0 and bkx_ is not None and len(bkx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkx is not long enough: Is %d, expected %d\" % (len(bkx_),((last_) - (first_))))\n if bkx_ is None:\n raise ValueError(\"Argument bkx cannot be None\")\n if bkx_ is None:\n raise ValueError(\"Argument bkx may not be None\")\n if bkx_ is not None:\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\n else:\n _bkx_tmp = None\n _blx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blx_ is not None and len(blx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blx is not long enough: Is %d, expected %d\" % (len(blx_),((last_) - (first_))))\n if blx_ is None:\n raise ValueError(\"Argument blx cannot be None\")\n if blx_ is None:\n raise ValueError(\"Argument blx may not be None\")\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\n _blx_copyarray = False\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blx_ is not None:\n _blx_copyarray = True\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\n _blx_np_tmp[:] = blx_\n assert _blx_np_tmp.flags.contiguous\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blx_copyarray = False\n _blx_tmp = None\n \n _bux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bux_ is not None and len(bux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bux is not long enough: Is %d, expected %d\" % (len(bux_),((last_) - (first_))))\n if bux_ is None:\n raise ValueError(\"Argument bux cannot be None\")\n if bux_ is None:\n raise ValueError(\"Argument bux may not be None\")\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\n _bux_copyarray = False\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bux_ is not None:\n _bux_copyarray = True\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\n _bux_np_tmp[:] = bux_\n assert _bux_np_tmp.flags.contiguous\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bux_copyarray = False\n _bux_tmp = None\n \n res = __library__.MSK_XX_putvarboundslice(self.__nativep,first_,last_,_bkx_tmp,_blx_tmp,_bux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundlist(self,sub,bk,bl,bu): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bk)\n elif num_ != len(bk):\n raise IndexError(\"Inconsistent length of array bk\")\n if num_ is None:\n num_ = len(bl)\n elif num_ != len(bl):\n raise IndexError(\"Inconsistent length of array bl\")\n if num_ is None:\n num_ = len(bu)\n elif num_ != len(bu):\n raise IndexError(\"Inconsistent length of array bu\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n res = self.__obj.putconboundlist(num_,sub_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def chgvarbound(self,j_,lower_,finite_,value_): # 3\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def chgvarbound(self,j_,lower_,finite_,value_):\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putboundlist(self,accmode_,sub,bk,bl,bu): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bk)\n elif num_ != len(bk):\n raise IndexError(\"Inconsistent length of array bk\")\n if num_ is None:\n num_ = len(bl)\n elif num_ != len(bl):\n raise IndexError(\"Inconsistent length of array bl\")\n if num_ is None:\n num_ = len(bu)\n elif num_ != len(bu):\n raise IndexError(\"Inconsistent length of array bu\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n res = self.__obj.putboundlist(accmode_,num_,sub_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _initialize_bounds(problem, bounds, get_bound, set_bound):\n for constraint in problem.constraints:\n root_expr = constraint.root_expr\n expr_bounds = Interval(constraint.lower_bound, constraint.upper_bound)\n if root_expr not in bounds:\n set_bound(root_expr, expr_bounds)\n else:\n existing_bounds = get_bound(root_expr)\n new_bounds = existing_bounds.intersect(expr_bounds)\n set_bound(root_expr, new_bounds)","def simple_bounds(child, lb, ub):\n assert len(lb) == len(ub), 'Lower and upper bounds have different #s of design variables in simple_bounds function.'\n assert len(lb) == len(child), 'Bounds and child have different #s of design variables in simple_bounds function.'\n for i in range(0, len(child), 1):\n if child[i] < lb[i]:\n child[i] = lb[i]\n\n for i in range(0, len(child), 1):\n if child[i] > ub[i]:\n child[i] = ub[i]\n\n return child","def apply_bound(x, var_min, var_max):\n x.position = np.maximum(x.position, var_min)\n x.position = np.minimum(x.position, var_max)","def getvarboundslice(self,first_,last_,bk_,bl_,bu_):\n _bk_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk is not long enough: Is %d, expected %d\" % (len(bk_),((last_) - (first_))))\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\n raise ValueError(\"Argument bk must be writable\")\n if bk_ is not None:\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\n else:\n _bk_tmp = None\n _bl_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl is not long enough: Is %d, expected %d\" % (len(bl_),((last_) - (first_))))\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\n raise ValueError(\"Argument bl must be writable\")\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\n _bl_copyarray = False\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bl_ is not None:\n _bl_copyarray = True\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\n _bl_np_tmp[:] = bl_\n assert _bl_np_tmp.flags.contiguous\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bl_copyarray = False\n _bl_tmp = None\n \n _bu_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu is not long enough: Is %d, expected %d\" % (len(bu_),((last_) - (first_))))\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\n raise ValueError(\"Argument bu must be writable\")\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\n _bu_copyarray = False\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bu_ is not None:\n _bu_copyarray = True\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\n _bu_np_tmp[:] = bu_\n assert _bu_np_tmp.flags.contiguous\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bu_copyarray = False\n _bu_tmp = None\n \n res = __library__.MSK_XX_getvarboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\n if _bl_copyarray:\n bl_[:] = _bl_np_tmp\n if _bu_copyarray:\n bu_[:] = _bu_np_tmp","def RestrictionRangeBound(self, compsIdList, lowerBound, upperBound):\n for i in range(len(compsIdList)): compsIdList[i] -= 1\n if self.solverTypeOptimize:\n self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) >= lowerBound)\n else:\n self.solver.assert_and_track(\n PbGe(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\n lowerBound), \"LabelRangeBound: \" + str(self.labelIdx))\n self.labelIdx += 1\n if self.solverTypeOptimize:\n PbLe(self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\n upperBound))\n else:\n self.solver.assert_and_track(\n sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) <= upperBound, \"LabelRangeBound: \" + str(self.labelIdx))\n self.labelIdx += 1","def set_bounds_atom(self,bounds):\n assert bounds.shape == (2,self.Phi.d)\n self.bounds = bounds # data bounds\n self.bounds_atom = bounds.T.tolist()\n for i in range(self.Phi.d): # bounds for the variance in each dimension\n max_variance_this_dimension = (bounds[1][i]-bounds[0][i])**2\n self.bounds_atom.append([self.variance_relative_lowerbound*max_variance_this_dimension,\n self.variance_relative_upperbound*max_variance_this_dimension])","def create_from_bounds(self, lbs, ubs):\n self.base_vertices = (np.array([lbs])+np.array([ubs])).T/2\n self.base_vectors = np.diag((np.array(ubs)-np.array(lbs))/2)","def getvarboundslice(self,first_,last_,bk,bl,bu): # 3\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])","def putconbound(self,i_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def compute_bounds(self, weights, add_coeff, dual_vars, lower_bounds, upper_bounds, store_primal=False):\n x0_coeff = -weights[0].backward(dual_vars.mus[0])\n x0 = torch.where(x0_coeff >= 0, lower_bounds[0].unsqueeze(1), upper_bounds[0].unsqueeze(1))\n bound = utils.bdot(x0, x0_coeff)\n if store_primal:\n self.bounds_primal = x0\n else:\n del x0\n del x0_coeff\n\n for lay_idx in range(1, len(weights)):\n lbs = lower_bounds[lay_idx].unsqueeze(1).clamp(None, 0)\n ubs = upper_bounds[lay_idx].unsqueeze(1).clamp(0, None)\n neg_bias = ((lbs * ubs) / (ubs - lbs))\n neg_bias.masked_fill_(ubs == lbs, 0) # cover case in which ubs & lbs coincide\n bound += utils.bdot(dual_vars.lambdas[lay_idx - 1].clamp(0, None), neg_bias)\n bound -= utils.bdot(dual_vars.mus[lay_idx - 1], weights[lay_idx - 1].get_bias())\n\n bound += utils.bdot(add_coeff, weights[-1].get_bias())\n return bound","def create_bound_for_scipy(lb, ub):\n lb = tuple(map(convert_inf_to_none, lb))\n ub = tuple(map(convert_inf_to_none, ub))\n return list((lb[i], ub[i]) for i in range(len(ub)))","def bounds(self, new_bounds: devices.PrimaryBounds) -> None:\n self._assert_bounds_are_valid(new_bounds)\n self._bounds = list(new_bounds)","def set_bounds(self, new_bounds):\n\n # Update the internal object stored dict\n self.pbounds.update(new_bounds)\n\n # Loop through the all bounds and reset the min-max bound matrix\n for row, key in enumerate(self.pbounds.keys()):\n\n # Reset all entries, even if the same.\n self.bounds[row] = self.pbounds[key]","def bounds(self, pos):","def removebarvars(self,subset_):\n num_ = None\n if num_ is None:\n num_ = len(subset_)\n elif num_ != len(subset_):\n raise IndexError(\"Inconsistent length of array subset\")\n if subset_ is None:\n raise ValueError(\"Argument subset cannot be None\")\n if subset_ is None:\n raise ValueError(\"Argument subset may not be None\")\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\n _subset_copyarray = False\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subset_ is not None:\n _subset_copyarray = True\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\n _subset_np_tmp[:] = subset_\n assert _subset_np_tmp.flags.contiguous\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subset_copyarray = False\n _subset_tmp = None\n \n res = __library__.MSK_XX_removebarvars(self.__nativep,num_,_subset_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def cb_bounds(self, variable, results_dict, keys, fixed_bounds):\n tas_bound, pr_bound = fixed_bounds\n if variable == \"tas\":\n if tas_bound:\n bound_limit = tas_bound\n else:\n bound_limit = self.find_abs_bound_range(results_dict, keys)\n cmap = plt.cm.RdBu_r\n else:\n if pr_bound:\n bound_limit = pr_bound\n else:\n bound_limit = self.find_abs_bound_range(results_dict,\n keys,\n avg_over=25)\n cmap = plt.cm.BrBG\n bounds = np.linspace(-1 * bound_limit, bound_limit, 11)\n return [bounds, cmap]","def _add_bound_configs(CONFIG):\n CONFIG.declare(\n 'obj_bound',\n ConfigValue(\n default=1e15,\n domain=PositiveFloat,\n description='Bound applied to the linearization of the objective function if main MIP is unbounded.',\n ),\n )\n CONFIG.declare(\n 'continuous_var_bound',\n ConfigValue(\n default=1e10,\n description='Default bound added to unbounded continuous variables in nonlinear constraint if single tree is activated.',\n domain=PositiveFloat,\n ),\n )\n CONFIG.declare(\n 'integer_var_bound',\n ConfigValue(\n default=1e9,\n description='Default bound added to unbounded integral variables in nonlinear constraint if single tree is activated.',\n domain=PositiveFloat,\n ),\n )\n CONFIG.declare(\n 'initial_bound_coef',\n ConfigValue(\n default=1e-1,\n domain=PositiveFloat,\n description='The coefficient used to approximate the initial primal/dual bound.',\n ),\n )","def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\n ...","def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\n ...","def __get_new_restriction_branch_and_bound(self, tableau, var_col, b_value):\n restriction_row = np.zeros((tableau.get_tableau_num_cols())).astype('object')\n restriction_row[var_col] = 1\n restriction_row[tableau.get_tableau_num_cols() - 1] = b_value\n\n for i in xrange(0, len(restriction_row)):\n restriction_row[i] = Fraction(restriction_row[i])\n\n return restriction_row","def RestrictionUpperLowerEqualBound(self, compsIdList, bound, operator):\n\n self.problem.logger.debug(\"RestrictionUpperLowerEqualBound: {} {} {} \".format(compsIdList, operator, bound))\n\n if operator == \"<=\":\n if self.solverTypeOptimize:\n bvars = [self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]\n self.solver.add(PbLe([(x, 1) for x in bvars], bound))\n else:\n self.__constMap[str(\"LabelUpperLowerEqualBound\" + str(self.labelIdx))] = sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) <= bound\n self.solver.assert_and_track(PbLe([(x, 1) for x in bvars], bound), \"LabelUpperLowerEqualBound\" + str(self.labelIdx))\n self.labelIdx += 1\n elif operator == \">=\":\n if self.solverTypeOptimize:\n bvars = [self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]\n self.solver.add(PbGe([(x, 1) for x in bvars], bound))\n else:\n self.__constMap[str(\"LabelUpperLowerEqualBound\" + str(self.labelIdx))] = sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) >= bound\n self.solver.assert_and_track(\n sum([If(self.a[compId * self.nrVM + j], 1, 0) for compId in compsIdList for j in range(self.nrVM)]) >= bound, \"LabelUpperLowerEqualBound\" + str(self.labelIdx))\n self.labelIdx += 1\n elif operator == \"=\":\n if self.solverTypeOptimize:\n bvars = [self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]\n self.solver.add(PbEq([(x, 1) for x in bvars], bound))\n else:\n self.__constMap[str(\"LabelUpperLowerEqualBound\" + str(self.labelIdx))] = sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) == bound\n\n self.solver.assert_and_track(\n sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) == bound, \"LabelUpperLowerEqualBound\" + str(self.labelIdx))\n self.labelIdx += 1\n else:\n self.problem.logger.info(\"Unknown operator\")","def set_bounds_atom(self,bounds):\n assert bounds.shape == (2,self.Phi.d)\n self.bounds = bounds # data bounds\n self.bounds_atom = bounds.T.tolist()","def extend_bounds(problem):\n\n num_vars = problem[\"num_vars\"]\n num_ff_vars = 2 ** find_smallest(num_vars)\n num_dummy_variables = num_ff_vars - num_vars\n\n bounds = list(problem[\"bounds\"])\n names = problem[\"names\"]\n if num_dummy_variables > 0:\n bounds.extend([[0, 1] for x in range(num_dummy_variables)])\n names.extend([\"dummy_\" + str(var) for var in range(num_dummy_variables)])\n problem[\"bounds\"] = bounds\n problem[\"names\"] = names\n problem[\"num_vars\"] = num_ff_vars\n\n return problem","def putconboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putconboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _add_boundaries(self):\n logger.info(\"add_boundaries: start of method\")\n\n import scipy.spatial as sptl\n import scipy.sparse as sprs\n Lx = self._Lx\n Ly = self._Ly\n Lz = self._Lz\n Np = self.num_pores()\n btype = self._btype\n boffset = 0.05\n\n #Translate internal pores to each face of domain\n poffset = np.zeros((7,3))\n poffset[[2,5],0] = [-Lx, Lx]\n poffset[[3,4],1] = [-Ly, Ly]\n poffset[[1,6],2] = [-Lz, Lz]\n pcoords = pcoords0 = self['pore.coords']\n for i in np.r_[1:7]:\n pcoords = np.concatenate((pcoords,pcoords0 + poffset[i,:]),axis=0)\n\n #Use some twisted logic to get bval list of + for boundary and - for periodic faces\n bval = [0, 1, 2, 3, 4, 5, 6]*(np.array([0, btype[2], btype[0], btype[1], btype[1], btype[0], btype[2]])*-2+1)\n ptype = np.zeros((Np,),dtype=int)\n for i in np.r_[1:7]:\n ptype = np.concatenate((ptype,np.ones((Np,),dtype=int)*bval[i]),axis=0)\n\n #pnum contains the internal ID number of the boundary pores (for connecting periodic points)\n pnum = self.pores()\n pnum = np.tile(pnum,7)\n\n Tri = sptl.Delaunay(pcoords)\n adjmat = sprs.lil_matrix((np.shape(pcoords)[0],np.shape(pcoords)[0]),dtype=int)\n for i in np.arange(0,np.shape(Tri.simplices)[0]):\n #Keep only simplices that are fully in real domain\n adjmat[Tri.simplices[i],Tri.simplices[i]] = 1\n adjmat = sprs.triu(adjmat,k=1,format=\"lil\")\n for i in np.arange(0,Np):\n #Add periodic throats to the netowrk (if any)\n tpore2 = pnum[adjmat.rows[i]][ptype[adjmat.rows[i]]<0]\n tpore1 = np.ones_like(tpore2,dtype=int)*i\n conns = self['throat.conns']\n conns = np.concatenate((conns,np.vstack((tpore1,tpore2)).T),axis=0)\n #Add boundary pores and throats to the network\n newporetyps = np.unique(ptype[adjmat.rows[i]][ptype[adjmat.rows[i]]>0])\n newporenums = np.r_[self.num_pores():self.num_pores()+np.size(newporetyps)]\n tpore2 = newporenums\n tpore1 = np.ones_like(tpore2,dtype=int)*i\n conns = np.concatenate((conns,np.vstack((tpore1,tpore2)).T),axis=0)\n self['throat.conns'] = conns\n bcoords = np.zeros((7,3),dtype=float)\n coords = self['pore.coords']\n bcoords[1,:] = [coords[i,0], coords[i,1], 0-Lz*boffset]\n bcoords[2,:] = [0-Lx*boffset, coords[i,1], coords[i,2]]\n bcoords[3,:] = [coords[i,0], -Ly*boffset, coords[i,2]]\n bcoords[4,:] = [coords[i,0], Ly+Ly*boffset, coords[i,2]]\n bcoords[5,:] = [Lx+Lx*boffset, coords[i,1], coords[i,2]]\n bcoords[6,:] = [coords[i,0], coords[i,1], Lz+Lz*boffset]\n newporecoords = bcoords[newporetyps,:]\n coords = np.concatenate((coords,newporecoords),axis=0)\n self['pore.coords'] = coords\n #Reset number of pores and throats (easier than tracking it)\n nums = np.r_[0:np.shape(coords)[0]]\n self['pore.numbering'] = nums\n self['pore.numbering'] = np.ones((nums[-1]+1,),dtype=bool)\n nums = np.r_[0:np.shape(conns)[0]]\n self['throat.numbering'] = nums\n self['throat.numbering'] = np.ones((nums[-1]+1,),dtype=bool)\n logger.debug(\"add_boundaries: end of method\")","def change_state_bounds(self, state, new_ub, new_lb, slack, comp=None,\n node=None):\n # TODO Adapt method so you can change only one of the settings?\n # TODO Put None as default parameter value and detect if other value is supplied\n comp_obj = self.get_component(comp, node)\n\n comp_obj.params[state].change_upper_bound(new_ub)\n comp_obj.params[state].change_lower_bound(new_lb)\n comp_obj.params[state].change_slack(slack)","def add_boundary(self, boundary):\n self._bounds.append(boundary)","def _set_bounds(b, x, n):\n for j in range(1, n - 1):\n for i in range(1, n - 1):\n x[index_of(i, j, 0, n)] = -x[index_of(i, j, 1, n)] if b == 3 else x[index_of(i, j, 1, n)]\n x[index_of(i, j, 0, n - 1)] = -x[index_of(i, j, 1, n - 2)] if b == 3 else x[index_of(i, j, 1, n - 2)]\n for k in range(1, n - 1):\n for i in range(1, n - 1):\n x[index_of(i, 0, k, n)] = -x[index_of(i, 1, k, n)] if b == 2 else x[index_of(i, 1, k, n)]\n x[index_of(i, n - 1, 0, n - 1)] = -x[index_of(i, n - 2, k, n - 2)] if b == 2 else x[\n index_of(i, n - 2, k, n - 2)]\n for k in range(1, n - 1):\n for j in range(1, n - 1):\n x[index_of(0, j, k, n)] = -x[index_of(1, j, k, n)] if b == 1 else x[index_of(1, j, k, n)]\n x[index_of(n - 1, j, k, n - 1)] = -x[index_of(n - 2, j, k, n)] if b == 1 else x[\n index_of(n - 2, j, k, n)]\n\n x[index_of(0, 0, 0, n)] = 1 / 3 * (x[index_of(1, 0, 0, n)] + x[index_of(0, 1, 0, n)] + x[index_of(0, 0, 1, n)])\n x[index_of(0, n - 1, 0, n)] = 1 / 3 * (\n x[index_of(1, n - 1, 0, n)] + x[index_of(0, n - 2, 0, n)] + x[index_of(0, n - 1, 1, n)])\n x[index_of(0, 0, n - 1, n)] = 1 / 3 * (\n x[index_of(1, 0, n - 1, n)] + x[index_of(0, 1, n - 1, n)] + x[index_of(0, 0, n - 2, n)])\n x[index_of(0, n - 1, n - 1, n)] = 1 / 3 * (\n x[index_of(1, n - 1, n - 1, n)] + x[index_of(0, n - 2, n - 1, n)] + x[index_of(0, n - 1, n - 2, n)])\n x[index_of(n - 1, 0, 0, n)] = 1 / 3 * (\n x[index_of(n - 2, 0, 0, n)] + x[index_of(n - 1, 1, 0, n)] + x[index_of(n - 1, 0, 1, n)])\n x[index_of(n - 1, n - 1, 0, n)] = 1 / 3 * (\n x[index_of(n - 2, n - 1, 0, n)] + x[index_of(n - 1, n - 2, 0, n)] + x[index_of(n - 1, n - 1, 1, n)])\n x[index_of(n - 1, 0, n - 1, n)] = 1 / 3 * (\n x[index_of(n - 2, 0, n - 1, n)] + x[index_of(n - 1, 1, n - 1, n)] + x[index_of(n - 1, 0, n - 2, n)])\n x[index_of(n - 1, n - 1, n - 1, n)] = 1 / 3 * (\n x[index_of(n - 2, n - 1, n - 1, n)] + x[index_of(n - 1, n - 2, n - 1, n)] + x[\n index_of(n - 1, n - 1, n - 2, n)])","def putvartypelist(self,subj_,vartype_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(vartype_)\n elif num_ != len(vartype_):\n raise IndexError(\"Inconsistent length of array vartype\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if vartype_ is None:\n raise ValueError(\"Argument vartype cannot be None\")\n if vartype_ is None:\n raise ValueError(\"Argument vartype may not be None\")\n if vartype_ is not None:\n _vartype_tmp = (ctypes.c_int32 * len(vartype_))(*vartype_)\n else:\n _vartype_tmp = None\n res = __library__.MSK_XX_putvartypelist(self.__nativep,num_,_subj_tmp,_vartype_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def get_variables_binds(self, predicate, bound_variables=None, variables_binds=None, recursion_level=1):\n\n # print(\"EXPLORING\", recursion_level, predicate, variables_binds)\n\n # Set of bound variables in predicate body\n if bound_variables is None:\n bound_variables = set()\n\n # Possible binds\n if variables_binds is None:\n variables_binds = [{}]\n\n recursion_level -= 1\n\n new_possible_binds = []\n\n for body_clause in predicate.body:\n adornments = self.compute_adornments(body_clause.parameters, bound_variables)\n\n # For each fact search if we can match every bound variable and assign free ones\n if body_clause.name in self._facts:\n for fact in self._facts[body_clause.name]:\n possible_binds = self.check_fact_with_adornment(fact, body_clause, adornments, variables_binds)\n if len(possible_binds):\n # A fact matched, we add variables binds to sup\n new_possible_binds.extend(possible_binds)\n\n # if len(new_possible_binds):\n # variables_binds = new_possible_binds\n\n if recursion_level > 0:\n # For each rule\n if body_clause.name in self._rules:\n for applicable_rule in self._rules[body_clause.name]:\n\n n_bound_variables = set()\n n_variables_binds = [{}]\n\n for index, argument in enumerate(body_clause.parameters):\n rule_corresponding_parameter = applicable_rule.head.parameters[index]\n\n if rule_corresponding_parameter.is_constant():\n if argument.is_constant():\n if rule_corresponding_parameter.value != argument.value:\n break\n else:\n if adornments[index]:\n if argument.is_constant():\n n_bound_variables.add(rule_corresponding_parameter.name)\n n_variables_binds[0][rule_corresponding_parameter.name] = argument.value\n elif argument.name in bound_variables and argument.name in variables_binds[0]:\n n_bound_variables.add(rule_corresponding_parameter.name)\n n_variables_binds[0][rule_corresponding_parameter.name] = variables_binds[0][argument.name]\n\n applicable_predicate_binds = self.get_variables_binds(applicable_rule, n_bound_variables, n_variables_binds, recursion_level)\n for n_bind in applicable_predicate_binds:\n adapted_bind = self.substitute_variable_names(n_bind, applicable_rule.head, body_clause)\n new_possible_binds.extend(adapted_bind)\n\n if len(new_possible_binds):\n variables_binds = new_possible_binds.copy()\n new_possible_binds.clear()\n else:\n variables_binds = [{}]\n\n new_possible_binds_no_duplicates = self.remove_duplicate_binds(variables_binds)\n\n if len(new_possible_binds_no_duplicates):\n yield new_possible_binds_no_duplicates","def putconboundslice(self,first_,last_,bkc_,blc_,buc_):\n _bkc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bkc_ is not None and len(bkc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkc is not long enough: Is %d, expected %d\" % (len(bkc_),((last_) - (first_))))\n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n _blc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blc_ is not None and len(blc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blc is not long enough: Is %d, expected %d\" % (len(blc_),((last_) - (first_))))\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n _buc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and buc_ is not None and len(buc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument buc is not long enough: Is %d, expected %d\" % (len(buc_),((last_) - (first_))))\n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundslice(self.__nativep,first_,last_,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def removebarvars(self,subset): # 3\n num_ = None\n if num_ is None:\n num_ = len(subset)\n elif num_ != len(subset):\n raise IndexError(\"Inconsistent length of array subset\")\n if num_ is None: num_ = 0\n if subset is None: raise TypeError(\"Invalid type for argument subset\")\n if subset is None:\n subset_ = None\n else:\n try:\n subset_ = memoryview(subset)\n except TypeError:\n try:\n _tmparr_subset = array.array(\"i\",subset)\n except TypeError:\n raise TypeError(\"Argument subset has wrong type\")\n else:\n subset_ = memoryview(_tmparr_subset)\n \n else:\n if subset_.format != \"i\":\n subset_ = memoryview(array.array(\"i\",subset))\n \n res = self.__obj.removebarvars(num_,subset_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _update_vids(cls, cnf, inp, vpool):\n\n top, vmap = max(inp + [vpool.top]), {} # current top and variable mapping\n\n # we are going to use this to check if literals are input\n inp = set([abs(l) for l in inp])\n\n # creating a new variable mapping, taking into\n # account variables marked as \"occupied\"\n while top < cnf.nv:\n top += 1\n\n # skipping the input literals\n if top in inp:\n vmap[top] = top\n continue\n\n vpool.top += 1\n\n while vpool._occupied and vpool.top >= vpool._occupied[0][0]:\n if vpool.top <= vpool._occupied[0][1] + 1:\n vpool.top = vpool._occupied[0][1] + 1\n\n vpool._occupied.pop(0)\n\n # mapping this literal to a free one\n vmap[top] = vpool.top\n\n # updating the clauses\n for cl in cnf.clauses:\n cl[:] = map(lambda l: int(math.copysign(vmap[abs(l)], l)) if abs(l) in vmap else l, cl)\n\n # updating the number of variables\n cnf.nv = vpool.top","def setSafeRange( azLow, azHigh, elLow, elHigh, ants, subarray=DEFAULT) :\n antlist = helpers.makeList(ants)\n multiSubarray('setSafeRange', subarray, azLow, azHigh, elLow, elHigh, \n antlist)","def putconbound(self,i_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def SetPRBinCatConstraint( self, model ) :\n tot = np.dot( self.wish.T, self.dispo )\n for val in tot :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def _manage_infinity_bounds(problem, _bounds, get_bound, set_bound):\n for variable in problem.variables:\n expr_bounds = get_bound(variable)\n lower_bound = expr_bounds.lower_bound\n upper_bound = expr_bounds.upper_bound\n\n if is_inf(lower_bound):\n new_lower_bound = None\n else:\n new_lower_bound = lower_bound\n\n if is_inf(upper_bound):\n new_upper_bound = None\n else:\n new_upper_bound = upper_bound\n\n set_bound(variable, Interval(new_lower_bound, new_upper_bound))","def removeBounded(self, bounds):\n if bounds==None or len(bounds)!=4:\n return\n x1,y1,x2,y2 = bounds\n if x1>x2 :\n temp=x1;x1=x2;x2=temp\n if y1>y2:\n temp=y1;y1=y2;y2=temp\n lst=[]\n for i in range(0,self.length()):\n x=self.x[i]; y=self.y[i]\n if (x>x1 and x<x2) and (y>y1 and y<y2): \n lst.append(i)\n self.removeMultiple(lst)\n return","def putacollist(self,sub_,ptrb_,ptre_,asub_,aval_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(ptrb_)\n elif num_ != len(ptrb_):\n raise IndexError(\"Inconsistent length of array ptrb\")\n if num_ is None:\n num_ = len(ptre_)\n elif num_ != len(ptre_):\n raise IndexError(\"Inconsistent length of array ptre\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if ptrb_ is None:\n raise ValueError(\"Argument ptrb cannot be None\")\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb may not be None\")\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\n _ptrb_copyarray = False\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptrb_ is not None:\n _ptrb_copyarray = True\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\n _ptrb_np_tmp[:] = ptrb_\n assert _ptrb_np_tmp.flags.contiguous\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptrb_copyarray = False\n _ptrb_tmp = None\n \n if ptre_ is None:\n raise ValueError(\"Argument ptre cannot be None\")\n if ptre_ is None:\n raise ValueError(\"Argument ptre may not be None\")\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\n _ptre_copyarray = False\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptre_ is not None:\n _ptre_copyarray = True\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\n _ptre_np_tmp[:] = ptre_\n assert _ptre_np_tmp.flags.contiguous\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptre_copyarray = False\n _ptre_tmp = None\n \n if asub_ is None:\n raise ValueError(\"Argument asub cannot be None\")\n if asub_ is None:\n raise ValueError(\"Argument asub may not be None\")\n if isinstance(asub_, numpy.ndarray) and asub_.dtype is numpy.dtype(numpy.int32) and asub_.flags.contiguous:\n _asub_copyarray = False\n _asub_tmp = ctypes.cast(asub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif asub_ is not None:\n _asub_copyarray = True\n _asub_np_tmp = numpy.zeros(len(asub_),numpy.dtype(numpy.int32))\n _asub_np_tmp[:] = asub_\n assert _asub_np_tmp.flags.contiguous\n _asub_tmp = ctypes.cast(_asub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _asub_copyarray = False\n _asub_tmp = None\n \n if aval_ is None:\n raise ValueError(\"Argument aval cannot be None\")\n if aval_ is None:\n raise ValueError(\"Argument aval may not be None\")\n if isinstance(aval_, numpy.ndarray) and aval_.dtype is numpy.dtype(numpy.float64) and aval_.flags.contiguous:\n _aval_copyarray = False\n _aval_tmp = ctypes.cast(aval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif aval_ is not None:\n _aval_copyarray = True\n _aval_np_tmp = numpy.zeros(len(aval_),numpy.dtype(numpy.float64))\n _aval_np_tmp[:] = aval_\n assert _aval_np_tmp.flags.contiguous\n _aval_tmp = ctypes.cast(_aval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _aval_copyarray = False\n _aval_tmp = None\n \n res = __library__.MSK_XX_putacollist64(self.__nativep,num_,_sub_tmp,_ptrb_tmp,_ptre_tmp,_asub_tmp,_aval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def removevars(self,subset_):\n num_ = None\n if num_ is None:\n num_ = len(subset_)\n elif num_ != len(subset_):\n raise IndexError(\"Inconsistent length of array subset\")\n if subset_ is None:\n raise ValueError(\"Argument subset cannot be None\")\n if subset_ is None:\n raise ValueError(\"Argument subset may not be None\")\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\n _subset_copyarray = False\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subset_ is not None:\n _subset_copyarray = True\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\n _subset_np_tmp[:] = subset_\n assert _subset_np_tmp.flags.contiguous\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subset_copyarray = False\n _subset_tmp = None\n \n res = __library__.MSK_XX_removevars(self.__nativep,num_,_subset_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __set__(self,obj,val):\n self._check_bounds(val)\n super(List,self).__set__(obj,val)","def putbound(self,accmode_,i_,bk_,bl_,bu_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putbound(accmode_,i_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def fix_all(self):\n for param_name in self._LIST_PARAMETERS:\n bounds_processed = self._process_bounds(param_name, False)\n setattr(self, '__'+param_name+'_bounds', bounds_processed)","def getconboundslice(self,first_,last_,bk_,bl_,bu_):\n _bk_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk is not long enough: Is %d, expected %d\" % (len(bk_),((last_) - (first_))))\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\n raise ValueError(\"Argument bk must be writable\")\n if bk_ is not None:\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\n else:\n _bk_tmp = None\n _bl_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl is not long enough: Is %d, expected %d\" % (len(bl_),((last_) - (first_))))\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\n raise ValueError(\"Argument bl must be writable\")\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\n _bl_copyarray = False\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bl_ is not None:\n _bl_copyarray = True\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\n _bl_np_tmp[:] = bl_\n assert _bl_np_tmp.flags.contiguous\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bl_copyarray = False\n _bl_tmp = None\n \n _bu_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu is not long enough: Is %d, expected %d\" % (len(bu_),((last_) - (first_))))\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\n raise ValueError(\"Argument bu must be writable\")\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\n _bu_copyarray = False\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bu_ is not None:\n _bu_copyarray = True\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\n _bu_np_tmp[:] = bu_\n assert _bu_np_tmp.flags.contiguous\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bu_copyarray = False\n _bu_tmp = None\n \n res = __library__.MSK_XX_getconboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\n if _bl_copyarray:\n bl_[:] = _bl_np_tmp\n if _bu_copyarray:\n bu_[:] = _bu_np_tmp","def variance_bounds(self, resids: NDArray, power: float = 2.0) -> NDArray:\n nobs = resids.shape[0]\n\n tau = min(75, nobs)\n w = 0.94 ** np.arange(tau)\n w = w / sum(w)\n var_bound = np.zeros(nobs)\n initial_value = w.dot(resids[:tau] ** 2.0)\n ewma_recursion(0.94, resids, var_bound, resids.shape[0], initial_value)\n\n var_bounds = np.vstack((var_bound / 1e6, var_bound * 1e6)).T\n var = resids.var()\n min_upper_bound = 1 + (resids ** 2.0).max()\n lower_bound, upper_bound = var / 1e8, 1e7 * (1 + (resids ** 2.0).max())\n var_bounds[var_bounds[:, 0] < lower_bound, 0] = lower_bound\n var_bounds[var_bounds[:, 1] < min_upper_bound, 1] = min_upper_bound\n var_bounds[var_bounds[:, 1] > upper_bound, 1] = upper_bound\n\n if power != 2.0:\n var_bounds **= power / 2.0\n\n return np.ascontiguousarray(var_bounds)","def CellBoundary(self, p_int, , vtkIdList):\n ...","def getvarbound(self,i_):\n bk_ = ctypes.c_int32()\n bl_ = ctypes.c_double()\n bu_ = ctypes.c_double()\n res = __library__.MSK_XX_getvarbound(self.__nativep,i_,ctypes.byref(bk_),ctypes.byref(bl_),ctypes.byref(bu_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _bk_return_value = boundkey(bk_.value)\n bl_ = bl_.value\n _bl_return_value = bl_\n bu_ = bu_.value\n _bu_return_value = bu_\n return (_bk_return_value,_bl_return_value,_bu_return_value)","def SetPRBinConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for val in tot :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def prune(self,domains,constraint):\n left_var = constraint.left[0]\n left_const_mult = constraint.left[1]\n left_val = constraint.left[2]\n\n right_var = constraint.right[0]\n right_const_mult = constraint.right[1]\n right_val = constraint.right[2]\n\n new_domains = deepcopy(domains)\n\n\n # Simple Variable-Value Labeling\n if (left_val == [0] and left_const_mult == [1]) and (right_const_mult == [0]):\n new_domains[left_var[0]] = [right_val[0]]\n \n # Simple Variable-Variable Labeling\n elif (left_val == [0] and left_const_mult == [1]) and (right_val == [0] and right_const_mult == [1]):\n new_set = set(new_domains[left_var[0]]) & set(new_domains[right_var[0]])\n new_domains[left_var[0]] = list(new_set)\n new_domains[right_var[0]] = list(new_set)\n\n else:\n l = 0\n for var,mult in zip(left_var,left_const_mult):\n l += mult*max(domains[var])\n for const in left_val:\n l += const\n\n r = 0\n for var,mult in zip(right_var,right_const_mult):\n r += mult*min(domains[var])\n for const in right_val:\n r += const\n\n # print(l,r)\n # print(new_domains)\n # print(constraint)\n\n for var,mult in zip(left_var,left_const_mult):\n max_var = max(domains[var])\n comp = (r-(l-mult*max_var)) / mult\n for elem in domains[var]:\n if elem < comp:\n new_domains[var].remove(elem)\n\n for var,mult in zip(right_var,right_const_mult):\n min_var = min(domains[var])\n comp = (l-(r-mult*min_var)) / mult\n for elem in domains[var]:\n if elem > comp:\n new_domains[var].remove(elem)\n\n # for i,domain in enumerate(new_domains):\n # if len(domain) == 0:\n # print(i,l,r)\n # print(\"Old:\",domains)\n # print(\"New:\",new_domains)\n # print(domains)\n # print(constraint)\n # print(\"------------------------\")\n # raise SystemError(\"Domain is Empty!!\")\n\n return new_domains","def putboundslice(self,con_,first_,last_,bk,bl,bu): # 3\n if not isinstance(con_,accmode): raise TypeError(\"Argument con has wrong type\")\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putboundslice(con_,first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _rename_bound_variables(self, formula, variables):\n new_vars = [self._bound_symbol(x) for x in variables]\n old_vars = [self.walk_symbol(x) for x in variables]\n new_formula = yicespy.yices_subst_term(len(variables), yicespy.make_term_array(new_vars),\n yicespy.make_term_array(old_vars), formula)\n return (new_formula, new_vars)","def _update_block_const(self,name,G1,remove_nodes,new_inst, const_list):\n\n def _list_replace(lst, old_value, new_value):\n for i, value in enumerate(lst):\n if value == old_value:\n lst[i] = new_value\n\n logger.debug(f\"update constraints with block in them for hierarchy {name} {remove_nodes}\")\n if self._if_const(name):\n for const in const_list:\n if hasattr(const, 'instances'):\n logger.debug(f\"checking instances in the constraint:{const.instances} {set(remove_nodes)}\")\n if set(const.instances) & set(remove_nodes):\n replace = True\n for block in remove_nodes:\n if replace:\n _list_replace(const.instances, block, new_inst)\n replace = False\n elif block in const.instances:\n const.instances.remove(block)\n logger.debug(f\"updated instances in the constraint:{const}\")","def add_boundaries(self):\n\n bound_conns=[]\n bound_coords=[]\n bound_vert_index=[]\n throat_vert_index=[]\n #Find boundary extent\n [x_min,x_max,y_min,y_max,z_min,z_max]=vo.vertex_dimension(self,self.pores(),parm='minmax')\n min_point = np.around(np.array([x_min,y_min,z_min]),10)\n max_point = np.around(np.array([x_max,y_max,z_max]),10)\n Np = self.num_pores()\n Nt = self.num_throats()\n new_throat_count = 0\n # ridge_dict contains a dictionary where the key is a set of 2 neighbouring pores and the value is the vertex indices\n # that form the throat or ridge between them\n for p,v in self._vor.ridge_dict.items():\n # if the vertex with index -1 is contained in list then the ridge is unbounded - ignore these\n if np.all(np.asarray(v) >=0):\n #boundary throats will be those connecting one pore inside the original set and one out\n if (p[0] in range(Np) and p[1] not in range(Np)) or\\\n (p[0] not in range(Np) and p[1] in range(Np)):\n # the dictionary key is not in numerical order so find the pore index inside\n if p[0] in range(Np):\n my_pore=p[0]\n else:\n my_pore=p[1]\n my_pore_coord = self[\"pore.coords\"][my_pore]\n new_pore_coord = my_pore_coord.copy()\n #rounding necessary here to identify the plane as Voronoi can have 1e-17 and smaller errors\n throat_verts = np.around(self._vor.vertices[v],10)\n #find which plane we are aligned with (if any) and align new_pore with throat plane\n if len(np.unique(throat_verts[:,0])) == 1:\n new_pore_coord[0]=np.unique(throat_verts[:,0])\n elif len(np.unique(throat_verts[:,1])) == 1:\n new_pore_coord[1]=np.unique(throat_verts[:,1])\n elif len(np.unique(throat_verts[:,2])) == 1:\n new_pore_coord[2]=np.unique(throat_verts[:,2])\n else:\n new_pore_coord = throat_verts.mean()\n bound_coords.append(new_pore_coord)\n bound_conns.append(np.array([my_pore,new_throat_count+Np]))\n bound_vert_index.append(dict(zip(v,throat_verts)))\n throat_vert_index.append(dict(zip(v,throat_verts)))\n new_throat_count += 1\n\n #Add new pores and connections\n self.extend(pore_coords=bound_coords, throat_conns=bound_conns)\n #Record new number of pores\n Mp = self.num_pores()\n Mt = self.num_throats()\n new_pore_ids = np.arange(Np,Mp)\n new_throat_ids = np.arange(Nt,Mt)\n #Identify which boundary the pore sits on\n front = self.pores()[self['pore.coords'][:,0]==min_point[0]]\n back = self.pores()[self['pore.coords'][:,0]==max_point[0]]\n left = self.pores()[self['pore.coords'][:,1]==min_point[1]]\n right = self.pores()[self['pore.coords'][:,1]==max_point[1]]\n bottom = self.pores()[self['pore.coords'][:,2]==min_point[2]]\n top = self.pores()[self['pore.coords'][:,2]==max_point[2]]\n #Assign labels\n self['pore.boundary'] = False\n self['pore.boundary'][new_pore_ids] = True\n self['pore.right_boundary'] = False\n self['pore.left_boundary'] = False\n self['pore.front_boundary'] = False\n self['pore.back_boundary'] = False\n self['pore.top_boundary'] = False\n self['pore.bottom_boundary'] = False\n self['pore.right_boundary'][right] = True\n self['pore.left_boundary'][left] = True\n self['pore.front_boundary'][front] = True\n self['pore.back_boundary'][back] = True\n self['pore.top_boundary'][top] = True\n self['pore.bottom_boundary'][bottom] = True\n #Save the throat verts\n self[\"pore.vert_index\"][new_pore_ids] = bound_vert_index\n self[\"throat.vert_index\"][new_throat_ids] = throat_vert_index","def normalize_bounds(self, bounds):\n scaled_bounds = []\n scalings = []\n intercepts = []\n \n non_fixed_params = []\n \n print(self.device)\n \n for name, domain in self.bounds.items():\n # Get any fixed parmeters\n if type(domain) == int or type(domain) == float:\n # Take note\n self.fixed_parameters.append(name)\n\n # Free parameters\n elif type(domain) == tuple:\n # Bookkeeping\n self.free_parameters.append(name)\n\n # Get scaling\n lower_bound = min(domain)\n upper_bound = max(domain)\n scale = upper_bound - lower_bound\n\n # Transform to [0, 1] domain\n #scaled_bound = {'name': name, 'type': 'continuous', 'domain': (0., 1.)} #torch.adjustment required\n non_fixed_params.append(name)\n \n # Store\n #scaled_bounds.append(scaled_bound)\n scalings.append(scale)\n intercepts.append(lower_bound)\n else:\n raise ValueError(\"Domain bounds not understood\")\n \n n_hyperparams = len(non_fixed_params)\n \n scaled_bounds = cat([zeros(1,n_hyperparams, device = self.device), \n ones(1, n_hyperparams, device = self.device)], 0)\n return scaled_bounds, tensor(scalings, device = self.device, requires_grad = False), tensor(intercepts, device = self.device, requires_grad = False) #torch.adjustment required","def compute_bounds(self):\n # Note: linear_constraints object has been been populated at this stage\n L_zero_var = []\n \n for constraint in self._linear_constraints.L_linear_constraints:\n lhs_string = constraint[0]\n rhs_string = constraint[1]\n if float(rhs_string)==0:\n #print \"rhs=0: forcing the variables to zero\"\n L_vars = re.split(r'[+-]',lhs_string)\n \n for var in L_vars:\n modform_var = var.strip()\n \n # forcing all the variables in this constraint to be zero\n self._linear_constraints.modform_space.D_PuLP_variables[modform_var] = pulp.LpVariable(modform_var, lowBound=0, upBound=0)\n #print \"var forced to zero: \", modform_var\n L_zero_var.append(modform_var)\n else: #if float(rhs)==0\n continue\n \n if len(L_zero_var)>0:\n print \"\\n####### Variables forced to zero (rhs = 0) ##########\"\n print \"variables forced to zero: \", set(L_zero_var)\n \n feasible_lc = flc.FeasibleLinearConstraints(self._linear_constraints)\n \n feasible_lc.get_feasible_linear_constraints()\n \n feasible_linear_constraints = feasible_lc.feasible_linear_constraints\n \n lp_solver = lps.LinearProgrammingSolver(feasible_linear_constraints)\n \n D_lower_bounds = {}; D_upper_bounds = {}\n \n for v in [self._linear_constraints.modform_space.D_PuLP_variables[k] for k in sorted(self._linear_constraints.modform_space.D_PuLP_variables.keys(), key=gbfunc.natural_keys)]:\n \n if str(v) in L_zero_var:\n D_lower_bounds[str(v)] = '0'\n D_upper_bounds[str(v)] = '0'\n continue\n #end if str(v) in L_zero_var\n \n objective_function_PuLP = v\n \n list_values_minimize = lp_solver.linear_programming_solver(objective_function_PuLP, pulp.LpMinimize)\n D_lower_bounds[str(v)] = \"%.3f\"%round(pulp.value(v),3)\n \n list_values_maximize = lp_solver.linear_programming_solver(objective_function_PuLP, pulp.LpMaximize)\n D_upper_bounds[str(v)] = \"%.3f\"%round(pulp.value(v),3)\n\n #end for v in ..\n\n return((D_lower_bounds, D_upper_bounds))","def getvarbound(self,i_): # 3\n res,resargs = self.__obj.getvarbound(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _bk_return_value,_bl_return_value,_bu_return_value = resargs\n _bk_return_value = boundkey(_bk_return_value)\n return _bk_return_value,_bl_return_value,_bu_return_value","def set_variable_slices(self, variables):\n # Set up y_slices and bounds\n y_slices = defaultdict(list)\n y_slices_explicit = defaultdict(list)\n start = 0\n end = 0\n lower_bounds = []\n upper_bounds = []\n # Iterate through unpacked variables, adding appropriate slices to y_slices\n for variable in variables:\n # Add up the size of all the domains in variable.domain\n if isinstance(variable, pybamm.ConcatenationVariable):\n start_ = start\n spatial_method = self.spatial_methods[variable.domain[0]]\n children = variable.children\n meshes = OrderedDict()\n for child in children:\n meshes[child] = [spatial_method.mesh[dom] for dom in child.domain]\n sec_points = spatial_method._get_auxiliary_domain_repeats(\n variable.domains\n )\n for i in range(sec_points):\n for child, mesh in meshes.items():\n for domain_mesh in mesh:\n end += domain_mesh.npts_for_broadcast_to_nodes\n # Add to slices\n y_slices[child].append(slice(start_, end))\n y_slices_explicit[child].append(slice(start_, end))\n # Increment start_\n start_ = end\n else:\n end += self._get_variable_size(variable)\n\n # Add to slices\n y_slices[variable].append(slice(start, end))\n y_slices_explicit[variable].append(slice(start, end))\n\n # Add to bounds\n def evaluate_bound(bound, side):\n if bound.has_symbol_of_classes(pybamm.InputParameter):\n if side == \"lower\":\n return -np.inf\n elif side == \"upper\":\n return np.inf\n else:\n return bound.evaluate()\n\n lower_bounds.extend(\n [evaluate_bound(variable.bounds[0], \"lower\")] * (end - start)\n )\n upper_bounds.extend(\n [evaluate_bound(variable.bounds[1], \"upper\")] * (end - start)\n )\n # Increment start\n start = end\n\n # Convert y_slices back to normal dictionary\n self.y_slices = dict(y_slices)\n # Also keep a record of what the y_slices are, to be stored in the model\n self.y_slices_explicit = dict(y_slices_explicit)\n\n # Also keep a record of bounds\n self.bounds = (np.array(lower_bounds), np.array(upper_bounds))\n\n # reset discretised_symbols\n self._discretised_symbols = {}","def bounds(self): # -> tuple[()]:\n ...","def add_time_bounds(nc, varname):\n THREE_HOURS = 60*60*3 # in seconds\n bnds_name = 'time_bnds'\n bounds_dim = 'nv'\n\n # Create bounds dimension\n nc.createDimension(bounds_dim, 2)\n\n # Get variable matching varname\n\n time_var = nc.variables['time']\n time_var.setncattr('bounds', bnds_name)\n time_data = time_var[:]\n time_length = len(time_data)\n\n # reshape time data\n bounds_data = np.dstack((time_data,time_data)).reshape(time_length,2)\n for i in bounds_data:\n i[0] = i[0] - (THREE_HOURS)\n bounds_var = nc.createVariable(bnds_name, time_var.dtype, ('time', bounds_dim), fill_value=9999)\n bounds_var[:] = bounds_data","def set_bounds_atom(self,bounds):\n self.bounds = bounds # data bounds\n raise NotImplementedError\n self.bounds_atom = None\n return None","def optimization_bounds(self, topology):\n bounds_low = np.zeros(self.number_of_parameters())\n bounds_up = np.zeros(self.number_of_parameters())\n\n for pkey, parameter in self.parameters.items():\n bounds_low[pkey] = parameter.bound_low(topology)\n bounds_up[pkey] = parameter.bound_up(topology)\n\n return bounds_low, bounds_up","def simple_bounds(s, Lb, Ub):\n\n\tfor i in range(len(s)):\n\t\tif s[i] > Ub[i]:\n\t\t\ts[i] = Ub[i]\n\t\telif s[i] < Lb[i]:\n\t\t\ts[i] = Lb[i] \n\treturn s","def add_variables(self, n_variables, lb=None, ub=None, var_type=None):\n curr_n_vars = self.problem.variables.get_num()\n\n lb = convert_cplex_val(lb)\n ub = convert_cplex_val(ub)\n\n if var_type.lower() == \"real\" or var_type.lower() == \"continuous\":\n vtype = cplex.Cplex.variables.type.continuous\n\n elif var_type.lower() == \"int\" or var_type.lower() == \"integer\":\n vtype = cplex.Cplex.variables.type.integer\n\n elif var_type.lower() == \"binary\" or var_type.lower() == \"bool\" or var_type.lower() == \"boolean\":\n vtype = cplex.Cplex.variables.type.binary\n\n elif var_type.lower() == \"auto\" or var_type is None:\n vtype = cplex.Cplex.variables.type.binary\n\n else:\n raise Exception(\"Vartype '{}' unsupported.\".format(var_type))\n\n if lb is not None and ub is not None:\n self.problem.variables.add(\n lb=[ lb ] * n_variables,\n ub=[ ub ] * n_variables,\n types=[ vtype ] * n_variables)\n\n elif lb is not None:\n self.problem.variables.add(\n lb=[ lb ] * n_variables,\n types=[ vtype ] * n_variables)\n\n elif ub is not None:\n self.problem.variables.add(\n ub=[ ub ] * n_variables,\n types=[ vtype ] * n_variables)\n\n else:\n self.problem.variables.add(\n types=[ vtype ] * n_variables)\n\n # Return the 0-based indexes of the new variables\n new_var_idxs = xrange(curr_n_vars, curr_n_vars + n_variables)\n return new_var_idxs","def set_in_bounds(self,obj,val):\n if not callable(val):\n bounded_val = self.crop_to_bounds(val)\n else:\n bounded_val = val\n super(Number,self).__set__(obj,bounded_val)","def clean_list(\n l: list,\n ub: int,\n lb: int = 0,\n ) -> list:\n\n # Initialisations\n l_temp = l[:]\n\n # Replace all out-of-bounds values with the boundary values\n l_temp = [clean_int(i, ub, lb = lb) for i in l_temp]\n\n # Sort the list and remove duplicates\n l_temp = list(set(l_temp))\n\n return l_temp","def setSublist(self, sublist):\n if self.cursor:\n self.cursor.sublist = sublist","def _check_bounds(self,val):\n if not (isinstance(val,list)):\n raise ValueError(\"List '%s' must be a list.\"%(self._attrib_name))\n\n if self.bounds is not None:\n min_length,max_length = self.bounds\n l=len(val)\n if min_length is not None and max_length is not None:\n if not (min_length <= l <= max_length):\n raise ValueError(\"%s: list length must be between %s and %s (inclusive)\"%(self._attrib_name,min_length,max_length))\n elif min_length is not None:\n if not min_length <= l: \n raise ValueError(\"%s: list length must be at least %s.\"%(self._attrib_name,min_length))\n elif max_length is not None:\n if not l <= max_length:\n raise ValueError(\"%s: list length must be at most %s.\"%(self._attrib_name,max_length))\n\n self._check_type(val)","def rejection_bounds(parent, child, stepSize, lb, ub):\n assert len(lb) == len(ub), 'Lower and upper bounds have different #s of design variables in rejection_bounds function.'\n assert len(lb) == len(child), 'Bounds and child have different #s of design variables in rejection_bounds function.'\n for i in range(0, len(child), 1):\n stepReductionCount = 0\n while child[i] < lb[i] or child[i] > ub[i]:\n if stepReductionCount >= 5:\n child[i] = cp.copy(parent[i])\n else:\n stepSize[i] = stepSize[i] / 2.0\n child[i] = child[i] - stepSize[i]\n stepReductionCount += 1\n\n return child","def get_bounds():\n return [0.00], [1.00]","def putbararowlist(self,subi_,ptrb_,ptre_,subj_,nummat_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(ptrb_)\n elif num_ != len(ptrb_):\n raise IndexError(\"Inconsistent length of array ptrb\")\n if num_ is None:\n num_ = len(ptre_)\n elif num_ != len(ptre_):\n raise IndexError(\"Inconsistent length of array ptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if ptrb_ is None:\n raise ValueError(\"Argument ptrb cannot be None\")\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb may not be None\")\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\n _ptrb_copyarray = False\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptrb_ is not None:\n _ptrb_copyarray = True\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\n _ptrb_np_tmp[:] = ptrb_\n assert _ptrb_np_tmp.flags.contiguous\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptrb_copyarray = False\n _ptrb_tmp = None\n \n if ptre_ is None:\n raise ValueError(\"Argument ptre cannot be None\")\n if ptre_ is None:\n raise ValueError(\"Argument ptre may not be None\")\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\n _ptre_copyarray = False\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptre_ is not None:\n _ptre_copyarray = True\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\n _ptre_np_tmp[:] = ptre_\n assert _ptre_np_tmp.flags.contiguous\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptre_copyarray = False\n _ptre_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _nummat_minlength = len((subj_))\n if len((subj_)) > 0 and nummat_ is not None and len(nummat_) != len((subj_)):\n raise ValueError(\"Array argument nummat is not long enough: Is %d, expected %d\" % (len(nummat_),len((subj_))))\n if nummat_ is None:\n raise ValueError(\"Argument nummat cannot be None\")\n if nummat_ is None:\n raise ValueError(\"Argument nummat may not be None\")\n if isinstance(nummat_, numpy.ndarray) and nummat_.dtype is numpy.dtype(numpy.int64) and nummat_.flags.contiguous:\n _nummat_copyarray = False\n _nummat_tmp = ctypes.cast(nummat_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nummat_ is not None:\n _nummat_copyarray = True\n _nummat_np_tmp = numpy.zeros(len(nummat_),numpy.dtype(numpy.int64))\n _nummat_np_tmp[:] = nummat_\n assert _nummat_np_tmp.flags.contiguous\n _nummat_tmp = ctypes.cast(_nummat_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nummat_copyarray = False\n _nummat_tmp = None\n \n _matidx_minlength = sum((nummat_))\n if sum((nummat_)) > 0 and matidx_ is not None and len(matidx_) != sum((nummat_)):\n raise ValueError(\"Array argument matidx is not long enough: Is %d, expected %d\" % (len(matidx_),sum((nummat_))))\n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n _weights_minlength = sum((nummat_))\n if sum((nummat_)) > 0 and weights_ is not None and len(weights_) != sum((nummat_)):\n raise ValueError(\"Array argument weights is not long enough: Is %d, expected %d\" % (len(weights_),sum((nummat_))))\n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbararowlist(self.__nativep,num_,_subi_tmp,_ptrb_tmp,_ptre_tmp,_subj_tmp,_nummat_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def incBound(self):\n # increment the bound for the local variables.\n self.incVariableList()\n super().incBound()\n\n # get the last variable.\n idx = len(self.stateNormalPath) - 1\n assert(idx > 0)\n\n # we add the constraints that specify the id of the transition\n self.addConstraintOnIdTransition(idx)","def setActiveBounded(self, bounds=None, status=1): \n if bounds==None or len(bounds)!=4:\n return\n x1,y1,x2,y2 = bounds\n if x1>x2 :\n temp=x1;x1=x2;x2=temp\n if y1>y2:\n temp=y1;y1=y2;y2=temp\n for i in range(0,self.length()):\n x=self.x[i]; y=self.y[i]\n if (x>x1 and x<x2) and (y>y1 and y<y2):\n self.active[i]= status \n return","def _top_to_bottom_translation(self, name, G1, Gsub, new_inst, sub_hierarchy_name, const_list):\n\n logger.debug(f\"transfering constraints from top {name} to bottom {sub_hierarchy_name} \")\n\n if self._if_const(name):\n if sub_hierarchy_name in self.hier_graph_dict and 'constraints' in self.hier_graph_dict[sub_hierarchy_name]:\n sub_const = self.hier_graph_dict[sub_hierarchy_name]['constraints']\n else:\n sub_const = []\n for const in list(const_list):\n if any(isinstance(const, x) for x in [constraint.HorizontalDistance,constraint.VerticalDistance,constraint.BlockDistance]):\n sub_const.append(const)\n logger.debug(f\"transferring global const {const}\")\n elif hasattr(const, \"instances\"):\n logger.debug(f\"checking if sub hierarchy instances are in const defined {Gsub} {new_inst} {const} \")\n sconst = {x:\n [Gsub[block] for block in const.instances if block in Gsub.keys()]\n if x == 'instances'\n else getattr(const, x)\n for x in const.__fields_set__}\n assert 'constraint' in sconst\n logger.debug(f\"transferred constraint instances {Gsub} from {const} to {sconst}\")\n sub_const.append(sconst)\n else:\n sub_const = []\n return sub_const","def __DimSiz_restriction_correct_list_parameter(self):\n\n strTestName = 'The size of a list dimension lower than a parameter (correct)'\n RxCSObject = _RxCSobject()\n\n # Firstly, let us define a reference parameter\n RxCSObject.paramAddMan('iParameter1', 'Int parameter')\n RxCSObject.paramType('iParameter1', int)\n\n # Now, let us define a list\n RxCSObject.paramAddMan('parameter1', 'List parameter')\n RxCSObject.paramType('parameter1', list)\n RxCSObject.paramDimL('parameter1', 'iParameter1', 0) # Size of dimension 0 must be lower than 'iParameter1'\n\n RxCSObject.iParameter1 = 5\n RxCSObject.parameter1 = [0, 1, 2, 4]\n\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)","def _overlap_cvars(self, cwrt, cvdef):\n (creg, cran, clen) = self._parse_reg_range(cwrt)\n s = cvdef.find(creg)\n if s != -1:\n (cvreg, cvran, cvlen) = self._parse_reg_range(cvdef[s:])\n if self._range_overapped(cran, cvran):\n return cvdef[s:s+cvlen]\n return None","def putacollist(self,sub,ptrb,ptre,asub,aval): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(ptrb)\n elif num_ != len(ptrb):\n raise IndexError(\"Inconsistent length of array ptrb\")\n if num_ is None:\n num_ = len(ptre)\n elif num_ != len(ptre):\n raise IndexError(\"Inconsistent length of array ptre\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if ptrb is None: raise TypeError(\"Invalid type for argument ptrb\")\n if ptrb is None:\n ptrb_ = None\n else:\n try:\n ptrb_ = memoryview(ptrb)\n except TypeError:\n try:\n _tmparr_ptrb = array.array(\"q\",ptrb)\n except TypeError:\n raise TypeError(\"Argument ptrb has wrong type\")\n else:\n ptrb_ = memoryview(_tmparr_ptrb)\n \n else:\n if ptrb_.format != \"q\":\n ptrb_ = memoryview(array.array(\"q\",ptrb))\n \n if ptre is None: raise TypeError(\"Invalid type for argument ptre\")\n if ptre is None:\n ptre_ = None\n else:\n try:\n ptre_ = memoryview(ptre)\n except TypeError:\n try:\n _tmparr_ptre = array.array(\"q\",ptre)\n except TypeError:\n raise TypeError(\"Argument ptre has wrong type\")\n else:\n ptre_ = memoryview(_tmparr_ptre)\n \n else:\n if ptre_.format != \"q\":\n ptre_ = memoryview(array.array(\"q\",ptre))\n \n if asub is None: raise TypeError(\"Invalid type for argument asub\")\n if asub is None:\n asub_ = None\n else:\n try:\n asub_ = memoryview(asub)\n except TypeError:\n try:\n _tmparr_asub = array.array(\"i\",asub)\n except TypeError:\n raise TypeError(\"Argument asub has wrong type\")\n else:\n asub_ = memoryview(_tmparr_asub)\n \n else:\n if asub_.format != \"i\":\n asub_ = memoryview(array.array(\"i\",asub))\n \n if aval is None: raise TypeError(\"Invalid type for argument aval\")\n if aval is None:\n aval_ = None\n else:\n try:\n aval_ = memoryview(aval)\n except TypeError:\n try:\n _tmparr_aval = array.array(\"d\",aval)\n except TypeError:\n raise TypeError(\"Argument aval has wrong type\")\n else:\n aval_ = memoryview(_tmparr_aval)\n \n else:\n if aval_.format != \"d\":\n aval_ = memoryview(array.array(\"d\",aval))\n \n res = self.__obj.putacollist64(num_,sub_,ptrb_,ptre_,asub_,aval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def glow_boundary(bound):\n assert bound < 4\n global layout\n temp = len(layout) - 1\n for i in range(bound, bound + len_square(bound)):\n for j in range(bound, bound + len_square(bound)): # TODO: assign this to a variable\t\n layout[i][j] = 1","def removevars(self,subset): # 3\n num_ = None\n if num_ is None:\n num_ = len(subset)\n elif num_ != len(subset):\n raise IndexError(\"Inconsistent length of array subset\")\n if num_ is None: num_ = 0\n if subset is None: raise TypeError(\"Invalid type for argument subset\")\n if subset is None:\n subset_ = None\n else:\n try:\n subset_ = memoryview(subset)\n except TypeError:\n try:\n _tmparr_subset = array.array(\"i\",subset)\n except TypeError:\n raise TypeError(\"Argument subset has wrong type\")\n else:\n subset_ = memoryview(_tmparr_subset)\n \n else:\n if subset_.format != \"i\":\n subset_ = memoryview(array.array(\"i\",subset))\n \n res = self.__obj.removevars(num_,subset_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def variable_bounds(problem):\n return ([\n problem['state_bounds'][var] if problem['state_bounds'][var] is not None else (-np.inf, np.inf)\n for _ in range(problem['N'] - 1)\n for var in range(problem['num_states'])\n ] + [\n problem['input_bounds'][inp] if problem['input_bounds'][inp] is not None else (-np.inf, np.inf)\n for _ in range(problem['N'] + 1)\n for inp in range(problem['num_inputs'])\n ]) * problem['Nv'] + ([(0.01, np.inf)] if problem['T'] == 0 else []) \\\n if problem['state_bounds'] is not None else None","def update_upper_bounds(self, B):\n for arc in self.arcs():\n if self.arc_info[arc[0]]['upper_bound'] == -1:\n self.arc_info[arc[0]]['upper_bound'] = B","def set_bc(self, problem):\n bcs = problem.bcs\n n_bound = cfg.const['N_GHOST_CELLS']\n # Left X-b.c.\n for i in range(0, self.i_min):\n for j in range(self.j_min, self.j_max):\n for k in range(self.k_min, self.k_max): \n if bcs[0] == 't': \n self.U[i][j][k] = self.U[self.i_min][j][k]\n elif bcs[0] == 'w':\n for num in [0, 2, 3, 4]: # 0 -> 3, 1 -> 2, i_min-1 -> i_min, i_min-2 -> i_min+1\n self.U[i][j][k][num] = self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\n for num in [1]:\n self.U[i][j][k][num] = - self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\n else:\n print(\"Errof field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Right X-b.c.\n for i in range(self.i_max, self.i_max+n_bound):\n for j in range(self.j_min, self.j_max):\n for k in range(self.k_min, self.k_max): \n if bcs[1] == 't':\n self.U[i][j][k] = self.U[self.i_max-1][j][k]\n elif bcs[1] == 'w':\n for num in [0, 2, 3, 4]: # i_max -> i_max-1 , i_max+1-> i_max-2\n self.U[i][j][k][num] = self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\n for num in [1]:\n self.U[i][j][k][num] = - self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Left Y-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(0, self.j_min):\n for k in range(self.k_min, self.k_max): \n if bcs[2] == 't':\n self.U[i][j][k] = self.U[i][self.j_min][k]\n elif bcs[2] == 'w':\n for num in [0, 1, 3, 4]:\n self.U[i][j][k][num] = self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\n for num in [2]:\n self.U[i][j][k][num] = - self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Right Y-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(self.j_max, self.j_max+n_bound):\n for k in range(self.k_min, self.k_max): \n if bcs[3] == 't':\n self.U[i][j][k] = self.U[i][self.j_max-1][k]\n elif bcs[3] == 'w':\n for num in [0, 1, 3, 4]:\n self.U[i][j][k][num] = self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\n for num in [2]:\n self.U[i][j][k][num] = -self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Left Z-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(0, self.j_max+n_bound):\n for k in range(0, self.k_min): \n if bcs[4] == 't':\n self.U[i][j][k] = self.U[i][j][self.k_min]\n elif bcs[4] == 'w':\n for num in [0, 1, 2, 4]:\n self.U[i][j][k][num] = self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\n for num in [3]:\n self.U[i][j][k][num] = - self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Right Z-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(0, self.j_max+n_bound):\n for k in range(self.k_max, self.k_max+n_bound):\n if bcs[5] == 't':\n self.U[i][j][k] = self.U[i][j][self.k_max-1]\n elif bcs[5] == 'w':\n for num in [0, 1, 2, 4]:\n self.U[i][j][k][num] = self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\n for num in [3]:\n self.U[i][j][k][num] = - self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")","def varcopy(self, vars):","def set_bounds(self, **kwargs):\n for name, bounds in kwargs.items():\n if name not in self._parameters:\n raise AttributeError('Unknown parameter %s for %s' % (name, self.__class__.__name__))\n param = self._parameters[name]\n # Set bounds\n lower_bound, upper_bound = bounds\n if torch.is_tensor(lower_bound) and torch.is_tensor(upper_bound):\n if lower_bound.size() != upper_bound.size() or \\\n lower_bound.size() != param.size():\n raise AttributeError('Lower bound, upper bound, and param should have the same size')\n elif not (isinstance(lower_bound, int) or isinstance(lower_bound, float)) or \\\n not (isinstance(upper_bound, int) or isinstance(upper_bound, float)):\n raise AttributeError('Unsupported argument types for parameter %s' % name)\n\n if name not in self._bounds:\n self._bounds[name] = [None, None]\n self._bounds[name][0] = lower_bound\n self._bounds[name][1] = upper_bound\n return self","def getx(v, lb, ub, i, B):\r\n x = lb + np.multiply((ub - lb), v)\r\n x[i] = B - (x.sum() - x[i])\r\n # Test if variable x[i] is within the bounds\r\n if x[i] <= ub[i] and x[i] >= lb[i]:\r\n return x\r\n else:\r\n return np.array([])","def update_bounds(self, temp):\n if (temp.extent[0] < self.extent[0]):\n self.extent[0] = np.copy(temp.extent[0])\n if (temp.extent[1] > self.extent[1]):\n self.extent[1] = np.copy(temp.extent[1])\n if (temp.extent[2] < self.extent[2]):\n self.extent[2] = np.copy(temp.extent[2])\n if (temp.extent[3] > self.extent[3]):\n self.extent[3] = np.copy(temp.extent[3])\n return self","def bounds(self, resids: NDArray) -> List[Tuple[float, float]]:"],"string":"[\n \"def putvarboundlist(self,sub_,bkx_,blx_,bux_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkx_)\\n elif num_ != len(bkx_):\\n raise IndexError(\\\"Inconsistent length of array bkx\\\")\\n if num_ is None:\\n num_ = len(blx_)\\n elif num_ != len(blx_):\\n raise IndexError(\\\"Inconsistent length of array blx\\\")\\n if num_ is None:\\n num_ = len(bux_)\\n elif num_ != len(bux_):\\n raise IndexError(\\\"Inconsistent length of array bux\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if bkx_ is None:\\n raise ValueError(\\\"Argument bkx cannot be None\\\")\\n if bkx_ is None:\\n raise ValueError(\\\"Argument bkx may not be None\\\")\\n if bkx_ is not None:\\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\\n else:\\n _bkx_tmp = None\\n if blx_ is None:\\n raise ValueError(\\\"Argument blx cannot be None\\\")\\n if blx_ is None:\\n raise ValueError(\\\"Argument blx may not be None\\\")\\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\\n _blx_copyarray = False\\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blx_ is not None:\\n _blx_copyarray = True\\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\\n _blx_np_tmp[:] = blx_\\n assert _blx_np_tmp.flags.contiguous\\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blx_copyarray = False\\n _blx_tmp = None\\n \\n if bux_ is None:\\n raise ValueError(\\\"Argument bux cannot be None\\\")\\n if bux_ is None:\\n raise ValueError(\\\"Argument bux may not be None\\\")\\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\\n _bux_copyarray = False\\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bux_ is not None:\\n _bux_copyarray = True\\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\\n _bux_np_tmp[:] = bux_\\n assert _bux_np_tmp.flags.contiguous\\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bux_copyarray = False\\n _bux_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundlist(self.__nativep,num_,_sub_tmp,_bkx_tmp,_blx_tmp,_bux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarboundlist(self,sub,bkx,blx,bux): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkx)\\n elif num_ != len(bkx):\\n raise IndexError(\\\"Inconsistent length of array bkx\\\")\\n if num_ is None:\\n num_ = len(blx)\\n elif num_ != len(blx):\\n raise IndexError(\\\"Inconsistent length of array blx\\\")\\n if num_ is None:\\n num_ = len(bux)\\n elif num_ != len(bux):\\n raise IndexError(\\\"Inconsistent length of array bux\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if bkx is None: raise TypeError(\\\"Invalid type for argument bkx\\\")\\n if bkx is None:\\n bkx_ = None\\n else:\\n try:\\n bkx_ = memoryview(bkx)\\n except TypeError:\\n try:\\n _tmparr_bkx = array.array(\\\"i\\\",bkx)\\n except TypeError:\\n raise TypeError(\\\"Argument bkx has wrong type\\\")\\n else:\\n bkx_ = memoryview(_tmparr_bkx)\\n \\n else:\\n if bkx_.format != \\\"i\\\":\\n bkx_ = memoryview(array.array(\\\"i\\\",bkx))\\n \\n if blx is None: raise TypeError(\\\"Invalid type for argument blx\\\")\\n if blx is None:\\n blx_ = None\\n else:\\n try:\\n blx_ = memoryview(blx)\\n except TypeError:\\n try:\\n _tmparr_blx = array.array(\\\"d\\\",blx)\\n except TypeError:\\n raise TypeError(\\\"Argument blx has wrong type\\\")\\n else:\\n blx_ = memoryview(_tmparr_blx)\\n \\n else:\\n if blx_.format != \\\"d\\\":\\n blx_ = memoryview(array.array(\\\"d\\\",blx))\\n \\n if bux is None: raise TypeError(\\\"Invalid type for argument bux\\\")\\n if bux is None:\\n bux_ = None\\n else:\\n try:\\n bux_ = memoryview(bux)\\n except TypeError:\\n try:\\n _tmparr_bux = array.array(\\\"d\\\",bux)\\n except TypeError:\\n raise TypeError(\\\"Argument bux has wrong type\\\")\\n else:\\n bux_ = memoryview(_tmparr_bux)\\n \\n else:\\n if bux_.format != \\\"d\\\":\\n bux_ = memoryview(array.array(\\\"d\\\",bux))\\n \\n res = self.__obj.putvarboundlist(num_,sub_,bkx_,blx_,bux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarbound(self,j_,bkx_,blx_,bux_):\\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarbound(self,j_,bk_,bl_,bu_): # 3\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundlist(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkc_)\\n elif num_ != len(bkc_):\\n raise IndexError(\\\"Inconsistent length of array bkc\\\")\\n if num_ is None:\\n num_ = len(blc_)\\n elif num_ != len(blc_):\\n raise IndexError(\\\"Inconsistent length of array blc\\\")\\n if num_ is None:\\n num_ = len(buc_)\\n elif num_ != len(buc_):\\n raise IndexError(\\\"Inconsistent length of array buc\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc cannot be None\\\")\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc may not be None\\\")\\n if bkc_ is not None:\\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\\n else:\\n _bkc_tmp = None\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc cannot be None\\\")\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc may not be None\\\")\\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\\n _blc_copyarray = False\\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blc_ is not None:\\n _blc_copyarray = True\\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\\n _blc_np_tmp[:] = blc_\\n assert _blc_np_tmp.flags.contiguous\\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blc_copyarray = False\\n _blc_tmp = None\\n \\n if buc_ is None:\\n raise ValueError(\\\"Argument buc cannot be None\\\")\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc may not be None\\\")\\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\\n _buc_copyarray = False\\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif buc_ is not None:\\n _buc_copyarray = True\\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\\n _buc_np_tmp[:] = buc_\\n assert _buc_np_tmp.flags.contiguous\\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _buc_copyarray = False\\n _buc_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarboundslice(self,first_,last_,bkx_,blx_,bux_):\\n _bkx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bkx_ is not None and len(bkx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bkx is not long enough: Is %d, expected %d\\\" % (len(bkx_),((last_) - (first_))))\\n if bkx_ is None:\\n raise ValueError(\\\"Argument bkx cannot be None\\\")\\n if bkx_ is None:\\n raise ValueError(\\\"Argument bkx may not be None\\\")\\n if bkx_ is not None:\\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\\n else:\\n _bkx_tmp = None\\n _blx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and blx_ is not None and len(blx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument blx is not long enough: Is %d, expected %d\\\" % (len(blx_),((last_) - (first_))))\\n if blx_ is None:\\n raise ValueError(\\\"Argument blx cannot be None\\\")\\n if blx_ is None:\\n raise ValueError(\\\"Argument blx may not be None\\\")\\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\\n _blx_copyarray = False\\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blx_ is not None:\\n _blx_copyarray = True\\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\\n _blx_np_tmp[:] = blx_\\n assert _blx_np_tmp.flags.contiguous\\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blx_copyarray = False\\n _blx_tmp = None\\n \\n _bux_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bux_ is not None and len(bux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bux is not long enough: Is %d, expected %d\\\" % (len(bux_),((last_) - (first_))))\\n if bux_ is None:\\n raise ValueError(\\\"Argument bux cannot be None\\\")\\n if bux_ is None:\\n raise ValueError(\\\"Argument bux may not be None\\\")\\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\\n _bux_copyarray = False\\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bux_ is not None:\\n _bux_copyarray = True\\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\\n _bux_np_tmp[:] = bux_\\n assert _bux_np_tmp.flags.contiguous\\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bux_copyarray = False\\n _bux_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundslice(self.__nativep,first_,last_,_bkx_tmp,_blx_tmp,_bux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundlist(self,sub,bk,bl,bu): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bk)\\n elif num_ != len(bk):\\n raise IndexError(\\\"Inconsistent length of array bk\\\")\\n if num_ is None:\\n num_ = len(bl)\\n elif num_ != len(bl):\\n raise IndexError(\\\"Inconsistent length of array bl\\\")\\n if num_ is None:\\n num_ = len(bu)\\n elif num_ != len(bu):\\n raise IndexError(\\\"Inconsistent length of array bu\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n res = self.__obj.putconboundlist(num_,sub_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def chgvarbound(self,j_,lower_,finite_,value_): # 3\\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def chgvarbound(self,j_,lower_,finite_,value_):\\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putboundlist(self,accmode_,sub,bk,bl,bu): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bk)\\n elif num_ != len(bk):\\n raise IndexError(\\\"Inconsistent length of array bk\\\")\\n if num_ is None:\\n num_ = len(bl)\\n elif num_ != len(bl):\\n raise IndexError(\\\"Inconsistent length of array bl\\\")\\n if num_ is None:\\n num_ = len(bu)\\n elif num_ != len(bu):\\n raise IndexError(\\\"Inconsistent length of array bu\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n res = self.__obj.putboundlist(accmode_,num_,sub_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _initialize_bounds(problem, bounds, get_bound, set_bound):\\n for constraint in problem.constraints:\\n root_expr = constraint.root_expr\\n expr_bounds = Interval(constraint.lower_bound, constraint.upper_bound)\\n if root_expr not in bounds:\\n set_bound(root_expr, expr_bounds)\\n else:\\n existing_bounds = get_bound(root_expr)\\n new_bounds = existing_bounds.intersect(expr_bounds)\\n set_bound(root_expr, new_bounds)\",\n \"def simple_bounds(child, lb, ub):\\n assert len(lb) == len(ub), 'Lower and upper bounds have different #s of design variables in simple_bounds function.'\\n assert len(lb) == len(child), 'Bounds and child have different #s of design variables in simple_bounds function.'\\n for i in range(0, len(child), 1):\\n if child[i] < lb[i]:\\n child[i] = lb[i]\\n\\n for i in range(0, len(child), 1):\\n if child[i] > ub[i]:\\n child[i] = ub[i]\\n\\n return child\",\n \"def apply_bound(x, var_min, var_max):\\n x.position = np.maximum(x.position, var_min)\\n x.position = np.minimum(x.position, var_max)\",\n \"def getvarboundslice(self,first_,last_,bk_,bl_,bu_):\\n _bk_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk is not long enough: Is %d, expected %d\\\" % (len(bk_),((last_) - (first_))))\\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\\n raise ValueError(\\\"Argument bk must be writable\\\")\\n if bk_ is not None:\\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\\n else:\\n _bk_tmp = None\\n _bl_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl is not long enough: Is %d, expected %d\\\" % (len(bl_),((last_) - (first_))))\\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\\n raise ValueError(\\\"Argument bl must be writable\\\")\\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\\n _bl_copyarray = False\\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bl_ is not None:\\n _bl_copyarray = True\\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\\n _bl_np_tmp[:] = bl_\\n assert _bl_np_tmp.flags.contiguous\\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bl_copyarray = False\\n _bl_tmp = None\\n \\n _bu_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu is not long enough: Is %d, expected %d\\\" % (len(bu_),((last_) - (first_))))\\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\\n raise ValueError(\\\"Argument bu must be writable\\\")\\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\\n _bu_copyarray = False\\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bu_ is not None:\\n _bu_copyarray = True\\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\\n _bu_np_tmp[:] = bu_\\n assert _bu_np_tmp.flags.contiguous\\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bu_copyarray = False\\n _bu_tmp = None\\n \\n res = __library__.MSK_XX_getvarboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\\n if _bl_copyarray:\\n bl_[:] = _bl_np_tmp\\n if _bu_copyarray:\\n bu_[:] = _bu_np_tmp\",\n \"def RestrictionRangeBound(self, compsIdList, lowerBound, upperBound):\\n for i in range(len(compsIdList)): compsIdList[i] -= 1\\n if self.solverTypeOptimize:\\n self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) >= lowerBound)\\n else:\\n self.solver.assert_and_track(\\n PbGe(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\\n lowerBound), \\\"LabelRangeBound: \\\" + str(self.labelIdx))\\n self.labelIdx += 1\\n if self.solverTypeOptimize:\\n PbLe(self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\\n upperBound))\\n else:\\n self.solver.assert_and_track(\\n sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) <= upperBound, \\\"LabelRangeBound: \\\" + str(self.labelIdx))\\n self.labelIdx += 1\",\n \"def set_bounds_atom(self,bounds):\\n assert bounds.shape == (2,self.Phi.d)\\n self.bounds = bounds # data bounds\\n self.bounds_atom = bounds.T.tolist()\\n for i in range(self.Phi.d): # bounds for the variance in each dimension\\n max_variance_this_dimension = (bounds[1][i]-bounds[0][i])**2\\n self.bounds_atom.append([self.variance_relative_lowerbound*max_variance_this_dimension,\\n self.variance_relative_upperbound*max_variance_this_dimension])\",\n \"def create_from_bounds(self, lbs, ubs):\\n self.base_vertices = (np.array([lbs])+np.array([ubs])).T/2\\n self.base_vectors = np.diag((np.array(ubs)-np.array(lbs))/2)\",\n \"def getvarboundslice(self,first_,last_,bk,bl,bu): # 3\\n _copyback_bk = False\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n _copyback_bk = True\\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n _copyback_bk = True\\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n _copyback_bl = False\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n _copyback_bl = True\\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n _copyback_bl = True\\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n _copyback_bu = False\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n _copyback_bu = True\\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n _copyback_bu = True\\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.getvarboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_bu:\\n bu[:] = _tmparr_bu\\n if _copyback_bl:\\n bl[:] = _tmparr_bl\\n if _copyback_bk:\\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])\",\n \"def putconbound(self,i_,bk_,bl_,bu_): # 3\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def compute_bounds(self, weights, add_coeff, dual_vars, lower_bounds, upper_bounds, store_primal=False):\\n x0_coeff = -weights[0].backward(dual_vars.mus[0])\\n x0 = torch.where(x0_coeff >= 0, lower_bounds[0].unsqueeze(1), upper_bounds[0].unsqueeze(1))\\n bound = utils.bdot(x0, x0_coeff)\\n if store_primal:\\n self.bounds_primal = x0\\n else:\\n del x0\\n del x0_coeff\\n\\n for lay_idx in range(1, len(weights)):\\n lbs = lower_bounds[lay_idx].unsqueeze(1).clamp(None, 0)\\n ubs = upper_bounds[lay_idx].unsqueeze(1).clamp(0, None)\\n neg_bias = ((lbs * ubs) / (ubs - lbs))\\n neg_bias.masked_fill_(ubs == lbs, 0) # cover case in which ubs & lbs coincide\\n bound += utils.bdot(dual_vars.lambdas[lay_idx - 1].clamp(0, None), neg_bias)\\n bound -= utils.bdot(dual_vars.mus[lay_idx - 1], weights[lay_idx - 1].get_bias())\\n\\n bound += utils.bdot(add_coeff, weights[-1].get_bias())\\n return bound\",\n \"def create_bound_for_scipy(lb, ub):\\n lb = tuple(map(convert_inf_to_none, lb))\\n ub = tuple(map(convert_inf_to_none, ub))\\n return list((lb[i], ub[i]) for i in range(len(ub)))\",\n \"def bounds(self, new_bounds: devices.PrimaryBounds) -> None:\\n self._assert_bounds_are_valid(new_bounds)\\n self._bounds = list(new_bounds)\",\n \"def set_bounds(self, new_bounds):\\n\\n # Update the internal object stored dict\\n self.pbounds.update(new_bounds)\\n\\n # Loop through the all bounds and reset the min-max bound matrix\\n for row, key in enumerate(self.pbounds.keys()):\\n\\n # Reset all entries, even if the same.\\n self.bounds[row] = self.pbounds[key]\",\n \"def bounds(self, pos):\",\n \"def removebarvars(self,subset_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subset_)\\n elif num_ != len(subset_):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset cannot be None\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset may not be None\\\")\\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\\n _subset_copyarray = False\\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subset_ is not None:\\n _subset_copyarray = True\\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\\n _subset_np_tmp[:] = subset_\\n assert _subset_np_tmp.flags.contiguous\\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subset_copyarray = False\\n _subset_tmp = None\\n \\n res = __library__.MSK_XX_removebarvars(self.__nativep,num_,_subset_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def cb_bounds(self, variable, results_dict, keys, fixed_bounds):\\n tas_bound, pr_bound = fixed_bounds\\n if variable == \\\"tas\\\":\\n if tas_bound:\\n bound_limit = tas_bound\\n else:\\n bound_limit = self.find_abs_bound_range(results_dict, keys)\\n cmap = plt.cm.RdBu_r\\n else:\\n if pr_bound:\\n bound_limit = pr_bound\\n else:\\n bound_limit = self.find_abs_bound_range(results_dict,\\n keys,\\n avg_over=25)\\n cmap = plt.cm.BrBG\\n bounds = np.linspace(-1 * bound_limit, bound_limit, 11)\\n return [bounds, cmap]\",\n \"def _add_bound_configs(CONFIG):\\n CONFIG.declare(\\n 'obj_bound',\\n ConfigValue(\\n default=1e15,\\n domain=PositiveFloat,\\n description='Bound applied to the linearization of the objective function if main MIP is unbounded.',\\n ),\\n )\\n CONFIG.declare(\\n 'continuous_var_bound',\\n ConfigValue(\\n default=1e10,\\n description='Default bound added to unbounded continuous variables in nonlinear constraint if single tree is activated.',\\n domain=PositiveFloat,\\n ),\\n )\\n CONFIG.declare(\\n 'integer_var_bound',\\n ConfigValue(\\n default=1e9,\\n description='Default bound added to unbounded integral variables in nonlinear constraint if single tree is activated.',\\n domain=PositiveFloat,\\n ),\\n )\\n CONFIG.declare(\\n 'initial_bound_coef',\\n ConfigValue(\\n default=1e-1,\\n domain=PositiveFloat,\\n description='The coefficient used to approximate the initial primal/dual bound.',\\n ),\\n )\",\n \"def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\\n ...\",\n \"def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\\n ...\",\n \"def __get_new_restriction_branch_and_bound(self, tableau, var_col, b_value):\\n restriction_row = np.zeros((tableau.get_tableau_num_cols())).astype('object')\\n restriction_row[var_col] = 1\\n restriction_row[tableau.get_tableau_num_cols() - 1] = b_value\\n\\n for i in xrange(0, len(restriction_row)):\\n restriction_row[i] = Fraction(restriction_row[i])\\n\\n return restriction_row\",\n \"def RestrictionUpperLowerEqualBound(self, compsIdList, bound, operator):\\n\\n self.problem.logger.debug(\\\"RestrictionUpperLowerEqualBound: {} {} {} \\\".format(compsIdList, operator, bound))\\n\\n if operator == \\\"<=\\\":\\n if self.solverTypeOptimize:\\n bvars = [self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]\\n self.solver.add(PbLe([(x, 1) for x in bvars], bound))\\n else:\\n self.__constMap[str(\\\"LabelUpperLowerEqualBound\\\" + str(self.labelIdx))] = sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) <= bound\\n self.solver.assert_and_track(PbLe([(x, 1) for x in bvars], bound), \\\"LabelUpperLowerEqualBound\\\" + str(self.labelIdx))\\n self.labelIdx += 1\\n elif operator == \\\">=\\\":\\n if self.solverTypeOptimize:\\n bvars = [self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]\\n self.solver.add(PbGe([(x, 1) for x in bvars], bound))\\n else:\\n self.__constMap[str(\\\"LabelUpperLowerEqualBound\\\" + str(self.labelIdx))] = sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) >= bound\\n self.solver.assert_and_track(\\n sum([If(self.a[compId * self.nrVM + j], 1, 0) for compId in compsIdList for j in range(self.nrVM)]) >= bound, \\\"LabelUpperLowerEqualBound\\\" + str(self.labelIdx))\\n self.labelIdx += 1\\n elif operator == \\\"=\\\":\\n if self.solverTypeOptimize:\\n bvars = [self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]\\n self.solver.add(PbEq([(x, 1) for x in bvars], bound))\\n else:\\n self.__constMap[str(\\\"LabelUpperLowerEqualBound\\\" + str(self.labelIdx))] = sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) == bound\\n\\n self.solver.assert_and_track(\\n sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) == bound, \\\"LabelUpperLowerEqualBound\\\" + str(self.labelIdx))\\n self.labelIdx += 1\\n else:\\n self.problem.logger.info(\\\"Unknown operator\\\")\",\n \"def set_bounds_atom(self,bounds):\\n assert bounds.shape == (2,self.Phi.d)\\n self.bounds = bounds # data bounds\\n self.bounds_atom = bounds.T.tolist()\",\n \"def extend_bounds(problem):\\n\\n num_vars = problem[\\\"num_vars\\\"]\\n num_ff_vars = 2 ** find_smallest(num_vars)\\n num_dummy_variables = num_ff_vars - num_vars\\n\\n bounds = list(problem[\\\"bounds\\\"])\\n names = problem[\\\"names\\\"]\\n if num_dummy_variables > 0:\\n bounds.extend([[0, 1] for x in range(num_dummy_variables)])\\n names.extend([\\\"dummy_\\\" + str(var) for var in range(num_dummy_variables)])\\n problem[\\\"bounds\\\"] = bounds\\n problem[\\\"names\\\"] = names\\n problem[\\\"num_vars\\\"] = num_ff_vars\\n\\n return problem\",\n \"def putconboundslice(self,first_,last_,bk,bl,bu): # 3\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putconboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _add_boundaries(self):\\n logger.info(\\\"add_boundaries: start of method\\\")\\n\\n import scipy.spatial as sptl\\n import scipy.sparse as sprs\\n Lx = self._Lx\\n Ly = self._Ly\\n Lz = self._Lz\\n Np = self.num_pores()\\n btype = self._btype\\n boffset = 0.05\\n\\n #Translate internal pores to each face of domain\\n poffset = np.zeros((7,3))\\n poffset[[2,5],0] = [-Lx, Lx]\\n poffset[[3,4],1] = [-Ly, Ly]\\n poffset[[1,6],2] = [-Lz, Lz]\\n pcoords = pcoords0 = self['pore.coords']\\n for i in np.r_[1:7]:\\n pcoords = np.concatenate((pcoords,pcoords0 + poffset[i,:]),axis=0)\\n\\n #Use some twisted logic to get bval list of + for boundary and - for periodic faces\\n bval = [0, 1, 2, 3, 4, 5, 6]*(np.array([0, btype[2], btype[0], btype[1], btype[1], btype[0], btype[2]])*-2+1)\\n ptype = np.zeros((Np,),dtype=int)\\n for i in np.r_[1:7]:\\n ptype = np.concatenate((ptype,np.ones((Np,),dtype=int)*bval[i]),axis=0)\\n\\n #pnum contains the internal ID number of the boundary pores (for connecting periodic points)\\n pnum = self.pores()\\n pnum = np.tile(pnum,7)\\n\\n Tri = sptl.Delaunay(pcoords)\\n adjmat = sprs.lil_matrix((np.shape(pcoords)[0],np.shape(pcoords)[0]),dtype=int)\\n for i in np.arange(0,np.shape(Tri.simplices)[0]):\\n #Keep only simplices that are fully in real domain\\n adjmat[Tri.simplices[i],Tri.simplices[i]] = 1\\n adjmat = sprs.triu(adjmat,k=1,format=\\\"lil\\\")\\n for i in np.arange(0,Np):\\n #Add periodic throats to the netowrk (if any)\\n tpore2 = pnum[adjmat.rows[i]][ptype[adjmat.rows[i]]<0]\\n tpore1 = np.ones_like(tpore2,dtype=int)*i\\n conns = self['throat.conns']\\n conns = np.concatenate((conns,np.vstack((tpore1,tpore2)).T),axis=0)\\n #Add boundary pores and throats to the network\\n newporetyps = np.unique(ptype[adjmat.rows[i]][ptype[adjmat.rows[i]]>0])\\n newporenums = np.r_[self.num_pores():self.num_pores()+np.size(newporetyps)]\\n tpore2 = newporenums\\n tpore1 = np.ones_like(tpore2,dtype=int)*i\\n conns = np.concatenate((conns,np.vstack((tpore1,tpore2)).T),axis=0)\\n self['throat.conns'] = conns\\n bcoords = np.zeros((7,3),dtype=float)\\n coords = self['pore.coords']\\n bcoords[1,:] = [coords[i,0], coords[i,1], 0-Lz*boffset]\\n bcoords[2,:] = [0-Lx*boffset, coords[i,1], coords[i,2]]\\n bcoords[3,:] = [coords[i,0], -Ly*boffset, coords[i,2]]\\n bcoords[4,:] = [coords[i,0], Ly+Ly*boffset, coords[i,2]]\\n bcoords[5,:] = [Lx+Lx*boffset, coords[i,1], coords[i,2]]\\n bcoords[6,:] = [coords[i,0], coords[i,1], Lz+Lz*boffset]\\n newporecoords = bcoords[newporetyps,:]\\n coords = np.concatenate((coords,newporecoords),axis=0)\\n self['pore.coords'] = coords\\n #Reset number of pores and throats (easier than tracking it)\\n nums = np.r_[0:np.shape(coords)[0]]\\n self['pore.numbering'] = nums\\n self['pore.numbering'] = np.ones((nums[-1]+1,),dtype=bool)\\n nums = np.r_[0:np.shape(conns)[0]]\\n self['throat.numbering'] = nums\\n self['throat.numbering'] = np.ones((nums[-1]+1,),dtype=bool)\\n logger.debug(\\\"add_boundaries: end of method\\\")\",\n \"def change_state_bounds(self, state, new_ub, new_lb, slack, comp=None,\\n node=None):\\n # TODO Adapt method so you can change only one of the settings?\\n # TODO Put None as default parameter value and detect if other value is supplied\\n comp_obj = self.get_component(comp, node)\\n\\n comp_obj.params[state].change_upper_bound(new_ub)\\n comp_obj.params[state].change_lower_bound(new_lb)\\n comp_obj.params[state].change_slack(slack)\",\n \"def add_boundary(self, boundary):\\n self._bounds.append(boundary)\",\n \"def _set_bounds(b, x, n):\\n for j in range(1, n - 1):\\n for i in range(1, n - 1):\\n x[index_of(i, j, 0, n)] = -x[index_of(i, j, 1, n)] if b == 3 else x[index_of(i, j, 1, n)]\\n x[index_of(i, j, 0, n - 1)] = -x[index_of(i, j, 1, n - 2)] if b == 3 else x[index_of(i, j, 1, n - 2)]\\n for k in range(1, n - 1):\\n for i in range(1, n - 1):\\n x[index_of(i, 0, k, n)] = -x[index_of(i, 1, k, n)] if b == 2 else x[index_of(i, 1, k, n)]\\n x[index_of(i, n - 1, 0, n - 1)] = -x[index_of(i, n - 2, k, n - 2)] if b == 2 else x[\\n index_of(i, n - 2, k, n - 2)]\\n for k in range(1, n - 1):\\n for j in range(1, n - 1):\\n x[index_of(0, j, k, n)] = -x[index_of(1, j, k, n)] if b == 1 else x[index_of(1, j, k, n)]\\n x[index_of(n - 1, j, k, n - 1)] = -x[index_of(n - 2, j, k, n)] if b == 1 else x[\\n index_of(n - 2, j, k, n)]\\n\\n x[index_of(0, 0, 0, n)] = 1 / 3 * (x[index_of(1, 0, 0, n)] + x[index_of(0, 1, 0, n)] + x[index_of(0, 0, 1, n)])\\n x[index_of(0, n - 1, 0, n)] = 1 / 3 * (\\n x[index_of(1, n - 1, 0, n)] + x[index_of(0, n - 2, 0, n)] + x[index_of(0, n - 1, 1, n)])\\n x[index_of(0, 0, n - 1, n)] = 1 / 3 * (\\n x[index_of(1, 0, n - 1, n)] + x[index_of(0, 1, n - 1, n)] + x[index_of(0, 0, n - 2, n)])\\n x[index_of(0, n - 1, n - 1, n)] = 1 / 3 * (\\n x[index_of(1, n - 1, n - 1, n)] + x[index_of(0, n - 2, n - 1, n)] + x[index_of(0, n - 1, n - 2, n)])\\n x[index_of(n - 1, 0, 0, n)] = 1 / 3 * (\\n x[index_of(n - 2, 0, 0, n)] + x[index_of(n - 1, 1, 0, n)] + x[index_of(n - 1, 0, 1, n)])\\n x[index_of(n - 1, n - 1, 0, n)] = 1 / 3 * (\\n x[index_of(n - 2, n - 1, 0, n)] + x[index_of(n - 1, n - 2, 0, n)] + x[index_of(n - 1, n - 1, 1, n)])\\n x[index_of(n - 1, 0, n - 1, n)] = 1 / 3 * (\\n x[index_of(n - 2, 0, n - 1, n)] + x[index_of(n - 1, 1, n - 1, n)] + x[index_of(n - 1, 0, n - 2, n)])\\n x[index_of(n - 1, n - 1, n - 1, n)] = 1 / 3 * (\\n x[index_of(n - 2, n - 1, n - 1, n)] + x[index_of(n - 1, n - 2, n - 1, n)] + x[\\n index_of(n - 1, n - 1, n - 2, n)])\",\n \"def putvartypelist(self,subj_,vartype_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(vartype_)\\n elif num_ != len(vartype_):\\n raise IndexError(\\\"Inconsistent length of array vartype\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if vartype_ is None:\\n raise ValueError(\\\"Argument vartype cannot be None\\\")\\n if vartype_ is None:\\n raise ValueError(\\\"Argument vartype may not be None\\\")\\n if vartype_ is not None:\\n _vartype_tmp = (ctypes.c_int32 * len(vartype_))(*vartype_)\\n else:\\n _vartype_tmp = None\\n res = __library__.MSK_XX_putvartypelist(self.__nativep,num_,_subj_tmp,_vartype_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def get_variables_binds(self, predicate, bound_variables=None, variables_binds=None, recursion_level=1):\\n\\n # print(\\\"EXPLORING\\\", recursion_level, predicate, variables_binds)\\n\\n # Set of bound variables in predicate body\\n if bound_variables is None:\\n bound_variables = set()\\n\\n # Possible binds\\n if variables_binds is None:\\n variables_binds = [{}]\\n\\n recursion_level -= 1\\n\\n new_possible_binds = []\\n\\n for body_clause in predicate.body:\\n adornments = self.compute_adornments(body_clause.parameters, bound_variables)\\n\\n # For each fact search if we can match every bound variable and assign free ones\\n if body_clause.name in self._facts:\\n for fact in self._facts[body_clause.name]:\\n possible_binds = self.check_fact_with_adornment(fact, body_clause, adornments, variables_binds)\\n if len(possible_binds):\\n # A fact matched, we add variables binds to sup\\n new_possible_binds.extend(possible_binds)\\n\\n # if len(new_possible_binds):\\n # variables_binds = new_possible_binds\\n\\n if recursion_level > 0:\\n # For each rule\\n if body_clause.name in self._rules:\\n for applicable_rule in self._rules[body_clause.name]:\\n\\n n_bound_variables = set()\\n n_variables_binds = [{}]\\n\\n for index, argument in enumerate(body_clause.parameters):\\n rule_corresponding_parameter = applicable_rule.head.parameters[index]\\n\\n if rule_corresponding_parameter.is_constant():\\n if argument.is_constant():\\n if rule_corresponding_parameter.value != argument.value:\\n break\\n else:\\n if adornments[index]:\\n if argument.is_constant():\\n n_bound_variables.add(rule_corresponding_parameter.name)\\n n_variables_binds[0][rule_corresponding_parameter.name] = argument.value\\n elif argument.name in bound_variables and argument.name in variables_binds[0]:\\n n_bound_variables.add(rule_corresponding_parameter.name)\\n n_variables_binds[0][rule_corresponding_parameter.name] = variables_binds[0][argument.name]\\n\\n applicable_predicate_binds = self.get_variables_binds(applicable_rule, n_bound_variables, n_variables_binds, recursion_level)\\n for n_bind in applicable_predicate_binds:\\n adapted_bind = self.substitute_variable_names(n_bind, applicable_rule.head, body_clause)\\n new_possible_binds.extend(adapted_bind)\\n\\n if len(new_possible_binds):\\n variables_binds = new_possible_binds.copy()\\n new_possible_binds.clear()\\n else:\\n variables_binds = [{}]\\n\\n new_possible_binds_no_duplicates = self.remove_duplicate_binds(variables_binds)\\n\\n if len(new_possible_binds_no_duplicates):\\n yield new_possible_binds_no_duplicates\",\n \"def putconboundslice(self,first_,last_,bkc_,blc_,buc_):\\n _bkc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bkc_ is not None and len(bkc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bkc is not long enough: Is %d, expected %d\\\" % (len(bkc_),((last_) - (first_))))\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc cannot be None\\\")\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc may not be None\\\")\\n if bkc_ is not None:\\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\\n else:\\n _bkc_tmp = None\\n _blc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and blc_ is not None and len(blc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument blc is not long enough: Is %d, expected %d\\\" % (len(blc_),((last_) - (first_))))\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc cannot be None\\\")\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc may not be None\\\")\\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\\n _blc_copyarray = False\\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blc_ is not None:\\n _blc_copyarray = True\\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\\n _blc_np_tmp[:] = blc_\\n assert _blc_np_tmp.flags.contiguous\\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blc_copyarray = False\\n _blc_tmp = None\\n \\n _buc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and buc_ is not None and len(buc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument buc is not long enough: Is %d, expected %d\\\" % (len(buc_),((last_) - (first_))))\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc cannot be None\\\")\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc may not be None\\\")\\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\\n _buc_copyarray = False\\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif buc_ is not None:\\n _buc_copyarray = True\\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\\n _buc_np_tmp[:] = buc_\\n assert _buc_np_tmp.flags.contiguous\\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _buc_copyarray = False\\n _buc_tmp = None\\n \\n res = __library__.MSK_XX_putconboundslice(self.__nativep,first_,last_,_bkc_tmp,_blc_tmp,_buc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def removebarvars(self,subset): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subset)\\n elif num_ != len(subset):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if num_ is None: num_ = 0\\n if subset is None: raise TypeError(\\\"Invalid type for argument subset\\\")\\n if subset is None:\\n subset_ = None\\n else:\\n try:\\n subset_ = memoryview(subset)\\n except TypeError:\\n try:\\n _tmparr_subset = array.array(\\\"i\\\",subset)\\n except TypeError:\\n raise TypeError(\\\"Argument subset has wrong type\\\")\\n else:\\n subset_ = memoryview(_tmparr_subset)\\n \\n else:\\n if subset_.format != \\\"i\\\":\\n subset_ = memoryview(array.array(\\\"i\\\",subset))\\n \\n res = self.__obj.removebarvars(num_,subset_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _update_vids(cls, cnf, inp, vpool):\\n\\n top, vmap = max(inp + [vpool.top]), {} # current top and variable mapping\\n\\n # we are going to use this to check if literals are input\\n inp = set([abs(l) for l in inp])\\n\\n # creating a new variable mapping, taking into\\n # account variables marked as \\\"occupied\\\"\\n while top < cnf.nv:\\n top += 1\\n\\n # skipping the input literals\\n if top in inp:\\n vmap[top] = top\\n continue\\n\\n vpool.top += 1\\n\\n while vpool._occupied and vpool.top >= vpool._occupied[0][0]:\\n if vpool.top <= vpool._occupied[0][1] + 1:\\n vpool.top = vpool._occupied[0][1] + 1\\n\\n vpool._occupied.pop(0)\\n\\n # mapping this literal to a free one\\n vmap[top] = vpool.top\\n\\n # updating the clauses\\n for cl in cnf.clauses:\\n cl[:] = map(lambda l: int(math.copysign(vmap[abs(l)], l)) if abs(l) in vmap else l, cl)\\n\\n # updating the number of variables\\n cnf.nv = vpool.top\",\n \"def setSafeRange( azLow, azHigh, elLow, elHigh, ants, subarray=DEFAULT) :\\n antlist = helpers.makeList(ants)\\n multiSubarray('setSafeRange', subarray, azLow, azHigh, elLow, elHigh, \\n antlist)\",\n \"def putconbound(self,i_,bkc_,blc_,buc_):\\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def SetPRBinCatConstraint( self, model ) :\\n tot = np.dot( self.wish.T, self.dispo )\\n for val in tot :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def _manage_infinity_bounds(problem, _bounds, get_bound, set_bound):\\n for variable in problem.variables:\\n expr_bounds = get_bound(variable)\\n lower_bound = expr_bounds.lower_bound\\n upper_bound = expr_bounds.upper_bound\\n\\n if is_inf(lower_bound):\\n new_lower_bound = None\\n else:\\n new_lower_bound = lower_bound\\n\\n if is_inf(upper_bound):\\n new_upper_bound = None\\n else:\\n new_upper_bound = upper_bound\\n\\n set_bound(variable, Interval(new_lower_bound, new_upper_bound))\",\n \"def removeBounded(self, bounds):\\n if bounds==None or len(bounds)!=4:\\n return\\n x1,y1,x2,y2 = bounds\\n if x1>x2 :\\n temp=x1;x1=x2;x2=temp\\n if y1>y2:\\n temp=y1;y1=y2;y2=temp\\n lst=[]\\n for i in range(0,self.length()):\\n x=self.x[i]; y=self.y[i]\\n if (x>x1 and x<x2) and (y>y1 and y<y2): \\n lst.append(i)\\n self.removeMultiple(lst)\\n return\",\n \"def putacollist(self,sub_,ptrb_,ptre_,asub_,aval_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(ptrb_)\\n elif num_ != len(ptrb_):\\n raise IndexError(\\\"Inconsistent length of array ptrb\\\")\\n if num_ is None:\\n num_ = len(ptre_)\\n elif num_ != len(ptre_):\\n raise IndexError(\\\"Inconsistent length of array ptre\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb cannot be None\\\")\\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb may not be None\\\")\\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\\n _ptrb_copyarray = False\\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptrb_ is not None:\\n _ptrb_copyarray = True\\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\\n _ptrb_np_tmp[:] = ptrb_\\n assert _ptrb_np_tmp.flags.contiguous\\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptrb_copyarray = False\\n _ptrb_tmp = None\\n \\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre cannot be None\\\")\\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre may not be None\\\")\\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\\n _ptre_copyarray = False\\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptre_ is not None:\\n _ptre_copyarray = True\\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\\n _ptre_np_tmp[:] = ptre_\\n assert _ptre_np_tmp.flags.contiguous\\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptre_copyarray = False\\n _ptre_tmp = None\\n \\n if asub_ is None:\\n raise ValueError(\\\"Argument asub cannot be None\\\")\\n if asub_ is None:\\n raise ValueError(\\\"Argument asub may not be None\\\")\\n if isinstance(asub_, numpy.ndarray) and asub_.dtype is numpy.dtype(numpy.int32) and asub_.flags.contiguous:\\n _asub_copyarray = False\\n _asub_tmp = ctypes.cast(asub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif asub_ is not None:\\n _asub_copyarray = True\\n _asub_np_tmp = numpy.zeros(len(asub_),numpy.dtype(numpy.int32))\\n _asub_np_tmp[:] = asub_\\n assert _asub_np_tmp.flags.contiguous\\n _asub_tmp = ctypes.cast(_asub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _asub_copyarray = False\\n _asub_tmp = None\\n \\n if aval_ is None:\\n raise ValueError(\\\"Argument aval cannot be None\\\")\\n if aval_ is None:\\n raise ValueError(\\\"Argument aval may not be None\\\")\\n if isinstance(aval_, numpy.ndarray) and aval_.dtype is numpy.dtype(numpy.float64) and aval_.flags.contiguous:\\n _aval_copyarray = False\\n _aval_tmp = ctypes.cast(aval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif aval_ is not None:\\n _aval_copyarray = True\\n _aval_np_tmp = numpy.zeros(len(aval_),numpy.dtype(numpy.float64))\\n _aval_np_tmp[:] = aval_\\n assert _aval_np_tmp.flags.contiguous\\n _aval_tmp = ctypes.cast(_aval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _aval_copyarray = False\\n _aval_tmp = None\\n \\n res = __library__.MSK_XX_putacollist64(self.__nativep,num_,_sub_tmp,_ptrb_tmp,_ptre_tmp,_asub_tmp,_aval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def removevars(self,subset_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subset_)\\n elif num_ != len(subset_):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset cannot be None\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset may not be None\\\")\\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\\n _subset_copyarray = False\\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subset_ is not None:\\n _subset_copyarray = True\\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\\n _subset_np_tmp[:] = subset_\\n assert _subset_np_tmp.flags.contiguous\\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subset_copyarray = False\\n _subset_tmp = None\\n \\n res = __library__.MSK_XX_removevars(self.__nativep,num_,_subset_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __set__(self,obj,val):\\n self._check_bounds(val)\\n super(List,self).__set__(obj,val)\",\n \"def putbound(self,accmode_,i_,bk_,bl_,bu_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putbound(accmode_,i_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def fix_all(self):\\n for param_name in self._LIST_PARAMETERS:\\n bounds_processed = self._process_bounds(param_name, False)\\n setattr(self, '__'+param_name+'_bounds', bounds_processed)\",\n \"def getconboundslice(self,first_,last_,bk_,bl_,bu_):\\n _bk_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk is not long enough: Is %d, expected %d\\\" % (len(bk_),((last_) - (first_))))\\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\\n raise ValueError(\\\"Argument bk must be writable\\\")\\n if bk_ is not None:\\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\\n else:\\n _bk_tmp = None\\n _bl_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl is not long enough: Is %d, expected %d\\\" % (len(bl_),((last_) - (first_))))\\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\\n raise ValueError(\\\"Argument bl must be writable\\\")\\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\\n _bl_copyarray = False\\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bl_ is not None:\\n _bl_copyarray = True\\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\\n _bl_np_tmp[:] = bl_\\n assert _bl_np_tmp.flags.contiguous\\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bl_copyarray = False\\n _bl_tmp = None\\n \\n _bu_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu is not long enough: Is %d, expected %d\\\" % (len(bu_),((last_) - (first_))))\\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\\n raise ValueError(\\\"Argument bu must be writable\\\")\\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\\n _bu_copyarray = False\\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bu_ is not None:\\n _bu_copyarray = True\\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\\n _bu_np_tmp[:] = bu_\\n assert _bu_np_tmp.flags.contiguous\\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bu_copyarray = False\\n _bu_tmp = None\\n \\n res = __library__.MSK_XX_getconboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\\n if _bl_copyarray:\\n bl_[:] = _bl_np_tmp\\n if _bu_copyarray:\\n bu_[:] = _bu_np_tmp\",\n \"def variance_bounds(self, resids: NDArray, power: float = 2.0) -> NDArray:\\n nobs = resids.shape[0]\\n\\n tau = min(75, nobs)\\n w = 0.94 ** np.arange(tau)\\n w = w / sum(w)\\n var_bound = np.zeros(nobs)\\n initial_value = w.dot(resids[:tau] ** 2.0)\\n ewma_recursion(0.94, resids, var_bound, resids.shape[0], initial_value)\\n\\n var_bounds = np.vstack((var_bound / 1e6, var_bound * 1e6)).T\\n var = resids.var()\\n min_upper_bound = 1 + (resids ** 2.0).max()\\n lower_bound, upper_bound = var / 1e8, 1e7 * (1 + (resids ** 2.0).max())\\n var_bounds[var_bounds[:, 0] < lower_bound, 0] = lower_bound\\n var_bounds[var_bounds[:, 1] < min_upper_bound, 1] = min_upper_bound\\n var_bounds[var_bounds[:, 1] > upper_bound, 1] = upper_bound\\n\\n if power != 2.0:\\n var_bounds **= power / 2.0\\n\\n return np.ascontiguousarray(var_bounds)\",\n \"def CellBoundary(self, p_int, , vtkIdList):\\n ...\",\n \"def getvarbound(self,i_):\\n bk_ = ctypes.c_int32()\\n bl_ = ctypes.c_double()\\n bu_ = ctypes.c_double()\\n res = __library__.MSK_XX_getvarbound(self.__nativep,i_,ctypes.byref(bk_),ctypes.byref(bl_),ctypes.byref(bu_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _bk_return_value = boundkey(bk_.value)\\n bl_ = bl_.value\\n _bl_return_value = bl_\\n bu_ = bu_.value\\n _bu_return_value = bu_\\n return (_bk_return_value,_bl_return_value,_bu_return_value)\",\n \"def SetPRBinConstraint(self, model ) :\\n tot = np.multiply(self.wish, self.dispo)\\n for val in tot :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def prune(self,domains,constraint):\\n left_var = constraint.left[0]\\n left_const_mult = constraint.left[1]\\n left_val = constraint.left[2]\\n\\n right_var = constraint.right[0]\\n right_const_mult = constraint.right[1]\\n right_val = constraint.right[2]\\n\\n new_domains = deepcopy(domains)\\n\\n\\n # Simple Variable-Value Labeling\\n if (left_val == [0] and left_const_mult == [1]) and (right_const_mult == [0]):\\n new_domains[left_var[0]] = [right_val[0]]\\n \\n # Simple Variable-Variable Labeling\\n elif (left_val == [0] and left_const_mult == [1]) and (right_val == [0] and right_const_mult == [1]):\\n new_set = set(new_domains[left_var[0]]) & set(new_domains[right_var[0]])\\n new_domains[left_var[0]] = list(new_set)\\n new_domains[right_var[0]] = list(new_set)\\n\\n else:\\n l = 0\\n for var,mult in zip(left_var,left_const_mult):\\n l += mult*max(domains[var])\\n for const in left_val:\\n l += const\\n\\n r = 0\\n for var,mult in zip(right_var,right_const_mult):\\n r += mult*min(domains[var])\\n for const in right_val:\\n r += const\\n\\n # print(l,r)\\n # print(new_domains)\\n # print(constraint)\\n\\n for var,mult in zip(left_var,left_const_mult):\\n max_var = max(domains[var])\\n comp = (r-(l-mult*max_var)) / mult\\n for elem in domains[var]:\\n if elem < comp:\\n new_domains[var].remove(elem)\\n\\n for var,mult in zip(right_var,right_const_mult):\\n min_var = min(domains[var])\\n comp = (l-(r-mult*min_var)) / mult\\n for elem in domains[var]:\\n if elem > comp:\\n new_domains[var].remove(elem)\\n\\n # for i,domain in enumerate(new_domains):\\n # if len(domain) == 0:\\n # print(i,l,r)\\n # print(\\\"Old:\\\",domains)\\n # print(\\\"New:\\\",new_domains)\\n # print(domains)\\n # print(constraint)\\n # print(\\\"------------------------\\\")\\n # raise SystemError(\\\"Domain is Empty!!\\\")\\n\\n return new_domains\",\n \"def putboundslice(self,con_,first_,last_,bk,bl,bu): # 3\\n if not isinstance(con_,accmode): raise TypeError(\\\"Argument con has wrong type\\\")\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putboundslice(con_,first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _rename_bound_variables(self, formula, variables):\\n new_vars = [self._bound_symbol(x) for x in variables]\\n old_vars = [self.walk_symbol(x) for x in variables]\\n new_formula = yicespy.yices_subst_term(len(variables), yicespy.make_term_array(new_vars),\\n yicespy.make_term_array(old_vars), formula)\\n return (new_formula, new_vars)\",\n \"def _update_block_const(self,name,G1,remove_nodes,new_inst, const_list):\\n\\n def _list_replace(lst, old_value, new_value):\\n for i, value in enumerate(lst):\\n if value == old_value:\\n lst[i] = new_value\\n\\n logger.debug(f\\\"update constraints with block in them for hierarchy {name} {remove_nodes}\\\")\\n if self._if_const(name):\\n for const in const_list:\\n if hasattr(const, 'instances'):\\n logger.debug(f\\\"checking instances in the constraint:{const.instances} {set(remove_nodes)}\\\")\\n if set(const.instances) & set(remove_nodes):\\n replace = True\\n for block in remove_nodes:\\n if replace:\\n _list_replace(const.instances, block, new_inst)\\n replace = False\\n elif block in const.instances:\\n const.instances.remove(block)\\n logger.debug(f\\\"updated instances in the constraint:{const}\\\")\",\n \"def add_boundaries(self):\\n\\n bound_conns=[]\\n bound_coords=[]\\n bound_vert_index=[]\\n throat_vert_index=[]\\n #Find boundary extent\\n [x_min,x_max,y_min,y_max,z_min,z_max]=vo.vertex_dimension(self,self.pores(),parm='minmax')\\n min_point = np.around(np.array([x_min,y_min,z_min]),10)\\n max_point = np.around(np.array([x_max,y_max,z_max]),10)\\n Np = self.num_pores()\\n Nt = self.num_throats()\\n new_throat_count = 0\\n # ridge_dict contains a dictionary where the key is a set of 2 neighbouring pores and the value is the vertex indices\\n # that form the throat or ridge between them\\n for p,v in self._vor.ridge_dict.items():\\n # if the vertex with index -1 is contained in list then the ridge is unbounded - ignore these\\n if np.all(np.asarray(v) >=0):\\n #boundary throats will be those connecting one pore inside the original set and one out\\n if (p[0] in range(Np) and p[1] not in range(Np)) or\\\\\\n (p[0] not in range(Np) and p[1] in range(Np)):\\n # the dictionary key is not in numerical order so find the pore index inside\\n if p[0] in range(Np):\\n my_pore=p[0]\\n else:\\n my_pore=p[1]\\n my_pore_coord = self[\\\"pore.coords\\\"][my_pore]\\n new_pore_coord = my_pore_coord.copy()\\n #rounding necessary here to identify the plane as Voronoi can have 1e-17 and smaller errors\\n throat_verts = np.around(self._vor.vertices[v],10)\\n #find which plane we are aligned with (if any) and align new_pore with throat plane\\n if len(np.unique(throat_verts[:,0])) == 1:\\n new_pore_coord[0]=np.unique(throat_verts[:,0])\\n elif len(np.unique(throat_verts[:,1])) == 1:\\n new_pore_coord[1]=np.unique(throat_verts[:,1])\\n elif len(np.unique(throat_verts[:,2])) == 1:\\n new_pore_coord[2]=np.unique(throat_verts[:,2])\\n else:\\n new_pore_coord = throat_verts.mean()\\n bound_coords.append(new_pore_coord)\\n bound_conns.append(np.array([my_pore,new_throat_count+Np]))\\n bound_vert_index.append(dict(zip(v,throat_verts)))\\n throat_vert_index.append(dict(zip(v,throat_verts)))\\n new_throat_count += 1\\n\\n #Add new pores and connections\\n self.extend(pore_coords=bound_coords, throat_conns=bound_conns)\\n #Record new number of pores\\n Mp = self.num_pores()\\n Mt = self.num_throats()\\n new_pore_ids = np.arange(Np,Mp)\\n new_throat_ids = np.arange(Nt,Mt)\\n #Identify which boundary the pore sits on\\n front = self.pores()[self['pore.coords'][:,0]==min_point[0]]\\n back = self.pores()[self['pore.coords'][:,0]==max_point[0]]\\n left = self.pores()[self['pore.coords'][:,1]==min_point[1]]\\n right = self.pores()[self['pore.coords'][:,1]==max_point[1]]\\n bottom = self.pores()[self['pore.coords'][:,2]==min_point[2]]\\n top = self.pores()[self['pore.coords'][:,2]==max_point[2]]\\n #Assign labels\\n self['pore.boundary'] = False\\n self['pore.boundary'][new_pore_ids] = True\\n self['pore.right_boundary'] = False\\n self['pore.left_boundary'] = False\\n self['pore.front_boundary'] = False\\n self['pore.back_boundary'] = False\\n self['pore.top_boundary'] = False\\n self['pore.bottom_boundary'] = False\\n self['pore.right_boundary'][right] = True\\n self['pore.left_boundary'][left] = True\\n self['pore.front_boundary'][front] = True\\n self['pore.back_boundary'][back] = True\\n self['pore.top_boundary'][top] = True\\n self['pore.bottom_boundary'][bottom] = True\\n #Save the throat verts\\n self[\\\"pore.vert_index\\\"][new_pore_ids] = bound_vert_index\\n self[\\\"throat.vert_index\\\"][new_throat_ids] = throat_vert_index\",\n \"def normalize_bounds(self, bounds):\\n scaled_bounds = []\\n scalings = []\\n intercepts = []\\n \\n non_fixed_params = []\\n \\n print(self.device)\\n \\n for name, domain in self.bounds.items():\\n # Get any fixed parmeters\\n if type(domain) == int or type(domain) == float:\\n # Take note\\n self.fixed_parameters.append(name)\\n\\n # Free parameters\\n elif type(domain) == tuple:\\n # Bookkeeping\\n self.free_parameters.append(name)\\n\\n # Get scaling\\n lower_bound = min(domain)\\n upper_bound = max(domain)\\n scale = upper_bound - lower_bound\\n\\n # Transform to [0, 1] domain\\n #scaled_bound = {'name': name, 'type': 'continuous', 'domain': (0., 1.)} #torch.adjustment required\\n non_fixed_params.append(name)\\n \\n # Store\\n #scaled_bounds.append(scaled_bound)\\n scalings.append(scale)\\n intercepts.append(lower_bound)\\n else:\\n raise ValueError(\\\"Domain bounds not understood\\\")\\n \\n n_hyperparams = len(non_fixed_params)\\n \\n scaled_bounds = cat([zeros(1,n_hyperparams, device = self.device), \\n ones(1, n_hyperparams, device = self.device)], 0)\\n return scaled_bounds, tensor(scalings, device = self.device, requires_grad = False), tensor(intercepts, device = self.device, requires_grad = False) #torch.adjustment required\",\n \"def compute_bounds(self):\\n # Note: linear_constraints object has been been populated at this stage\\n L_zero_var = []\\n \\n for constraint in self._linear_constraints.L_linear_constraints:\\n lhs_string = constraint[0]\\n rhs_string = constraint[1]\\n if float(rhs_string)==0:\\n #print \\\"rhs=0: forcing the variables to zero\\\"\\n L_vars = re.split(r'[+-]',lhs_string)\\n \\n for var in L_vars:\\n modform_var = var.strip()\\n \\n # forcing all the variables in this constraint to be zero\\n self._linear_constraints.modform_space.D_PuLP_variables[modform_var] = pulp.LpVariable(modform_var, lowBound=0, upBound=0)\\n #print \\\"var forced to zero: \\\", modform_var\\n L_zero_var.append(modform_var)\\n else: #if float(rhs)==0\\n continue\\n \\n if len(L_zero_var)>0:\\n print \\\"\\\\n####### Variables forced to zero (rhs = 0) ##########\\\"\\n print \\\"variables forced to zero: \\\", set(L_zero_var)\\n \\n feasible_lc = flc.FeasibleLinearConstraints(self._linear_constraints)\\n \\n feasible_lc.get_feasible_linear_constraints()\\n \\n feasible_linear_constraints = feasible_lc.feasible_linear_constraints\\n \\n lp_solver = lps.LinearProgrammingSolver(feasible_linear_constraints)\\n \\n D_lower_bounds = {}; D_upper_bounds = {}\\n \\n for v in [self._linear_constraints.modform_space.D_PuLP_variables[k] for k in sorted(self._linear_constraints.modform_space.D_PuLP_variables.keys(), key=gbfunc.natural_keys)]:\\n \\n if str(v) in L_zero_var:\\n D_lower_bounds[str(v)] = '0'\\n D_upper_bounds[str(v)] = '0'\\n continue\\n #end if str(v) in L_zero_var\\n \\n objective_function_PuLP = v\\n \\n list_values_minimize = lp_solver.linear_programming_solver(objective_function_PuLP, pulp.LpMinimize)\\n D_lower_bounds[str(v)] = \\\"%.3f\\\"%round(pulp.value(v),3)\\n \\n list_values_maximize = lp_solver.linear_programming_solver(objective_function_PuLP, pulp.LpMaximize)\\n D_upper_bounds[str(v)] = \\\"%.3f\\\"%round(pulp.value(v),3)\\n\\n #end for v in ..\\n\\n return((D_lower_bounds, D_upper_bounds))\",\n \"def getvarbound(self,i_): # 3\\n res,resargs = self.__obj.getvarbound(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _bk_return_value,_bl_return_value,_bu_return_value = resargs\\n _bk_return_value = boundkey(_bk_return_value)\\n return _bk_return_value,_bl_return_value,_bu_return_value\",\n \"def set_variable_slices(self, variables):\\n # Set up y_slices and bounds\\n y_slices = defaultdict(list)\\n y_slices_explicit = defaultdict(list)\\n start = 0\\n end = 0\\n lower_bounds = []\\n upper_bounds = []\\n # Iterate through unpacked variables, adding appropriate slices to y_slices\\n for variable in variables:\\n # Add up the size of all the domains in variable.domain\\n if isinstance(variable, pybamm.ConcatenationVariable):\\n start_ = start\\n spatial_method = self.spatial_methods[variable.domain[0]]\\n children = variable.children\\n meshes = OrderedDict()\\n for child in children:\\n meshes[child] = [spatial_method.mesh[dom] for dom in child.domain]\\n sec_points = spatial_method._get_auxiliary_domain_repeats(\\n variable.domains\\n )\\n for i in range(sec_points):\\n for child, mesh in meshes.items():\\n for domain_mesh in mesh:\\n end += domain_mesh.npts_for_broadcast_to_nodes\\n # Add to slices\\n y_slices[child].append(slice(start_, end))\\n y_slices_explicit[child].append(slice(start_, end))\\n # Increment start_\\n start_ = end\\n else:\\n end += self._get_variable_size(variable)\\n\\n # Add to slices\\n y_slices[variable].append(slice(start, end))\\n y_slices_explicit[variable].append(slice(start, end))\\n\\n # Add to bounds\\n def evaluate_bound(bound, side):\\n if bound.has_symbol_of_classes(pybamm.InputParameter):\\n if side == \\\"lower\\\":\\n return -np.inf\\n elif side == \\\"upper\\\":\\n return np.inf\\n else:\\n return bound.evaluate()\\n\\n lower_bounds.extend(\\n [evaluate_bound(variable.bounds[0], \\\"lower\\\")] * (end - start)\\n )\\n upper_bounds.extend(\\n [evaluate_bound(variable.bounds[1], \\\"upper\\\")] * (end - start)\\n )\\n # Increment start\\n start = end\\n\\n # Convert y_slices back to normal dictionary\\n self.y_slices = dict(y_slices)\\n # Also keep a record of what the y_slices are, to be stored in the model\\n self.y_slices_explicit = dict(y_slices_explicit)\\n\\n # Also keep a record of bounds\\n self.bounds = (np.array(lower_bounds), np.array(upper_bounds))\\n\\n # reset discretised_symbols\\n self._discretised_symbols = {}\",\n \"def bounds(self): # -> tuple[()]:\\n ...\",\n \"def add_time_bounds(nc, varname):\\n THREE_HOURS = 60*60*3 # in seconds\\n bnds_name = 'time_bnds'\\n bounds_dim = 'nv'\\n\\n # Create bounds dimension\\n nc.createDimension(bounds_dim, 2)\\n\\n # Get variable matching varname\\n\\n time_var = nc.variables['time']\\n time_var.setncattr('bounds', bnds_name)\\n time_data = time_var[:]\\n time_length = len(time_data)\\n\\n # reshape time data\\n bounds_data = np.dstack((time_data,time_data)).reshape(time_length,2)\\n for i in bounds_data:\\n i[0] = i[0] - (THREE_HOURS)\\n bounds_var = nc.createVariable(bnds_name, time_var.dtype, ('time', bounds_dim), fill_value=9999)\\n bounds_var[:] = bounds_data\",\n \"def set_bounds_atom(self,bounds):\\n self.bounds = bounds # data bounds\\n raise NotImplementedError\\n self.bounds_atom = None\\n return None\",\n \"def optimization_bounds(self, topology):\\n bounds_low = np.zeros(self.number_of_parameters())\\n bounds_up = np.zeros(self.number_of_parameters())\\n\\n for pkey, parameter in self.parameters.items():\\n bounds_low[pkey] = parameter.bound_low(topology)\\n bounds_up[pkey] = parameter.bound_up(topology)\\n\\n return bounds_low, bounds_up\",\n \"def simple_bounds(s, Lb, Ub):\\n\\n\\tfor i in range(len(s)):\\n\\t\\tif s[i] > Ub[i]:\\n\\t\\t\\ts[i] = Ub[i]\\n\\t\\telif s[i] < Lb[i]:\\n\\t\\t\\ts[i] = Lb[i] \\n\\treturn s\",\n \"def add_variables(self, n_variables, lb=None, ub=None, var_type=None):\\n curr_n_vars = self.problem.variables.get_num()\\n\\n lb = convert_cplex_val(lb)\\n ub = convert_cplex_val(ub)\\n\\n if var_type.lower() == \\\"real\\\" or var_type.lower() == \\\"continuous\\\":\\n vtype = cplex.Cplex.variables.type.continuous\\n\\n elif var_type.lower() == \\\"int\\\" or var_type.lower() == \\\"integer\\\":\\n vtype = cplex.Cplex.variables.type.integer\\n\\n elif var_type.lower() == \\\"binary\\\" or var_type.lower() == \\\"bool\\\" or var_type.lower() == \\\"boolean\\\":\\n vtype = cplex.Cplex.variables.type.binary\\n\\n elif var_type.lower() == \\\"auto\\\" or var_type is None:\\n vtype = cplex.Cplex.variables.type.binary\\n\\n else:\\n raise Exception(\\\"Vartype '{}' unsupported.\\\".format(var_type))\\n\\n if lb is not None and ub is not None:\\n self.problem.variables.add(\\n lb=[ lb ] * n_variables,\\n ub=[ ub ] * n_variables,\\n types=[ vtype ] * n_variables)\\n\\n elif lb is not None:\\n self.problem.variables.add(\\n lb=[ lb ] * n_variables,\\n types=[ vtype ] * n_variables)\\n\\n elif ub is not None:\\n self.problem.variables.add(\\n ub=[ ub ] * n_variables,\\n types=[ vtype ] * n_variables)\\n\\n else:\\n self.problem.variables.add(\\n types=[ vtype ] * n_variables)\\n\\n # Return the 0-based indexes of the new variables\\n new_var_idxs = xrange(curr_n_vars, curr_n_vars + n_variables)\\n return new_var_idxs\",\n \"def set_in_bounds(self,obj,val):\\n if not callable(val):\\n bounded_val = self.crop_to_bounds(val)\\n else:\\n bounded_val = val\\n super(Number,self).__set__(obj,bounded_val)\",\n \"def clean_list(\\n l: list,\\n ub: int,\\n lb: int = 0,\\n ) -> list:\\n\\n # Initialisations\\n l_temp = l[:]\\n\\n # Replace all out-of-bounds values with the boundary values\\n l_temp = [clean_int(i, ub, lb = lb) for i in l_temp]\\n\\n # Sort the list and remove duplicates\\n l_temp = list(set(l_temp))\\n\\n return l_temp\",\n \"def setSublist(self, sublist):\\n if self.cursor:\\n self.cursor.sublist = sublist\",\n \"def _check_bounds(self,val):\\n if not (isinstance(val,list)):\\n raise ValueError(\\\"List '%s' must be a list.\\\"%(self._attrib_name))\\n\\n if self.bounds is not None:\\n min_length,max_length = self.bounds\\n l=len(val)\\n if min_length is not None and max_length is not None:\\n if not (min_length <= l <= max_length):\\n raise ValueError(\\\"%s: list length must be between %s and %s (inclusive)\\\"%(self._attrib_name,min_length,max_length))\\n elif min_length is not None:\\n if not min_length <= l: \\n raise ValueError(\\\"%s: list length must be at least %s.\\\"%(self._attrib_name,min_length))\\n elif max_length is not None:\\n if not l <= max_length:\\n raise ValueError(\\\"%s: list length must be at most %s.\\\"%(self._attrib_name,max_length))\\n\\n self._check_type(val)\",\n \"def rejection_bounds(parent, child, stepSize, lb, ub):\\n assert len(lb) == len(ub), 'Lower and upper bounds have different #s of design variables in rejection_bounds function.'\\n assert len(lb) == len(child), 'Bounds and child have different #s of design variables in rejection_bounds function.'\\n for i in range(0, len(child), 1):\\n stepReductionCount = 0\\n while child[i] < lb[i] or child[i] > ub[i]:\\n if stepReductionCount >= 5:\\n child[i] = cp.copy(parent[i])\\n else:\\n stepSize[i] = stepSize[i] / 2.0\\n child[i] = child[i] - stepSize[i]\\n stepReductionCount += 1\\n\\n return child\",\n \"def get_bounds():\\n return [0.00], [1.00]\",\n \"def putbararowlist(self,subi_,ptrb_,ptre_,subj_,nummat_,matidx_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(ptrb_)\\n elif num_ != len(ptrb_):\\n raise IndexError(\\\"Inconsistent length of array ptrb\\\")\\n if num_ is None:\\n num_ = len(ptre_)\\n elif num_ != len(ptre_):\\n raise IndexError(\\\"Inconsistent length of array ptre\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb cannot be None\\\")\\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb may not be None\\\")\\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\\n _ptrb_copyarray = False\\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptrb_ is not None:\\n _ptrb_copyarray = True\\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\\n _ptrb_np_tmp[:] = ptrb_\\n assert _ptrb_np_tmp.flags.contiguous\\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptrb_copyarray = False\\n _ptrb_tmp = None\\n \\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre cannot be None\\\")\\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre may not be None\\\")\\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\\n _ptre_copyarray = False\\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptre_ is not None:\\n _ptre_copyarray = True\\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\\n _ptre_np_tmp[:] = ptre_\\n assert _ptre_np_tmp.flags.contiguous\\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptre_copyarray = False\\n _ptre_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _nummat_minlength = len((subj_))\\n if len((subj_)) > 0 and nummat_ is not None and len(nummat_) != len((subj_)):\\n raise ValueError(\\\"Array argument nummat is not long enough: Is %d, expected %d\\\" % (len(nummat_),len((subj_))))\\n if nummat_ is None:\\n raise ValueError(\\\"Argument nummat cannot be None\\\")\\n if nummat_ is None:\\n raise ValueError(\\\"Argument nummat may not be None\\\")\\n if isinstance(nummat_, numpy.ndarray) and nummat_.dtype is numpy.dtype(numpy.int64) and nummat_.flags.contiguous:\\n _nummat_copyarray = False\\n _nummat_tmp = ctypes.cast(nummat_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif nummat_ is not None:\\n _nummat_copyarray = True\\n _nummat_np_tmp = numpy.zeros(len(nummat_),numpy.dtype(numpy.int64))\\n _nummat_np_tmp[:] = nummat_\\n assert _nummat_np_tmp.flags.contiguous\\n _nummat_tmp = ctypes.cast(_nummat_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _nummat_copyarray = False\\n _nummat_tmp = None\\n \\n _matidx_minlength = sum((nummat_))\\n if sum((nummat_)) > 0 and matidx_ is not None and len(matidx_) != sum((nummat_)):\\n raise ValueError(\\\"Array argument matidx is not long enough: Is %d, expected %d\\\" % (len(matidx_),sum((nummat_))))\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx cannot be None\\\")\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx may not be None\\\")\\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\\n _matidx_copyarray = False\\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif matidx_ is not None:\\n _matidx_copyarray = True\\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\\n _matidx_np_tmp[:] = matidx_\\n assert _matidx_np_tmp.flags.contiguous\\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _matidx_copyarray = False\\n _matidx_tmp = None\\n \\n _weights_minlength = sum((nummat_))\\n if sum((nummat_)) > 0 and weights_ is not None and len(weights_) != sum((nummat_)):\\n raise ValueError(\\\"Array argument weights is not long enough: Is %d, expected %d\\\" % (len(weights_),sum((nummat_))))\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbararowlist(self.__nativep,num_,_subi_tmp,_ptrb_tmp,_ptre_tmp,_subj_tmp,_nummat_tmp,_matidx_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def incBound(self):\\n # increment the bound for the local variables.\\n self.incVariableList()\\n super().incBound()\\n\\n # get the last variable.\\n idx = len(self.stateNormalPath) - 1\\n assert(idx > 0)\\n\\n # we add the constraints that specify the id of the transition\\n self.addConstraintOnIdTransition(idx)\",\n \"def setActiveBounded(self, bounds=None, status=1): \\n if bounds==None or len(bounds)!=4:\\n return\\n x1,y1,x2,y2 = bounds\\n if x1>x2 :\\n temp=x1;x1=x2;x2=temp\\n if y1>y2:\\n temp=y1;y1=y2;y2=temp\\n for i in range(0,self.length()):\\n x=self.x[i]; y=self.y[i]\\n if (x>x1 and x<x2) and (y>y1 and y<y2):\\n self.active[i]= status \\n return\",\n \"def _top_to_bottom_translation(self, name, G1, Gsub, new_inst, sub_hierarchy_name, const_list):\\n\\n logger.debug(f\\\"transfering constraints from top {name} to bottom {sub_hierarchy_name} \\\")\\n\\n if self._if_const(name):\\n if sub_hierarchy_name in self.hier_graph_dict and 'constraints' in self.hier_graph_dict[sub_hierarchy_name]:\\n sub_const = self.hier_graph_dict[sub_hierarchy_name]['constraints']\\n else:\\n sub_const = []\\n for const in list(const_list):\\n if any(isinstance(const, x) for x in [constraint.HorizontalDistance,constraint.VerticalDistance,constraint.BlockDistance]):\\n sub_const.append(const)\\n logger.debug(f\\\"transferring global const {const}\\\")\\n elif hasattr(const, \\\"instances\\\"):\\n logger.debug(f\\\"checking if sub hierarchy instances are in const defined {Gsub} {new_inst} {const} \\\")\\n sconst = {x:\\n [Gsub[block] for block in const.instances if block in Gsub.keys()]\\n if x == 'instances'\\n else getattr(const, x)\\n for x in const.__fields_set__}\\n assert 'constraint' in sconst\\n logger.debug(f\\\"transferred constraint instances {Gsub} from {const} to {sconst}\\\")\\n sub_const.append(sconst)\\n else:\\n sub_const = []\\n return sub_const\",\n \"def __DimSiz_restriction_correct_list_parameter(self):\\n\\n strTestName = 'The size of a list dimension lower than a parameter (correct)'\\n RxCSObject = _RxCSobject()\\n\\n # Firstly, let us define a reference parameter\\n RxCSObject.paramAddMan('iParameter1', 'Int parameter')\\n RxCSObject.paramType('iParameter1', int)\\n\\n # Now, let us define a list\\n RxCSObject.paramAddMan('parameter1', 'List parameter')\\n RxCSObject.paramType('parameter1', list)\\n RxCSObject.paramDimL('parameter1', 'iParameter1', 0) # Size of dimension 0 must be lower than 'iParameter1'\\n\\n RxCSObject.iParameter1 = 5\\n RxCSObject.parameter1 = [0, 1, 2, 4]\\n\\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)\",\n \"def _overlap_cvars(self, cwrt, cvdef):\\n (creg, cran, clen) = self._parse_reg_range(cwrt)\\n s = cvdef.find(creg)\\n if s != -1:\\n (cvreg, cvran, cvlen) = self._parse_reg_range(cvdef[s:])\\n if self._range_overapped(cran, cvran):\\n return cvdef[s:s+cvlen]\\n return None\",\n \"def putacollist(self,sub,ptrb,ptre,asub,aval): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(ptrb)\\n elif num_ != len(ptrb):\\n raise IndexError(\\\"Inconsistent length of array ptrb\\\")\\n if num_ is None:\\n num_ = len(ptre)\\n elif num_ != len(ptre):\\n raise IndexError(\\\"Inconsistent length of array ptre\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if ptrb is None: raise TypeError(\\\"Invalid type for argument ptrb\\\")\\n if ptrb is None:\\n ptrb_ = None\\n else:\\n try:\\n ptrb_ = memoryview(ptrb)\\n except TypeError:\\n try:\\n _tmparr_ptrb = array.array(\\\"q\\\",ptrb)\\n except TypeError:\\n raise TypeError(\\\"Argument ptrb has wrong type\\\")\\n else:\\n ptrb_ = memoryview(_tmparr_ptrb)\\n \\n else:\\n if ptrb_.format != \\\"q\\\":\\n ptrb_ = memoryview(array.array(\\\"q\\\",ptrb))\\n \\n if ptre is None: raise TypeError(\\\"Invalid type for argument ptre\\\")\\n if ptre is None:\\n ptre_ = None\\n else:\\n try:\\n ptre_ = memoryview(ptre)\\n except TypeError:\\n try:\\n _tmparr_ptre = array.array(\\\"q\\\",ptre)\\n except TypeError:\\n raise TypeError(\\\"Argument ptre has wrong type\\\")\\n else:\\n ptre_ = memoryview(_tmparr_ptre)\\n \\n else:\\n if ptre_.format != \\\"q\\\":\\n ptre_ = memoryview(array.array(\\\"q\\\",ptre))\\n \\n if asub is None: raise TypeError(\\\"Invalid type for argument asub\\\")\\n if asub is None:\\n asub_ = None\\n else:\\n try:\\n asub_ = memoryview(asub)\\n except TypeError:\\n try:\\n _tmparr_asub = array.array(\\\"i\\\",asub)\\n except TypeError:\\n raise TypeError(\\\"Argument asub has wrong type\\\")\\n else:\\n asub_ = memoryview(_tmparr_asub)\\n \\n else:\\n if asub_.format != \\\"i\\\":\\n asub_ = memoryview(array.array(\\\"i\\\",asub))\\n \\n if aval is None: raise TypeError(\\\"Invalid type for argument aval\\\")\\n if aval is None:\\n aval_ = None\\n else:\\n try:\\n aval_ = memoryview(aval)\\n except TypeError:\\n try:\\n _tmparr_aval = array.array(\\\"d\\\",aval)\\n except TypeError:\\n raise TypeError(\\\"Argument aval has wrong type\\\")\\n else:\\n aval_ = memoryview(_tmparr_aval)\\n \\n else:\\n if aval_.format != \\\"d\\\":\\n aval_ = memoryview(array.array(\\\"d\\\",aval))\\n \\n res = self.__obj.putacollist64(num_,sub_,ptrb_,ptre_,asub_,aval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def glow_boundary(bound):\\n assert bound < 4\\n global layout\\n temp = len(layout) - 1\\n for i in range(bound, bound + len_square(bound)):\\n for j in range(bound, bound + len_square(bound)): # TODO: assign this to a variable\\t\\n layout[i][j] = 1\",\n \"def removevars(self,subset): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subset)\\n elif num_ != len(subset):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if num_ is None: num_ = 0\\n if subset is None: raise TypeError(\\\"Invalid type for argument subset\\\")\\n if subset is None:\\n subset_ = None\\n else:\\n try:\\n subset_ = memoryview(subset)\\n except TypeError:\\n try:\\n _tmparr_subset = array.array(\\\"i\\\",subset)\\n except TypeError:\\n raise TypeError(\\\"Argument subset has wrong type\\\")\\n else:\\n subset_ = memoryview(_tmparr_subset)\\n \\n else:\\n if subset_.format != \\\"i\\\":\\n subset_ = memoryview(array.array(\\\"i\\\",subset))\\n \\n res = self.__obj.removevars(num_,subset_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def variable_bounds(problem):\\n return ([\\n problem['state_bounds'][var] if problem['state_bounds'][var] is not None else (-np.inf, np.inf)\\n for _ in range(problem['N'] - 1)\\n for var in range(problem['num_states'])\\n ] + [\\n problem['input_bounds'][inp] if problem['input_bounds'][inp] is not None else (-np.inf, np.inf)\\n for _ in range(problem['N'] + 1)\\n for inp in range(problem['num_inputs'])\\n ]) * problem['Nv'] + ([(0.01, np.inf)] if problem['T'] == 0 else []) \\\\\\n if problem['state_bounds'] is not None else None\",\n \"def update_upper_bounds(self, B):\\n for arc in self.arcs():\\n if self.arc_info[arc[0]]['upper_bound'] == -1:\\n self.arc_info[arc[0]]['upper_bound'] = B\",\n \"def set_bc(self, problem):\\n bcs = problem.bcs\\n n_bound = cfg.const['N_GHOST_CELLS']\\n # Left X-b.c.\\n for i in range(0, self.i_min):\\n for j in range(self.j_min, self.j_max):\\n for k in range(self.k_min, self.k_max): \\n if bcs[0] == 't': \\n self.U[i][j][k] = self.U[self.i_min][j][k]\\n elif bcs[0] == 'w':\\n for num in [0, 2, 3, 4]: # 0 -> 3, 1 -> 2, i_min-1 -> i_min, i_min-2 -> i_min+1\\n self.U[i][j][k][num] = self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\\n for num in [1]:\\n self.U[i][j][k][num] = - self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\\n else:\\n print(\\\"Errof field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Right X-b.c.\\n for i in range(self.i_max, self.i_max+n_bound):\\n for j in range(self.j_min, self.j_max):\\n for k in range(self.k_min, self.k_max): \\n if bcs[1] == 't':\\n self.U[i][j][k] = self.U[self.i_max-1][j][k]\\n elif bcs[1] == 'w':\\n for num in [0, 2, 3, 4]: # i_max -> i_max-1 , i_max+1-> i_max-2\\n self.U[i][j][k][num] = self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\\n for num in [1]:\\n self.U[i][j][k][num] = - self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Left Y-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(0, self.j_min):\\n for k in range(self.k_min, self.k_max): \\n if bcs[2] == 't':\\n self.U[i][j][k] = self.U[i][self.j_min][k]\\n elif bcs[2] == 'w':\\n for num in [0, 1, 3, 4]:\\n self.U[i][j][k][num] = self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\\n for num in [2]:\\n self.U[i][j][k][num] = - self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Right Y-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(self.j_max, self.j_max+n_bound):\\n for k in range(self.k_min, self.k_max): \\n if bcs[3] == 't':\\n self.U[i][j][k] = self.U[i][self.j_max-1][k]\\n elif bcs[3] == 'w':\\n for num in [0, 1, 3, 4]:\\n self.U[i][j][k][num] = self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\\n for num in [2]:\\n self.U[i][j][k][num] = -self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Left Z-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(0, self.j_max+n_bound):\\n for k in range(0, self.k_min): \\n if bcs[4] == 't':\\n self.U[i][j][k] = self.U[i][j][self.k_min]\\n elif bcs[4] == 'w':\\n for num in [0, 1, 2, 4]:\\n self.U[i][j][k][num] = self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\\n for num in [3]:\\n self.U[i][j][k][num] = - self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Right Z-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(0, self.j_max+n_bound):\\n for k in range(self.k_max, self.k_max+n_bound):\\n if bcs[5] == 't':\\n self.U[i][j][k] = self.U[i][j][self.k_max-1]\\n elif bcs[5] == 'w':\\n for num in [0, 1, 2, 4]:\\n self.U[i][j][k][num] = self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\\n for num in [3]:\\n self.U[i][j][k][num] = - self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\",\n \"def varcopy(self, vars):\",\n \"def set_bounds(self, **kwargs):\\n for name, bounds in kwargs.items():\\n if name not in self._parameters:\\n raise AttributeError('Unknown parameter %s for %s' % (name, self.__class__.__name__))\\n param = self._parameters[name]\\n # Set bounds\\n lower_bound, upper_bound = bounds\\n if torch.is_tensor(lower_bound) and torch.is_tensor(upper_bound):\\n if lower_bound.size() != upper_bound.size() or \\\\\\n lower_bound.size() != param.size():\\n raise AttributeError('Lower bound, upper bound, and param should have the same size')\\n elif not (isinstance(lower_bound, int) or isinstance(lower_bound, float)) or \\\\\\n not (isinstance(upper_bound, int) or isinstance(upper_bound, float)):\\n raise AttributeError('Unsupported argument types for parameter %s' % name)\\n\\n if name not in self._bounds:\\n self._bounds[name] = [None, None]\\n self._bounds[name][0] = lower_bound\\n self._bounds[name][1] = upper_bound\\n return self\",\n \"def getx(v, lb, ub, i, B):\\r\\n x = lb + np.multiply((ub - lb), v)\\r\\n x[i] = B - (x.sum() - x[i])\\r\\n # Test if variable x[i] is within the bounds\\r\\n if x[i] <= ub[i] and x[i] >= lb[i]:\\r\\n return x\\r\\n else:\\r\\n return np.array([])\",\n \"def update_bounds(self, temp):\\n if (temp.extent[0] < self.extent[0]):\\n self.extent[0] = np.copy(temp.extent[0])\\n if (temp.extent[1] > self.extent[1]):\\n self.extent[1] = np.copy(temp.extent[1])\\n if (temp.extent[2] < self.extent[2]):\\n self.extent[2] = np.copy(temp.extent[2])\\n if (temp.extent[3] > self.extent[3]):\\n self.extent[3] = np.copy(temp.extent[3])\\n return self\",\n \"def bounds(self, resids: NDArray) -> List[Tuple[float, float]]:\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7802839","0.74736947","0.7351789","0.71760994","0.68241155","0.65913326","0.65883243","0.6351352","0.63493115","0.62824434","0.62661535","0.6100925","0.60384923","0.59712017","0.58503205","0.5785788","0.57782364","0.5726529","0.5715237","0.5681339","0.5545659","0.5491208","0.54890114","0.5483636","0.546886","0.539461","0.5387674","0.5384747","0.5364006","0.5359506","0.52942514","0.526751","0.526751","0.52517074","0.52512807","0.52485764","0.5212598","0.5163835","0.51559985","0.5136705","0.5122905","0.50980645","0.5091878","0.50853205","0.50768936","0.5050554","0.5049465","0.50361884","0.5030731","0.5024565","0.50220263","0.50206536","0.5016745","0.50128233","0.5011484","0.49964276","0.49903536","0.49721953","0.49649483","0.4929862","0.492163","0.49199656","0.4917423","0.49145764","0.48915958","0.48897234","0.48812637","0.48744318","0.48705423","0.48666108","0.48408568","0.48390138","0.48301685","0.48239547","0.48192626","0.48147142","0.48017812","0.4797252","0.47939426","0.47889856","0.47876903","0.47816214","0.4763094","0.4758455","0.47470576","0.4746261","0.47434625","0.473868","0.47378445","0.4721357","0.4720737","0.47129658","0.4702835","0.47024792","0.47023082","0.46968636","0.46960503","0.4691799","0.46880385","0.468298"],"string":"[\n \"0.7802839\",\n \"0.74736947\",\n \"0.7351789\",\n \"0.71760994\",\n \"0.68241155\",\n \"0.65913326\",\n \"0.65883243\",\n \"0.6351352\",\n \"0.63493115\",\n \"0.62824434\",\n \"0.62661535\",\n \"0.6100925\",\n \"0.60384923\",\n \"0.59712017\",\n \"0.58503205\",\n \"0.5785788\",\n \"0.57782364\",\n \"0.5726529\",\n \"0.5715237\",\n \"0.5681339\",\n \"0.5545659\",\n \"0.5491208\",\n \"0.54890114\",\n \"0.5483636\",\n \"0.546886\",\n \"0.539461\",\n \"0.5387674\",\n \"0.5384747\",\n \"0.5364006\",\n \"0.5359506\",\n \"0.52942514\",\n \"0.526751\",\n \"0.526751\",\n \"0.52517074\",\n \"0.52512807\",\n \"0.52485764\",\n \"0.5212598\",\n \"0.5163835\",\n \"0.51559985\",\n \"0.5136705\",\n \"0.5122905\",\n \"0.50980645\",\n \"0.5091878\",\n \"0.50853205\",\n \"0.50768936\",\n \"0.5050554\",\n \"0.5049465\",\n \"0.50361884\",\n \"0.5030731\",\n \"0.5024565\",\n \"0.50220263\",\n \"0.50206536\",\n \"0.5016745\",\n \"0.50128233\",\n \"0.5011484\",\n \"0.49964276\",\n \"0.49903536\",\n \"0.49721953\",\n \"0.49649483\",\n \"0.4929862\",\n \"0.492163\",\n \"0.49199656\",\n \"0.4917423\",\n \"0.49145764\",\n \"0.48915958\",\n \"0.48897234\",\n \"0.48812637\",\n \"0.48744318\",\n \"0.48705423\",\n \"0.48666108\",\n \"0.48408568\",\n \"0.48390138\",\n \"0.48301685\",\n \"0.48239547\",\n \"0.48192626\",\n \"0.48147142\",\n \"0.48017812\",\n \"0.4797252\",\n \"0.47939426\",\n \"0.47889856\",\n \"0.47876903\",\n \"0.47816214\",\n \"0.4763094\",\n \"0.4758455\",\n \"0.47470576\",\n \"0.4746261\",\n \"0.47434625\",\n \"0.473868\",\n \"0.47378445\",\n \"0.4721357\",\n \"0.4720737\",\n \"0.47129658\",\n \"0.4702835\",\n \"0.47024792\",\n \"0.47023082\",\n \"0.46968636\",\n \"0.46960503\",\n \"0.4691799\",\n \"0.46880385\",\n \"0.468298\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8611551"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11960,"cells":{"query":{"kind":"string","value":"Changes the bounds for a slice of the variables. putvarboundslice(self,first_,last_,bkx_,blx_,bux_)"},"document":{"kind":"string","value":"def putvarboundslice(self,first_,last_,bkx_,blx_,bux_):\n _bkx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bkx_ is not None and len(bkx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkx is not long enough: Is %d, expected %d\" % (len(bkx_),((last_) - (first_))))\n if bkx_ is None:\n raise ValueError(\"Argument bkx cannot be None\")\n if bkx_ is None:\n raise ValueError(\"Argument bkx may not be None\")\n if bkx_ is not None:\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\n else:\n _bkx_tmp = None\n _blx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blx_ is not None and len(blx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blx is not long enough: Is %d, expected %d\" % (len(blx_),((last_) - (first_))))\n if blx_ is None:\n raise ValueError(\"Argument blx cannot be None\")\n if blx_ is None:\n raise ValueError(\"Argument blx may not be None\")\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\n _blx_copyarray = False\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blx_ is not None:\n _blx_copyarray = True\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\n _blx_np_tmp[:] = blx_\n assert _blx_np_tmp.flags.contiguous\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blx_copyarray = False\n _blx_tmp = None\n \n _bux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bux_ is not None and len(bux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bux is not long enough: Is %d, expected %d\" % (len(bux_),((last_) - (first_))))\n if bux_ is None:\n raise ValueError(\"Argument bux cannot be None\")\n if bux_ is None:\n raise ValueError(\"Argument bux may not be None\")\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\n _bux_copyarray = False\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bux_ is not None:\n _bux_copyarray = True\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\n _bux_np_tmp[:] = bux_\n assert _bux_np_tmp.flags.contiguous\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bux_copyarray = False\n _bux_tmp = None\n \n res = __library__.MSK_XX_putvarboundslice(self.__nativep,first_,last_,_bkx_tmp,_blx_tmp,_bux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getvarboundslice(self,first_,last_,bk,bl,bu): # 3\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])","def getvarboundslice(self,first_,last_,bk_,bl_,bu_):\n _bk_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk is not long enough: Is %d, expected %d\" % (len(bk_),((last_) - (first_))))\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\n raise ValueError(\"Argument bk must be writable\")\n if bk_ is not None:\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\n else:\n _bk_tmp = None\n _bl_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl is not long enough: Is %d, expected %d\" % (len(bl_),((last_) - (first_))))\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\n raise ValueError(\"Argument bl must be writable\")\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\n _bl_copyarray = False\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bl_ is not None:\n _bl_copyarray = True\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\n _bl_np_tmp[:] = bl_\n assert _bl_np_tmp.flags.contiguous\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bl_copyarray = False\n _bl_tmp = None\n \n _bu_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu is not long enough: Is %d, expected %d\" % (len(bu_),((last_) - (first_))))\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\n raise ValueError(\"Argument bu must be writable\")\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\n _bu_copyarray = False\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bu_ is not None:\n _bu_copyarray = True\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\n _bu_np_tmp[:] = bu_\n assert _bu_np_tmp.flags.contiguous\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bu_copyarray = False\n _bu_tmp = None\n \n res = __library__.MSK_XX_getvarboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\n if _bl_copyarray:\n bl_[:] = _bl_np_tmp\n if _bu_copyarray:\n bu_[:] = _bu_np_tmp","def putboundslice(self,con_,first_,last_,bk,bl,bu): # 3\n if not isinstance(con_,accmode): raise TypeError(\"Argument con has wrong type\")\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putboundslice(con_,first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putconboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_variable_slices(self, variables):\n # Set up y_slices and bounds\n y_slices = defaultdict(list)\n y_slices_explicit = defaultdict(list)\n start = 0\n end = 0\n lower_bounds = []\n upper_bounds = []\n # Iterate through unpacked variables, adding appropriate slices to y_slices\n for variable in variables:\n # Add up the size of all the domains in variable.domain\n if isinstance(variable, pybamm.ConcatenationVariable):\n start_ = start\n spatial_method = self.spatial_methods[variable.domain[0]]\n children = variable.children\n meshes = OrderedDict()\n for child in children:\n meshes[child] = [spatial_method.mesh[dom] for dom in child.domain]\n sec_points = spatial_method._get_auxiliary_domain_repeats(\n variable.domains\n )\n for i in range(sec_points):\n for child, mesh in meshes.items():\n for domain_mesh in mesh:\n end += domain_mesh.npts_for_broadcast_to_nodes\n # Add to slices\n y_slices[child].append(slice(start_, end))\n y_slices_explicit[child].append(slice(start_, end))\n # Increment start_\n start_ = end\n else:\n end += self._get_variable_size(variable)\n\n # Add to slices\n y_slices[variable].append(slice(start, end))\n y_slices_explicit[variable].append(slice(start, end))\n\n # Add to bounds\n def evaluate_bound(bound, side):\n if bound.has_symbol_of_classes(pybamm.InputParameter):\n if side == \"lower\":\n return -np.inf\n elif side == \"upper\":\n return np.inf\n else:\n return bound.evaluate()\n\n lower_bounds.extend(\n [evaluate_bound(variable.bounds[0], \"lower\")] * (end - start)\n )\n upper_bounds.extend(\n [evaluate_bound(variable.bounds[1], \"upper\")] * (end - start)\n )\n # Increment start\n start = end\n\n # Convert y_slices back to normal dictionary\n self.y_slices = dict(y_slices)\n # Also keep a record of what the y_slices are, to be stored in the model\n self.y_slices_explicit = dict(y_slices_explicit)\n\n # Also keep a record of bounds\n self.bounds = (np.array(lower_bounds), np.array(upper_bounds))\n\n # reset discretised_symbols\n self._discretised_symbols = {}","def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarboundlist(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkx_)\n elif num_ != len(bkx_):\n raise IndexError(\"Inconsistent length of array bkx\")\n if num_ is None:\n num_ = len(blx_)\n elif num_ != len(blx_):\n raise IndexError(\"Inconsistent length of array blx\")\n if num_ is None:\n num_ = len(bux_)\n elif num_ != len(bux_):\n raise IndexError(\"Inconsistent length of array bux\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkx_ is None:\n raise ValueError(\"Argument bkx cannot be None\")\n if bkx_ is None:\n raise ValueError(\"Argument bkx may not be None\")\n if bkx_ is not None:\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\n else:\n _bkx_tmp = None\n if blx_ is None:\n raise ValueError(\"Argument blx cannot be None\")\n if blx_ is None:\n raise ValueError(\"Argument blx may not be None\")\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\n _blx_copyarray = False\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blx_ is not None:\n _blx_copyarray = True\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\n _blx_np_tmp[:] = blx_\n assert _blx_np_tmp.flags.contiguous\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blx_copyarray = False\n _blx_tmp = None\n \n if bux_ is None:\n raise ValueError(\"Argument bux cannot be None\")\n if bux_ is None:\n raise ValueError(\"Argument bux may not be None\")\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\n _bux_copyarray = False\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bux_ is not None:\n _bux_copyarray = True\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\n _bux_np_tmp[:] = bux_\n assert _bux_np_tmp.flags.contiguous\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bux_copyarray = False\n _bux_tmp = None\n \n res = __library__.MSK_XX_putvarboundlist(self.__nativep,num_,_sub_tmp,_bkx_tmp,_blx_tmp,_bux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putxxslice(self,whichsol_,first_,last_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xx is None: raise TypeError(\"Invalid type for argument xx\")\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.putxxslice(whichsol_,first_,last_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarbound(self,j_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putxxslice(self,whichsol_,first_,last_,xx_):\n _xx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),((last_) - (first_))))\n if xx_ is None:\n raise ValueError(\"Argument xx cannot be None\")\n if xx_ is None:\n raise ValueError(\"Argument xx may not be None\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_putxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarboundlist(self,sub,bkx,blx,bux): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkx)\n elif num_ != len(bkx):\n raise IndexError(\"Inconsistent length of array bkx\")\n if num_ is None:\n num_ = len(blx)\n elif num_ != len(blx):\n raise IndexError(\"Inconsistent length of array blx\")\n if num_ is None:\n num_ = len(bux)\n elif num_ != len(bux):\n raise IndexError(\"Inconsistent length of array bux\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bkx is None: raise TypeError(\"Invalid type for argument bkx\")\n if bkx is None:\n bkx_ = None\n else:\n try:\n bkx_ = memoryview(bkx)\n except TypeError:\n try:\n _tmparr_bkx = array.array(\"i\",bkx)\n except TypeError:\n raise TypeError(\"Argument bkx has wrong type\")\n else:\n bkx_ = memoryview(_tmparr_bkx)\n \n else:\n if bkx_.format != \"i\":\n bkx_ = memoryview(array.array(\"i\",bkx))\n \n if blx is None: raise TypeError(\"Invalid type for argument blx\")\n if blx is None:\n blx_ = None\n else:\n try:\n blx_ = memoryview(blx)\n except TypeError:\n try:\n _tmparr_blx = array.array(\"d\",blx)\n except TypeError:\n raise TypeError(\"Argument blx has wrong type\")\n else:\n blx_ = memoryview(_tmparr_blx)\n \n else:\n if blx_.format != \"d\":\n blx_ = memoryview(array.array(\"d\",blx))\n \n if bux is None: raise TypeError(\"Invalid type for argument bux\")\n if bux is None:\n bux_ = None\n else:\n try:\n bux_ = memoryview(bux)\n except TypeError:\n try:\n _tmparr_bux = array.array(\"d\",bux)\n except TypeError:\n raise TypeError(\"Argument bux has wrong type\")\n else:\n bux_ = memoryview(_tmparr_bux)\n \n else:\n if bux_.format != \"d\":\n bux_ = memoryview(array.array(\"d\",bux))\n \n res = self.__obj.putvarboundlist(num_,sub_,bkx_,blx_,bux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getconboundslice(self,first_,last_,bk,bl,bu): # 3\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getconboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])","def getconboundslice(self,first_,last_,bk_,bl_,bu_):\n _bk_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk is not long enough: Is %d, expected %d\" % (len(bk_),((last_) - (first_))))\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\n raise ValueError(\"Argument bk must be writable\")\n if bk_ is not None:\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\n else:\n _bk_tmp = None\n _bl_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl is not long enough: Is %d, expected %d\" % (len(bl_),((last_) - (first_))))\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\n raise ValueError(\"Argument bl must be writable\")\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\n _bl_copyarray = False\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bl_ is not None:\n _bl_copyarray = True\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\n _bl_np_tmp[:] = bl_\n assert _bl_np_tmp.flags.contiguous\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bl_copyarray = False\n _bl_tmp = None\n \n _bu_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu is not long enough: Is %d, expected %d\" % (len(bu_),((last_) - (first_))))\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\n raise ValueError(\"Argument bu must be writable\")\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\n _bu_copyarray = False\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bu_ is not None:\n _bu_copyarray = True\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\n _bu_np_tmp[:] = bu_\n assert _bu_np_tmp.flags.contiguous\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bu_copyarray = False\n _bu_tmp = None\n \n res = __library__.MSK_XX_getconboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\n if _bl_copyarray:\n bl_[:] = _bl_np_tmp\n if _bu_copyarray:\n bu_[:] = _bu_np_tmp","def apply_bound(x, var_min, var_max):\n x.position = np.maximum(x.position, var_min)\n x.position = np.minimum(x.position, var_max)","def set_bounds_atom(self,bounds):\n assert bounds.shape == (2,self.Phi.d)\n self.bounds = bounds # data bounds\n self.bounds_atom = bounds.T.tolist()\n for i in range(self.Phi.d): # bounds for the variance in each dimension\n max_variance_this_dimension = (bounds[1][i]-bounds[0][i])**2\n self.bounds_atom.append([self.variance_relative_lowerbound*max_variance_this_dimension,\n self.variance_relative_upperbound*max_variance_this_dimension])","def putconboundslice(self,first_,last_,bkc_,blc_,buc_):\n _bkc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bkc_ is not None and len(bkc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkc is not long enough: Is %d, expected %d\" % (len(bkc_),((last_) - (first_))))\n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n _blc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blc_ is not None and len(blc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blc is not long enough: Is %d, expected %d\" % (len(blc_),((last_) - (first_))))\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n _buc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and buc_ is not None and len(buc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument buc is not long enough: Is %d, expected %d\" % (len(buc_),((last_) - (first_))))\n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundslice(self.__nativep,first_,last_,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putcslice(self,first_,last_,slice_):\n _slice_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slice_ is not None and len(slice_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slice is not long enough: Is %d, expected %d\" % (len(slice_),((last_) - (first_))))\n if slice_ is None:\n raise ValueError(\"Argument slice cannot be None\")\n if slice_ is None:\n raise ValueError(\"Argument slice may not be None\")\n if isinstance(slice_, numpy.ndarray) and slice_.dtype is numpy.dtype(numpy.float64) and slice_.flags.contiguous:\n _slice_copyarray = False\n _slice_tmp = ctypes.cast(slice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slice_ is not None:\n _slice_copyarray = True\n _slice_np_tmp = numpy.zeros(len(slice_),numpy.dtype(numpy.float64))\n _slice_np_tmp[:] = slice_\n assert _slice_np_tmp.flags.contiguous\n _slice_tmp = ctypes.cast(_slice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slice_copyarray = False\n _slice_tmp = None\n \n res = __library__.MSK_XX_putcslice(self.__nativep,first_,last_,_slice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarbound(self,j_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getboundslice(self,accmode_,first_,last_,bk,bl,bu): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getboundslice(accmode_,first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])","def set_bounds(self, new_bounds):\n\n # Update the internal object stored dict\n self.pbounds.update(new_bounds)\n\n # Loop through the all bounds and reset the min-max bound matrix\n for row, key in enumerate(self.pbounds.keys()):\n\n # Reset all entries, even if the same.\n self.bounds[row] = self.pbounds[key]","def chgvarbound(self,j_,lower_,finite_,value_): # 3\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getbarxslice(self,whichsol_,first_,last_,slicesize_,barxslice_):\n _barxslice_minlength = (slicesize_)\n if (slicesize_) > 0 and barxslice_ is not None and len(barxslice_) != (slicesize_):\n raise ValueError(\"Array argument barxslice is not long enough: Is %d, expected %d\" % (len(barxslice_),(slicesize_)))\n if isinstance(barxslice_,numpy.ndarray) and not barxslice_.flags.writeable:\n raise ValueError(\"Argument barxslice must be writable\")\n if barxslice_ is None:\n raise ValueError(\"Argument barxslice may not be None\")\n if isinstance(barxslice_, numpy.ndarray) and barxslice_.dtype is numpy.dtype(numpy.float64) and barxslice_.flags.contiguous:\n _barxslice_copyarray = False\n _barxslice_tmp = ctypes.cast(barxslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxslice_ is not None:\n _barxslice_copyarray = True\n _barxslice_np_tmp = numpy.zeros(len(barxslice_),numpy.dtype(numpy.float64))\n _barxslice_np_tmp[:] = barxslice_\n assert _barxslice_np_tmp.flags.contiguous\n _barxslice_tmp = ctypes.cast(_barxslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxslice_copyarray = False\n _barxslice_tmp = None\n \n res = __library__.MSK_XX_getbarxslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barxslice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barxslice_copyarray:\n barxslice_[:] = _barxslice_np_tmp","def getxxslice(self,whichsol_,first_,last_,xx_):\n _xx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),((last_) - (first_))))\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\n raise ValueError(\"Argument xx must be writable\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_getxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xx_copyarray:\n xx_[:] = _xx_np_tmp","def getbarsslice(self,whichsol_,first_,last_,slicesize_,barsslice_):\n _barsslice_minlength = (slicesize_)\n if (slicesize_) > 0 and barsslice_ is not None and len(barsslice_) != (slicesize_):\n raise ValueError(\"Array argument barsslice is not long enough: Is %d, expected %d\" % (len(barsslice_),(slicesize_)))\n if isinstance(barsslice_,numpy.ndarray) and not barsslice_.flags.writeable:\n raise ValueError(\"Argument barsslice must be writable\")\n if barsslice_ is None:\n raise ValueError(\"Argument barsslice may not be None\")\n if isinstance(barsslice_, numpy.ndarray) and barsslice_.dtype is numpy.dtype(numpy.float64) and barsslice_.flags.contiguous:\n _barsslice_copyarray = False\n _barsslice_tmp = ctypes.cast(barsslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsslice_ is not None:\n _barsslice_copyarray = True\n _barsslice_np_tmp = numpy.zeros(len(barsslice_),numpy.dtype(numpy.float64))\n _barsslice_np_tmp[:] = barsslice_\n assert _barsslice_np_tmp.flags.contiguous\n _barsslice_tmp = ctypes.cast(_barsslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsslice_copyarray = False\n _barsslice_tmp = None\n \n res = __library__.MSK_XX_getbarsslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barsslice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barsslice_copyarray:\n barsslice_[:] = _barsslice_np_tmp","def putcslice(self,first_,last_,slice): # 3\n if slice is None: raise TypeError(\"Invalid type for argument slice\")\n if slice is None:\n slice_ = None\n else:\n try:\n slice_ = memoryview(slice)\n except TypeError:\n try:\n _tmparr_slice = array.array(\"d\",slice)\n except TypeError:\n raise TypeError(\"Argument slice has wrong type\")\n else:\n slice_ = memoryview(_tmparr_slice)\n \n else:\n if slice_.format != \"d\":\n slice_ = memoryview(array.array(\"d\",slice))\n \n if slice_ is not None and len(slice_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slice has wrong length\")\n res = self.__obj.putcslice(first_,last_,slice_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def chgvarbound(self,j_,lower_,finite_,value_):\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getxxslice(self,whichsol_,first_,last_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_xx = False\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n _copyback_xx = True\n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n _copyback_xx = True\n if xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.getxxslice(whichsol_,first_,last_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_xx:\n xx[:] = _tmparr_xx","def _initialize_bounds(problem, bounds, get_bound, set_bound):\n for constraint in problem.constraints:\n root_expr = constraint.root_expr\n expr_bounds = Interval(constraint.lower_bound, constraint.upper_bound)\n if root_expr not in bounds:\n set_bound(root_expr, expr_bounds)\n else:\n existing_bounds = get_bound(root_expr)\n new_bounds = existing_bounds.intersect(expr_bounds)\n set_bound(root_expr, new_bounds)","def set_bounds_atom(self,bounds):\n assert bounds.shape == (2,self.Phi.d)\n self.bounds = bounds # data bounds\n self.bounds_atom = bounds.T.tolist()","def extend_bounds(problem):\n\n num_vars = problem[\"num_vars\"]\n num_ff_vars = 2 ** find_smallest(num_vars)\n num_dummy_variables = num_ff_vars - num_vars\n\n bounds = list(problem[\"bounds\"])\n names = problem[\"names\"]\n if num_dummy_variables > 0:\n bounds.extend([[0, 1] for x in range(num_dummy_variables)])\n names.extend([\"dummy_\" + str(var) for var in range(num_dummy_variables)])\n problem[\"bounds\"] = bounds\n problem[\"names\"] = names\n problem[\"num_vars\"] = num_ff_vars\n\n return problem","def bounds(self, pos):","def putsuxslice(self,whichsol_,first_,last_,sux_):\n _sux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),((last_) - (first_))))\n if sux_ is None:\n raise ValueError(\"Argument sux cannot be None\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_putsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def removebarvars(self,subset_):\n num_ = None\n if num_ is None:\n num_ = len(subset_)\n elif num_ != len(subset_):\n raise IndexError(\"Inconsistent length of array subset\")\n if subset_ is None:\n raise ValueError(\"Argument subset cannot be None\")\n if subset_ is None:\n raise ValueError(\"Argument subset may not be None\")\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\n _subset_copyarray = False\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subset_ is not None:\n _subset_copyarray = True\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\n _subset_np_tmp[:] = subset_\n assert _subset_np_tmp.flags.contiguous\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subset_copyarray = False\n _subset_tmp = None\n \n res = __library__.MSK_XX_removebarvars(self.__nativep,num_,_subset_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsnxslice(self,whichsol_,first_,last_,snx_):\n _snx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and snx_ is not None and len(snx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument snx is not long enough: Is %d, expected %d\" % (len(snx_),((last_) - (first_))))\n if snx_ is None:\n raise ValueError(\"Argument snx cannot be None\")\n if snx_ is None:\n raise ValueError(\"Argument snx may not be None\")\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsnxslice(self.__nativep,whichsol_,first_,last_,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def bounds(self, new_bounds: devices.PrimaryBounds) -> None:\n self._assert_bounds_are_valid(new_bounds)\n self._bounds = list(new_bounds)","def putskxslice(self,whichsol_,first_,last_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskxslice(whichsol_,first_,last_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsuxslice(self,whichsol_,first_,last_,sux_):\n _sux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),((last_) - (first_))))\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\n raise ValueError(\"Argument sux must be writable\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_getsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _sux_copyarray:\n sux_[:] = _sux_np_tmp","def cb_bounds(self, variable, results_dict, keys, fixed_bounds):\n tas_bound, pr_bound = fixed_bounds\n if variable == \"tas\":\n if tas_bound:\n bound_limit = tas_bound\n else:\n bound_limit = self.find_abs_bound_range(results_dict, keys)\n cmap = plt.cm.RdBu_r\n else:\n if pr_bound:\n bound_limit = pr_bound\n else:\n bound_limit = self.find_abs_bound_range(results_dict,\n keys,\n avg_over=25)\n cmap = plt.cm.BrBG\n bounds = np.linspace(-1 * bound_limit, bound_limit, 11)\n return [bounds, cmap]","def set_bounds_atom(self,bounds):\n self.bounds = bounds # data bounds\n raise NotImplementedError\n self.bounds_atom = None\n return None","def putsuxslice(self,whichsol_,first_,last_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsuxslice(whichsol_,first_,last_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def write_bounds(self):\n optimized_par_df = \\\n self.parameter_df.loc[self.parameter_df.estimate == 1\n & (~self.parameter_df.index.isin(\n self.amici_model.getFixedParameterIds())), :]\n self.f.require_dataset('/parameters/lowerBound',\n shape=optimized_par_df.lowerBound.shape,\n data=optimized_par_df.lowerBound, dtype='f8')\n self.f.require_dataset('/parameters/upperBound',\n shape=optimized_par_df.upperBound.shape,\n data=optimized_par_df.upperBound, dtype='f8')","def subspace(self,varset_values):\n vector = np.empty(self.dimensions)\n \n for varname, value in varset_values:\n try: \n vector[self.slices[varname]] = np.ravel(value)\n except KeyError:\n pass\n \n return vector","def getsuxslice(self,whichsol_,first_,last_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_sux = False\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n _copyback_sux = True\n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n _copyback_sux = True\n if sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.getsuxslice(whichsol_,first_,last_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_sux:\n sux[:] = _tmparr_sux","def putsnxslice(self,whichsol_,first_,last_,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if snx is None: raise TypeError(\"Invalid type for argument snx\")\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n if snx_ is not None and len(snx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument snx has wrong length\")\n res = self.__obj.putsnxslice(whichsol_,first_,last_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def normalize_bounds(self, bounds):\n scaled_bounds = []\n scalings = []\n intercepts = []\n \n non_fixed_params = []\n \n print(self.device)\n \n for name, domain in self.bounds.items():\n # Get any fixed parmeters\n if type(domain) == int or type(domain) == float:\n # Take note\n self.fixed_parameters.append(name)\n\n # Free parameters\n elif type(domain) == tuple:\n # Bookkeeping\n self.free_parameters.append(name)\n\n # Get scaling\n lower_bound = min(domain)\n upper_bound = max(domain)\n scale = upper_bound - lower_bound\n\n # Transform to [0, 1] domain\n #scaled_bound = {'name': name, 'type': 'continuous', 'domain': (0., 1.)} #torch.adjustment required\n non_fixed_params.append(name)\n \n # Store\n #scaled_bounds.append(scaled_bound)\n scalings.append(scale)\n intercepts.append(lower_bound)\n else:\n raise ValueError(\"Domain bounds not understood\")\n \n n_hyperparams = len(non_fixed_params)\n \n scaled_bounds = cat([zeros(1,n_hyperparams, device = self.device), \n ones(1, n_hyperparams, device = self.device)], 0)\n return scaled_bounds, tensor(scalings, device = self.device, requires_grad = False), tensor(intercepts, device = self.device, requires_grad = False) #torch.adjustment required","def putskxslice(self,whichsol_,first_,last_,skx_):\n _skx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),((last_) - (first_))))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def update_bounds(self, temp):\n if (temp.extent[0] < self.extent[0]):\n self.extent[0] = np.copy(temp.extent[0])\n if (temp.extent[1] > self.extent[1]):\n self.extent[1] = np.copy(temp.extent[1])\n if (temp.extent[2] < self.extent[2]):\n self.extent[2] = np.copy(temp.extent[2])\n if (temp.extent[3] > self.extent[3]):\n self.extent[3] = np.copy(temp.extent[3])\n return self","def init_range_variables(self):\n self.range_start_vars_array = []\n self.range_end_vars_array = []\n\n for idx in range(len(self._pk_for_filter)):\n self.range_start_vars_array.append(\"@range_start_{}\".format(idx))\n self.range_end_vars_array.append(\"@range_end_{}\".format(idx))\n self.range_start_vars = \",\".join(self.range_start_vars_array)\n self.range_end_vars = \",\".join(self.range_end_vars_array)","def set_slices(self, start=0.0, end=1.0, step=None, num=None):\n\n if step is None:\n s = (end - start) / float(num)\n self._slices = frange(start, end, s)\n elif num is None:\n self._slices = frange(start, end, step)\n else:\n raise RuntimeError()\n\n LOG.info('Num slices: %d', len(self._slices))\n LOG.info('Slices: %s', self._slices)","def removevars(self,subset_):\n num_ = None\n if num_ is None:\n num_ = len(subset_)\n elif num_ != len(subset_):\n raise IndexError(\"Inconsistent length of array subset\")\n if subset_ is None:\n raise ValueError(\"Argument subset cannot be None\")\n if subset_ is None:\n raise ValueError(\"Argument subset may not be None\")\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\n _subset_copyarray = False\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subset_ is not None:\n _subset_copyarray = True\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\n _subset_np_tmp[:] = subset_\n assert _subset_np_tmp.flags.contiguous\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subset_copyarray = False\n _subset_tmp = None\n \n res = __library__.MSK_XX_removevars(self.__nativep,num_,_subset_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def removebarvars(self,subset): # 3\n num_ = None\n if num_ is None:\n num_ = len(subset)\n elif num_ != len(subset):\n raise IndexError(\"Inconsistent length of array subset\")\n if num_ is None: num_ = 0\n if subset is None: raise TypeError(\"Invalid type for argument subset\")\n if subset is None:\n subset_ = None\n else:\n try:\n subset_ = memoryview(subset)\n except TypeError:\n try:\n _tmparr_subset = array.array(\"i\",subset)\n except TypeError:\n raise TypeError(\"Argument subset has wrong type\")\n else:\n subset_ = memoryview(_tmparr_subset)\n \n else:\n if subset_.format != \"i\":\n subset_ = memoryview(array.array(\"i\",subset))\n \n res = self.__obj.removebarvars(num_,subset_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_bounds(self, **kwargs):\n for name, bounds in kwargs.items():\n if name not in self._parameters:\n raise AttributeError('Unknown parameter %s for %s' % (name, self.__class__.__name__))\n param = self._parameters[name]\n # Set bounds\n lower_bound, upper_bound = bounds\n if torch.is_tensor(lower_bound) and torch.is_tensor(upper_bound):\n if lower_bound.size() != upper_bound.size() or \\\n lower_bound.size() != param.size():\n raise AttributeError('Lower bound, upper bound, and param should have the same size')\n elif not (isinstance(lower_bound, int) or isinstance(lower_bound, float)) or \\\n not (isinstance(upper_bound, int) or isinstance(upper_bound, float)):\n raise AttributeError('Unsupported argument types for parameter %s' % name)\n\n if name not in self._bounds:\n self._bounds[name] = [None, None]\n self._bounds[name][0] = lower_bound\n self._bounds[name][1] = upper_bound\n return self","def putxcslice(self,whichsol_,first_,last_,xc_):\n _xc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xc_ is not None and len(xc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),((last_) - (first_))))\n if xc_ is None:\n raise ValueError(\"Argument xc cannot be None\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxcslice(self.__nativep,whichsol_,first_,last_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _manage_infinity_bounds(problem, _bounds, get_bound, set_bound):\n for variable in problem.variables:\n expr_bounds = get_bound(variable)\n lower_bound = expr_bounds.lower_bound\n upper_bound = expr_bounds.upper_bound\n\n if is_inf(lower_bound):\n new_lower_bound = None\n else:\n new_lower_bound = lower_bound\n\n if is_inf(upper_bound):\n new_upper_bound = None\n else:\n new_upper_bound = upper_bound\n\n set_bound(variable, Interval(new_lower_bound, new_upper_bound))","def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\n ...","def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\n ...","def putxcslice(self,whichsol_,first_,last_,xc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xc is None: raise TypeError(\"Invalid type for argument xc\")\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xc_ is not None and len(xc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xc has wrong length\")\n res = self.__obj.putxcslice(whichsol_,first_,last_,xc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putyslice(self,whichsol_,first_,last_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.putyslice(whichsol_,first_,last_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putacolslice(self,first_,last_,ptrb,ptre,asub,aval): # 3\n if ptrb is None: raise TypeError(\"Invalid type for argument ptrb\")\n if ptrb is None:\n ptrb_ = None\n else:\n try:\n ptrb_ = memoryview(ptrb)\n except TypeError:\n try:\n _tmparr_ptrb = array.array(\"q\",ptrb)\n except TypeError:\n raise TypeError(\"Argument ptrb has wrong type\")\n else:\n ptrb_ = memoryview(_tmparr_ptrb)\n \n else:\n if ptrb_.format != \"q\":\n ptrb_ = memoryview(array.array(\"q\",ptrb))\n \n if ptre is None: raise TypeError(\"Invalid type for argument ptre\")\n if ptre is None:\n ptre_ = None\n else:\n try:\n ptre_ = memoryview(ptre)\n except TypeError:\n try:\n _tmparr_ptre = array.array(\"q\",ptre)\n except TypeError:\n raise TypeError(\"Argument ptre has wrong type\")\n else:\n ptre_ = memoryview(_tmparr_ptre)\n \n else:\n if ptre_.format != \"q\":\n ptre_ = memoryview(array.array(\"q\",ptre))\n \n if asub is None: raise TypeError(\"Invalid type for argument asub\")\n if asub is None:\n asub_ = None\n else:\n try:\n asub_ = memoryview(asub)\n except TypeError:\n try:\n _tmparr_asub = array.array(\"i\",asub)\n except TypeError:\n raise TypeError(\"Argument asub has wrong type\")\n else:\n asub_ = memoryview(_tmparr_asub)\n \n else:\n if asub_.format != \"i\":\n asub_ = memoryview(array.array(\"i\",asub))\n \n if aval is None: raise TypeError(\"Invalid type for argument aval\")\n if aval is None:\n aval_ = None\n else:\n try:\n aval_ = memoryview(aval)\n except TypeError:\n try:\n _tmparr_aval = array.array(\"d\",aval)\n except TypeError:\n raise TypeError(\"Argument aval has wrong type\")\n else:\n aval_ = memoryview(_tmparr_aval)\n \n else:\n if aval_.format != \"d\":\n aval_ = memoryview(array.array(\"d\",aval))\n \n res = self.__obj.putacolslice64(first_,last_,ptrb_,ptre_,asub_,aval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def get_bounds_parameters(self):\n bounds = []\n bounds += self.var_noise.bounds\n bounds += self.mean.bounds\n bounds += self.kernel.get_bounds_parameters()\n\n return bounds","def putacolslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb cannot be None\")\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb may not be None\")\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\n _ptrb_copyarray = False\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptrb_ is not None:\n _ptrb_copyarray = True\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\n _ptrb_np_tmp[:] = ptrb_\n assert _ptrb_np_tmp.flags.contiguous\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptrb_copyarray = False\n _ptrb_tmp = None\n \n if ptre_ is None:\n raise ValueError(\"Argument ptre cannot be None\")\n if ptre_ is None:\n raise ValueError(\"Argument ptre may not be None\")\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\n _ptre_copyarray = False\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptre_ is not None:\n _ptre_copyarray = True\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\n _ptre_np_tmp[:] = ptre_\n assert _ptre_np_tmp.flags.contiguous\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptre_copyarray = False\n _ptre_tmp = None\n \n if asub_ is None:\n raise ValueError(\"Argument asub cannot be None\")\n if asub_ is None:\n raise ValueError(\"Argument asub may not be None\")\n if isinstance(asub_, numpy.ndarray) and asub_.dtype is numpy.dtype(numpy.int32) and asub_.flags.contiguous:\n _asub_copyarray = False\n _asub_tmp = ctypes.cast(asub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif asub_ is not None:\n _asub_copyarray = True\n _asub_np_tmp = numpy.zeros(len(asub_),numpy.dtype(numpy.int32))\n _asub_np_tmp[:] = asub_\n assert _asub_np_tmp.flags.contiguous\n _asub_tmp = ctypes.cast(_asub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _asub_copyarray = False\n _asub_tmp = None\n \n if aval_ is None:\n raise ValueError(\"Argument aval cannot be None\")\n if aval_ is None:\n raise ValueError(\"Argument aval may not be None\")\n if isinstance(aval_, numpy.ndarray) and aval_.dtype is numpy.dtype(numpy.float64) and aval_.flags.contiguous:\n _aval_copyarray = False\n _aval_tmp = ctypes.cast(aval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif aval_ is not None:\n _aval_copyarray = True\n _aval_np_tmp = numpy.zeros(len(aval_),numpy.dtype(numpy.float64))\n _aval_np_tmp[:] = aval_\n assert _aval_np_tmp.flags.contiguous\n _aval_tmp = ctypes.cast(_aval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _aval_copyarray = False\n _aval_tmp = None\n \n res = __library__.MSK_XX_putacolslice64(self.__nativep,first_,last_,_ptrb_tmp,_ptre_tmp,_asub_tmp,_aval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putyslice(self,whichsol_,first_,last_,y_):\n _y_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),((last_) - (first_))))\n if y_ is None:\n raise ValueError(\"Argument y cannot be None\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_putyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _set_neighs_slice(self, key):\n ## Condition to use slice type\n self._constant_neighs = True\n self.ks = range(1) if self.ks is None else self.ks\n ## Possible options\n if key is None:\n self.idxs = slice(0, self._n, 1)\n elif isinstance(key, slice):\n start = 0 if key.start is None else key.start\n stop = self._n if key.stop is None else key.stop\n stop = self._n if key.stop > 10*16 else key.stop\n step = 1 if key.step is None else key.step\n self.idxs = slice(start, stop, step)\n elif type(key) in inttypes:\n self.idxs = slice(0, key, 1)\n elif type(key) == tuple:\n self.idxs = slice(key[0], key[1], 1)\n self._setted = True","def putslxslice(self,whichsol_,first_,last_,slx_):\n _slx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slx_ is not None and len(slx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),((last_) - (first_))))\n if slx_ is None:\n raise ValueError(\"Argument slx cannot be None\")\n if slx_ is None:\n raise ValueError(\"Argument slx may not be None\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n res = __library__.MSK_XX_putslxslice(self.__nativep,whichsol_,first_,last_,_slx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def variable_bounds(problem):\n return ([\n problem['state_bounds'][var] if problem['state_bounds'][var] is not None else (-np.inf, np.inf)\n for _ in range(problem['N'] - 1)\n for var in range(problem['num_states'])\n ] + [\n problem['input_bounds'][inp] if problem['input_bounds'][inp] is not None else (-np.inf, np.inf)\n for _ in range(problem['N'] + 1)\n for inp in range(problem['num_inputs'])\n ]) * problem['Nv'] + ([(0.01, np.inf)] if problem['T'] == 0 else []) \\\n if problem['state_bounds'] is not None else None","def only_bounds(must_print):\n\n #Extracting input.\n input = find_input()\n\n #Running the experiment.\n result = bound.execute_script(input, must_print, True)[2:]\n #result = bound2.execute_script(input, must_print, True)[2:]\n\n #Storing output.\n store_output(result) #result = [P_BOUND, R_BOUND]","def removeBounded(self, bounds):\n if bounds==None or len(bounds)!=4:\n return\n x1,y1,x2,y2 = bounds\n if x1>x2 :\n temp=x1;x1=x2;x2=temp\n if y1>y2:\n temp=y1;y1=y2;y2=temp\n lst=[]\n for i in range(0,self.length()):\n x=self.x[i]; y=self.y[i]\n if (x>x1 and x<x2) and (y>y1 and y<y2): \n lst.append(i)\n self.removeMultiple(lst)\n return","def _extend_slice(self, sl, extent, dim_max, dim_min=0):\n\n x_start = max(sl.start - extent, dim_min)\n x_end = min(sl.stop + extent, dim_max)\n return np.s_[x_start:x_end]","def removevars(self,subset): # 3\n num_ = None\n if num_ is None:\n num_ = len(subset)\n elif num_ != len(subset):\n raise IndexError(\"Inconsistent length of array subset\")\n if num_ is None: num_ = 0\n if subset is None: raise TypeError(\"Invalid type for argument subset\")\n if subset is None:\n subset_ = None\n else:\n try:\n subset_ = memoryview(subset)\n except TypeError:\n try:\n _tmparr_subset = array.array(\"i\",subset)\n except TypeError:\n raise TypeError(\"Argument subset has wrong type\")\n else:\n subset_ = memoryview(_tmparr_subset)\n \n else:\n if subset_.format != \"i\":\n subset_ = memoryview(array.array(\"i\",subset))\n \n res = self.__obj.removevars(num_,subset_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def bounds(self, axis, view=None):\n if view is None:\n view = self\n if axis not in self._vshare.bounds:\n self._vshare.bounds[axis] = self._compute_bounds(axis, view)\n return self._vshare.bounds[axis]","def appendbarvars(self,dim_):\n num_ = None\n if num_ is None:\n num_ = len(dim_)\n elif num_ != len(dim_):\n raise IndexError(\"Inconsistent length of array dim\")\n if dim_ is None:\n raise ValueError(\"Argument dim cannot be None\")\n if dim_ is None:\n raise ValueError(\"Argument dim may not be None\")\n if isinstance(dim_, numpy.ndarray) and dim_.dtype is numpy.dtype(numpy.int32) and dim_.flags.contiguous:\n _dim_copyarray = False\n _dim_tmp = ctypes.cast(dim_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dim_ is not None:\n _dim_copyarray = True\n _dim_np_tmp = numpy.zeros(len(dim_),numpy.dtype(numpy.int32))\n _dim_np_tmp[:] = dim_\n assert _dim_np_tmp.flags.contiguous\n _dim_tmp = ctypes.cast(_dim_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dim_copyarray = False\n _dim_tmp = None\n \n res = __library__.MSK_XX_appendbarvars(self.__nativep,num_,_dim_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putslxslice(self,whichsol_,first_,last_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslxslice(whichsol_,first_,last_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setSafeRange( azLow, azHigh, elLow, elHigh, ants, subarray=DEFAULT) :\n antlist = helpers.makeList(ants)\n multiSubarray('setSafeRange', subarray, azLow, azHigh, elLow, elHigh, \n antlist)","def getvarbound(self,i_): # 3\n res,resargs = self.__obj.getvarbound(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _bk_return_value,_bl_return_value,_bu_return_value = resargs\n _bk_return_value = boundkey(_bk_return_value)\n return _bk_return_value,_bl_return_value,_bu_return_value","def add_time_bounds(nc, varname):\n THREE_HOURS = 60*60*3 # in seconds\n bnds_name = 'time_bnds'\n bounds_dim = 'nv'\n\n # Create bounds dimension\n nc.createDimension(bounds_dim, 2)\n\n # Get variable matching varname\n\n time_var = nc.variables['time']\n time_var.setncattr('bounds', bnds_name)\n time_data = time_var[:]\n time_length = len(time_data)\n\n # reshape time data\n bounds_data = np.dstack((time_data,time_data)).reshape(time_length,2)\n for i in bounds_data:\n i[0] = i[0] - (THREE_HOURS)\n bounds_var = nc.createVariable(bnds_name, time_var.dtype, ('time', bounds_dim), fill_value=9999)\n bounds_var[:] = bounds_data","def putskcslice(self,whichsol_,first_,last_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskcslice(whichsol_,first_,last_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getvarbound(self,i_):\n bk_ = ctypes.c_int32()\n bl_ = ctypes.c_double()\n bu_ = ctypes.c_double()\n res = __library__.MSK_XX_getvarbound(self.__nativep,i_,ctypes.byref(bk_),ctypes.byref(bl_),ctypes.byref(bu_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _bk_return_value = boundkey(bk_.value)\n bl_ = bl_.value\n _bl_return_value = bl_\n bu_ = bu_.value\n _bu_return_value = bu_\n return (_bk_return_value,_bl_return_value,_bu_return_value)","def update_i_bnds(self):\n\n # Get old and new boundaries.\n i_bnds_old = self.i_bounds\n i_bnds_new = self._get_i_bnds()\n\n for i_order in range(self.n_orders):\n\n # Take most restrictive lower bound.\n low_bnds = [i_bnds_new[i_order][0], i_bnds_old[i_order][0]]\n i_bnds_new[i_order][0] = np.max(low_bnds)\n\n # Take most restrictive upper bound.\n up_bnds = [i_bnds_new[i_order][1], i_bnds_old[i_order][1]]\n i_bnds_new[i_order][1] = np.min(up_bnds)\n\n # Update attribute.\n self.i_bounds = i_bnds_new\n\n return","def create_from_bounds(self, lbs, ubs):\n self.base_vertices = (np.array([lbs])+np.array([ubs])).T/2\n self.base_vectors = np.diag((np.array(ubs)-np.array(lbs))/2)","def _fix_slice(self, inputs, new_attr):\n begin = new_attr.get('begin')\n end = new_attr.get('end')\n axes = new_attr.get('axis', tuple(range(len(begin))))\n slice_op = mx.sym.slice_axis(inputs[0], axis=axes[0], begin=begin[0], end=end[0])\n if len(axes) > 1:\n for i, axis in enumerate(axes):\n slice_op = mx.sym.slice_axis(slice_op, axis=axis, begin=begin[i], end=end[i])\n return slice_op","def setActiveBounded(self, bounds=None, status=1): \n if bounds==None or len(bounds)!=4:\n return\n x1,y1,x2,y2 = bounds\n if x1>x2 :\n temp=x1;x1=x2;x2=temp\n if y1>y2:\n temp=y1;y1=y2;y2=temp\n for i in range(0,self.length()):\n x=self.x[i]; y=self.y[i]\n if (x>x1 and x<x2) and (y>y1 and y<y2):\n self.active[i]= status \n return","def _parse_var_modelspace(self,varname) :\n\n\t\tmodelspace = self.ss.constraint.modelspace\n\t\tparams = getattr(modelspace,varname)\n\t\tnvars = len(self.ss.variables) # num of variables\n\n\t\tif varname in ('alpha','beta') : \n\t\t\tkeys = params.keys()\n\t\t\tvar_range = (params['defaultLowerBound'],\\\n\t\t\t\tparams['defaultUpperBound'])\n\t\t\tself.modelspace[varname] = [var_range]*nvars\n\t\t\tfor key in keys : \n\t\t\t\tif re.match(varname+'_\\d+',key)\t:\n\t\t\t\t\tidx = int(key.split('_')[1])\t\t\t\t\n\t\t\t\t\tself.modelspace[varname][idx-1] = params[key]\n\n\t\telif varname in ('g','h') :\n\t\t\tkeys = params.keys()\n\t\t\tvar_range = (params['defaultLowerBound'],\\\n\t\t\t\tparams['defaultUpperBound'])\n\n\t\t\t# This step is purely there cuz [[var_range]*nvars]*nvars\n\t\t\t# does not work\n\t\t\tvarlist = []\n\t\t\tfor ii in range(nvars) : \n\t\t\t\tvarlist.append([var_range]*nvars)\n\t\t\tself.modelspace[varname] = varlist\n\t\t\tfor key in keys : \n\t\t\t\tif re.match(varname+'_\\d+_\\d+',key)\t:\n\t\t\t\t\tidr,idc = map(int,(key.split('_')[1:3]))\n\t\t\t\t\tself.modelspace[varname][idr-1][idc-1] = params[key]\n\t\t\n\t\telse :\n\t\t\tlogging.error(\"Unrecognized varname %s quitting..\" \\\n\t\t\t%(varname))\n\t\t\tsys.exit(1)","def split_var(self, x):\n cum_dims = list(np.cumsum(self.dims))\n out = []\n for slice_from, slice_to, dist in zip([0] + cum_dims, cum_dims, self.dists):\n sliced = x[:, slice_from:slice_to]\n out.append(sliced)\n return out","def var_slice(colnames, x):\n # TODO: produces bahavior similar to df.loc[:, \"V1\":\"V3\"], but can reverse\n # TODO: make DRY\n # TODO: reverse not including end points\n if isinstance(x.start, Var):\n start_indx = (colnames == x.start.name).idxmax()\n elif isinstance(x.start, str):\n start_indx = (colnames == x.start).idxmax()\n else:\n start_indx = x.start or 0\n\n if isinstance(x.stop, Var):\n stop_indx = (colnames == x.stop.name).idxmax() + 1\n elif isinstance(x.stop, str):\n stop_indx = (colnames == x.stop).idxmax() + 1\n else:\n stop_indx = x.stop or len(colnames)\n\n if start_indx > stop_indx:\n return stop_indx, start_indx\n else:\n return start_indx, stop_indx","def set_grid(self, bounds, dx):\n self.dx = dx\n self.X1 = np.arange(bounds[0], bounds[1], dx)\n self.X = np.array(list(it.product(*[self.X1]*self.n_dim)))","def checkranges(self, var, name):\r\n\r\n # reshape var\r\n assert len(var) == 2, \"%s must have two elements,\" % name\r\n var = np.array([float(v) for v in var])\r\n\r\n # check values\r\n if name in [\"arange\", \"Rprange\", \"Mprange\"]:\r\n assert np.all(var > 0), \"%s values must be strictly positive\" % name\r\n if name in [\"erange\", \"prange\"]:\r\n assert np.all(var >= 0) and np.all(var <= 1), (\r\n \"%s values must be between 0 and 1\" % name\r\n )\r\n\r\n # the second element must be greater or equal to the first\r\n if var[1] < var[0]:\r\n var = var[::-1]\r\n\r\n return var","def draw_bounds():\n\n pass","def putarowslice(self,first_,last_,ptrb,ptre,asub,aval): # 3\n if ptrb is None: raise TypeError(\"Invalid type for argument ptrb\")\n if ptrb is None:\n ptrb_ = None\n else:\n try:\n ptrb_ = memoryview(ptrb)\n except TypeError:\n try:\n _tmparr_ptrb = array.array(\"q\",ptrb)\n except TypeError:\n raise TypeError(\"Argument ptrb has wrong type\")\n else:\n ptrb_ = memoryview(_tmparr_ptrb)\n \n else:\n if ptrb_.format != \"q\":\n ptrb_ = memoryview(array.array(\"q\",ptrb))\n \n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptrb has wrong length\")\n if ptre is None: raise TypeError(\"Invalid type for argument ptre\")\n if ptre is None:\n ptre_ = None\n else:\n try:\n ptre_ = memoryview(ptre)\n except TypeError:\n try:\n _tmparr_ptre = array.array(\"q\",ptre)\n except TypeError:\n raise TypeError(\"Argument ptre has wrong type\")\n else:\n ptre_ = memoryview(_tmparr_ptre)\n \n else:\n if ptre_.format != \"q\":\n ptre_ = memoryview(array.array(\"q\",ptre))\n \n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptre has wrong length\")\n if asub is None: raise TypeError(\"Invalid type for argument asub\")\n if asub is None:\n asub_ = None\n else:\n try:\n asub_ = memoryview(asub)\n except TypeError:\n try:\n _tmparr_asub = array.array(\"i\",asub)\n except TypeError:\n raise TypeError(\"Argument asub has wrong type\")\n else:\n asub_ = memoryview(_tmparr_asub)\n \n else:\n if asub_.format != \"i\":\n asub_ = memoryview(array.array(\"i\",asub))\n \n if aval is None: raise TypeError(\"Invalid type for argument aval\")\n if aval is None:\n aval_ = None\n else:\n try:\n aval_ = memoryview(aval)\n except TypeError:\n try:\n _tmparr_aval = array.array(\"d\",aval)\n except TypeError:\n raise TypeError(\"Argument aval has wrong type\")\n else:\n aval_ = memoryview(_tmparr_aval)\n \n else:\n if aval_.format != \"d\":\n aval_ = memoryview(array.array(\"d\",aval))\n \n res = self.__obj.putarowslice64(first_,last_,ptrb_,ptre_,asub_,aval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_slider_bounds(self,lower,upper,inclusive_bounds=None):\n self.bounds = (lower,upper)\n\n if inclusive_bounds is not None:\n self.inclusive_bounds = inclusive_bounds\n\n epsilon = max(self.slider['resolution'],0.00000000001)\n\n if self.inclusive_bounds[0] is False:\n lower+=epsilon\n if self.inclusive_bounds[1] is False:\n upper-=epsilon\n self.slider.config(from_=lower,to=upper)","def repair(self, x, bounds=None, copy=False, copy_always=False):\r\n # TODO (old data): CPU(N,lam,iter=20,200,100): 3.3s of 8s for two bounds, 1.8s of 6.5s for one bound\r\n # TODO: test whether np.max([bounds[0], x], axis=0) etc is speed relevant\r\n\r\n if bounds is None:\r\n bounds = self.bounds\r\n if copy_always:\r\n x_out = array(x, copy=True)\r\n if bounds not in (None, [None, None], (None, None)): # solely for effiency\r\n x_out = array(x, copy=True) if copy and not copy_always else x\r\n if bounds[0] is not None:\r\n if np.isscalar(bounds[0]):\r\n for i in xrange(len(x)):\r\n x_out[i] = max([bounds[0], x[i]])\r\n else:\r\n for i in xrange(len(x)):\r\n if bounds[0][i] is not None:\r\n x_out[i] = max([bounds[0][i], x[i]])\r\n if bounds[1] is not None:\r\n if np.isscalar(bounds[1]):\r\n for i in xrange(len(x)):\r\n x_out[i] = min([bounds[1], x[i]])\r\n else:\r\n for i in xrange(len(x)):\r\n if bounds[1][i] is not None:\r\n x_out[i] = min([bounds[1][i], x[i]])\r\n return x_out # convenience return\r","def point_into_bound(bounds, point, array_name):\n\theight, width = bounds #get ndarray shape\n\tx, y = point #get the components of stamp_point\n\t#validate in x\n\tassert (x >= 0 and x < width), \"out of {} bounds in x axis in stamp_point\".format(array_name)\n\t#validate in y\n\tassert (y >= 0 and y < height), \"out of {} bounds in y axis in stamp_point\".format(array_name)","def set_range_indexing(self, var_idx: int) -> None:\n if len(self._carry_register_) < var_idx or len(self._carry_register_) == 0:\n return # TODO: Throw an error\n if self._carry_variables_[self._carry_register_[var_idx]] not in [List, int, list]:\n return # TODO: Throw an error\n\n # Done checking\n self.indexing = self._get_available_var_()\n self.__prefix__ += self.indexing + \" in range(\"\n if self._carry_variables_[self._carry_register_[var_idx]] == int:\n self.__prefix__ += self._carry_register_[var_idx] + \")\"\n else:\n self.__prefix__ += \"len(\" + self._carry_register_[var_idx] + \"))\"\n self.__prefix__ += \":\"","def get_bounds(self):\n return ([self.t_min] * self.dim,[self.t_max] * self.dim)","def select_bounds(ds, bounds):\n \n xs = slice(bounds[0][0], bounds[1][0])\n ys = slice(bounds[1][1], bounds[0][1])\n # select over x and y axis\n return ds.sel(x=xs, y=ys)","def bounds(self):\n if self.change_dimensionality:\n return [self._bounds[0]] * self.N\n else:\n return self._bounds","def putslcslice(self,whichsol_,first_,last_,slc_):\n _slc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),((last_) - (first_))))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def bound(self, points):\n if self.bounds is None:\n return points\n\n bounded = np.copy(points) # SLOW!!!\n # Because numpy doesn't handle multi-dimensional arrays the same as 1-dimensional ones, it's easiest to just make it always look like a multi-dim array\n points_shape = points.shape\n cPoints = points_shape[0]\n if cPoints == 1:\n bounded = np.array([bounded, np.zeros_like(bounded)])\n _ignore, cDims = bounded.shape\n for i in xrange(cDims):\n xs = bounded[:,i]\n min_b, max_b = self.bounds[i]\n assert min_b < max_b\n width = max_b - min_b\n # (EDIT: Wrong!) Because of the way that mod works (it wraps negative values around, rather than returning -(abs(x) % abs(y))), we can just use it straight\n # Need to treat neg and pos values different because of behavior of mod operator\n # On second thought, don't use mod, just assume small jumps\n too_far_neg_ixs = xs < min_b\n xs[too_far_neg_ixs] += width\n too_far_pos_ixs = xs > max_b\n xs[too_far_pos_ixs] -= width\n bounded[:,i] = xs # is this necessary? seems so\n if cPoints == 1:\n bounded = bounded[0] # pull back out the 1-dim array\n return bounded"],"string":"[\n \"def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getvarboundslice(self,first_,last_,bk,bl,bu): # 3\\n _copyback_bk = False\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n _copyback_bk = True\\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n _copyback_bk = True\\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n _copyback_bl = False\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n _copyback_bl = True\\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n _copyback_bl = True\\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n _copyback_bu = False\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n _copyback_bu = True\\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n _copyback_bu = True\\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.getvarboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_bu:\\n bu[:] = _tmparr_bu\\n if _copyback_bl:\\n bl[:] = _tmparr_bl\\n if _copyback_bk:\\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])\",\n \"def getvarboundslice(self,first_,last_,bk_,bl_,bu_):\\n _bk_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk is not long enough: Is %d, expected %d\\\" % (len(bk_),((last_) - (first_))))\\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\\n raise ValueError(\\\"Argument bk must be writable\\\")\\n if bk_ is not None:\\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\\n else:\\n _bk_tmp = None\\n _bl_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl is not long enough: Is %d, expected %d\\\" % (len(bl_),((last_) - (first_))))\\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\\n raise ValueError(\\\"Argument bl must be writable\\\")\\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\\n _bl_copyarray = False\\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bl_ is not None:\\n _bl_copyarray = True\\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\\n _bl_np_tmp[:] = bl_\\n assert _bl_np_tmp.flags.contiguous\\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bl_copyarray = False\\n _bl_tmp = None\\n \\n _bu_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu is not long enough: Is %d, expected %d\\\" % (len(bu_),((last_) - (first_))))\\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\\n raise ValueError(\\\"Argument bu must be writable\\\")\\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\\n _bu_copyarray = False\\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bu_ is not None:\\n _bu_copyarray = True\\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\\n _bu_np_tmp[:] = bu_\\n assert _bu_np_tmp.flags.contiguous\\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bu_copyarray = False\\n _bu_tmp = None\\n \\n res = __library__.MSK_XX_getvarboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\\n if _bl_copyarray:\\n bl_[:] = _bl_np_tmp\\n if _bu_copyarray:\\n bu_[:] = _bu_np_tmp\",\n \"def putboundslice(self,con_,first_,last_,bk,bl,bu): # 3\\n if not isinstance(con_,accmode): raise TypeError(\\\"Argument con has wrong type\\\")\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putboundslice(con_,first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundslice(self,first_,last_,bk,bl,bu): # 3\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putconboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_variable_slices(self, variables):\\n # Set up y_slices and bounds\\n y_slices = defaultdict(list)\\n y_slices_explicit = defaultdict(list)\\n start = 0\\n end = 0\\n lower_bounds = []\\n upper_bounds = []\\n # Iterate through unpacked variables, adding appropriate slices to y_slices\\n for variable in variables:\\n # Add up the size of all the domains in variable.domain\\n if isinstance(variable, pybamm.ConcatenationVariable):\\n start_ = start\\n spatial_method = self.spatial_methods[variable.domain[0]]\\n children = variable.children\\n meshes = OrderedDict()\\n for child in children:\\n meshes[child] = [spatial_method.mesh[dom] for dom in child.domain]\\n sec_points = spatial_method._get_auxiliary_domain_repeats(\\n variable.domains\\n )\\n for i in range(sec_points):\\n for child, mesh in meshes.items():\\n for domain_mesh in mesh:\\n end += domain_mesh.npts_for_broadcast_to_nodes\\n # Add to slices\\n y_slices[child].append(slice(start_, end))\\n y_slices_explicit[child].append(slice(start_, end))\\n # Increment start_\\n start_ = end\\n else:\\n end += self._get_variable_size(variable)\\n\\n # Add to slices\\n y_slices[variable].append(slice(start, end))\\n y_slices_explicit[variable].append(slice(start, end))\\n\\n # Add to bounds\\n def evaluate_bound(bound, side):\\n if bound.has_symbol_of_classes(pybamm.InputParameter):\\n if side == \\\"lower\\\":\\n return -np.inf\\n elif side == \\\"upper\\\":\\n return np.inf\\n else:\\n return bound.evaluate()\\n\\n lower_bounds.extend(\\n [evaluate_bound(variable.bounds[0], \\\"lower\\\")] * (end - start)\\n )\\n upper_bounds.extend(\\n [evaluate_bound(variable.bounds[1], \\\"upper\\\")] * (end - start)\\n )\\n # Increment start\\n start = end\\n\\n # Convert y_slices back to normal dictionary\\n self.y_slices = dict(y_slices)\\n # Also keep a record of what the y_slices are, to be stored in the model\\n self.y_slices_explicit = dict(y_slices_explicit)\\n\\n # Also keep a record of bounds\\n self.bounds = (np.array(lower_bounds), np.array(upper_bounds))\\n\\n # reset discretised_symbols\\n self._discretised_symbols = {}\",\n \"def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarboundlist(self,sub_,bkx_,blx_,bux_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkx_)\\n elif num_ != len(bkx_):\\n raise IndexError(\\\"Inconsistent length of array bkx\\\")\\n if num_ is None:\\n num_ = len(blx_)\\n elif num_ != len(blx_):\\n raise IndexError(\\\"Inconsistent length of array blx\\\")\\n if num_ is None:\\n num_ = len(bux_)\\n elif num_ != len(bux_):\\n raise IndexError(\\\"Inconsistent length of array bux\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if bkx_ is None:\\n raise ValueError(\\\"Argument bkx cannot be None\\\")\\n if bkx_ is None:\\n raise ValueError(\\\"Argument bkx may not be None\\\")\\n if bkx_ is not None:\\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\\n else:\\n _bkx_tmp = None\\n if blx_ is None:\\n raise ValueError(\\\"Argument blx cannot be None\\\")\\n if blx_ is None:\\n raise ValueError(\\\"Argument blx may not be None\\\")\\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\\n _blx_copyarray = False\\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blx_ is not None:\\n _blx_copyarray = True\\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\\n _blx_np_tmp[:] = blx_\\n assert _blx_np_tmp.flags.contiguous\\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blx_copyarray = False\\n _blx_tmp = None\\n \\n if bux_ is None:\\n raise ValueError(\\\"Argument bux cannot be None\\\")\\n if bux_ is None:\\n raise ValueError(\\\"Argument bux may not be None\\\")\\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\\n _bux_copyarray = False\\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bux_ is not None:\\n _bux_copyarray = True\\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\\n _bux_np_tmp[:] = bux_\\n assert _bux_np_tmp.flags.contiguous\\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bux_copyarray = False\\n _bux_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundlist(self.__nativep,num_,_sub_tmp,_bkx_tmp,_blx_tmp,_bux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putxxslice(self,whichsol_,first_,last_,xx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xx is None: raise TypeError(\\\"Invalid type for argument xx\\\")\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n res = self.__obj.putxxslice(whichsol_,first_,last_,xx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarbound(self,j_,bkx_,blx_,bux_):\\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putxxslice(self,whichsol_,first_,last_,xx_):\\n _xx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),((last_) - (first_))))\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx cannot be None\\\")\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx may not be None\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n res = __library__.MSK_XX_putxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarboundlist(self,sub,bkx,blx,bux): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkx)\\n elif num_ != len(bkx):\\n raise IndexError(\\\"Inconsistent length of array bkx\\\")\\n if num_ is None:\\n num_ = len(blx)\\n elif num_ != len(blx):\\n raise IndexError(\\\"Inconsistent length of array blx\\\")\\n if num_ is None:\\n num_ = len(bux)\\n elif num_ != len(bux):\\n raise IndexError(\\\"Inconsistent length of array bux\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if bkx is None: raise TypeError(\\\"Invalid type for argument bkx\\\")\\n if bkx is None:\\n bkx_ = None\\n else:\\n try:\\n bkx_ = memoryview(bkx)\\n except TypeError:\\n try:\\n _tmparr_bkx = array.array(\\\"i\\\",bkx)\\n except TypeError:\\n raise TypeError(\\\"Argument bkx has wrong type\\\")\\n else:\\n bkx_ = memoryview(_tmparr_bkx)\\n \\n else:\\n if bkx_.format != \\\"i\\\":\\n bkx_ = memoryview(array.array(\\\"i\\\",bkx))\\n \\n if blx is None: raise TypeError(\\\"Invalid type for argument blx\\\")\\n if blx is None:\\n blx_ = None\\n else:\\n try:\\n blx_ = memoryview(blx)\\n except TypeError:\\n try:\\n _tmparr_blx = array.array(\\\"d\\\",blx)\\n except TypeError:\\n raise TypeError(\\\"Argument blx has wrong type\\\")\\n else:\\n blx_ = memoryview(_tmparr_blx)\\n \\n else:\\n if blx_.format != \\\"d\\\":\\n blx_ = memoryview(array.array(\\\"d\\\",blx))\\n \\n if bux is None: raise TypeError(\\\"Invalid type for argument bux\\\")\\n if bux is None:\\n bux_ = None\\n else:\\n try:\\n bux_ = memoryview(bux)\\n except TypeError:\\n try:\\n _tmparr_bux = array.array(\\\"d\\\",bux)\\n except TypeError:\\n raise TypeError(\\\"Argument bux has wrong type\\\")\\n else:\\n bux_ = memoryview(_tmparr_bux)\\n \\n else:\\n if bux_.format != \\\"d\\\":\\n bux_ = memoryview(array.array(\\\"d\\\",bux))\\n \\n res = self.__obj.putvarboundlist(num_,sub_,bkx_,blx_,bux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getconboundslice(self,first_,last_,bk,bl,bu): # 3\\n _copyback_bk = False\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n _copyback_bk = True\\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n _copyback_bk = True\\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n _copyback_bl = False\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n _copyback_bl = True\\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n _copyback_bl = True\\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n _copyback_bu = False\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n _copyback_bu = True\\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n _copyback_bu = True\\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.getconboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_bu:\\n bu[:] = _tmparr_bu\\n if _copyback_bl:\\n bl[:] = _tmparr_bl\\n if _copyback_bk:\\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])\",\n \"def getconboundslice(self,first_,last_,bk_,bl_,bu_):\\n _bk_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk is not long enough: Is %d, expected %d\\\" % (len(bk_),((last_) - (first_))))\\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\\n raise ValueError(\\\"Argument bk must be writable\\\")\\n if bk_ is not None:\\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\\n else:\\n _bk_tmp = None\\n _bl_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl is not long enough: Is %d, expected %d\\\" % (len(bl_),((last_) - (first_))))\\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\\n raise ValueError(\\\"Argument bl must be writable\\\")\\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\\n _bl_copyarray = False\\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bl_ is not None:\\n _bl_copyarray = True\\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\\n _bl_np_tmp[:] = bl_\\n assert _bl_np_tmp.flags.contiguous\\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bl_copyarray = False\\n _bl_tmp = None\\n \\n _bu_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu is not long enough: Is %d, expected %d\\\" % (len(bu_),((last_) - (first_))))\\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\\n raise ValueError(\\\"Argument bu must be writable\\\")\\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\\n _bu_copyarray = False\\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bu_ is not None:\\n _bu_copyarray = True\\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\\n _bu_np_tmp[:] = bu_\\n assert _bu_np_tmp.flags.contiguous\\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bu_copyarray = False\\n _bu_tmp = None\\n \\n res = __library__.MSK_XX_getconboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\\n if _bl_copyarray:\\n bl_[:] = _bl_np_tmp\\n if _bu_copyarray:\\n bu_[:] = _bu_np_tmp\",\n \"def apply_bound(x, var_min, var_max):\\n x.position = np.maximum(x.position, var_min)\\n x.position = np.minimum(x.position, var_max)\",\n \"def set_bounds_atom(self,bounds):\\n assert bounds.shape == (2,self.Phi.d)\\n self.bounds = bounds # data bounds\\n self.bounds_atom = bounds.T.tolist()\\n for i in range(self.Phi.d): # bounds for the variance in each dimension\\n max_variance_this_dimension = (bounds[1][i]-bounds[0][i])**2\\n self.bounds_atom.append([self.variance_relative_lowerbound*max_variance_this_dimension,\\n self.variance_relative_upperbound*max_variance_this_dimension])\",\n \"def putconboundslice(self,first_,last_,bkc_,blc_,buc_):\\n _bkc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bkc_ is not None and len(bkc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bkc is not long enough: Is %d, expected %d\\\" % (len(bkc_),((last_) - (first_))))\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc cannot be None\\\")\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc may not be None\\\")\\n if bkc_ is not None:\\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\\n else:\\n _bkc_tmp = None\\n _blc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and blc_ is not None and len(blc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument blc is not long enough: Is %d, expected %d\\\" % (len(blc_),((last_) - (first_))))\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc cannot be None\\\")\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc may not be None\\\")\\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\\n _blc_copyarray = False\\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blc_ is not None:\\n _blc_copyarray = True\\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\\n _blc_np_tmp[:] = blc_\\n assert _blc_np_tmp.flags.contiguous\\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blc_copyarray = False\\n _blc_tmp = None\\n \\n _buc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and buc_ is not None and len(buc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument buc is not long enough: Is %d, expected %d\\\" % (len(buc_),((last_) - (first_))))\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc cannot be None\\\")\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc may not be None\\\")\\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\\n _buc_copyarray = False\\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif buc_ is not None:\\n _buc_copyarray = True\\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\\n _buc_np_tmp[:] = buc_\\n assert _buc_np_tmp.flags.contiguous\\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _buc_copyarray = False\\n _buc_tmp = None\\n \\n res = __library__.MSK_XX_putconboundslice(self.__nativep,first_,last_,_bkc_tmp,_blc_tmp,_buc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putcslice(self,first_,last_,slice_):\\n _slice_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slice_ is not None and len(slice_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slice is not long enough: Is %d, expected %d\\\" % (len(slice_),((last_) - (first_))))\\n if slice_ is None:\\n raise ValueError(\\\"Argument slice cannot be None\\\")\\n if slice_ is None:\\n raise ValueError(\\\"Argument slice may not be None\\\")\\n if isinstance(slice_, numpy.ndarray) and slice_.dtype is numpy.dtype(numpy.float64) and slice_.flags.contiguous:\\n _slice_copyarray = False\\n _slice_tmp = ctypes.cast(slice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slice_ is not None:\\n _slice_copyarray = True\\n _slice_np_tmp = numpy.zeros(len(slice_),numpy.dtype(numpy.float64))\\n _slice_np_tmp[:] = slice_\\n assert _slice_np_tmp.flags.contiguous\\n _slice_tmp = ctypes.cast(_slice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slice_copyarray = False\\n _slice_tmp = None\\n \\n res = __library__.MSK_XX_putcslice(self.__nativep,first_,last_,_slice_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarbound(self,j_,bk_,bl_,bu_): # 3\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getboundslice(self,accmode_,first_,last_,bk,bl,bu): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n _copyback_bk = False\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n _copyback_bk = True\\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n _copyback_bk = True\\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n _copyback_bl = False\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n _copyback_bl = True\\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n _copyback_bl = True\\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n _copyback_bu = False\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n _copyback_bu = True\\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n _copyback_bu = True\\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.getboundslice(accmode_,first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_bu:\\n bu[:] = _tmparr_bu\\n if _copyback_bl:\\n bl[:] = _tmparr_bl\\n if _copyback_bk:\\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])\",\n \"def set_bounds(self, new_bounds):\\n\\n # Update the internal object stored dict\\n self.pbounds.update(new_bounds)\\n\\n # Loop through the all bounds and reset the min-max bound matrix\\n for row, key in enumerate(self.pbounds.keys()):\\n\\n # Reset all entries, even if the same.\\n self.bounds[row] = self.pbounds[key]\",\n \"def chgvarbound(self,j_,lower_,finite_,value_): # 3\\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getbarxslice(self,whichsol_,first_,last_,slicesize_,barxslice_):\\n _barxslice_minlength = (slicesize_)\\n if (slicesize_) > 0 and barxslice_ is not None and len(barxslice_) != (slicesize_):\\n raise ValueError(\\\"Array argument barxslice is not long enough: Is %d, expected %d\\\" % (len(barxslice_),(slicesize_)))\\n if isinstance(barxslice_,numpy.ndarray) and not barxslice_.flags.writeable:\\n raise ValueError(\\\"Argument barxslice must be writable\\\")\\n if barxslice_ is None:\\n raise ValueError(\\\"Argument barxslice may not be None\\\")\\n if isinstance(barxslice_, numpy.ndarray) and barxslice_.dtype is numpy.dtype(numpy.float64) and barxslice_.flags.contiguous:\\n _barxslice_copyarray = False\\n _barxslice_tmp = ctypes.cast(barxslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barxslice_ is not None:\\n _barxslice_copyarray = True\\n _barxslice_np_tmp = numpy.zeros(len(barxslice_),numpy.dtype(numpy.float64))\\n _barxslice_np_tmp[:] = barxslice_\\n assert _barxslice_np_tmp.flags.contiguous\\n _barxslice_tmp = ctypes.cast(_barxslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barxslice_copyarray = False\\n _barxslice_tmp = None\\n \\n res = __library__.MSK_XX_getbarxslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barxslice_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _barxslice_copyarray:\\n barxslice_[:] = _barxslice_np_tmp\",\n \"def getxxslice(self,whichsol_,first_,last_,xx_):\\n _xx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),((last_) - (first_))))\\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\\n raise ValueError(\\\"Argument xx must be writable\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n res = __library__.MSK_XX_getxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _xx_copyarray:\\n xx_[:] = _xx_np_tmp\",\n \"def getbarsslice(self,whichsol_,first_,last_,slicesize_,barsslice_):\\n _barsslice_minlength = (slicesize_)\\n if (slicesize_) > 0 and barsslice_ is not None and len(barsslice_) != (slicesize_):\\n raise ValueError(\\\"Array argument barsslice is not long enough: Is %d, expected %d\\\" % (len(barsslice_),(slicesize_)))\\n if isinstance(barsslice_,numpy.ndarray) and not barsslice_.flags.writeable:\\n raise ValueError(\\\"Argument barsslice must be writable\\\")\\n if barsslice_ is None:\\n raise ValueError(\\\"Argument barsslice may not be None\\\")\\n if isinstance(barsslice_, numpy.ndarray) and barsslice_.dtype is numpy.dtype(numpy.float64) and barsslice_.flags.contiguous:\\n _barsslice_copyarray = False\\n _barsslice_tmp = ctypes.cast(barsslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barsslice_ is not None:\\n _barsslice_copyarray = True\\n _barsslice_np_tmp = numpy.zeros(len(barsslice_),numpy.dtype(numpy.float64))\\n _barsslice_np_tmp[:] = barsslice_\\n assert _barsslice_np_tmp.flags.contiguous\\n _barsslice_tmp = ctypes.cast(_barsslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barsslice_copyarray = False\\n _barsslice_tmp = None\\n \\n res = __library__.MSK_XX_getbarsslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barsslice_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _barsslice_copyarray:\\n barsslice_[:] = _barsslice_np_tmp\",\n \"def putcslice(self,first_,last_,slice): # 3\\n if slice is None: raise TypeError(\\\"Invalid type for argument slice\\\")\\n if slice is None:\\n slice_ = None\\n else:\\n try:\\n slice_ = memoryview(slice)\\n except TypeError:\\n try:\\n _tmparr_slice = array.array(\\\"d\\\",slice)\\n except TypeError:\\n raise TypeError(\\\"Argument slice has wrong type\\\")\\n else:\\n slice_ = memoryview(_tmparr_slice)\\n \\n else:\\n if slice_.format != \\\"d\\\":\\n slice_ = memoryview(array.array(\\\"d\\\",slice))\\n \\n if slice_ is not None and len(slice_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slice has wrong length\\\")\\n res = self.__obj.putcslice(first_,last_,slice_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def chgvarbound(self,j_,lower_,finite_,value_):\\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getxxslice(self,whichsol_,first_,last_,xx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_xx = False\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n _copyback_xx = True\\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n _copyback_xx = True\\n if xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n res = self.__obj.getxxslice(whichsol_,first_,last_,xx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_xx:\\n xx[:] = _tmparr_xx\",\n \"def _initialize_bounds(problem, bounds, get_bound, set_bound):\\n for constraint in problem.constraints:\\n root_expr = constraint.root_expr\\n expr_bounds = Interval(constraint.lower_bound, constraint.upper_bound)\\n if root_expr not in bounds:\\n set_bound(root_expr, expr_bounds)\\n else:\\n existing_bounds = get_bound(root_expr)\\n new_bounds = existing_bounds.intersect(expr_bounds)\\n set_bound(root_expr, new_bounds)\",\n \"def set_bounds_atom(self,bounds):\\n assert bounds.shape == (2,self.Phi.d)\\n self.bounds = bounds # data bounds\\n self.bounds_atom = bounds.T.tolist()\",\n \"def extend_bounds(problem):\\n\\n num_vars = problem[\\\"num_vars\\\"]\\n num_ff_vars = 2 ** find_smallest(num_vars)\\n num_dummy_variables = num_ff_vars - num_vars\\n\\n bounds = list(problem[\\\"bounds\\\"])\\n names = problem[\\\"names\\\"]\\n if num_dummy_variables > 0:\\n bounds.extend([[0, 1] for x in range(num_dummy_variables)])\\n names.extend([\\\"dummy_\\\" + str(var) for var in range(num_dummy_variables)])\\n problem[\\\"bounds\\\"] = bounds\\n problem[\\\"names\\\"] = names\\n problem[\\\"num_vars\\\"] = num_ff_vars\\n\\n return problem\",\n \"def bounds(self, pos):\",\n \"def putsuxslice(self,whichsol_,first_,last_,sux_):\\n _sux_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),((last_) - (first_))))\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux cannot be None\\\")\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux may not be None\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_putsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def removebarvars(self,subset_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subset_)\\n elif num_ != len(subset_):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset cannot be None\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset may not be None\\\")\\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\\n _subset_copyarray = False\\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subset_ is not None:\\n _subset_copyarray = True\\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\\n _subset_np_tmp[:] = subset_\\n assert _subset_np_tmp.flags.contiguous\\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subset_copyarray = False\\n _subset_tmp = None\\n \\n res = __library__.MSK_XX_removebarvars(self.__nativep,num_,_subset_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsnxslice(self,whichsol_,first_,last_,snx_):\\n _snx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and snx_ is not None and len(snx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument snx is not long enough: Is %d, expected %d\\\" % (len(snx_),((last_) - (first_))))\\n if snx_ is None:\\n raise ValueError(\\\"Argument snx cannot be None\\\")\\n if snx_ is None:\\n raise ValueError(\\\"Argument snx may not be None\\\")\\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_putsnxslice(self.__nativep,whichsol_,first_,last_,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def bounds(self, new_bounds: devices.PrimaryBounds) -> None:\\n self._assert_bounds_are_valid(new_bounds)\\n self._bounds = list(new_bounds)\",\n \"def putskxslice(self,whichsol_,first_,last_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skx is None: raise TypeError(\\\"Invalid type for argument skx\\\")\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skx_ is not None and len(skx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.putskxslice(whichsol_,first_,last_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsuxslice(self,whichsol_,first_,last_,sux_):\\n _sux_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),((last_) - (first_))))\\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\\n raise ValueError(\\\"Argument sux must be writable\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_getsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _sux_copyarray:\\n sux_[:] = _sux_np_tmp\",\n \"def cb_bounds(self, variable, results_dict, keys, fixed_bounds):\\n tas_bound, pr_bound = fixed_bounds\\n if variable == \\\"tas\\\":\\n if tas_bound:\\n bound_limit = tas_bound\\n else:\\n bound_limit = self.find_abs_bound_range(results_dict, keys)\\n cmap = plt.cm.RdBu_r\\n else:\\n if pr_bound:\\n bound_limit = pr_bound\\n else:\\n bound_limit = self.find_abs_bound_range(results_dict,\\n keys,\\n avg_over=25)\\n cmap = plt.cm.BrBG\\n bounds = np.linspace(-1 * bound_limit, bound_limit, 11)\\n return [bounds, cmap]\",\n \"def set_bounds_atom(self,bounds):\\n self.bounds = bounds # data bounds\\n raise NotImplementedError\\n self.bounds_atom = None\\n return None\",\n \"def putsuxslice(self,whichsol_,first_,last_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if sux is None: raise TypeError(\\\"Invalid type for argument sux\\\")\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.putsuxslice(whichsol_,first_,last_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def write_bounds(self):\\n optimized_par_df = \\\\\\n self.parameter_df.loc[self.parameter_df.estimate == 1\\n & (~self.parameter_df.index.isin(\\n self.amici_model.getFixedParameterIds())), :]\\n self.f.require_dataset('/parameters/lowerBound',\\n shape=optimized_par_df.lowerBound.shape,\\n data=optimized_par_df.lowerBound, dtype='f8')\\n self.f.require_dataset('/parameters/upperBound',\\n shape=optimized_par_df.upperBound.shape,\\n data=optimized_par_df.upperBound, dtype='f8')\",\n \"def subspace(self,varset_values):\\n vector = np.empty(self.dimensions)\\n \\n for varname, value in varset_values:\\n try: \\n vector[self.slices[varname]] = np.ravel(value)\\n except KeyError:\\n pass\\n \\n return vector\",\n \"def getsuxslice(self,whichsol_,first_,last_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_sux = False\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n _copyback_sux = True\\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n _copyback_sux = True\\n if sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.getsuxslice(whichsol_,first_,last_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_sux:\\n sux[:] = _tmparr_sux\",\n \"def putsnxslice(self,whichsol_,first_,last_,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if snx is None: raise TypeError(\\\"Invalid type for argument snx\\\")\\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n \\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n \\n if snx_ is not None and len(snx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument snx has wrong length\\\")\\n res = self.__obj.putsnxslice(whichsol_,first_,last_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def normalize_bounds(self, bounds):\\n scaled_bounds = []\\n scalings = []\\n intercepts = []\\n \\n non_fixed_params = []\\n \\n print(self.device)\\n \\n for name, domain in self.bounds.items():\\n # Get any fixed parmeters\\n if type(domain) == int or type(domain) == float:\\n # Take note\\n self.fixed_parameters.append(name)\\n\\n # Free parameters\\n elif type(domain) == tuple:\\n # Bookkeeping\\n self.free_parameters.append(name)\\n\\n # Get scaling\\n lower_bound = min(domain)\\n upper_bound = max(domain)\\n scale = upper_bound - lower_bound\\n\\n # Transform to [0, 1] domain\\n #scaled_bound = {'name': name, 'type': 'continuous', 'domain': (0., 1.)} #torch.adjustment required\\n non_fixed_params.append(name)\\n \\n # Store\\n #scaled_bounds.append(scaled_bound)\\n scalings.append(scale)\\n intercepts.append(lower_bound)\\n else:\\n raise ValueError(\\\"Domain bounds not understood\\\")\\n \\n n_hyperparams = len(non_fixed_params)\\n \\n scaled_bounds = cat([zeros(1,n_hyperparams, device = self.device), \\n ones(1, n_hyperparams, device = self.device)], 0)\\n return scaled_bounds, tensor(scalings, device = self.device, requires_grad = False), tensor(intercepts, device = self.device, requires_grad = False) #torch.adjustment required\",\n \"def putskxslice(self,whichsol_,first_,last_,skx_):\\n _skx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),((last_) - (first_))))\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx cannot be None\\\")\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx may not be None\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_putskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def update_bounds(self, temp):\\n if (temp.extent[0] < self.extent[0]):\\n self.extent[0] = np.copy(temp.extent[0])\\n if (temp.extent[1] > self.extent[1]):\\n self.extent[1] = np.copy(temp.extent[1])\\n if (temp.extent[2] < self.extent[2]):\\n self.extent[2] = np.copy(temp.extent[2])\\n if (temp.extent[3] > self.extent[3]):\\n self.extent[3] = np.copy(temp.extent[3])\\n return self\",\n \"def init_range_variables(self):\\n self.range_start_vars_array = []\\n self.range_end_vars_array = []\\n\\n for idx in range(len(self._pk_for_filter)):\\n self.range_start_vars_array.append(\\\"@range_start_{}\\\".format(idx))\\n self.range_end_vars_array.append(\\\"@range_end_{}\\\".format(idx))\\n self.range_start_vars = \\\",\\\".join(self.range_start_vars_array)\\n self.range_end_vars = \\\",\\\".join(self.range_end_vars_array)\",\n \"def set_slices(self, start=0.0, end=1.0, step=None, num=None):\\n\\n if step is None:\\n s = (end - start) / float(num)\\n self._slices = frange(start, end, s)\\n elif num is None:\\n self._slices = frange(start, end, step)\\n else:\\n raise RuntimeError()\\n\\n LOG.info('Num slices: %d', len(self._slices))\\n LOG.info('Slices: %s', self._slices)\",\n \"def removevars(self,subset_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subset_)\\n elif num_ != len(subset_):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset cannot be None\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset may not be None\\\")\\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\\n _subset_copyarray = False\\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subset_ is not None:\\n _subset_copyarray = True\\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\\n _subset_np_tmp[:] = subset_\\n assert _subset_np_tmp.flags.contiguous\\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subset_copyarray = False\\n _subset_tmp = None\\n \\n res = __library__.MSK_XX_removevars(self.__nativep,num_,_subset_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def removebarvars(self,subset): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subset)\\n elif num_ != len(subset):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if num_ is None: num_ = 0\\n if subset is None: raise TypeError(\\\"Invalid type for argument subset\\\")\\n if subset is None:\\n subset_ = None\\n else:\\n try:\\n subset_ = memoryview(subset)\\n except TypeError:\\n try:\\n _tmparr_subset = array.array(\\\"i\\\",subset)\\n except TypeError:\\n raise TypeError(\\\"Argument subset has wrong type\\\")\\n else:\\n subset_ = memoryview(_tmparr_subset)\\n \\n else:\\n if subset_.format != \\\"i\\\":\\n subset_ = memoryview(array.array(\\\"i\\\",subset))\\n \\n res = self.__obj.removebarvars(num_,subset_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_bounds(self, **kwargs):\\n for name, bounds in kwargs.items():\\n if name not in self._parameters:\\n raise AttributeError('Unknown parameter %s for %s' % (name, self.__class__.__name__))\\n param = self._parameters[name]\\n # Set bounds\\n lower_bound, upper_bound = bounds\\n if torch.is_tensor(lower_bound) and torch.is_tensor(upper_bound):\\n if lower_bound.size() != upper_bound.size() or \\\\\\n lower_bound.size() != param.size():\\n raise AttributeError('Lower bound, upper bound, and param should have the same size')\\n elif not (isinstance(lower_bound, int) or isinstance(lower_bound, float)) or \\\\\\n not (isinstance(upper_bound, int) or isinstance(upper_bound, float)):\\n raise AttributeError('Unsupported argument types for parameter %s' % name)\\n\\n if name not in self._bounds:\\n self._bounds[name] = [None, None]\\n self._bounds[name][0] = lower_bound\\n self._bounds[name][1] = upper_bound\\n return self\",\n \"def putxcslice(self,whichsol_,first_,last_,xc_):\\n _xc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and xc_ is not None and len(xc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),((last_) - (first_))))\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc cannot be None\\\")\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc may not be None\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n res = __library__.MSK_XX_putxcslice(self.__nativep,whichsol_,first_,last_,_xc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _manage_infinity_bounds(problem, _bounds, get_bound, set_bound):\\n for variable in problem.variables:\\n expr_bounds = get_bound(variable)\\n lower_bound = expr_bounds.lower_bound\\n upper_bound = expr_bounds.upper_bound\\n\\n if is_inf(lower_bound):\\n new_lower_bound = None\\n else:\\n new_lower_bound = lower_bound\\n\\n if is_inf(upper_bound):\\n new_upper_bound = None\\n else:\\n new_upper_bound = upper_bound\\n\\n set_bound(variable, Interval(new_lower_bound, new_upper_bound))\",\n \"def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\\n ...\",\n \"def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\\n ...\",\n \"def putxcslice(self,whichsol_,first_,last_,xc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xc is None: raise TypeError(\\\"Invalid type for argument xc\\\")\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n \\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n \\n if xc_ is not None and len(xc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n res = self.__obj.putxcslice(whichsol_,first_,last_,xc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putyslice(self,whichsol_,first_,last_,y): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if y is None: raise TypeError(\\\"Invalid type for argument y\\\")\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n res = self.__obj.putyslice(whichsol_,first_,last_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putacolslice(self,first_,last_,ptrb,ptre,asub,aval): # 3\\n if ptrb is None: raise TypeError(\\\"Invalid type for argument ptrb\\\")\\n if ptrb is None:\\n ptrb_ = None\\n else:\\n try:\\n ptrb_ = memoryview(ptrb)\\n except TypeError:\\n try:\\n _tmparr_ptrb = array.array(\\\"q\\\",ptrb)\\n except TypeError:\\n raise TypeError(\\\"Argument ptrb has wrong type\\\")\\n else:\\n ptrb_ = memoryview(_tmparr_ptrb)\\n \\n else:\\n if ptrb_.format != \\\"q\\\":\\n ptrb_ = memoryview(array.array(\\\"q\\\",ptrb))\\n \\n if ptre is None: raise TypeError(\\\"Invalid type for argument ptre\\\")\\n if ptre is None:\\n ptre_ = None\\n else:\\n try:\\n ptre_ = memoryview(ptre)\\n except TypeError:\\n try:\\n _tmparr_ptre = array.array(\\\"q\\\",ptre)\\n except TypeError:\\n raise TypeError(\\\"Argument ptre has wrong type\\\")\\n else:\\n ptre_ = memoryview(_tmparr_ptre)\\n \\n else:\\n if ptre_.format != \\\"q\\\":\\n ptre_ = memoryview(array.array(\\\"q\\\",ptre))\\n \\n if asub is None: raise TypeError(\\\"Invalid type for argument asub\\\")\\n if asub is None:\\n asub_ = None\\n else:\\n try:\\n asub_ = memoryview(asub)\\n except TypeError:\\n try:\\n _tmparr_asub = array.array(\\\"i\\\",asub)\\n except TypeError:\\n raise TypeError(\\\"Argument asub has wrong type\\\")\\n else:\\n asub_ = memoryview(_tmparr_asub)\\n \\n else:\\n if asub_.format != \\\"i\\\":\\n asub_ = memoryview(array.array(\\\"i\\\",asub))\\n \\n if aval is None: raise TypeError(\\\"Invalid type for argument aval\\\")\\n if aval is None:\\n aval_ = None\\n else:\\n try:\\n aval_ = memoryview(aval)\\n except TypeError:\\n try:\\n _tmparr_aval = array.array(\\\"d\\\",aval)\\n except TypeError:\\n raise TypeError(\\\"Argument aval has wrong type\\\")\\n else:\\n aval_ = memoryview(_tmparr_aval)\\n \\n else:\\n if aval_.format != \\\"d\\\":\\n aval_ = memoryview(array.array(\\\"d\\\",aval))\\n \\n res = self.__obj.putacolslice64(first_,last_,ptrb_,ptre_,asub_,aval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def get_bounds_parameters(self):\\n bounds = []\\n bounds += self.var_noise.bounds\\n bounds += self.mean.bounds\\n bounds += self.kernel.get_bounds_parameters()\\n\\n return bounds\",\n \"def putacolslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb cannot be None\\\")\\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb may not be None\\\")\\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\\n _ptrb_copyarray = False\\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptrb_ is not None:\\n _ptrb_copyarray = True\\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\\n _ptrb_np_tmp[:] = ptrb_\\n assert _ptrb_np_tmp.flags.contiguous\\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptrb_copyarray = False\\n _ptrb_tmp = None\\n \\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre cannot be None\\\")\\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre may not be None\\\")\\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\\n _ptre_copyarray = False\\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptre_ is not None:\\n _ptre_copyarray = True\\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\\n _ptre_np_tmp[:] = ptre_\\n assert _ptre_np_tmp.flags.contiguous\\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptre_copyarray = False\\n _ptre_tmp = None\\n \\n if asub_ is None:\\n raise ValueError(\\\"Argument asub cannot be None\\\")\\n if asub_ is None:\\n raise ValueError(\\\"Argument asub may not be None\\\")\\n if isinstance(asub_, numpy.ndarray) and asub_.dtype is numpy.dtype(numpy.int32) and asub_.flags.contiguous:\\n _asub_copyarray = False\\n _asub_tmp = ctypes.cast(asub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif asub_ is not None:\\n _asub_copyarray = True\\n _asub_np_tmp = numpy.zeros(len(asub_),numpy.dtype(numpy.int32))\\n _asub_np_tmp[:] = asub_\\n assert _asub_np_tmp.flags.contiguous\\n _asub_tmp = ctypes.cast(_asub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _asub_copyarray = False\\n _asub_tmp = None\\n \\n if aval_ is None:\\n raise ValueError(\\\"Argument aval cannot be None\\\")\\n if aval_ is None:\\n raise ValueError(\\\"Argument aval may not be None\\\")\\n if isinstance(aval_, numpy.ndarray) and aval_.dtype is numpy.dtype(numpy.float64) and aval_.flags.contiguous:\\n _aval_copyarray = False\\n _aval_tmp = ctypes.cast(aval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif aval_ is not None:\\n _aval_copyarray = True\\n _aval_np_tmp = numpy.zeros(len(aval_),numpy.dtype(numpy.float64))\\n _aval_np_tmp[:] = aval_\\n assert _aval_np_tmp.flags.contiguous\\n _aval_tmp = ctypes.cast(_aval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _aval_copyarray = False\\n _aval_tmp = None\\n \\n res = __library__.MSK_XX_putacolslice64(self.__nativep,first_,last_,_ptrb_tmp,_ptre_tmp,_asub_tmp,_aval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putyslice(self,whichsol_,first_,last_,y_):\\n _y_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),((last_) - (first_))))\\n if y_ is None:\\n raise ValueError(\\\"Argument y cannot be None\\\")\\n if y_ is None:\\n raise ValueError(\\\"Argument y may not be None\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n res = __library__.MSK_XX_putyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _set_neighs_slice(self, key):\\n ## Condition to use slice type\\n self._constant_neighs = True\\n self.ks = range(1) if self.ks is None else self.ks\\n ## Possible options\\n if key is None:\\n self.idxs = slice(0, self._n, 1)\\n elif isinstance(key, slice):\\n start = 0 if key.start is None else key.start\\n stop = self._n if key.stop is None else key.stop\\n stop = self._n if key.stop > 10*16 else key.stop\\n step = 1 if key.step is None else key.step\\n self.idxs = slice(start, stop, step)\\n elif type(key) in inttypes:\\n self.idxs = slice(0, key, 1)\\n elif type(key) == tuple:\\n self.idxs = slice(key[0], key[1], 1)\\n self._setted = True\",\n \"def putslxslice(self,whichsol_,first_,last_,slx_):\\n _slx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slx_ is not None and len(slx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slx is not long enough: Is %d, expected %d\\\" % (len(slx_),((last_) - (first_))))\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx cannot be None\\\")\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx may not be None\\\")\\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n res = __library__.MSK_XX_putslxslice(self.__nativep,whichsol_,first_,last_,_slx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def variable_bounds(problem):\\n return ([\\n problem['state_bounds'][var] if problem['state_bounds'][var] is not None else (-np.inf, np.inf)\\n for _ in range(problem['N'] - 1)\\n for var in range(problem['num_states'])\\n ] + [\\n problem['input_bounds'][inp] if problem['input_bounds'][inp] is not None else (-np.inf, np.inf)\\n for _ in range(problem['N'] + 1)\\n for inp in range(problem['num_inputs'])\\n ]) * problem['Nv'] + ([(0.01, np.inf)] if problem['T'] == 0 else []) \\\\\\n if problem['state_bounds'] is not None else None\",\n \"def only_bounds(must_print):\\n\\n #Extracting input.\\n input = find_input()\\n\\n #Running the experiment.\\n result = bound.execute_script(input, must_print, True)[2:]\\n #result = bound2.execute_script(input, must_print, True)[2:]\\n\\n #Storing output.\\n store_output(result) #result = [P_BOUND, R_BOUND]\",\n \"def removeBounded(self, bounds):\\n if bounds==None or len(bounds)!=4:\\n return\\n x1,y1,x2,y2 = bounds\\n if x1>x2 :\\n temp=x1;x1=x2;x2=temp\\n if y1>y2:\\n temp=y1;y1=y2;y2=temp\\n lst=[]\\n for i in range(0,self.length()):\\n x=self.x[i]; y=self.y[i]\\n if (x>x1 and x<x2) and (y>y1 and y<y2): \\n lst.append(i)\\n self.removeMultiple(lst)\\n return\",\n \"def _extend_slice(self, sl, extent, dim_max, dim_min=0):\\n\\n x_start = max(sl.start - extent, dim_min)\\n x_end = min(sl.stop + extent, dim_max)\\n return np.s_[x_start:x_end]\",\n \"def removevars(self,subset): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subset)\\n elif num_ != len(subset):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if num_ is None: num_ = 0\\n if subset is None: raise TypeError(\\\"Invalid type for argument subset\\\")\\n if subset is None:\\n subset_ = None\\n else:\\n try:\\n subset_ = memoryview(subset)\\n except TypeError:\\n try:\\n _tmparr_subset = array.array(\\\"i\\\",subset)\\n except TypeError:\\n raise TypeError(\\\"Argument subset has wrong type\\\")\\n else:\\n subset_ = memoryview(_tmparr_subset)\\n \\n else:\\n if subset_.format != \\\"i\\\":\\n subset_ = memoryview(array.array(\\\"i\\\",subset))\\n \\n res = self.__obj.removevars(num_,subset_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def bounds(self, axis, view=None):\\n if view is None:\\n view = self\\n if axis not in self._vshare.bounds:\\n self._vshare.bounds[axis] = self._compute_bounds(axis, view)\\n return self._vshare.bounds[axis]\",\n \"def appendbarvars(self,dim_):\\n num_ = None\\n if num_ is None:\\n num_ = len(dim_)\\n elif num_ != len(dim_):\\n raise IndexError(\\\"Inconsistent length of array dim\\\")\\n if dim_ is None:\\n raise ValueError(\\\"Argument dim cannot be None\\\")\\n if dim_ is None:\\n raise ValueError(\\\"Argument dim may not be None\\\")\\n if isinstance(dim_, numpy.ndarray) and dim_.dtype is numpy.dtype(numpy.int32) and dim_.flags.contiguous:\\n _dim_copyarray = False\\n _dim_tmp = ctypes.cast(dim_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif dim_ is not None:\\n _dim_copyarray = True\\n _dim_np_tmp = numpy.zeros(len(dim_),numpy.dtype(numpy.int32))\\n _dim_np_tmp[:] = dim_\\n assert _dim_np_tmp.flags.contiguous\\n _dim_tmp = ctypes.cast(_dim_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _dim_copyarray = False\\n _dim_tmp = None\\n \\n res = __library__.MSK_XX_appendbarvars(self.__nativep,num_,_dim_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putslxslice(self,whichsol_,first_,last_,slx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slx is None: raise TypeError(\\\"Invalid type for argument slx\\\")\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if slx_ is not None and len(slx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n res = self.__obj.putslxslice(whichsol_,first_,last_,slx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setSafeRange( azLow, azHigh, elLow, elHigh, ants, subarray=DEFAULT) :\\n antlist = helpers.makeList(ants)\\n multiSubarray('setSafeRange', subarray, azLow, azHigh, elLow, elHigh, \\n antlist)\",\n \"def getvarbound(self,i_): # 3\\n res,resargs = self.__obj.getvarbound(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _bk_return_value,_bl_return_value,_bu_return_value = resargs\\n _bk_return_value = boundkey(_bk_return_value)\\n return _bk_return_value,_bl_return_value,_bu_return_value\",\n \"def add_time_bounds(nc, varname):\\n THREE_HOURS = 60*60*3 # in seconds\\n bnds_name = 'time_bnds'\\n bounds_dim = 'nv'\\n\\n # Create bounds dimension\\n nc.createDimension(bounds_dim, 2)\\n\\n # Get variable matching varname\\n\\n time_var = nc.variables['time']\\n time_var.setncattr('bounds', bnds_name)\\n time_data = time_var[:]\\n time_length = len(time_data)\\n\\n # reshape time data\\n bounds_data = np.dstack((time_data,time_data)).reshape(time_length,2)\\n for i in bounds_data:\\n i[0] = i[0] - (THREE_HOURS)\\n bounds_var = nc.createVariable(bnds_name, time_var.dtype, ('time', bounds_dim), fill_value=9999)\\n bounds_var[:] = bounds_data\",\n \"def putskcslice(self,whichsol_,first_,last_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skc_ is not None and len(skc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.putskcslice(whichsol_,first_,last_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getvarbound(self,i_):\\n bk_ = ctypes.c_int32()\\n bl_ = ctypes.c_double()\\n bu_ = ctypes.c_double()\\n res = __library__.MSK_XX_getvarbound(self.__nativep,i_,ctypes.byref(bk_),ctypes.byref(bl_),ctypes.byref(bu_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _bk_return_value = boundkey(bk_.value)\\n bl_ = bl_.value\\n _bl_return_value = bl_\\n bu_ = bu_.value\\n _bu_return_value = bu_\\n return (_bk_return_value,_bl_return_value,_bu_return_value)\",\n \"def update_i_bnds(self):\\n\\n # Get old and new boundaries.\\n i_bnds_old = self.i_bounds\\n i_bnds_new = self._get_i_bnds()\\n\\n for i_order in range(self.n_orders):\\n\\n # Take most restrictive lower bound.\\n low_bnds = [i_bnds_new[i_order][0], i_bnds_old[i_order][0]]\\n i_bnds_new[i_order][0] = np.max(low_bnds)\\n\\n # Take most restrictive upper bound.\\n up_bnds = [i_bnds_new[i_order][1], i_bnds_old[i_order][1]]\\n i_bnds_new[i_order][1] = np.min(up_bnds)\\n\\n # Update attribute.\\n self.i_bounds = i_bnds_new\\n\\n return\",\n \"def create_from_bounds(self, lbs, ubs):\\n self.base_vertices = (np.array([lbs])+np.array([ubs])).T/2\\n self.base_vectors = np.diag((np.array(ubs)-np.array(lbs))/2)\",\n \"def _fix_slice(self, inputs, new_attr):\\n begin = new_attr.get('begin')\\n end = new_attr.get('end')\\n axes = new_attr.get('axis', tuple(range(len(begin))))\\n slice_op = mx.sym.slice_axis(inputs[0], axis=axes[0], begin=begin[0], end=end[0])\\n if len(axes) > 1:\\n for i, axis in enumerate(axes):\\n slice_op = mx.sym.slice_axis(slice_op, axis=axis, begin=begin[i], end=end[i])\\n return slice_op\",\n \"def setActiveBounded(self, bounds=None, status=1): \\n if bounds==None or len(bounds)!=4:\\n return\\n x1,y1,x2,y2 = bounds\\n if x1>x2 :\\n temp=x1;x1=x2;x2=temp\\n if y1>y2:\\n temp=y1;y1=y2;y2=temp\\n for i in range(0,self.length()):\\n x=self.x[i]; y=self.y[i]\\n if (x>x1 and x<x2) and (y>y1 and y<y2):\\n self.active[i]= status \\n return\",\n \"def _parse_var_modelspace(self,varname) :\\n\\n\\t\\tmodelspace = self.ss.constraint.modelspace\\n\\t\\tparams = getattr(modelspace,varname)\\n\\t\\tnvars = len(self.ss.variables) # num of variables\\n\\n\\t\\tif varname in ('alpha','beta') : \\n\\t\\t\\tkeys = params.keys()\\n\\t\\t\\tvar_range = (params['defaultLowerBound'],\\\\\\n\\t\\t\\t\\tparams['defaultUpperBound'])\\n\\t\\t\\tself.modelspace[varname] = [var_range]*nvars\\n\\t\\t\\tfor key in keys : \\n\\t\\t\\t\\tif re.match(varname+'_\\\\d+',key)\\t:\\n\\t\\t\\t\\t\\tidx = int(key.split('_')[1])\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tself.modelspace[varname][idx-1] = params[key]\\n\\n\\t\\telif varname in ('g','h') :\\n\\t\\t\\tkeys = params.keys()\\n\\t\\t\\tvar_range = (params['defaultLowerBound'],\\\\\\n\\t\\t\\t\\tparams['defaultUpperBound'])\\n\\n\\t\\t\\t# This step is purely there cuz [[var_range]*nvars]*nvars\\n\\t\\t\\t# does not work\\n\\t\\t\\tvarlist = []\\n\\t\\t\\tfor ii in range(nvars) : \\n\\t\\t\\t\\tvarlist.append([var_range]*nvars)\\n\\t\\t\\tself.modelspace[varname] = varlist\\n\\t\\t\\tfor key in keys : \\n\\t\\t\\t\\tif re.match(varname+'_\\\\d+_\\\\d+',key)\\t:\\n\\t\\t\\t\\t\\tidr,idc = map(int,(key.split('_')[1:3]))\\n\\t\\t\\t\\t\\tself.modelspace[varname][idr-1][idc-1] = params[key]\\n\\t\\t\\n\\t\\telse :\\n\\t\\t\\tlogging.error(\\\"Unrecognized varname %s quitting..\\\" \\\\\\n\\t\\t\\t%(varname))\\n\\t\\t\\tsys.exit(1)\",\n \"def split_var(self, x):\\n cum_dims = list(np.cumsum(self.dims))\\n out = []\\n for slice_from, slice_to, dist in zip([0] + cum_dims, cum_dims, self.dists):\\n sliced = x[:, slice_from:slice_to]\\n out.append(sliced)\\n return out\",\n \"def var_slice(colnames, x):\\n # TODO: produces bahavior similar to df.loc[:, \\\"V1\\\":\\\"V3\\\"], but can reverse\\n # TODO: make DRY\\n # TODO: reverse not including end points\\n if isinstance(x.start, Var):\\n start_indx = (colnames == x.start.name).idxmax()\\n elif isinstance(x.start, str):\\n start_indx = (colnames == x.start).idxmax()\\n else:\\n start_indx = x.start or 0\\n\\n if isinstance(x.stop, Var):\\n stop_indx = (colnames == x.stop.name).idxmax() + 1\\n elif isinstance(x.stop, str):\\n stop_indx = (colnames == x.stop).idxmax() + 1\\n else:\\n stop_indx = x.stop or len(colnames)\\n\\n if start_indx > stop_indx:\\n return stop_indx, start_indx\\n else:\\n return start_indx, stop_indx\",\n \"def set_grid(self, bounds, dx):\\n self.dx = dx\\n self.X1 = np.arange(bounds[0], bounds[1], dx)\\n self.X = np.array(list(it.product(*[self.X1]*self.n_dim)))\",\n \"def checkranges(self, var, name):\\r\\n\\r\\n # reshape var\\r\\n assert len(var) == 2, \\\"%s must have two elements,\\\" % name\\r\\n var = np.array([float(v) for v in var])\\r\\n\\r\\n # check values\\r\\n if name in [\\\"arange\\\", \\\"Rprange\\\", \\\"Mprange\\\"]:\\r\\n assert np.all(var > 0), \\\"%s values must be strictly positive\\\" % name\\r\\n if name in [\\\"erange\\\", \\\"prange\\\"]:\\r\\n assert np.all(var >= 0) and np.all(var <= 1), (\\r\\n \\\"%s values must be between 0 and 1\\\" % name\\r\\n )\\r\\n\\r\\n # the second element must be greater or equal to the first\\r\\n if var[1] < var[0]:\\r\\n var = var[::-1]\\r\\n\\r\\n return var\",\n \"def draw_bounds():\\n\\n pass\",\n \"def putarowslice(self,first_,last_,ptrb,ptre,asub,aval): # 3\\n if ptrb is None: raise TypeError(\\\"Invalid type for argument ptrb\\\")\\n if ptrb is None:\\n ptrb_ = None\\n else:\\n try:\\n ptrb_ = memoryview(ptrb)\\n except TypeError:\\n try:\\n _tmparr_ptrb = array.array(\\\"q\\\",ptrb)\\n except TypeError:\\n raise TypeError(\\\"Argument ptrb has wrong type\\\")\\n else:\\n ptrb_ = memoryview(_tmparr_ptrb)\\n \\n else:\\n if ptrb_.format != \\\"q\\\":\\n ptrb_ = memoryview(array.array(\\\"q\\\",ptrb))\\n \\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptrb has wrong length\\\")\\n if ptre is None: raise TypeError(\\\"Invalid type for argument ptre\\\")\\n if ptre is None:\\n ptre_ = None\\n else:\\n try:\\n ptre_ = memoryview(ptre)\\n except TypeError:\\n try:\\n _tmparr_ptre = array.array(\\\"q\\\",ptre)\\n except TypeError:\\n raise TypeError(\\\"Argument ptre has wrong type\\\")\\n else:\\n ptre_ = memoryview(_tmparr_ptre)\\n \\n else:\\n if ptre_.format != \\\"q\\\":\\n ptre_ = memoryview(array.array(\\\"q\\\",ptre))\\n \\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptre has wrong length\\\")\\n if asub is None: raise TypeError(\\\"Invalid type for argument asub\\\")\\n if asub is None:\\n asub_ = None\\n else:\\n try:\\n asub_ = memoryview(asub)\\n except TypeError:\\n try:\\n _tmparr_asub = array.array(\\\"i\\\",asub)\\n except TypeError:\\n raise TypeError(\\\"Argument asub has wrong type\\\")\\n else:\\n asub_ = memoryview(_tmparr_asub)\\n \\n else:\\n if asub_.format != \\\"i\\\":\\n asub_ = memoryview(array.array(\\\"i\\\",asub))\\n \\n if aval is None: raise TypeError(\\\"Invalid type for argument aval\\\")\\n if aval is None:\\n aval_ = None\\n else:\\n try:\\n aval_ = memoryview(aval)\\n except TypeError:\\n try:\\n _tmparr_aval = array.array(\\\"d\\\",aval)\\n except TypeError:\\n raise TypeError(\\\"Argument aval has wrong type\\\")\\n else:\\n aval_ = memoryview(_tmparr_aval)\\n \\n else:\\n if aval_.format != \\\"d\\\":\\n aval_ = memoryview(array.array(\\\"d\\\",aval))\\n \\n res = self.__obj.putarowslice64(first_,last_,ptrb_,ptre_,asub_,aval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_slider_bounds(self,lower,upper,inclusive_bounds=None):\\n self.bounds = (lower,upper)\\n\\n if inclusive_bounds is not None:\\n self.inclusive_bounds = inclusive_bounds\\n\\n epsilon = max(self.slider['resolution'],0.00000000001)\\n\\n if self.inclusive_bounds[0] is False:\\n lower+=epsilon\\n if self.inclusive_bounds[1] is False:\\n upper-=epsilon\\n self.slider.config(from_=lower,to=upper)\",\n \"def repair(self, x, bounds=None, copy=False, copy_always=False):\\r\\n # TODO (old data): CPU(N,lam,iter=20,200,100): 3.3s of 8s for two bounds, 1.8s of 6.5s for one bound\\r\\n # TODO: test whether np.max([bounds[0], x], axis=0) etc is speed relevant\\r\\n\\r\\n if bounds is None:\\r\\n bounds = self.bounds\\r\\n if copy_always:\\r\\n x_out = array(x, copy=True)\\r\\n if bounds not in (None, [None, None], (None, None)): # solely for effiency\\r\\n x_out = array(x, copy=True) if copy and not copy_always else x\\r\\n if bounds[0] is not None:\\r\\n if np.isscalar(bounds[0]):\\r\\n for i in xrange(len(x)):\\r\\n x_out[i] = max([bounds[0], x[i]])\\r\\n else:\\r\\n for i in xrange(len(x)):\\r\\n if bounds[0][i] is not None:\\r\\n x_out[i] = max([bounds[0][i], x[i]])\\r\\n if bounds[1] is not None:\\r\\n if np.isscalar(bounds[1]):\\r\\n for i in xrange(len(x)):\\r\\n x_out[i] = min([bounds[1], x[i]])\\r\\n else:\\r\\n for i in xrange(len(x)):\\r\\n if bounds[1][i] is not None:\\r\\n x_out[i] = min([bounds[1][i], x[i]])\\r\\n return x_out # convenience return\\r\",\n \"def point_into_bound(bounds, point, array_name):\\n\\theight, width = bounds #get ndarray shape\\n\\tx, y = point #get the components of stamp_point\\n\\t#validate in x\\n\\tassert (x >= 0 and x < width), \\\"out of {} bounds in x axis in stamp_point\\\".format(array_name)\\n\\t#validate in y\\n\\tassert (y >= 0 and y < height), \\\"out of {} bounds in y axis in stamp_point\\\".format(array_name)\",\n \"def set_range_indexing(self, var_idx: int) -> None:\\n if len(self._carry_register_) < var_idx or len(self._carry_register_) == 0:\\n return # TODO: Throw an error\\n if self._carry_variables_[self._carry_register_[var_idx]] not in [List, int, list]:\\n return # TODO: Throw an error\\n\\n # Done checking\\n self.indexing = self._get_available_var_()\\n self.__prefix__ += self.indexing + \\\" in range(\\\"\\n if self._carry_variables_[self._carry_register_[var_idx]] == int:\\n self.__prefix__ += self._carry_register_[var_idx] + \\\")\\\"\\n else:\\n self.__prefix__ += \\\"len(\\\" + self._carry_register_[var_idx] + \\\"))\\\"\\n self.__prefix__ += \\\":\\\"\",\n \"def get_bounds(self):\\n return ([self.t_min] * self.dim,[self.t_max] * self.dim)\",\n \"def select_bounds(ds, bounds):\\n \\n xs = slice(bounds[0][0], bounds[1][0])\\n ys = slice(bounds[1][1], bounds[0][1])\\n # select over x and y axis\\n return ds.sel(x=xs, y=ys)\",\n \"def bounds(self):\\n if self.change_dimensionality:\\n return [self._bounds[0]] * self.N\\n else:\\n return self._bounds\",\n \"def putslcslice(self,whichsol_,first_,last_,slc_):\\n _slc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),((last_) - (first_))))\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc cannot be None\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def bound(self, points):\\n if self.bounds is None:\\n return points\\n\\n bounded = np.copy(points) # SLOW!!!\\n # Because numpy doesn't handle multi-dimensional arrays the same as 1-dimensional ones, it's easiest to just make it always look like a multi-dim array\\n points_shape = points.shape\\n cPoints = points_shape[0]\\n if cPoints == 1:\\n bounded = np.array([bounded, np.zeros_like(bounded)])\\n _ignore, cDims = bounded.shape\\n for i in xrange(cDims):\\n xs = bounded[:,i]\\n min_b, max_b = self.bounds[i]\\n assert min_b < max_b\\n width = max_b - min_b\\n # (EDIT: Wrong!) Because of the way that mod works (it wraps negative values around, rather than returning -(abs(x) % abs(y))), we can just use it straight\\n # Need to treat neg and pos values different because of behavior of mod operator\\n # On second thought, don't use mod, just assume small jumps\\n too_far_neg_ixs = xs < min_b\\n xs[too_far_neg_ixs] += width\\n too_far_pos_ixs = xs > max_b\\n xs[too_far_pos_ixs] -= width\\n bounded[:,i] = xs # is this necessary? seems so\\n if cPoints == 1:\\n bounded = bounded[0] # pull back out the 1-dim array\\n return bounded\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.85273284","0.8044646","0.7539585","0.7323293","0.67018676","0.66931504","0.659911","0.6440455","0.6379589","0.63711655","0.63274574","0.63001","0.62871003","0.6224053","0.6090941","0.6087178","0.6076162","0.60601884","0.6049202","0.596602","0.59273034","0.5910438","0.58734","0.5819123","0.5774795","0.570598","0.57015616","0.5701106","0.56925607","0.5629376","0.5626502","0.55965585","0.55484545","0.5546123","0.5505856","0.5499518","0.54787904","0.5467946","0.5466423","0.5465936","0.5462976","0.5452153","0.54391617","0.54306376","0.5421511","0.5348755","0.5310868","0.53107715","0.5298228","0.52940786","0.5272882","0.5251993","0.52454036","0.5243332","0.5216946","0.5209061","0.5201773","0.5170938","0.5170938","0.51624984","0.5160753","0.5147273","0.51453143","0.5135048","0.5102867","0.5084365","0.50807244","0.5067659","0.5031485","0.50206983","0.50161886","0.50007963","0.49991566","0.49949554","0.49924874","0.49911043","0.49841654","0.49801534","0.49781078","0.49754527","0.49644262","0.49447703","0.4942279","0.49278164","0.4927809","0.49160215","0.49139288","0.490907","0.48824874","0.48666608","0.4852613","0.48506007","0.48435536","0.48326945","0.48299584","0.482612","0.48252815","0.48231763","0.48207545","0.4817522"],"string":"[\n \"0.85273284\",\n \"0.8044646\",\n \"0.7539585\",\n \"0.7323293\",\n \"0.67018676\",\n \"0.66931504\",\n \"0.659911\",\n \"0.6440455\",\n \"0.6379589\",\n \"0.63711655\",\n \"0.63274574\",\n \"0.63001\",\n \"0.62871003\",\n \"0.6224053\",\n \"0.6090941\",\n \"0.6087178\",\n \"0.6076162\",\n \"0.60601884\",\n \"0.6049202\",\n \"0.596602\",\n \"0.59273034\",\n \"0.5910438\",\n \"0.58734\",\n \"0.5819123\",\n \"0.5774795\",\n \"0.570598\",\n \"0.57015616\",\n \"0.5701106\",\n \"0.56925607\",\n \"0.5629376\",\n \"0.5626502\",\n \"0.55965585\",\n \"0.55484545\",\n \"0.5546123\",\n \"0.5505856\",\n \"0.5499518\",\n \"0.54787904\",\n \"0.5467946\",\n \"0.5466423\",\n \"0.5465936\",\n \"0.5462976\",\n \"0.5452153\",\n \"0.54391617\",\n \"0.54306376\",\n \"0.5421511\",\n \"0.5348755\",\n \"0.5310868\",\n \"0.53107715\",\n \"0.5298228\",\n \"0.52940786\",\n \"0.5272882\",\n \"0.5251993\",\n \"0.52454036\",\n \"0.5243332\",\n \"0.5216946\",\n \"0.5209061\",\n \"0.5201773\",\n \"0.5170938\",\n \"0.5170938\",\n \"0.51624984\",\n \"0.5160753\",\n \"0.5147273\",\n \"0.51453143\",\n \"0.5135048\",\n \"0.5102867\",\n \"0.5084365\",\n \"0.50807244\",\n \"0.5067659\",\n \"0.5031485\",\n \"0.50206983\",\n \"0.50161886\",\n \"0.50007963\",\n \"0.49991566\",\n \"0.49949554\",\n \"0.49924874\",\n \"0.49911043\",\n \"0.49841654\",\n \"0.49801534\",\n \"0.49781078\",\n \"0.49754527\",\n \"0.49644262\",\n \"0.49447703\",\n \"0.4942279\",\n \"0.49278164\",\n \"0.4927809\",\n \"0.49160215\",\n \"0.49139288\",\n \"0.490907\",\n \"0.48824874\",\n \"0.48666608\",\n \"0.4852613\",\n \"0.48506007\",\n \"0.48435536\",\n \"0.48326945\",\n \"0.48299584\",\n \"0.482612\",\n \"0.48252815\",\n \"0.48231763\",\n \"0.48207545\",\n \"0.4817522\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7870629"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":11961,"cells":{"query":{"kind":"string","value":"Changes the bounds for a slice of the variables. putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_)"},"document":{"kind":"string","value":"def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarboundslice(self,first_,last_,bkx_,blx_,bux_):\n _bkx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bkx_ is not None and len(bkx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkx is not long enough: Is %d, expected %d\" % (len(bkx_),((last_) - (first_))))\n if bkx_ is None:\n raise ValueError(\"Argument bkx cannot be None\")\n if bkx_ is None:\n raise ValueError(\"Argument bkx may not be None\")\n if bkx_ is not None:\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\n else:\n _bkx_tmp = None\n _blx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blx_ is not None and len(blx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blx is not long enough: Is %d, expected %d\" % (len(blx_),((last_) - (first_))))\n if blx_ is None:\n raise ValueError(\"Argument blx cannot be None\")\n if blx_ is None:\n raise ValueError(\"Argument blx may not be None\")\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\n _blx_copyarray = False\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blx_ is not None:\n _blx_copyarray = True\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\n _blx_np_tmp[:] = blx_\n assert _blx_np_tmp.flags.contiguous\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blx_copyarray = False\n _blx_tmp = None\n \n _bux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bux_ is not None and len(bux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bux is not long enough: Is %d, expected %d\" % (len(bux_),((last_) - (first_))))\n if bux_ is None:\n raise ValueError(\"Argument bux cannot be None\")\n if bux_ is None:\n raise ValueError(\"Argument bux may not be None\")\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\n _bux_copyarray = False\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bux_ is not None:\n _bux_copyarray = True\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\n _bux_np_tmp[:] = bux_\n assert _bux_np_tmp.flags.contiguous\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bux_copyarray = False\n _bux_tmp = None\n \n res = __library__.MSK_XX_putvarboundslice(self.__nativep,first_,last_,_bkx_tmp,_blx_tmp,_bux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getvarboundslice(self,first_,last_,bk,bl,bu): # 3\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])","def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getvarboundslice(self,first_,last_,bk_,bl_,bu_):\n _bk_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk is not long enough: Is %d, expected %d\" % (len(bk_),((last_) - (first_))))\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\n raise ValueError(\"Argument bk must be writable\")\n if bk_ is not None:\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\n else:\n _bk_tmp = None\n _bl_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl is not long enough: Is %d, expected %d\" % (len(bl_),((last_) - (first_))))\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\n raise ValueError(\"Argument bl must be writable\")\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\n _bl_copyarray = False\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bl_ is not None:\n _bl_copyarray = True\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\n _bl_np_tmp[:] = bl_\n assert _bl_np_tmp.flags.contiguous\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bl_copyarray = False\n _bl_tmp = None\n \n _bu_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu is not long enough: Is %d, expected %d\" % (len(bu_),((last_) - (first_))))\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\n raise ValueError(\"Argument bu must be writable\")\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\n _bu_copyarray = False\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bu_ is not None:\n _bu_copyarray = True\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\n _bu_np_tmp[:] = bu_\n assert _bu_np_tmp.flags.contiguous\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bu_copyarray = False\n _bu_tmp = None\n \n res = __library__.MSK_XX_getvarboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\n if _bl_copyarray:\n bl_[:] = _bl_np_tmp\n if _bu_copyarray:\n bu_[:] = _bu_np_tmp","def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putconboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putboundslice(self,con_,first_,last_,bk,bl,bu): # 3\n if not isinstance(con_,accmode): raise TypeError(\"Argument con has wrong type\")\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putboundslice(con_,first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundslice(self,first_,last_,bkc_,blc_,buc_):\n _bkc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bkc_ is not None and len(bkc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkc is not long enough: Is %d, expected %d\" % (len(bkc_),((last_) - (first_))))\n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n _blc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blc_ is not None and len(blc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blc is not long enough: Is %d, expected %d\" % (len(blc_),((last_) - (first_))))\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n _buc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and buc_ is not None and len(buc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument buc is not long enough: Is %d, expected %d\" % (len(buc_),((last_) - (first_))))\n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundslice(self.__nativep,first_,last_,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarboundlist(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkx_)\n elif num_ != len(bkx_):\n raise IndexError(\"Inconsistent length of array bkx\")\n if num_ is None:\n num_ = len(blx_)\n elif num_ != len(blx_):\n raise IndexError(\"Inconsistent length of array blx\")\n if num_ is None:\n num_ = len(bux_)\n elif num_ != len(bux_):\n raise IndexError(\"Inconsistent length of array bux\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkx_ is None:\n raise ValueError(\"Argument bkx cannot be None\")\n if bkx_ is None:\n raise ValueError(\"Argument bkx may not be None\")\n if bkx_ is not None:\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\n else:\n _bkx_tmp = None\n if blx_ is None:\n raise ValueError(\"Argument blx cannot be None\")\n if blx_ is None:\n raise ValueError(\"Argument blx may not be None\")\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\n _blx_copyarray = False\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blx_ is not None:\n _blx_copyarray = True\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\n _blx_np_tmp[:] = blx_\n assert _blx_np_tmp.flags.contiguous\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blx_copyarray = False\n _blx_tmp = None\n \n if bux_ is None:\n raise ValueError(\"Argument bux cannot be None\")\n if bux_ is None:\n raise ValueError(\"Argument bux may not be None\")\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\n _bux_copyarray = False\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bux_ is not None:\n _bux_copyarray = True\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\n _bux_np_tmp[:] = bux_\n assert _bux_np_tmp.flags.contiguous\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bux_copyarray = False\n _bux_tmp = None\n \n res = __library__.MSK_XX_putvarboundlist(self.__nativep,num_,_sub_tmp,_bkx_tmp,_blx_tmp,_bux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getconboundslice(self,first_,last_,bk_,bl_,bu_):\n _bk_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk is not long enough: Is %d, expected %d\" % (len(bk_),((last_) - (first_))))\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\n raise ValueError(\"Argument bk must be writable\")\n if bk_ is not None:\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\n else:\n _bk_tmp = None\n _bl_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl is not long enough: Is %d, expected %d\" % (len(bl_),((last_) - (first_))))\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\n raise ValueError(\"Argument bl must be writable\")\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\n _bl_copyarray = False\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bl_ is not None:\n _bl_copyarray = True\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\n _bl_np_tmp[:] = bl_\n assert _bl_np_tmp.flags.contiguous\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bl_copyarray = False\n _bl_tmp = None\n \n _bu_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu is not long enough: Is %d, expected %d\" % (len(bu_),((last_) - (first_))))\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\n raise ValueError(\"Argument bu must be writable\")\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\n _bu_copyarray = False\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bu_ is not None:\n _bu_copyarray = True\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\n _bu_np_tmp[:] = bu_\n assert _bu_np_tmp.flags.contiguous\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bu_copyarray = False\n _bu_tmp = None\n \n res = __library__.MSK_XX_getconboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\n if _bl_copyarray:\n bl_[:] = _bl_np_tmp\n if _bu_copyarray:\n bu_[:] = _bu_np_tmp","def putxxslice(self,whichsol_,first_,last_,xx_):\n _xx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),((last_) - (first_))))\n if xx_ is None:\n raise ValueError(\"Argument xx cannot be None\")\n if xx_ is None:\n raise ValueError(\"Argument xx may not be None\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_putxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getconboundslice(self,first_,last_,bk,bl,bu): # 3\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getconboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])","def putxxslice(self,whichsol_,first_,last_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xx is None: raise TypeError(\"Invalid type for argument xx\")\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.putxxslice(whichsol_,first_,last_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putcslice(self,first_,last_,slice_):\n _slice_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slice_ is not None and len(slice_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slice is not long enough: Is %d, expected %d\" % (len(slice_),((last_) - (first_))))\n if slice_ is None:\n raise ValueError(\"Argument slice cannot be None\")\n if slice_ is None:\n raise ValueError(\"Argument slice may not be None\")\n if isinstance(slice_, numpy.ndarray) and slice_.dtype is numpy.dtype(numpy.float64) and slice_.flags.contiguous:\n _slice_copyarray = False\n _slice_tmp = ctypes.cast(slice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slice_ is not None:\n _slice_copyarray = True\n _slice_np_tmp = numpy.zeros(len(slice_),numpy.dtype(numpy.float64))\n _slice_np_tmp[:] = slice_\n assert _slice_np_tmp.flags.contiguous\n _slice_tmp = ctypes.cast(_slice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slice_copyarray = False\n _slice_tmp = None\n \n res = __library__.MSK_XX_putcslice(self.__nativep,first_,last_,_slice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarbound(self,j_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_variable_slices(self, variables):\n # Set up y_slices and bounds\n y_slices = defaultdict(list)\n y_slices_explicit = defaultdict(list)\n start = 0\n end = 0\n lower_bounds = []\n upper_bounds = []\n # Iterate through unpacked variables, adding appropriate slices to y_slices\n for variable in variables:\n # Add up the size of all the domains in variable.domain\n if isinstance(variable, pybamm.ConcatenationVariable):\n start_ = start\n spatial_method = self.spatial_methods[variable.domain[0]]\n children = variable.children\n meshes = OrderedDict()\n for child in children:\n meshes[child] = [spatial_method.mesh[dom] for dom in child.domain]\n sec_points = spatial_method._get_auxiliary_domain_repeats(\n variable.domains\n )\n for i in range(sec_points):\n for child, mesh in meshes.items():\n for domain_mesh in mesh:\n end += domain_mesh.npts_for_broadcast_to_nodes\n # Add to slices\n y_slices[child].append(slice(start_, end))\n y_slices_explicit[child].append(slice(start_, end))\n # Increment start_\n start_ = end\n else:\n end += self._get_variable_size(variable)\n\n # Add to slices\n y_slices[variable].append(slice(start, end))\n y_slices_explicit[variable].append(slice(start, end))\n\n # Add to bounds\n def evaluate_bound(bound, side):\n if bound.has_symbol_of_classes(pybamm.InputParameter):\n if side == \"lower\":\n return -np.inf\n elif side == \"upper\":\n return np.inf\n else:\n return bound.evaluate()\n\n lower_bounds.extend(\n [evaluate_bound(variable.bounds[0], \"lower\")] * (end - start)\n )\n upper_bounds.extend(\n [evaluate_bound(variable.bounds[1], \"upper\")] * (end - start)\n )\n # Increment start\n start = end\n\n # Convert y_slices back to normal dictionary\n self.y_slices = dict(y_slices)\n # Also keep a record of what the y_slices are, to be stored in the model\n self.y_slices_explicit = dict(y_slices_explicit)\n\n # Also keep a record of bounds\n self.bounds = (np.array(lower_bounds), np.array(upper_bounds))\n\n # reset discretised_symbols\n self._discretised_symbols = {}","def apply_bound(x, var_min, var_max):\n x.position = np.maximum(x.position, var_min)\n x.position = np.minimum(x.position, var_max)","def chgvarbound(self,j_,lower_,finite_,value_): # 3\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarboundlist(self,sub,bkx,blx,bux): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkx)\n elif num_ != len(bkx):\n raise IndexError(\"Inconsistent length of array bkx\")\n if num_ is None:\n num_ = len(blx)\n elif num_ != len(blx):\n raise IndexError(\"Inconsistent length of array blx\")\n if num_ is None:\n num_ = len(bux)\n elif num_ != len(bux):\n raise IndexError(\"Inconsistent length of array bux\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bkx is None: raise TypeError(\"Invalid type for argument bkx\")\n if bkx is None:\n bkx_ = None\n else:\n try:\n bkx_ = memoryview(bkx)\n except TypeError:\n try:\n _tmparr_bkx = array.array(\"i\",bkx)\n except TypeError:\n raise TypeError(\"Argument bkx has wrong type\")\n else:\n bkx_ = memoryview(_tmparr_bkx)\n \n else:\n if bkx_.format != \"i\":\n bkx_ = memoryview(array.array(\"i\",bkx))\n \n if blx is None: raise TypeError(\"Invalid type for argument blx\")\n if blx is None:\n blx_ = None\n else:\n try:\n blx_ = memoryview(blx)\n except TypeError:\n try:\n _tmparr_blx = array.array(\"d\",blx)\n except TypeError:\n raise TypeError(\"Argument blx has wrong type\")\n else:\n blx_ = memoryview(_tmparr_blx)\n \n else:\n if blx_.format != \"d\":\n blx_ = memoryview(array.array(\"d\",blx))\n \n if bux is None: raise TypeError(\"Invalid type for argument bux\")\n if bux is None:\n bux_ = None\n else:\n try:\n bux_ = memoryview(bux)\n except TypeError:\n try:\n _tmparr_bux = array.array(\"d\",bux)\n except TypeError:\n raise TypeError(\"Argument bux has wrong type\")\n else:\n bux_ = memoryview(_tmparr_bux)\n \n else:\n if bux_.format != \"d\":\n bux_ = memoryview(array.array(\"d\",bux))\n \n res = self.__obj.putvarboundlist(num_,sub_,bkx_,blx_,bux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def chgvarbound(self,j_,lower_,finite_,value_):\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putcslice(self,first_,last_,slice): # 3\n if slice is None: raise TypeError(\"Invalid type for argument slice\")\n if slice is None:\n slice_ = None\n else:\n try:\n slice_ = memoryview(slice)\n except TypeError:\n try:\n _tmparr_slice = array.array(\"d\",slice)\n except TypeError:\n raise TypeError(\"Argument slice has wrong type\")\n else:\n slice_ = memoryview(_tmparr_slice)\n \n else:\n if slice_.format != \"d\":\n slice_ = memoryview(array.array(\"d\",slice))\n \n if slice_ is not None and len(slice_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slice has wrong length\")\n res = self.__obj.putcslice(first_,last_,slice_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_bounds_atom(self,bounds):\n assert bounds.shape == (2,self.Phi.d)\n self.bounds = bounds # data bounds\n self.bounds_atom = bounds.T.tolist()\n for i in range(self.Phi.d): # bounds for the variance in each dimension\n max_variance_this_dimension = (bounds[1][i]-bounds[0][i])**2\n self.bounds_atom.append([self.variance_relative_lowerbound*max_variance_this_dimension,\n self.variance_relative_upperbound*max_variance_this_dimension])","def getbarxslice(self,whichsol_,first_,last_,slicesize_,barxslice_):\n _barxslice_minlength = (slicesize_)\n if (slicesize_) > 0 and barxslice_ is not None and len(barxslice_) != (slicesize_):\n raise ValueError(\"Array argument barxslice is not long enough: Is %d, expected %d\" % (len(barxslice_),(slicesize_)))\n if isinstance(barxslice_,numpy.ndarray) and not barxslice_.flags.writeable:\n raise ValueError(\"Argument barxslice must be writable\")\n if barxslice_ is None:\n raise ValueError(\"Argument barxslice may not be None\")\n if isinstance(barxslice_, numpy.ndarray) and barxslice_.dtype is numpy.dtype(numpy.float64) and barxslice_.flags.contiguous:\n _barxslice_copyarray = False\n _barxslice_tmp = ctypes.cast(barxslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxslice_ is not None:\n _barxslice_copyarray = True\n _barxslice_np_tmp = numpy.zeros(len(barxslice_),numpy.dtype(numpy.float64))\n _barxslice_np_tmp[:] = barxslice_\n assert _barxslice_np_tmp.flags.contiguous\n _barxslice_tmp = ctypes.cast(_barxslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxslice_copyarray = False\n _barxslice_tmp = None\n \n res = __library__.MSK_XX_getbarxslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barxslice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barxslice_copyarray:\n barxslice_[:] = _barxslice_np_tmp","def getbarsslice(self,whichsol_,first_,last_,slicesize_,barsslice_):\n _barsslice_minlength = (slicesize_)\n if (slicesize_) > 0 and barsslice_ is not None and len(barsslice_) != (slicesize_):\n raise ValueError(\"Array argument barsslice is not long enough: Is %d, expected %d\" % (len(barsslice_),(slicesize_)))\n if isinstance(barsslice_,numpy.ndarray) and not barsslice_.flags.writeable:\n raise ValueError(\"Argument barsslice must be writable\")\n if barsslice_ is None:\n raise ValueError(\"Argument barsslice may not be None\")\n if isinstance(barsslice_, numpy.ndarray) and barsslice_.dtype is numpy.dtype(numpy.float64) and barsslice_.flags.contiguous:\n _barsslice_copyarray = False\n _barsslice_tmp = ctypes.cast(barsslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsslice_ is not None:\n _barsslice_copyarray = True\n _barsslice_np_tmp = numpy.zeros(len(barsslice_),numpy.dtype(numpy.float64))\n _barsslice_np_tmp[:] = barsslice_\n assert _barsslice_np_tmp.flags.contiguous\n _barsslice_tmp = ctypes.cast(_barsslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsslice_copyarray = False\n _barsslice_tmp = None\n \n res = __library__.MSK_XX_getbarsslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barsslice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barsslice_copyarray:\n barsslice_[:] = _barsslice_np_tmp","def getboundslice(self,accmode_,first_,last_,bk,bl,bu): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getboundslice(accmode_,first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])","def putvarbound(self,j_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getxxslice(self,whichsol_,first_,last_,xx_):\n _xx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),((last_) - (first_))))\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\n raise ValueError(\"Argument xx must be writable\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_getxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xx_copyarray:\n xx_[:] = _xx_np_tmp","def set_bounds(self, new_bounds):\n\n # Update the internal object stored dict\n self.pbounds.update(new_bounds)\n\n # Loop through the all bounds and reset the min-max bound matrix\n for row, key in enumerate(self.pbounds.keys()):\n\n # Reset all entries, even if the same.\n self.bounds[row] = self.pbounds[key]","def _initialize_bounds(problem, bounds, get_bound, set_bound):\n for constraint in problem.constraints:\n root_expr = constraint.root_expr\n expr_bounds = Interval(constraint.lower_bound, constraint.upper_bound)\n if root_expr not in bounds:\n set_bound(root_expr, expr_bounds)\n else:\n existing_bounds = get_bound(root_expr)\n new_bounds = existing_bounds.intersect(expr_bounds)\n set_bound(root_expr, new_bounds)","def getxxslice(self,whichsol_,first_,last_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_xx = False\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n _copyback_xx = True\n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n _copyback_xx = True\n if xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.getxxslice(whichsol_,first_,last_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_xx:\n xx[:] = _tmparr_xx","def cb_bounds(self, variable, results_dict, keys, fixed_bounds):\n tas_bound, pr_bound = fixed_bounds\n if variable == \"tas\":\n if tas_bound:\n bound_limit = tas_bound\n else:\n bound_limit = self.find_abs_bound_range(results_dict, keys)\n cmap = plt.cm.RdBu_r\n else:\n if pr_bound:\n bound_limit = pr_bound\n else:\n bound_limit = self.find_abs_bound_range(results_dict,\n keys,\n avg_over=25)\n cmap = plt.cm.BrBG\n bounds = np.linspace(-1 * bound_limit, bound_limit, 11)\n return [bounds, cmap]","def putsuxslice(self,whichsol_,first_,last_,sux_):\n _sux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),((last_) - (first_))))\n if sux_ is None:\n raise ValueError(\"Argument sux cannot be None\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_putsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsnxslice(self,whichsol_,first_,last_,snx_):\n _snx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and snx_ is not None and len(snx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument snx is not long enough: Is %d, expected %d\" % (len(snx_),((last_) - (first_))))\n if snx_ is None:\n raise ValueError(\"Argument snx cannot be None\")\n if snx_ is None:\n raise ValueError(\"Argument snx may not be None\")\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsnxslice(self.__nativep,whichsol_,first_,last_,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def removebarvars(self,subset_):\n num_ = None\n if num_ is None:\n num_ = len(subset_)\n elif num_ != len(subset_):\n raise IndexError(\"Inconsistent length of array subset\")\n if subset_ is None:\n raise ValueError(\"Argument subset cannot be None\")\n if subset_ is None:\n raise ValueError(\"Argument subset may not be None\")\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\n _subset_copyarray = False\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subset_ is not None:\n _subset_copyarray = True\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\n _subset_np_tmp[:] = subset_\n assert _subset_np_tmp.flags.contiguous\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subset_copyarray = False\n _subset_tmp = None\n \n res = __library__.MSK_XX_removebarvars(self.__nativep,num_,_subset_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsuxslice(self,whichsol_,first_,last_,sux_):\n _sux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),((last_) - (first_))))\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\n raise ValueError(\"Argument sux must be writable\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_getsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _sux_copyarray:\n sux_[:] = _sux_np_tmp","def putxcslice(self,whichsol_,first_,last_,xc_):\n _xc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xc_ is not None and len(xc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),((last_) - (first_))))\n if xc_ is None:\n raise ValueError(\"Argument xc cannot be None\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxcslice(self.__nativep,whichsol_,first_,last_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def bounds(self, new_bounds: devices.PrimaryBounds) -> None:\n self._assert_bounds_are_valid(new_bounds)\n self._bounds = list(new_bounds)","def set_bounds_atom(self,bounds):\n assert bounds.shape == (2,self.Phi.d)\n self.bounds = bounds # data bounds\n self.bounds_atom = bounds.T.tolist()","def extend_bounds(problem):\n\n num_vars = problem[\"num_vars\"]\n num_ff_vars = 2 ** find_smallest(num_vars)\n num_dummy_variables = num_ff_vars - num_vars\n\n bounds = list(problem[\"bounds\"])\n names = problem[\"names\"]\n if num_dummy_variables > 0:\n bounds.extend([[0, 1] for x in range(num_dummy_variables)])\n names.extend([\"dummy_\" + str(var) for var in range(num_dummy_variables)])\n problem[\"bounds\"] = bounds\n problem[\"names\"] = names\n problem[\"num_vars\"] = num_ff_vars\n\n return problem","def putskxslice(self,whichsol_,first_,last_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskxslice(whichsol_,first_,last_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def write_bounds(self):\n optimized_par_df = \\\n self.parameter_df.loc[self.parameter_df.estimate == 1\n & (~self.parameter_df.index.isin(\n self.amici_model.getFixedParameterIds())), :]\n self.f.require_dataset('/parameters/lowerBound',\n shape=optimized_par_df.lowerBound.shape,\n data=optimized_par_df.lowerBound, dtype='f8')\n self.f.require_dataset('/parameters/upperBound',\n shape=optimized_par_df.upperBound.shape,\n data=optimized_par_df.upperBound, dtype='f8')","def putxcslice(self,whichsol_,first_,last_,xc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xc is None: raise TypeError(\"Invalid type for argument xc\")\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xc_ is not None and len(xc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xc has wrong length\")\n res = self.__obj.putxcslice(whichsol_,first_,last_,xc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def subspace(self,varset_values):\n vector = np.empty(self.dimensions)\n \n for varname, value in varset_values:\n try: \n vector[self.slices[varname]] = np.ravel(value)\n except KeyError:\n pass\n \n return vector","def update_bounds(self, temp):\n if (temp.extent[0] < self.extent[0]):\n self.extent[0] = np.copy(temp.extent[0])\n if (temp.extent[1] > self.extent[1]):\n self.extent[1] = np.copy(temp.extent[1])\n if (temp.extent[2] < self.extent[2]):\n self.extent[2] = np.copy(temp.extent[2])\n if (temp.extent[3] > self.extent[3]):\n self.extent[3] = np.copy(temp.extent[3])\n return self","def bounds(self, pos):","def putsuxslice(self,whichsol_,first_,last_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsuxslice(whichsol_,first_,last_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_bounds_atom(self,bounds):\n self.bounds = bounds # data bounds\n raise NotImplementedError\n self.bounds_atom = None\n return None","def getsuxslice(self,whichsol_,first_,last_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_sux = False\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n _copyback_sux = True\n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n _copyback_sux = True\n if sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.getsuxslice(whichsol_,first_,last_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_sux:\n sux[:] = _tmparr_sux","def putskxslice(self,whichsol_,first_,last_,skx_):\n _skx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),((last_) - (first_))))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def normalize_bounds(self, bounds):\n scaled_bounds = []\n scalings = []\n intercepts = []\n \n non_fixed_params = []\n \n print(self.device)\n \n for name, domain in self.bounds.items():\n # Get any fixed parmeters\n if type(domain) == int or type(domain) == float:\n # Take note\n self.fixed_parameters.append(name)\n\n # Free parameters\n elif type(domain) == tuple:\n # Bookkeeping\n self.free_parameters.append(name)\n\n # Get scaling\n lower_bound = min(domain)\n upper_bound = max(domain)\n scale = upper_bound - lower_bound\n\n # Transform to [0, 1] domain\n #scaled_bound = {'name': name, 'type': 'continuous', 'domain': (0., 1.)} #torch.adjustment required\n non_fixed_params.append(name)\n \n # Store\n #scaled_bounds.append(scaled_bound)\n scalings.append(scale)\n intercepts.append(lower_bound)\n else:\n raise ValueError(\"Domain bounds not understood\")\n \n n_hyperparams = len(non_fixed_params)\n \n scaled_bounds = cat([zeros(1,n_hyperparams, device = self.device), \n ones(1, n_hyperparams, device = self.device)], 0)\n return scaled_bounds, tensor(scalings, device = self.device, requires_grad = False), tensor(intercepts, device = self.device, requires_grad = False) #torch.adjustment required","def putsnxslice(self,whichsol_,first_,last_,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if snx is None: raise TypeError(\"Invalid type for argument snx\")\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n if snx_ is not None and len(snx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument snx has wrong length\")\n res = self.__obj.putsnxslice(whichsol_,first_,last_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskcslice(self,whichsol_,first_,last_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskcslice(whichsol_,first_,last_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putacolslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb cannot be None\")\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb may not be None\")\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\n _ptrb_copyarray = False\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptrb_ is not None:\n _ptrb_copyarray = True\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\n _ptrb_np_tmp[:] = ptrb_\n assert _ptrb_np_tmp.flags.contiguous\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptrb_copyarray = False\n _ptrb_tmp = None\n \n if ptre_ is None:\n raise ValueError(\"Argument ptre cannot be None\")\n if ptre_ is None:\n raise ValueError(\"Argument ptre may not be None\")\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\n _ptre_copyarray = False\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptre_ is not None:\n _ptre_copyarray = True\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\n _ptre_np_tmp[:] = ptre_\n assert _ptre_np_tmp.flags.contiguous\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptre_copyarray = False\n _ptre_tmp = None\n \n if asub_ is None:\n raise ValueError(\"Argument asub cannot be None\")\n if asub_ is None:\n raise ValueError(\"Argument asub may not be None\")\n if isinstance(asub_, numpy.ndarray) and asub_.dtype is numpy.dtype(numpy.int32) and asub_.flags.contiguous:\n _asub_copyarray = False\n _asub_tmp = ctypes.cast(asub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif asub_ is not None:\n _asub_copyarray = True\n _asub_np_tmp = numpy.zeros(len(asub_),numpy.dtype(numpy.int32))\n _asub_np_tmp[:] = asub_\n assert _asub_np_tmp.flags.contiguous\n _asub_tmp = ctypes.cast(_asub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _asub_copyarray = False\n _asub_tmp = None\n \n if aval_ is None:\n raise ValueError(\"Argument aval cannot be None\")\n if aval_ is None:\n raise ValueError(\"Argument aval may not be None\")\n if isinstance(aval_, numpy.ndarray) and aval_.dtype is numpy.dtype(numpy.float64) and aval_.flags.contiguous:\n _aval_copyarray = False\n _aval_tmp = ctypes.cast(aval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif aval_ is not None:\n _aval_copyarray = True\n _aval_np_tmp = numpy.zeros(len(aval_),numpy.dtype(numpy.float64))\n _aval_np_tmp[:] = aval_\n assert _aval_np_tmp.flags.contiguous\n _aval_tmp = ctypes.cast(_aval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _aval_copyarray = False\n _aval_tmp = None\n \n res = __library__.MSK_XX_putacolslice64(self.__nativep,first_,last_,_ptrb_tmp,_ptre_tmp,_asub_tmp,_aval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setSafeRange( azLow, azHigh, elLow, elHigh, ants, subarray=DEFAULT) :\n antlist = helpers.makeList(ants)\n multiSubarray('setSafeRange', subarray, azLow, azHigh, elLow, elHigh, \n antlist)","def removevars(self,subset_):\n num_ = None\n if num_ is None:\n num_ = len(subset_)\n elif num_ != len(subset_):\n raise IndexError(\"Inconsistent length of array subset\")\n if subset_ is None:\n raise ValueError(\"Argument subset cannot be None\")\n if subset_ is None:\n raise ValueError(\"Argument subset may not be None\")\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\n _subset_copyarray = False\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subset_ is not None:\n _subset_copyarray = True\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\n _subset_np_tmp[:] = subset_\n assert _subset_np_tmp.flags.contiguous\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subset_copyarray = False\n _subset_tmp = None\n \n res = __library__.MSK_XX_removevars(self.__nativep,num_,_subset_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def removebarvars(self,subset): # 3\n num_ = None\n if num_ is None:\n num_ = len(subset)\n elif num_ != len(subset):\n raise IndexError(\"Inconsistent length of array subset\")\n if num_ is None: num_ = 0\n if subset is None: raise TypeError(\"Invalid type for argument subset\")\n if subset is None:\n subset_ = None\n else:\n try:\n subset_ = memoryview(subset)\n except TypeError:\n try:\n _tmparr_subset = array.array(\"i\",subset)\n except TypeError:\n raise TypeError(\"Argument subset has wrong type\")\n else:\n subset_ = memoryview(_tmparr_subset)\n \n else:\n if subset_.format != \"i\":\n subset_ = memoryview(array.array(\"i\",subset))\n \n res = self.__obj.removebarvars(num_,subset_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def repair(self, x, bounds=None, copy=False, copy_always=False):\r\n # TODO (old data): CPU(N,lam,iter=20,200,100): 3.3s of 8s for two bounds, 1.8s of 6.5s for one bound\r\n # TODO: test whether np.max([bounds[0], x], axis=0) etc is speed relevant\r\n\r\n if bounds is None:\r\n bounds = self.bounds\r\n if copy_always:\r\n x_out = array(x, copy=True)\r\n if bounds not in (None, [None, None], (None, None)): # solely for effiency\r\n x_out = array(x, copy=True) if copy and not copy_always else x\r\n if bounds[0] is not None:\r\n if np.isscalar(bounds[0]):\r\n for i in xrange(len(x)):\r\n x_out[i] = max([bounds[0], x[i]])\r\n else:\r\n for i in xrange(len(x)):\r\n if bounds[0][i] is not None:\r\n x_out[i] = max([bounds[0][i], x[i]])\r\n if bounds[1] is not None:\r\n if np.isscalar(bounds[1]):\r\n for i in xrange(len(x)):\r\n x_out[i] = min([bounds[1], x[i]])\r\n else:\r\n for i in xrange(len(x)):\r\n if bounds[1][i] is not None:\r\n x_out[i] = min([bounds[1][i], x[i]])\r\n return x_out # convenience return\r","def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\n ...","def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\n ...","def putslcslice(self,whichsol_,first_,last_,slc_):\n _slc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),((last_) - (first_))))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _manage_infinity_bounds(problem, _bounds, get_bound, set_bound):\n for variable in problem.variables:\n expr_bounds = get_bound(variable)\n lower_bound = expr_bounds.lower_bound\n upper_bound = expr_bounds.upper_bound\n\n if is_inf(lower_bound):\n new_lower_bound = None\n else:\n new_lower_bound = lower_bound\n\n if is_inf(upper_bound):\n new_upper_bound = None\n else:\n new_upper_bound = upper_bound\n\n set_bound(variable, Interval(new_lower_bound, new_upper_bound))","def putslxslice(self,whichsol_,first_,last_,slx_):\n _slx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slx_ is not None and len(slx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),((last_) - (first_))))\n if slx_ is None:\n raise ValueError(\"Argument slx cannot be None\")\n if slx_ is None:\n raise ValueError(\"Argument slx may not be None\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n res = __library__.MSK_XX_putslxslice(self.__nativep,whichsol_,first_,last_,_slx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _set_neighs_slice(self, key):\n ## Condition to use slice type\n self._constant_neighs = True\n self.ks = range(1) if self.ks is None else self.ks\n ## Possible options\n if key is None:\n self.idxs = slice(0, self._n, 1)\n elif isinstance(key, slice):\n start = 0 if key.start is None else key.start\n stop = self._n if key.stop is None else key.stop\n stop = self._n if key.stop > 10*16 else key.stop\n step = 1 if key.step is None else key.step\n self.idxs = slice(start, stop, step)\n elif type(key) in inttypes:\n self.idxs = slice(0, key, 1)\n elif type(key) == tuple:\n self.idxs = slice(key[0], key[1], 1)\n self._setted = True","def init_range_variables(self):\n self.range_start_vars_array = []\n self.range_end_vars_array = []\n\n for idx in range(len(self._pk_for_filter)):\n self.range_start_vars_array.append(\"@range_start_{}\".format(idx))\n self.range_end_vars_array.append(\"@range_end_{}\".format(idx))\n self.range_start_vars = \",\".join(self.range_start_vars_array)\n self.range_end_vars = \",\".join(self.range_end_vars_array)","def putacolslice(self,first_,last_,ptrb,ptre,asub,aval): # 3\n if ptrb is None: raise TypeError(\"Invalid type for argument ptrb\")\n if ptrb is None:\n ptrb_ = None\n else:\n try:\n ptrb_ = memoryview(ptrb)\n except TypeError:\n try:\n _tmparr_ptrb = array.array(\"q\",ptrb)\n except TypeError:\n raise TypeError(\"Argument ptrb has wrong type\")\n else:\n ptrb_ = memoryview(_tmparr_ptrb)\n \n else:\n if ptrb_.format != \"q\":\n ptrb_ = memoryview(array.array(\"q\",ptrb))\n \n if ptre is None: raise TypeError(\"Invalid type for argument ptre\")\n if ptre is None:\n ptre_ = None\n else:\n try:\n ptre_ = memoryview(ptre)\n except TypeError:\n try:\n _tmparr_ptre = array.array(\"q\",ptre)\n except TypeError:\n raise TypeError(\"Argument ptre has wrong type\")\n else:\n ptre_ = memoryview(_tmparr_ptre)\n \n else:\n if ptre_.format != \"q\":\n ptre_ = memoryview(array.array(\"q\",ptre))\n \n if asub is None: raise TypeError(\"Invalid type for argument asub\")\n if asub is None:\n asub_ = None\n else:\n try:\n asub_ = memoryview(asub)\n except TypeError:\n try:\n _tmparr_asub = array.array(\"i\",asub)\n except TypeError:\n raise TypeError(\"Argument asub has wrong type\")\n else:\n asub_ = memoryview(_tmparr_asub)\n \n else:\n if asub_.format != \"i\":\n asub_ = memoryview(array.array(\"i\",asub))\n \n if aval is None: raise TypeError(\"Invalid type for argument aval\")\n if aval is None:\n aval_ = None\n else:\n try:\n aval_ = memoryview(aval)\n except TypeError:\n try:\n _tmparr_aval = array.array(\"d\",aval)\n except TypeError:\n raise TypeError(\"Argument aval has wrong type\")\n else:\n aval_ = memoryview(_tmparr_aval)\n \n else:\n if aval_.format != \"d\":\n aval_ = memoryview(array.array(\"d\",aval))\n \n res = self.__obj.putacolslice64(first_,last_,ptrb_,ptre_,asub_,aval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundlist(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkc_)\n elif num_ != len(bkc_):\n raise IndexError(\"Inconsistent length of array bkc\")\n if num_ is None:\n num_ = len(blc_)\n elif num_ != len(blc_):\n raise IndexError(\"Inconsistent length of array blc\")\n if num_ is None:\n num_ = len(buc_)\n elif num_ != len(buc_):\n raise IndexError(\"Inconsistent length of array buc\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def variable_bounds(problem):\n return ([\n problem['state_bounds'][var] if problem['state_bounds'][var] is not None else (-np.inf, np.inf)\n for _ in range(problem['N'] - 1)\n for var in range(problem['num_states'])\n ] + [\n problem['input_bounds'][inp] if problem['input_bounds'][inp] is not None else (-np.inf, np.inf)\n for _ in range(problem['N'] + 1)\n for inp in range(problem['num_inputs'])\n ]) * problem['Nv'] + ([(0.01, np.inf)] if problem['T'] == 0 else []) \\\n if problem['state_bounds'] is not None else None","def set_slices(self, start=0.0, end=1.0, step=None, num=None):\n\n if step is None:\n s = (end - start) / float(num)\n self._slices = frange(start, end, s)\n elif num is None:\n self._slices = frange(start, end, step)\n else:\n raise RuntimeError()\n\n LOG.info('Num slices: %d', len(self._slices))\n LOG.info('Slices: %s', self._slices)","def putyslice(self,whichsol_,first_,last_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.putyslice(whichsol_,first_,last_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskcslice(self,whichsol_,first_,last_,skc_):\n _skc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),((last_) - (first_))))\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskcslice(self.__nativep,whichsol_,first_,last_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def RestrictionRangeBound(self, compsIdList, lowerBound, upperBound):\n for i in range(len(compsIdList)): compsIdList[i] -= 1\n if self.solverTypeOptimize:\n self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) >= lowerBound)\n else:\n self.solver.assert_and_track(\n PbGe(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\n lowerBound), \"LabelRangeBound: \" + str(self.labelIdx))\n self.labelIdx += 1\n if self.solverTypeOptimize:\n PbLe(self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\n upperBound))\n else:\n self.solver.assert_and_track(\n sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) <= upperBound, \"LabelRangeBound: \" + str(self.labelIdx))\n self.labelIdx += 1","def compute_bounds(self, weights, add_coeff, dual_vars, lower_bounds, upper_bounds, store_primal=False):\n x0_coeff = -weights[0].backward(dual_vars.mus[0])\n x0 = torch.where(x0_coeff >= 0, lower_bounds[0].unsqueeze(1), upper_bounds[0].unsqueeze(1))\n bound = utils.bdot(x0, x0_coeff)\n if store_primal:\n self.bounds_primal = x0\n else:\n del x0\n del x0_coeff\n\n for lay_idx in range(1, len(weights)):\n lbs = lower_bounds[lay_idx].unsqueeze(1).clamp(None, 0)\n ubs = upper_bounds[lay_idx].unsqueeze(1).clamp(0, None)\n neg_bias = ((lbs * ubs) / (ubs - lbs))\n neg_bias.masked_fill_(ubs == lbs, 0) # cover case in which ubs & lbs coincide\n bound += utils.bdot(dual_vars.lambdas[lay_idx - 1].clamp(0, None), neg_bias)\n bound -= utils.bdot(dual_vars.mus[lay_idx - 1], weights[lay_idx - 1].get_bias())\n\n bound += utils.bdot(add_coeff, weights[-1].get_bias())\n return bound","def putyslice(self,whichsol_,first_,last_,y_):\n _y_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),((last_) - (first_))))\n if y_ is None:\n raise ValueError(\"Argument y cannot be None\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_putyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __get_new_restriction_branch_and_bound(self, tableau, var_col, b_value):\n restriction_row = np.zeros((tableau.get_tableau_num_cols())).astype('object')\n restriction_row[var_col] = 1\n restriction_row[tableau.get_tableau_num_cols() - 1] = b_value\n\n for i in xrange(0, len(restriction_row)):\n restriction_row[i] = Fraction(restriction_row[i])\n\n return restriction_row","def _set_bounds(b, x, n):\n for j in range(1, n - 1):\n for i in range(1, n - 1):\n x[index_of(i, j, 0, n)] = -x[index_of(i, j, 1, n)] if b == 3 else x[index_of(i, j, 1, n)]\n x[index_of(i, j, 0, n - 1)] = -x[index_of(i, j, 1, n - 2)] if b == 3 else x[index_of(i, j, 1, n - 2)]\n for k in range(1, n - 1):\n for i in range(1, n - 1):\n x[index_of(i, 0, k, n)] = -x[index_of(i, 1, k, n)] if b == 2 else x[index_of(i, 1, k, n)]\n x[index_of(i, n - 1, 0, n - 1)] = -x[index_of(i, n - 2, k, n - 2)] if b == 2 else x[\n index_of(i, n - 2, k, n - 2)]\n for k in range(1, n - 1):\n for j in range(1, n - 1):\n x[index_of(0, j, k, n)] = -x[index_of(1, j, k, n)] if b == 1 else x[index_of(1, j, k, n)]\n x[index_of(n - 1, j, k, n - 1)] = -x[index_of(n - 2, j, k, n)] if b == 1 else x[\n index_of(n - 2, j, k, n)]\n\n x[index_of(0, 0, 0, n)] = 1 / 3 * (x[index_of(1, 0, 0, n)] + x[index_of(0, 1, 0, n)] + x[index_of(0, 0, 1, n)])\n x[index_of(0, n - 1, 0, n)] = 1 / 3 * (\n x[index_of(1, n - 1, 0, n)] + x[index_of(0, n - 2, 0, n)] + x[index_of(0, n - 1, 1, n)])\n x[index_of(0, 0, n - 1, n)] = 1 / 3 * (\n x[index_of(1, 0, n - 1, n)] + x[index_of(0, 1, n - 1, n)] + x[index_of(0, 0, n - 2, n)])\n x[index_of(0, n - 1, n - 1, n)] = 1 / 3 * (\n x[index_of(1, n - 1, n - 1, n)] + x[index_of(0, n - 2, n - 1, n)] + x[index_of(0, n - 1, n - 2, n)])\n x[index_of(n - 1, 0, 0, n)] = 1 / 3 * (\n x[index_of(n - 2, 0, 0, n)] + x[index_of(n - 1, 1, 0, n)] + x[index_of(n - 1, 0, 1, n)])\n x[index_of(n - 1, n - 1, 0, n)] = 1 / 3 * (\n x[index_of(n - 2, n - 1, 0, n)] + x[index_of(n - 1, n - 2, 0, n)] + x[index_of(n - 1, n - 1, 1, n)])\n x[index_of(n - 1, 0, n - 1, n)] = 1 / 3 * (\n x[index_of(n - 2, 0, n - 1, n)] + x[index_of(n - 1, 1, n - 1, n)] + x[index_of(n - 1, 0, n - 2, n)])\n x[index_of(n - 1, n - 1, n - 1, n)] = 1 / 3 * (\n x[index_of(n - 2, n - 1, n - 1, n)] + x[index_of(n - 1, n - 2, n - 1, n)] + x[\n index_of(n - 1, n - 1, n - 2, n)])","def removeBounded(self, bounds):\n if bounds==None or len(bounds)!=4:\n return\n x1,y1,x2,y2 = bounds\n if x1>x2 :\n temp=x1;x1=x2;x2=temp\n if y1>y2:\n temp=y1;y1=y2;y2=temp\n lst=[]\n for i in range(0,self.length()):\n x=self.x[i]; y=self.y[i]\n if (x>x1 and x<x2) and (y>y1 and y<y2): \n lst.append(i)\n self.removeMultiple(lst)\n return","def test_slice_setslice_forbidden(self):\n global setVal\n class foo:\n def __setslice__(self, i, j, value):\n global setVal\n setVal = i, j, value\n def __setitem__(self, index, value):\n global setVal\n setVal = index, value\n\n foo()[::] = 23\n self.assertEqual(setVal, (slice(None, None, None), 23))\n foo()[::None] = 23\n self.assertEqual(setVal, (slice(None, None, None), 23))","def set_bounds(self, **kwargs):\n for name, bounds in kwargs.items():\n if name not in self._parameters:\n raise AttributeError('Unknown parameter %s for %s' % (name, self.__class__.__name__))\n param = self._parameters[name]\n # Set bounds\n lower_bound, upper_bound = bounds\n if torch.is_tensor(lower_bound) and torch.is_tensor(upper_bound):\n if lower_bound.size() != upper_bound.size() or \\\n lower_bound.size() != param.size():\n raise AttributeError('Lower bound, upper bound, and param should have the same size')\n elif not (isinstance(lower_bound, int) or isinstance(lower_bound, float)) or \\\n not (isinstance(upper_bound, int) or isinstance(upper_bound, float)):\n raise AttributeError('Unsupported argument types for parameter %s' % name)\n\n if name not in self._bounds:\n self._bounds[name] = [None, None]\n self._bounds[name][0] = lower_bound\n self._bounds[name][1] = upper_bound\n return self","def getacolslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _ptrb_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptrb is not long enough: Is %d, expected %d\" % (len(ptrb_),((last_) - (first_))))\n if isinstance(ptrb_,numpy.ndarray) and not ptrb_.flags.writeable:\n raise ValueError(\"Argument ptrb must be writable\")\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb may not be None\")\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\n _ptrb_copyarray = False\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptrb_ is not None:\n _ptrb_copyarray = True\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\n _ptrb_np_tmp[:] = ptrb_\n assert _ptrb_np_tmp.flags.contiguous\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptrb_copyarray = False\n _ptrb_tmp = None\n \n _ptre_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and ptre_ is not None and len(ptre_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptre is not long enough: Is %d, expected %d\" % (len(ptre_),((last_) - (first_))))\n if isinstance(ptre_,numpy.ndarray) and not ptre_.flags.writeable:\n raise ValueError(\"Argument ptre must be writable\")\n if ptre_ is None:\n raise ValueError(\"Argument ptre may not be None\")\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\n _ptre_copyarray = False\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptre_ is not None:\n _ptre_copyarray = True\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\n _ptre_np_tmp[:] = ptre_\n assert _ptre_np_tmp.flags.contiguous\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptre_copyarray = False\n _ptre_tmp = None\n \n _sub_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and sub_ is not None and len(sub_) != (maxnumnz_):\n raise ValueError(\"Array argument sub is not long enough: Is %d, expected %d\" % (len(sub_),(maxnumnz_)))\n if isinstance(sub_,numpy.ndarray) and not sub_.flags.writeable:\n raise ValueError(\"Argument sub must be writable\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _val_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and val_ is not None and len(val_) != (maxnumnz_):\n raise ValueError(\"Array argument val is not long enough: Is %d, expected %d\" % (len(val_),(maxnumnz_)))\n if isinstance(val_,numpy.ndarray) and not val_.flags.writeable:\n raise ValueError(\"Argument val must be writable\")\n if val_ is None:\n raise ValueError(\"Argument val may not be None\")\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\n _val_copyarray = False\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif val_ is not None:\n _val_copyarray = True\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\n _val_np_tmp[:] = val_\n assert _val_np_tmp.flags.contiguous\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _val_copyarray = False\n _val_tmp = None\n \n surp_ = ctypes.c_int64(_sub_minlength)\n res = __library__.MSK_XX_getacolslice64(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_ptrb_tmp,_ptre_tmp,_sub_tmp,_val_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _ptrb_copyarray:\n ptrb_[:] = _ptrb_np_tmp\n if _ptre_copyarray:\n ptre_[:] = _ptre_np_tmp\n if _sub_copyarray:\n sub_[:] = _sub_np_tmp\n if _val_copyarray:\n val_[:] = _val_np_tmp","def getvarbound(self,i_):\n bk_ = ctypes.c_int32()\n bl_ = ctypes.c_double()\n bu_ = ctypes.c_double()\n res = __library__.MSK_XX_getvarbound(self.__nativep,i_,ctypes.byref(bk_),ctypes.byref(bl_),ctypes.byref(bu_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _bk_return_value = boundkey(bk_.value)\n bl_ = bl_.value\n _bl_return_value = bl_\n bu_ = bu_.value\n _bu_return_value = bu_\n return (_bk_return_value,_bl_return_value,_bu_return_value)","def putslxslice(self,whichsol_,first_,last_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslxslice(whichsol_,first_,last_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _fix_slice(self, inputs, new_attr):\n begin = new_attr.get('begin')\n end = new_attr.get('end')\n axes = new_attr.get('axis', tuple(range(len(begin))))\n slice_op = mx.sym.slice_axis(inputs[0], axis=axes[0], begin=begin[0], end=end[0])\n if len(axes) > 1:\n for i, axis in enumerate(axes):\n slice_op = mx.sym.slice_axis(slice_op, axis=axis, begin=begin[i], end=end[i])\n return slice_op","def update_i_bnds(self):\n\n # Get old and new boundaries.\n i_bnds_old = self.i_bounds\n i_bnds_new = self._get_i_bnds()\n\n for i_order in range(self.n_orders):\n\n # Take most restrictive lower bound.\n low_bnds = [i_bnds_new[i_order][0], i_bnds_old[i_order][0]]\n i_bnds_new[i_order][0] = np.max(low_bnds)\n\n # Take most restrictive upper bound.\n up_bnds = [i_bnds_new[i_order][1], i_bnds_old[i_order][1]]\n i_bnds_new[i_order][1] = np.min(up_bnds)\n\n # Update attribute.\n self.i_bounds = i_bnds_new\n\n return","def getvarbound(self,i_): # 3\n res,resargs = self.__obj.getvarbound(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _bk_return_value,_bl_return_value,_bu_return_value = resargs\n _bk_return_value = boundkey(_bk_return_value)\n return _bk_return_value,_bl_return_value,_bu_return_value","def appendbarvars(self,dim_):\n num_ = None\n if num_ is None:\n num_ = len(dim_)\n elif num_ != len(dim_):\n raise IndexError(\"Inconsistent length of array dim\")\n if dim_ is None:\n raise ValueError(\"Argument dim cannot be None\")\n if dim_ is None:\n raise ValueError(\"Argument dim may not be None\")\n if isinstance(dim_, numpy.ndarray) and dim_.dtype is numpy.dtype(numpy.int32) and dim_.flags.contiguous:\n _dim_copyarray = False\n _dim_tmp = ctypes.cast(dim_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dim_ is not None:\n _dim_copyarray = True\n _dim_np_tmp = numpy.zeros(len(dim_),numpy.dtype(numpy.int32))\n _dim_np_tmp[:] = dim_\n assert _dim_np_tmp.flags.contiguous\n _dim_tmp = ctypes.cast(_dim_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dim_copyarray = False\n _dim_tmp = None\n \n res = __library__.MSK_XX_appendbarvars(self.__nativep,num_,_dim_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def only_bounds(must_print):\n\n #Extracting input.\n input = find_input()\n\n #Running the experiment.\n result = bound.execute_script(input, must_print, True)[2:]\n #result = bound2.execute_script(input, must_print, True)[2:]\n\n #Storing output.\n store_output(result) #result = [P_BOUND, R_BOUND]","def glow_boundary(bound):\n assert bound < 4\n global layout\n temp = len(layout) - 1\n for i in range(bound, bound + len_square(bound)):\n for j in range(bound, bound + len_square(bound)): # TODO: assign this to a variable\t\n layout[i][j] = 1","def putslcslice(self,whichsol_,first_,last_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slc is None: raise TypeError(\"Invalid type for argument slc\")\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.putslcslice(whichsol_,first_,last_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def get_bounds_parameters(self):\n bounds = []\n bounds += self.var_noise.bounds\n bounds += self.mean.bounds\n bounds += self.kernel.get_bounds_parameters()\n\n return bounds","def removevars(self,subset): # 3\n num_ = None\n if num_ is None:\n num_ = len(subset)\n elif num_ != len(subset):\n raise IndexError(\"Inconsistent length of array subset\")\n if num_ is None: num_ = 0\n if subset is None: raise TypeError(\"Invalid type for argument subset\")\n if subset is None:\n subset_ = None\n else:\n try:\n subset_ = memoryview(subset)\n except TypeError:\n try:\n _tmparr_subset = array.array(\"i\",subset)\n except TypeError:\n raise TypeError(\"Argument subset has wrong type\")\n else:\n subset_ = memoryview(_tmparr_subset)\n \n else:\n if subset_.format != \"i\":\n subset_ = memoryview(array.array(\"i\",subset))\n \n res = self.__obj.removevars(num_,subset_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundlist(self,sub,bk,bl,bu): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bk)\n elif num_ != len(bk):\n raise IndexError(\"Inconsistent length of array bk\")\n if num_ is None:\n num_ = len(bl)\n elif num_ != len(bl):\n raise IndexError(\"Inconsistent length of array bl\")\n if num_ is None:\n num_ = len(bu)\n elif num_ != len(bu):\n raise IndexError(\"Inconsistent length of array bu\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n res = self.__obj.putconboundlist(num_,sub_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def bounds(self, axis, view=None):\n if view is None:\n view = self\n if axis not in self._vshare.bounds:\n self._vshare.bounds[axis] = self._compute_bounds(axis, view)\n return self._vshare.bounds[axis]","def slice_parameters(self, slice_parameters: SliceParamsIm):\n\n self._slice_parameters = slice_parameters","def create_from_bounds(self, lbs, ubs):\n self.base_vertices = (np.array([lbs])+np.array([ubs])).T/2\n self.base_vectors = np.diag((np.array(ubs)-np.array(lbs))/2)","def getacolslicetrip(self,first_,last_,subi,subj,val): # 3\n maxnumnz_ = self.getaslicenumnz(accmode.var,(first_),(last_))\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_val = False\n if val is None:\n val_ = None\n else:\n try:\n val_ = memoryview(val)\n except TypeError:\n try:\n _tmparr_val = array.array(\"d\",val)\n except TypeError:\n raise TypeError(\"Argument val has wrong type\")\n else:\n val_ = memoryview(_tmparr_val)\n _copyback_val = True\n else:\n if val_.format != \"d\":\n val_ = memoryview(array.array(\"d\",val))\n _copyback_val = True\n if val_ is not None and len(val_) != (maxnumnz_):\n raise ValueError(\"Array argument val has wrong length\")\n res = self.__obj.getacolslicetrip(first_,last_,maxnumnz_,len(subi),subi_,subj_,val_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_val:\n val[:] = _tmparr_val\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi","def into_bounds(self, y, bounds=None, copy_never=False, copy_always=False):\r\n bounds = bounds if bounds is not None else self.bounds\r\n if bounds in (None, [None, None]):\r\n return y if not copy_always else array(y, copy=True)\r\n if bounds[0] is not None:\r\n if len(bounds[0]) not in (1, len(y)):\r\n raise ValueError('len(bounds[0]) = ' + str(len(bounds[0])) +\r\n ' and len of initial solution (' + str(len(y)) + ') disagree')\r\n if copy_never: # is rather slower\r\n for i in xrange(len(y)):\r\n y[i] = max(bounds[0][i], y[i])\r\n else:\r\n y = np.max([bounds[0], y], axis=0)\r\n if bounds[1] is not None:\r\n if len(bounds[1]) not in (1, len(y)):\r\n raise ValueError('len(bounds[1]) = ' + str(len(bounds[1])) +\r\n ' and initial solution (' + str(len(y)) + ') disagree')\r\n if copy_never:\r\n for i in xrange(len(y)):\r\n y[i] = min(bounds[1][i], y[i])\r\n else:\r\n y = np.min([bounds[1], y], axis=0)\r\n return y","def clip_by_bound(self) -> None:\n\n pass","def getsnxslice(self,whichsol_,first_,last_,snx_):\n _snx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and snx_ is not None and len(snx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument snx is not long enough: Is %d, expected %d\" % (len(snx_),((last_) - (first_))))\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\n raise ValueError(\"Argument snx must be writable\")\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_getsnxslice(self.__nativep,whichsol_,first_,last_,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _snx_copyarray:\n snx_[:] = _snx_np_tmp"],"string":"[\n \"def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarboundslice(self,first_,last_,bkx_,blx_,bux_):\\n _bkx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bkx_ is not None and len(bkx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bkx is not long enough: Is %d, expected %d\\\" % (len(bkx_),((last_) - (first_))))\\n if bkx_ is None:\\n raise ValueError(\\\"Argument bkx cannot be None\\\")\\n if bkx_ is None:\\n raise ValueError(\\\"Argument bkx may not be None\\\")\\n if bkx_ is not None:\\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\\n else:\\n _bkx_tmp = None\\n _blx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and blx_ is not None and len(blx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument blx is not long enough: Is %d, expected %d\\\" % (len(blx_),((last_) - (first_))))\\n if blx_ is None:\\n raise ValueError(\\\"Argument blx cannot be None\\\")\\n if blx_ is None:\\n raise ValueError(\\\"Argument blx may not be None\\\")\\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\\n _blx_copyarray = False\\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blx_ is not None:\\n _blx_copyarray = True\\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\\n _blx_np_tmp[:] = blx_\\n assert _blx_np_tmp.flags.contiguous\\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blx_copyarray = False\\n _blx_tmp = None\\n \\n _bux_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bux_ is not None and len(bux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bux is not long enough: Is %d, expected %d\\\" % (len(bux_),((last_) - (first_))))\\n if bux_ is None:\\n raise ValueError(\\\"Argument bux cannot be None\\\")\\n if bux_ is None:\\n raise ValueError(\\\"Argument bux may not be None\\\")\\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\\n _bux_copyarray = False\\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bux_ is not None:\\n _bux_copyarray = True\\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\\n _bux_np_tmp[:] = bux_\\n assert _bux_np_tmp.flags.contiguous\\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bux_copyarray = False\\n _bux_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundslice(self.__nativep,first_,last_,_bkx_tmp,_blx_tmp,_bux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getvarboundslice(self,first_,last_,bk,bl,bu): # 3\\n _copyback_bk = False\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n _copyback_bk = True\\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n _copyback_bk = True\\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n _copyback_bl = False\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n _copyback_bl = True\\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n _copyback_bl = True\\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n _copyback_bu = False\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n _copyback_bu = True\\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n _copyback_bu = True\\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.getvarboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_bu:\\n bu[:] = _tmparr_bu\\n if _copyback_bl:\\n bl[:] = _tmparr_bl\\n if _copyback_bk:\\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])\",\n \"def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getvarboundslice(self,first_,last_,bk_,bl_,bu_):\\n _bk_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk is not long enough: Is %d, expected %d\\\" % (len(bk_),((last_) - (first_))))\\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\\n raise ValueError(\\\"Argument bk must be writable\\\")\\n if bk_ is not None:\\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\\n else:\\n _bk_tmp = None\\n _bl_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl is not long enough: Is %d, expected %d\\\" % (len(bl_),((last_) - (first_))))\\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\\n raise ValueError(\\\"Argument bl must be writable\\\")\\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\\n _bl_copyarray = False\\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bl_ is not None:\\n _bl_copyarray = True\\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\\n _bl_np_tmp[:] = bl_\\n assert _bl_np_tmp.flags.contiguous\\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bl_copyarray = False\\n _bl_tmp = None\\n \\n _bu_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu is not long enough: Is %d, expected %d\\\" % (len(bu_),((last_) - (first_))))\\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\\n raise ValueError(\\\"Argument bu must be writable\\\")\\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\\n _bu_copyarray = False\\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bu_ is not None:\\n _bu_copyarray = True\\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\\n _bu_np_tmp[:] = bu_\\n assert _bu_np_tmp.flags.contiguous\\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bu_copyarray = False\\n _bu_tmp = None\\n \\n res = __library__.MSK_XX_getvarboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\\n if _bl_copyarray:\\n bl_[:] = _bl_np_tmp\\n if _bu_copyarray:\\n bu_[:] = _bu_np_tmp\",\n \"def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundslice(self,first_,last_,bk,bl,bu): # 3\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putconboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putboundslice(self,con_,first_,last_,bk,bl,bu): # 3\\n if not isinstance(con_,accmode): raise TypeError(\\\"Argument con has wrong type\\\")\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putboundslice(con_,first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundslice(self,first_,last_,bkc_,blc_,buc_):\\n _bkc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bkc_ is not None and len(bkc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bkc is not long enough: Is %d, expected %d\\\" % (len(bkc_),((last_) - (first_))))\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc cannot be None\\\")\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc may not be None\\\")\\n if bkc_ is not None:\\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\\n else:\\n _bkc_tmp = None\\n _blc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and blc_ is not None and len(blc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument blc is not long enough: Is %d, expected %d\\\" % (len(blc_),((last_) - (first_))))\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc cannot be None\\\")\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc may not be None\\\")\\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\\n _blc_copyarray = False\\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blc_ is not None:\\n _blc_copyarray = True\\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\\n _blc_np_tmp[:] = blc_\\n assert _blc_np_tmp.flags.contiguous\\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blc_copyarray = False\\n _blc_tmp = None\\n \\n _buc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and buc_ is not None and len(buc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument buc is not long enough: Is %d, expected %d\\\" % (len(buc_),((last_) - (first_))))\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc cannot be None\\\")\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc may not be None\\\")\\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\\n _buc_copyarray = False\\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif buc_ is not None:\\n _buc_copyarray = True\\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\\n _buc_np_tmp[:] = buc_\\n assert _buc_np_tmp.flags.contiguous\\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _buc_copyarray = False\\n _buc_tmp = None\\n \\n res = __library__.MSK_XX_putconboundslice(self.__nativep,first_,last_,_bkc_tmp,_blc_tmp,_buc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarboundlist(self,sub_,bkx_,blx_,bux_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkx_)\\n elif num_ != len(bkx_):\\n raise IndexError(\\\"Inconsistent length of array bkx\\\")\\n if num_ is None:\\n num_ = len(blx_)\\n elif num_ != len(blx_):\\n raise IndexError(\\\"Inconsistent length of array blx\\\")\\n if num_ is None:\\n num_ = len(bux_)\\n elif num_ != len(bux_):\\n raise IndexError(\\\"Inconsistent length of array bux\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if bkx_ is None:\\n raise ValueError(\\\"Argument bkx cannot be None\\\")\\n if bkx_ is None:\\n raise ValueError(\\\"Argument bkx may not be None\\\")\\n if bkx_ is not None:\\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\\n else:\\n _bkx_tmp = None\\n if blx_ is None:\\n raise ValueError(\\\"Argument blx cannot be None\\\")\\n if blx_ is None:\\n raise ValueError(\\\"Argument blx may not be None\\\")\\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\\n _blx_copyarray = False\\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blx_ is not None:\\n _blx_copyarray = True\\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\\n _blx_np_tmp[:] = blx_\\n assert _blx_np_tmp.flags.contiguous\\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blx_copyarray = False\\n _blx_tmp = None\\n \\n if bux_ is None:\\n raise ValueError(\\\"Argument bux cannot be None\\\")\\n if bux_ is None:\\n raise ValueError(\\\"Argument bux may not be None\\\")\\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\\n _bux_copyarray = False\\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bux_ is not None:\\n _bux_copyarray = True\\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\\n _bux_np_tmp[:] = bux_\\n assert _bux_np_tmp.flags.contiguous\\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bux_copyarray = False\\n _bux_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundlist(self.__nativep,num_,_sub_tmp,_bkx_tmp,_blx_tmp,_bux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getconboundslice(self,first_,last_,bk_,bl_,bu_):\\n _bk_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk is not long enough: Is %d, expected %d\\\" % (len(bk_),((last_) - (first_))))\\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\\n raise ValueError(\\\"Argument bk must be writable\\\")\\n if bk_ is not None:\\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\\n else:\\n _bk_tmp = None\\n _bl_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl is not long enough: Is %d, expected %d\\\" % (len(bl_),((last_) - (first_))))\\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\\n raise ValueError(\\\"Argument bl must be writable\\\")\\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\\n _bl_copyarray = False\\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bl_ is not None:\\n _bl_copyarray = True\\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\\n _bl_np_tmp[:] = bl_\\n assert _bl_np_tmp.flags.contiguous\\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bl_copyarray = False\\n _bl_tmp = None\\n \\n _bu_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu is not long enough: Is %d, expected %d\\\" % (len(bu_),((last_) - (first_))))\\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\\n raise ValueError(\\\"Argument bu must be writable\\\")\\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\\n _bu_copyarray = False\\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif bu_ is not None:\\n _bu_copyarray = True\\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\\n _bu_np_tmp[:] = bu_\\n assert _bu_np_tmp.flags.contiguous\\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _bu_copyarray = False\\n _bu_tmp = None\\n \\n res = __library__.MSK_XX_getconboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\\n if _bl_copyarray:\\n bl_[:] = _bl_np_tmp\\n if _bu_copyarray:\\n bu_[:] = _bu_np_tmp\",\n \"def putxxslice(self,whichsol_,first_,last_,xx_):\\n _xx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),((last_) - (first_))))\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx cannot be None\\\")\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx may not be None\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n res = __library__.MSK_XX_putxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getconboundslice(self,first_,last_,bk,bl,bu): # 3\\n _copyback_bk = False\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n _copyback_bk = True\\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n _copyback_bk = True\\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n _copyback_bl = False\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n _copyback_bl = True\\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n _copyback_bl = True\\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n _copyback_bu = False\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n _copyback_bu = True\\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n _copyback_bu = True\\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.getconboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_bu:\\n bu[:] = _tmparr_bu\\n if _copyback_bl:\\n bl[:] = _tmparr_bl\\n if _copyback_bk:\\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])\",\n \"def putxxslice(self,whichsol_,first_,last_,xx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xx is None: raise TypeError(\\\"Invalid type for argument xx\\\")\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n res = self.__obj.putxxslice(whichsol_,first_,last_,xx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putcslice(self,first_,last_,slice_):\\n _slice_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slice_ is not None and len(slice_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slice is not long enough: Is %d, expected %d\\\" % (len(slice_),((last_) - (first_))))\\n if slice_ is None:\\n raise ValueError(\\\"Argument slice cannot be None\\\")\\n if slice_ is None:\\n raise ValueError(\\\"Argument slice may not be None\\\")\\n if isinstance(slice_, numpy.ndarray) and slice_.dtype is numpy.dtype(numpy.float64) and slice_.flags.contiguous:\\n _slice_copyarray = False\\n _slice_tmp = ctypes.cast(slice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slice_ is not None:\\n _slice_copyarray = True\\n _slice_np_tmp = numpy.zeros(len(slice_),numpy.dtype(numpy.float64))\\n _slice_np_tmp[:] = slice_\\n assert _slice_np_tmp.flags.contiguous\\n _slice_tmp = ctypes.cast(_slice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slice_copyarray = False\\n _slice_tmp = None\\n \\n res = __library__.MSK_XX_putcslice(self.__nativep,first_,last_,_slice_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarbound(self,j_,bkx_,blx_,bux_):\\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_variable_slices(self, variables):\\n # Set up y_slices and bounds\\n y_slices = defaultdict(list)\\n y_slices_explicit = defaultdict(list)\\n start = 0\\n end = 0\\n lower_bounds = []\\n upper_bounds = []\\n # Iterate through unpacked variables, adding appropriate slices to y_slices\\n for variable in variables:\\n # Add up the size of all the domains in variable.domain\\n if isinstance(variable, pybamm.ConcatenationVariable):\\n start_ = start\\n spatial_method = self.spatial_methods[variable.domain[0]]\\n children = variable.children\\n meshes = OrderedDict()\\n for child in children:\\n meshes[child] = [spatial_method.mesh[dom] for dom in child.domain]\\n sec_points = spatial_method._get_auxiliary_domain_repeats(\\n variable.domains\\n )\\n for i in range(sec_points):\\n for child, mesh in meshes.items():\\n for domain_mesh in mesh:\\n end += domain_mesh.npts_for_broadcast_to_nodes\\n # Add to slices\\n y_slices[child].append(slice(start_, end))\\n y_slices_explicit[child].append(slice(start_, end))\\n # Increment start_\\n start_ = end\\n else:\\n end += self._get_variable_size(variable)\\n\\n # Add to slices\\n y_slices[variable].append(slice(start, end))\\n y_slices_explicit[variable].append(slice(start, end))\\n\\n # Add to bounds\\n def evaluate_bound(bound, side):\\n if bound.has_symbol_of_classes(pybamm.InputParameter):\\n if side == \\\"lower\\\":\\n return -np.inf\\n elif side == \\\"upper\\\":\\n return np.inf\\n else:\\n return bound.evaluate()\\n\\n lower_bounds.extend(\\n [evaluate_bound(variable.bounds[0], \\\"lower\\\")] * (end - start)\\n )\\n upper_bounds.extend(\\n [evaluate_bound(variable.bounds[1], \\\"upper\\\")] * (end - start)\\n )\\n # Increment start\\n start = end\\n\\n # Convert y_slices back to normal dictionary\\n self.y_slices = dict(y_slices)\\n # Also keep a record of what the y_slices are, to be stored in the model\\n self.y_slices_explicit = dict(y_slices_explicit)\\n\\n # Also keep a record of bounds\\n self.bounds = (np.array(lower_bounds), np.array(upper_bounds))\\n\\n # reset discretised_symbols\\n self._discretised_symbols = {}\",\n \"def apply_bound(x, var_min, var_max):\\n x.position = np.maximum(x.position, var_min)\\n x.position = np.minimum(x.position, var_max)\",\n \"def chgvarbound(self,j_,lower_,finite_,value_): # 3\\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarboundlist(self,sub,bkx,blx,bux): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkx)\\n elif num_ != len(bkx):\\n raise IndexError(\\\"Inconsistent length of array bkx\\\")\\n if num_ is None:\\n num_ = len(blx)\\n elif num_ != len(blx):\\n raise IndexError(\\\"Inconsistent length of array blx\\\")\\n if num_ is None:\\n num_ = len(bux)\\n elif num_ != len(bux):\\n raise IndexError(\\\"Inconsistent length of array bux\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if bkx is None: raise TypeError(\\\"Invalid type for argument bkx\\\")\\n if bkx is None:\\n bkx_ = None\\n else:\\n try:\\n bkx_ = memoryview(bkx)\\n except TypeError:\\n try:\\n _tmparr_bkx = array.array(\\\"i\\\",bkx)\\n except TypeError:\\n raise TypeError(\\\"Argument bkx has wrong type\\\")\\n else:\\n bkx_ = memoryview(_tmparr_bkx)\\n \\n else:\\n if bkx_.format != \\\"i\\\":\\n bkx_ = memoryview(array.array(\\\"i\\\",bkx))\\n \\n if blx is None: raise TypeError(\\\"Invalid type for argument blx\\\")\\n if blx is None:\\n blx_ = None\\n else:\\n try:\\n blx_ = memoryview(blx)\\n except TypeError:\\n try:\\n _tmparr_blx = array.array(\\\"d\\\",blx)\\n except TypeError:\\n raise TypeError(\\\"Argument blx has wrong type\\\")\\n else:\\n blx_ = memoryview(_tmparr_blx)\\n \\n else:\\n if blx_.format != \\\"d\\\":\\n blx_ = memoryview(array.array(\\\"d\\\",blx))\\n \\n if bux is None: raise TypeError(\\\"Invalid type for argument bux\\\")\\n if bux is None:\\n bux_ = None\\n else:\\n try:\\n bux_ = memoryview(bux)\\n except TypeError:\\n try:\\n _tmparr_bux = array.array(\\\"d\\\",bux)\\n except TypeError:\\n raise TypeError(\\\"Argument bux has wrong type\\\")\\n else:\\n bux_ = memoryview(_tmparr_bux)\\n \\n else:\\n if bux_.format != \\\"d\\\":\\n bux_ = memoryview(array.array(\\\"d\\\",bux))\\n \\n res = self.__obj.putvarboundlist(num_,sub_,bkx_,blx_,bux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def chgvarbound(self,j_,lower_,finite_,value_):\\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putcslice(self,first_,last_,slice): # 3\\n if slice is None: raise TypeError(\\\"Invalid type for argument slice\\\")\\n if slice is None:\\n slice_ = None\\n else:\\n try:\\n slice_ = memoryview(slice)\\n except TypeError:\\n try:\\n _tmparr_slice = array.array(\\\"d\\\",slice)\\n except TypeError:\\n raise TypeError(\\\"Argument slice has wrong type\\\")\\n else:\\n slice_ = memoryview(_tmparr_slice)\\n \\n else:\\n if slice_.format != \\\"d\\\":\\n slice_ = memoryview(array.array(\\\"d\\\",slice))\\n \\n if slice_ is not None and len(slice_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slice has wrong length\\\")\\n res = self.__obj.putcslice(first_,last_,slice_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_bounds_atom(self,bounds):\\n assert bounds.shape == (2,self.Phi.d)\\n self.bounds = bounds # data bounds\\n self.bounds_atom = bounds.T.tolist()\\n for i in range(self.Phi.d): # bounds for the variance in each dimension\\n max_variance_this_dimension = (bounds[1][i]-bounds[0][i])**2\\n self.bounds_atom.append([self.variance_relative_lowerbound*max_variance_this_dimension,\\n self.variance_relative_upperbound*max_variance_this_dimension])\",\n \"def getbarxslice(self,whichsol_,first_,last_,slicesize_,barxslice_):\\n _barxslice_minlength = (slicesize_)\\n if (slicesize_) > 0 and barxslice_ is not None and len(barxslice_) != (slicesize_):\\n raise ValueError(\\\"Array argument barxslice is not long enough: Is %d, expected %d\\\" % (len(barxslice_),(slicesize_)))\\n if isinstance(barxslice_,numpy.ndarray) and not barxslice_.flags.writeable:\\n raise ValueError(\\\"Argument barxslice must be writable\\\")\\n if barxslice_ is None:\\n raise ValueError(\\\"Argument barxslice may not be None\\\")\\n if isinstance(barxslice_, numpy.ndarray) and barxslice_.dtype is numpy.dtype(numpy.float64) and barxslice_.flags.contiguous:\\n _barxslice_copyarray = False\\n _barxslice_tmp = ctypes.cast(barxslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barxslice_ is not None:\\n _barxslice_copyarray = True\\n _barxslice_np_tmp = numpy.zeros(len(barxslice_),numpy.dtype(numpy.float64))\\n _barxslice_np_tmp[:] = barxslice_\\n assert _barxslice_np_tmp.flags.contiguous\\n _barxslice_tmp = ctypes.cast(_barxslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barxslice_copyarray = False\\n _barxslice_tmp = None\\n \\n res = __library__.MSK_XX_getbarxslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barxslice_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _barxslice_copyarray:\\n barxslice_[:] = _barxslice_np_tmp\",\n \"def getbarsslice(self,whichsol_,first_,last_,slicesize_,barsslice_):\\n _barsslice_minlength = (slicesize_)\\n if (slicesize_) > 0 and barsslice_ is not None and len(barsslice_) != (slicesize_):\\n raise ValueError(\\\"Array argument barsslice is not long enough: Is %d, expected %d\\\" % (len(barsslice_),(slicesize_)))\\n if isinstance(barsslice_,numpy.ndarray) and not barsslice_.flags.writeable:\\n raise ValueError(\\\"Argument barsslice must be writable\\\")\\n if barsslice_ is None:\\n raise ValueError(\\\"Argument barsslice may not be None\\\")\\n if isinstance(barsslice_, numpy.ndarray) and barsslice_.dtype is numpy.dtype(numpy.float64) and barsslice_.flags.contiguous:\\n _barsslice_copyarray = False\\n _barsslice_tmp = ctypes.cast(barsslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barsslice_ is not None:\\n _barsslice_copyarray = True\\n _barsslice_np_tmp = numpy.zeros(len(barsslice_),numpy.dtype(numpy.float64))\\n _barsslice_np_tmp[:] = barsslice_\\n assert _barsslice_np_tmp.flags.contiguous\\n _barsslice_tmp = ctypes.cast(_barsslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barsslice_copyarray = False\\n _barsslice_tmp = None\\n \\n res = __library__.MSK_XX_getbarsslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barsslice_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _barsslice_copyarray:\\n barsslice_[:] = _barsslice_np_tmp\",\n \"def getboundslice(self,accmode_,first_,last_,bk,bl,bu): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n _copyback_bk = False\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n _copyback_bk = True\\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n _copyback_bk = True\\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n _copyback_bl = False\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n _copyback_bl = True\\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n _copyback_bl = True\\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n _copyback_bu = False\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n _copyback_bu = True\\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n _copyback_bu = True\\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.getboundslice(accmode_,first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_bu:\\n bu[:] = _tmparr_bu\\n if _copyback_bl:\\n bl[:] = _tmparr_bl\\n if _copyback_bk:\\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])\",\n \"def putvarbound(self,j_,bk_,bl_,bu_): # 3\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getxxslice(self,whichsol_,first_,last_,xx_):\\n _xx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),((last_) - (first_))))\\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\\n raise ValueError(\\\"Argument xx must be writable\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n res = __library__.MSK_XX_getxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _xx_copyarray:\\n xx_[:] = _xx_np_tmp\",\n \"def set_bounds(self, new_bounds):\\n\\n # Update the internal object stored dict\\n self.pbounds.update(new_bounds)\\n\\n # Loop through the all bounds and reset the min-max bound matrix\\n for row, key in enumerate(self.pbounds.keys()):\\n\\n # Reset all entries, even if the same.\\n self.bounds[row] = self.pbounds[key]\",\n \"def _initialize_bounds(problem, bounds, get_bound, set_bound):\\n for constraint in problem.constraints:\\n root_expr = constraint.root_expr\\n expr_bounds = Interval(constraint.lower_bound, constraint.upper_bound)\\n if root_expr not in bounds:\\n set_bound(root_expr, expr_bounds)\\n else:\\n existing_bounds = get_bound(root_expr)\\n new_bounds = existing_bounds.intersect(expr_bounds)\\n set_bound(root_expr, new_bounds)\",\n \"def getxxslice(self,whichsol_,first_,last_,xx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_xx = False\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n _copyback_xx = True\\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n _copyback_xx = True\\n if xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n res = self.__obj.getxxslice(whichsol_,first_,last_,xx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_xx:\\n xx[:] = _tmparr_xx\",\n \"def cb_bounds(self, variable, results_dict, keys, fixed_bounds):\\n tas_bound, pr_bound = fixed_bounds\\n if variable == \\\"tas\\\":\\n if tas_bound:\\n bound_limit = tas_bound\\n else:\\n bound_limit = self.find_abs_bound_range(results_dict, keys)\\n cmap = plt.cm.RdBu_r\\n else:\\n if pr_bound:\\n bound_limit = pr_bound\\n else:\\n bound_limit = self.find_abs_bound_range(results_dict,\\n keys,\\n avg_over=25)\\n cmap = plt.cm.BrBG\\n bounds = np.linspace(-1 * bound_limit, bound_limit, 11)\\n return [bounds, cmap]\",\n \"def putsuxslice(self,whichsol_,first_,last_,sux_):\\n _sux_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),((last_) - (first_))))\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux cannot be None\\\")\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux may not be None\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_putsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsnxslice(self,whichsol_,first_,last_,snx_):\\n _snx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and snx_ is not None and len(snx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument snx is not long enough: Is %d, expected %d\\\" % (len(snx_),((last_) - (first_))))\\n if snx_ is None:\\n raise ValueError(\\\"Argument snx cannot be None\\\")\\n if snx_ is None:\\n raise ValueError(\\\"Argument snx may not be None\\\")\\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_putsnxslice(self.__nativep,whichsol_,first_,last_,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def removebarvars(self,subset_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subset_)\\n elif num_ != len(subset_):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset cannot be None\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset may not be None\\\")\\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\\n _subset_copyarray = False\\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subset_ is not None:\\n _subset_copyarray = True\\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\\n _subset_np_tmp[:] = subset_\\n assert _subset_np_tmp.flags.contiguous\\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subset_copyarray = False\\n _subset_tmp = None\\n \\n res = __library__.MSK_XX_removebarvars(self.__nativep,num_,_subset_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsuxslice(self,whichsol_,first_,last_,sux_):\\n _sux_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),((last_) - (first_))))\\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\\n raise ValueError(\\\"Argument sux must be writable\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_getsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _sux_copyarray:\\n sux_[:] = _sux_np_tmp\",\n \"def putxcslice(self,whichsol_,first_,last_,xc_):\\n _xc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and xc_ is not None and len(xc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),((last_) - (first_))))\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc cannot be None\\\")\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc may not be None\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n res = __library__.MSK_XX_putxcslice(self.__nativep,whichsol_,first_,last_,_xc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def bounds(self, new_bounds: devices.PrimaryBounds) -> None:\\n self._assert_bounds_are_valid(new_bounds)\\n self._bounds = list(new_bounds)\",\n \"def set_bounds_atom(self,bounds):\\n assert bounds.shape == (2,self.Phi.d)\\n self.bounds = bounds # data bounds\\n self.bounds_atom = bounds.T.tolist()\",\n \"def extend_bounds(problem):\\n\\n num_vars = problem[\\\"num_vars\\\"]\\n num_ff_vars = 2 ** find_smallest(num_vars)\\n num_dummy_variables = num_ff_vars - num_vars\\n\\n bounds = list(problem[\\\"bounds\\\"])\\n names = problem[\\\"names\\\"]\\n if num_dummy_variables > 0:\\n bounds.extend([[0, 1] for x in range(num_dummy_variables)])\\n names.extend([\\\"dummy_\\\" + str(var) for var in range(num_dummy_variables)])\\n problem[\\\"bounds\\\"] = bounds\\n problem[\\\"names\\\"] = names\\n problem[\\\"num_vars\\\"] = num_ff_vars\\n\\n return problem\",\n \"def putskxslice(self,whichsol_,first_,last_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skx is None: raise TypeError(\\\"Invalid type for argument skx\\\")\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skx_ is not None and len(skx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.putskxslice(whichsol_,first_,last_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def write_bounds(self):\\n optimized_par_df = \\\\\\n self.parameter_df.loc[self.parameter_df.estimate == 1\\n & (~self.parameter_df.index.isin(\\n self.amici_model.getFixedParameterIds())), :]\\n self.f.require_dataset('/parameters/lowerBound',\\n shape=optimized_par_df.lowerBound.shape,\\n data=optimized_par_df.lowerBound, dtype='f8')\\n self.f.require_dataset('/parameters/upperBound',\\n shape=optimized_par_df.upperBound.shape,\\n data=optimized_par_df.upperBound, dtype='f8')\",\n \"def putxcslice(self,whichsol_,first_,last_,xc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xc is None: raise TypeError(\\\"Invalid type for argument xc\\\")\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n \\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n \\n if xc_ is not None and len(xc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n res = self.__obj.putxcslice(whichsol_,first_,last_,xc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def subspace(self,varset_values):\\n vector = np.empty(self.dimensions)\\n \\n for varname, value in varset_values:\\n try: \\n vector[self.slices[varname]] = np.ravel(value)\\n except KeyError:\\n pass\\n \\n return vector\",\n \"def update_bounds(self, temp):\\n if (temp.extent[0] < self.extent[0]):\\n self.extent[0] = np.copy(temp.extent[0])\\n if (temp.extent[1] > self.extent[1]):\\n self.extent[1] = np.copy(temp.extent[1])\\n if (temp.extent[2] < self.extent[2]):\\n self.extent[2] = np.copy(temp.extent[2])\\n if (temp.extent[3] > self.extent[3]):\\n self.extent[3] = np.copy(temp.extent[3])\\n return self\",\n \"def bounds(self, pos):\",\n \"def putsuxslice(self,whichsol_,first_,last_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if sux is None: raise TypeError(\\\"Invalid type for argument sux\\\")\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.putsuxslice(whichsol_,first_,last_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_bounds_atom(self,bounds):\\n self.bounds = bounds # data bounds\\n raise NotImplementedError\\n self.bounds_atom = None\\n return None\",\n \"def getsuxslice(self,whichsol_,first_,last_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_sux = False\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n _copyback_sux = True\\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n _copyback_sux = True\\n if sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.getsuxslice(whichsol_,first_,last_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_sux:\\n sux[:] = _tmparr_sux\",\n \"def putskxslice(self,whichsol_,first_,last_,skx_):\\n _skx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),((last_) - (first_))))\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx cannot be None\\\")\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx may not be None\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_putskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def normalize_bounds(self, bounds):\\n scaled_bounds = []\\n scalings = []\\n intercepts = []\\n \\n non_fixed_params = []\\n \\n print(self.device)\\n \\n for name, domain in self.bounds.items():\\n # Get any fixed parmeters\\n if type(domain) == int or type(domain) == float:\\n # Take note\\n self.fixed_parameters.append(name)\\n\\n # Free parameters\\n elif type(domain) == tuple:\\n # Bookkeeping\\n self.free_parameters.append(name)\\n\\n # Get scaling\\n lower_bound = min(domain)\\n upper_bound = max(domain)\\n scale = upper_bound - lower_bound\\n\\n # Transform to [0, 1] domain\\n #scaled_bound = {'name': name, 'type': 'continuous', 'domain': (0., 1.)} #torch.adjustment required\\n non_fixed_params.append(name)\\n \\n # Store\\n #scaled_bounds.append(scaled_bound)\\n scalings.append(scale)\\n intercepts.append(lower_bound)\\n else:\\n raise ValueError(\\\"Domain bounds not understood\\\")\\n \\n n_hyperparams = len(non_fixed_params)\\n \\n scaled_bounds = cat([zeros(1,n_hyperparams, device = self.device), \\n ones(1, n_hyperparams, device = self.device)], 0)\\n return scaled_bounds, tensor(scalings, device = self.device, requires_grad = False), tensor(intercepts, device = self.device, requires_grad = False) #torch.adjustment required\",\n \"def putsnxslice(self,whichsol_,first_,last_,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if snx is None: raise TypeError(\\\"Invalid type for argument snx\\\")\\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n \\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n \\n if snx_ is not None and len(snx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument snx has wrong length\\\")\\n res = self.__obj.putsnxslice(whichsol_,first_,last_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskcslice(self,whichsol_,first_,last_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skc_ is not None and len(skc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.putskcslice(whichsol_,first_,last_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putacolslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb cannot be None\\\")\\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb may not be None\\\")\\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\\n _ptrb_copyarray = False\\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptrb_ is not None:\\n _ptrb_copyarray = True\\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\\n _ptrb_np_tmp[:] = ptrb_\\n assert _ptrb_np_tmp.flags.contiguous\\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptrb_copyarray = False\\n _ptrb_tmp = None\\n \\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre cannot be None\\\")\\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre may not be None\\\")\\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\\n _ptre_copyarray = False\\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptre_ is not None:\\n _ptre_copyarray = True\\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\\n _ptre_np_tmp[:] = ptre_\\n assert _ptre_np_tmp.flags.contiguous\\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptre_copyarray = False\\n _ptre_tmp = None\\n \\n if asub_ is None:\\n raise ValueError(\\\"Argument asub cannot be None\\\")\\n if asub_ is None:\\n raise ValueError(\\\"Argument asub may not be None\\\")\\n if isinstance(asub_, numpy.ndarray) and asub_.dtype is numpy.dtype(numpy.int32) and asub_.flags.contiguous:\\n _asub_copyarray = False\\n _asub_tmp = ctypes.cast(asub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif asub_ is not None:\\n _asub_copyarray = True\\n _asub_np_tmp = numpy.zeros(len(asub_),numpy.dtype(numpy.int32))\\n _asub_np_tmp[:] = asub_\\n assert _asub_np_tmp.flags.contiguous\\n _asub_tmp = ctypes.cast(_asub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _asub_copyarray = False\\n _asub_tmp = None\\n \\n if aval_ is None:\\n raise ValueError(\\\"Argument aval cannot be None\\\")\\n if aval_ is None:\\n raise ValueError(\\\"Argument aval may not be None\\\")\\n if isinstance(aval_, numpy.ndarray) and aval_.dtype is numpy.dtype(numpy.float64) and aval_.flags.contiguous:\\n _aval_copyarray = False\\n _aval_tmp = ctypes.cast(aval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif aval_ is not None:\\n _aval_copyarray = True\\n _aval_np_tmp = numpy.zeros(len(aval_),numpy.dtype(numpy.float64))\\n _aval_np_tmp[:] = aval_\\n assert _aval_np_tmp.flags.contiguous\\n _aval_tmp = ctypes.cast(_aval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _aval_copyarray = False\\n _aval_tmp = None\\n \\n res = __library__.MSK_XX_putacolslice64(self.__nativep,first_,last_,_ptrb_tmp,_ptre_tmp,_asub_tmp,_aval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setSafeRange( azLow, azHigh, elLow, elHigh, ants, subarray=DEFAULT) :\\n antlist = helpers.makeList(ants)\\n multiSubarray('setSafeRange', subarray, azLow, azHigh, elLow, elHigh, \\n antlist)\",\n \"def removevars(self,subset_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subset_)\\n elif num_ != len(subset_):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset cannot be None\\\")\\n if subset_ is None:\\n raise ValueError(\\\"Argument subset may not be None\\\")\\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\\n _subset_copyarray = False\\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subset_ is not None:\\n _subset_copyarray = True\\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\\n _subset_np_tmp[:] = subset_\\n assert _subset_np_tmp.flags.contiguous\\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subset_copyarray = False\\n _subset_tmp = None\\n \\n res = __library__.MSK_XX_removevars(self.__nativep,num_,_subset_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def removebarvars(self,subset): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subset)\\n elif num_ != len(subset):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if num_ is None: num_ = 0\\n if subset is None: raise TypeError(\\\"Invalid type for argument subset\\\")\\n if subset is None:\\n subset_ = None\\n else:\\n try:\\n subset_ = memoryview(subset)\\n except TypeError:\\n try:\\n _tmparr_subset = array.array(\\\"i\\\",subset)\\n except TypeError:\\n raise TypeError(\\\"Argument subset has wrong type\\\")\\n else:\\n subset_ = memoryview(_tmparr_subset)\\n \\n else:\\n if subset_.format != \\\"i\\\":\\n subset_ = memoryview(array.array(\\\"i\\\",subset))\\n \\n res = self.__obj.removebarvars(num_,subset_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def repair(self, x, bounds=None, copy=False, copy_always=False):\\r\\n # TODO (old data): CPU(N,lam,iter=20,200,100): 3.3s of 8s for two bounds, 1.8s of 6.5s for one bound\\r\\n # TODO: test whether np.max([bounds[0], x], axis=0) etc is speed relevant\\r\\n\\r\\n if bounds is None:\\r\\n bounds = self.bounds\\r\\n if copy_always:\\r\\n x_out = array(x, copy=True)\\r\\n if bounds not in (None, [None, None], (None, None)): # solely for effiency\\r\\n x_out = array(x, copy=True) if copy and not copy_always else x\\r\\n if bounds[0] is not None:\\r\\n if np.isscalar(bounds[0]):\\r\\n for i in xrange(len(x)):\\r\\n x_out[i] = max([bounds[0], x[i]])\\r\\n else:\\r\\n for i in xrange(len(x)):\\r\\n if bounds[0][i] is not None:\\r\\n x_out[i] = max([bounds[0][i], x[i]])\\r\\n if bounds[1] is not None:\\r\\n if np.isscalar(bounds[1]):\\r\\n for i in xrange(len(x)):\\r\\n x_out[i] = min([bounds[1], x[i]])\\r\\n else:\\r\\n for i in xrange(len(x)):\\r\\n if bounds[1][i] is not None:\\r\\n x_out[i] = min([bounds[1][i], x[i]])\\r\\n return x_out # convenience return\\r\",\n \"def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\\n ...\",\n \"def SetBounds(self, p_float, p_float_1, p_float_2, p_float_3, p_float_4, p_float_5):\\n ...\",\n \"def putslcslice(self,whichsol_,first_,last_,slc_):\\n _slc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),((last_) - (first_))))\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc cannot be None\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _manage_infinity_bounds(problem, _bounds, get_bound, set_bound):\\n for variable in problem.variables:\\n expr_bounds = get_bound(variable)\\n lower_bound = expr_bounds.lower_bound\\n upper_bound = expr_bounds.upper_bound\\n\\n if is_inf(lower_bound):\\n new_lower_bound = None\\n else:\\n new_lower_bound = lower_bound\\n\\n if is_inf(upper_bound):\\n new_upper_bound = None\\n else:\\n new_upper_bound = upper_bound\\n\\n set_bound(variable, Interval(new_lower_bound, new_upper_bound))\",\n \"def putslxslice(self,whichsol_,first_,last_,slx_):\\n _slx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slx_ is not None and len(slx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slx is not long enough: Is %d, expected %d\\\" % (len(slx_),((last_) - (first_))))\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx cannot be None\\\")\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx may not be None\\\")\\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n res = __library__.MSK_XX_putslxslice(self.__nativep,whichsol_,first_,last_,_slx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _set_neighs_slice(self, key):\\n ## Condition to use slice type\\n self._constant_neighs = True\\n self.ks = range(1) if self.ks is None else self.ks\\n ## Possible options\\n if key is None:\\n self.idxs = slice(0, self._n, 1)\\n elif isinstance(key, slice):\\n start = 0 if key.start is None else key.start\\n stop = self._n if key.stop is None else key.stop\\n stop = self._n if key.stop > 10*16 else key.stop\\n step = 1 if key.step is None else key.step\\n self.idxs = slice(start, stop, step)\\n elif type(key) in inttypes:\\n self.idxs = slice(0, key, 1)\\n elif type(key) == tuple:\\n self.idxs = slice(key[0], key[1], 1)\\n self._setted = True\",\n \"def init_range_variables(self):\\n self.range_start_vars_array = []\\n self.range_end_vars_array = []\\n\\n for idx in range(len(self._pk_for_filter)):\\n self.range_start_vars_array.append(\\\"@range_start_{}\\\".format(idx))\\n self.range_end_vars_array.append(\\\"@range_end_{}\\\".format(idx))\\n self.range_start_vars = \\\",\\\".join(self.range_start_vars_array)\\n self.range_end_vars = \\\",\\\".join(self.range_end_vars_array)\",\n \"def putacolslice(self,first_,last_,ptrb,ptre,asub,aval): # 3\\n if ptrb is None: raise TypeError(\\\"Invalid type for argument ptrb\\\")\\n if ptrb is None:\\n ptrb_ = None\\n else:\\n try:\\n ptrb_ = memoryview(ptrb)\\n except TypeError:\\n try:\\n _tmparr_ptrb = array.array(\\\"q\\\",ptrb)\\n except TypeError:\\n raise TypeError(\\\"Argument ptrb has wrong type\\\")\\n else:\\n ptrb_ = memoryview(_tmparr_ptrb)\\n \\n else:\\n if ptrb_.format != \\\"q\\\":\\n ptrb_ = memoryview(array.array(\\\"q\\\",ptrb))\\n \\n if ptre is None: raise TypeError(\\\"Invalid type for argument ptre\\\")\\n if ptre is None:\\n ptre_ = None\\n else:\\n try:\\n ptre_ = memoryview(ptre)\\n except TypeError:\\n try:\\n _tmparr_ptre = array.array(\\\"q\\\",ptre)\\n except TypeError:\\n raise TypeError(\\\"Argument ptre has wrong type\\\")\\n else:\\n ptre_ = memoryview(_tmparr_ptre)\\n \\n else:\\n if ptre_.format != \\\"q\\\":\\n ptre_ = memoryview(array.array(\\\"q\\\",ptre))\\n \\n if asub is None: raise TypeError(\\\"Invalid type for argument asub\\\")\\n if asub is None:\\n asub_ = None\\n else:\\n try:\\n asub_ = memoryview(asub)\\n except TypeError:\\n try:\\n _tmparr_asub = array.array(\\\"i\\\",asub)\\n except TypeError:\\n raise TypeError(\\\"Argument asub has wrong type\\\")\\n else:\\n asub_ = memoryview(_tmparr_asub)\\n \\n else:\\n if asub_.format != \\\"i\\\":\\n asub_ = memoryview(array.array(\\\"i\\\",asub))\\n \\n if aval is None: raise TypeError(\\\"Invalid type for argument aval\\\")\\n if aval is None:\\n aval_ = None\\n else:\\n try:\\n aval_ = memoryview(aval)\\n except TypeError:\\n try:\\n _tmparr_aval = array.array(\\\"d\\\",aval)\\n except TypeError:\\n raise TypeError(\\\"Argument aval has wrong type\\\")\\n else:\\n aval_ = memoryview(_tmparr_aval)\\n \\n else:\\n if aval_.format != \\\"d\\\":\\n aval_ = memoryview(array.array(\\\"d\\\",aval))\\n \\n res = self.__obj.putacolslice64(first_,last_,ptrb_,ptre_,asub_,aval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundlist(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkc_)\\n elif num_ != len(bkc_):\\n raise IndexError(\\\"Inconsistent length of array bkc\\\")\\n if num_ is None:\\n num_ = len(blc_)\\n elif num_ != len(blc_):\\n raise IndexError(\\\"Inconsistent length of array blc\\\")\\n if num_ is None:\\n num_ = len(buc_)\\n elif num_ != len(buc_):\\n raise IndexError(\\\"Inconsistent length of array buc\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc cannot be None\\\")\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc may not be None\\\")\\n if bkc_ is not None:\\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\\n else:\\n _bkc_tmp = None\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc cannot be None\\\")\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc may not be None\\\")\\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\\n _blc_copyarray = False\\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blc_ is not None:\\n _blc_copyarray = True\\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\\n _blc_np_tmp[:] = blc_\\n assert _blc_np_tmp.flags.contiguous\\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blc_copyarray = False\\n _blc_tmp = None\\n \\n if buc_ is None:\\n raise ValueError(\\\"Argument buc cannot be None\\\")\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc may not be None\\\")\\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\\n _buc_copyarray = False\\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif buc_ is not None:\\n _buc_copyarray = True\\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\\n _buc_np_tmp[:] = buc_\\n assert _buc_np_tmp.flags.contiguous\\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _buc_copyarray = False\\n _buc_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def variable_bounds(problem):\\n return ([\\n problem['state_bounds'][var] if problem['state_bounds'][var] is not None else (-np.inf, np.inf)\\n for _ in range(problem['N'] - 1)\\n for var in range(problem['num_states'])\\n ] + [\\n problem['input_bounds'][inp] if problem['input_bounds'][inp] is not None else (-np.inf, np.inf)\\n for _ in range(problem['N'] + 1)\\n for inp in range(problem['num_inputs'])\\n ]) * problem['Nv'] + ([(0.01, np.inf)] if problem['T'] == 0 else []) \\\\\\n if problem['state_bounds'] is not None else None\",\n \"def set_slices(self, start=0.0, end=1.0, step=None, num=None):\\n\\n if step is None:\\n s = (end - start) / float(num)\\n self._slices = frange(start, end, s)\\n elif num is None:\\n self._slices = frange(start, end, step)\\n else:\\n raise RuntimeError()\\n\\n LOG.info('Num slices: %d', len(self._slices))\\n LOG.info('Slices: %s', self._slices)\",\n \"def putyslice(self,whichsol_,first_,last_,y): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if y is None: raise TypeError(\\\"Invalid type for argument y\\\")\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n res = self.__obj.putyslice(whichsol_,first_,last_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskcslice(self,whichsol_,first_,last_,skc_):\\n _skc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and skc_ is not None and len(skc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),((last_) - (first_))))\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_putskcslice(self.__nativep,whichsol_,first_,last_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def RestrictionRangeBound(self, compsIdList, lowerBound, upperBound):\\n for i in range(len(compsIdList)): compsIdList[i] -= 1\\n if self.solverTypeOptimize:\\n self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) >= lowerBound)\\n else:\\n self.solver.assert_and_track(\\n PbGe(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\\n lowerBound), \\\"LabelRangeBound: \\\" + str(self.labelIdx))\\n self.labelIdx += 1\\n if self.solverTypeOptimize:\\n PbLe(self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\\n upperBound))\\n else:\\n self.solver.assert_and_track(\\n sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) <= upperBound, \\\"LabelRangeBound: \\\" + str(self.labelIdx))\\n self.labelIdx += 1\",\n \"def compute_bounds(self, weights, add_coeff, dual_vars, lower_bounds, upper_bounds, store_primal=False):\\n x0_coeff = -weights[0].backward(dual_vars.mus[0])\\n x0 = torch.where(x0_coeff >= 0, lower_bounds[0].unsqueeze(1), upper_bounds[0].unsqueeze(1))\\n bound = utils.bdot(x0, x0_coeff)\\n if store_primal:\\n self.bounds_primal = x0\\n else:\\n del x0\\n del x0_coeff\\n\\n for lay_idx in range(1, len(weights)):\\n lbs = lower_bounds[lay_idx].unsqueeze(1).clamp(None, 0)\\n ubs = upper_bounds[lay_idx].unsqueeze(1).clamp(0, None)\\n neg_bias = ((lbs * ubs) / (ubs - lbs))\\n neg_bias.masked_fill_(ubs == lbs, 0) # cover case in which ubs & lbs coincide\\n bound += utils.bdot(dual_vars.lambdas[lay_idx - 1].clamp(0, None), neg_bias)\\n bound -= utils.bdot(dual_vars.mus[lay_idx - 1], weights[lay_idx - 1].get_bias())\\n\\n bound += utils.bdot(add_coeff, weights[-1].get_bias())\\n return bound\",\n \"def putyslice(self,whichsol_,first_,last_,y_):\\n _y_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),((last_) - (first_))))\\n if y_ is None:\\n raise ValueError(\\\"Argument y cannot be None\\\")\\n if y_ is None:\\n raise ValueError(\\\"Argument y may not be None\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n res = __library__.MSK_XX_putyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __get_new_restriction_branch_and_bound(self, tableau, var_col, b_value):\\n restriction_row = np.zeros((tableau.get_tableau_num_cols())).astype('object')\\n restriction_row[var_col] = 1\\n restriction_row[tableau.get_tableau_num_cols() - 1] = b_value\\n\\n for i in xrange(0, len(restriction_row)):\\n restriction_row[i] = Fraction(restriction_row[i])\\n\\n return restriction_row\",\n \"def _set_bounds(b, x, n):\\n for j in range(1, n - 1):\\n for i in range(1, n - 1):\\n x[index_of(i, j, 0, n)] = -x[index_of(i, j, 1, n)] if b == 3 else x[index_of(i, j, 1, n)]\\n x[index_of(i, j, 0, n - 1)] = -x[index_of(i, j, 1, n - 2)] if b == 3 else x[index_of(i, j, 1, n - 2)]\\n for k in range(1, n - 1):\\n for i in range(1, n - 1):\\n x[index_of(i, 0, k, n)] = -x[index_of(i, 1, k, n)] if b == 2 else x[index_of(i, 1, k, n)]\\n x[index_of(i, n - 1, 0, n - 1)] = -x[index_of(i, n - 2, k, n - 2)] if b == 2 else x[\\n index_of(i, n - 2, k, n - 2)]\\n for k in range(1, n - 1):\\n for j in range(1, n - 1):\\n x[index_of(0, j, k, n)] = -x[index_of(1, j, k, n)] if b == 1 else x[index_of(1, j, k, n)]\\n x[index_of(n - 1, j, k, n - 1)] = -x[index_of(n - 2, j, k, n)] if b == 1 else x[\\n index_of(n - 2, j, k, n)]\\n\\n x[index_of(0, 0, 0, n)] = 1 / 3 * (x[index_of(1, 0, 0, n)] + x[index_of(0, 1, 0, n)] + x[index_of(0, 0, 1, n)])\\n x[index_of(0, n - 1, 0, n)] = 1 / 3 * (\\n x[index_of(1, n - 1, 0, n)] + x[index_of(0, n - 2, 0, n)] + x[index_of(0, n - 1, 1, n)])\\n x[index_of(0, 0, n - 1, n)] = 1 / 3 * (\\n x[index_of(1, 0, n - 1, n)] + x[index_of(0, 1, n - 1, n)] + x[index_of(0, 0, n - 2, n)])\\n x[index_of(0, n - 1, n - 1, n)] = 1 / 3 * (\\n x[index_of(1, n - 1, n - 1, n)] + x[index_of(0, n - 2, n - 1, n)] + x[index_of(0, n - 1, n - 2, n)])\\n x[index_of(n - 1, 0, 0, n)] = 1 / 3 * (\\n x[index_of(n - 2, 0, 0, n)] + x[index_of(n - 1, 1, 0, n)] + x[index_of(n - 1, 0, 1, n)])\\n x[index_of(n - 1, n - 1, 0, n)] = 1 / 3 * (\\n x[index_of(n - 2, n - 1, 0, n)] + x[index_of(n - 1, n - 2, 0, n)] + x[index_of(n - 1, n - 1, 1, n)])\\n x[index_of(n - 1, 0, n - 1, n)] = 1 / 3 * (\\n x[index_of(n - 2, 0, n - 1, n)] + x[index_of(n - 1, 1, n - 1, n)] + x[index_of(n - 1, 0, n - 2, n)])\\n x[index_of(n - 1, n - 1, n - 1, n)] = 1 / 3 * (\\n x[index_of(n - 2, n - 1, n - 1, n)] + x[index_of(n - 1, n - 2, n - 1, n)] + x[\\n index_of(n - 1, n - 1, n - 2, n)])\",\n \"def removeBounded(self, bounds):\\n if bounds==None or len(bounds)!=4:\\n return\\n x1,y1,x2,y2 = bounds\\n if x1>x2 :\\n temp=x1;x1=x2;x2=temp\\n if y1>y2:\\n temp=y1;y1=y2;y2=temp\\n lst=[]\\n for i in range(0,self.length()):\\n x=self.x[i]; y=self.y[i]\\n if (x>x1 and x<x2) and (y>y1 and y<y2): \\n lst.append(i)\\n self.removeMultiple(lst)\\n return\",\n \"def test_slice_setslice_forbidden(self):\\n global setVal\\n class foo:\\n def __setslice__(self, i, j, value):\\n global setVal\\n setVal = i, j, value\\n def __setitem__(self, index, value):\\n global setVal\\n setVal = index, value\\n\\n foo()[::] = 23\\n self.assertEqual(setVal, (slice(None, None, None), 23))\\n foo()[::None] = 23\\n self.assertEqual(setVal, (slice(None, None, None), 23))\",\n \"def set_bounds(self, **kwargs):\\n for name, bounds in kwargs.items():\\n if name not in self._parameters:\\n raise AttributeError('Unknown parameter %s for %s' % (name, self.__class__.__name__))\\n param = self._parameters[name]\\n # Set bounds\\n lower_bound, upper_bound = bounds\\n if torch.is_tensor(lower_bound) and torch.is_tensor(upper_bound):\\n if lower_bound.size() != upper_bound.size() or \\\\\\n lower_bound.size() != param.size():\\n raise AttributeError('Lower bound, upper bound, and param should have the same size')\\n elif not (isinstance(lower_bound, int) or isinstance(lower_bound, float)) or \\\\\\n not (isinstance(upper_bound, int) or isinstance(upper_bound, float)):\\n raise AttributeError('Unsupported argument types for parameter %s' % name)\\n\\n if name not in self._bounds:\\n self._bounds[name] = [None, None]\\n self._bounds[name][0] = lower_bound\\n self._bounds[name][1] = upper_bound\\n return self\",\n \"def getacolslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\\n _ptrb_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptrb is not long enough: Is %d, expected %d\\\" % (len(ptrb_),((last_) - (first_))))\\n if isinstance(ptrb_,numpy.ndarray) and not ptrb_.flags.writeable:\\n raise ValueError(\\\"Argument ptrb must be writable\\\")\\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb may not be None\\\")\\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\\n _ptrb_copyarray = False\\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptrb_ is not None:\\n _ptrb_copyarray = True\\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\\n _ptrb_np_tmp[:] = ptrb_\\n assert _ptrb_np_tmp.flags.contiguous\\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptrb_copyarray = False\\n _ptrb_tmp = None\\n \\n _ptre_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and ptre_ is not None and len(ptre_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptre is not long enough: Is %d, expected %d\\\" % (len(ptre_),((last_) - (first_))))\\n if isinstance(ptre_,numpy.ndarray) and not ptre_.flags.writeable:\\n raise ValueError(\\\"Argument ptre must be writable\\\")\\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre may not be None\\\")\\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\\n _ptre_copyarray = False\\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptre_ is not None:\\n _ptre_copyarray = True\\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\\n _ptre_np_tmp[:] = ptre_\\n assert _ptre_np_tmp.flags.contiguous\\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptre_copyarray = False\\n _ptre_tmp = None\\n \\n _sub_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and sub_ is not None and len(sub_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument sub is not long enough: Is %d, expected %d\\\" % (len(sub_),(maxnumnz_)))\\n if isinstance(sub_,numpy.ndarray) and not sub_.flags.writeable:\\n raise ValueError(\\\"Argument sub must be writable\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _val_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and val_ is not None and len(val_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument val is not long enough: Is %d, expected %d\\\" % (len(val_),(maxnumnz_)))\\n if isinstance(val_,numpy.ndarray) and not val_.flags.writeable:\\n raise ValueError(\\\"Argument val must be writable\\\")\\n if val_ is None:\\n raise ValueError(\\\"Argument val may not be None\\\")\\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\\n _val_copyarray = False\\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif val_ is not None:\\n _val_copyarray = True\\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\\n _val_np_tmp[:] = val_\\n assert _val_np_tmp.flags.contiguous\\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _val_copyarray = False\\n _val_tmp = None\\n \\n surp_ = ctypes.c_int64(_sub_minlength)\\n res = __library__.MSK_XX_getacolslice64(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_ptrb_tmp,_ptre_tmp,_sub_tmp,_val_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _ptrb_copyarray:\\n ptrb_[:] = _ptrb_np_tmp\\n if _ptre_copyarray:\\n ptre_[:] = _ptre_np_tmp\\n if _sub_copyarray:\\n sub_[:] = _sub_np_tmp\\n if _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def getvarbound(self,i_):\\n bk_ = ctypes.c_int32()\\n bl_ = ctypes.c_double()\\n bu_ = ctypes.c_double()\\n res = __library__.MSK_XX_getvarbound(self.__nativep,i_,ctypes.byref(bk_),ctypes.byref(bl_),ctypes.byref(bu_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _bk_return_value = boundkey(bk_.value)\\n bl_ = bl_.value\\n _bl_return_value = bl_\\n bu_ = bu_.value\\n _bu_return_value = bu_\\n return (_bk_return_value,_bl_return_value,_bu_return_value)\",\n \"def putslxslice(self,whichsol_,first_,last_,slx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slx is None: raise TypeError(\\\"Invalid type for argument slx\\\")\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if slx_ is not None and len(slx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n res = self.__obj.putslxslice(whichsol_,first_,last_,slx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _fix_slice(self, inputs, new_attr):\\n begin = new_attr.get('begin')\\n end = new_attr.get('end')\\n axes = new_attr.get('axis', tuple(range(len(begin))))\\n slice_op = mx.sym.slice_axis(inputs[0], axis=axes[0], begin=begin[0], end=end[0])\\n if len(axes) > 1:\\n for i, axis in enumerate(axes):\\n slice_op = mx.sym.slice_axis(slice_op, axis=axis, begin=begin[i], end=end[i])\\n return slice_op\",\n \"def update_i_bnds(self):\\n\\n # Get old and new boundaries.\\n i_bnds_old = self.i_bounds\\n i_bnds_new = self._get_i_bnds()\\n\\n for i_order in range(self.n_orders):\\n\\n # Take most restrictive lower bound.\\n low_bnds = [i_bnds_new[i_order][0], i_bnds_old[i_order][0]]\\n i_bnds_new[i_order][0] = np.max(low_bnds)\\n\\n # Take most restrictive upper bound.\\n up_bnds = [i_bnds_new[i_order][1], i_bnds_old[i_order][1]]\\n i_bnds_new[i_order][1] = np.min(up_bnds)\\n\\n # Update attribute.\\n self.i_bounds = i_bnds_new\\n\\n return\",\n \"def getvarbound(self,i_): # 3\\n res,resargs = self.__obj.getvarbound(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _bk_return_value,_bl_return_value,_bu_return_value = resargs\\n _bk_return_value = boundkey(_bk_return_value)\\n return _bk_return_value,_bl_return_value,_bu_return_value\",\n \"def appendbarvars(self,dim_):\\n num_ = None\\n if num_ is None:\\n num_ = len(dim_)\\n elif num_ != len(dim_):\\n raise IndexError(\\\"Inconsistent length of array dim\\\")\\n if dim_ is None:\\n raise ValueError(\\\"Argument dim cannot be None\\\")\\n if dim_ is None:\\n raise ValueError(\\\"Argument dim may not be None\\\")\\n if isinstance(dim_, numpy.ndarray) and dim_.dtype is numpy.dtype(numpy.int32) and dim_.flags.contiguous:\\n _dim_copyarray = False\\n _dim_tmp = ctypes.cast(dim_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif dim_ is not None:\\n _dim_copyarray = True\\n _dim_np_tmp = numpy.zeros(len(dim_),numpy.dtype(numpy.int32))\\n _dim_np_tmp[:] = dim_\\n assert _dim_np_tmp.flags.contiguous\\n _dim_tmp = ctypes.cast(_dim_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _dim_copyarray = False\\n _dim_tmp = None\\n \\n res = __library__.MSK_XX_appendbarvars(self.__nativep,num_,_dim_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def only_bounds(must_print):\\n\\n #Extracting input.\\n input = find_input()\\n\\n #Running the experiment.\\n result = bound.execute_script(input, must_print, True)[2:]\\n #result = bound2.execute_script(input, must_print, True)[2:]\\n\\n #Storing output.\\n store_output(result) #result = [P_BOUND, R_BOUND]\",\n \"def glow_boundary(bound):\\n assert bound < 4\\n global layout\\n temp = len(layout) - 1\\n for i in range(bound, bound + len_square(bound)):\\n for j in range(bound, bound + len_square(bound)): # TODO: assign this to a variable\\t\\n layout[i][j] = 1\",\n \"def putslcslice(self,whichsol_,first_,last_,slc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slc is None: raise TypeError(\\\"Invalid type for argument slc\\\")\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n \\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n \\n if slc_ is not None and len(slc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n res = self.__obj.putslcslice(whichsol_,first_,last_,slc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def get_bounds_parameters(self):\\n bounds = []\\n bounds += self.var_noise.bounds\\n bounds += self.mean.bounds\\n bounds += self.kernel.get_bounds_parameters()\\n\\n return bounds\",\n \"def removevars(self,subset): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subset)\\n elif num_ != len(subset):\\n raise IndexError(\\\"Inconsistent length of array subset\\\")\\n if num_ is None: num_ = 0\\n if subset is None: raise TypeError(\\\"Invalid type for argument subset\\\")\\n if subset is None:\\n subset_ = None\\n else:\\n try:\\n subset_ = memoryview(subset)\\n except TypeError:\\n try:\\n _tmparr_subset = array.array(\\\"i\\\",subset)\\n except TypeError:\\n raise TypeError(\\\"Argument subset has wrong type\\\")\\n else:\\n subset_ = memoryview(_tmparr_subset)\\n \\n else:\\n if subset_.format != \\\"i\\\":\\n subset_ = memoryview(array.array(\\\"i\\\",subset))\\n \\n res = self.__obj.removevars(num_,subset_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundlist(self,sub,bk,bl,bu): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bk)\\n elif num_ != len(bk):\\n raise IndexError(\\\"Inconsistent length of array bk\\\")\\n if num_ is None:\\n num_ = len(bl)\\n elif num_ != len(bl):\\n raise IndexError(\\\"Inconsistent length of array bl\\\")\\n if num_ is None:\\n num_ = len(bu)\\n elif num_ != len(bu):\\n raise IndexError(\\\"Inconsistent length of array bu\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n res = self.__obj.putconboundlist(num_,sub_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def bounds(self, axis, view=None):\\n if view is None:\\n view = self\\n if axis not in self._vshare.bounds:\\n self._vshare.bounds[axis] = self._compute_bounds(axis, view)\\n return self._vshare.bounds[axis]\",\n \"def slice_parameters(self, slice_parameters: SliceParamsIm):\\n\\n self._slice_parameters = slice_parameters\",\n \"def create_from_bounds(self, lbs, ubs):\\n self.base_vertices = (np.array([lbs])+np.array([ubs])).T/2\\n self.base_vectors = np.diag((np.array(ubs)-np.array(lbs))/2)\",\n \"def getacolslicetrip(self,first_,last_,subi,subj,val): # 3\\n maxnumnz_ = self.getaslicenumnz(accmode.var,(first_),(last_))\\n _copyback_subi = False\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n _copyback_subi = True\\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n _copyback_subi = True\\n if subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n _copyback_val = False\\n if val is None:\\n val_ = None\\n else:\\n try:\\n val_ = memoryview(val)\\n except TypeError:\\n try:\\n _tmparr_val = array.array(\\\"d\\\",val)\\n except TypeError:\\n raise TypeError(\\\"Argument val has wrong type\\\")\\n else:\\n val_ = memoryview(_tmparr_val)\\n _copyback_val = True\\n else:\\n if val_.format != \\\"d\\\":\\n val_ = memoryview(array.array(\\\"d\\\",val))\\n _copyback_val = True\\n if val_ is not None and len(val_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument val has wrong length\\\")\\n res = self.__obj.getacolslicetrip(first_,last_,maxnumnz_,len(subi),subi_,subj_,val_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_val:\\n val[:] = _tmparr_val\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n if _copyback_subi:\\n subi[:] = _tmparr_subi\",\n \"def into_bounds(self, y, bounds=None, copy_never=False, copy_always=False):\\r\\n bounds = bounds if bounds is not None else self.bounds\\r\\n if bounds in (None, [None, None]):\\r\\n return y if not copy_always else array(y, copy=True)\\r\\n if bounds[0] is not None:\\r\\n if len(bounds[0]) not in (1, len(y)):\\r\\n raise ValueError('len(bounds[0]) = ' + str(len(bounds[0])) +\\r\\n ' and len of initial solution (' + str(len(y)) + ') disagree')\\r\\n if copy_never: # is rather slower\\r\\n for i in xrange(len(y)):\\r\\n y[i] = max(bounds[0][i], y[i])\\r\\n else:\\r\\n y = np.max([bounds[0], y], axis=0)\\r\\n if bounds[1] is not None:\\r\\n if len(bounds[1]) not in (1, len(y)):\\r\\n raise ValueError('len(bounds[1]) = ' + str(len(bounds[1])) +\\r\\n ' and initial solution (' + str(len(y)) + ') disagree')\\r\\n if copy_never:\\r\\n for i in xrange(len(y)):\\r\\n y[i] = min(bounds[1][i], y[i])\\r\\n else:\\r\\n y = np.min([bounds[1], y], axis=0)\\r\\n return y\",\n \"def clip_by_bound(self) -> None:\\n\\n pass\",\n \"def getsnxslice(self,whichsol_,first_,last_,snx_):\\n _snx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and snx_ is not None and len(snx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument snx is not long enough: Is %d, expected %d\\\" % (len(snx_),((last_) - (first_))))\\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\\n raise ValueError(\\\"Argument snx must be writable\\\")\\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_getsnxslice(self.__nativep,whichsol_,first_,last_,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _snx_copyarray:\\n snx_[:] = _snx_np_tmp\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78326833","0.7734105","0.7384246","0.73374903","0.7264578","0.676517","0.6706333","0.65771973","0.6273341","0.6261887","0.62256247","0.6213124","0.61776054","0.6171745","0.6162715","0.61598974","0.61103725","0.6071546","0.601926","0.6003408","0.58926064","0.5874434","0.5857309","0.582052","0.5790033","0.57805425","0.57727635","0.5750098","0.5706496","0.5672962","0.5631143","0.54858583","0.54576933","0.54352766","0.54302645","0.5426259","0.54165095","0.54136944","0.53971756","0.5390014","0.5378196","0.5322155","0.5307615","0.53074425","0.53027976","0.53023666","0.5293272","0.529069","0.5262014","0.52491003","0.52468014","0.5202993","0.52021885","0.5199621","0.51743364","0.51664394","0.5150409","0.5123688","0.51233387","0.51188284","0.51188284","0.5109922","0.50866616","0.5076692","0.5063672","0.5059641","0.50409573","0.5019271","0.4998933","0.49863642","0.4984684","0.49797356","0.49791965","0.49768525","0.49725536","0.49648982","0.49597657","0.4958631","0.49518517","0.4948172","0.49376953","0.4937505","0.49310213","0.49228483","0.4917201","0.49103472","0.49050447","0.49025","0.48915538","0.48889753","0.4855632","0.4852399","0.4836302","0.48346302","0.4826363","0.48227015","0.48205718","0.4814966","0.4806546","0.48063394"],"string":"[\n \"0.78326833\",\n \"0.7734105\",\n \"0.7384246\",\n \"0.73374903\",\n \"0.7264578\",\n \"0.676517\",\n \"0.6706333\",\n \"0.65771973\",\n \"0.6273341\",\n \"0.6261887\",\n \"0.62256247\",\n \"0.6213124\",\n \"0.61776054\",\n \"0.6171745\",\n \"0.6162715\",\n \"0.61598974\",\n \"0.61103725\",\n \"0.6071546\",\n \"0.601926\",\n \"0.6003408\",\n \"0.58926064\",\n \"0.5874434\",\n \"0.5857309\",\n \"0.582052\",\n \"0.5790033\",\n \"0.57805425\",\n \"0.57727635\",\n \"0.5750098\",\n \"0.5706496\",\n \"0.5672962\",\n \"0.5631143\",\n \"0.54858583\",\n \"0.54576933\",\n \"0.54352766\",\n \"0.54302645\",\n \"0.5426259\",\n \"0.54165095\",\n \"0.54136944\",\n \"0.53971756\",\n \"0.5390014\",\n \"0.5378196\",\n \"0.5322155\",\n \"0.5307615\",\n \"0.53074425\",\n \"0.53027976\",\n \"0.53023666\",\n \"0.5293272\",\n \"0.529069\",\n \"0.5262014\",\n \"0.52491003\",\n \"0.52468014\",\n \"0.5202993\",\n \"0.52021885\",\n \"0.5199621\",\n \"0.51743364\",\n \"0.51664394\",\n \"0.5150409\",\n \"0.5123688\",\n \"0.51233387\",\n \"0.51188284\",\n \"0.51188284\",\n \"0.5109922\",\n \"0.50866616\",\n \"0.5076692\",\n \"0.5063672\",\n \"0.5059641\",\n \"0.50409573\",\n \"0.5019271\",\n \"0.4998933\",\n \"0.49863642\",\n \"0.4984684\",\n \"0.49797356\",\n \"0.49791965\",\n \"0.49768525\",\n \"0.49725536\",\n \"0.49648982\",\n \"0.49597657\",\n \"0.4958631\",\n \"0.49518517\",\n \"0.4948172\",\n \"0.49376953\",\n \"0.4937505\",\n \"0.49310213\",\n \"0.49228483\",\n \"0.4917201\",\n \"0.49103472\",\n \"0.49050447\",\n \"0.49025\",\n \"0.48915538\",\n \"0.48889753\",\n \"0.4855632\",\n \"0.4852399\",\n \"0.4836302\",\n \"0.48346302\",\n \"0.4826363\",\n \"0.48227015\",\n \"0.48205718\",\n \"0.4814966\",\n \"0.4806546\",\n \"0.48063394\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.89807767"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11962,"cells":{"query":{"kind":"string","value":"Replaces the fixed term in the objective. putcfix(self,cfix_)"},"document":{"kind":"string","value":"def putcfix(self,cfix_):\n res = __library__.MSK_XX_putcfix(self.__nativep,cfix_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putcfix(self,cfix_): # 3\n res = self.__obj.putcfix(cfix_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def fixC(self,i,value):\n if self.coeffPattern[2] == None:\n m,n=self.m,self.n\n self.coeffPattern[2] = [None]*m\n self.coeffPattern[2][i]=value\n self._updateEstimatorSize(i)","def f_fixed(self):\n self.fx_free = self.fy_free = self.fz_free = False\n return self","def x(self, new_x):\n if new_x is None:\n logging.warning(\"Cochain features were set to None. \")\n else:\n assert self.num_cells == len(new_x)\n self.__x = new_x","def removeFixedEffect(self, index=None):\n if self._n_terms==0:\n pass\n if index is None or index==(self._n_terms-1):\n\n self._n_terms-=1\n F = self._F.pop() #= self.F[:-1]\n A = self._A.pop() #= self.A[:-1]\n self._A_identity.pop() #= self.A_identity[:-1]\n REML_term = self._REML_term.pop()# = self.REML_term[:-1]\n self._B.pop()# = self.B[:-1]\n self._n_fixed_effs-=F.shape[1]*A.shape[0]\n if REML_term:\n self._n_fixed_effs_REML-=F.shape[1]*A.shape[0]\n\n pass\n elif index >= self.n_terms:\n raise Exception(\"index exceeds max index of terms\")\n else:\n raise NotImplementedError(\"currently only last term can be removed\")\n pass\n self._rebuild_indicator()\n self.clear_cache('Fstar','Astar','Xstar','Xhat',\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\n 'LRLdiag_Xhat_tens','Areml_grad',\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')","def m_fixed(self):\n self.mx_free = self.my_free = self.mz_free = False\n return self","def cpf(self, cpf):\n self._cpf = cpf","def __init__(self, f, index_value_pairs):\n # super(FixVariables, self).__init__(\n ComposedFunction.__init__(self, [f, self.insert_variables])\n self.index_value_pairs = dict(index_value_pairs)","def addFixedEffect(self,F=None,A=None, REML=True, index=None):\n if F is None: F = np.ones((self.N,1))\n if A is None:\n A = np.eye(self.P)\n A_identity = True\n elif (A.shape == (self.P,self.P)) & (A==np.eye(self.P)).all():\n A_identity = True\n else:\n A_identity = False\n\n assert F.shape[0]==self.N, \"F dimension mismatch\"\n assert A.shape[1]==self.P, \"A dimension mismatch\"\n if index is None or index==self.n_terms:\n self.F.append(F)\n self.A.append(A)\n self.A_identity.append(A_identity)\n self.REML_term.append(REML)\n # build B matrix and indicator\n self.B.append(np.zeros((F.shape[1],A.shape[0])))\n self._n_terms+=1\n self._update_indicator(F.shape[1],A.shape[0])\n elif index >self.n_terms:\n raise Exception(\"index exceeds max index of terms\")\n else:\n self._n_fixed_effs-=self.F[index].shape[1]*self.A[index].shape[0]\n if self.REML_term[index]:\n self._n_fixed_effs_REML-=self.F[index].shape[1]*self.A[index].shape[0]\n self.F[index] = F\n self.A[index] = A\n self.A_identity[index] = A_identity\n self.REML_term[index]=REML\n self.B[index] = np.zeros((F.shape[1],A.shape[0]))\n self._rebuild_indicator()\n\n self._n_fixed_effs+=F.shape[1]*A.shape[0]\n if REML:\n self._n_fixed_effs_REML+=F.shape[1]*A.shape[0]\n self.clear_cache('Fstar','Astar','Xstar','Xhat',\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\n 'LRLdiag_Xhat_tens','Areml_grad',\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')","def fixB(self,i,j,value):\n if self.coeffPattern[1] == None:\n m,n=self.m,self.n\n self.coeffPattern = [[None]*n for i in range(m)]\n self.coeffPattern[1][i][j]=value\n self._updateEstimatorSize(i)","def _set_fixed(o, d):\n if d:\n o.fix()\n else:\n o.unfix()","def problem_reduction_single(self, i, val):\n y_update = - val * self.A.getcol(i).toarray().flatten()\n self.y += y_update\n self.A = sparse.hstack([self.A[:, :i], self.A[:, i + 1:]], format='csr')\n z_index = self.mask.searchsorted(i)\n self.mask = np.insert(self.mask, z_index, i)\n self.z = np.insert(self.z, z_index, val)","def fixA(self,i,j,value):\n if self.coeffPattern[0] == None:\n m,n=self.m,self.n\n self.coeffPattern[0] = [[None]*m for i in range(m)]\n self.coeffPattern[0][i][j]=value\n self._updateEstimatorSize(i)","def putxc(self,whichsol_,xc_):\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp","def clearFixedEffect(self):\n self._A = []\n self._F = []\n self._B = []\n self._A_identity = []\n self._REML_term = []\n self._n_terms = 0\n self._n_fixed_effs = 0\n self._n_fixed_effs_REML = 0\n self.indicator = {'term':np.array([]),\n 'row':np.array([]),\n 'col':np.array([])}\n self.clear_cache('Fstar','Astar','Xstar','Xhat',\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\n 'LRLdiag_Xhat_tens','Areml_grad',\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')","def getcfix(self):\n cfix_ = ctypes.c_double()\n res = __library__.MSK_XX_getcfix(self.__nativep,ctypes.byref(cfix_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n cfix_ = cfix_.value\n _cfix_return_value = cfix_\n return (_cfix_return_value)","def tctfdfc(x):\n if isinstance(x,Fdf) :\n pass\n else : \n x = Fdf.constant(x)\n return x","def apply_qc(self, qf=1):\n logger = logging.getLogger(\"timeseries_logger\")\n logger.info('Applying mask to the data')\n self.temperature = np.ma.masked_where(self.temperatureQF != qf, self.temperature)\n self.salinity = np.ma.masked_where(self.salinityQF !=qf, self.salinity)","def MakeFixed(self,content):\n return self.register(Fixed(content,reg=self))","def affect_cnf(dimacs_cnf, literal_index, literal_value):\n remove_indexes = []\n for clause_index in range(dimacs_cnf.shape[0]):\n if dimacs_cnf[clause_index][literal_index] * literal_value == 1:\n dimacs_cnf[clause_index] = 1\n remove_indexes.append(clause_index)\n elif dimacs_cnf[clause_index][literal_index] * literal_value == -1:\n dimacs_cnf[clause_index][literal_index] = 0\n return np.delete(dimacs_cnf, remove_indexes, axis=0)","def fixed_cost(self):\n return np.einsum('i->', self.c[self.f])","def Coaxial(movableAxis: str, fixedAxis: str, flip: Boolean) -> \"Feature\":\n return Feature()","def set_ctf(ima, p):\n\tfrom utilities import generate_ctf\n\tctf = generate_ctf( p )\n\tima.set_attr( \"ctf\", ctf )","def putxc(self,whichsol_,xc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xc is None: raise TypeError(\"Invalid type for argument xc\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n res = self.__obj.putxc(whichsol_,xc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_xc:\n xc[:] = _tmparr_xc","def __init__(self, word_fd, quadgram_fd, ii, iii, ixi, ixxi, iixi, ixii):\n AbstractCollocationFinder.__init__(self, word_fd, quadgram_fd)\n self.iii = iii\n self.ii = ii\n self.ixi = ixi\n self.ixxi = ixxi\n self.iixi = iixi\n self.ixii = ixii","def subst_i(term):\n return Term(term.sums, term.amp.xreplace({i: k}), term.vecs)","def set_idx(self, i, other, tensor_value):\n for k, v in self.variables.items():\n if k not in other.variables:\n self.variables[k][i] *= 0\n\n for k, v in other.variables.items():\n if k not in self.variables:\n self.variables[k] = np.zeros(tensor_value.shape)\n self.variables[k][i] = other.variables[k]","def fusion(self, x):\n new_x = x.copy()\n for i in range(self.z.size):\n new_x[self.mask[i]] = self.z[i]\n return new_x","def modify(self, feature, newValues):\n self.data[:,self._getFIdx(feature)] = newValues\n return 0","def setCoefficient(self, *args):\n return _libsbml.FluxObjective_setCoefficient(self, *args)","def __setitem__(self, i, v):\n raise TypeError(\"'Factorization' object does not support item assignment\")","def replace(self, index, value):\n index += self.n\n self.data[index] = value\n index //= 2\n while index > 0:\n self.data[index] = self.func(self.data[2*index], self.data[2*index+1])\n index //= 2","def fixed_point(is_zero, plus, minus, f, x):\n\n @memo_Y\n def _fixed_point(fixed_point_fun):\n def __fixed_point(collected, new):\n diff = minus(new, collected)\n if is_zero(diff):\n return collected\n return fixed_point_fun(plus(collected, diff), f(diff))\n return __fixed_point\n\n return _fixed_point(x, f(x))","def fixed_amount(self, fixed_amount):\n\n self._fixed_amount = fixed_amount","def fixed_amount(self, fixed_amount):\n\n self._fixed_amount = fixed_amount","def phenix_real_space_refinement(self, mtz_in, pdb_in, column_labels): \n \n mtz_name = get_name(mtz_in)\n \n #Spacify phenix version dependent parameters\n try:\n phenix_version = int(re.search(r\"phenix-1\\.(.+?)\\.\", miller.__file__).group(1)) #This is not so robust. relies on the format being 'phenix.1.18.something' or 'phenix.1.18-something'\n except ValueError:\n phenix_version = int(re.search(r\"phenix-1\\.(.+?)\\-\", miller.__file__).group(1))\n except AttributeError:\n print('Update phenix! Verify that you are using at least Phenix.1.17.')\n phenix_version = 19 #let's assume then that the latest phenix is installed in case this fails for other reasons than a very old phenix version\n \n print(\"Phenix version 1.%d\" %(phenix_version))\n \n if phenix_version >= 19:\n output_prefix = 'output.prefix=%s_independent'%(mtz_name)\n model_format = 'model_format=pdb'\n # outpdb = \"%s_independent_real_space_refined_000.pdb\"%(mtz_name)\n else:\n output_prefix = 'output.file_name_prefix=%s_independent'%(mtz_name)\n model_format = 'output.model_format=pdb'\n # outpdb = \"%s_independent_real_space_refined.pdb\"%(mtz_name)\n\n # print(\"phenix.real_space_refine %s %s %s %s %s %s label='%s'\" % (self.real_phil,\n # mtz_in, self.additional, pdb_in, output_prefix, model_format, column_labels))\n real = os.system(\"phenix.real_space_refine %s %s %s %s %s %s label='%s' scattering_table=n_gaussian\" %(\n self.real_phil, mtz_in, self.additional, pdb_in, output_prefix, model_format, column_labels))\n\n #Find output file\n if real == 0 : #os.system has correctly finished. Then search for the last refined structure\n if phenix_version >= 19:\n try:\n pdb_fles = glob.glob(\"%s_independent_real_space_refined_???.pdb\"%(mtz_name))\n # [fle for fle in os.listdir(os.getcwd()) if \"%s_independent_real_space_refined_0\"%(mtz_name) in\n # fle and fle.endswith('pdb')]\n pdb_fles.sort()\n outpdb = pdb_fles[-1]\n except IndexError:\n outpdb = \"%s_independent_real_space_refined_000.pdb\" % (mtz_name)\n else:\n try:\n pdb_fles = glob.glob(\"%s_independent_real_space_refined.pdb\"%(mtz_name))\n # [fle for fle in os.listdir(os.getcwd()) if\n # \"%s_independent_real_space_refined\" % (mtz_name) in fle and fle.endswith('pdb')]\n pdb_fles.sort()\n outpdb = pdb_fles[-1]\n except IndexError:\n outpdb = \"%s_independent_real_space_refined.pdb\" % (mtz_name)\n else: # os.systenm has nog correctly finished\n outpdb = \"not_a_file\"\n\n return outpdb","def fixed_location(self, fixed_location):\n\n self._fixed_location = fixed_location","def constrain_fixed(self, value=None, warning=True, trigger_parent=True):\n if value is not None:\n self[:] = value\n\n index = self.unconstrain()\n index = self._add_to_index_operations(self.constraints, index, __fixed__, warning)\n self._highest_parent_._set_fixed(self, index)\n self.notify_observers(self, None if trigger_parent else -np.inf)\n return index","def _exch(self, ix_1, ix_2):\n tmp = self._vals[ix_1]\n self._vals[ix_1] = self._vals[ix_2]\n self._vals[ix_2] = tmp","def reset_f(self):\n self.f = np.copy(self.f_uniq)\n self.f_covered = np.any(self.a[:,self.f], axis=1)","def fixed(self):\n for i in range(15):\n self.factors[i].fixed()\n self.transition.fixed()","def fcp(self, var='x'):\n return self.charpoly(var).factor()","def fix_curvature(self) -> None:\n self.n1.fix = True\n self.n2.fix = True","def resize_coeff(x, new_x):\n return new_x / x","def resize_coeff(x, new_x):\n return new_x / x","def chgconbound(self,i_,lower_,finite_,value_):\n res = __library__.MSK_XX_chgconbound(self.__nativep,i_,lower_,finite_,value_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putxx(self,whichsol_,xx_):\n _xx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),self.getnumvar()))\n if xx_ is None:\n raise ValueError(\"Argument xx cannot be None\")\n if xx_ is None:\n raise ValueError(\"Argument xx may not be None\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_putxx(self.__nativep,whichsol_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def fobj(self, value):\n self._fobj = CArray(value)","def set_sky_coords(self, FOV, npix):\n self.x_sky = np.linspace(-FOV / 2.0, FOV / 2.0, npix)\n self.x_sky, self.y_sky = np.meshgrid(self.x_sky, self.x_sky)\n self.cell_sky = np.diff(self.x_sky).mean()\n self.npix = npix\n self.FOV = FOV","def chi2sf_inplace(x, k):","def fix(self, i, j):\n\n fixable = self._fix_table[self._linear_index(i, j)]\n if fixable is None:\n fixable = self._fix(i, j)\n self._fix_table[self._linear_index(i, j)] = fixable\n return fixable","def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _manipulate_coupling(self, mappingf, prefactorf):\n\n ndata = {}\n for okey in self:\n for nkey in mappingf(okey):\n pref = prefactorf(okey, nkey)\n if math.isclose(pref, 0, rel_tol=0, abs_tol=1e-12):\n continue\n\n b = np.multiply(pref, self[okey])\n if nkey in ndata:\n ndata[nkey] += b\n else:\n ndata[nkey] = b\n self._data = ndata","def x0(self, x0):\n self._x0 = base.getvector(x0, 3)","def updateValue(self,i,x):\n assert 0 <= i < len(self)\n self.__update_aux(0,0,len(self),i,x)","def restrict(self, variable, value):\n # 新因子变量列表为原因子变量列表减去被求和的变量\n restricted_variable = self.var_list.index(variable)\n new_var_list = self.var_list[:restricted_variable] + self.var_list[restricted_variable + 1:]\n\n # 对于新因子CPT中的表项，其值为原因子中与新因子变量取值相同且限制变量取对应值的的单个表项的值\n new_cpt = {}\n if restricted_variable == 0:\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\n if len(self.var_list) == 1:\n postfix = ''\n new_cpt[postfix] = self.cpt[str(value) + postfix]\n elif restricted_variable == len(self.var_list) - 1:\n for i in range(pow(2, restricted_variable)):\n prefix = Util.to_binary(i, restricted_variable)\n new_cpt[prefix] = self.cpt[prefix + str(value)]\n else:\n for i in range(pow(2, restricted_variable)):\n prefix = Util.to_binary(i, restricted_variable)\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\n new_cpt[prefix + postfix] = self.cpt[prefix + str(value) + postfix]\n new_node = Node('f' + str(new_var_list), new_var_list)\n new_node.set_cpt(new_cpt)\n return new_node","def _setbeneficiary_customer_59F(self, val):\n self.swift_obj.BeneficiaryCustomer_F = val\n self.swift_obj.BeneficiaryCustomer_F.swiftTag = '59F'","def _add_force(Fg: np.ndarray, dof_map: Dict[Tuple[int, int], int], model: BDF,\n load, offset: int, ndof_per_grid: int, cid: int=0, show_warning: bool=True):\n #cid = load.cid\n nid = load.node\n node_ref = load.node_ref\n ndofi = ndof_per_grid if node_ref.type == 'GRID' else 1\n assert ndofi == 6, f'GRID must have 6 DOF for structural analysis\\n{node_ref}'\n\n if node_ref.cd == cid:\n fglobal = load.mag * load.xyz\n elif node_ref.cd != cid:\n fbasic = load.to_global()\n if show_warning:\n model.log.warning(f'differing cid & cd is not supported; cid={cid} cd={node_ref.cd}')\n show_warning = False\n cd_ref = node_ref.cd_ref\n Tbg = cd_ref.beta()\n fglobal = _force_to_local(cd_ref, fbasic)\n\n if 0: # pragma: no cover\n if cd_ref.type[-1] in ['C', 'S']:\n ex = Tbg[0, :]\n ey = Tbg[1, :]\n #ez = Tbg[2, :]\n xyz_local = node_ref.get_position_wrt(model, node_ref.cd)\n if cd_ref.type[-1] == 'C':\n theta = radians(xyz_local[1])\n ct = cos(theta)\n st = sin(theta)\n T = np.array([\n [ct, -st, 0.],\n [st, ct, 0.],\n [0., 0., 1.],\n ])\n Tbg = Tbg @ T\n else:\n from pyNastran.bdf.cards.coordinate_systems import CORD2S\n rho, thetad, phid = xyz_local\n coord = CORD2S.add_ijk(-1, origin=cd_ref.origin, i=ex, j=ey, k=None, rid=0, comment='')\n beta = coord.beta()\n Tbg = Tbg @ beta\n coord.transform_vector_to_local([rho, thetad, phid])\n #theta = radians(xyz_local[1])\n #phi = radians(xyz_local[2])\n #ct = cos(theta)\n #st = sin(theta)\n\n #cp = cos(phi)\n #sp = sin(phi)\n\n str(xyz_local)\n else:\n # rectangular\n pass\n Tgb = Tbg.T\n fglobal = Tgb @ fbasic\n else:\n raise NotImplementedError(f'node_ref.cd={node_ref.cd} cid={cid} load:\\n{str(load)}')\n\n for dof in range(3):\n irow = dof_map[(nid, dof+offset)]\n Fg[irow] += fglobal[dof]\n return show_warning","def _fracidfun(x0, y, u, t, opti):\n\n # Cannot identify if both polynomials fixed\n if isinstance(opti.funcFix, (list,np.ndarray, tuple)):\n # Check fixpoly\n if opti.funcFix[0] == 0 and opti.funcFix[1] == 0:\n opti.findDelay = True\n raise ValueError('FRACIDFUN:BothPolynomialsFixed: Cannot identify model because both polynomials are set to be fixed')\n else:\n numSize = int(opti.funcFix[0])\n denSize = int(opti.funcFix[1])\n else:\n raise ValueError('FRACIDFUN: wrong type for opti.polyFix: list, tuple, ndarray allowed')\n\n\n # Get initial model parameters\n [inum, innum, iden, inden,delay] = fotfparam(opti.G)\n opt = opti.optiFix\n if opti.findDelay:\n delay = x0[-1]\n x0 = x0[:-1]\n\n # Pole polynomial is fixed\n if numSize > 0 and denSize == 0:\n #Update numerators (Zeroes)\n [inum, innum] = _fracidfun_getpolyparam(opt, numSize, x0, inum, innum)\n\n # Zero polynomial is fixed\n elif numSize == 0 and denSize > 0:\n #Update Denominator (Poles)\n [iden, inden] = _fracidfun_getpolyparam(opt, denSize, x0, iden, inden)\n\n # Free identification\n elif numSize > 0 and denSize > 0:\n # Update both (Zeros & Poles)\n [inum, innum, iden, inden] = _fracidfun_getfotfparam(opt, numSize, denSize, x0, iden, inden, inum, innum)\n\n # Get identified fotf object\n G = FOTransFunc(inum, innum, iden, inden,delay)\n\n # Build model based on type\n if opti.type == simMethod.grunwaldLetnikov:\n y_id = lsim(G,u,t)\n elif opti.type == simMethod.oustaloop and opti.oustaOpt is not None:\n wb = opti.oustaOpt[0]\n wh = opti.oustaOpt[1]\n N = opti.oustaOpt[2]\n newG = G.oustaloop(wb, wh, N)\n (y_id, t, x00) = controlsim(newG,u, t)\n else:\n raise ValueError(\"utilities._fracidfun: Unknown simulation type 'simMethod' specified!\")\n err = y - y_id\n return err","def update_from_problem(\n self,\n dim_full: int,\n x_free_indices: List[int],\n x_fixed_indices: List[int],\n x_fixed_vals: List[int]):\n\n pre_post_processor = FixedParametersProcessor(\n dim_full=dim_full,\n x_free_indices=x_free_indices,\n x_fixed_indices=x_fixed_indices,\n x_fixed_vals=x_fixed_vals)\n\n self.pre_post_processor = pre_post_processor","def setInfixes(self, i):\n return self._set(infixes=i)","def applyForce(self, F, dT):","def fill(self, value: complex) -> None:\n self.coeff.fill(value)","def _update_farness_map(self,ind):","def __update_jacobian(self, x, F):\n old_err = _n.seterr(divide='raise')\n\n try:\n y = F - self.F\n s = x - self.x\n\n zt = None\n if self.update_type == BroydenSolver.UPDATE_ICUM:\n maxi = abs(_n.ravel(y)).argmax()\n zt = _n.transpose(_n.zeros((1,self.n), _n.float_))\n zt[0, maxi] = 1\n elif self.update_type == BroydenSolver.UPDATE_GOOD_BROYDEN:\n # (Good) Broyden update\n zt = _n.dot(_n.transpose(s), self.H)\n elif self.update_type == BroydenSolver.UPDATE_BAD_BROYDEN:\n # (Bad) Broyden update\n zt = _n.transpose(y)\n else:\n raise ValueError(\"Unknown update type %s\" % (self.update_type))\n\n self.H = self.H \\\n + _n.dot(s - _n.dot(self.H, y), zt) / _n.dot(zt, y)\n except FloatingPointError:\n warnings.warn(\"%% Broyden reset: singular\", BroydenWarning)\n self.H = _n.identity(self.n) / self.initial_scale\n\n _n.seterr(**old_err)","def update(self):\n\n terms_toRemove = []\n\n for termIndex, [term_constantFactor, term_unknowns_attributeAddresses] in enumerate(self.LHS):\n\n # Check if coefficient is 0 - then no need to process any of the unknowns since term will be 0 anyways\n if term_constantFactor == 0:\n terms_toRemove.append(termIndex)\n continue # continue to next term, no need to resolve the unknowns of this term since the product will be 0 anyways\n\n # Check if any unknowns became known\n unknowns_toRemove = []\n for unknown_attributeAddress in term_unknowns_attributeAddresses:\n attribute = getattr_fromAddress(*unknown_attributeAddress)\n if isNumeric(attribute):\n # object.attribute which had previously been identified as unknown now has a value, add it to the constant factor product and remove from the unknowns\n self.LHS[termIndex][0] *= attribute # multiply it with the constant factor product\n unknowns_toRemove.append([termIndex, unknown_attributeAddress])\n for termIndex, unknown_attributeAddress in unknowns_toRemove: # remove unknowns which have become known in the end\n # removing in the end not to tamper with the iteration of the above loop\n self.LHS[termIndex][1].remove(unknown_attributeAddress)\n\n # Move constants to RHS\n if self.LHS[termIndex][1] == []:\n # if term has no unknowns, it is a constant, move to RHS\n self.RHS -= self.LHS[termIndex][0]\n self.LHS.pop(termIndex)\n\n for termIndex in reversed(terms_toRemove): # reversed - otherwise would tamper with indices of items identified for removal\n self.LHS.pop(termIndex)\n\n self._gatherUnknowns()","def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def compress(self):\n gamma = -1\n A = self.A\n A_dict = self.A_dict\n A_dict_inv = self.A_dict_inv\n chi = tuple([i for i in range(len(self.p)) if self.p[i] != i])\n for alpha in self.omega:\n gamma += 1\n if gamma != alpha:\n # replace α by γ in coset table\n for x in A:\n beta = self.table[alpha][A_dict[x]]\n self.table[gamma][A_dict[x]] = beta\n self.table[beta][A_dict_inv[x]] == gamma\n # all the cosets in the table are live cosets\n self.p = list(range(gamma + 1))\n # delete the useless coloumns\n del self.table[len(self.p):]\n # re-define values\n for row in self.table:\n for j in range(len(self.A)):\n row[j] -= bisect_left(chi, row[j])","def replace_function(self, pfunc, index = -1):\n raise NotImplementedError()","def hessian(self, x, y, obj_factor):\n pass","def __call__(self):\n\n if self.indices is []:\n return\n\n\n #-----------------------------------------\n # Here is where the important formula is applied\n #----------------------------------------\n if self.indices is None:\n height = self.stage_c - self.elev_c\n self.friction_c[:] = self.friction(height)\n else:\n ind = self.indices\n height = self.stage_c[ind] - self.elev_c[ind]\n self.friction_c[ind] = self.friction(height)","def update_F_matrix(self, F_matrix):\n self.F_matrix = F_matrix","def set_idf(self):\n for word, word_info in kc_util.gen_db(self.dictionary_db.cursor()):\n word_info.idf = self.idf(word_info.number)\n self.dictionary_db.replace(word, pickle.dumps(word_info))","def replace_contractions(self):\n self.text = contractions.fix(self.text)\n return self","def SetForegroundValue(self, _arg: 'short const') -> \"void\":\n return _itkBinaryContourImageFilterPython.itkBinaryContourImageFilterISS3ISS3_SetForegroundValue(self, _arg)","def Increase_Dictionary(dictio, data, index):\n for line in data:\n split = line.split()\n name = split[0] + \" \" + split[1]\n fmi = float(split[2])\n dictio.setdefault(name, [np.Inf, np.Inf, np.Inf])[index] = fmi\n return dictio","def ccut(value,arg):\n return value.replace(arg, '')","def compute_clique_potentials(self,F):\r\n\r\n for i in self.nodes():\r\n self.node[i]['fac'] = factor([],[],[])\r\n \r\n for f in F.factors: # assign each factor to a clique\r\n for j,data in self.nodes_iter(data=True):\r\n if len(scipy.setdiff1d(f.var,data['clique']) ) ==0:\r\n self.node[j]['fac'] *= f\r\n self.nop += scipy.prod(self.node[j]['fac'].card)\r\n break","def Truncate(self, f, fcut, below=True):\n fout = copy.copy(f)\n ind = thresh(f,fcut)\n if below:\n fout = fout[0:ind]\n else:\n fout = fout[ind:]\n \n keys=['Gxx','Gyy','Gxy']\n\n for curkey in keys:\n curitem = colwise(getattr(self,curkey))\n\n if below:\n curitem = curitem[0:ind,:]\n else:\n curitem = curitem[ind:,:]\n \n setattr(self,curkey,squeeze(curitem))\n return fout","def setFriction(self):\n if int(self.vx) != 0:\n self.ff = -abs(self.vx)/self.vx*self.mu*abs(self.fn)\n else:\n self.ff = 0","def set_iqpt_fine(self, iqpt):\n self.qptanalyzer.wtq = self.wtq_fine[iqpt]\n self.qptanalyzer.ddb.fname = self.ddb_fine_fnames[iqpt]\n self.qptanalyzer.eigq.fname = self.eigq_fine_fnames[iqpt]\n self.qptanalyzer.gkk.fname = self.gkk_fine_fnames[iqpt]\n self.qptanalyzer.read_nonzero_files()","def putskx(self,whichsol_,skx_):\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskx(self.__nativep,whichsol_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _ReplaceCompound(self, from_id, to_id):\n if from_id == to_id:\n return\n \n # set the coefficient of the original compound to 0\n i = self._FindCompoundIndex(from_id)\n if i is None:\n return\n how_many = self.reactants[i].coeff\n self.reactants[i].coeff = 0\n\n # create a new compound with the new kegg_id and the same coefficient\n # or add the number to the coefficient if it already is a reactant\n j = self._FindCompoundIndex(to_id)\n if j is None:\n self.reactants[i] = CompoundWithCoeff.FromId(how_many, to_id)\n else:\n self.reactants[j].coeff += how_many\n self._Dedup()\n\n # clear the cache since the reaction has changed\n self._catalyzing_enzymes = None","def fixed(self):\n return self.f_fixed().m_fixed()","def _update_feature_vec(fvec, word, tag_ngram):","def CZ(self, qubit_expr):\n self.apply_gate_operation(cirq.ops.CZ, qubit_expr)","def XX(self, qubit_expr):\n self.apply_gate_operation(cirq.ops.XX, qubit_expr)","def getcfix(self): # 3\n res,resargs = self.__obj.getcfix()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _cfix_return_value = resargs\n return _cfix_return_value","def replace(self, key, val, expiry_time=0, min_compress_len=0):\n\t\treturn self._set(\"replace\", key, val, expiry_time, min_compress_len)","def end_with_ipa_vector(self, ipa):\n self._ipa_full_vector = ipa","def CX(self, qubit_expr):\n self.apply_gate_operation(cirq.ops.CX, qubit_expr)","def set_collocation_points(self, X_f):\n self.t = self.tensor(X_f[:,0:1])\n self.x = self.tensor(X_f[:,1:2])\n self.y = self.tensor(X_f[:,2:3])","def SetForegroundValue(self, _arg: 'unsigned short const') -> \"void\":\n return _itkBinaryContourImageFilterPython.itkBinaryContourImageFilterIUS3IUS3_SetForegroundValue(self, _arg)","def _calc_C(self, lambdify=True):\n\n C = None\n C_func = None\n # check to see if we have our term saved in file\n C, C_func = self._load_from_file('C', lambdify)\n\n if C is None and C_func is None:\n # if no saved file was loaded, generate function\n print('Generating centrifugal and Coriolis compensation function')\n\n # first get the inertia matrix\n M = self._calc_M(lambdify=False)\n\n # C_{kj} = sum_i c_{ijk}(q) \\dot{q}_i\n # c_{ijk} = 1/2 * sum_i (\\frac{\\partial M_{kj}}{\\partial q_j} +\n # \\frac{\\partial M_{ki}}{\\partial q_j} - \\frac{\\partial M_{ij}}\n # {\\partial q_k})\n C = sp.zeros(self.N_JOINTS, self.N_JOINTS)\n for kk in range(self.N_JOINTS):\n for jj in range(self.N_JOINTS):\n for ii in range(self.N_JOINTS):\n dMkjdqi = M[kk, jj].diff(self.q[ii])\n dMkidqj = M[kk, ii].diff(self.q[jj])\n dMijdqk = M[ii, jj].diff(self.q[kk])\n C[kk, jj] += .5 * (dMkjdqi + dMkidqj - dMijdqk) * self.dq[ii]\n C[kk, jj] = C[kk, jj]\n C = sp.Matrix(C)\n\n # save to file\n abr_control.utils.os_utils.makedirs(\n '%s/C' % self.config_folder)\n cloudpickle.dump(C, open(\n '%s/C/C' % self.config_folder, 'wb'))\n\n if lambdify is False:\n # if should return expression not function\n return C\n\n if C_func is None:\n C_func = self._generate_and_save_function(\n filename='C', expression=C,\n parameters=self.q+self.dq)\n return C_func","def start_location(f):\r\n f[0][0] = 'x'\r\n return f","def fixities(self):\n return [self.fx_free, self.fy_free, self.fz_free,\n self.mx_free, self.my_free, self.mz_free]","def test10(self):\n a, b = np.arange(self.N), np.arange(1, self.N+1)\n c, d = bcolz.carray(a, rootdir=self.rootdir), b\n c[\"a + 2 * d - 3 > 1000\"] = 0\n a[(a + 2 * b - 3) > 1000] = 0\n # print \"carray ->\", c\n # print \"numpy ->\", a\n assert_array_equal(c[:], a, \"carray[expr] = v does not work correctly\")","def SetForegroundValue(self, _arg: 'float const') -> \"void\":\n return _itkBinaryContourImageFilterPython.itkBinaryContourImageFilterIF3IF3_SetForegroundValue(self, _arg)","def objective(self, x):\n pass","def objective(self, x):\n pass"],"string":"[\n \"def putcfix(self,cfix_): # 3\\n res = self.__obj.putcfix(cfix_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def fixC(self,i,value):\\n if self.coeffPattern[2] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[2] = [None]*m\\n self.coeffPattern[2][i]=value\\n self._updateEstimatorSize(i)\",\n \"def f_fixed(self):\\n self.fx_free = self.fy_free = self.fz_free = False\\n return self\",\n \"def x(self, new_x):\\n if new_x is None:\\n logging.warning(\\\"Cochain features were set to None. \\\")\\n else:\\n assert self.num_cells == len(new_x)\\n self.__x = new_x\",\n \"def removeFixedEffect(self, index=None):\\n if self._n_terms==0:\\n pass\\n if index is None or index==(self._n_terms-1):\\n\\n self._n_terms-=1\\n F = self._F.pop() #= self.F[:-1]\\n A = self._A.pop() #= self.A[:-1]\\n self._A_identity.pop() #= self.A_identity[:-1]\\n REML_term = self._REML_term.pop()# = self.REML_term[:-1]\\n self._B.pop()# = self.B[:-1]\\n self._n_fixed_effs-=F.shape[1]*A.shape[0]\\n if REML_term:\\n self._n_fixed_effs_REML-=F.shape[1]*A.shape[0]\\n\\n pass\\n elif index >= self.n_terms:\\n raise Exception(\\\"index exceeds max index of terms\\\")\\n else:\\n raise NotImplementedError(\\\"currently only last term can be removed\\\")\\n pass\\n self._rebuild_indicator()\\n self.clear_cache('Fstar','Astar','Xstar','Xhat',\\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\\n 'LRLdiag_Xhat_tens','Areml_grad',\\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')\",\n \"def m_fixed(self):\\n self.mx_free = self.my_free = self.mz_free = False\\n return self\",\n \"def cpf(self, cpf):\\n self._cpf = cpf\",\n \"def __init__(self, f, index_value_pairs):\\n # super(FixVariables, self).__init__(\\n ComposedFunction.__init__(self, [f, self.insert_variables])\\n self.index_value_pairs = dict(index_value_pairs)\",\n \"def addFixedEffect(self,F=None,A=None, REML=True, index=None):\\n if F is None: F = np.ones((self.N,1))\\n if A is None:\\n A = np.eye(self.P)\\n A_identity = True\\n elif (A.shape == (self.P,self.P)) & (A==np.eye(self.P)).all():\\n A_identity = True\\n else:\\n A_identity = False\\n\\n assert F.shape[0]==self.N, \\\"F dimension mismatch\\\"\\n assert A.shape[1]==self.P, \\\"A dimension mismatch\\\"\\n if index is None or index==self.n_terms:\\n self.F.append(F)\\n self.A.append(A)\\n self.A_identity.append(A_identity)\\n self.REML_term.append(REML)\\n # build B matrix and indicator\\n self.B.append(np.zeros((F.shape[1],A.shape[0])))\\n self._n_terms+=1\\n self._update_indicator(F.shape[1],A.shape[0])\\n elif index >self.n_terms:\\n raise Exception(\\\"index exceeds max index of terms\\\")\\n else:\\n self._n_fixed_effs-=self.F[index].shape[1]*self.A[index].shape[0]\\n if self.REML_term[index]:\\n self._n_fixed_effs_REML-=self.F[index].shape[1]*self.A[index].shape[0]\\n self.F[index] = F\\n self.A[index] = A\\n self.A_identity[index] = A_identity\\n self.REML_term[index]=REML\\n self.B[index] = np.zeros((F.shape[1],A.shape[0]))\\n self._rebuild_indicator()\\n\\n self._n_fixed_effs+=F.shape[1]*A.shape[0]\\n if REML:\\n self._n_fixed_effs_REML+=F.shape[1]*A.shape[0]\\n self.clear_cache('Fstar','Astar','Xstar','Xhat',\\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\\n 'LRLdiag_Xhat_tens','Areml_grad',\\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')\",\n \"def fixB(self,i,j,value):\\n if self.coeffPattern[1] == None:\\n m,n=self.m,self.n\\n self.coeffPattern = [[None]*n for i in range(m)]\\n self.coeffPattern[1][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def _set_fixed(o, d):\\n if d:\\n o.fix()\\n else:\\n o.unfix()\",\n \"def problem_reduction_single(self, i, val):\\n y_update = - val * self.A.getcol(i).toarray().flatten()\\n self.y += y_update\\n self.A = sparse.hstack([self.A[:, :i], self.A[:, i + 1:]], format='csr')\\n z_index = self.mask.searchsorted(i)\\n self.mask = np.insert(self.mask, z_index, i)\\n self.z = np.insert(self.z, z_index, val)\",\n \"def fixA(self,i,j,value):\\n if self.coeffPattern[0] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[0] = [[None]*m for i in range(m)]\\n self.coeffPattern[0][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def putxc(self,whichsol_,xc_):\\n _xc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),self.getnumcon()))\\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\\n raise ValueError(\\\"Argument xc must be writable\\\")\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc may not be None\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _xc_copyarray:\\n xc_[:] = _xc_np_tmp\",\n \"def clearFixedEffect(self):\\n self._A = []\\n self._F = []\\n self._B = []\\n self._A_identity = []\\n self._REML_term = []\\n self._n_terms = 0\\n self._n_fixed_effs = 0\\n self._n_fixed_effs_REML = 0\\n self.indicator = {'term':np.array([]),\\n 'row':np.array([]),\\n 'col':np.array([])}\\n self.clear_cache('Fstar','Astar','Xstar','Xhat',\\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\\n 'LRLdiag_Xhat_tens','Areml_grad',\\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')\",\n \"def getcfix(self):\\n cfix_ = ctypes.c_double()\\n res = __library__.MSK_XX_getcfix(self.__nativep,ctypes.byref(cfix_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n cfix_ = cfix_.value\\n _cfix_return_value = cfix_\\n return (_cfix_return_value)\",\n \"def tctfdfc(x):\\n if isinstance(x,Fdf) :\\n pass\\n else : \\n x = Fdf.constant(x)\\n return x\",\n \"def apply_qc(self, qf=1):\\n logger = logging.getLogger(\\\"timeseries_logger\\\")\\n logger.info('Applying mask to the data')\\n self.temperature = np.ma.masked_where(self.temperatureQF != qf, self.temperature)\\n self.salinity = np.ma.masked_where(self.salinityQF !=qf, self.salinity)\",\n \"def MakeFixed(self,content):\\n return self.register(Fixed(content,reg=self))\",\n \"def affect_cnf(dimacs_cnf, literal_index, literal_value):\\n remove_indexes = []\\n for clause_index in range(dimacs_cnf.shape[0]):\\n if dimacs_cnf[clause_index][literal_index] * literal_value == 1:\\n dimacs_cnf[clause_index] = 1\\n remove_indexes.append(clause_index)\\n elif dimacs_cnf[clause_index][literal_index] * literal_value == -1:\\n dimacs_cnf[clause_index][literal_index] = 0\\n return np.delete(dimacs_cnf, remove_indexes, axis=0)\",\n \"def fixed_cost(self):\\n return np.einsum('i->', self.c[self.f])\",\n \"def Coaxial(movableAxis: str, fixedAxis: str, flip: Boolean) -> \\\"Feature\\\":\\n return Feature()\",\n \"def set_ctf(ima, p):\\n\\tfrom utilities import generate_ctf\\n\\tctf = generate_ctf( p )\\n\\tima.set_attr( \\\"ctf\\\", ctf )\",\n \"def putxc(self,whichsol_,xc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xc is None: raise TypeError(\\\"Invalid type for argument xc\\\")\\n _copyback_xc = False\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n _copyback_xc = True\\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n _copyback_xc = True\\n if xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n res = self.__obj.putxc(whichsol_,xc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_xc:\\n xc[:] = _tmparr_xc\",\n \"def __init__(self, word_fd, quadgram_fd, ii, iii, ixi, ixxi, iixi, ixii):\\n AbstractCollocationFinder.__init__(self, word_fd, quadgram_fd)\\n self.iii = iii\\n self.ii = ii\\n self.ixi = ixi\\n self.ixxi = ixxi\\n self.iixi = iixi\\n self.ixii = ixii\",\n \"def subst_i(term):\\n return Term(term.sums, term.amp.xreplace({i: k}), term.vecs)\",\n \"def set_idx(self, i, other, tensor_value):\\n for k, v in self.variables.items():\\n if k not in other.variables:\\n self.variables[k][i] *= 0\\n\\n for k, v in other.variables.items():\\n if k not in self.variables:\\n self.variables[k] = np.zeros(tensor_value.shape)\\n self.variables[k][i] = other.variables[k]\",\n \"def fusion(self, x):\\n new_x = x.copy()\\n for i in range(self.z.size):\\n new_x[self.mask[i]] = self.z[i]\\n return new_x\",\n \"def modify(self, feature, newValues):\\n self.data[:,self._getFIdx(feature)] = newValues\\n return 0\",\n \"def setCoefficient(self, *args):\\n return _libsbml.FluxObjective_setCoefficient(self, *args)\",\n \"def __setitem__(self, i, v):\\n raise TypeError(\\\"'Factorization' object does not support item assignment\\\")\",\n \"def replace(self, index, value):\\n index += self.n\\n self.data[index] = value\\n index //= 2\\n while index > 0:\\n self.data[index] = self.func(self.data[2*index], self.data[2*index+1])\\n index //= 2\",\n \"def fixed_point(is_zero, plus, minus, f, x):\\n\\n @memo_Y\\n def _fixed_point(fixed_point_fun):\\n def __fixed_point(collected, new):\\n diff = minus(new, collected)\\n if is_zero(diff):\\n return collected\\n return fixed_point_fun(plus(collected, diff), f(diff))\\n return __fixed_point\\n\\n return _fixed_point(x, f(x))\",\n \"def fixed_amount(self, fixed_amount):\\n\\n self._fixed_amount = fixed_amount\",\n \"def fixed_amount(self, fixed_amount):\\n\\n self._fixed_amount = fixed_amount\",\n \"def phenix_real_space_refinement(self, mtz_in, pdb_in, column_labels): \\n \\n mtz_name = get_name(mtz_in)\\n \\n #Spacify phenix version dependent parameters\\n try:\\n phenix_version = int(re.search(r\\\"phenix-1\\\\.(.+?)\\\\.\\\", miller.__file__).group(1)) #This is not so robust. relies on the format being 'phenix.1.18.something' or 'phenix.1.18-something'\\n except ValueError:\\n phenix_version = int(re.search(r\\\"phenix-1\\\\.(.+?)\\\\-\\\", miller.__file__).group(1))\\n except AttributeError:\\n print('Update phenix! Verify that you are using at least Phenix.1.17.')\\n phenix_version = 19 #let's assume then that the latest phenix is installed in case this fails for other reasons than a very old phenix version\\n \\n print(\\\"Phenix version 1.%d\\\" %(phenix_version))\\n \\n if phenix_version >= 19:\\n output_prefix = 'output.prefix=%s_independent'%(mtz_name)\\n model_format = 'model_format=pdb'\\n # outpdb = \\\"%s_independent_real_space_refined_000.pdb\\\"%(mtz_name)\\n else:\\n output_prefix = 'output.file_name_prefix=%s_independent'%(mtz_name)\\n model_format = 'output.model_format=pdb'\\n # outpdb = \\\"%s_independent_real_space_refined.pdb\\\"%(mtz_name)\\n\\n # print(\\\"phenix.real_space_refine %s %s %s %s %s %s label='%s'\\\" % (self.real_phil,\\n # mtz_in, self.additional, pdb_in, output_prefix, model_format, column_labels))\\n real = os.system(\\\"phenix.real_space_refine %s %s %s %s %s %s label='%s' scattering_table=n_gaussian\\\" %(\\n self.real_phil, mtz_in, self.additional, pdb_in, output_prefix, model_format, column_labels))\\n\\n #Find output file\\n if real == 0 : #os.system has correctly finished. Then search for the last refined structure\\n if phenix_version >= 19:\\n try:\\n pdb_fles = glob.glob(\\\"%s_independent_real_space_refined_???.pdb\\\"%(mtz_name))\\n # [fle for fle in os.listdir(os.getcwd()) if \\\"%s_independent_real_space_refined_0\\\"%(mtz_name) in\\n # fle and fle.endswith('pdb')]\\n pdb_fles.sort()\\n outpdb = pdb_fles[-1]\\n except IndexError:\\n outpdb = \\\"%s_independent_real_space_refined_000.pdb\\\" % (mtz_name)\\n else:\\n try:\\n pdb_fles = glob.glob(\\\"%s_independent_real_space_refined.pdb\\\"%(mtz_name))\\n # [fle for fle in os.listdir(os.getcwd()) if\\n # \\\"%s_independent_real_space_refined\\\" % (mtz_name) in fle and fle.endswith('pdb')]\\n pdb_fles.sort()\\n outpdb = pdb_fles[-1]\\n except IndexError:\\n outpdb = \\\"%s_independent_real_space_refined.pdb\\\" % (mtz_name)\\n else: # os.systenm has nog correctly finished\\n outpdb = \\\"not_a_file\\\"\\n\\n return outpdb\",\n \"def fixed_location(self, fixed_location):\\n\\n self._fixed_location = fixed_location\",\n \"def constrain_fixed(self, value=None, warning=True, trigger_parent=True):\\n if value is not None:\\n self[:] = value\\n\\n index = self.unconstrain()\\n index = self._add_to_index_operations(self.constraints, index, __fixed__, warning)\\n self._highest_parent_._set_fixed(self, index)\\n self.notify_observers(self, None if trigger_parent else -np.inf)\\n return index\",\n \"def _exch(self, ix_1, ix_2):\\n tmp = self._vals[ix_1]\\n self._vals[ix_1] = self._vals[ix_2]\\n self._vals[ix_2] = tmp\",\n \"def reset_f(self):\\n self.f = np.copy(self.f_uniq)\\n self.f_covered = np.any(self.a[:,self.f], axis=1)\",\n \"def fixed(self):\\n for i in range(15):\\n self.factors[i].fixed()\\n self.transition.fixed()\",\n \"def fcp(self, var='x'):\\n return self.charpoly(var).factor()\",\n \"def fix_curvature(self) -> None:\\n self.n1.fix = True\\n self.n2.fix = True\",\n \"def resize_coeff(x, new_x):\\n return new_x / x\",\n \"def resize_coeff(x, new_x):\\n return new_x / x\",\n \"def chgconbound(self,i_,lower_,finite_,value_):\\n res = __library__.MSK_XX_chgconbound(self.__nativep,i_,lower_,finite_,value_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putxx(self,whichsol_,xx_):\\n _xx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),self.getnumvar()))\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx cannot be None\\\")\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx may not be None\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n res = __library__.MSK_XX_putxx(self.__nativep,whichsol_,_xx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def fobj(self, value):\\n self._fobj = CArray(value)\",\n \"def set_sky_coords(self, FOV, npix):\\n self.x_sky = np.linspace(-FOV / 2.0, FOV / 2.0, npix)\\n self.x_sky, self.y_sky = np.meshgrid(self.x_sky, self.x_sky)\\n self.cell_sky = np.diff(self.x_sky).mean()\\n self.npix = npix\\n self.FOV = FOV\",\n \"def chi2sf_inplace(x, k):\",\n \"def fix(self, i, j):\\n\\n fixable = self._fix_table[self._linear_index(i, j)]\\n if fixable is None:\\n fixable = self._fix(i, j)\\n self._fix_table[self._linear_index(i, j)] = fixable\\n return fixable\",\n \"def putsolutionyi(self,i_,whichsol_,y_):\\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _manipulate_coupling(self, mappingf, prefactorf):\\n\\n ndata = {}\\n for okey in self:\\n for nkey in mappingf(okey):\\n pref = prefactorf(okey, nkey)\\n if math.isclose(pref, 0, rel_tol=0, abs_tol=1e-12):\\n continue\\n\\n b = np.multiply(pref, self[okey])\\n if nkey in ndata:\\n ndata[nkey] += b\\n else:\\n ndata[nkey] = b\\n self._data = ndata\",\n \"def x0(self, x0):\\n self._x0 = base.getvector(x0, 3)\",\n \"def updateValue(self,i,x):\\n assert 0 <= i < len(self)\\n self.__update_aux(0,0,len(self),i,x)\",\n \"def restrict(self, variable, value):\\n # 新因子变量列表为原因子变量列表减去被求和的变量\\n restricted_variable = self.var_list.index(variable)\\n new_var_list = self.var_list[:restricted_variable] + self.var_list[restricted_variable + 1:]\\n\\n # 对于新因子CPT中的表项，其值为原因子中与新因子变量取值相同且限制变量取对应值的的单个表项的值\\n new_cpt = {}\\n if restricted_variable == 0:\\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\\n if len(self.var_list) == 1:\\n postfix = ''\\n new_cpt[postfix] = self.cpt[str(value) + postfix]\\n elif restricted_variable == len(self.var_list) - 1:\\n for i in range(pow(2, restricted_variable)):\\n prefix = Util.to_binary(i, restricted_variable)\\n new_cpt[prefix] = self.cpt[prefix + str(value)]\\n else:\\n for i in range(pow(2, restricted_variable)):\\n prefix = Util.to_binary(i, restricted_variable)\\n for j in range(pow(2, len(new_var_list) - restricted_variable)):\\n postfix = Util.to_binary(j, len(new_var_list) - restricted_variable)\\n new_cpt[prefix + postfix] = self.cpt[prefix + str(value) + postfix]\\n new_node = Node('f' + str(new_var_list), new_var_list)\\n new_node.set_cpt(new_cpt)\\n return new_node\",\n \"def _setbeneficiary_customer_59F(self, val):\\n self.swift_obj.BeneficiaryCustomer_F = val\\n self.swift_obj.BeneficiaryCustomer_F.swiftTag = '59F'\",\n \"def _add_force(Fg: np.ndarray, dof_map: Dict[Tuple[int, int], int], model: BDF,\\n load, offset: int, ndof_per_grid: int, cid: int=0, show_warning: bool=True):\\n #cid = load.cid\\n nid = load.node\\n node_ref = load.node_ref\\n ndofi = ndof_per_grid if node_ref.type == 'GRID' else 1\\n assert ndofi == 6, f'GRID must have 6 DOF for structural analysis\\\\n{node_ref}'\\n\\n if node_ref.cd == cid:\\n fglobal = load.mag * load.xyz\\n elif node_ref.cd != cid:\\n fbasic = load.to_global()\\n if show_warning:\\n model.log.warning(f'differing cid & cd is not supported; cid={cid} cd={node_ref.cd}')\\n show_warning = False\\n cd_ref = node_ref.cd_ref\\n Tbg = cd_ref.beta()\\n fglobal = _force_to_local(cd_ref, fbasic)\\n\\n if 0: # pragma: no cover\\n if cd_ref.type[-1] in ['C', 'S']:\\n ex = Tbg[0, :]\\n ey = Tbg[1, :]\\n #ez = Tbg[2, :]\\n xyz_local = node_ref.get_position_wrt(model, node_ref.cd)\\n if cd_ref.type[-1] == 'C':\\n theta = radians(xyz_local[1])\\n ct = cos(theta)\\n st = sin(theta)\\n T = np.array([\\n [ct, -st, 0.],\\n [st, ct, 0.],\\n [0., 0., 1.],\\n ])\\n Tbg = Tbg @ T\\n else:\\n from pyNastran.bdf.cards.coordinate_systems import CORD2S\\n rho, thetad, phid = xyz_local\\n coord = CORD2S.add_ijk(-1, origin=cd_ref.origin, i=ex, j=ey, k=None, rid=0, comment='')\\n beta = coord.beta()\\n Tbg = Tbg @ beta\\n coord.transform_vector_to_local([rho, thetad, phid])\\n #theta = radians(xyz_local[1])\\n #phi = radians(xyz_local[2])\\n #ct = cos(theta)\\n #st = sin(theta)\\n\\n #cp = cos(phi)\\n #sp = sin(phi)\\n\\n str(xyz_local)\\n else:\\n # rectangular\\n pass\\n Tgb = Tbg.T\\n fglobal = Tgb @ fbasic\\n else:\\n raise NotImplementedError(f'node_ref.cd={node_ref.cd} cid={cid} load:\\\\n{str(load)}')\\n\\n for dof in range(3):\\n irow = dof_map[(nid, dof+offset)]\\n Fg[irow] += fglobal[dof]\\n return show_warning\",\n \"def _fracidfun(x0, y, u, t, opti):\\n\\n # Cannot identify if both polynomials fixed\\n if isinstance(opti.funcFix, (list,np.ndarray, tuple)):\\n # Check fixpoly\\n if opti.funcFix[0] == 0 and opti.funcFix[1] == 0:\\n opti.findDelay = True\\n raise ValueError('FRACIDFUN:BothPolynomialsFixed: Cannot identify model because both polynomials are set to be fixed')\\n else:\\n numSize = int(opti.funcFix[0])\\n denSize = int(opti.funcFix[1])\\n else:\\n raise ValueError('FRACIDFUN: wrong type for opti.polyFix: list, tuple, ndarray allowed')\\n\\n\\n # Get initial model parameters\\n [inum, innum, iden, inden,delay] = fotfparam(opti.G)\\n opt = opti.optiFix\\n if opti.findDelay:\\n delay = x0[-1]\\n x0 = x0[:-1]\\n\\n # Pole polynomial is fixed\\n if numSize > 0 and denSize == 0:\\n #Update numerators (Zeroes)\\n [inum, innum] = _fracidfun_getpolyparam(opt, numSize, x0, inum, innum)\\n\\n # Zero polynomial is fixed\\n elif numSize == 0 and denSize > 0:\\n #Update Denominator (Poles)\\n [iden, inden] = _fracidfun_getpolyparam(opt, denSize, x0, iden, inden)\\n\\n # Free identification\\n elif numSize > 0 and denSize > 0:\\n # Update both (Zeros & Poles)\\n [inum, innum, iden, inden] = _fracidfun_getfotfparam(opt, numSize, denSize, x0, iden, inden, inum, innum)\\n\\n # Get identified fotf object\\n G = FOTransFunc(inum, innum, iden, inden,delay)\\n\\n # Build model based on type\\n if opti.type == simMethod.grunwaldLetnikov:\\n y_id = lsim(G,u,t)\\n elif opti.type == simMethod.oustaloop and opti.oustaOpt is not None:\\n wb = opti.oustaOpt[0]\\n wh = opti.oustaOpt[1]\\n N = opti.oustaOpt[2]\\n newG = G.oustaloop(wb, wh, N)\\n (y_id, t, x00) = controlsim(newG,u, t)\\n else:\\n raise ValueError(\\\"utilities._fracidfun: Unknown simulation type 'simMethod' specified!\\\")\\n err = y - y_id\\n return err\",\n \"def update_from_problem(\\n self,\\n dim_full: int,\\n x_free_indices: List[int],\\n x_fixed_indices: List[int],\\n x_fixed_vals: List[int]):\\n\\n pre_post_processor = FixedParametersProcessor(\\n dim_full=dim_full,\\n x_free_indices=x_free_indices,\\n x_fixed_indices=x_fixed_indices,\\n x_fixed_vals=x_fixed_vals)\\n\\n self.pre_post_processor = pre_post_processor\",\n \"def setInfixes(self, i):\\n return self._set(infixes=i)\",\n \"def applyForce(self, F, dT):\",\n \"def fill(self, value: complex) -> None:\\n self.coeff.fill(value)\",\n \"def _update_farness_map(self,ind):\",\n \"def __update_jacobian(self, x, F):\\n old_err = _n.seterr(divide='raise')\\n\\n try:\\n y = F - self.F\\n s = x - self.x\\n\\n zt = None\\n if self.update_type == BroydenSolver.UPDATE_ICUM:\\n maxi = abs(_n.ravel(y)).argmax()\\n zt = _n.transpose(_n.zeros((1,self.n), _n.float_))\\n zt[0, maxi] = 1\\n elif self.update_type == BroydenSolver.UPDATE_GOOD_BROYDEN:\\n # (Good) Broyden update\\n zt = _n.dot(_n.transpose(s), self.H)\\n elif self.update_type == BroydenSolver.UPDATE_BAD_BROYDEN:\\n # (Bad) Broyden update\\n zt = _n.transpose(y)\\n else:\\n raise ValueError(\\\"Unknown update type %s\\\" % (self.update_type))\\n\\n self.H = self.H \\\\\\n + _n.dot(s - _n.dot(self.H, y), zt) / _n.dot(zt, y)\\n except FloatingPointError:\\n warnings.warn(\\\"%% Broyden reset: singular\\\", BroydenWarning)\\n self.H = _n.identity(self.n) / self.initial_scale\\n\\n _n.seterr(**old_err)\",\n \"def update(self):\\n\\n terms_toRemove = []\\n\\n for termIndex, [term_constantFactor, term_unknowns_attributeAddresses] in enumerate(self.LHS):\\n\\n # Check if coefficient is 0 - then no need to process any of the unknowns since term will be 0 anyways\\n if term_constantFactor == 0:\\n terms_toRemove.append(termIndex)\\n continue # continue to next term, no need to resolve the unknowns of this term since the product will be 0 anyways\\n\\n # Check if any unknowns became known\\n unknowns_toRemove = []\\n for unknown_attributeAddress in term_unknowns_attributeAddresses:\\n attribute = getattr_fromAddress(*unknown_attributeAddress)\\n if isNumeric(attribute):\\n # object.attribute which had previously been identified as unknown now has a value, add it to the constant factor product and remove from the unknowns\\n self.LHS[termIndex][0] *= attribute # multiply it with the constant factor product\\n unknowns_toRemove.append([termIndex, unknown_attributeAddress])\\n for termIndex, unknown_attributeAddress in unknowns_toRemove: # remove unknowns which have become known in the end\\n # removing in the end not to tamper with the iteration of the above loop\\n self.LHS[termIndex][1].remove(unknown_attributeAddress)\\n\\n # Move constants to RHS\\n if self.LHS[termIndex][1] == []:\\n # if term has no unknowns, it is a constant, move to RHS\\n self.RHS -= self.LHS[termIndex][0]\\n self.LHS.pop(termIndex)\\n\\n for termIndex in reversed(terms_toRemove): # reversed - otherwise would tamper with indices of items identified for removal\\n self.LHS.pop(termIndex)\\n\\n self._gatherUnknowns()\",\n \"def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def compress(self):\\n gamma = -1\\n A = self.A\\n A_dict = self.A_dict\\n A_dict_inv = self.A_dict_inv\\n chi = tuple([i for i in range(len(self.p)) if self.p[i] != i])\\n for alpha in self.omega:\\n gamma += 1\\n if gamma != alpha:\\n # replace α by γ in coset table\\n for x in A:\\n beta = self.table[alpha][A_dict[x]]\\n self.table[gamma][A_dict[x]] = beta\\n self.table[beta][A_dict_inv[x]] == gamma\\n # all the cosets in the table are live cosets\\n self.p = list(range(gamma + 1))\\n # delete the useless coloumns\\n del self.table[len(self.p):]\\n # re-define values\\n for row in self.table:\\n for j in range(len(self.A)):\\n row[j] -= bisect_left(chi, row[j])\",\n \"def replace_function(self, pfunc, index = -1):\\n raise NotImplementedError()\",\n \"def hessian(self, x, y, obj_factor):\\n pass\",\n \"def __call__(self):\\n\\n if self.indices is []:\\n return\\n\\n\\n #-----------------------------------------\\n # Here is where the important formula is applied\\n #----------------------------------------\\n if self.indices is None:\\n height = self.stage_c - self.elev_c\\n self.friction_c[:] = self.friction(height)\\n else:\\n ind = self.indices\\n height = self.stage_c[ind] - self.elev_c[ind]\\n self.friction_c[ind] = self.friction(height)\",\n \"def update_F_matrix(self, F_matrix):\\n self.F_matrix = F_matrix\",\n \"def set_idf(self):\\n for word, word_info in kc_util.gen_db(self.dictionary_db.cursor()):\\n word_info.idf = self.idf(word_info.number)\\n self.dictionary_db.replace(word, pickle.dumps(word_info))\",\n \"def replace_contractions(self):\\n self.text = contractions.fix(self.text)\\n return self\",\n \"def SetForegroundValue(self, _arg: 'short const') -> \\\"void\\\":\\n return _itkBinaryContourImageFilterPython.itkBinaryContourImageFilterISS3ISS3_SetForegroundValue(self, _arg)\",\n \"def Increase_Dictionary(dictio, data, index):\\n for line in data:\\n split = line.split()\\n name = split[0] + \\\" \\\" + split[1]\\n fmi = float(split[2])\\n dictio.setdefault(name, [np.Inf, np.Inf, np.Inf])[index] = fmi\\n return dictio\",\n \"def ccut(value,arg):\\n return value.replace(arg, '')\",\n \"def compute_clique_potentials(self,F):\\r\\n\\r\\n for i in self.nodes():\\r\\n self.node[i]['fac'] = factor([],[],[])\\r\\n \\r\\n for f in F.factors: # assign each factor to a clique\\r\\n for j,data in self.nodes_iter(data=True):\\r\\n if len(scipy.setdiff1d(f.var,data['clique']) ) ==0:\\r\\n self.node[j]['fac'] *= f\\r\\n self.nop += scipy.prod(self.node[j]['fac'].card)\\r\\n break\",\n \"def Truncate(self, f, fcut, below=True):\\n fout = copy.copy(f)\\n ind = thresh(f,fcut)\\n if below:\\n fout = fout[0:ind]\\n else:\\n fout = fout[ind:]\\n \\n keys=['Gxx','Gyy','Gxy']\\n\\n for curkey in keys:\\n curitem = colwise(getattr(self,curkey))\\n\\n if below:\\n curitem = curitem[0:ind,:]\\n else:\\n curitem = curitem[ind:,:]\\n \\n setattr(self,curkey,squeeze(curitem))\\n return fout\",\n \"def setFriction(self):\\n if int(self.vx) != 0:\\n self.ff = -abs(self.vx)/self.vx*self.mu*abs(self.fn)\\n else:\\n self.ff = 0\",\n \"def set_iqpt_fine(self, iqpt):\\n self.qptanalyzer.wtq = self.wtq_fine[iqpt]\\n self.qptanalyzer.ddb.fname = self.ddb_fine_fnames[iqpt]\\n self.qptanalyzer.eigq.fname = self.eigq_fine_fnames[iqpt]\\n self.qptanalyzer.gkk.fname = self.gkk_fine_fnames[iqpt]\\n self.qptanalyzer.read_nonzero_files()\",\n \"def putskx(self,whichsol_,skx_):\\n _skx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),self.getnumvar()))\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx cannot be None\\\")\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx may not be None\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_putskx(self.__nativep,whichsol_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _ReplaceCompound(self, from_id, to_id):\\n if from_id == to_id:\\n return\\n \\n # set the coefficient of the original compound to 0\\n i = self._FindCompoundIndex(from_id)\\n if i is None:\\n return\\n how_many = self.reactants[i].coeff\\n self.reactants[i].coeff = 0\\n\\n # create a new compound with the new kegg_id and the same coefficient\\n # or add the number to the coefficient if it already is a reactant\\n j = self._FindCompoundIndex(to_id)\\n if j is None:\\n self.reactants[i] = CompoundWithCoeff.FromId(how_many, to_id)\\n else:\\n self.reactants[j].coeff += how_many\\n self._Dedup()\\n\\n # clear the cache since the reaction has changed\\n self._catalyzing_enzymes = None\",\n \"def fixed(self):\\n return self.f_fixed().m_fixed()\",\n \"def _update_feature_vec(fvec, word, tag_ngram):\",\n \"def CZ(self, qubit_expr):\\n self.apply_gate_operation(cirq.ops.CZ, qubit_expr)\",\n \"def XX(self, qubit_expr):\\n self.apply_gate_operation(cirq.ops.XX, qubit_expr)\",\n \"def getcfix(self): # 3\\n res,resargs = self.__obj.getcfix()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _cfix_return_value = resargs\\n return _cfix_return_value\",\n \"def replace(self, key, val, expiry_time=0, min_compress_len=0):\\n\\t\\treturn self._set(\\\"replace\\\", key, val, expiry_time, min_compress_len)\",\n \"def end_with_ipa_vector(self, ipa):\\n self._ipa_full_vector = ipa\",\n \"def CX(self, qubit_expr):\\n self.apply_gate_operation(cirq.ops.CX, qubit_expr)\",\n \"def set_collocation_points(self, X_f):\\n self.t = self.tensor(X_f[:,0:1])\\n self.x = self.tensor(X_f[:,1:2])\\n self.y = self.tensor(X_f[:,2:3])\",\n \"def SetForegroundValue(self, _arg: 'unsigned short const') -> \\\"void\\\":\\n return _itkBinaryContourImageFilterPython.itkBinaryContourImageFilterIUS3IUS3_SetForegroundValue(self, _arg)\",\n \"def _calc_C(self, lambdify=True):\\n\\n C = None\\n C_func = None\\n # check to see if we have our term saved in file\\n C, C_func = self._load_from_file('C', lambdify)\\n\\n if C is None and C_func is None:\\n # if no saved file was loaded, generate function\\n print('Generating centrifugal and Coriolis compensation function')\\n\\n # first get the inertia matrix\\n M = self._calc_M(lambdify=False)\\n\\n # C_{kj} = sum_i c_{ijk}(q) \\\\dot{q}_i\\n # c_{ijk} = 1/2 * sum_i (\\\\frac{\\\\partial M_{kj}}{\\\\partial q_j} +\\n # \\\\frac{\\\\partial M_{ki}}{\\\\partial q_j} - \\\\frac{\\\\partial M_{ij}}\\n # {\\\\partial q_k})\\n C = sp.zeros(self.N_JOINTS, self.N_JOINTS)\\n for kk in range(self.N_JOINTS):\\n for jj in range(self.N_JOINTS):\\n for ii in range(self.N_JOINTS):\\n dMkjdqi = M[kk, jj].diff(self.q[ii])\\n dMkidqj = M[kk, ii].diff(self.q[jj])\\n dMijdqk = M[ii, jj].diff(self.q[kk])\\n C[kk, jj] += .5 * (dMkjdqi + dMkidqj - dMijdqk) * self.dq[ii]\\n C[kk, jj] = C[kk, jj]\\n C = sp.Matrix(C)\\n\\n # save to file\\n abr_control.utils.os_utils.makedirs(\\n '%s/C' % self.config_folder)\\n cloudpickle.dump(C, open(\\n '%s/C/C' % self.config_folder, 'wb'))\\n\\n if lambdify is False:\\n # if should return expression not function\\n return C\\n\\n if C_func is None:\\n C_func = self._generate_and_save_function(\\n filename='C', expression=C,\\n parameters=self.q+self.dq)\\n return C_func\",\n \"def start_location(f):\\r\\n f[0][0] = 'x'\\r\\n return f\",\n \"def fixities(self):\\n return [self.fx_free, self.fy_free, self.fz_free,\\n self.mx_free, self.my_free, self.mz_free]\",\n \"def test10(self):\\n a, b = np.arange(self.N), np.arange(1, self.N+1)\\n c, d = bcolz.carray(a, rootdir=self.rootdir), b\\n c[\\\"a + 2 * d - 3 > 1000\\\"] = 0\\n a[(a + 2 * b - 3) > 1000] = 0\\n # print \\\"carray ->\\\", c\\n # print \\\"numpy ->\\\", a\\n assert_array_equal(c[:], a, \\\"carray[expr] = v does not work correctly\\\")\",\n \"def SetForegroundValue(self, _arg: 'float const') -> \\\"void\\\":\\n return _itkBinaryContourImageFilterPython.itkBinaryContourImageFilterIF3IF3_SetForegroundValue(self, _arg)\",\n \"def objective(self, x):\\n pass\",\n \"def objective(self, x):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.708022","0.6352814","0.5975144","0.58999455","0.5676646","0.5464873","0.53494346","0.5349058","0.5343659","0.53432745","0.53168523","0.52869856","0.5279766","0.5264671","0.51587206","0.5142443","0.5114193","0.51119894","0.5060143","0.5042062","0.5015368","0.50101244","0.49665415","0.4955151","0.493829","0.49057582","0.48905075","0.48514515","0.4827352","0.48138055","0.4804691","0.47960508","0.4782272","0.47641975","0.47641975","0.4749602","0.47476658","0.47290856","0.47269675","0.47235885","0.4719877","0.4717961","0.46963567","0.467618","0.467618","0.46760678","0.46751547","0.46535906","0.46515507","0.46487895","0.464582","0.4634238","0.46305954","0.46274242","0.46228462","0.46194625","0.46191916","0.4616066","0.46068478","0.46041438","0.46005437","0.45943704","0.45934314","0.4587059","0.4586462","0.45831385","0.45817226","0.45706186","0.45685774","0.4557025","0.45562828","0.45557946","0.45546883","0.45446694","0.45380753","0.45373243","0.4536156","0.45295557","0.45259175","0.45160094","0.4515868","0.45157567","0.45102605","0.45088038","0.45062664","0.45040682","0.45027792","0.44977257","0.44974458","0.44951528","0.4495148","0.4488767","0.44874316","0.448518","0.44795048","0.44793174","0.44780728","0.44651613","0.44644687","0.44644687"],"string":"[\n \"0.708022\",\n \"0.6352814\",\n \"0.5975144\",\n \"0.58999455\",\n \"0.5676646\",\n \"0.5464873\",\n \"0.53494346\",\n \"0.5349058\",\n \"0.5343659\",\n \"0.53432745\",\n \"0.53168523\",\n \"0.52869856\",\n \"0.5279766\",\n \"0.5264671\",\n \"0.51587206\",\n \"0.5142443\",\n \"0.5114193\",\n \"0.51119894\",\n \"0.5060143\",\n \"0.5042062\",\n \"0.5015368\",\n \"0.50101244\",\n \"0.49665415\",\n \"0.4955151\",\n \"0.493829\",\n \"0.49057582\",\n \"0.48905075\",\n \"0.48514515\",\n \"0.4827352\",\n \"0.48138055\",\n \"0.4804691\",\n \"0.47960508\",\n \"0.4782272\",\n \"0.47641975\",\n \"0.47641975\",\n \"0.4749602\",\n \"0.47476658\",\n \"0.47290856\",\n \"0.47269675\",\n \"0.47235885\",\n \"0.4719877\",\n \"0.4717961\",\n \"0.46963567\",\n \"0.467618\",\n \"0.467618\",\n \"0.46760678\",\n \"0.46751547\",\n \"0.46535906\",\n \"0.46515507\",\n \"0.46487895\",\n \"0.464582\",\n \"0.4634238\",\n \"0.46305954\",\n \"0.46274242\",\n \"0.46228462\",\n \"0.46194625\",\n \"0.46191916\",\n \"0.4616066\",\n \"0.46068478\",\n \"0.46041438\",\n \"0.46005437\",\n \"0.45943704\",\n \"0.45934314\",\n \"0.4587059\",\n \"0.4586462\",\n \"0.45831385\",\n \"0.45817226\",\n \"0.45706186\",\n \"0.45685774\",\n \"0.4557025\",\n \"0.45562828\",\n \"0.45557946\",\n \"0.45546883\",\n \"0.45446694\",\n \"0.45380753\",\n \"0.45373243\",\n \"0.4536156\",\n \"0.45295557\",\n \"0.45259175\",\n \"0.45160094\",\n \"0.4515868\",\n \"0.45157567\",\n \"0.45102605\",\n \"0.45088038\",\n \"0.45062664\",\n \"0.45040682\",\n \"0.45027792\",\n \"0.44977257\",\n \"0.44974458\",\n \"0.44951528\",\n \"0.4495148\",\n \"0.4488767\",\n \"0.44874316\",\n \"0.448518\",\n \"0.44795048\",\n \"0.44793174\",\n \"0.44780728\",\n \"0.44651613\",\n \"0.44644687\",\n \"0.44644687\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69024557"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11963,"cells":{"query":{"kind":"string","value":"Modifies one linear coefficient in the objective. putcj(self,j_,cj_)"},"document":{"kind":"string","value":"def putcj(self,j_,cj_):\n res = __library__.MSK_XX_putcj(self.__nativep,j_,cj_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putcj(self,j_,cj_): # 3\n res = self.__obj.putcj(j_,cj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def jacobian(self, c):\n\n raise NotImplementedError","def getcj(self,j_): # 3\n res,resargs = self.__obj.getcj(j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _cj_return_value = resargs\n return _cj_return_value","def substitute_cost(self, i, j):\n raise NotImplementedError","def getcj(self,j_):\n cj_ = ctypes.c_double()\n res = __library__.MSK_XX_getcj(self.__nativep,j_,ctypes.byref(cj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n cj_ = cj_.value\n _cj_return_value = cj_\n return (_cj_return_value)","def jacobian(self, x):\n pass","def setCoefficient(self, *args):\n return _libsbml.FluxObjective_setCoefficient(self, *args)","def putaij(self,i_,j_,aij_): # 3\n res = self.__obj.putaij(i_,j_,aij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putaij(self,i_,j_,aij_):\n res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def jac(self):\n return self.unit_jac if self._jac is None else self._jac","def JCoeff(l, m, s, lp, mp, sp):\n coeff = int((l == lp) & (m == -mp) & (s == sp))*1j*(-1)**(int(m-1/2))*s\n return coeff","def fixA(self,i,j,value):\n if self.coeffPattern[0] == None:\n m,n=self.m,self.n\n self.coeffPattern[0] = [[None]*m for i in range(m)]\n self.coeffPattern[0][i][j]=value\n self._updateEstimatorSize(i)","def _update_objective(self):\n # rewrap the cost if the solver has been run\n self.Finalize()\n return","def objective(self, x):\n pass","def objective(self, x):\n pass","def insert_cost(self, i, j):\n raise NotImplementedError","def jaccard_coeff(self):\n a, c, _, b = self.to_ccw()\n return _div(a, a + b + c)","def problem_reduction_single(self, i, val):\n y_update = - val * self.A.getcol(i).toarray().flatten()\n self.y += y_update\n self.A = sparse.hstack([self.A[:, :i], self.A[:, i + 1:]], format='csr')\n z_index = self.mask.searchsorted(i)\n self.mask = np.insert(self.mask, z_index, i)\n self.z = np.insert(self.z, z_index, val)","def compute_j(self, trajectory):\r\n J = 0\r\n for i, (_,_,r,_) in enumerate(trajectory):\r\n J += (self.domain.discount**i) * r\r\n return J","def jacobian(self,x,y,l,a):\n J = np.zeros([*x.shape,2,2])\n\n J = _jacobian(x,y,l,a,J)\n\n return J","def J_plus_component(j_prime: int, m_prime: int, j: int, m: int) -> float:\n if (j_prime != j) or (m_prime != m + 1):\n return 0\n return J_plus_coefficient(j, m)","def add(self, i, j):\n \n # use running average to update CoM coordinates.\n self._x = (self._x * self._P + i) / (self._P + 1)\n self._y = (self._y * self._P + j) / (self._P + 1)\n # increment mass\n self._P += 1","def jacobian(self, dt):\n raise NotImplementedError","def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def jacobian_c(self, x, out=None, **kwargs):\n return empty_matrix(0, self.nx)","def _partial_min_solution(self, j):\n beta_without_j = np.delete(self.betas, j, axis=0)\n X_without_j = np.delete(self.X, j, axis=0)\n X_j = self.X[j] # these are the X values for the jth feature in the model\n # Make predictions and obtain residuals on the full set of Ys, without the effect of the jth predictor included\n R_without_j = (self.Y - (beta_without_j.T @ X_without_j))\n c_j = 2/self.n * (X_j @ R_without_j) # This quantity is described in the notes\n # The following if statements are due to the subgradient of the L1 penality\n if abs(c_j) <= self.lam: # this step is what causes the lasso to shrink coefficients to 0 based on lambda\n return 0\n a_j = 2 * sum(X_j**2) # also described in notes\n if c_j < -self.lam:\n return (c_j + self.lam) / (a_j / self.n)\n elif c_j > self.lam:\n return (c_j - self.lam) / (a_j / self.n)","def add_com_jac(ui):\n global com_jac_list\n\n content = content_fk_jac_loops(ui, \"com_jac\")\n if content in com_jac_list:\n return\n com_jac_list.append(content)\n ui.listWidget_com_jac.addItem(f\"Center of Mass Jacobian \"\n f\"{parse_content(content)}\")","def optimize_cjp_displacements(self, method='lm', init_coeffs=None):\n if init_coeffs is None:\n init_coeffs = np.random.rand(5)\n else:\n init_coeffs += np.random.rand(5)\n # optimize least squares\n return optimize.least_squares(fun=self.residuals_cjp_displacements,\n x0=init_coeffs,\n method=method)","def lasso_cd_weight_update(\n x: FloatTensor,\n r: FloatTensor,\n j: int,\n w_j: Union[float, FloatTensor],\n col_l2: float,\n lmb: float,\n) -> float:\n if col_l2 == 0.0:\n return 0.0\n n, d = x.size()\n # quick hack for multiclass (tensor instead of float) check\n multiclass = hasattr(w_j, \"__len__\")\n z = (n * lmb) / 2\n if multiclass:\n # w_j is tensor; multiclass regression\n rho = x[:, j].matmul(\n r + x[:, j].unsqueeze(1).matmul(w_j.unsqueeze(0))\n ) # type: ignore\n return torch.nn.functional.softshrink(rho, z).data / col_l2\n else:\n # w_j is scalar; scalar regression\n rho = x[:, j].matmul(r + x[:, j] * w_j)\n return soft_thresh(rho, z) / col_l2","def _append_cx(self, i, j):\n\n if not 0 <= i < self.num_qubits or not 0 <= j < self.num_qubits:\n raise QiskitError(\"CX qubits are out of bounds.\")\n self.linear[j] = (self.linear[i] + self.linear[j]) % 2\n self.shift[j] = (self.shift[i] + self.shift[j]) % 2","def fixB(self,i,j,value):\n if self.coeffPattern[1] == None:\n m,n=self.m,self.n\n self.coeffPattern = [[None]*n for i in range(m)]\n self.coeffPattern[1][i][j]=value\n self._updateEstimatorSize(i)","def ols_cd_weight_update(\n x: FloatTensor,\n r: FloatTensor,\n j: int,\n w_j: Union[float, FloatTensor],\n col_l2: float,\n _: float,\n) -> float:\n return w_j + x[:, j].matmul(r) / col_l2 if col_l2 != 0.0 else 0.0","def comb_lignes(self,ki,kj,i,j):\n f = self.F\n g = lambda r,c : ki*f(i,c) + kj*f(j,c) if r == i else f(r,c)\n return Mat(self.D,g)","def conj(self, o): \n return (o.inv()) * self * o","def jacobian(self,var,g=None):\n if (g==None):g=self.g\n jac=np.zeros([self.n+1,self.n])\n for i in range(self.n):\n for j in range(self.n):\n if(i==j): jac[i][j]=2.*(var[i]+1.)-g*np.sum([self.XXZ.Z(i,k) for k in range(self.n) if k!=i])\n else: jac[i][j]=g*self.XXZ.Z(i,j)\n for i in range(self.n):\n jac[self.n][i]=1.\n return jac","def setitem(self, i, j, value):\n # XXX: flint matrices do not support negative indices\n # XXX: They also raise ValueError instead of IndexError\n m, n = self.shape\n if i < 0:\n i += m\n if j < 0:\n j += n\n try:\n self.rep[i, j] = value\n except ValueError:\n raise IndexError(f\"Invalid indices ({i}, {j}) for Matrix of shape {self.shape}\")","def objective(self):\n pass","def fixC(self,i,value):\n if self.coeffPattern[2] == None:\n m,n=self.m,self.n\n self.coeffPattern[2] = [None]*m\n self.coeffPattern[2][i]=value\n self._updateEstimatorSize(i)","def _do_Jsum(om, A_J, D_J):\n Dmat=npufunc.Jomega.outer(D_J,om)\n return np.einsum('...j,jk',A_J,Dmat)","def coefficients_from_j(j, minimal_twist=True):\n try:\n K = j.parent()\n except AttributeError:\n K = rings.RationalField()\n if K not in _Fields:\n K = K.fraction_field()\n\n char = K.characteristic()\n if char == 2:\n if j == 0:\n return Sequence([0, 0, 1, 0, 0], universe=K)\n else:\n return Sequence([1, 0, 0, 0, 1/j], universe=K)\n if char == 3:\n if j == 0:\n return Sequence([0, 0, 0, 1, 0], universe=K)\n else:\n return Sequence([0, j, 0, 0, -j**2], universe=K)\n\n if K is rings.RationalField():\n # we construct the minimal twist, i.e. the curve with minimal\n # conductor with this j_invariant:\n if j == 0:\n return Sequence([0, 0, 1, 0, 0], universe=K) # 27a3\n if j == 1728:\n return Sequence([0, 0, 0, -1, 0], universe=K) # 32a2\n\n if not minimal_twist:\n k = j-1728\n return Sequence([0, 0, 0, -3*j*k, -2*j*k**2], universe=K)\n\n n = j.numerator()\n m = n-1728*j.denominator()\n a4 = -3*n*m\n a6 = -2*n*m**2\n\n # Now E=[0,0,0,a4,a6] has j-invariant j=n/d\n from sage.sets.set import Set\n for p in Set(n.prime_divisors()+m.prime_divisors()):\n e = min(a4.valuation(p)//2, a6.valuation(p)//3)\n if e & gt\n 0:\n p = p**e\n a4 /= p**2\n a6 /= p**3\n\n # Now E=[0,0,0,a4,a6] is minimal at all p != 2,3\n tw = [-1, 2, -2, 3, -3, 6, -6]\n E1 = EllipticCurve([0, 0, 0, a4, a6])\n Elist = [E1] + [E1.quadratic_twist(t) for t in tw]\n Elist.sort(key=lambda E: E.conductor())\n return Sequence(Elist[0].ainvs())\n\n # defaults for all other fields:\n if j == 0:\n return Sequence([0, 0, 0, 0, 1], universe=K)\n if j == 1728:\n return Sequence([0, 0, 0, 1, 0], universe=K)\n k = j-1728\n return Sequence([0, 0, 0, -3*j*k, -2*j*k**2], universe=K)","def ridge_cd_weight_update(\n x: FloatTensor,\n r: FloatTensor,\n j: int,\n w_j: Union[float, FloatTensor],\n col_l2: float,\n lmb: float,\n) -> float:\n if col_l2 == 0.0:\n return 0.0\n n, d = x.size()\n return w_j + (x[:, j].matmul(r) - n * lmb * w_j) / col_l2","def delete_cost(self, i, j):\n raise NotImplementedError","def set_current(self,Jc):\n self.raw_parameters[\"Jc\"] = Jc\n self.parameters = NormalizedParameters(self.raw_parameters)\n self._load()","def inverseIntermediateJac(self,x):\n \n Ri = self._rotation.T\n si = (1./self._scaled).reshape((1,1,self._dim))\n \n Jac = self.intermediateJacPol2Rot(x)\n \n #Ri.J\n Jac = np.einsum(\"jk,ikl->ijl\",Ri,Jac)\n #(Ri.J).diag(si)\n Jac *= si\n \n return Jac","def cost(self, X, y) :\n ### ========== TODO : START ========== ###\n # part d: compute J(theta)\n #we know for linear/polynomial regression, the cost is the square of the errors\n X = self.generate_polynomial_features(X)\n y_pred_vector = np.dot(X, self.coef_)\n cost = np.dot((y-y_pred_vector).transpose(),(y-y_pred_vector))#write in the matrix form\n ### ========== TODO : END ========== ###\n return cost","def threej(self, coeff):\n j1, m1, j2, m2, j3, m3 = coeff\n myargs = tuple(int(2*x) for x in (j1, j2, j3, m1, m2, m3))\n L1, L2, L3, M1, M2, M3 = myargs\n if M1 + M2 + M3 != 0:\n return 0\n elif abs(L1 - L2) > L3:\n return 0\n elif L1 + L2 < L3:\n return 0\n else:\n return py3nj.wigner3j(*myargs)","def jacobian(self, x1, x2, out=None):\n raise NotImplementedError","def update_coeff(self, **kwargs: float) -> None:\n for rule_name, coeff in kwargs.items():\n if rule_name not in self.rules:\n raise ValueError(f\"Behavioral rule {rule_name} does not exist\")\n else:\n self.rules[getattr(self, rule_name)] = coeff","def spin_J_set(j, *, dtype=None):\n return jmat(j, dtype=dtype)","def add_objective(self): \n \n if \"CSS\" in self.algorithm:\n \n if self.num_hidden == 0:\n \n data_term = self.compute_energy(self.x, self.batch_size)\n \n else:\n \n data_term = self.compute_free_energy(self.x)\n \n normalizer_term = self.add_css_approximation(data_term)\n \n if \"CD\" in self.algorithm and self.num_hidden ==0:\n \n data_term = self.compute_energy(self.x, self.batch_size)\n \n normalizer_term = self.compute_energy(self.x_gibbs, \n self.batch_size)\n \n normalizer_term = -T.mean(normalizer_term)\n \n if \"CD\" in self.algorithm and self.num_hidden > 0:\n \n data_term = self.compute_free_energy(self.x)\n \n normalizer_term = self.compute_free_energy(self.rbm_cd_samples)\n \n normalizer_term = -T.mean(normalizer_term)\n \n # cost is negative log likelihood \n self.cost = T.mean(data_term) + normalizer_term","def jacobian_c(self, x, out=None, **kwargs):\n return self._base_nlp.jacobian_c(x, out=out, **kwargs)","def putbarcj(self,j_,sub_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights_)\n elif num_ != len(weights_):\n raise IndexError(\"Inconsistent length of array weights\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbarcj(self.__nativep,j_,num_,_sub_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarcj(self,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexError(\"Inconsistent length of array weights\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n \n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n \n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n \n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def j_nc_from_j(self, j, inverse=False, check_bounds=False):\n if not inverse:\n if check_bounds:\n assert_between(j, 0, self._grid_shape[1]-1)\n j_nc = self._nc_j0 + j * self._nc_jskip\n if check_bounds:\n assert_between(j_nc, 0, self._nc_xdim)\n return j_nc\n else:\n j_nc = j\n if check_bounds:\n assert_between(j_nc, 0, self._nc_ydim)\n j = (j_nc - self._nc_j0)/self._nc_jskip\n if check_bounds:\n assert_between(j, 0, self._grid_shape[1]-1)\n return j","def JacobianFunction(p,x,y,z):\n \n n = len(x)\n \n J = np.array([ np.ones((n)),x,x**2,y,y**2,x*y ])\n \n return J","def jacobian_i(self, x):\n return np.matrix([-x**3, -x**2, -x, -1])","def transform_and_compute_jacobian(self, xj):\n x = xj[:, :self.d].detach()\n log_j = xj[:, -1]\n\n x.requires_grad = True\n y = self.flow_(x)\n\n n_batch = xj.shape[0]\n\n jx = torch.zeros(n_batch, self.d, self.d).to(log_j.device)\n directions = torch.eye(self.d).to(log_j).unsqueeze(0).repeat(n_batch, 1, 1)\n\n for i in range(self.d):\n jx[:, i, :] = torch.autograd.grad(y, x, directions[:, i, :],\n allow_unused=True, create_graph=True, retain_graph=True)[0]\n x.requires_grad = False\n x.grad = None\n\n log_det_j = torch.log(torch.abs(torch.det(jx)))\n return torch.cat([y.detach(), (log_j + log_det_j).unsqueeze(1)], 1)","def _reduce(self, x, y):\n coef = self._update_parallel_coef_constraints(x)\n self.coef_ = coef.T","def doublec(self, j):\n\t\tif j < (self.k0 + 1):\n\t\t\treturn 0\n\t\tif (self.b[j] != self.b[j-1]):\n\t\t\treturn 0\n\t\treturn self.cons(j)","def objective(self, objective):\n\n self._objective = objective","def merge_j(self, tensor: Tensor):\n return tensor.merge(consts=self._j_exprs).map2amps(factor)","def __setitem__(self, j, val):\n\t\tself._coords[j] = val","def __setitem__( self, l, c_l ) :\n\n if( l == len( self ) ) :\n self.coefficients.append( float( c_l ) )\n else :\n self.coefficients[l] = float( c_l )","def coefficient(self) -> float:\n ...","def _J_combine_LS_anisotropic(w, S2, tau_int, A_J, D_J):\n dims=len(A_J)\n J=np.zeros( (dims,len(w)) )\n for k in range(dims):\n D_eff=D_J[k]+1.0/tau_int\n J[i] = S2[i]*A_J[k]*D_J[k]/(D_J[k]**2+w**2) + (1-S2) * D_eff/(D_eff**2 + w**2)\n return J.sum(axis=0)","def jac_rot(self):\n J = self.sim.data.get_body_jacr(self.end_effector)\n J = J.reshape(3, -1)[:, 0:7].T\n return J","def _get_jacobian(self):\n srcs, recs = self.srcs, self.recs\n if not self.sparse:\n jac = numpy.array(\n [ttime2d.straight([cell], '', srcs, recs, velocity=1.)\n for cell in self.mesh]).T\n else:\n shoot = ttime2d.straight\n nonzero = []\n extend = nonzero.extend\n for j, c in enumerate(self.mesh):\n extend((i, j, tt)\n for i, tt in enumerate(shoot([c], '', srcs, recs,\n velocity=1.))\n if tt != 0)\n row, col, val = numpy.array(nonzero).T\n shape = (self.ndata, self.nparams)\n jac = scipy.sparse.csr_matrix((val, (row, col)), shape)\n return jac","def setSmoothCost(self, cost):\n if cost.shape[0] != cost.shape[1] or (cost != cost.T).any():\n raise DataTypeNotSupportedError('Cost matrix not square or not symmetric')\n if cost.shape[0] != self.numLabels:\n raise ShapeMismatchError('Cost matrix not of size numLabels * numLabels')\n\n # Just a reference\n self._smoothCost = self._convertSmoothCostArray(cost)\n _cgco.gcoSetSmoothCost(self.handle, self._smoothCost)","def __call__(self, W=None):\n if W is None:\n W = sparse.csr_matrix(self.W[-1])\n else:\n W = sparse.csr_matrix(W)\n J = self.mask.T*(self.mask.multiply(W.T))\n self.J = J\n LogJ = J.copy()\n LogJ.data = np.log(LogJ.data)\n obj = (self.mask.T*self.mask).multiply(LogJ).sum()\n if self.rho is not None:\n obj = (1. - self.rho)*obj + self.rho*self.regularizer(W.todense())[0]\n return obj","def jot(self):\r\n\t\t\r\n\t\t# empty list?\r\n\t\tif len(self) < 1:\r\n\t\t\r\n\t\t\treturn '(0)'\r\n\t\t\r\n\t\t# go through terms\r\n\t\ts = ''\r\n\t\tfor i in self:\r\n\t\t\t\r\n\t\t\t# positive or negative\r\n\t\t\tif i.look('i') % 4 in (0,1):\r\n\t\t\t\ts += ' +('\r\n\t\t\tif i.look('i') % 4 in (2,3):\r\n\t\t\t\ts += ' -('\r\n\t\t\t\t\r\n\t\t\t# list of variables\r\n\t\t\ty = i.keys()\r\n\t\t\ty = [str(j) for j in y if j != 'i']\r\n\t\t\ty = [j for j in y if j.isalpha()]\r\n\t\t\ty = Pa._tidy(y)\r\n\t\t\t\t\r\n\t\t\t# coefficient\r\n\t\t\tn,d = i.fuse()\r\n\t\t\tif n > 1 or d > 1 or len(y) < 1:\r\n\t\t\t\ts += str(n)\r\n\t\t\tif d > 1:\r\n\t\t\t\ts += '/' + str(d)\r\n\t\t\tif i.look('i') % 4 in (1,3):\r\n\t\t\t\ts += 'i'\r\n\t\t\t\r\n\t\t\t# add variables to string\r\n\t\t\tfor k in y:\r\n\t\t\t\tif s[-1] == '(':\r\n\t\t\t\t\ts += k\r\n\t\t\t\telse:\r\n\t\t\t\t\ts += ' ' + k\r\n\t\t\t\tif i[k] != 1:\r\n\t\t\t\t\ts += str(i[k])\r\n\t\t\t\t\t\r\n\t\t\t# close\r\n\t\t\ts += ')'\r\n\t\t\t\t\r\n\t\t# remove leading ' '\r\n\t\tif s[0] == ' ':\r\n\t\t\ts = s[1:]\r\n\t\t\t\t\r\n\t\t# remove leading +\r\n\t\tif s[0] == '+':\r\n\t\t\ts = s[1:]\r\n\t\t\t\t\r\n\t\treturn s","def putqobjij(self,i_,j_,qoij_): # 3\n res = self.__obj.putqobjij(i_,j_,qoij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def objective(self, adjacency=None, R=None):\n raise NotImplementedError()","def intermediateJac(self, x, isRot=False):\n if not isRot:\n x = dot(self._rotation,x)\n\n #Due to how it is constructed,\n #J_rot2polar is its own inverse (and symmetric)\n \n Jac = self.intermediateJacRot2Polar(x)\n \n #Jac = S.J.R\n \n s=self._scaled.reshape((1,self._dim,1))\n R = self._rotation\n \n #Compute J.R\n Jac = np.einsum(\"ijk,kl->ijl\",Jac,R)\n #Left multiply with S\n #S.(J.R)\n Jac *= s #A left multiplication with a diagonal matrix is like scaling the rows\n \n return Jac","def jacobian(self, dt):\n return self._F_cache","def put_proj_coeffs(self, dest):\n if parallel.is_rank_zero():\n self.put_array(self.proj_coeffs, dest)\n parallel.barrier()","def jacobian(theta, event, parameters_to_fit):\n for (key, val) in enumerate(parameters_to_fit):\n setattr(event.model.parameters, val, theta[key])\n return event.chi2_gradient(parameters_to_fit)","def coupling_6j((J1,J2,J3),(J4,J5,J6)):\n cj6=pygsl.sf.coupling_6j(int(2*J1),int(2*J2), int(2*J3), int(2*J4),int(2*J5), int(2*J6))[0]\n cj6=(-1)**(J1+J2+J4+J5)*hat(J3)*hat(J6)*cj6 \n return cj6","def J(cst, x):\n [u0, v0, u1, v1, u2, v2, coeffs] = cst\n [u, v, g1, g2, g3] = x\n df1du = 2*u*g3**2 - 2*g3*u0 + 2*g3*coeffs[3]*(g1*u1-u0) + 2*g3*coeffs[4]*(g2*u2-u0)\n df1dv = -2*v*g3**2 + 2*g3*v0 - 2*g3*coeffs[3]*(g1*v1-v0) - 2*g3*coeffs[4]*(g2*v2-v0)\n df1dg1 = 2*g1*coeffs[0]*(u1**2-v1**2) + 2*(v1*v0-u1*u0)*(coeffs[0]+coeffs[1]+coeffs[3]) + 2*g2*coeffs[1]*(u1*u2-v1*v2) + 2*g3*coeffs[3]*(u1*u-v1*v)\n df1dg2 = 2*g2*coeffs[2]*(u2**2-v2**2) + 2*(v2*v0-u2*u0)*(coeffs[1]+coeffs[2]+coeffs[4]) + 2*g1*coeffs[1]*(u1*u2-v1*v2) + 2*g3*coeffs[4]*(u2*u-v2*v)\n df1dg3 = 2*g3*(u**2-v**2) + 2*(v*v0-u*u0)*(coeffs[3]+coeffs[4]+1) + 2*g1*coeffs[3]*(u1*u-v1*v) + 2*g2*coeffs[4]*(u2*u-v2*v)\n\n df2du = 0\n df2dv = 2*v*g3**2 + 2*g3*(-v0 + coeffs[3]*(g1*v1-v0) + coeffs[4]*(g2*v2-v0))\n df2dg1 = 2*g1*coeffs[0]*(v1**2-1) + 2*(1-v1*v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + 2*g2*coeffs[1]*(v1*v2-1) + 2*g3*coeffs[3]*(v1*v-1)\n df2dg2 = 2*g2*coeffs[2]*(v2**2-1) + 2*(1-v2*v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + 2*g1*coeffs[1]*(v1*v2-1) + 2*g3*coeffs[4]*(v2*v-1)\n df2dg3 = 2*g3*(v**2-1) + 2*(1-v*v0)*(coeffs[3]+coeffs[4]+1) + 2*g1*coeffs[3]*(v1*v-1) + 2*g2*coeffs[4]*(v2*v-1)\n\n df3du = g3*coeffs[3]*(g1*v1-v0) + g3*coeffs[4]*(g2*v2-v0) + g3*(g3*v-v0)\n df3dv = g3*coeffs[3]*(g1*u1-u0) + g3*coeffs[4]*(g2*u2-u0) + g3*(g3*u-u0)\n df3dg1 = 2*g1*coeffs[0]*u1*v1 - (v1*u0+u1*v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(u1*v2+v1*u2) + g3*coeffs[3]*(v1*u+u1*v)\n df3dg2 = 2*g2*coeffs[2]*u2*v2 - (v2*u0+u2*v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(u1*v2+v1*u2) + g3*coeffs[4]*(v2*u+u2*v)\n df3dg3 = 2*g3*u*v - (u*v0+v*u0)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(v1*u+u1*v) + g2*coeffs[4]*(v2*u+u2*v)\n\n df4du = g3*coeffs[3]*(g1-1) + g3*coeffs[4]*(g2-1) + g3*(g3-1)\n df4dv = 0\n df4dg1 = 2*g1*coeffs[0]*u1 - (u0+u1)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(u1+u2) + g3*coeffs[3]*(u+u1)\n df4dg2 = 2*g2*coeffs[2]*u2 - (u0+u2)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(u1+u2) + g3*coeffs[4]*(u+u2)\n df4dg3 = 2*g3*u - (u+u0)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(u+u1) + g2*coeffs[4]*(u+u2)\n\n df5du = 0\n df5dv = g3*coeffs[3]*(g1-1) + g3*coeffs[4]*(g2-1) + g3*(g3-1)\n df5dg1 = 2*g1*coeffs[0]*v1 - (v1+v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(v2+v1) + g3*coeffs[3]*(v1+v)\n df5dg2 = 2*g2*coeffs[2]*v2 - (v2+v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(v2+v1) + g3*coeffs[4]*(v2+v)\n df5dg3 = 2*g3*v - (v0+v)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(v1+v) + g2*coeffs[4]*(v2+v)\n\n return np.array([\n [df1du, df1dv, df1dg1, df1dg2, df1dg3],\n [df2du, df2dv, df2dg1, df2dg2, df2dg3],\n [df3du, df3dv, df3dg1, df3dg2, df3dg3],\n [df4du, df4dv, df4dg1, df4dg2, df4dg3],\n [df5du, df5dv, df5dg1, df5dg2, df5dg3],\n ])","def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\n self.coeff[self._core.index_alpha(key[0]),\n self._core.index_beta(key[1])] = value","def R(self, s, j, **params):\n s_,j_ = (deepcopy(s)+1,deepcopy(j))\n return s_,j_","def total_cost(J_content, J_style, alpha=10, beta=40):\n\n J = alpha * J_content + beta * J_style\n\n return J","def J(self):\n return self._J","def J (self, n):","def total_cost(J_content, J_style, alpha = 10, beta = 40):\n\n J = alpha * J_content + beta * J_style\n\n return J","def amalgamate(self,i,j):\n # conserve momentum\n self.v[i] = (self.v[i]*self.m[i]+self.v[j]*self.m[j])/ \\\n (self.m[i]+self.m[j])\n self.r[i] = (self.r[j] - self.r[i])/2 + self.r[j] \n self.m[i] = self.m[i] + self.m[j]\n self.r[j] = self.r[self.n-1]\n self.v[j] = self.v[self.n-1]\n self.m[j] = self.m[self.n-1]\n self.n = self.n - 1","def user_cons_hJ(h, Jac, mbs_data, tsim):\n\n # Example: Compute the expression of h and Jac then assign the values.\n # h[1] = mbs_data.q[1]-mbs_data.q[2]*mbs_data.q[2]\n # Jac[1,1] = 1.\n # Jac[1,2] = -2*mbs_data.q[2].\n # IMPORTANT: NEVER REASSIGN h => h = np.array([0,mbs_data.q[1]-mbs_data.q[2]*mbs_data.q[2],0])\n # NEVER REASSIGN Jac => Jac = np.array([[0,0,0,0],[0,1,-2*mbs_data.q[2],0])\n # Both command will change the values of h, Jac in this function\n # but they will not be modified outside the scope of this function.\n rwt = RwtTrackGeometry(mbs_data, pointer = mbs_data.user_model['addons']['rwt'])\n rwc = RwcMain(pointer = mbs_data.user_model['addons']['rwc'])\n \n rwt.cons_hJ(mbs_data, h, Jac)\n rwc.compute_constraints(mbs_data, h, Jac)\n \n \"\"\"id1 = mbs_data.joint_id[\"R1_caisse1\"]\n id2 = mbs_data.joint_id[\"R1_caisse2\"]\n id3 = mbs_data.joint_id[\"R1_chassis1\"]\n id4 = mbs_data.joint_id[\"R1_chassis2\"]\n\n # define the value of the constraint\n h[1] = (mbs_data.q[id1] + mbs_data.q[id3]) - (mbs_data.q[id2] + mbs_data.q[id4])\n\n # define the value of the jacobian matrix\n Jac[1,id1] = 1\n Jac[1,id2] = -1\n Jac[1,id3] = 1\n Jac[1,id4] = -1\"\"\"\n \n return","def putacol(self,j_,subj_,valj_):\n nzj_ = None\n if nzj_ is None:\n nzj_ = len(subj_)\n elif nzj_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nzj_ is None:\n nzj_ = len(valj_)\n elif nzj_ != len(valj_):\n raise IndexError(\"Inconsistent length of array valj\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valj_ is None:\n raise ValueError(\"Argument valj cannot be None\")\n if valj_ is None:\n raise ValueError(\"Argument valj may not be None\")\n if isinstance(valj_, numpy.ndarray) and valj_.dtype is numpy.dtype(numpy.float64) and valj_.flags.contiguous:\n _valj_copyarray = False\n _valj_tmp = ctypes.cast(valj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valj_ is not None:\n _valj_copyarray = True\n _valj_np_tmp = numpy.zeros(len(valj_),numpy.dtype(numpy.float64))\n _valj_np_tmp[:] = valj_\n assert _valj_np_tmp.flags.contiguous\n _valj_tmp = ctypes.cast(_valj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valj_copyarray = False\n _valj_tmp = None\n \n res = __library__.MSK_XX_putacol(self.__nativep,j_,nzj_,_subj_tmp,_valj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def jacobi_solver(self, mat, rhs):\n x = np.zeros_like(rhs)\n for it_count in range(self.iterations_number):\n x_new = np.zeros_like(x)\n if self.verbose > 1:\n print(\"Iteration {0}: {1}\".format(it_count, x))\n for i in range(mat.shape[0]):\n s1 = np.dot(mat[i, :i], x[:i])\n s2 = np.dot(mat[i, i + 1:], x[i + 1:])\n x_new[i] = (rhs[i] - s1 - s2) / mat[i, i]\n if np.allclose(x, x_new, rtol=1e-8):\n break\n x = x_new\n return x","def __call__(self, W=None):\n if W is None:\n W = sparse.csr_matrix(self.W[-1])\n else:\n W = sparse.csr_matrix(W)\n J = self.mask.T*(self.mask.multiply(W.T))\n self.J = J\n LogJ = J.copy()\n LogJ.data = np.log(LogJ.data)\n obj = (-J.multiply(LogJ)).sum()\n if self.rho is not None:\n obj = (1. - self.rho)*obj + self.rho*self.regularizer(W.todense())[0]\n return obj","def coeff_mul(self, coeff):\n return self","def get_jac(wcs, cenx, ceny):\n import galsim\n\n crpix = wcs.crpix\n galsim_pos = galsim.PositionD(x=crpix[0], y=crpix[1])\n\n galsim_jac = wcs.jacobian(image_pos=galsim_pos)\n\n return ngmix.Jacobian(\n x=cenx,\n y=ceny,\n dudx=galsim_jac.dudx,\n dudy=galsim_jac.dudy,\n dvdx=galsim_jac.dvdx,\n dvdy=galsim_jac.dvdy,\n )","def _weight_jac_mat_prod(self, module, g_inp, g_out, mat):\n raise NotImplementedError","def jac_res(self, params, **kwargs):\n e = kwargs.get(\"e\", self.problem.data_e)\n\n jac = self.jacobian.eval(params, **kwargs)\n return - jac / e[:, None]","def switch(self, i, j0, j1):\n if not self._mutable:\n raise ValueError(\"this constellation is immutable.\"\n \" Take a mutable copy first.\")\n S = SymmetricGroup(list(range(self.degree())))\n tr = S((j0, j1))\n i = int(i)\n if i < 0 or i >= len(self._g):\n raise ValueError(\"index out of range\")\n\n ii = i + 1\n if ii == len(self._g):\n ii = 0\n self._g[i] = self._g[i] * tr\n self._g[ii] = tr * self._g[ii]","def relax_rate(self, temperature, j_i, j_j, fit_param=None):\n # Energies of the rovibrational states involved.\n Ej_i = self.ls_factors.term_values(0, j_i, 'Fv')\n del_E = self.ls_factors.term_values(0, j_j, 'Fv') - Ej_i\n\n # For a transition from j_i to j_j with i<j:\n if abs(j_i-j_j) % 2 != 0:\n # For N2 gamma_ji is zero when del_j is odd due to nuclear-spin\n # selection rules.\n gamma_ji = 0\n gamma_ij = 0\n else:\n alpha, beta, sigma, m = fit_param\n\n _term_1 = (1-np.exp(-m))/(\n 1-np.exp(-m*temperature/295))*(295/temperature)**0.5\n _term_2 = ((1+1.5*1.44*Ej_i/temperature/sigma)/(\n 1+1.5*1.44*Ej_i/temperature))**2\n\n gamma_ji = alpha*self.pressure/1.01325*_term_1*_term_2*np.exp(\n -beta*del_E*1.44/temperature)\n gamma_ij = gamma_ji*(2*j_i+1)/(2*j_j+1)*np.exp(\n del_E*1.44/temperature)\n\n return gamma_ji, gamma_ij","def add_coefficients(self, L, overwrite=False):\n if not isinstance(L, dict):\n raise ValueError(\"Call with dictionary as argument!\")\n\n for p in L.keys():\n c = mpmath.mpmathify(L[p])\n # print \"c=\",c\n cd = ceil(mpmath.log10(abs(c)))\n if(cd > self.maxdigs):\n self.maxdigs = cd\n # print \"p=\",p\n if(is_int(p)):\n (r, n) = rn_from_D(self._space.WR, p)\n elif(isinstance(p, tuple)):\n (r, n) = p\n if r in self._coeffs:\n if n in self._coeffs[r]:\n c_old = self._coeffs[r][n]\n # Try to determine (heuristically) if the new coefficient is really better\n d1 = dist_from_int(c)[0]\n d2 = dist_from_int(c_old)[0]\n if(overwrite):\n self._coeffs[r][n] = c\n else:\n self._coeffs[r][n] = c\n else:\n # see if it is a possible index at all\n if not r < 0 or r > self._space.multiplier().ambient_rank():\n raise ValueError(\"Key {0} corr to (r,n)=({1},{2}) is invalid for the current space!\".format(p, r, n))\n elif r not in self._space.multiplier().D():\n if self._space._sym_type == -1 and (r == 0 or r == self._space.multiplier().N):\n # Should be 0 by symmetry\n if abs(c) > 10**(1 - self.prec):\n raise ValueError(\"Coefficient should be zero by symmetry. Got c({0},{1})={2}!\".format(r, n, c))\n else:\n self._coeffs[r][n] = 0\n else:\n # is equal to +- c(-r,n)\n mr = 2 * self.multiplier().N - r\n if mr in self._coeffs:\n if n in self._coeffs[mr]:\n c_old = self._coeffs[mr][n]\n if abs(c - self._space.multiplier()._sym_type * c_old) > 10**(1 - self.prec):\n st = \"Might add an erroneous coefficient! Got c({0},{1})={2}. \".format(r, n, c)\n st += \"From previous coefficients should have {0}\".format(self._space._sym_type * c_old)\n raise ValueError(st)\n if overwrite:\n self._coeffs[mr][n] = c\n else:\n raise ValueError(\"Coefficient should be zero by symmetry. Got c({0},{1})={2}!\" .format(r, n, c))","def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def J(self) -> float:\n return self._J","def lpj2pjc(lpj: to.Tensor):\n up_lpg_bound = 0.0\n shft = up_lpg_bound - lpj.max(dim=1, keepdim=True)[0]\n tmp = to.exp(lpj + shft)\n return tmp.div_(tmp.sum(dim=1, keepdim=True))","def put_C_reduced(self, dest):\n _parallel.call_from_rank_zero(self.put_mat, self.C_reduced, dest)\n _parallel.barrier()"],"string":"[\n \"def putcj(self,j_,cj_): # 3\\n res = self.__obj.putcj(j_,cj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def jacobian(self, c):\\n\\n raise NotImplementedError\",\n \"def getcj(self,j_): # 3\\n res,resargs = self.__obj.getcj(j_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _cj_return_value = resargs\\n return _cj_return_value\",\n \"def substitute_cost(self, i, j):\\n raise NotImplementedError\",\n \"def getcj(self,j_):\\n cj_ = ctypes.c_double()\\n res = __library__.MSK_XX_getcj(self.__nativep,j_,ctypes.byref(cj_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n cj_ = cj_.value\\n _cj_return_value = cj_\\n return (_cj_return_value)\",\n \"def jacobian(self, x):\\n pass\",\n \"def setCoefficient(self, *args):\\n return _libsbml.FluxObjective_setCoefficient(self, *args)\",\n \"def putaij(self,i_,j_,aij_): # 3\\n res = self.__obj.putaij(i_,j_,aij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putaij(self,i_,j_,aij_):\\n res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def jac(self):\\n return self.unit_jac if self._jac is None else self._jac\",\n \"def JCoeff(l, m, s, lp, mp, sp):\\n coeff = int((l == lp) & (m == -mp) & (s == sp))*1j*(-1)**(int(m-1/2))*s\\n return coeff\",\n \"def fixA(self,i,j,value):\\n if self.coeffPattern[0] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[0] = [[None]*m for i in range(m)]\\n self.coeffPattern[0][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def _update_objective(self):\\n # rewrap the cost if the solver has been run\\n self.Finalize()\\n return\",\n \"def objective(self, x):\\n pass\",\n \"def objective(self, x):\\n pass\",\n \"def insert_cost(self, i, j):\\n raise NotImplementedError\",\n \"def jaccard_coeff(self):\\n a, c, _, b = self.to_ccw()\\n return _div(a, a + b + c)\",\n \"def problem_reduction_single(self, i, val):\\n y_update = - val * self.A.getcol(i).toarray().flatten()\\n self.y += y_update\\n self.A = sparse.hstack([self.A[:, :i], self.A[:, i + 1:]], format='csr')\\n z_index = self.mask.searchsorted(i)\\n self.mask = np.insert(self.mask, z_index, i)\\n self.z = np.insert(self.z, z_index, val)\",\n \"def compute_j(self, trajectory):\\r\\n J = 0\\r\\n for i, (_,_,r,_) in enumerate(trajectory):\\r\\n J += (self.domain.discount**i) * r\\r\\n return J\",\n \"def jacobian(self,x,y,l,a):\\n J = np.zeros([*x.shape,2,2])\\n\\n J = _jacobian(x,y,l,a,J)\\n\\n return J\",\n \"def J_plus_component(j_prime: int, m_prime: int, j: int, m: int) -> float:\\n if (j_prime != j) or (m_prime != m + 1):\\n return 0\\n return J_plus_coefficient(j, m)\",\n \"def add(self, i, j):\\n \\n # use running average to update CoM coordinates.\\n self._x = (self._x * self._P + i) / (self._P + 1)\\n self._y = (self._y * self._P + j) / (self._P + 1)\\n # increment mass\\n self._P += 1\",\n \"def jacobian(self, dt):\\n raise NotImplementedError\",\n \"def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def jacobian_c(self, x, out=None, **kwargs):\\n return empty_matrix(0, self.nx)\",\n \"def _partial_min_solution(self, j):\\n beta_without_j = np.delete(self.betas, j, axis=0)\\n X_without_j = np.delete(self.X, j, axis=0)\\n X_j = self.X[j] # these are the X values for the jth feature in the model\\n # Make predictions and obtain residuals on the full set of Ys, without the effect of the jth predictor included\\n R_without_j = (self.Y - (beta_without_j.T @ X_without_j))\\n c_j = 2/self.n * (X_j @ R_without_j) # This quantity is described in the notes\\n # The following if statements are due to the subgradient of the L1 penality\\n if abs(c_j) <= self.lam: # this step is what causes the lasso to shrink coefficients to 0 based on lambda\\n return 0\\n a_j = 2 * sum(X_j**2) # also described in notes\\n if c_j < -self.lam:\\n return (c_j + self.lam) / (a_j / self.n)\\n elif c_j > self.lam:\\n return (c_j - self.lam) / (a_j / self.n)\",\n \"def add_com_jac(ui):\\n global com_jac_list\\n\\n content = content_fk_jac_loops(ui, \\\"com_jac\\\")\\n if content in com_jac_list:\\n return\\n com_jac_list.append(content)\\n ui.listWidget_com_jac.addItem(f\\\"Center of Mass Jacobian \\\"\\n f\\\"{parse_content(content)}\\\")\",\n \"def optimize_cjp_displacements(self, method='lm', init_coeffs=None):\\n if init_coeffs is None:\\n init_coeffs = np.random.rand(5)\\n else:\\n init_coeffs += np.random.rand(5)\\n # optimize least squares\\n return optimize.least_squares(fun=self.residuals_cjp_displacements,\\n x0=init_coeffs,\\n method=method)\",\n \"def lasso_cd_weight_update(\\n x: FloatTensor,\\n r: FloatTensor,\\n j: int,\\n w_j: Union[float, FloatTensor],\\n col_l2: float,\\n lmb: float,\\n) -> float:\\n if col_l2 == 0.0:\\n return 0.0\\n n, d = x.size()\\n # quick hack for multiclass (tensor instead of float) check\\n multiclass = hasattr(w_j, \\\"__len__\\\")\\n z = (n * lmb) / 2\\n if multiclass:\\n # w_j is tensor; multiclass regression\\n rho = x[:, j].matmul(\\n r + x[:, j].unsqueeze(1).matmul(w_j.unsqueeze(0))\\n ) # type: ignore\\n return torch.nn.functional.softshrink(rho, z).data / col_l2\\n else:\\n # w_j is scalar; scalar regression\\n rho = x[:, j].matmul(r + x[:, j] * w_j)\\n return soft_thresh(rho, z) / col_l2\",\n \"def _append_cx(self, i, j):\\n\\n if not 0 <= i < self.num_qubits or not 0 <= j < self.num_qubits:\\n raise QiskitError(\\\"CX qubits are out of bounds.\\\")\\n self.linear[j] = (self.linear[i] + self.linear[j]) % 2\\n self.shift[j] = (self.shift[i] + self.shift[j]) % 2\",\n \"def fixB(self,i,j,value):\\n if self.coeffPattern[1] == None:\\n m,n=self.m,self.n\\n self.coeffPattern = [[None]*n for i in range(m)]\\n self.coeffPattern[1][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def ols_cd_weight_update(\\n x: FloatTensor,\\n r: FloatTensor,\\n j: int,\\n w_j: Union[float, FloatTensor],\\n col_l2: float,\\n _: float,\\n) -> float:\\n return w_j + x[:, j].matmul(r) / col_l2 if col_l2 != 0.0 else 0.0\",\n \"def comb_lignes(self,ki,kj,i,j):\\n f = self.F\\n g = lambda r,c : ki*f(i,c) + kj*f(j,c) if r == i else f(r,c)\\n return Mat(self.D,g)\",\n \"def conj(self, o): \\n return (o.inv()) * self * o\",\n \"def jacobian(self,var,g=None):\\n if (g==None):g=self.g\\n jac=np.zeros([self.n+1,self.n])\\n for i in range(self.n):\\n for j in range(self.n):\\n if(i==j): jac[i][j]=2.*(var[i]+1.)-g*np.sum([self.XXZ.Z(i,k) for k in range(self.n) if k!=i])\\n else: jac[i][j]=g*self.XXZ.Z(i,j)\\n for i in range(self.n):\\n jac[self.n][i]=1.\\n return jac\",\n \"def setitem(self, i, j, value):\\n # XXX: flint matrices do not support negative indices\\n # XXX: They also raise ValueError instead of IndexError\\n m, n = self.shape\\n if i < 0:\\n i += m\\n if j < 0:\\n j += n\\n try:\\n self.rep[i, j] = value\\n except ValueError:\\n raise IndexError(f\\\"Invalid indices ({i}, {j}) for Matrix of shape {self.shape}\\\")\",\n \"def objective(self):\\n pass\",\n \"def fixC(self,i,value):\\n if self.coeffPattern[2] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[2] = [None]*m\\n self.coeffPattern[2][i]=value\\n self._updateEstimatorSize(i)\",\n \"def _do_Jsum(om, A_J, D_J):\\n Dmat=npufunc.Jomega.outer(D_J,om)\\n return np.einsum('...j,jk',A_J,Dmat)\",\n \"def coefficients_from_j(j, minimal_twist=True):\\n try:\\n K = j.parent()\\n except AttributeError:\\n K = rings.RationalField()\\n if K not in _Fields:\\n K = K.fraction_field()\\n\\n char = K.characteristic()\\n if char == 2:\\n if j == 0:\\n return Sequence([0, 0, 1, 0, 0], universe=K)\\n else:\\n return Sequence([1, 0, 0, 0, 1/j], universe=K)\\n if char == 3:\\n if j == 0:\\n return Sequence([0, 0, 0, 1, 0], universe=K)\\n else:\\n return Sequence([0, j, 0, 0, -j**2], universe=K)\\n\\n if K is rings.RationalField():\\n # we construct the minimal twist, i.e. the curve with minimal\\n # conductor with this j_invariant:\\n if j == 0:\\n return Sequence([0, 0, 1, 0, 0], universe=K) # 27a3\\n if j == 1728:\\n return Sequence([0, 0, 0, -1, 0], universe=K) # 32a2\\n\\n if not minimal_twist:\\n k = j-1728\\n return Sequence([0, 0, 0, -3*j*k, -2*j*k**2], universe=K)\\n\\n n = j.numerator()\\n m = n-1728*j.denominator()\\n a4 = -3*n*m\\n a6 = -2*n*m**2\\n\\n # Now E=[0,0,0,a4,a6] has j-invariant j=n/d\\n from sage.sets.set import Set\\n for p in Set(n.prime_divisors()+m.prime_divisors()):\\n e = min(a4.valuation(p)//2, a6.valuation(p)//3)\\n if e & gt\\n 0:\\n p = p**e\\n a4 /= p**2\\n a6 /= p**3\\n\\n # Now E=[0,0,0,a4,a6] is minimal at all p != 2,3\\n tw = [-1, 2, -2, 3, -3, 6, -6]\\n E1 = EllipticCurve([0, 0, 0, a4, a6])\\n Elist = [E1] + [E1.quadratic_twist(t) for t in tw]\\n Elist.sort(key=lambda E: E.conductor())\\n return Sequence(Elist[0].ainvs())\\n\\n # defaults for all other fields:\\n if j == 0:\\n return Sequence([0, 0, 0, 0, 1], universe=K)\\n if j == 1728:\\n return Sequence([0, 0, 0, 1, 0], universe=K)\\n k = j-1728\\n return Sequence([0, 0, 0, -3*j*k, -2*j*k**2], universe=K)\",\n \"def ridge_cd_weight_update(\\n x: FloatTensor,\\n r: FloatTensor,\\n j: int,\\n w_j: Union[float, FloatTensor],\\n col_l2: float,\\n lmb: float,\\n) -> float:\\n if col_l2 == 0.0:\\n return 0.0\\n n, d = x.size()\\n return w_j + (x[:, j].matmul(r) - n * lmb * w_j) / col_l2\",\n \"def delete_cost(self, i, j):\\n raise NotImplementedError\",\n \"def set_current(self,Jc):\\n self.raw_parameters[\\\"Jc\\\"] = Jc\\n self.parameters = NormalizedParameters(self.raw_parameters)\\n self._load()\",\n \"def inverseIntermediateJac(self,x):\\n \\n Ri = self._rotation.T\\n si = (1./self._scaled).reshape((1,1,self._dim))\\n \\n Jac = self.intermediateJacPol2Rot(x)\\n \\n #Ri.J\\n Jac = np.einsum(\\\"jk,ikl->ijl\\\",Ri,Jac)\\n #(Ri.J).diag(si)\\n Jac *= si\\n \\n return Jac\",\n \"def cost(self, X, y) :\\n ### ========== TODO : START ========== ###\\n # part d: compute J(theta)\\n #we know for linear/polynomial regression, the cost is the square of the errors\\n X = self.generate_polynomial_features(X)\\n y_pred_vector = np.dot(X, self.coef_)\\n cost = np.dot((y-y_pred_vector).transpose(),(y-y_pred_vector))#write in the matrix form\\n ### ========== TODO : END ========== ###\\n return cost\",\n \"def threej(self, coeff):\\n j1, m1, j2, m2, j3, m3 = coeff\\n myargs = tuple(int(2*x) for x in (j1, j2, j3, m1, m2, m3))\\n L1, L2, L3, M1, M2, M3 = myargs\\n if M1 + M2 + M3 != 0:\\n return 0\\n elif abs(L1 - L2) > L3:\\n return 0\\n elif L1 + L2 < L3:\\n return 0\\n else:\\n return py3nj.wigner3j(*myargs)\",\n \"def jacobian(self, x1, x2, out=None):\\n raise NotImplementedError\",\n \"def update_coeff(self, **kwargs: float) -> None:\\n for rule_name, coeff in kwargs.items():\\n if rule_name not in self.rules:\\n raise ValueError(f\\\"Behavioral rule {rule_name} does not exist\\\")\\n else:\\n self.rules[getattr(self, rule_name)] = coeff\",\n \"def spin_J_set(j, *, dtype=None):\\n return jmat(j, dtype=dtype)\",\n \"def add_objective(self): \\n \\n if \\\"CSS\\\" in self.algorithm:\\n \\n if self.num_hidden == 0:\\n \\n data_term = self.compute_energy(self.x, self.batch_size)\\n \\n else:\\n \\n data_term = self.compute_free_energy(self.x)\\n \\n normalizer_term = self.add_css_approximation(data_term)\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden ==0:\\n \\n data_term = self.compute_energy(self.x, self.batch_size)\\n \\n normalizer_term = self.compute_energy(self.x_gibbs, \\n self.batch_size)\\n \\n normalizer_term = -T.mean(normalizer_term)\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden > 0:\\n \\n data_term = self.compute_free_energy(self.x)\\n \\n normalizer_term = self.compute_free_energy(self.rbm_cd_samples)\\n \\n normalizer_term = -T.mean(normalizer_term)\\n \\n # cost is negative log likelihood \\n self.cost = T.mean(data_term) + normalizer_term\",\n \"def jacobian_c(self, x, out=None, **kwargs):\\n return self._base_nlp.jacobian_c(x, out=out, **kwargs)\",\n \"def putbarcj(self,j_,sub_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights_)\\n elif num_ != len(weights_):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbarcj(self.__nativep,j_,num_,_sub_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarcj(self,j_,sub,weights): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights)\\n elif num_ != len(weights):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"q\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"q\\\":\\n sub_ = memoryview(array.array(\\\"q\\\",sub))\\n \\n if weights is None: raise TypeError(\\\"Invalid type for argument weights\\\")\\n if weights is None:\\n weights_ = None\\n else:\\n try:\\n weights_ = memoryview(weights)\\n except TypeError:\\n try:\\n _tmparr_weights = array.array(\\\"d\\\",weights)\\n except TypeError:\\n raise TypeError(\\\"Argument weights has wrong type\\\")\\n else:\\n weights_ = memoryview(_tmparr_weights)\\n \\n else:\\n if weights_.format != \\\"d\\\":\\n weights_ = memoryview(array.array(\\\"d\\\",weights))\\n \\n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def j_nc_from_j(self, j, inverse=False, check_bounds=False):\\n if not inverse:\\n if check_bounds:\\n assert_between(j, 0, self._grid_shape[1]-1)\\n j_nc = self._nc_j0 + j * self._nc_jskip\\n if check_bounds:\\n assert_between(j_nc, 0, self._nc_xdim)\\n return j_nc\\n else:\\n j_nc = j\\n if check_bounds:\\n assert_between(j_nc, 0, self._nc_ydim)\\n j = (j_nc - self._nc_j0)/self._nc_jskip\\n if check_bounds:\\n assert_between(j, 0, self._grid_shape[1]-1)\\n return j\",\n \"def JacobianFunction(p,x,y,z):\\n \\n n = len(x)\\n \\n J = np.array([ np.ones((n)),x,x**2,y,y**2,x*y ])\\n \\n return J\",\n \"def jacobian_i(self, x):\\n return np.matrix([-x**3, -x**2, -x, -1])\",\n \"def transform_and_compute_jacobian(self, xj):\\n x = xj[:, :self.d].detach()\\n log_j = xj[:, -1]\\n\\n x.requires_grad = True\\n y = self.flow_(x)\\n\\n n_batch = xj.shape[0]\\n\\n jx = torch.zeros(n_batch, self.d, self.d).to(log_j.device)\\n directions = torch.eye(self.d).to(log_j).unsqueeze(0).repeat(n_batch, 1, 1)\\n\\n for i in range(self.d):\\n jx[:, i, :] = torch.autograd.grad(y, x, directions[:, i, :],\\n allow_unused=True, create_graph=True, retain_graph=True)[0]\\n x.requires_grad = False\\n x.grad = None\\n\\n log_det_j = torch.log(torch.abs(torch.det(jx)))\\n return torch.cat([y.detach(), (log_j + log_det_j).unsqueeze(1)], 1)\",\n \"def _reduce(self, x, y):\\n coef = self._update_parallel_coef_constraints(x)\\n self.coef_ = coef.T\",\n \"def doublec(self, j):\\n\\t\\tif j < (self.k0 + 1):\\n\\t\\t\\treturn 0\\n\\t\\tif (self.b[j] != self.b[j-1]):\\n\\t\\t\\treturn 0\\n\\t\\treturn self.cons(j)\",\n \"def objective(self, objective):\\n\\n self._objective = objective\",\n \"def merge_j(self, tensor: Tensor):\\n return tensor.merge(consts=self._j_exprs).map2amps(factor)\",\n \"def __setitem__(self, j, val):\\n\\t\\tself._coords[j] = val\",\n \"def __setitem__( self, l, c_l ) :\\n\\n if( l == len( self ) ) :\\n self.coefficients.append( float( c_l ) )\\n else :\\n self.coefficients[l] = float( c_l )\",\n \"def coefficient(self) -> float:\\n ...\",\n \"def _J_combine_LS_anisotropic(w, S2, tau_int, A_J, D_J):\\n dims=len(A_J)\\n J=np.zeros( (dims,len(w)) )\\n for k in range(dims):\\n D_eff=D_J[k]+1.0/tau_int\\n J[i] = S2[i]*A_J[k]*D_J[k]/(D_J[k]**2+w**2) + (1-S2) * D_eff/(D_eff**2 + w**2)\\n return J.sum(axis=0)\",\n \"def jac_rot(self):\\n J = self.sim.data.get_body_jacr(self.end_effector)\\n J = J.reshape(3, -1)[:, 0:7].T\\n return J\",\n \"def _get_jacobian(self):\\n srcs, recs = self.srcs, self.recs\\n if not self.sparse:\\n jac = numpy.array(\\n [ttime2d.straight([cell], '', srcs, recs, velocity=1.)\\n for cell in self.mesh]).T\\n else:\\n shoot = ttime2d.straight\\n nonzero = []\\n extend = nonzero.extend\\n for j, c in enumerate(self.mesh):\\n extend((i, j, tt)\\n for i, tt in enumerate(shoot([c], '', srcs, recs,\\n velocity=1.))\\n if tt != 0)\\n row, col, val = numpy.array(nonzero).T\\n shape = (self.ndata, self.nparams)\\n jac = scipy.sparse.csr_matrix((val, (row, col)), shape)\\n return jac\",\n \"def setSmoothCost(self, cost):\\n if cost.shape[0] != cost.shape[1] or (cost != cost.T).any():\\n raise DataTypeNotSupportedError('Cost matrix not square or not symmetric')\\n if cost.shape[0] != self.numLabels:\\n raise ShapeMismatchError('Cost matrix not of size numLabels * numLabels')\\n\\n # Just a reference\\n self._smoothCost = self._convertSmoothCostArray(cost)\\n _cgco.gcoSetSmoothCost(self.handle, self._smoothCost)\",\n \"def __call__(self, W=None):\\n if W is None:\\n W = sparse.csr_matrix(self.W[-1])\\n else:\\n W = sparse.csr_matrix(W)\\n J = self.mask.T*(self.mask.multiply(W.T))\\n self.J = J\\n LogJ = J.copy()\\n LogJ.data = np.log(LogJ.data)\\n obj = (self.mask.T*self.mask).multiply(LogJ).sum()\\n if self.rho is not None:\\n obj = (1. - self.rho)*obj + self.rho*self.regularizer(W.todense())[0]\\n return obj\",\n \"def jot(self):\\r\\n\\t\\t\\r\\n\\t\\t# empty list?\\r\\n\\t\\tif len(self) < 1:\\r\\n\\t\\t\\r\\n\\t\\t\\treturn '(0)'\\r\\n\\t\\t\\r\\n\\t\\t# go through terms\\r\\n\\t\\ts = ''\\r\\n\\t\\tfor i in self:\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# positive or negative\\r\\n\\t\\t\\tif i.look('i') % 4 in (0,1):\\r\\n\\t\\t\\t\\ts += ' +('\\r\\n\\t\\t\\tif i.look('i') % 4 in (2,3):\\r\\n\\t\\t\\t\\ts += ' -('\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t# list of variables\\r\\n\\t\\t\\ty = i.keys()\\r\\n\\t\\t\\ty = [str(j) for j in y if j != 'i']\\r\\n\\t\\t\\ty = [j for j in y if j.isalpha()]\\r\\n\\t\\t\\ty = Pa._tidy(y)\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t# coefficient\\r\\n\\t\\t\\tn,d = i.fuse()\\r\\n\\t\\t\\tif n > 1 or d > 1 or len(y) < 1:\\r\\n\\t\\t\\t\\ts += str(n)\\r\\n\\t\\t\\tif d > 1:\\r\\n\\t\\t\\t\\ts += '/' + str(d)\\r\\n\\t\\t\\tif i.look('i') % 4 in (1,3):\\r\\n\\t\\t\\t\\ts += 'i'\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# add variables to string\\r\\n\\t\\t\\tfor k in y:\\r\\n\\t\\t\\t\\tif s[-1] == '(':\\r\\n\\t\\t\\t\\t\\ts += k\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\ts += ' ' + k\\r\\n\\t\\t\\t\\tif i[k] != 1:\\r\\n\\t\\t\\t\\t\\ts += str(i[k])\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t# close\\r\\n\\t\\t\\ts += ')'\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t# remove leading ' '\\r\\n\\t\\tif s[0] == ' ':\\r\\n\\t\\t\\ts = s[1:]\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t# remove leading +\\r\\n\\t\\tif s[0] == '+':\\r\\n\\t\\t\\ts = s[1:]\\r\\n\\t\\t\\t\\t\\r\\n\\t\\treturn s\",\n \"def putqobjij(self,i_,j_,qoij_): # 3\\n res = self.__obj.putqobjij(i_,j_,qoij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def objective(self, adjacency=None, R=None):\\n raise NotImplementedError()\",\n \"def intermediateJac(self, x, isRot=False):\\n if not isRot:\\n x = dot(self._rotation,x)\\n\\n #Due to how it is constructed,\\n #J_rot2polar is its own inverse (and symmetric)\\n \\n Jac = self.intermediateJacRot2Polar(x)\\n \\n #Jac = S.J.R\\n \\n s=self._scaled.reshape((1,self._dim,1))\\n R = self._rotation\\n \\n #Compute J.R\\n Jac = np.einsum(\\\"ijk,kl->ijl\\\",Jac,R)\\n #Left multiply with S\\n #S.(J.R)\\n Jac *= s #A left multiplication with a diagonal matrix is like scaling the rows\\n \\n return Jac\",\n \"def jacobian(self, dt):\\n return self._F_cache\",\n \"def put_proj_coeffs(self, dest):\\n if parallel.is_rank_zero():\\n self.put_array(self.proj_coeffs, dest)\\n parallel.barrier()\",\n \"def jacobian(theta, event, parameters_to_fit):\\n for (key, val) in enumerate(parameters_to_fit):\\n setattr(event.model.parameters, val, theta[key])\\n return event.chi2_gradient(parameters_to_fit)\",\n \"def coupling_6j((J1,J2,J3),(J4,J5,J6)):\\n cj6=pygsl.sf.coupling_6j(int(2*J1),int(2*J2), int(2*J3), int(2*J4),int(2*J5), int(2*J6))[0]\\n cj6=(-1)**(J1+J2+J4+J5)*hat(J3)*hat(J6)*cj6 \\n return cj6\",\n \"def J(cst, x):\\n [u0, v0, u1, v1, u2, v2, coeffs] = cst\\n [u, v, g1, g2, g3] = x\\n df1du = 2*u*g3**2 - 2*g3*u0 + 2*g3*coeffs[3]*(g1*u1-u0) + 2*g3*coeffs[4]*(g2*u2-u0)\\n df1dv = -2*v*g3**2 + 2*g3*v0 - 2*g3*coeffs[3]*(g1*v1-v0) - 2*g3*coeffs[4]*(g2*v2-v0)\\n df1dg1 = 2*g1*coeffs[0]*(u1**2-v1**2) + 2*(v1*v0-u1*u0)*(coeffs[0]+coeffs[1]+coeffs[3]) + 2*g2*coeffs[1]*(u1*u2-v1*v2) + 2*g3*coeffs[3]*(u1*u-v1*v)\\n df1dg2 = 2*g2*coeffs[2]*(u2**2-v2**2) + 2*(v2*v0-u2*u0)*(coeffs[1]+coeffs[2]+coeffs[4]) + 2*g1*coeffs[1]*(u1*u2-v1*v2) + 2*g3*coeffs[4]*(u2*u-v2*v)\\n df1dg3 = 2*g3*(u**2-v**2) + 2*(v*v0-u*u0)*(coeffs[3]+coeffs[4]+1) + 2*g1*coeffs[3]*(u1*u-v1*v) + 2*g2*coeffs[4]*(u2*u-v2*v)\\n\\n df2du = 0\\n df2dv = 2*v*g3**2 + 2*g3*(-v0 + coeffs[3]*(g1*v1-v0) + coeffs[4]*(g2*v2-v0))\\n df2dg1 = 2*g1*coeffs[0]*(v1**2-1) + 2*(1-v1*v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + 2*g2*coeffs[1]*(v1*v2-1) + 2*g3*coeffs[3]*(v1*v-1)\\n df2dg2 = 2*g2*coeffs[2]*(v2**2-1) + 2*(1-v2*v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + 2*g1*coeffs[1]*(v1*v2-1) + 2*g3*coeffs[4]*(v2*v-1)\\n df2dg3 = 2*g3*(v**2-1) + 2*(1-v*v0)*(coeffs[3]+coeffs[4]+1) + 2*g1*coeffs[3]*(v1*v-1) + 2*g2*coeffs[4]*(v2*v-1)\\n\\n df3du = g3*coeffs[3]*(g1*v1-v0) + g3*coeffs[4]*(g2*v2-v0) + g3*(g3*v-v0)\\n df3dv = g3*coeffs[3]*(g1*u1-u0) + g3*coeffs[4]*(g2*u2-u0) + g3*(g3*u-u0)\\n df3dg1 = 2*g1*coeffs[0]*u1*v1 - (v1*u0+u1*v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(u1*v2+v1*u2) + g3*coeffs[3]*(v1*u+u1*v)\\n df3dg2 = 2*g2*coeffs[2]*u2*v2 - (v2*u0+u2*v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(u1*v2+v1*u2) + g3*coeffs[4]*(v2*u+u2*v)\\n df3dg3 = 2*g3*u*v - (u*v0+v*u0)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(v1*u+u1*v) + g2*coeffs[4]*(v2*u+u2*v)\\n\\n df4du = g3*coeffs[3]*(g1-1) + g3*coeffs[4]*(g2-1) + g3*(g3-1)\\n df4dv = 0\\n df4dg1 = 2*g1*coeffs[0]*u1 - (u0+u1)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(u1+u2) + g3*coeffs[3]*(u+u1)\\n df4dg2 = 2*g2*coeffs[2]*u2 - (u0+u2)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(u1+u2) + g3*coeffs[4]*(u+u2)\\n df4dg3 = 2*g3*u - (u+u0)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(u+u1) + g2*coeffs[4]*(u+u2)\\n\\n df5du = 0\\n df5dv = g3*coeffs[3]*(g1-1) + g3*coeffs[4]*(g2-1) + g3*(g3-1)\\n df5dg1 = 2*g1*coeffs[0]*v1 - (v1+v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(v2+v1) + g3*coeffs[3]*(v1+v)\\n df5dg2 = 2*g2*coeffs[2]*v2 - (v2+v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(v2+v1) + g3*coeffs[4]*(v2+v)\\n df5dg3 = 2*g3*v - (v0+v)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(v1+v) + g2*coeffs[4]*(v2+v)\\n\\n return np.array([\\n [df1du, df1dv, df1dg1, df1dg2, df1dg3],\\n [df2du, df2dv, df2dg1, df2dg2, df2dg3],\\n [df3du, df3dv, df3dg1, df3dg2, df3dg3],\\n [df4du, df4dv, df4dg1, df4dg2, df4dg3],\\n [df5du, df5dv, df5dg1, df5dg2, df5dg3],\\n ])\",\n \"def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\\n self.coeff[self._core.index_alpha(key[0]),\\n self._core.index_beta(key[1])] = value\",\n \"def R(self, s, j, **params):\\n s_,j_ = (deepcopy(s)+1,deepcopy(j))\\n return s_,j_\",\n \"def total_cost(J_content, J_style, alpha=10, beta=40):\\n\\n J = alpha * J_content + beta * J_style\\n\\n return J\",\n \"def J(self):\\n return self._J\",\n \"def J (self, n):\",\n \"def total_cost(J_content, J_style, alpha = 10, beta = 40):\\n\\n J = alpha * J_content + beta * J_style\\n\\n return J\",\n \"def amalgamate(self,i,j):\\n # conserve momentum\\n self.v[i] = (self.v[i]*self.m[i]+self.v[j]*self.m[j])/ \\\\\\n (self.m[i]+self.m[j])\\n self.r[i] = (self.r[j] - self.r[i])/2 + self.r[j] \\n self.m[i] = self.m[i] + self.m[j]\\n self.r[j] = self.r[self.n-1]\\n self.v[j] = self.v[self.n-1]\\n self.m[j] = self.m[self.n-1]\\n self.n = self.n - 1\",\n \"def user_cons_hJ(h, Jac, mbs_data, tsim):\\n\\n # Example: Compute the expression of h and Jac then assign the values.\\n # h[1] = mbs_data.q[1]-mbs_data.q[2]*mbs_data.q[2]\\n # Jac[1,1] = 1.\\n # Jac[1,2] = -2*mbs_data.q[2].\\n # IMPORTANT: NEVER REASSIGN h => h = np.array([0,mbs_data.q[1]-mbs_data.q[2]*mbs_data.q[2],0])\\n # NEVER REASSIGN Jac => Jac = np.array([[0,0,0,0],[0,1,-2*mbs_data.q[2],0])\\n # Both command will change the values of h, Jac in this function\\n # but they will not be modified outside the scope of this function.\\n rwt = RwtTrackGeometry(mbs_data, pointer = mbs_data.user_model['addons']['rwt'])\\n rwc = RwcMain(pointer = mbs_data.user_model['addons']['rwc'])\\n \\n rwt.cons_hJ(mbs_data, h, Jac)\\n rwc.compute_constraints(mbs_data, h, Jac)\\n \\n \\\"\\\"\\\"id1 = mbs_data.joint_id[\\\"R1_caisse1\\\"]\\n id2 = mbs_data.joint_id[\\\"R1_caisse2\\\"]\\n id3 = mbs_data.joint_id[\\\"R1_chassis1\\\"]\\n id4 = mbs_data.joint_id[\\\"R1_chassis2\\\"]\\n\\n # define the value of the constraint\\n h[1] = (mbs_data.q[id1] + mbs_data.q[id3]) - (mbs_data.q[id2] + mbs_data.q[id4])\\n\\n # define the value of the jacobian matrix\\n Jac[1,id1] = 1\\n Jac[1,id2] = -1\\n Jac[1,id3] = 1\\n Jac[1,id4] = -1\\\"\\\"\\\"\\n \\n return\",\n \"def putacol(self,j_,subj_,valj_):\\n nzj_ = None\\n if nzj_ is None:\\n nzj_ = len(subj_)\\n elif nzj_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nzj_ is None:\\n nzj_ = len(valj_)\\n elif nzj_ != len(valj_):\\n raise IndexError(\\\"Inconsistent length of array valj\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valj_ is None:\\n raise ValueError(\\\"Argument valj cannot be None\\\")\\n if valj_ is None:\\n raise ValueError(\\\"Argument valj may not be None\\\")\\n if isinstance(valj_, numpy.ndarray) and valj_.dtype is numpy.dtype(numpy.float64) and valj_.flags.contiguous:\\n _valj_copyarray = False\\n _valj_tmp = ctypes.cast(valj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valj_ is not None:\\n _valj_copyarray = True\\n _valj_np_tmp = numpy.zeros(len(valj_),numpy.dtype(numpy.float64))\\n _valj_np_tmp[:] = valj_\\n assert _valj_np_tmp.flags.contiguous\\n _valj_tmp = ctypes.cast(_valj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valj_copyarray = False\\n _valj_tmp = None\\n \\n res = __library__.MSK_XX_putacol(self.__nativep,j_,nzj_,_subj_tmp,_valj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def jacobi_solver(self, mat, rhs):\\n x = np.zeros_like(rhs)\\n for it_count in range(self.iterations_number):\\n x_new = np.zeros_like(x)\\n if self.verbose > 1:\\n print(\\\"Iteration {0}: {1}\\\".format(it_count, x))\\n for i in range(mat.shape[0]):\\n s1 = np.dot(mat[i, :i], x[:i])\\n s2 = np.dot(mat[i, i + 1:], x[i + 1:])\\n x_new[i] = (rhs[i] - s1 - s2) / mat[i, i]\\n if np.allclose(x, x_new, rtol=1e-8):\\n break\\n x = x_new\\n return x\",\n \"def __call__(self, W=None):\\n if W is None:\\n W = sparse.csr_matrix(self.W[-1])\\n else:\\n W = sparse.csr_matrix(W)\\n J = self.mask.T*(self.mask.multiply(W.T))\\n self.J = J\\n LogJ = J.copy()\\n LogJ.data = np.log(LogJ.data)\\n obj = (-J.multiply(LogJ)).sum()\\n if self.rho is not None:\\n obj = (1. - self.rho)*obj + self.rho*self.regularizer(W.todense())[0]\\n return obj\",\n \"def coeff_mul(self, coeff):\\n return self\",\n \"def get_jac(wcs, cenx, ceny):\\n import galsim\\n\\n crpix = wcs.crpix\\n galsim_pos = galsim.PositionD(x=crpix[0], y=crpix[1])\\n\\n galsim_jac = wcs.jacobian(image_pos=galsim_pos)\\n\\n return ngmix.Jacobian(\\n x=cenx,\\n y=ceny,\\n dudx=galsim_jac.dudx,\\n dudy=galsim_jac.dudy,\\n dvdx=galsim_jac.dvdx,\\n dvdy=galsim_jac.dvdy,\\n )\",\n \"def _weight_jac_mat_prod(self, module, g_inp, g_out, mat):\\n raise NotImplementedError\",\n \"def jac_res(self, params, **kwargs):\\n e = kwargs.get(\\\"e\\\", self.problem.data_e)\\n\\n jac = self.jacobian.eval(params, **kwargs)\\n return - jac / e[:, None]\",\n \"def switch(self, i, j0, j1):\\n if not self._mutable:\\n raise ValueError(\\\"this constellation is immutable.\\\"\\n \\\" Take a mutable copy first.\\\")\\n S = SymmetricGroup(list(range(self.degree())))\\n tr = S((j0, j1))\\n i = int(i)\\n if i < 0 or i >= len(self._g):\\n raise ValueError(\\\"index out of range\\\")\\n\\n ii = i + 1\\n if ii == len(self._g):\\n ii = 0\\n self._g[i] = self._g[i] * tr\\n self._g[ii] = tr * self._g[ii]\",\n \"def relax_rate(self, temperature, j_i, j_j, fit_param=None):\\n # Energies of the rovibrational states involved.\\n Ej_i = self.ls_factors.term_values(0, j_i, 'Fv')\\n del_E = self.ls_factors.term_values(0, j_j, 'Fv') - Ej_i\\n\\n # For a transition from j_i to j_j with i<j:\\n if abs(j_i-j_j) % 2 != 0:\\n # For N2 gamma_ji is zero when del_j is odd due to nuclear-spin\\n # selection rules.\\n gamma_ji = 0\\n gamma_ij = 0\\n else:\\n alpha, beta, sigma, m = fit_param\\n\\n _term_1 = (1-np.exp(-m))/(\\n 1-np.exp(-m*temperature/295))*(295/temperature)**0.5\\n _term_2 = ((1+1.5*1.44*Ej_i/temperature/sigma)/(\\n 1+1.5*1.44*Ej_i/temperature))**2\\n\\n gamma_ji = alpha*self.pressure/1.01325*_term_1*_term_2*np.exp(\\n -beta*del_E*1.44/temperature)\\n gamma_ij = gamma_ji*(2*j_i+1)/(2*j_j+1)*np.exp(\\n del_E*1.44/temperature)\\n\\n return gamma_ji, gamma_ij\",\n \"def add_coefficients(self, L, overwrite=False):\\n if not isinstance(L, dict):\\n raise ValueError(\\\"Call with dictionary as argument!\\\")\\n\\n for p in L.keys():\\n c = mpmath.mpmathify(L[p])\\n # print \\\"c=\\\",c\\n cd = ceil(mpmath.log10(abs(c)))\\n if(cd > self.maxdigs):\\n self.maxdigs = cd\\n # print \\\"p=\\\",p\\n if(is_int(p)):\\n (r, n) = rn_from_D(self._space.WR, p)\\n elif(isinstance(p, tuple)):\\n (r, n) = p\\n if r in self._coeffs:\\n if n in self._coeffs[r]:\\n c_old = self._coeffs[r][n]\\n # Try to determine (heuristically) if the new coefficient is really better\\n d1 = dist_from_int(c)[0]\\n d2 = dist_from_int(c_old)[0]\\n if(overwrite):\\n self._coeffs[r][n] = c\\n else:\\n self._coeffs[r][n] = c\\n else:\\n # see if it is a possible index at all\\n if not r < 0 or r > self._space.multiplier().ambient_rank():\\n raise ValueError(\\\"Key {0} corr to (r,n)=({1},{2}) is invalid for the current space!\\\".format(p, r, n))\\n elif r not in self._space.multiplier().D():\\n if self._space._sym_type == -1 and (r == 0 or r == self._space.multiplier().N):\\n # Should be 0 by symmetry\\n if abs(c) > 10**(1 - self.prec):\\n raise ValueError(\\\"Coefficient should be zero by symmetry. Got c({0},{1})={2}!\\\".format(r, n, c))\\n else:\\n self._coeffs[r][n] = 0\\n else:\\n # is equal to +- c(-r,n)\\n mr = 2 * self.multiplier().N - r\\n if mr in self._coeffs:\\n if n in self._coeffs[mr]:\\n c_old = self._coeffs[mr][n]\\n if abs(c - self._space.multiplier()._sym_type * c_old) > 10**(1 - self.prec):\\n st = \\\"Might add an erroneous coefficient! Got c({0},{1})={2}. \\\".format(r, n, c)\\n st += \\\"From previous coefficients should have {0}\\\".format(self._space._sym_type * c_old)\\n raise ValueError(st)\\n if overwrite:\\n self._coeffs[mr][n] = c\\n else:\\n raise ValueError(\\\"Coefficient should be zero by symmetry. Got c({0},{1})={2}!\\\" .format(r, n, c))\",\n \"def putsolutionyi(self,i_,whichsol_,y_):\\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def J(self) -> float:\\n return self._J\",\n \"def lpj2pjc(lpj: to.Tensor):\\n up_lpg_bound = 0.0\\n shft = up_lpg_bound - lpj.max(dim=1, keepdim=True)[0]\\n tmp = to.exp(lpj + shft)\\n return tmp.div_(tmp.sum(dim=1, keepdim=True))\",\n \"def put_C_reduced(self, dest):\\n _parallel.call_from_rank_zero(self.put_mat, self.C_reduced, dest)\\n _parallel.barrier()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71400785","0.6213591","0.61696875","0.6164791","0.60605234","0.59805757","0.58748615","0.57106733","0.5628812","0.56003463","0.55685896","0.5553388","0.5516792","0.5434863","0.5434863","0.5425381","0.540043","0.5388786","0.5384671","0.53840244","0.5376267","0.5371235","0.5354306","0.5333264","0.53274924","0.53267163","0.5322429","0.5313385","0.52935594","0.5290578","0.528203","0.5247122","0.52451473","0.5241435","0.5239954","0.5239542","0.5212098","0.5210151","0.51985526","0.5163057","0.5150458","0.51390374","0.51260006","0.51140386","0.51067364","0.5106533","0.509801","0.50909734","0.50880504","0.50805444","0.50746447","0.50737745","0.50686157","0.5064995","0.50647867","0.5047045","0.5046875","0.5035524","0.50183815","0.50137496","0.50103253","0.5007907","0.49889848","0.49748567","0.4969866","0.49542886","0.49477628","0.49398473","0.49324587","0.49323145","0.49306238","0.49201122","0.49187353","0.49160802","0.49154314","0.4911832","0.4911619","0.49105662","0.49040768","0.4898352","0.48956996","0.48940435","0.48896036","0.4886338","0.48837346","0.48801762","0.48800114","0.48781997","0.48777553","0.4873995","0.487339","0.48628366","0.48623466","0.48567456","0.4848527","0.48479795","0.48417616","0.48405084","0.4840463","0.4830791"],"string":"[\n \"0.71400785\",\n \"0.6213591\",\n \"0.61696875\",\n \"0.6164791\",\n \"0.60605234\",\n \"0.59805757\",\n \"0.58748615\",\n \"0.57106733\",\n \"0.5628812\",\n \"0.56003463\",\n \"0.55685896\",\n \"0.5553388\",\n \"0.5516792\",\n \"0.5434863\",\n \"0.5434863\",\n \"0.5425381\",\n \"0.540043\",\n \"0.5388786\",\n \"0.5384671\",\n \"0.53840244\",\n \"0.5376267\",\n \"0.5371235\",\n \"0.5354306\",\n \"0.5333264\",\n \"0.53274924\",\n \"0.53267163\",\n \"0.5322429\",\n \"0.5313385\",\n \"0.52935594\",\n \"0.5290578\",\n \"0.528203\",\n \"0.5247122\",\n \"0.52451473\",\n \"0.5241435\",\n \"0.5239954\",\n \"0.5239542\",\n \"0.5212098\",\n \"0.5210151\",\n \"0.51985526\",\n \"0.5163057\",\n \"0.5150458\",\n \"0.51390374\",\n \"0.51260006\",\n \"0.51140386\",\n \"0.51067364\",\n \"0.5106533\",\n \"0.509801\",\n \"0.50909734\",\n \"0.50880504\",\n \"0.50805444\",\n \"0.50746447\",\n \"0.50737745\",\n \"0.50686157\",\n \"0.5064995\",\n \"0.50647867\",\n \"0.5047045\",\n \"0.5046875\",\n \"0.5035524\",\n \"0.50183815\",\n \"0.50137496\",\n \"0.50103253\",\n \"0.5007907\",\n \"0.49889848\",\n \"0.49748567\",\n \"0.4969866\",\n \"0.49542886\",\n \"0.49477628\",\n \"0.49398473\",\n \"0.49324587\",\n \"0.49323145\",\n \"0.49306238\",\n \"0.49201122\",\n \"0.49187353\",\n \"0.49160802\",\n \"0.49154314\",\n \"0.4911832\",\n \"0.4911619\",\n \"0.49105662\",\n \"0.49040768\",\n \"0.4898352\",\n \"0.48956996\",\n \"0.48940435\",\n \"0.48896036\",\n \"0.4886338\",\n \"0.48837346\",\n \"0.48801762\",\n \"0.48800114\",\n \"0.48781997\",\n \"0.48777553\",\n \"0.4873995\",\n \"0.487339\",\n \"0.48628366\",\n \"0.48623466\",\n \"0.48567456\",\n \"0.4848527\",\n \"0.48479795\",\n \"0.48417616\",\n \"0.48405084\",\n \"0.4840463\",\n \"0.4830791\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70735407"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11964,"cells":{"query":{"kind":"string","value":"Sets the objective sense. putobjsense(self,sense_)"},"document":{"kind":"string","value":"def putobjsense(self,sense_):\n res = __library__.MSK_XX_putobjsense(self.__nativep,sense_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putobjsense(self,sense_): # 3\n if not isinstance(sense_,objsense): raise TypeError(\"Argument sense has wrong type\")\n res = self.__obj.putobjsense(sense_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def objective(self, objective):\n\n self._objective = objective","def optimize(self, objective_sense=None, **kwargs):\n\n if objective_sense:\n self.objective.direction = objective_sense\n\n try:\n # self._hidden_optimize_call(kwargs)\n Model.optimize(self, **kwargs)\n solution = self.get_solution()\n self.solution = solution\n return solution\n except SolverError as SE:\n status = self.solver.status\n self.logger.error(SE)\n self.logger.warning('Solver status: {}'.format(status))\n raise (SE)","def getobjsense(self): # 3\n res,resargs = self.__obj.getobjsense()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sense_return_value = resargs\n _sense_return_value = objsense(_sense_return_value)\n return _sense_return_value","def put(cls, obj):\n pass","def set_occupant(self, obj):\n\t\tpass","def setMode(self):\n if self.currentTarget != None and self.finishedAssault == 0:\n if self.isAssault == 1:\n if self.currentTarget != None:\n self.mode = 'assault'\n else:\n self.mode = 'escape'\n else:\n self.log.debug('COUNT: %s: %s TARGET-> %s' % (self.myGalaxy.count, self.name, self.currentTarget.name))\n ##self.myGalaxy.resultList.append('COUNT: %s: %s TARGET-> %s' % (self.myGalaxy.count, self.name, self.currentTarget.name))\n if ((len(self.activeWeapons) == 0 or (self.currentISP/self.myShipHull.maxISP) < 0.7)) and self.__module__ == 'anw.war.ship':\n self.mode = 'escape'\n else:\n range = funcs.getTargetRange(self.posX, self.posY, self.currentTarget.posX, self.currentTarget.posY)\n if range <= self.range:\n self.mode = 'engage'\n else:\n self.mode = 'close'\n else:\n self.mode == 'escape'\n if globals.serverMode == 0:\n self.shipsim.updateShipMode()","def add_objective(self, objective):\n self.objectives.append(objective)","def set(self, obj, value):\n pass","def set(self, obj, value):\n raise NotImplementedError","def set_sensible_obstacles(self, obstacles):\n self.sensible_obstacles = obstacles","def SetObject(self, obj):\n return _gmat_py.EphemManager_SetObject(self, obj)","def sense_and_act(self):\n pass","def sense(self):\n self.robot.update_state_position()\n self.robot.update_state_proximity(self.world_props)\n self.robot.update_state_compass()\n self.robot.update_state_odometry()","def set_solo(self, track, xclip, ident, value = None):\n if track in self.song().tracks + self.song().return_tracks:\n if value in KEYWORDS:\n track.solo = KEYWORDS[value]\n else:\n track.solo = not(track.solo)","def on_sense_sonar(self, dist):\n raise NotImplementedError()","def __set__(self, obj, value):\n # We need to take a copy in case these are the class topics\n topics = obj._help_topics.copy()\n topics.update(value)\n obj._help_topics = topics","def save(self):\r\n for obs_name in self.__dict__.keys():\r\n if obs_name is not \"_ObjetSimu__obs\":\r\n if not obs_name in self.__sous_objets:\r\n if obs_name in self.__obs.keys():\r\n if \"copy\" in dir(self.__dict__[obs_name]):\r\n self.__obs[obs_name].append(self.__dict__[obs_name].copy())\r\n else:\r\n self.__obs[obs_name].append(self.__dict__[obs_name])\r\n else:\r\n self.__dict__[obs_name].save()","def set_sm_userdata(self, obj_desc):\n rospy.loginfo('Getting parameters from server')\n\n #obj_desc = rospy.get_param('obj_desc','{\"type\" : \"Bar\"}')\n\n self.sm.userdata.sm_obj_desc = json.loads(obj_desc)\n\n self.sm.userdata.state = 'pose_selection' \n \n self.sm.userdata.sm_min_objs = rospy.get_param('min_objs',1)\n self.sm.userdata.sm_max_objs = rospy.get_param('max_objs',1)\n self.sm.userdata.sm_max_time = rospy.get_param('max_time', 120)\n self.sm.userdata.sm_max_poses = rospy.get_param('max_poses', 10)\n\n rospy.loginfo(\"Search for %s\", self.sm.userdata.sm_obj_desc)\n rospy.loginfo(\"min_objs: %s\", self.sm.userdata.sm_min_objs)\n rospy.loginfo(\"max_objs: %s\", self.sm.userdata.sm_max_objs)\n rospy.loginfo(\"max_time: %s\", self.sm.userdata.sm_max_time)\n rospy.loginfo(\"max_poses: %s\", self.sm.userdata.sm_max_poses)\n \n # initialize empty obj list\n self.sm.userdata.sm_obj_list = []","def slo(self, objective=99.99):\n self.objective = objective\n return objective","def getobjsense(self):\n sense_ = ctypes.c_int32()\n res = __library__.MSK_XX_getobjsense(self.__nativep,ctypes.byref(sense_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sense_return_value = objsense(sense_.value)\n return (_sense_return_value)","def _sense_and_act(self):\n pass","def getObjSense(self, problemname : str, x : pd.Series):\n if problemname in self.objsensedict:\n return self.objsensedict[problemname]\n elif not pd.isnull(x.get(Key.ObjectiveSense, None)):\n return x.get(Key.ObjectiveSense)\n else:\n logger.warning(\"No objective sense for {}, assuming minimization\".format(problemname))\n return ObjectiveSenseCode.MINIMIZE","def observe(self, obs):\n self.observation = obs\n self.selected = obs.selected\n \n #############################\n # Update of turn statistics #\n #############################\n if self.id == (obs.step % 6):\n # Store base locations\n if self.__class__.home_base is None:\n self.__class__.home_base = (obs.loc[0]+16, obs.loc[1]+8)\n self.__class__.enemy_base = \\\n self.getSymmetricOpposite(self.__class__.home_base)\n \n # Reset trendingSpot\n self.__class__.trendingSpot = {}\n \n # Update friendly CPs\n self.__class__.friendlyCPs = map(lambda x: x[0:2], \n filter(lambda x: x[2] == self.team, obs.cps))\n \n # Update enemy CPs\n self.__class__.enemyCPs = map(lambda x:x[0:2], \n filter(lambda x: x[2] != self.team, obs.cps))\n \n # Update ammo packs \n ammopacks = filter(lambda x: x[2] == \"Ammo\", obs.objects)\n if ammopacks:\n self.updateAllAmmoSpots(ammopacks)\n\n # Update inFriendlyHands stat\n if SETTINGS_DOMINATION_ADDS_UP:\n inFriendlyHands = self.__class__.inFriendlyHands\n else:\n inFriendlyHands = {}\n for cp in self.__class__.friendlyCPs:\n if cp in self.__class__.inFriendlyHands:\n inFriendlyHands[cp] = self.__class__.inFriendlyHands[cp] + 1\n else:\n inFriendlyHands[cp] = 1\n self.__class__.inFriendlyHands = inFriendlyHands","def define_objective(self, objective, capability_param, capability_value,\n assignment_path):\n RATIO = 20\n # pdcli must receive a long number - cant receive float.\n if capability_value > RATIO:\n if objective == Obj.DONT:\n ctx.cluster.cli.basic_objective_create(**{\n 'name': capability_param, capability_param:\n long(capability_value) * RATIO})\n\n self._logger.info('Objective {0} was set to {1}'.format(\n capability_param, str(capability_value * RATIO)))\n\n else:\n ctx.cluster.cli.basic_objective_create(**{\n 'name': capability_param, capability_param:\n long(capability_value)})\n\n self._logger.info('Objective {0} was set to {1}'.format(\n capability_param, str(capability_value)))\n\n ctx.cluster.cli.share_objective_add(name=self.s_name,\n objective=capability_param,\n path=assignment_path)","def set_arm(self, track, xclip, ident, value = None):\n if track in self.song().tracks and track.can_be_armed:\n if value in KEYWORDS:\n track.arm = KEYWORDS[value]\n else:\n track.arm = not(track.arm)","def _update_optimizer(self, hyperparameters, score, fit=True):\n if self.do_maximize:\n score = -score\n self.optimizer_result = self.optimizer.tell(hyperparameters, score, fit=fit)","def __set__(self, obj, value):\r\n pass","def set_object_description(self, agent, Description):\n\n self.send_ObjectDescription(agent, agent.agent_id, agent.session_id, {1:[self.LocalID, Description]})","def putOn(self,obj):\n if obj not in self.on:\n self.on.append(obj)\n if self not in obj.on:\n obj.putOn(self)\n if obj not in self.road.on:\n self.road.putOn(obj)","def postInitialization(self, objectives = None):\n self.objectives = objectives","def do_tune(self, cmd):\n self.params.set('tune', float(cmd) / 16.0, 'global')","def apply_to_world(self, world):\n # add the current obstacles\n for obstacle in self.current_obstacles:\n world.add_obstacle(obstacle)\n\n # program the robot supervisors\n for robot in world.robots:\n robot.supervisor.goal = self.current_goal[:]","def set_use_case(self, title: str=None, domain: str=None, overview: str=None, scope: str=None,\n situation: str=None, opportunity: str=None, actions: str=None, project_name: str=None,\n project_lead: str=None, project_contact: str=None, stakeholder_domain: str=None,\n stakeholder_group: str=None, stakeholder_lead: str=None, stakeholder_contact: str=None,\n save: bool=None):\n self.pm.set_use_case(title=title, domain=domain, overview=overview, scope=scope, situation=situation,\n opportunity=opportunity, actions=actions, project_name=project_name,\n project_lead=project_lead, project_contact=project_contact,\n stakeholder_domain=stakeholder_domain, stakeholder_group=stakeholder_group,\n stakeholder_lead=stakeholder_lead, stakeholder_contact=stakeholder_contact)\n self.pm_persist(save=save)","def expense(self, expense):\n self._expense = expense","def put(self, obj):\n\n if obj is None:\n return\n\n obj = self.put_process(obj)\n return Placeholder(obj).write()","def opponent(self, opponent):\n\n self._opponent = opponent","def set_word(self, w, v):\n assert isinstance(v, Hypothesis)\n\n self.value[w] = v","def __set__(self,obj,val):\n assert len(val) == len(self.attribs),\"Compound parameter '%s' got the wrong number of values (needed %d, but got %d).\" % (self._attrib_name,len(self.attribs),len(val))\n \n if not obj:\n for a,v in zip(self.attribs,val):\n setattr(self.objtype,a,v)\n else:\n for a,v in zip(self.attribs,val):\n setattr(obj,a,v)","def quickdef(world, obname, kind, props={}, **kwargs) :\n world.activity.def_obj(obname, kind)\n for prop, val in props.iteritems() :\n world[prop(obname)] = val\n if \"put_in\" in kwargs :\n world.activity.put_in(obname, kwargs[\"put_in\"])\n if \"put_on\" in kwargs :\n world.activity.put_on(obname, kwargs[\"put_on\"])\n if \"make_part_of\" in kwargs :\n world.activity.make_part_of(obname, kwargs[\"make_part_of\"])","def add_objective(self): \n \n if \"CSS\" in self.algorithm:\n \n if self.num_hidden == 0:\n \n data_term = self.compute_energy(self.x, self.batch_size)\n \n else:\n \n data_term = self.compute_free_energy(self.x)\n \n normalizer_term = self.add_css_approximation(data_term)\n \n if \"CD\" in self.algorithm and self.num_hidden ==0:\n \n data_term = self.compute_energy(self.x, self.batch_size)\n \n normalizer_term = self.compute_energy(self.x_gibbs, \n self.batch_size)\n \n normalizer_term = -T.mean(normalizer_term)\n \n if \"CD\" in self.algorithm and self.num_hidden > 0:\n \n data_term = self.compute_free_energy(self.x)\n \n normalizer_term = self.compute_free_energy(self.rbm_cd_samples)\n \n normalizer_term = -T.mean(normalizer_term)\n \n # cost is negative log likelihood \n self.cost = T.mean(data_term) + normalizer_term","def put(self):\n self._val = True","def emotion(self, emotion: float):\n\n self._emotion = emotion","def save(self, *args, **kwargs):\n self.phs = self.set_phs()\n super(Study, self).save(*args, **kwargs)","def setType(self, *args):\n return _libsbml.Objective_setType(self, *args)","def save_m2m(self, bundle):\n keywords = bundle.data.get('keywords', [])\n keywords = [k.lower() for k in keywords]\n bundle.obj.keywords.set(*keywords)\n return super(StoryResource, self).save_m2m(bundle)","def set_wildtype_phenotype(self, value):\n self.epistasis.data.values[0] = value\n self.build()","def putOn(self,obj):\n if obj not in self.on:\n self.on.append(obj)\n if self not in obj.on:\n obj.putOn(self)","def putOn(self,obj):\n if obj not in self.on:\n self.on.append(obj)\n if self not in obj.on:\n obj.putOn(self)","def optimize(self, X, y):\n print(\"Performing TPOT genetic optimization.\")\n self.model.fit(X, y)\n self.optimized = True","def setActiveObjective(self, *args):\n return _libsbml.ListOfObjectives_setActiveObjective(self, *args)","def save(self, obj):\n raise NotImplementedError","def save(self, **kwargs):\n if len(self.words) > 0:\n self.words = self.words.strip().rstrip()\n self.word_count = self._calculate_word_count()\n super(Term, self).save(**kwargs)","def add(self, obj):\n self.objects.append(obj)\n if obj.gravity == 0:\n obj.gravity = self.gravity\n if obj.gravity_z == 0:\n obj.gravity_z = self.gravity_z","def update(self, obj):\n\n self.cfg.update(obj)","def _update_objective(self):\n # rewrap the cost if the solver has been run\n self.Finalize()\n return","def updateCavity(self, obj):\n obj.Shape = self.getCavityShape(obj.Structure, obj.Position)\n obj.Volume = obj.Shape.Volume","def setEvolution(self,evolution):\n\t\tself.evolution = evolution","def update(self, game):\n super().update(game)\n self.nn_def.set_score(self.score)","def addObjective(self, *args):\n return _libsbml.FbcModelPlugin_addObjective(self, *args)","async def setheist(self, ctx):\r\n\r\n pass","def __setattr__(self, name, value):\n if name in [\"sampling_function\", \"env\", \"fit_dist\", \"reset\"]:\n object.__setattr__(self, name, value)\n else:\n setattr(self.env, name, value)","def set_single_rxn_objective(species_mod, obj_id, obj_direction=\"max\"):\n for strain in tqdm(species_mod.strains):\n species_mod.strains.get_by_id(strain.id).cobra_model.objective=obj_id\n species_mod.strains.get_by_id(strain.id).cobra_model.direction=obj_direction\n return species_mod","def set_objective(self, n_evidence=None):\n if n_evidence is None:\n n_evidence = self.objective.get('n_evidence', self.n_evidence)\n\n if n_evidence < self.n_evidence:\n logger.warning(\n 'Requesting less evidence than there already exists')\n\n self.objective['n_evidence'] = n_evidence\n self.objective['n_sim'] = n_evidence - self.n_precomputed_evidence","def set_score(self,score):\n self._score = score","def setVocabulary(self, vocabulary): # real signature unknown; restored from __doc__\n pass","def set(self, X, y, feature_name):\n pass","def shots_allowed(self, shots_allowed):\n\n self._shots_allowed = shots_allowed","def setHardness(self, hardness):\n self.__hardness = hardness\n self.scene.setShaderInput(\"props\", self.__ambient, self.__hardness, 0, 1)","def init_SMOTE(self, k_neighbors, n_jobs):\n self.object =\\\n over_sampling.SMOTE(random_state=self.random_state,\n k_neighbors=k_neighbors,\n n_jobs=n_jobs)\n\n self.k_neighbors = k_neighbors\n self.n_jobs = n_jobs","def update_obstacles(self, new_obs):\n self.obstacles = new_obs","def set(self, obj: _T) -> None:\n\n self.registry[self.scopefunc()] = obj","def put(self, obj):\n\n if obj is None:\n return\n\n return obj","def put(self, obj):\r\n self.staging_buffer.append(obj)\r\n\r\n if self.is_full():\r\n self._flush()","def set_fold(self, track, xclip, ident, value = None):\n if track.is_foldable:\n if value in KEYWORDS:\n track.fold_state = KEYWORDS[value]\n else:\n track.fold_state = not(track.fold_state)","def init(self, obs, infos):\n # reset agent, get vocabulary masks for verbs / adjectives / nouns\n self.scores = []\n self.dones = []\n self.prev_actions = [[\"\" for _ in range(len(obs))]]\n self.prev_step_is_still_interacting = np.ones((len(obs),), dtype=\"float32\") # 1s and starts to be 0 when previous action is \"stop\"\n self.naozi.reset(batch_size=len(obs))\n self.kg.reset(node_capacity=self.num_nodes, relation_capacity=self.num_relations, batch_size=len(obs))\n self.not_finished_yet = None","def update_spaces_threatened(self):\n self.spaces_threatened = self.all_possible_moves(return_spaces_threatened=True)\n update_threatening_king(self)","def update_spaces_threatened(self):\n self.spaces_threatened = self.all_possible_moves(return_spaces_threatened=True)\n update_threatening_king(self)","def set_swish(self, memory_efficient: bool = True) -> None:\n self._swish = Act[\"memswish\"](inplace=True) if memory_efficient else Act[\"swish\"](alpha=1.0)","def contribute_to_object(self, obj):\n pass","def set_observations(self, factor):\r\n relevant_obs = set(self.obs).intersection(set(factor.get_variables()))\r\n if relevant_obs:\r\n factor.set_observations({x:self.obs[x] for x in relevant_obs})","def setSuggestionStrategy(self, value):\n if value == SuggestionStrategy.OCR:\n self.setBooleanOption(8, True)\n elif value == SuggestionStrategy.TYPO:\n self.setBooleanOption(8, False)\n else:\n raise VoikkoException(\"Invalid suggestion strategy\")","def replaceOOV(word):\n if word in self.vocab: return word\n else: return self.oov","def put(self, *_, **__): # pylint: disable=arguments-differ\n pass","def put(self, *_, **__): # pylint: disable=arguments-differ\n pass","def __init__(self, name, defense):\n self.name = name\n self.defense = defense","def updateObservation(self, obs):\n self.settingsDb.updateObservation(self.observationsTableName(), obs)","def save(self, *args, **kwargs):\n self.small_particles = (int(self.dylos_bin_1) +\n int(self.dylos_bin_2) + int(self.dylos_bin_3))\n self.big_particles = int(self.dylos_bin_4)\n super(Dylos, self).save(*args, **kwargs)","def __setitem__(self, key, obj):\n self.add(key, obj, self._mode)","def write_to_nc(self, nc_handle):\n if self.name in nc_handle.variables:\n return\n process_var = nc_handle.createVariable(self.name, int)\n setattr(process_var, \"PROV__Activity\", self.process_step)\n #setattr(process_var, \"Source\", self.source)\n\n self.attributes.pop('process_step')\n\n # Adds the remainder of the attributes to the variable.\n for name, value in self.attributes.iteritems():\n if name == 'source':\n name = 'PROV__Used'\n setattr(process_var, name, value)","def add_synapses(self, obj):\n synapse_ct = lems.ComponentType('Synapse')\n dynamics_synapse = lems.Dynamics()\n synapse_ct.add(dynamics_synapse)","def objective(self):\n pass","def setStem(self, stem):\n\t\tself.stem=stem;","def set_obs(self, num_obs):\n curr_obs = self._nobs\n if num_obs < curr_obs:\n raise ValueError(\"num_obs must be >= \" + str(curr_obs))\n if num_obs == curr_obs:\n return\n isstrvar = self._isstrvar\n empty_row = ['' if isstrvar(i) else MISSING for i in range(self._nvar)]\n self._varvals += [copy.copy(empty_row) \n for _ in range(num_obs - curr_obs)]\n self._nobs = num_obs\n self._changed = True\n # Need to clear srtlist. If there are string variables, there \n # might now be empty strings after non-empty string. If there \n # are numerical variables with extended missing, there will now \n # be \".\" missing after extended missing. Issue pointed out at\n # http://www.stata.com/statalist/archive/2013-08/msg00576.html\n self._srtlist = [None]*self._nvar","def set_objective_fn(self, objective_fn):\n self.objective_fn = objective_fn","def _insert_vocab(self, json_obj, mode=InsertVocabMode.ALL):\n raise NotImplementedError","def put(self, obj=None): #py:UR.put\n if obj is None:\n RUR._UR.put_(self.body)\n else:\n RUR._UR.put_(self.body, obj)","def save(self, *args, **kwargs):\n self.trait_flavor_name = self.set_trait_flavor_name()\n # Call the \"real\" save method.\n super(HarmonizedTrait, self).save(*args, **kwargs)","def at_object_creation(self):\n self.db.max_hp = 100 # Set maximum HP to 100\n self.db.hp = self.db.max_hp # Set current HP to maximum\n self.db.spells_known = [] # Set empty spells known list\n self.db.max_mp = 20 # Set maximum MP to 20\n self.db.mp = self.db.max_mp # Set current MP to maximum","def set(self,obj,value):\r\n\t\tvalue = self.parse(value)\r\n\t\tsetattr(obj,self.name,value)"],"string":"[\n \"def putobjsense(self,sense_): # 3\\n if not isinstance(sense_,objsense): raise TypeError(\\\"Argument sense has wrong type\\\")\\n res = self.__obj.putobjsense(sense_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def objective(self, objective):\\n\\n self._objective = objective\",\n \"def optimize(self, objective_sense=None, **kwargs):\\n\\n if objective_sense:\\n self.objective.direction = objective_sense\\n\\n try:\\n # self._hidden_optimize_call(kwargs)\\n Model.optimize(self, **kwargs)\\n solution = self.get_solution()\\n self.solution = solution\\n return solution\\n except SolverError as SE:\\n status = self.solver.status\\n self.logger.error(SE)\\n self.logger.warning('Solver status: {}'.format(status))\\n raise (SE)\",\n \"def getobjsense(self): # 3\\n res,resargs = self.__obj.getobjsense()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _sense_return_value = resargs\\n _sense_return_value = objsense(_sense_return_value)\\n return _sense_return_value\",\n \"def put(cls, obj):\\n pass\",\n \"def set_occupant(self, obj):\\n\\t\\tpass\",\n \"def setMode(self):\\n if self.currentTarget != None and self.finishedAssault == 0:\\n if self.isAssault == 1:\\n if self.currentTarget != None:\\n self.mode = 'assault'\\n else:\\n self.mode = 'escape'\\n else:\\n self.log.debug('COUNT: %s: %s TARGET-> %s' % (self.myGalaxy.count, self.name, self.currentTarget.name))\\n ##self.myGalaxy.resultList.append('COUNT: %s: %s TARGET-> %s' % (self.myGalaxy.count, self.name, self.currentTarget.name))\\n if ((len(self.activeWeapons) == 0 or (self.currentISP/self.myShipHull.maxISP) < 0.7)) and self.__module__ == 'anw.war.ship':\\n self.mode = 'escape'\\n else:\\n range = funcs.getTargetRange(self.posX, self.posY, self.currentTarget.posX, self.currentTarget.posY)\\n if range <= self.range:\\n self.mode = 'engage'\\n else:\\n self.mode = 'close'\\n else:\\n self.mode == 'escape'\\n if globals.serverMode == 0:\\n self.shipsim.updateShipMode()\",\n \"def add_objective(self, objective):\\n self.objectives.append(objective)\",\n \"def set(self, obj, value):\\n pass\",\n \"def set(self, obj, value):\\n raise NotImplementedError\",\n \"def set_sensible_obstacles(self, obstacles):\\n self.sensible_obstacles = obstacles\",\n \"def SetObject(self, obj):\\n return _gmat_py.EphemManager_SetObject(self, obj)\",\n \"def sense_and_act(self):\\n pass\",\n \"def sense(self):\\n self.robot.update_state_position()\\n self.robot.update_state_proximity(self.world_props)\\n self.robot.update_state_compass()\\n self.robot.update_state_odometry()\",\n \"def set_solo(self, track, xclip, ident, value = None):\\n if track in self.song().tracks + self.song().return_tracks:\\n if value in KEYWORDS:\\n track.solo = KEYWORDS[value]\\n else:\\n track.solo = not(track.solo)\",\n \"def on_sense_sonar(self, dist):\\n raise NotImplementedError()\",\n \"def __set__(self, obj, value):\\n # We need to take a copy in case these are the class topics\\n topics = obj._help_topics.copy()\\n topics.update(value)\\n obj._help_topics = topics\",\n \"def save(self):\\r\\n for obs_name in self.__dict__.keys():\\r\\n if obs_name is not \\\"_ObjetSimu__obs\\\":\\r\\n if not obs_name in self.__sous_objets:\\r\\n if obs_name in self.__obs.keys():\\r\\n if \\\"copy\\\" in dir(self.__dict__[obs_name]):\\r\\n self.__obs[obs_name].append(self.__dict__[obs_name].copy())\\r\\n else:\\r\\n self.__obs[obs_name].append(self.__dict__[obs_name])\\r\\n else:\\r\\n self.__dict__[obs_name].save()\",\n \"def set_sm_userdata(self, obj_desc):\\n rospy.loginfo('Getting parameters from server')\\n\\n #obj_desc = rospy.get_param('obj_desc','{\\\"type\\\" : \\\"Bar\\\"}')\\n\\n self.sm.userdata.sm_obj_desc = json.loads(obj_desc)\\n\\n self.sm.userdata.state = 'pose_selection' \\n \\n self.sm.userdata.sm_min_objs = rospy.get_param('min_objs',1)\\n self.sm.userdata.sm_max_objs = rospy.get_param('max_objs',1)\\n self.sm.userdata.sm_max_time = rospy.get_param('max_time', 120)\\n self.sm.userdata.sm_max_poses = rospy.get_param('max_poses', 10)\\n\\n rospy.loginfo(\\\"Search for %s\\\", self.sm.userdata.sm_obj_desc)\\n rospy.loginfo(\\\"min_objs: %s\\\", self.sm.userdata.sm_min_objs)\\n rospy.loginfo(\\\"max_objs: %s\\\", self.sm.userdata.sm_max_objs)\\n rospy.loginfo(\\\"max_time: %s\\\", self.sm.userdata.sm_max_time)\\n rospy.loginfo(\\\"max_poses: %s\\\", self.sm.userdata.sm_max_poses)\\n \\n # initialize empty obj list\\n self.sm.userdata.sm_obj_list = []\",\n \"def slo(self, objective=99.99):\\n self.objective = objective\\n return objective\",\n \"def getobjsense(self):\\n sense_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getobjsense(self.__nativep,ctypes.byref(sense_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _sense_return_value = objsense(sense_.value)\\n return (_sense_return_value)\",\n \"def _sense_and_act(self):\\n pass\",\n \"def getObjSense(self, problemname : str, x : pd.Series):\\n if problemname in self.objsensedict:\\n return self.objsensedict[problemname]\\n elif not pd.isnull(x.get(Key.ObjectiveSense, None)):\\n return x.get(Key.ObjectiveSense)\\n else:\\n logger.warning(\\\"No objective sense for {}, assuming minimization\\\".format(problemname))\\n return ObjectiveSenseCode.MINIMIZE\",\n \"def observe(self, obs):\\n self.observation = obs\\n self.selected = obs.selected\\n \\n #############################\\n # Update of turn statistics #\\n #############################\\n if self.id == (obs.step % 6):\\n # Store base locations\\n if self.__class__.home_base is None:\\n self.__class__.home_base = (obs.loc[0]+16, obs.loc[1]+8)\\n self.__class__.enemy_base = \\\\\\n self.getSymmetricOpposite(self.__class__.home_base)\\n \\n # Reset trendingSpot\\n self.__class__.trendingSpot = {}\\n \\n # Update friendly CPs\\n self.__class__.friendlyCPs = map(lambda x: x[0:2], \\n filter(lambda x: x[2] == self.team, obs.cps))\\n \\n # Update enemy CPs\\n self.__class__.enemyCPs = map(lambda x:x[0:2], \\n filter(lambda x: x[2] != self.team, obs.cps))\\n \\n # Update ammo packs \\n ammopacks = filter(lambda x: x[2] == \\\"Ammo\\\", obs.objects)\\n if ammopacks:\\n self.updateAllAmmoSpots(ammopacks)\\n\\n # Update inFriendlyHands stat\\n if SETTINGS_DOMINATION_ADDS_UP:\\n inFriendlyHands = self.__class__.inFriendlyHands\\n else:\\n inFriendlyHands = {}\\n for cp in self.__class__.friendlyCPs:\\n if cp in self.__class__.inFriendlyHands:\\n inFriendlyHands[cp] = self.__class__.inFriendlyHands[cp] + 1\\n else:\\n inFriendlyHands[cp] = 1\\n self.__class__.inFriendlyHands = inFriendlyHands\",\n \"def define_objective(self, objective, capability_param, capability_value,\\n assignment_path):\\n RATIO = 20\\n # pdcli must receive a long number - cant receive float.\\n if capability_value > RATIO:\\n if objective == Obj.DONT:\\n ctx.cluster.cli.basic_objective_create(**{\\n 'name': capability_param, capability_param:\\n long(capability_value) * RATIO})\\n\\n self._logger.info('Objective {0} was set to {1}'.format(\\n capability_param, str(capability_value * RATIO)))\\n\\n else:\\n ctx.cluster.cli.basic_objective_create(**{\\n 'name': capability_param, capability_param:\\n long(capability_value)})\\n\\n self._logger.info('Objective {0} was set to {1}'.format(\\n capability_param, str(capability_value)))\\n\\n ctx.cluster.cli.share_objective_add(name=self.s_name,\\n objective=capability_param,\\n path=assignment_path)\",\n \"def set_arm(self, track, xclip, ident, value = None):\\n if track in self.song().tracks and track.can_be_armed:\\n if value in KEYWORDS:\\n track.arm = KEYWORDS[value]\\n else:\\n track.arm = not(track.arm)\",\n \"def _update_optimizer(self, hyperparameters, score, fit=True):\\n if self.do_maximize:\\n score = -score\\n self.optimizer_result = self.optimizer.tell(hyperparameters, score, fit=fit)\",\n \"def __set__(self, obj, value):\\r\\n pass\",\n \"def set_object_description(self, agent, Description):\\n\\n self.send_ObjectDescription(agent, agent.agent_id, agent.session_id, {1:[self.LocalID, Description]})\",\n \"def putOn(self,obj):\\n if obj not in self.on:\\n self.on.append(obj)\\n if self not in obj.on:\\n obj.putOn(self)\\n if obj not in self.road.on:\\n self.road.putOn(obj)\",\n \"def postInitialization(self, objectives = None):\\n self.objectives = objectives\",\n \"def do_tune(self, cmd):\\n self.params.set('tune', float(cmd) / 16.0, 'global')\",\n \"def apply_to_world(self, world):\\n # add the current obstacles\\n for obstacle in self.current_obstacles:\\n world.add_obstacle(obstacle)\\n\\n # program the robot supervisors\\n for robot in world.robots:\\n robot.supervisor.goal = self.current_goal[:]\",\n \"def set_use_case(self, title: str=None, domain: str=None, overview: str=None, scope: str=None,\\n situation: str=None, opportunity: str=None, actions: str=None, project_name: str=None,\\n project_lead: str=None, project_contact: str=None, stakeholder_domain: str=None,\\n stakeholder_group: str=None, stakeholder_lead: str=None, stakeholder_contact: str=None,\\n save: bool=None):\\n self.pm.set_use_case(title=title, domain=domain, overview=overview, scope=scope, situation=situation,\\n opportunity=opportunity, actions=actions, project_name=project_name,\\n project_lead=project_lead, project_contact=project_contact,\\n stakeholder_domain=stakeholder_domain, stakeholder_group=stakeholder_group,\\n stakeholder_lead=stakeholder_lead, stakeholder_contact=stakeholder_contact)\\n self.pm_persist(save=save)\",\n \"def expense(self, expense):\\n self._expense = expense\",\n \"def put(self, obj):\\n\\n if obj is None:\\n return\\n\\n obj = self.put_process(obj)\\n return Placeholder(obj).write()\",\n \"def opponent(self, opponent):\\n\\n self._opponent = opponent\",\n \"def set_word(self, w, v):\\n assert isinstance(v, Hypothesis)\\n\\n self.value[w] = v\",\n \"def __set__(self,obj,val):\\n assert len(val) == len(self.attribs),\\\"Compound parameter '%s' got the wrong number of values (needed %d, but got %d).\\\" % (self._attrib_name,len(self.attribs),len(val))\\n \\n if not obj:\\n for a,v in zip(self.attribs,val):\\n setattr(self.objtype,a,v)\\n else:\\n for a,v in zip(self.attribs,val):\\n setattr(obj,a,v)\",\n \"def quickdef(world, obname, kind, props={}, **kwargs) :\\n world.activity.def_obj(obname, kind)\\n for prop, val in props.iteritems() :\\n world[prop(obname)] = val\\n if \\\"put_in\\\" in kwargs :\\n world.activity.put_in(obname, kwargs[\\\"put_in\\\"])\\n if \\\"put_on\\\" in kwargs :\\n world.activity.put_on(obname, kwargs[\\\"put_on\\\"])\\n if \\\"make_part_of\\\" in kwargs :\\n world.activity.make_part_of(obname, kwargs[\\\"make_part_of\\\"])\",\n \"def add_objective(self): \\n \\n if \\\"CSS\\\" in self.algorithm:\\n \\n if self.num_hidden == 0:\\n \\n data_term = self.compute_energy(self.x, self.batch_size)\\n \\n else:\\n \\n data_term = self.compute_free_energy(self.x)\\n \\n normalizer_term = self.add_css_approximation(data_term)\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden ==0:\\n \\n data_term = self.compute_energy(self.x, self.batch_size)\\n \\n normalizer_term = self.compute_energy(self.x_gibbs, \\n self.batch_size)\\n \\n normalizer_term = -T.mean(normalizer_term)\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden > 0:\\n \\n data_term = self.compute_free_energy(self.x)\\n \\n normalizer_term = self.compute_free_energy(self.rbm_cd_samples)\\n \\n normalizer_term = -T.mean(normalizer_term)\\n \\n # cost is negative log likelihood \\n self.cost = T.mean(data_term) + normalizer_term\",\n \"def put(self):\\n self._val = True\",\n \"def emotion(self, emotion: float):\\n\\n self._emotion = emotion\",\n \"def save(self, *args, **kwargs):\\n self.phs = self.set_phs()\\n super(Study, self).save(*args, **kwargs)\",\n \"def setType(self, *args):\\n return _libsbml.Objective_setType(self, *args)\",\n \"def save_m2m(self, bundle):\\n keywords = bundle.data.get('keywords', [])\\n keywords = [k.lower() for k in keywords]\\n bundle.obj.keywords.set(*keywords)\\n return super(StoryResource, self).save_m2m(bundle)\",\n \"def set_wildtype_phenotype(self, value):\\n self.epistasis.data.values[0] = value\\n self.build()\",\n \"def putOn(self,obj):\\n if obj not in self.on:\\n self.on.append(obj)\\n if self not in obj.on:\\n obj.putOn(self)\",\n \"def putOn(self,obj):\\n if obj not in self.on:\\n self.on.append(obj)\\n if self not in obj.on:\\n obj.putOn(self)\",\n \"def optimize(self, X, y):\\n print(\\\"Performing TPOT genetic optimization.\\\")\\n self.model.fit(X, y)\\n self.optimized = True\",\n \"def setActiveObjective(self, *args):\\n return _libsbml.ListOfObjectives_setActiveObjective(self, *args)\",\n \"def save(self, obj):\\n raise NotImplementedError\",\n \"def save(self, **kwargs):\\n if len(self.words) > 0:\\n self.words = self.words.strip().rstrip()\\n self.word_count = self._calculate_word_count()\\n super(Term, self).save(**kwargs)\",\n \"def add(self, obj):\\n self.objects.append(obj)\\n if obj.gravity == 0:\\n obj.gravity = self.gravity\\n if obj.gravity_z == 0:\\n obj.gravity_z = self.gravity_z\",\n \"def update(self, obj):\\n\\n self.cfg.update(obj)\",\n \"def _update_objective(self):\\n # rewrap the cost if the solver has been run\\n self.Finalize()\\n return\",\n \"def updateCavity(self, obj):\\n obj.Shape = self.getCavityShape(obj.Structure, obj.Position)\\n obj.Volume = obj.Shape.Volume\",\n \"def setEvolution(self,evolution):\\n\\t\\tself.evolution = evolution\",\n \"def update(self, game):\\n super().update(game)\\n self.nn_def.set_score(self.score)\",\n \"def addObjective(self, *args):\\n return _libsbml.FbcModelPlugin_addObjective(self, *args)\",\n \"async def setheist(self, ctx):\\r\\n\\r\\n pass\",\n \"def __setattr__(self, name, value):\\n if name in [\\\"sampling_function\\\", \\\"env\\\", \\\"fit_dist\\\", \\\"reset\\\"]:\\n object.__setattr__(self, name, value)\\n else:\\n setattr(self.env, name, value)\",\n \"def set_single_rxn_objective(species_mod, obj_id, obj_direction=\\\"max\\\"):\\n for strain in tqdm(species_mod.strains):\\n species_mod.strains.get_by_id(strain.id).cobra_model.objective=obj_id\\n species_mod.strains.get_by_id(strain.id).cobra_model.direction=obj_direction\\n return species_mod\",\n \"def set_objective(self, n_evidence=None):\\n if n_evidence is None:\\n n_evidence = self.objective.get('n_evidence', self.n_evidence)\\n\\n if n_evidence < self.n_evidence:\\n logger.warning(\\n 'Requesting less evidence than there already exists')\\n\\n self.objective['n_evidence'] = n_evidence\\n self.objective['n_sim'] = n_evidence - self.n_precomputed_evidence\",\n \"def set_score(self,score):\\n self._score = score\",\n \"def setVocabulary(self, vocabulary): # real signature unknown; restored from __doc__\\n pass\",\n \"def set(self, X, y, feature_name):\\n pass\",\n \"def shots_allowed(self, shots_allowed):\\n\\n self._shots_allowed = shots_allowed\",\n \"def setHardness(self, hardness):\\n self.__hardness = hardness\\n self.scene.setShaderInput(\\\"props\\\", self.__ambient, self.__hardness, 0, 1)\",\n \"def init_SMOTE(self, k_neighbors, n_jobs):\\n self.object =\\\\\\n over_sampling.SMOTE(random_state=self.random_state,\\n k_neighbors=k_neighbors,\\n n_jobs=n_jobs)\\n\\n self.k_neighbors = k_neighbors\\n self.n_jobs = n_jobs\",\n \"def update_obstacles(self, new_obs):\\n self.obstacles = new_obs\",\n \"def set(self, obj: _T) -> None:\\n\\n self.registry[self.scopefunc()] = obj\",\n \"def put(self, obj):\\n\\n if obj is None:\\n return\\n\\n return obj\",\n \"def put(self, obj):\\r\\n self.staging_buffer.append(obj)\\r\\n\\r\\n if self.is_full():\\r\\n self._flush()\",\n \"def set_fold(self, track, xclip, ident, value = None):\\n if track.is_foldable:\\n if value in KEYWORDS:\\n track.fold_state = KEYWORDS[value]\\n else:\\n track.fold_state = not(track.fold_state)\",\n \"def init(self, obs, infos):\\n # reset agent, get vocabulary masks for verbs / adjectives / nouns\\n self.scores = []\\n self.dones = []\\n self.prev_actions = [[\\\"\\\" for _ in range(len(obs))]]\\n self.prev_step_is_still_interacting = np.ones((len(obs),), dtype=\\\"float32\\\") # 1s and starts to be 0 when previous action is \\\"stop\\\"\\n self.naozi.reset(batch_size=len(obs))\\n self.kg.reset(node_capacity=self.num_nodes, relation_capacity=self.num_relations, batch_size=len(obs))\\n self.not_finished_yet = None\",\n \"def update_spaces_threatened(self):\\n self.spaces_threatened = self.all_possible_moves(return_spaces_threatened=True)\\n update_threatening_king(self)\",\n \"def update_spaces_threatened(self):\\n self.spaces_threatened = self.all_possible_moves(return_spaces_threatened=True)\\n update_threatening_king(self)\",\n \"def set_swish(self, memory_efficient: bool = True) -> None:\\n self._swish = Act[\\\"memswish\\\"](inplace=True) if memory_efficient else Act[\\\"swish\\\"](alpha=1.0)\",\n \"def contribute_to_object(self, obj):\\n pass\",\n \"def set_observations(self, factor):\\r\\n relevant_obs = set(self.obs).intersection(set(factor.get_variables()))\\r\\n if relevant_obs:\\r\\n factor.set_observations({x:self.obs[x] for x in relevant_obs})\",\n \"def setSuggestionStrategy(self, value):\\n if value == SuggestionStrategy.OCR:\\n self.setBooleanOption(8, True)\\n elif value == SuggestionStrategy.TYPO:\\n self.setBooleanOption(8, False)\\n else:\\n raise VoikkoException(\\\"Invalid suggestion strategy\\\")\",\n \"def replaceOOV(word):\\n if word in self.vocab: return word\\n else: return self.oov\",\n \"def put(self, *_, **__): # pylint: disable=arguments-differ\\n pass\",\n \"def put(self, *_, **__): # pylint: disable=arguments-differ\\n pass\",\n \"def __init__(self, name, defense):\\n self.name = name\\n self.defense = defense\",\n \"def updateObservation(self, obs):\\n self.settingsDb.updateObservation(self.observationsTableName(), obs)\",\n \"def save(self, *args, **kwargs):\\n self.small_particles = (int(self.dylos_bin_1) +\\n int(self.dylos_bin_2) + int(self.dylos_bin_3))\\n self.big_particles = int(self.dylos_bin_4)\\n super(Dylos, self).save(*args, **kwargs)\",\n \"def __setitem__(self, key, obj):\\n self.add(key, obj, self._mode)\",\n \"def write_to_nc(self, nc_handle):\\n if self.name in nc_handle.variables:\\n return\\n process_var = nc_handle.createVariable(self.name, int)\\n setattr(process_var, \\\"PROV__Activity\\\", self.process_step)\\n #setattr(process_var, \\\"Source\\\", self.source)\\n\\n self.attributes.pop('process_step')\\n\\n # Adds the remainder of the attributes to the variable.\\n for name, value in self.attributes.iteritems():\\n if name == 'source':\\n name = 'PROV__Used'\\n setattr(process_var, name, value)\",\n \"def add_synapses(self, obj):\\n synapse_ct = lems.ComponentType('Synapse')\\n dynamics_synapse = lems.Dynamics()\\n synapse_ct.add(dynamics_synapse)\",\n \"def objective(self):\\n pass\",\n \"def setStem(self, stem):\\n\\t\\tself.stem=stem;\",\n \"def set_obs(self, num_obs):\\n curr_obs = self._nobs\\n if num_obs < curr_obs:\\n raise ValueError(\\\"num_obs must be >= \\\" + str(curr_obs))\\n if num_obs == curr_obs:\\n return\\n isstrvar = self._isstrvar\\n empty_row = ['' if isstrvar(i) else MISSING for i in range(self._nvar)]\\n self._varvals += [copy.copy(empty_row) \\n for _ in range(num_obs - curr_obs)]\\n self._nobs = num_obs\\n self._changed = True\\n # Need to clear srtlist. If there are string variables, there \\n # might now be empty strings after non-empty string. If there \\n # are numerical variables with extended missing, there will now \\n # be \\\".\\\" missing after extended missing. Issue pointed out at\\n # http://www.stata.com/statalist/archive/2013-08/msg00576.html\\n self._srtlist = [None]*self._nvar\",\n \"def set_objective_fn(self, objective_fn):\\n self.objective_fn = objective_fn\",\n \"def _insert_vocab(self, json_obj, mode=InsertVocabMode.ALL):\\n raise NotImplementedError\",\n \"def put(self, obj=None): #py:UR.put\\n if obj is None:\\n RUR._UR.put_(self.body)\\n else:\\n RUR._UR.put_(self.body, obj)\",\n \"def save(self, *args, **kwargs):\\n self.trait_flavor_name = self.set_trait_flavor_name()\\n # Call the \\\"real\\\" save method.\\n super(HarmonizedTrait, self).save(*args, **kwargs)\",\n \"def at_object_creation(self):\\n self.db.max_hp = 100 # Set maximum HP to 100\\n self.db.hp = self.db.max_hp # Set current HP to maximum\\n self.db.spells_known = [] # Set empty spells known list\\n self.db.max_mp = 20 # Set maximum MP to 20\\n self.db.mp = self.db.max_mp # Set current MP to maximum\",\n \"def set(self,obj,value):\\r\\n\\t\\tvalue = self.parse(value)\\r\\n\\t\\tsetattr(obj,self.name,value)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79471225","0.52841735","0.5171148","0.5096876","0.50680315","0.48795915","0.48363435","0.48355487","0.48335677","0.48176384","0.47935885","0.47814864","0.4768401","0.47334376","0.4723602","0.469889","0.46933207","0.46930823","0.4692844","0.46748367","0.46448252","0.4631909","0.46222875","0.46169978","0.46156844","0.45625475","0.45423335","0.45310906","0.45248115","0.45099097","0.4488542","0.44882116","0.4474676","0.4472379","0.44616288","0.44500247","0.4443907","0.44373438","0.44325256","0.44298673","0.442271","0.44205758","0.44177192","0.44166493","0.44023976","0.44014812","0.43962395","0.4394077","0.4394077","0.43910927","0.43842268","0.43795228","0.4379161","0.43785092","0.43784824","0.43770754","0.4376121","0.4375661","0.43738958","0.4361227","0.4359378","0.43567398","0.4336055","0.432382","0.43232313","0.43133137","0.43109927","0.431095","0.430775","0.43025023","0.43004847","0.4299235","0.429761","0.42945668","0.42916963","0.42916194","0.42861423","0.42861423","0.4285845","0.4285734","0.4282672","0.4271256","0.42706555","0.4264846","0.4264846","0.4264506","0.42641208","0.42552873","0.42534778","0.42526475","0.42412585","0.42360404","0.4230574","0.42232278","0.4222213","0.42154744","0.4215227","0.42107078","0.42101866","0.42086288"],"string":"[\n \"0.79471225\",\n \"0.52841735\",\n \"0.5171148\",\n \"0.5096876\",\n \"0.50680315\",\n \"0.48795915\",\n \"0.48363435\",\n \"0.48355487\",\n \"0.48335677\",\n \"0.48176384\",\n \"0.47935885\",\n \"0.47814864\",\n \"0.4768401\",\n \"0.47334376\",\n \"0.4723602\",\n \"0.469889\",\n \"0.46933207\",\n \"0.46930823\",\n \"0.4692844\",\n \"0.46748367\",\n \"0.46448252\",\n \"0.4631909\",\n \"0.46222875\",\n \"0.46169978\",\n \"0.46156844\",\n \"0.45625475\",\n \"0.45423335\",\n \"0.45310906\",\n \"0.45248115\",\n \"0.45099097\",\n \"0.4488542\",\n \"0.44882116\",\n \"0.4474676\",\n \"0.4472379\",\n \"0.44616288\",\n \"0.44500247\",\n \"0.4443907\",\n \"0.44373438\",\n \"0.44325256\",\n \"0.44298673\",\n \"0.442271\",\n \"0.44205758\",\n \"0.44177192\",\n \"0.44166493\",\n \"0.44023976\",\n \"0.44014812\",\n \"0.43962395\",\n \"0.4394077\",\n \"0.4394077\",\n \"0.43910927\",\n \"0.43842268\",\n \"0.43795228\",\n \"0.4379161\",\n \"0.43785092\",\n \"0.43784824\",\n \"0.43770754\",\n \"0.4376121\",\n \"0.4375661\",\n \"0.43738958\",\n \"0.4361227\",\n \"0.4359378\",\n \"0.43567398\",\n \"0.4336055\",\n \"0.432382\",\n \"0.43232313\",\n \"0.43133137\",\n \"0.43109927\",\n \"0.431095\",\n \"0.430775\",\n \"0.43025023\",\n \"0.43004847\",\n \"0.4299235\",\n \"0.429761\",\n \"0.42945668\",\n \"0.42916963\",\n \"0.42916194\",\n \"0.42861423\",\n \"0.42861423\",\n \"0.4285845\",\n \"0.4285734\",\n \"0.4282672\",\n \"0.4271256\",\n \"0.42706555\",\n \"0.4264846\",\n \"0.4264846\",\n \"0.4264506\",\n \"0.42641208\",\n \"0.42552873\",\n \"0.42534778\",\n \"0.42526475\",\n \"0.42412585\",\n \"0.42360404\",\n \"0.4230574\",\n \"0.42232278\",\n \"0.4222213\",\n \"0.42154744\",\n \"0.4215227\",\n \"0.42107078\",\n \"0.42101866\",\n \"0.42086288\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75217026"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11965,"cells":{"query":{"kind":"string","value":"Gets the objective sense. getobjsense(self)"},"document":{"kind":"string","value":"def getobjsense(self):\n sense_ = ctypes.c_int32()\n res = __library__.MSK_XX_getobjsense(self.__nativep,ctypes.byref(sense_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sense_return_value = objsense(sense_.value)\n return (_sense_return_value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getobjsense(self): # 3\n res,resargs = self.__obj.getobjsense()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sense_return_value = resargs\n _sense_return_value = objsense(_sense_return_value)\n return _sense_return_value","def objective(self):\n return self._objective","def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def offense(self):\n #return self.stats.strength + self.stats.level\n return self.stats.offense","def getObjSense(self, problemname : str, x : pd.Series):\n if problemname in self.objsensedict:\n return self.objsensedict[problemname]\n elif not pd.isnull(x.get(Key.ObjectiveSense, None)):\n return x.get(Key.ObjectiveSense)\n else:\n logger.warning(\"No objective sense for {}, assuming minimization\".format(problemname))\n return ObjectiveSenseCode.MINIMIZE","def getActiveObjective(self):\n return _libsbml.ListOfObjectives_getActiveObjective(self)","def objective_val(self):\n return self.m.ObjVal","def getObjectiveType(self):\n return _libsbml.Objective_getObjectiveType(self)","def get_objectives(self, scaled=True, use_indices=True):\n return self._get_variables_of_type('objective', scaled, use_indices)","def get_requisite_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def getObjective(self, *args):\n return _libsbml.FbcModelPlugin_getObjective(self, *args)","def objective(self):\n pass","def get_observed_objective(self) -> float:\n # pylint: disable=invalid-name\n obj = 0.\n for gr in self.grounded.values():\n dist = gr.get_observed_dist_to_satisfaction()\n obj += 1 - self.weight * max(0, dist) ** 2\n return obj","def get_objectives(self):\n return copy.deepcopy(self.objectives), self.gates_names","def get_equivalent_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def stats(self):\n return self._solution","def stats(self):\n return self._solution","def stats(self):\n return self._solution","def getType(self):\n return _libsbml.Objective_getType(self)","def getActiveObjective(self, *args):\n return _libsbml.FbcModelPlugin_getActiveObjective(self, *args)","def get_contenu(self):\n return self.objets","def expense(self):\n return self._expense","def get_dependent_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def defense(self):\n #return self.stats.dexterity + (self.stats.reiatsu * self.stats.density)\n return self.stats.defense","def getObservationCount(self):\r\n return self._s_obs","def get_ancestor_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def getObservation(self):\n sensors = self.env.getSensors()\n if self.sensor_limits:\n sensors = self.normalize(sensors)\n return sensors","def getNumObjectives(self):\n return _libsbml.FbcModelPlugin_getNumObjectives(self)","def createObjective(self):\n return _libsbml.ListOfObjectives_createObjective(self)","def get_descendant_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_best_individual(self):\n return self._best_indv","def get_objective(self):\n # Note that this makes the generic objectives call to Handcar\n # without specifying the objectiveBank:\n url_str = (self._base_url + '/objectives/' +\n self._my_map['objectiveId'])\n return Objective(self._load_json(url_str))","def evolution(self):\n return self._evolution","def get(self, *args):\n return _libsbml.ListOfObjectives_get(self, *args)","def objective(self) -> Optional[Union[int, float]]:\n if self.solution is not None:\n if isinstance(self.solution, list):\n return getattr(self.solution[-1], \"objective\", None)\n else:\n return getattr(self.solution, \"objective\", None)\n else:\n return None","def getNumObjectives(self):\n return _libsbml.ListOfObjectives_getNumObjectives(self)","def evidence_from_occupancy_costmap(self) -> List[jpt.variables.LabelAssignment]:\n\n # create Occupancy costmap for the target object\n position, orientation = self.target.get_position_and_orientation()\n position = list(position)\n position[-1] = 0\n\n ocm = OccupancyCostmap(distance_to_obstacle=0.3, from_ros=False, size=200, resolution=0.02,\n origin=(position, orientation))\n # ocm.visualize()\n\n # working on a copy of the costmap, since found rectangles are deleted\n map = np.copy(ocm.map)\n\n # initialize result\n queries = []\n\n origin = np.array([ocm.height/2, ocm.width/2])\n\n # for every index pair (i, j) in the occupancy map\n for i in range(0, map.shape[0]):\n for j in range(0, map.shape[1]):\n\n # if this index has not been used yet\n if map[i][j] > 0:\n\n # get consecutive box\n width = ocm._find_consectuive_line((i, j), map)\n height = ocm._find_max_box_height((i, j), width, map)\n\n # mark box as used\n map[i:i+height, j:j+width] = 0\n\n # calculate to coordinates relative to the objects pose\n pose = np.array([i, j])\n lower_corner = (pose - origin) * ocm.resolution\n upper_corner = (pose - origin + np.array([height, width])) * ocm.resolution\n rectangle = np.array([lower_corner, upper_corner]).T\n\n # transform to jpt query\n query = self.model.bind({\"x\": list(rectangle[0]), \"y\": list(rectangle[1])})\n queries.append(query)\n\n return queries","def getDefense(self):\n\t\treturn(self.maneuverability * self.protection)","def getCoefficient(self):\n return _libsbml.FluxObjective_getCoefficient(self)","def oob_score_(self):\n return self.predictor.oob_score_","def evaluate(self):\n scores = []\n scores.append(self.word_analogy())\n print(\"Word Analogy (acc): \", scores[0])\n scores.append(self.word_similarity())\n print(\"Word Similarity (MSE): \", scores[1])\n scores.append(self.concept_categorization())\n print(\"Concept Categorization (purity): \", scores[2])\n scores.append(self.sentiment_analysis())\n print(\"Sentiment Analysis (acc): \", scores[3])\n return scores","def GetObjects(self): \r\n return self.model.GetObjects()","def get_experience(self):\n return sum([i.get_experience for i in self.__units])","def getResistence(self):\n return self.resistence","def coherence(self):\r\n coherence = np.abs(self.coherency ** 2)\r\n\r\n return coherence","def knobs(self):\n return self.Knobs(self)","def get_o(self):\n return self.o","def get_scores(self):\n return self.score","def getObservation(self):\n return self._cur_state","def GetPts(self):\n return self.best","def get(self) -> list:\n return self.__expedition","def getOptimizationMetrics(self):\n return self.__unwrapResults().optimizationMetrics","def get_cognitive_process_terms(self):\n return # osid.grading.GradeQueryInspector","def get_energy(self):\r\n return self._energy","def evaluate(self):\n\n\t\tself.model_score = self.model.evaluate(self.x_test, self.y_test, batch_size=2048)\n\t\tprint(\"%s score = %f\\n\" %(self.modelName, self.model_score[1]))\n\t\treturn self.model_score","def productivity(self):\n return self.zmwMetric(\"Productivity\")","def get_objective_bank_terms(self):\n return # osid.learning.ObjectiveBankQueryInspector","def get_objective_bank_terms(self):\n return # osid.learning.ObjectiveBankQueryInspector","def get_objective_bank_terms(self):\n return # osid.learning.ObjectiveBankQueryInspector","def best_individual(self):\n return self.population.best_individual()","def get_omnipy(self):\n return _SALOMERuntime.RuntimeSALOME_get_omnipy(self)","def getObs(self):\n return self.listener.obs","def get_relevances(self):\n\n # Backpropagate softmax value\n # relevances = [tf.nn.softmax(self.activations[0])*tf.cast(self.y, tf.float32)]\n\n # Backpropagate a value from given labels y\n relevances = [tf.cast(self.y, tf.float32)]\n\n loc_poly = [pol for pol in self.polynomials]\n loc_pooling = [p for p in self.model.p]\n print(\"\\n Relevance calculation:\")\n for i in range(1, len(\n self.activations)): # start from 1 (not 0) penultimate activations since relevances already contains logits.\n name = self.activations[i - 1].name.split('/')\n if 'logits' in name[0] or 'fc' in name[0]:\n print(\"\\tFully connected:\", name[0])\n relevances.append(self.prop_fc(name[0], self.activations[i], relevances[-1]))\n elif 'flatten' in name[0]:\n print(\"\\tFlatten layer:\", name[0])\n relevances.append(self.prop_flatten(self.activations[i], relevances[-1]))\n # print(\"\\n\")\n elif 'pooling' in name[1]:\n # TODO: incorporate pooling type and value into name\n print(\"\\tPooling:\", name[0] + \" \" + name[1])\n p = loc_pooling.pop()\n relevances.append(self.prop_max_pool(self.activations[i], relevances[-1], ksize=[1, p, 1, 1],\n strides=[1, p, 1, 1]))\n elif 'conv' in name[0]:\n if len(loc_poly) > 1:\n print(\"\\tConvolution: \", name[0], \"\\n\")\n relevances.append(self.prop_gconv(name[0], self.activations[i], relevances[-1],\n polynomials=loc_poly.pop()))\n else:\n print(\"\\tConvolution, the first layer:\", name[0], \"\\n\")\n relevances.append(self.prop_gconv_first_conv_layer(name[0], self.activations[i], relevances[-1],\n polynomials=loc_poly.pop()))\n else:\n raise 'Error parsing layer'\n\n return relevances","def censored(self):\n if not self._vocab:\n self.vocab()\n return self._censored","def coherence(self):\r\n return np.abs(self.coherency) ** 2","def get_progression(self):\n return self._harmonies","def evaluate_obj(self, hparams):\n\n return [self.id, hparams, self.objective(hparams, self.device)]","def get_valued_metrics(self):\n return self._valued_metrics","def getName(self):\n return _libsbml.Objective_getName(self)","def get(self):\n return self.__expedition","def compute_overall_opinions(self):\r\n opinions_list = []\r\n global CATEGORIES\r\n i=0\r\n for cat in CATEGORIES:\r\n opinions_list.append([])\r\n for citizen in self.citizens:\r\n opinions_list[i].append(citizen.opinions[cat].weight)\r\n i+=1\r\n \r\n i=0;\r\n for cat in CATEGORIES:\r\n mean = stats.describe(opinions_list[i])[2]\r\n std = math.sqrt(stats.describe(opinions_list[i])[3])\r\n print \"Category: %d - Mean: %f - STD: %f\" % (cat, mean, std)\r\n i+=1","def top_dimensionality(self):\n return self._vocab_size","def Om(self):\n return self._Om","def __repr__(self):\n return self.scores()","def expenses(self):\n\n return Expenses.objects.filter(\n house=self.house,\n )","def GetObjVal(self) :\n if not self.maxWeights : return 0\n elif 'pp' in self.__type : return pulp.lpSum(self.prodVars )\n elif self.__type == 'prBinCat' : return np.dot(self.wish.T, self.dispo ).sum() \n elif 'pr' in self.__type : return np.multiply(self.wish, self.dispo).sum()\n else : return 0","def get_observation(self):\n return np.array(self.env.mode, dtype=np.int32)","def get_objectives(data):\n objectives = [math.log(population[0][\"objective\"]) for population in data]\n # objectives = [population[0][\"objective\"] for population in data]\n return objectives","def relevance(self):\n return self.metadata.relevance","def sngl_obj_evo(self, lacking):\n prob, algo = self.probinit('jde', 0)\n l = list()\n u = 6+(self.N-3)*4\n for i in range(lacking):\n archi = archipelago(algo,prob,8,16, topology=fully_connected())\n for j in range(u):\n archi.evolve(5)\n stdout.write(\"\\r{0} / {1}\".format(i*u+j+1, lacking*u))\n stdout.flush()\n tmp = [isl for isl in archi]\n tmp.sort(key = lambda x: x.population.champion.f[0]);\n l.append(tmp[0].population.champion)\n stdout.write(\" Done. \")\n return l, prob","def get_true_propositions(self):\n ret = \"\"\n if self.agent in self.objects:\n ret += self.objects[self.agent]\n return ret","def get_action(self, obs):\n obs = torch.FloatTensor(obs).to(self.device)\n value_int, value_ext = self.ppo.critic(obs)\n action, policy = self.ppo.explore(obs)\n return action, policy, value_ext.data.cpu().numpy(), value_int.data.cpu().numpy()","def intensities(self):\n return self._intensities.copy()","def result(self):\r\n # TODO: how about xcurrent?\r\n return self.best.get() + (\r\n self.countevals, self.countiter, self.gp.pheno(self.mean), self.gp.scales * self.sigma * self.sigma_vec * self.dC**0.5)","def opschrift(self):\n return self._opschrift.get_waarde()","def dispersion(self):\n return self._dispersion","def get_queryset(self):\n return Objective.objects.filter(perspective__description='Learning and Capacity').order_by('code')","def energy(self):\n return self._energy","def get_life(self):\r\n return self.__lives","def get_dispersion_virial(self):\n if self._dispersion_virial is None:\n self._dispersion_virial = self._get_potential(self._system._dispersion)\n return self._dispersion_virial","def emotion(self) -> float:\n return self._emotion","def get_Objective(self):\r\n \r\n ans = 0\r\n for i in range(len(self.X.shape)):\r\n ans += self.parameter['phi'][i] * self.parameter['lambda1'] *norm(tl.unfold(self.Wlist[i], i), 'nuc') + (1 / self.parameter['m']) * norm(tl.unfold(self.X - self.Wlist[i] - self.V, i))\r\n\r\n # Augmented part is calculated seperately. \r\n augment_part1 = 0.5 * self.parameter['rho1'] * norm(self.V - self.T + self.F1)\r\n augment_part2 = 0.5 * self.parameter['rho2'] * norm(tl.fold(np.dot(self.Dmatrix, tl.unfold(self.T, 0)), 0, self.T.shape) - self.S + self.F2)\r\n\r\n # Combine the result for final objective function\r\n ans += self.parameter['beta1'] * norm(self.V.reshape(self.totaldim), 1) + self.parameter['beta2'] * norm(self.S.reshape(self.totaldim), 1) + augment_part1 + augment_part2 \r\n return ans","def get_complexity(self):\n if self.layer_type() == nn.Conv2d:\n return pow(self.layer_type.get_sub_value(\"conv_window_size\"), 2) * self.layer_type.get_sub_value(\n \"out_features\")\n elif self.layer_type() == nn.Linear:\n return self.layer_type.get_sub_value(\"out_features\")\n else:\n raise Exception()","def objective(self):\n hinge_loss = tf.losses.hinge_loss(self.labels, self.predictions)\n\n return hinge_loss","def get_requisite_objective_id_terms(self):\n return # osid.search.terms.IdTerm","def get_mood(self):\n return self.mood","def incident_energy(self):\n return self._incident_energy","def putobjsense(self,sense_): # 3\n if not isinstance(sense_,objsense): raise TypeError(\"Argument sense has wrong type\")\n res = self.__obj.putobjsense(sense_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"],"string":"[\n \"def getobjsense(self): # 3\\n res,resargs = self.__obj.getobjsense()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _sense_return_value = resargs\\n _sense_return_value = objsense(_sense_return_value)\\n return _sense_return_value\",\n \"def objective(self):\\n return self._objective\",\n \"def get_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def offense(self):\\n #return self.stats.strength + self.stats.level\\n return self.stats.offense\",\n \"def getObjSense(self, problemname : str, x : pd.Series):\\n if problemname in self.objsensedict:\\n return self.objsensedict[problemname]\\n elif not pd.isnull(x.get(Key.ObjectiveSense, None)):\\n return x.get(Key.ObjectiveSense)\\n else:\\n logger.warning(\\\"No objective sense for {}, assuming minimization\\\".format(problemname))\\n return ObjectiveSenseCode.MINIMIZE\",\n \"def getActiveObjective(self):\\n return _libsbml.ListOfObjectives_getActiveObjective(self)\",\n \"def objective_val(self):\\n return self.m.ObjVal\",\n \"def getObjectiveType(self):\\n return _libsbml.Objective_getObjectiveType(self)\",\n \"def get_objectives(self, scaled=True, use_indices=True):\\n return self._get_variables_of_type('objective', scaled, use_indices)\",\n \"def get_requisite_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def getObjective(self, *args):\\n return _libsbml.FbcModelPlugin_getObjective(self, *args)\",\n \"def objective(self):\\n pass\",\n \"def get_observed_objective(self) -> float:\\n # pylint: disable=invalid-name\\n obj = 0.\\n for gr in self.grounded.values():\\n dist = gr.get_observed_dist_to_satisfaction()\\n obj += 1 - self.weight * max(0, dist) ** 2\\n return obj\",\n \"def get_objectives(self):\\n return copy.deepcopy(self.objectives), self.gates_names\",\n \"def get_equivalent_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def stats(self):\\n return self._solution\",\n \"def stats(self):\\n return self._solution\",\n \"def stats(self):\\n return self._solution\",\n \"def getType(self):\\n return _libsbml.Objective_getType(self)\",\n \"def getActiveObjective(self, *args):\\n return _libsbml.FbcModelPlugin_getActiveObjective(self, *args)\",\n \"def get_contenu(self):\\n return self.objets\",\n \"def expense(self):\\n return self._expense\",\n \"def get_dependent_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def defense(self):\\n #return self.stats.dexterity + (self.stats.reiatsu * self.stats.density)\\n return self.stats.defense\",\n \"def getObservationCount(self):\\r\\n return self._s_obs\",\n \"def get_ancestor_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def getObservation(self):\\n sensors = self.env.getSensors()\\n if self.sensor_limits:\\n sensors = self.normalize(sensors)\\n return sensors\",\n \"def getNumObjectives(self):\\n return _libsbml.FbcModelPlugin_getNumObjectives(self)\",\n \"def createObjective(self):\\n return _libsbml.ListOfObjectives_createObjective(self)\",\n \"def get_descendant_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_best_individual(self):\\n return self._best_indv\",\n \"def get_objective(self):\\n # Note that this makes the generic objectives call to Handcar\\n # without specifying the objectiveBank:\\n url_str = (self._base_url + '/objectives/' +\\n self._my_map['objectiveId'])\\n return Objective(self._load_json(url_str))\",\n \"def evolution(self):\\n return self._evolution\",\n \"def get(self, *args):\\n return _libsbml.ListOfObjectives_get(self, *args)\",\n \"def objective(self) -> Optional[Union[int, float]]:\\n if self.solution is not None:\\n if isinstance(self.solution, list):\\n return getattr(self.solution[-1], \\\"objective\\\", None)\\n else:\\n return getattr(self.solution, \\\"objective\\\", None)\\n else:\\n return None\",\n \"def getNumObjectives(self):\\n return _libsbml.ListOfObjectives_getNumObjectives(self)\",\n \"def evidence_from_occupancy_costmap(self) -> List[jpt.variables.LabelAssignment]:\\n\\n # create Occupancy costmap for the target object\\n position, orientation = self.target.get_position_and_orientation()\\n position = list(position)\\n position[-1] = 0\\n\\n ocm = OccupancyCostmap(distance_to_obstacle=0.3, from_ros=False, size=200, resolution=0.02,\\n origin=(position, orientation))\\n # ocm.visualize()\\n\\n # working on a copy of the costmap, since found rectangles are deleted\\n map = np.copy(ocm.map)\\n\\n # initialize result\\n queries = []\\n\\n origin = np.array([ocm.height/2, ocm.width/2])\\n\\n # for every index pair (i, j) in the occupancy map\\n for i in range(0, map.shape[0]):\\n for j in range(0, map.shape[1]):\\n\\n # if this index has not been used yet\\n if map[i][j] > 0:\\n\\n # get consecutive box\\n width = ocm._find_consectuive_line((i, j), map)\\n height = ocm._find_max_box_height((i, j), width, map)\\n\\n # mark box as used\\n map[i:i+height, j:j+width] = 0\\n\\n # calculate to coordinates relative to the objects pose\\n pose = np.array([i, j])\\n lower_corner = (pose - origin) * ocm.resolution\\n upper_corner = (pose - origin + np.array([height, width])) * ocm.resolution\\n rectangle = np.array([lower_corner, upper_corner]).T\\n\\n # transform to jpt query\\n query = self.model.bind({\\\"x\\\": list(rectangle[0]), \\\"y\\\": list(rectangle[1])})\\n queries.append(query)\\n\\n return queries\",\n \"def getDefense(self):\\n\\t\\treturn(self.maneuverability * self.protection)\",\n \"def getCoefficient(self):\\n return _libsbml.FluxObjective_getCoefficient(self)\",\n \"def oob_score_(self):\\n return self.predictor.oob_score_\",\n \"def evaluate(self):\\n scores = []\\n scores.append(self.word_analogy())\\n print(\\\"Word Analogy (acc): \\\", scores[0])\\n scores.append(self.word_similarity())\\n print(\\\"Word Similarity (MSE): \\\", scores[1])\\n scores.append(self.concept_categorization())\\n print(\\\"Concept Categorization (purity): \\\", scores[2])\\n scores.append(self.sentiment_analysis())\\n print(\\\"Sentiment Analysis (acc): \\\", scores[3])\\n return scores\",\n \"def GetObjects(self): \\r\\n return self.model.GetObjects()\",\n \"def get_experience(self):\\n return sum([i.get_experience for i in self.__units])\",\n \"def getResistence(self):\\n return self.resistence\",\n \"def coherence(self):\\r\\n coherence = np.abs(self.coherency ** 2)\\r\\n\\r\\n return coherence\",\n \"def knobs(self):\\n return self.Knobs(self)\",\n \"def get_o(self):\\n return self.o\",\n \"def get_scores(self):\\n return self.score\",\n \"def getObservation(self):\\n return self._cur_state\",\n \"def GetPts(self):\\n return self.best\",\n \"def get(self) -> list:\\n return self.__expedition\",\n \"def getOptimizationMetrics(self):\\n return self.__unwrapResults().optimizationMetrics\",\n \"def get_cognitive_process_terms(self):\\n return # osid.grading.GradeQueryInspector\",\n \"def get_energy(self):\\r\\n return self._energy\",\n \"def evaluate(self):\\n\\n\\t\\tself.model_score = self.model.evaluate(self.x_test, self.y_test, batch_size=2048)\\n\\t\\tprint(\\\"%s score = %f\\\\n\\\" %(self.modelName, self.model_score[1]))\\n\\t\\treturn self.model_score\",\n \"def productivity(self):\\n return self.zmwMetric(\\\"Productivity\\\")\",\n \"def get_objective_bank_terms(self):\\n return # osid.learning.ObjectiveBankQueryInspector\",\n \"def get_objective_bank_terms(self):\\n return # osid.learning.ObjectiveBankQueryInspector\",\n \"def get_objective_bank_terms(self):\\n return # osid.learning.ObjectiveBankQueryInspector\",\n \"def best_individual(self):\\n return self.population.best_individual()\",\n \"def get_omnipy(self):\\n return _SALOMERuntime.RuntimeSALOME_get_omnipy(self)\",\n \"def getObs(self):\\n return self.listener.obs\",\n \"def get_relevances(self):\\n\\n # Backpropagate softmax value\\n # relevances = [tf.nn.softmax(self.activations[0])*tf.cast(self.y, tf.float32)]\\n\\n # Backpropagate a value from given labels y\\n relevances = [tf.cast(self.y, tf.float32)]\\n\\n loc_poly = [pol for pol in self.polynomials]\\n loc_pooling = [p for p in self.model.p]\\n print(\\\"\\\\n Relevance calculation:\\\")\\n for i in range(1, len(\\n self.activations)): # start from 1 (not 0) penultimate activations since relevances already contains logits.\\n name = self.activations[i - 1].name.split('/')\\n if 'logits' in name[0] or 'fc' in name[0]:\\n print(\\\"\\\\tFully connected:\\\", name[0])\\n relevances.append(self.prop_fc(name[0], self.activations[i], relevances[-1]))\\n elif 'flatten' in name[0]:\\n print(\\\"\\\\tFlatten layer:\\\", name[0])\\n relevances.append(self.prop_flatten(self.activations[i], relevances[-1]))\\n # print(\\\"\\\\n\\\")\\n elif 'pooling' in name[1]:\\n # TODO: incorporate pooling type and value into name\\n print(\\\"\\\\tPooling:\\\", name[0] + \\\" \\\" + name[1])\\n p = loc_pooling.pop()\\n relevances.append(self.prop_max_pool(self.activations[i], relevances[-1], ksize=[1, p, 1, 1],\\n strides=[1, p, 1, 1]))\\n elif 'conv' in name[0]:\\n if len(loc_poly) > 1:\\n print(\\\"\\\\tConvolution: \\\", name[0], \\\"\\\\n\\\")\\n relevances.append(self.prop_gconv(name[0], self.activations[i], relevances[-1],\\n polynomials=loc_poly.pop()))\\n else:\\n print(\\\"\\\\tConvolution, the first layer:\\\", name[0], \\\"\\\\n\\\")\\n relevances.append(self.prop_gconv_first_conv_layer(name[0], self.activations[i], relevances[-1],\\n polynomials=loc_poly.pop()))\\n else:\\n raise 'Error parsing layer'\\n\\n return relevances\",\n \"def censored(self):\\n if not self._vocab:\\n self.vocab()\\n return self._censored\",\n \"def coherence(self):\\r\\n return np.abs(self.coherency) ** 2\",\n \"def get_progression(self):\\n return self._harmonies\",\n \"def evaluate_obj(self, hparams):\\n\\n return [self.id, hparams, self.objective(hparams, self.device)]\",\n \"def get_valued_metrics(self):\\n return self._valued_metrics\",\n \"def getName(self):\\n return _libsbml.Objective_getName(self)\",\n \"def get(self):\\n return self.__expedition\",\n \"def compute_overall_opinions(self):\\r\\n opinions_list = []\\r\\n global CATEGORIES\\r\\n i=0\\r\\n for cat in CATEGORIES:\\r\\n opinions_list.append([])\\r\\n for citizen in self.citizens:\\r\\n opinions_list[i].append(citizen.opinions[cat].weight)\\r\\n i+=1\\r\\n \\r\\n i=0;\\r\\n for cat in CATEGORIES:\\r\\n mean = stats.describe(opinions_list[i])[2]\\r\\n std = math.sqrt(stats.describe(opinions_list[i])[3])\\r\\n print \\\"Category: %d - Mean: %f - STD: %f\\\" % (cat, mean, std)\\r\\n i+=1\",\n \"def top_dimensionality(self):\\n return self._vocab_size\",\n \"def Om(self):\\n return self._Om\",\n \"def __repr__(self):\\n return self.scores()\",\n \"def expenses(self):\\n\\n return Expenses.objects.filter(\\n house=self.house,\\n )\",\n \"def GetObjVal(self) :\\n if not self.maxWeights : return 0\\n elif 'pp' in self.__type : return pulp.lpSum(self.prodVars )\\n elif self.__type == 'prBinCat' : return np.dot(self.wish.T, self.dispo ).sum() \\n elif 'pr' in self.__type : return np.multiply(self.wish, self.dispo).sum()\\n else : return 0\",\n \"def get_observation(self):\\n return np.array(self.env.mode, dtype=np.int32)\",\n \"def get_objectives(data):\\n objectives = [math.log(population[0][\\\"objective\\\"]) for population in data]\\n # objectives = [population[0][\\\"objective\\\"] for population in data]\\n return objectives\",\n \"def relevance(self):\\n return self.metadata.relevance\",\n \"def sngl_obj_evo(self, lacking):\\n prob, algo = self.probinit('jde', 0)\\n l = list()\\n u = 6+(self.N-3)*4\\n for i in range(lacking):\\n archi = archipelago(algo,prob,8,16, topology=fully_connected())\\n for j in range(u):\\n archi.evolve(5)\\n stdout.write(\\\"\\\\r{0} / {1}\\\".format(i*u+j+1, lacking*u))\\n stdout.flush()\\n tmp = [isl for isl in archi]\\n tmp.sort(key = lambda x: x.population.champion.f[0]);\\n l.append(tmp[0].population.champion)\\n stdout.write(\\\" Done. \\\")\\n return l, prob\",\n \"def get_true_propositions(self):\\n ret = \\\"\\\"\\n if self.agent in self.objects:\\n ret += self.objects[self.agent]\\n return ret\",\n \"def get_action(self, obs):\\n obs = torch.FloatTensor(obs).to(self.device)\\n value_int, value_ext = self.ppo.critic(obs)\\n action, policy = self.ppo.explore(obs)\\n return action, policy, value_ext.data.cpu().numpy(), value_int.data.cpu().numpy()\",\n \"def intensities(self):\\n return self._intensities.copy()\",\n \"def result(self):\\r\\n # TODO: how about xcurrent?\\r\\n return self.best.get() + (\\r\\n self.countevals, self.countiter, self.gp.pheno(self.mean), self.gp.scales * self.sigma * self.sigma_vec * self.dC**0.5)\",\n \"def opschrift(self):\\n return self._opschrift.get_waarde()\",\n \"def dispersion(self):\\n return self._dispersion\",\n \"def get_queryset(self):\\n return Objective.objects.filter(perspective__description='Learning and Capacity').order_by('code')\",\n \"def energy(self):\\n return self._energy\",\n \"def get_life(self):\\r\\n return self.__lives\",\n \"def get_dispersion_virial(self):\\n if self._dispersion_virial is None:\\n self._dispersion_virial = self._get_potential(self._system._dispersion)\\n return self._dispersion_virial\",\n \"def emotion(self) -> float:\\n return self._emotion\",\n \"def get_Objective(self):\\r\\n \\r\\n ans = 0\\r\\n for i in range(len(self.X.shape)):\\r\\n ans += self.parameter['phi'][i] * self.parameter['lambda1'] *norm(tl.unfold(self.Wlist[i], i), 'nuc') + (1 / self.parameter['m']) * norm(tl.unfold(self.X - self.Wlist[i] - self.V, i))\\r\\n\\r\\n # Augmented part is calculated seperately. \\r\\n augment_part1 = 0.5 * self.parameter['rho1'] * norm(self.V - self.T + self.F1)\\r\\n augment_part2 = 0.5 * self.parameter['rho2'] * norm(tl.fold(np.dot(self.Dmatrix, tl.unfold(self.T, 0)), 0, self.T.shape) - self.S + self.F2)\\r\\n\\r\\n # Combine the result for final objective function\\r\\n ans += self.parameter['beta1'] * norm(self.V.reshape(self.totaldim), 1) + self.parameter['beta2'] * norm(self.S.reshape(self.totaldim), 1) + augment_part1 + augment_part2 \\r\\n return ans\",\n \"def get_complexity(self):\\n if self.layer_type() == nn.Conv2d:\\n return pow(self.layer_type.get_sub_value(\\\"conv_window_size\\\"), 2) * self.layer_type.get_sub_value(\\n \\\"out_features\\\")\\n elif self.layer_type() == nn.Linear:\\n return self.layer_type.get_sub_value(\\\"out_features\\\")\\n else:\\n raise Exception()\",\n \"def objective(self):\\n hinge_loss = tf.losses.hinge_loss(self.labels, self.predictions)\\n\\n return hinge_loss\",\n \"def get_requisite_objective_id_terms(self):\\n return # osid.search.terms.IdTerm\",\n \"def get_mood(self):\\n return self.mood\",\n \"def incident_energy(self):\\n return self._incident_energy\",\n \"def putobjsense(self,sense_): # 3\\n if not isinstance(sense_,objsense): raise TypeError(\\\"Argument sense has wrong type\\\")\\n res = self.__obj.putobjsense(sense_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8077817","0.6475919","0.62005496","0.62005496","0.62005496","0.61305094","0.6124314","0.6089001","0.5931077","0.58792186","0.5827234","0.58187944","0.57927275","0.5724244","0.57183784","0.5676843","0.56714916","0.5586467","0.5586467","0.5586467","0.5568052","0.5497707","0.54583293","0.54484195","0.5361648","0.53509575","0.533727","0.5314603","0.5296677","0.52641076","0.5256195","0.52362114","0.52337027","0.5233127","0.5229976","0.5199817","0.51608074","0.51454175","0.51431143","0.51226014","0.5083851","0.50801474","0.5079618","0.50747555","0.5074162","0.50711566","0.5069178","0.5056927","0.5052318","0.50513744","0.5045002","0.50433356","0.50273603","0.50198966","0.5019522","0.501033","0.50077736","0.50072026","0.50053066","0.50053066","0.50053066","0.49875885","0.49788773","0.4978121","0.49743617","0.49743274","0.49737132","0.4973462","0.4963637","0.4960496","0.49498644","0.49393693","0.4932445","0.49293202","0.49288484","0.49284112","0.49220267","0.4922023","0.4915953","0.49092108","0.4906665","0.4905215","0.48926324","0.48908833","0.48832914","0.48826456","0.48784062","0.4873668","0.4871737","0.48715806","0.48687115","0.48677933","0.4849688","0.4848665","0.4839286","0.48364937","0.48353875","0.48353243","0.48324","0.48312306"],"string":"[\n \"0.8077817\",\n \"0.6475919\",\n \"0.62005496\",\n \"0.62005496\",\n \"0.62005496\",\n \"0.61305094\",\n \"0.6124314\",\n \"0.6089001\",\n \"0.5931077\",\n \"0.58792186\",\n \"0.5827234\",\n \"0.58187944\",\n \"0.57927275\",\n \"0.5724244\",\n \"0.57183784\",\n \"0.5676843\",\n \"0.56714916\",\n \"0.5586467\",\n \"0.5586467\",\n \"0.5586467\",\n \"0.5568052\",\n \"0.5497707\",\n \"0.54583293\",\n \"0.54484195\",\n \"0.5361648\",\n \"0.53509575\",\n \"0.533727\",\n \"0.5314603\",\n \"0.5296677\",\n \"0.52641076\",\n \"0.5256195\",\n \"0.52362114\",\n \"0.52337027\",\n \"0.5233127\",\n \"0.5229976\",\n \"0.5199817\",\n \"0.51608074\",\n \"0.51454175\",\n \"0.51431143\",\n \"0.51226014\",\n \"0.5083851\",\n \"0.50801474\",\n \"0.5079618\",\n \"0.50747555\",\n \"0.5074162\",\n \"0.50711566\",\n \"0.5069178\",\n \"0.5056927\",\n \"0.5052318\",\n \"0.50513744\",\n \"0.5045002\",\n \"0.50433356\",\n \"0.50273603\",\n \"0.50198966\",\n \"0.5019522\",\n \"0.501033\",\n \"0.50077736\",\n \"0.50072026\",\n \"0.50053066\",\n \"0.50053066\",\n \"0.50053066\",\n \"0.49875885\",\n \"0.49788773\",\n \"0.4978121\",\n \"0.49743617\",\n \"0.49743274\",\n \"0.49737132\",\n \"0.4973462\",\n \"0.4963637\",\n \"0.4960496\",\n \"0.49498644\",\n \"0.49393693\",\n \"0.4932445\",\n \"0.49293202\",\n \"0.49288484\",\n \"0.49284112\",\n \"0.49220267\",\n \"0.4922023\",\n \"0.4915953\",\n \"0.49092108\",\n \"0.4906665\",\n \"0.4905215\",\n \"0.48926324\",\n \"0.48908833\",\n \"0.48832914\",\n \"0.48826456\",\n \"0.48784062\",\n \"0.4873668\",\n \"0.4871737\",\n \"0.48715806\",\n \"0.48687115\",\n \"0.48677933\",\n \"0.4849688\",\n \"0.4848665\",\n \"0.4839286\",\n \"0.48364937\",\n \"0.48353875\",\n \"0.48353243\",\n \"0.48324\",\n \"0.48312306\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73985374"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11966,"cells":{"query":{"kind":"string","value":"Modifies a part of the linear objective coefficients. putclist(self,subj_,val_)"},"document":{"kind":"string","value":"def putclist(self,subj_,val_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(val_)\n elif num_ != len(val_):\n raise IndexError(\"Inconsistent length of array val\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if val_ is None:\n raise ValueError(\"Argument val cannot be None\")\n if val_ is None:\n raise ValueError(\"Argument val may not be None\")\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\n _val_copyarray = False\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif val_ is not None:\n _val_copyarray = True\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\n _val_np_tmp[:] = val_\n assert _val_np_tmp.flags.contiguous\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _val_copyarray = False\n _val_tmp = None\n \n res = __library__.MSK_XX_putclist(self.__nativep,num_,_subj_tmp,_val_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putclist(self,subj,val): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(val)\n elif num_ != len(val):\n raise IndexError(\"Inconsistent length of array val\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if val is None: raise TypeError(\"Invalid type for argument val\")\n if val is None:\n val_ = None\n else:\n try:\n val_ = memoryview(val)\n except TypeError:\n try:\n _tmparr_val = array.array(\"d\",val)\n except TypeError:\n raise TypeError(\"Argument val has wrong type\")\n else:\n val_ = memoryview(_tmparr_val)\n \n else:\n if val_.format != \"d\":\n val_ = memoryview(array.array(\"d\",val))\n \n res = self.__obj.putclist(num_,subj_,val_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __setitem__( self, l, c_l ) :\n\n if( l == len( self ) ) :\n self.coefficients.append( float( c_l ) )\n else :\n self.coefficients[l] = float( c_l )","def putaijlist(self,subi_,subj_,valij_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij_)\n elif num_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getclist(self,subj_,c_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _c_minlength = (num_)\n if (num_) > 0 and c_ is not None and len(c_) != (num_):\n raise ValueError(\"Array argument c is not long enough: Is %d, expected %d\" % (len(c_),(num_)))\n if isinstance(c_,numpy.ndarray) and not c_.flags.writeable:\n raise ValueError(\"Argument c must be writable\")\n if c_ is None:\n raise ValueError(\"Argument c may not be None\")\n if isinstance(c_, numpy.ndarray) and c_.dtype is numpy.dtype(numpy.float64) and c_.flags.contiguous:\n _c_copyarray = False\n _c_tmp = ctypes.cast(c_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif c_ is not None:\n _c_copyarray = True\n _c_np_tmp = numpy.zeros(len(c_),numpy.dtype(numpy.float64))\n _c_np_tmp[:] = c_\n assert _c_np_tmp.flags.contiguous\n _c_tmp = ctypes.cast(_c_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _c_copyarray = False\n _c_tmp = None\n \n res = __library__.MSK_XX_getclist(self.__nativep,num_,_subj_tmp,_c_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _c_copyarray:\n c_[:] = _c_np_tmp","def putaijlist(self,subi,subj,valij): # 3\n num_ = None\n if num_ is None:\n num_ = len(subi)\n elif num_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij)\n elif num_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if num_ is None: num_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res = self.__obj.putaijlist64(num_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _set_coef(self, coef_):\n self.coef_ = np.asfortranarray(array2d(coef_))","def putvartypelist(self,subj_,vartype_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(vartype_)\n elif num_ != len(vartype_):\n raise IndexError(\"Inconsistent length of array vartype\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if vartype_ is None:\n raise ValueError(\"Argument vartype cannot be None\")\n if vartype_ is None:\n raise ValueError(\"Argument vartype may not be None\")\n if vartype_ is not None:\n _vartype_tmp = (ctypes.c_int32 * len(vartype_))(*vartype_)\n else:\n _vartype_tmp = None\n res = __library__.MSK_XX_putvartypelist(self.__nativep,num_,_subj_tmp,_vartype_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\n self.coeff[self._core.index_alpha(key[0]),\n self._core.index_beta(key[1])] = value","def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):\n num_ = None\n if num_ is None:\n num_ = len(dims_)\n elif num_ != len(dims_):\n raise IndexError(\"Inconsistent length of array dims\")\n if num_ is None:\n num_ = len(nz_)\n elif num_ != len(nz_):\n raise IndexError(\"Inconsistent length of array nz\")\n if dims_ is None:\n raise ValueError(\"Argument dims cannot be None\")\n if dims_ is None:\n raise ValueError(\"Argument dims may not be None\")\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\n _dims_copyarray = False\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dims_ is not None:\n _dims_copyarray = True\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\n _dims_np_tmp[:] = dims_\n assert _dims_np_tmp.flags.contiguous\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dims_copyarray = False\n _dims_tmp = None\n \n if nz_ is None:\n raise ValueError(\"Argument nz cannot be None\")\n if nz_ is None:\n raise ValueError(\"Argument nz may not be None\")\n if isinstance(nz_, numpy.ndarray) and nz_.dtype is numpy.dtype(numpy.int64) and nz_.flags.contiguous:\n _nz_copyarray = False\n _nz_tmp = ctypes.cast(nz_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nz_ is not None:\n _nz_copyarray = True\n _nz_np_tmp = numpy.zeros(len(nz_),numpy.dtype(numpy.int64))\n _nz_np_tmp[:] = nz_\n assert _nz_np_tmp.flags.contiguous\n _nz_tmp = ctypes.cast(_nz_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nz_copyarray = False\n _nz_tmp = None\n \n _subi_minlength = sum((nz_))\n if sum((nz_)) > 0 and subi_ is not None and len(subi_) != sum((nz_)):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),sum((nz_))))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = sum((nz_))\n if sum((nz_)) > 0 and subj_ is not None and len(subj_) != sum((nz_)):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),sum((nz_))))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = sum((nz_))\n if sum((nz_)) > 0 and valij_ is not None and len(valij_) != sum((nz_)):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),sum((nz_))))\n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n _idx_minlength = (num_)\n if (num_) > 0 and idx_ is not None and len(idx_) != (num_):\n raise ValueError(\"Array argument idx is not long enough: Is %d, expected %d\" % (len(idx_),(num_)))\n if isinstance(idx_,numpy.ndarray) and not idx_.flags.writeable:\n raise ValueError(\"Argument idx must be writable\")\n if idx_ is None:\n raise ValueError(\"Argument idx may not be None\")\n if isinstance(idx_, numpy.ndarray) and idx_.dtype is numpy.dtype(numpy.int64) and idx_.flags.contiguous:\n _idx_copyarray = False\n _idx_tmp = ctypes.cast(idx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif idx_ is not None:\n _idx_copyarray = True\n _idx_np_tmp = numpy.zeros(len(idx_),numpy.dtype(numpy.int64))\n _idx_np_tmp[:] = idx_\n assert _idx_np_tmp.flags.contiguous\n _idx_tmp = ctypes.cast(_idx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _idx_copyarray = False\n _idx_tmp = None\n \n res = __library__.MSK_XX_appendsparsesymmatlist(self.__nativep,num_,_dims_tmp,_nz_tmp,_subi_tmp,_subj_tmp,_valij_tmp,_idx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _idx_copyarray:\n idx_[:] = _idx_np_tmp","def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def store(self, subj):\n if subj in self.__lst:\n raise ValueError('Disciplina exista deja')\n self.__lst.append(subj)","def _store_2D_coefficients(hdr, poly_model, coeff_prefix, keeplinear=False):\n mindeg = int(not keeplinear)\n degree = poly_model.degree\n for i in range(0, degree + 1):\n for j in range(0, degree + 1):\n if (i + j) > mindeg and (i + j < degree + 1):\n hdr[f'{coeff_prefix}_{i}_{j}'] = getattr(poly_model, f'c{i}_{j}').value","def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)","def parameters_polynomial(cobj, prop, prop_units, alist, blist):\n for i, aval in enumerate(alist):\n if i == 0:\n param_units = prop_units\n else:\n param_units = prop_units / pyunits.K**i\n\n coeff = Var(doc=\"A parameter for CoolProp polynomial form\", units=param_units)\n cobj.add_component(prop + \"_coeff_A\" + str(i), coeff)\n coeff.fix(aval)\n\n for i, bval in enumerate(blist):\n if i == 0:\n param_units = pyunits.dimensionless\n else:\n param_units = pyunits.K**-i\n\n coeff = Var(doc=\"B parameter for CoolProp exponential form\", units=param_units)\n cobj.add_component(prop + \"_coeff_B\" + str(i), coeff)\n coeff.fix(bval)","def add_coefficients(self, L, overwrite=False):\n if not isinstance(L, dict):\n raise ValueError(\"Call with dictionary as argument!\")\n\n for p in L.keys():\n c = mpmath.mpmathify(L[p])\n # print \"c=\",c\n cd = ceil(mpmath.log10(abs(c)))\n if(cd > self.maxdigs):\n self.maxdigs = cd\n # print \"p=\",p\n if(is_int(p)):\n (r, n) = rn_from_D(self._space.WR, p)\n elif(isinstance(p, tuple)):\n (r, n) = p\n if r in self._coeffs:\n if n in self._coeffs[r]:\n c_old = self._coeffs[r][n]\n # Try to determine (heuristically) if the new coefficient is really better\n d1 = dist_from_int(c)[0]\n d2 = dist_from_int(c_old)[0]\n if(overwrite):\n self._coeffs[r][n] = c\n else:\n self._coeffs[r][n] = c\n else:\n # see if it is a possible index at all\n if not r < 0 or r > self._space.multiplier().ambient_rank():\n raise ValueError(\"Key {0} corr to (r,n)=({1},{2}) is invalid for the current space!\".format(p, r, n))\n elif r not in self._space.multiplier().D():\n if self._space._sym_type == -1 and (r == 0 or r == self._space.multiplier().N):\n # Should be 0 by symmetry\n if abs(c) > 10**(1 - self.prec):\n raise ValueError(\"Coefficient should be zero by symmetry. Got c({0},{1})={2}!\".format(r, n, c))\n else:\n self._coeffs[r][n] = 0\n else:\n # is equal to +- c(-r,n)\n mr = 2 * self.multiplier().N - r\n if mr in self._coeffs:\n if n in self._coeffs[mr]:\n c_old = self._coeffs[mr][n]\n if abs(c - self._space.multiplier()._sym_type * c_old) > 10**(1 - self.prec):\n st = \"Might add an erroneous coefficient! Got c({0},{1})={2}. \".format(r, n, c)\n st += \"From previous coefficients should have {0}\".format(self._space._sym_type * c_old)\n raise ValueError(st)\n if overwrite:\n self._coeffs[mr][n] = c\n else:\n raise ValueError(\"Coefficient should be zero by symmetry. Got c({0},{1})={2}!\" .format(r, n, c))","def perturbe_clist(cl_array,bins,amount):\n cltt_list=[]\n for i in range(len(bins)):\n cl=cl_array.copy()\n cl[int(bins[i])]=amount*cl_array[int(bins[i])]\n cltt_list.append(cl)\n return cltt_list","def putslc(self,whichsol_,slc_):\n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslc(self.__nativep,whichsol_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __getitem__( self, l ) :\n\n return( self.coefficients[l] )","def update_coeff(self, **kwargs: float) -> None:\n for rule_name, coeff in kwargs.items():\n if rule_name not in self.rules:\n raise ValueError(f\"Behavioral rule {rule_name} does not exist\")\n else:\n self.rules[getattr(self, rule_name)] = coeff","def add_com_jac(ui):\n global com_jac_list\n\n content = content_fk_jac_loops(ui, \"com_jac\")\n if content in com_jac_list:\n return\n com_jac_list.append(content)\n ui.listWidget_com_jac.addItem(f\"Center of Mass Jacobian \"\n f\"{parse_content(content)}\")","def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def UpdateCostMatrix( self, extraXs ):\n for x in extraXs:\n newRow = [ self.EuclideanDistanceSq(x,y) for y in self.Y ]\n self.C.append(newRow)","def setListPunctCmplx(self, list):\n\t\tself.list_punct_cmplx = list","def __init__(self, coef_list):\n assert type(coef_list) is list, 'error message indicating that coef is not a list'\n self.degree = len(coef_list) - 1\n self.coefs = []\n for coef in coef_list:\n self.coefs.append(coef)","def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij)\n elif nz_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if nz_ is None: nz_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _idx_return_value = resargs\n return _idx_return_value","def __init__(self, sect_list, coef=1):\n self.subsectors = copy.deepcopy(sect_list)\n self.ds = {}\n self.coef = coef\n self._UpdateDS()\n self.domains = []\n self.excluded_vars = list()","def __init__(self, coefficients):\n self.coefficients = coefficients","def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(alphaptrb_)\n elif num_ != len(alphaptrb_):\n raise IndexError(\"Inconsistent length of array alphaptrb\")\n if num_ is None:\n num_ = len(alphaptre_)\n elif num_ != len(alphaptre_):\n raise IndexError(\"Inconsistent length of array alphaptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb cannot be None\")\n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb may not be None\")\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptrb_ is not None:\n _alphaptrb_copyarray = True\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\n _alphaptrb_np_tmp[:] = alphaptrb_\n assert _alphaptrb_np_tmp.flags.contiguous\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = None\n \n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre cannot be None\")\n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre may not be None\")\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\n _alphaptre_copyarray = False\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptre_ is not None:\n _alphaptre_copyarray = True\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\n _alphaptre_np_tmp[:] = alphaptre_\n assert _alphaptre_np_tmp.flags.contiguous\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptre_copyarray = False\n _alphaptre_tmp = None\n \n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvartypelist(self,subj,vartype): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(vartype)\n elif num_ != len(vartype):\n raise IndexError(\"Inconsistent length of array vartype\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if vartype is None: raise TypeError(\"Invalid type for argument vartype\")\n if vartype is None:\n vartype_ = None\n else:\n try:\n vartype_ = memoryview(vartype)\n except TypeError:\n try:\n _tmparr_vartype = array.array(\"i\",vartype)\n except TypeError:\n raise TypeError(\"Argument vartype has wrong type\")\n else:\n vartype_ = memoryview(_tmparr_vartype)\n \n else:\n if vartype_.format != \"i\":\n vartype_ = memoryview(array.array(\"i\",vartype))\n \n res = self.__obj.putvartypelist(num_,subj_,vartype_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putacol(self,j_,subj_,valj_):\n nzj_ = None\n if nzj_ is None:\n nzj_ = len(subj_)\n elif nzj_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nzj_ is None:\n nzj_ = len(valj_)\n elif nzj_ != len(valj_):\n raise IndexError(\"Inconsistent length of array valj\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valj_ is None:\n raise ValueError(\"Argument valj cannot be None\")\n if valj_ is None:\n raise ValueError(\"Argument valj may not be None\")\n if isinstance(valj_, numpy.ndarray) and valj_.dtype is numpy.dtype(numpy.float64) and valj_.flags.contiguous:\n _valj_copyarray = False\n _valj_tmp = ctypes.cast(valj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valj_ is not None:\n _valj_copyarray = True\n _valj_np_tmp = numpy.zeros(len(valj_),numpy.dtype(numpy.float64))\n _valj_np_tmp[:] = valj_\n assert _valj_np_tmp.flags.contiguous\n _valj_tmp = ctypes.cast(_valj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valj_copyarray = False\n _valj_tmp = None\n \n res = __library__.MSK_XX_putacol(self.__nativep,j_,nzj_,_subj_tmp,_valj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def redcloth_list(self, text):\n pattern = re.compile(r\"^([-]+%s[ .].*:=.*)$(?![^-])\" % self.lc,\n re.M | re.U | re.S)\n return pattern.sub(self.fRCList, text)","def SetPRCatConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for line in tot :\n for val in line :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def putacol(self,j_,subj,valj): # 3\n nzj_ = None\n if nzj_ is None:\n nzj_ = len(subj)\n elif nzj_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nzj_ is None:\n nzj_ = len(valj)\n elif nzj_ != len(valj):\n raise IndexError(\"Inconsistent length of array valj\")\n if nzj_ is None: nzj_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valj is None: raise TypeError(\"Invalid type for argument valj\")\n if valj is None:\n valj_ = None\n else:\n try:\n valj_ = memoryview(valj)\n except TypeError:\n try:\n _tmparr_valj = array.array(\"d\",valj)\n except TypeError:\n raise TypeError(\"Argument valj has wrong type\")\n else:\n valj_ = memoryview(_tmparr_valj)\n \n else:\n if valj_.format != \"d\":\n valj_ = memoryview(array.array(\"d\",valj))\n \n res = self.__obj.putacol(j_,nzj_,subj_,valj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubk is None: raise TypeError(\"Invalid type for argument qcsubk\")\n if qcsubk is None:\n qcsubk_ = None\n else:\n try:\n qcsubk_ = memoryview(qcsubk)\n except TypeError:\n try:\n _tmparr_qcsubk = array.array(\"i\",qcsubk)\n except TypeError:\n raise TypeError(\"Argument qcsubk has wrong type\")\n else:\n qcsubk_ = memoryview(_tmparr_qcsubk)\n \n else:\n if qcsubk_.format != \"i\":\n qcsubk_ = memoryview(array.array(\"i\",qcsubk))\n \n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setCoefficient(self, *args):\n return _libsbml.FluxObjective_setCoefficient(self, *args)","def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n \n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n \n if subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n \n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n \n if subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valjkl is None: raise TypeError(\"Invalid type for argument valjkl\")\n if valjkl is None:\n valjkl_ = None\n else:\n try:\n valjkl_ = memoryview(valjkl)\n except TypeError:\n try:\n _tmparr_valjkl = array.array(\"d\",valjkl)\n except TypeError:\n raise TypeError(\"Argument valjkl has wrong type\")\n else:\n valjkl_ = memoryview(_tmparr_valjkl)\n \n else:\n if valjkl_.format != \"d\":\n valjkl_ = memoryview(array.array(\"d\",valjkl))\n \n if valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl has wrong length\")\n res = self.__obj.putbarcblocktriplet(num_,subj_,subk_,subl_,valjkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __set__(self,obj,val):\n self._check_bounds(val)\n super(List,self).__set__(obj,val)","def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valjkl_minlength = (num_)\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl is not long enough: Is %d, expected %d\" % (len(valjkl_),(num_)))\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl cannot be None\")\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl may not be None\")\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\n _valjkl_copyarray = False\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valjkl_ is not None:\n _valjkl_copyarray = True\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\n _valjkl_np_tmp[:] = valjkl_\n assert _valjkl_np_tmp.flags.contiguous\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valjkl_copyarray = False\n _valjkl_tmp = None\n \n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def lin_reg_step(phen_vals, sd, cof_chr_pos_list, progress_file_writer=None, plot_prefix=None):\n import gwaResults as gr\n import bisect\n s1 = time.time()\n lm = LinearModel(phen_vals)\n\n h0_rss = lm.get_rss()\n step_dict = {}\n step_dict['h0_rss'] = h0_rss\n\n chrom_pos_list = sd.get_chr_pos_list()\n print 'Looking up cofactors'\n cof_indices = []\n for chrom_pos in cof_chr_pos_list:\n i = bisect.bisect(chrom_pos_list, chrom_pos) - 1\n assert chrom_pos_list[i] == chrom_pos, 'Cofactor missing??'\n cof_indices.append(i)\n snps = sd.get_snps()\n cof_snps = [snps[i] for i in cof_indices]\n lm.set_factors(cof_snps)\n\n if not progress_file_writer == None:\n progress_file_writer.update_progress_bar(progress=0.20, task_status='Performing linear regression')\n progress_file_writer.set_step(0.05)\n\n\n print 'Performing linear regression.'\n r = lm.fast_f_test(snps, progress_file_writer=progress_file_writer)\n min_pval_i = sp.argmin(r['ps'])\n step_dict['min_pval_i'] = min_pval_i\n step_dict['min_pval'] = r['ps'][min_pval_i]\n step_dict['mahalanobis_rss'] = r['rss'][min_pval_i]\n step_dict['min_pval_chr_pos'] = chrom_pos_list[min_pval_i]\n\n num_snps = len(snps)\n num_par = lm.X.shape[1] + 1\n step_dict['num_snps'] = num_snps\n step_dict['num_par'] = num_par\n rss = lm.get_rss()\n ll = lm.get_ll(rss)\n (bic, extended_bic, modified_bic) = _calc_bic_(ll, num_snps, num_par, lm.n) # Calculate the BICs\n step_dict['ebic'] = extended_bic\n step_dict['mbic'] = modified_bic\n step_dict['bic'] = bic\n\n step_dict['rss'] = rss\n perc_var_expl = 1.0 - (rss / h0_rss)\n step_dict['perc_var_expl'] = perc_var_expl\n\n # Calculate maximum cofactor p-value\\\n print 'Updating the cofactor p-values'\n cof_pvals = []\n cof_chrom_pos_pval_list = []\n for i, snp in enumerate(cof_snps):\n t_cofactors = cof_snps[:]\n del t_cofactors[i]\n lm.set_factors(t_cofactors)\n res = lm.fast_f_test([snp])\n cof_pval = res['ps'][0]\n cof_pvals.append(cof_pval)\n cof_chrom_pos_pval_list.append((cof_chr_pos_list[i][0], cof_chr_pos_list[i][1], -math.log10(cof_pval)))\n for i, pval in zip(cof_indices, cof_pvals):\n r['ps'][i] = pval\n if len(cof_pvals):\n step_dict['max_cof_pval'] = max(cof_pvals)\n else:\n step_dict['max_cof_pval'] = 0.0\n secs = time.time() - s1\n if secs > 60:\n mins = int(secs) / 60\n secs = secs - mins * 60\n print 'Took %d mins and %f seconds.' % (mins, secs)\n else:\n print 'Took %f seconds.' % (secs)\n\n if plot_prefix != None:\n res = gr.Result(scores=list(r['ps']), snps_data=sd)\n png_file_name = '%s_%s_manhattan.png' % (plot_prefix, _cofactors_to_string_(cof_chrom_pos_pval_list))\n res.neg_log_trans()\n res.filter_percentile(0.1)\n res.plot_manhattan(png_file=png_file_name, plot_bonferroni=True, highlight_markers=cof_chrom_pos_pval_list)\n\n return {'stats':step_dict, 'res':r}","def fixC(self,i,value):\n if self.coeffPattern[2] == None:\n m,n=self.m,self.n\n self.coeffPattern[2] = [None]*m\n self.coeffPattern[2][i]=value\n self._updateEstimatorSize(i)","def set_coeffs(self, sol):\n # TODO: look for bugs here!\n self.log_debug(\"Set spline coefficients\")\n\n # task: find which of the free parameters (coeffs) belong to which spline object\n sol_bak = sol.copy()\n subs = dict()\n\n # iterate over the OrderedDict {'x1': [cx1_..., ...], 'u1': [cu1_...]}\n for k, v in list(self.indep_vars.items()):\n i = len(v)\n # TODO: improve comment\n subs[k] = sol[:i] # set numerical value to symbolical value\n sol = sol[i:] ##:: sol = []\n \n if self._parameters['use_chains']:\n for var in self.sys.states + self.sys.inputs:\n for ic in self._chains:\n if var in ic: ##:: ('x1','x2','u1') and ('x3','x4')\n subs[var] = subs[ic.upper] ##:: elements in the same chain have the same coefficients (number, not symbol).\n \n # set numerical coefficients for each spline and derivative\n # TODO: handle `!!`-comments after talking to yx \n ##!! spline_key_plus_k = self.splines.keys().append('k')\n for k in list(self.splines.keys()): ##:: ['x1','x3']\n self.splines[k].set_coefficients(free_coeffs=subs[k])\n ##:: self._indep_vars = free_coeffs (self.splines[k]._indep_coeffs=free_coeffs) makes symbols changing into numbers. {'x1': <Spline object>, 'x3': <Spline object>}, Spline._P[k] saves the polynomial.\n \n # yet another dictionary for solution and coeffs\n# ##!! indep_vars['z_par'] = np.array([sp.symbols('k')])\n# ##!! self.indep_vars = indep_vars\n\n coeffs_sol = OrderedDict()\n\n # used for indexing\n i = 0\n j = 0\n\n for k, v in list(self.indep_vars.items()):\n ##:: ['x1': array([0.12,0.13,...,]), 'x3':...] symbols change into numbers\n j += len(v)\n coeffs_sol[k] = sol_bak[i:j]\n i = j\n\n self.coeffs_sol = coeffs_sol\n ##:: {'x1': array([ 25.94485709, 16.38313857, -35.65010072, ..., 2.28427004, 2.82974712, 1.88490863]), 'x3': array([-34.33884269, 45.13959025, 1.3272378 , -4.15546318,# 5.3863866 , -5.39286006, -8.86559812, -6.11620983, -2.95630206])}\n\n ##!! return self.coeffs_sol['z_par'].tolist()","def setta_cubi(lst):\n for i in range(len(lst)):\n lst[i] = lst[i]**3","def __init__(self, valList):\r\n self.possibleVals = valList[:]","def updateParList(self, parList):\n counter = 0\n for modelName in self._modelList:\n model = self.__modelDict[modelName]\n modelParDict = model.parFitDict\n for parName in modelParDict.keys():\n if modelParDict[parName][\"vary\"]:\n modelParDict[parName][\"value\"] = parList[counter]\n counter += 1\n else:\n pass","def set_values(self, value_list, chat_id):\n coin, value, action = value_list\n self.clients[chat_id][0].criptos[coin] = []\n self.clients[chat_id][0].criptos[coin].append(float(value))\n self.clients[chat_id][0].criptos[coin].append(action)\n\n if coin not in self.clients[chat_id][0].pairs:\n self.clients[chat_id][0].pairs.append(coin)","def putconboundlist(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkc_)\n elif num_ != len(bkc_):\n raise IndexError(\"Inconsistent length of array bkc\")\n if num_ is None:\n num_ = len(blc_)\n elif num_ != len(blc_):\n raise IndexError(\"Inconsistent length of array blc\")\n if num_ is None:\n num_ = len(buc_)\n elif num_ != len(buc_):\n raise IndexError(\"Inconsistent length of array buc\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __init__(self, valList):\n self.possibleVals = valList[:]","def edit_polynomial_trajectory_condition(ui):\n\n if ui.tableWidget_poly_conditions.currentItem() is None:\n return\n\n # Get selected list item .................................................\n\n global polynomial_trajectories\n\n selection = ui.listWidget_poly.selectedItems()\n\n index_list = None\n for item in selection:\n index_list = ui.listWidget_poly.row(item)\n if index_list is None:\n return\n\n # Find the cell to edit ..................................................\n\n row = ui.tableWidget_poly_conditions.currentItem().row()\n col = ui.tableWidget_poly_conditions.currentItem().column()\n\n content = ui.tableWidget_poly_conditions.currentItem().text()\n\n # Check if the number is correct .........................................\n\n content = content.replace(\",\", \".\")\n\n try:\n float(content)\n\n # It's a symbolic variable\n except ValueError:\n\n # Avoiding non alphanumeric characters ...............................\n\n i = 0\n while i < len(content):\n char = content[i]\n # Avoiding whitespaces and -\n if char in [\" \", \"-\", \",\", \";\"]:\n content = content[:i] + \"_\" + content[i + 1:]\n i += 1\n elif not (char.isascii() and char.isalnum() or char == \"_\"):\n content = content[:i] + content[i + 1:]\n else:\n i += 1\n\n # Avoiding name starting by a number .................................\n\n if content[0:1].isdigit():\n letters = [\"k\", \"t\", \"x\"]\n content = letters[col] + content\n\n if col == 0:\n try:\n int(content)\n if int(content) > 0:\n content = str(int(content))\n else:\n content = \"0\"\n except ValueError:\n content = \"0\"\n\n ui.tableWidget_poly_conditions.currentItem().setText(content)\n\n if content == \"\":\n content = \"0\"\n\n polynomial_trajectories[index_list][\"conditions\"][row][col] = content","def putbararowlist(self,subi_,ptrb_,ptre_,subj_,nummat_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(ptrb_)\n elif num_ != len(ptrb_):\n raise IndexError(\"Inconsistent length of array ptrb\")\n if num_ is None:\n num_ = len(ptre_)\n elif num_ != len(ptre_):\n raise IndexError(\"Inconsistent length of array ptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if ptrb_ is None:\n raise ValueError(\"Argument ptrb cannot be None\")\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb may not be None\")\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\n _ptrb_copyarray = False\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptrb_ is not None:\n _ptrb_copyarray = True\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\n _ptrb_np_tmp[:] = ptrb_\n assert _ptrb_np_tmp.flags.contiguous\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptrb_copyarray = False\n _ptrb_tmp = None\n \n if ptre_ is None:\n raise ValueError(\"Argument ptre cannot be None\")\n if ptre_ is None:\n raise ValueError(\"Argument ptre may not be None\")\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\n _ptre_copyarray = False\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptre_ is not None:\n _ptre_copyarray = True\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\n _ptre_np_tmp[:] = ptre_\n assert _ptre_np_tmp.flags.contiguous\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptre_copyarray = False\n _ptre_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _nummat_minlength = len((subj_))\n if len((subj_)) > 0 and nummat_ is not None and len(nummat_) != len((subj_)):\n raise ValueError(\"Array argument nummat is not long enough: Is %d, expected %d\" % (len(nummat_),len((subj_))))\n if nummat_ is None:\n raise ValueError(\"Argument nummat cannot be None\")\n if nummat_ is None:\n raise ValueError(\"Argument nummat may not be None\")\n if isinstance(nummat_, numpy.ndarray) and nummat_.dtype is numpy.dtype(numpy.int64) and nummat_.flags.contiguous:\n _nummat_copyarray = False\n _nummat_tmp = ctypes.cast(nummat_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nummat_ is not None:\n _nummat_copyarray = True\n _nummat_np_tmp = numpy.zeros(len(nummat_),numpy.dtype(numpy.int64))\n _nummat_np_tmp[:] = nummat_\n assert _nummat_np_tmp.flags.contiguous\n _nummat_tmp = ctypes.cast(_nummat_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nummat_copyarray = False\n _nummat_tmp = None\n \n _matidx_minlength = sum((nummat_))\n if sum((nummat_)) > 0 and matidx_ is not None and len(matidx_) != sum((nummat_)):\n raise ValueError(\"Array argument matidx is not long enough: Is %d, expected %d\" % (len(matidx_),sum((nummat_))))\n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n _weights_minlength = sum((nummat_))\n if sum((nummat_)) > 0 and weights_ is not None and len(weights_) != sum((nummat_)):\n raise ValueError(\"Array argument weights is not long enough: Is %d, expected %d\" % (len(weights_),sum((nummat_))))\n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbararowlist(self.__nativep,num_,_subi_tmp,_ptrb_tmp,_ptre_tmp,_subj_tmp,_nummat_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __setitem__(self, key, val):\n self[key][...] = val","def add_prim_value(self, var_name, val):\n if not self.pdv_to_csv: # if not save to csv file, just store the value\n self.add_other_value(self.prim_var, var_name, val)\n else: # otherwise, we save the value to a csv file and add the file pos to self.prim_var[var_name]\n if not var_name in self.prim_var.keys(): # if the var is not established in the dictionary\n self.prim_var[var_name] = []\n pos = len(self.prim_var[var_name])\n f_path = os.path.join(self.root_dir, 'prim_vars', var_name, str(pos) + '.csv')\n self.add_file(f_path) # we should create the file at first\n np.savetxt(f_path, np.asmatrix(val), delimiter = ',')\n self.prim_var[var_name].append(pos)","def updateCoeff(self, **args):\n for par in args:\n self.rateCoeffMeta[par] = args[par]\n meta = self.rateCoeffMeta\n if self.rateCoeffMeta['type'] ==\"constant\":\n self.k = cp.k_const(meta['k'])\n elif self.rateCoeffMeta['type'] ==\"Arrhenius\":\n self.k = cp.k_arr(meta['A'], meta['E'], meta['T'], meta['R'])\n elif self.rateCoeffMeta['type'] ==\"modifiedArrhenius\":\n self.k = cp.k_mod_arr(meta['A'], meta['b'], meta['E'], meta['T'], meta['R'])\n else:\n # Other type of reaction rate coeff\n self.k = None # k = cp.newMethodToComputeK(...)\n return","def add_objective(self): \n \n if \"CSS\" in self.algorithm:\n \n if self.num_hidden == 0:\n \n data_term = self.compute_energy(self.x, self.batch_size)\n \n else:\n \n data_term = self.compute_free_energy(self.x)\n \n normalizer_term = self.add_css_approximation(data_term)\n \n if \"CD\" in self.algorithm and self.num_hidden ==0:\n \n data_term = self.compute_energy(self.x, self.batch_size)\n \n normalizer_term = self.compute_energy(self.x_gibbs, \n self.batch_size)\n \n normalizer_term = -T.mean(normalizer_term)\n \n if \"CD\" in self.algorithm and self.num_hidden > 0:\n \n data_term = self.compute_free_energy(self.x)\n \n normalizer_term = self.compute_free_energy(self.rbm_cd_samples)\n \n normalizer_term = -T.mean(normalizer_term)\n \n # cost is negative log likelihood \n self.cost = T.mean(data_term) + normalizer_term","def circ_j_ctrl_multiPauli(reg_target: List[QRegStorage], reg_ctrlling: List[QRegStorage], int_j: int,\n str_Pauli_term: str, if_minus: bool,\n reg_borrowed: Optional[List[QRegStorage]] = None) -> None:\n if reg_borrowed is None:\n reg_borrowed = []\n list_str_single_Pauli_term = re.split(r',\\s*', str_Pauli_term.upper())\n # we can transform such j-ctrl gate into multictrl gate by conjugating operation with several X gates\n for int_k in range(len(reg_ctrlling)):\n if (int_j >> int_k) % 2 == 0:\n X(reg_ctrlling[-1 - int_k])\n for str_single_Pauli_term in list_str_single_Pauli_term:\n match = re.match(r'([XYZ])([0-9]+)(\\w+)', str_single_Pauli_term, flags=re.I)\n while match:\n circ_multictrl_Pauli(reg_target[int(match.group(2))], reg_ctrlling, match.group(1).upper(),\n reg_borrowed=reg_target[:int(match.group(2))] + reg_target[int(match.group(2)) + 1:] +\n reg_borrowed)\n str_single_Pauli_term = match.group(3)\n match = re.match(r'([XYZ])([0-9]+)(\\w+)', str_single_Pauli_term, flags=re.I)\n match = re.match(r'([XYZ])([0-9]+)', str_single_Pauli_term, flags=re.I)\n if match:\n circ_multictrl_Pauli(reg_target[int(match.group(2))], reg_ctrlling, match.group(1).upper(),\n reg_borrowed=reg_target[:int(match.group(2))] + reg_target[int(match.group(2)) + 1:] +\n reg_borrowed)\n # We need to implement a multictrl-(-ID) gate,\n # which just equals to a multictrl-Z gate operated on those ctrlling qubits.\n if if_minus and len(reg_ctrlling) > 0:\n circ_multictrl_Z(reg_ctrlling[-1], reg_ctrlling[:-1], reg_borrowed=reg_target + reg_borrowed)\n # the other part of the conjugating operation with several X gates\n for int_k in range(len(reg_ctrlling)):\n if (int_j >> int_k) % 2 == 0:\n X(reg_ctrlling[-1 - int_k])","def __setitem__(self, j, val):\n\t\tself._coords[j] = val","def translate_list(self, oov_list, show_progbar=True, n_jobs=1, debug=False):\n raise NotImplementedError","def putslcslice(self,whichsol_,first_,last_,slc_):\n _slc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),((last_) - (first_))))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _list_coefficients_by_components(self, fd=True, pos=True, neg=True, printimag=False, norm_neg=True, nmin=0, nmax=0, latex=False, nd=0, Lvals=False, prime=False):\n sig = 1\n if(self._space.WR.is_dual()):\n sig = -1\n maxi = max(self._coeffs.keys())\n w1 = len(str(maxi))\n w2 = max(list(map(len, str(self._space.WR.D()).split())))\n maxn = max(self._coeffs[list(self._coeffs.keys())[0]].keys())\n w3 = len(str(maxn)) + 1\n C = self._coeffs\n mp0 = mpmath.mpf(0)\n mpold = mpmath.mp.dps\n N = self._space.WR.N\n if(mpmath.mp.dps < self.maxdigs):\n mpmath.mp.dps = self.maxdigs\n if norm_neg:\n cnorm = 0\n tnorm = (0, 0)\n for j in range(1, 100):\n t = rn_from_D(self.space.WR, -j * sig)\n if(t is None):\n continue\n if(t[1] + self._space.WR.Qv[t[0]] >= 0):\n continue\n c1 = self.get_coefficient(t[0], t[1])\n if c1 is None:\n continue\n if abs(c1) > self._prec:\n cnorm = c1\n tnorm = t\n print(\"c1=c({0})={1}\".format(tnorm, cnorm))\n break\n for r in C.keys():\n for n in range(min(C[r].keys()), max(C[r].keys()) + 1):\n if nmin > 0 and abs(n) < nmin:\n continue\n if nmax > 0 and abs(n) > nmax:\n continue\n nn = n + self._space.WR.Qv[r]\n if not neg and nn < 0:\n continue\n if not pos and nn >= 0:\n continue\n D = self._space.D_from_rn((r, n))\n if(fd):\n if fd and not is_fundamental_discriminant(D) and D != 1:\n continue\n if prime and gcd(D, N) > 1:\n continue\n c = self.get_coefficient(r, n)\n cs = \"\"\n if c != 0 and c is not None:\n if(nn >= 0):\n ss = \"+\"\n if(nn < 0):\n ss = \"-\"\n if(norm_neg):\n # print \"r,n=\",r,n\n # print \"cnorm=\",cnorm\n # print \"tnorm=\",tnorm\n D = self._space.D_from_rn((r, n))\n if ((r, n) != tnorm) and cnorm != 0:\n c = c / cnorm * mpmath.sqrt(mpmath.mpf(abs(D)))\n if c.real() >= 0:\n cs = \" \"\n if not printimag:\n if nd > 0:\n cs = str(c.real()).strip()\n cs = sci_pretty_print(cs, nd, latex_pow=latex)\n else:\n cs = str(c.real())\n else:\n cs = cs + str(c)\n if Lvals and list(self._Lv.keys()).count(D) == 1:\n ls = \"\\t\" + str(self._Lv[D])\n else:\n if latex:\n ls = \"\\\\\\\\ \\n\"\n else:\n ls = \"\"\n if latex:\n D = self._space.WR.D()[r]\n if(is_int(D)):\n p = numerator(D)\n q = denominator(D)\n sr = \"\\\\frac{\" + str(p) + \"}{\" + str(q) + \"}\"\n else:\n sr = str(D)\n ss = \"\"\n print(\"$C{0}({1},{2}) $ & $ {3} $ {4}\".format(ss, sr.ljust(w1), str(n).ljust(w3), cs, ls))\n else:\n print(\"C^{0}[{1}][{2}] = {3}\".format(ss, str(r).ljust(w1), str(n).ljust(w3), cs + ls))\n mpmath.mp.dps = mpold","def la_coeff(f1, coeff, cov, var_names, digits=3):\n f1.write(r\"\\begin{align}\" + \"\\n\")\n\n for j, co in enumerate(coeff):\n str_co = \"\\t\" + var_names[j]\n #var = uc.ufloat(coeff[j]\n #str_co += \" &=& {:L} \\\\\\\\\".format(var) # uncertainties format\n str_co += (\" &= %.\" + str(digits) + \"f \\\\\\\\\")%coeff[j] # specified format withouh errors\n str_co = em(str_co)\n f1.write(str_co +\"\\n\")\n\n f1.write(\"\\t\\mathrm{cov} &=\\n\")\n f1.write(\"\\t\\\\begin{pmatrix}\\n\")\n for row in cov:\n str_row = \"\\t\\t\"\n for var in row:\n str_row += (\"%.\" + str(1) + \"e &\")%var\n str_row = str_row[:-1] + r\"\\\\\"\n str_row = em(str_row)\n f1.write(str_row + \"\\n\")\n f1.write(\"\\t\\end{pmatrix} \\n\")\n f1.write(\"\\end{align}\\n\\n\")\n return 0","def score_coefficient(self, value):\n self.predictor._score_coefficient = value","def coefficients(self) :\n raise NotImplementedError","def _update_parallel_coef_constraints(self, x):\n n_features = x.shape[1]\n xi_final = np.zeros((n_features, n_features))\n\n # Todo: parallelize this for loop with Multiprocessing/joblib\n if self.model_subset is None:\n self.model_subset = range(n_features)\n elif np.max(np.abs(self.model_subset)) >= n_features:\n raise ValueError(\n \"A value in model_subset is larger than the number \"\n \"of features in the candidate library\"\n )\n for i in self.model_subset:\n print(\"Model \", i)\n xi = cp.Variable(n_features)\n # Note that norm choice below must be convex,\n # so thresholder must be L1 or L2\n if (self.thresholder).lower() in (\"l1\", \"weighted_l1\"):\n if self.thresholds is None:\n cost = cp.sum_squares(x[:, i] - x @ xi) + self.threshold * cp.norm1(\n xi\n )\n else:\n cost = cp.sum_squares(x[:, i] - x @ xi) + cp.norm1(\n self.thresholds[i, :] @ xi\n )\n if (self.thresholder).lower() in (\"l2\", \"weighted_l2\"):\n if self.thresholds is None:\n cost = (\n cp.sum_squares(x[:, i] - x @ xi)\n + self.threshold * cp.norm2(xi) ** 2\n )\n else:\n cost = (\n cp.sum_squares(x[:, i] - x @ xi)\n + cp.norm2(self.thresholds[i, :] @ xi) ** 2\n )\n prob = cp.Problem(\n cp.Minimize(cost),\n [xi[i] == 0.0],\n )\n try:\n prob.solve(\n max_iter=self.max_iter,\n eps_abs=self.tol,\n eps_rel=self.tol,\n verbose=self.verbose_cvxpy,\n )\n if xi.value is None:\n warnings.warn(\n \"Infeasible solve on iteration \"\n + str(i)\n + \", try changing your library\",\n ConvergenceWarning,\n )\n xi_final[:, i] = xi.value\n # Annoying error coming from L2 norm switching to use the ECOS\n # solver, which uses \"max_iters\" instead of \"max_iter\", and\n # similar semantic changes for the other variables.\n except TypeError:\n prob.solve(\n max_iters=self.max_iter,\n abstol=self.tol,\n reltol=self.tol,\n verbose=self.verbose_cvxpy,\n )\n if xi.value is None:\n warnings.warn(\n \"Infeasible solve on iteration \"\n + str(i)\n + \", try changing your library\",\n ConvergenceWarning,\n )\n xi_final[:, i] = xi.value\n except cp.error.SolverError:\n print(\"Solver failed on model \", str(i), \", setting coefs to zeros\")\n xi_final[:, i] = np.zeros(n_features)\n return xi_final","def __setSpectrum__(self):\n \n self.Ck = []\n TempCk = []\n TempOneCk = OneCk()\n \n # Process 1st frequency\n Tb = self.freqs[1].totalJ\n \n for b in range(-Tb, Tb, 1):\n TempOneCk.freq = b*self.freqs[1].Vph\n TempOneCk.Amp = self.freqs[1].Cjk(b)\n self.Ck.append(TempOneCk)\n \n # Process additional frequencies\n CkSize = len(self.Ck)\n Added = FALSE\n \n for f in range(2, len(self.freqs), 1):\n # Reset temporary variables\n Tb = self.freqs[f].totalJ\n TempCk = []\n \n # Calculate each Ck coefficient\n for b in range(-Tb, Tb, 1):\n for k in range(CkSize):\n TempOneCk.Amp = Ck[k].Amp * self.freq[f].Cjk(b)\n \n # Check to see if Amp is big enough to keep\n if( abs(TempOneCk.Amp) > self.min_Ck ):\n Added = FALSE\n TempOneCk.freq = self.Ck[k].freq + b*self.freqs.Vph\n \n # If freq is already in Ck, add new value to old,\n # if not, add new value and freq to spectrum\n for c in TempCk:\n if abs(c.freq-TempOneCk.freq < DOUBLE_PRECISION):\n c.Amp += TempOneCk.Amp\n Added = TRUE\n break\n \n if (not Added):\n TempCk.append(TempOneCk)\n \n self.Ck = TempCk\n CkSize = len(self.Ck)","def fixA(self,i,j,value):\n if self.coeffPattern[0] == None:\n m,n=self.m,self.n\n self.coeffPattern[0] = [[None]*m for i in range(m)]\n self.coeffPattern[0][i][j]=value\n self._updateEstimatorSize(i)","def objective(self, x):\n pass","def objective(self, x):\n pass","def add(self, *args):\n return _libsbml.ListWrapperCVTerm_add(self, *args)","def objective(self, param):\n self.__init__(param, self.data)\n # return self.rmse() + self.penalty()\n return self.rmse() + self.penalty()","def build_coeff_dict(l):\n coeff_dict = {}\n for triplet in l:\n coeff_dict[triplet[0]] = triplet[2]\n return coeff_dict","def __setitem__(self, idx, val):\n self.rows[idx[0]][idx[1]] = val","def put_proj_coeffs(self, dest):\n if parallel.is_rank_zero():\n self.put_array(self.proj_coeffs, dest)\n parallel.barrier()","def update_co_occ_mat(hc_ro, i_s_real, co_occs, co_occ_list, idx, nsteps):\n\n # Go to next element in circular array.\n idx = (idx + 1) % nsteps\n\n # Remove estimate from left hand side of time window.\n if len(co_occ_list[idx]):\n i_s_real_left, hc_ro_left = co_occ_list[idx]\n co_occs[:, i_s_real_left] -= hc_ro_left\n\n # Add current estimate to matrix and list.\n co_occs[:, i_s_real] += hc_ro\n co_occ_list[idx] = [i_s_real, hc_ro]\n\n # Fix float imprecision.\n co_occs[(co_occs < 0) & (co_occs > -1e-10)] = 0\n\n return idx","def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_valij = False\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n _copyback_valij = True\n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n _copyback_valij = True\n if valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij has wrong length\")\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_valij:\n valij[:] = _tmparr_valij\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi","def lcs(hh, vv):\n B=LCS.getB(hh,vv)\n trac=LCS.backtrack(B);\n cs=[ hh[h-1] for v,h,k in trac if k=='1' ]\n return cs","def enqueue(self, value): ################# <-\n self.lst = self.lst +[value]","def __init__(self, coefficients):\n \n if not isinstance(coefficients, list):\n raise TypeError(\"The coefficients variable must be a list of numbers\")\n\n if sum(coefficients) != 1:\n raise ValueError(\"The sum of coefficients must be equal to 1\")\n\n self.coef = coefficients\n self.samples = [0.0] * len(self.coef)","def coeffs_from_vec(self, coeffs_vec):\n coeffs = [self.coeffs[0].copy(), self.coeffs[1].copy()]\n coeffs[0].iloc[1:] = coeffs_vec[:self.index_ncoeffs[0]].copy()\n coeffs[1].iloc[1:] = coeffs_vec[self.index_ncoeffs[0]:].copy()\n return coeffs","def update_listed_eqns(self):\n self.clear_rightside()\n self.lst_eqns.clear()\n self.lst_eqns.addItems(list(self.eqn_data[self.cmb_sections.currentText()]))","def putboundlist(self,accmode_,sub,bk,bl,bu): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bk)\n elif num_ != len(bk):\n raise IndexError(\"Inconsistent length of array bk\")\n if num_ is None:\n num_ = len(bl)\n elif num_ != len(bl):\n raise IndexError(\"Inconsistent length of array bl\")\n if num_ is None:\n num_ = len(bu)\n elif num_ != len(bu):\n raise IndexError(\"Inconsistent length of array bu\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n res = self.__obj.putboundlist(accmode_,num_,sub_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def extend_step_tmp_vals_at_index(self, tmp_vals, index):\n try:\n self[index][3].extend(tmp_vals)\n except IndexError:\n self[index].append(tmp_vals)","def cont_leg(leg_val, tens_list, leg_list, ent_list):\n New_Tens = tens_list\n New_Legs = leg_list\n New_Schm = ent_list\n # search for desired leg value\n o1, o2 = leg_find(leg_val,leg_list)\n # new contracted tensor\n N_T= np.tensordot(New_Tens[o1[0]],New_Tens[o2[0]],[[o1[1]],[o2[1]]])\n # remove contracted legs and update legs data\n New_Legs[o1[0]].remove(leg_val)\n New_Legs[o2[0]].remove(leg_val)\n N_L = New_Legs[o1[0]] + New_Legs[o2[0]]\n m_big = max(o1[0],o2[0])\n m_small = min(o1[0],o2[0])\n # delete old tens_list/Legs elements\n del New_Tens[m_big]\n del New_Tens[m_small]\n del New_Legs[m_big]\n del New_Legs[m_small]\n # append new tensor with updated leg indicies\n New_Tens.append(N_T)\n New_Legs.append(N_L)\n New_Schm[leg_val] = np.array([0])\n return New_Tens, New_Legs, New_Schm","def add_cost_value(self, var_name, val):\n self.add_other_value(self.cost, var_name, val)\n\n # add the change of cost automatically\n if len(self.cost[var_name]) > 1: # if the cost is not empty\n last_val = self.get_last_last_cost_val(var_name)\n cost_change = abs(val - last_val) / last_val\n self.add_other_value(self.cost_change, var_name + '_change', cost_change)","def fixB(self,i,j,value):\n if self.coeffPattern[1] == None:\n m,n=self.m,self.n\n self.coeffPattern = [[None]*n for i in range(m)]\n self.coeffPattern[1][i][j]=value\n self._updateEstimatorSize(i)","def add_value(trajectories, val_func):\n for trajectory in trajectories:\n observes = trajectory['observes']\n values = val_func.predict(observes)\n trajectory['values'] = values","def getbarcblocktriplet(self,subj,subk,subl,valjkl): # 3\n maxnum_ = self.getnumbarcblocktriplets()\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxnum_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n _copyback_subk = False\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n _copyback_subk = True\n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n _copyback_subk = True\n if subk_ is not None and len(subk_) != (maxnum_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n _copyback_subl = False\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n _copyback_subl = True\n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n _copyback_subl = True\n if subl_ is not None and len(subl_) != (maxnum_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valjkl is None: raise TypeError(\"Invalid type for argument valjkl\")\n _copyback_valjkl = False\n if valjkl is None:\n valjkl_ = None\n else:\n try:\n valjkl_ = memoryview(valjkl)\n except TypeError:\n try:\n _tmparr_valjkl = array.array(\"d\",valjkl)\n except TypeError:\n raise TypeError(\"Argument valjkl has wrong type\")\n else:\n valjkl_ = memoryview(_tmparr_valjkl)\n _copyback_valjkl = True\n else:\n if valjkl_.format != \"d\":\n valjkl_ = memoryview(array.array(\"d\",valjkl))\n _copyback_valjkl = True\n if valjkl_ is not None and len(valjkl_) != (maxnum_):\n raise ValueError(\"Array argument valjkl has wrong length\")\n res,resargs = self.__obj.getbarcblocktriplet(maxnum_,subj_,subk_,subl_,valjkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n if _copyback_valjkl:\n valjkl[:] = _tmparr_valjkl\n if _copyback_subl:\n subl[:] = _tmparr_subl\n if _copyback_subk:\n subk[:] = _tmparr_subk\n if _copyback_subj:\n subj[:] = _tmparr_subj\n return _num_return_value","def addObjective(self, *args):\n return _libsbml.FbcModelPlugin_addObjective(self, *args)","def createObjective(self, solver, objective, conflict_vars_d, sched_d, conflicts_d, courses):\n\n\n # Put a little pressure on to not use slots 6 or 7\n for asl in self.vars_actualslots:\n if asl[1] == \"6\":\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_SLOT_6']) \n if asl[1] == \"7\":\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_SLOT_7']) \n\n if asl[0]==\"T\" and asl[1] in [\"4\", \"5\"]:\n if self.name in non_FAS_instructor:\n # for non-FAS faculty, favor teaching on Tuesday 3pm-5pm\n if asl[1] == \"4\":\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_FAVOR_COURSES_TU_3_TO_5'])\n else: \n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_FAVOR_COURSES_TU_3_TO_5']*3)\n else:\n # avoid teaching on Tuesday 3pm-5pm for FAS instructors\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_COURSES_TU_3_TO_5'])\n\n if asl[0]==\"F\":\n # avoid Friday teaching, to mimic faculty preferences\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_COURSES_FRIDAY']) \n \n \n \n # Add to objective function for bad course conflicts\n for other in conflicts_d.get(self.name, []):\n assert is_cross_list_canonical(other), other\n\n if other not in courses and other not in sched_d:\n # we're not scheduling the other course, so we can ignore it\n continue\n\n if other in courses and not (self.name < other):\n # we will let the processing for the other course handle this weight\n continue\n\n # create a variable that indicates if self and other conflict, add add the weight\n # to the objective function\n v_conflicts = solver.IntVar(0, 1, self.name + \" and \" + other + \" conflict\")\n objective.SetCoefficient(v_conflicts, PARAMS['WEIGHT_BAD_CONFLICT_FACTOR'] * int(conflicts_d[self.name][other]))\n\n # Record the variable so we can look at it later\n if self.name not in conflict_vars_d:\n conflict_vars_d[self.name] = {}\n assert other not in conflict_vars_d[self.name]\n conflict_vars_d[self.name][other] = v_conflicts\n \n if other not in courses:\n # the other course already has a fixed schedule.\n # Go through each actual slot and see if it intersects with other course\n disjuncts = []\n for s in self.vars_actualslots:\n my_course_time = ss.meeting_time_to_course_time([s])\n \n if sct.courses_conflict([my_course_time], sched_d[other]):\n # slots s conflicts with the time for other\n disjuncts.append(self.vars_actualslots[s])\n\n if len(disjuncts) > 0:\n makeDisjunction(solver, v_conflicts, disjuncts)\n \n if other in courses:\n # we are scheduling the other course, so we need to use its variables.\n # Create a variable for each actual slot that indicates if both self and other use that slot.\n d = courses[other]\n vs_d_same_slot = []\n for asl in self.vars_actualslots:\n if asl in d.vars_actualslots:\n # actual slot represented by vas is in both courses self and d.\n v_both_use_slot = solver.IntVar(0, 1, self.name + \" and \" + other + \" using slot \" + asl)\n makeConjunction(solver, v_both_use_slot, [self.vars_actualslots[asl], d.vars_actualslots[asl]])\n vs_d_same_slot.append(v_both_use_slot)\n\n if len(vs_d_same_slot) > 0:\n makeDisjunction(solver, v_conflicts, vs_d_same_slot)","def __init__(self):\r\n self.vals = []","def putacollist(self,sub_,ptrb_,ptre_,asub_,aval_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(ptrb_)\n elif num_ != len(ptrb_):\n raise IndexError(\"Inconsistent length of array ptrb\")\n if num_ is None:\n num_ = len(ptre_)\n elif num_ != len(ptre_):\n raise IndexError(\"Inconsistent length of array ptre\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if ptrb_ is None:\n raise ValueError(\"Argument ptrb cannot be None\")\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb may not be None\")\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\n _ptrb_copyarray = False\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptrb_ is not None:\n _ptrb_copyarray = True\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\n _ptrb_np_tmp[:] = ptrb_\n assert _ptrb_np_tmp.flags.contiguous\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptrb_copyarray = False\n _ptrb_tmp = None\n \n if ptre_ is None:\n raise ValueError(\"Argument ptre cannot be None\")\n if ptre_ is None:\n raise ValueError(\"Argument ptre may not be None\")\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\n _ptre_copyarray = False\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptre_ is not None:\n _ptre_copyarray = True\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\n _ptre_np_tmp[:] = ptre_\n assert _ptre_np_tmp.flags.contiguous\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptre_copyarray = False\n _ptre_tmp = None\n \n if asub_ is None:\n raise ValueError(\"Argument asub cannot be None\")\n if asub_ is None:\n raise ValueError(\"Argument asub may not be None\")\n if isinstance(asub_, numpy.ndarray) and asub_.dtype is numpy.dtype(numpy.int32) and asub_.flags.contiguous:\n _asub_copyarray = False\n _asub_tmp = ctypes.cast(asub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif asub_ is not None:\n _asub_copyarray = True\n _asub_np_tmp = numpy.zeros(len(asub_),numpy.dtype(numpy.int32))\n _asub_np_tmp[:] = asub_\n assert _asub_np_tmp.flags.contiguous\n _asub_tmp = ctypes.cast(_asub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _asub_copyarray = False\n _asub_tmp = None\n \n if aval_ is None:\n raise ValueError(\"Argument aval cannot be None\")\n if aval_ is None:\n raise ValueError(\"Argument aval may not be None\")\n if isinstance(aval_, numpy.ndarray) and aval_.dtype is numpy.dtype(numpy.float64) and aval_.flags.contiguous:\n _aval_copyarray = False\n _aval_tmp = ctypes.cast(aval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif aval_ is not None:\n _aval_copyarray = True\n _aval_np_tmp = numpy.zeros(len(aval_),numpy.dtype(numpy.float64))\n _aval_np_tmp[:] = aval_\n assert _aval_np_tmp.flags.contiguous\n _aval_tmp = ctypes.cast(_aval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _aval_copyarray = False\n _aval_tmp = None\n \n res = __library__.MSK_XX_putacollist64(self.__nativep,num_,_sub_tmp,_ptrb_tmp,_ptre_tmp,_asub_tmp,_aval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def new_condition_polynomial_trajectory(ui):\n\n # Add the row ............................................................\n\n pos = ui.tableWidget_poly_conditions.rowCount()\n ui.tableWidget_poly_conditions.insertRow(pos)\n\n ui.tableWidget_poly_conditions.setItem(pos, 0, QTableWidgetItem(\"0\"))\n ui.tableWidget_poly_conditions.setItem(pos, 1, QTableWidgetItem(\"0\"))\n ui.tableWidget_poly_conditions.setItem(pos, 2, QTableWidgetItem(\"0\"))\n\n # Save to global variable ................................................\n\n global polynomial_trajectories\n\n selection = ui.listWidget_poly.selectedItems()\n\n index = None\n for item in selection:\n index = ui.listWidget_poly.row(item)\n if index is None:\n return\n\n polynomial_trajectories[index][\"conditions\"].append([\"0\", \"0\", \"0\"])","def update_output(self, latent_mat, weight_mat, y_list):","def __getitem__(self, idx):\n return [self.y[idx], self.cont_X[idx], self.cat_X[idx]]","def __setitem__(self, name, val):\n\n if name in self.vars:\n l[name].setVal(val)\n else:\n l[name] = YPFVal(name, val)","def __setitem__(self, key, item):\n assert isinstance(key,list) and isinstance(item,list) and len(key)==2 and len(item)==2\n self._data[self.__ptBin(key[0])][self.__etaBin(key[1])] = item","def update(self):\n\n terms_toRemove = []\n\n for termIndex, [term_constantFactor, term_unknowns_attributeAddresses] in enumerate(self.LHS):\n\n # Check if coefficient is 0 - then no need to process any of the unknowns since term will be 0 anyways\n if term_constantFactor == 0:\n terms_toRemove.append(termIndex)\n continue # continue to next term, no need to resolve the unknowns of this term since the product will be 0 anyways\n\n # Check if any unknowns became known\n unknowns_toRemove = []\n for unknown_attributeAddress in term_unknowns_attributeAddresses:\n attribute = getattr_fromAddress(*unknown_attributeAddress)\n if isNumeric(attribute):\n # object.attribute which had previously been identified as unknown now has a value, add it to the constant factor product and remove from the unknowns\n self.LHS[termIndex][0] *= attribute # multiply it with the constant factor product\n unknowns_toRemove.append([termIndex, unknown_attributeAddress])\n for termIndex, unknown_attributeAddress in unknowns_toRemove: # remove unknowns which have become known in the end\n # removing in the end not to tamper with the iteration of the above loop\n self.LHS[termIndex][1].remove(unknown_attributeAddress)\n\n # Move constants to RHS\n if self.LHS[termIndex][1] == []:\n # if term has no unknowns, it is a constant, move to RHS\n self.RHS -= self.LHS[termIndex][0]\n self.LHS.pop(termIndex)\n\n for termIndex in reversed(terms_toRemove): # reversed - otherwise would tamper with indices of items identified for removal\n self.LHS.pop(termIndex)\n\n self._gatherUnknowns()","def putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_):\n _subi_minlength = (num_)\n if (num_) > 0 and subi_ is not None and len(subi_) != (num_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(num_)))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valijkl_minlength = (num_)\n if (num_) > 0 and valijkl_ is not None and len(valijkl_) != (num_):\n raise ValueError(\"Array argument valijkl is not long enough: Is %d, expected %d\" % (len(valijkl_),(num_)))\n if valijkl_ is None:\n raise ValueError(\"Argument valijkl cannot be None\")\n if valijkl_ is None:\n raise ValueError(\"Argument valijkl may not be None\")\n if isinstance(valijkl_, numpy.ndarray) and valijkl_.dtype is numpy.dtype(numpy.float64) and valijkl_.flags.contiguous:\n _valijkl_copyarray = False\n _valijkl_tmp = ctypes.cast(valijkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valijkl_ is not None:\n _valijkl_copyarray = True\n _valijkl_np_tmp = numpy.zeros(len(valijkl_),numpy.dtype(numpy.float64))\n _valijkl_np_tmp[:] = valijkl_\n assert _valijkl_np_tmp.flags.contiguous\n _valijkl_tmp = ctypes.cast(_valijkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valijkl_copyarray = False\n _valijkl_tmp = None\n \n res = __library__.MSK_XX_putbarablocktriplet(self.__nativep,num_,_subi_tmp,_subj_tmp,_subk_tmp,_subl_tmp,_valijkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def JCoeff(l, m, s, lp, mp, sp):\n coeff = int((l == lp) & (m == -mp) & (s == sp))*1j*(-1)**(int(m-1/2))*s\n return coeff"],"string":"[\n \"def putclist(self,subj,val): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subj)\\n elif num_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(val)\\n elif num_ != len(val):\\n raise IndexError(\\\"Inconsistent length of array val\\\")\\n if num_ is None: num_ = 0\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if val is None: raise TypeError(\\\"Invalid type for argument val\\\")\\n if val is None:\\n val_ = None\\n else:\\n try:\\n val_ = memoryview(val)\\n except TypeError:\\n try:\\n _tmparr_val = array.array(\\\"d\\\",val)\\n except TypeError:\\n raise TypeError(\\\"Argument val has wrong type\\\")\\n else:\\n val_ = memoryview(_tmparr_val)\\n \\n else:\\n if val_.format != \\\"d\\\":\\n val_ = memoryview(array.array(\\\"d\\\",val))\\n \\n res = self.__obj.putclist(num_,subj_,val_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __setitem__( self, l, c_l ) :\\n\\n if( l == len( self ) ) :\\n self.coefficients.append( float( c_l ) )\\n else :\\n self.coefficients[l] = float( c_l )\",\n \"def putaijlist(self,subi_,subj_,valij_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(valij_)\\n elif num_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getclist(self,subj_,c_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _c_minlength = (num_)\\n if (num_) > 0 and c_ is not None and len(c_) != (num_):\\n raise ValueError(\\\"Array argument c is not long enough: Is %d, expected %d\\\" % (len(c_),(num_)))\\n if isinstance(c_,numpy.ndarray) and not c_.flags.writeable:\\n raise ValueError(\\\"Argument c must be writable\\\")\\n if c_ is None:\\n raise ValueError(\\\"Argument c may not be None\\\")\\n if isinstance(c_, numpy.ndarray) and c_.dtype is numpy.dtype(numpy.float64) and c_.flags.contiguous:\\n _c_copyarray = False\\n _c_tmp = ctypes.cast(c_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif c_ is not None:\\n _c_copyarray = True\\n _c_np_tmp = numpy.zeros(len(c_),numpy.dtype(numpy.float64))\\n _c_np_tmp[:] = c_\\n assert _c_np_tmp.flags.contiguous\\n _c_tmp = ctypes.cast(_c_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _c_copyarray = False\\n _c_tmp = None\\n \\n res = __library__.MSK_XX_getclist(self.__nativep,num_,_subj_tmp,_c_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _c_copyarray:\\n c_[:] = _c_np_tmp\",\n \"def putaijlist(self,subi,subj,valij): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subi)\\n elif num_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj)\\n elif num_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(valij)\\n elif num_ != len(valij):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if num_ is None: num_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valij is None: raise TypeError(\\\"Invalid type for argument valij\\\")\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n \\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n \\n res = self.__obj.putaijlist64(num_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _set_coef(self, coef_):\\n self.coef_ = np.asfortranarray(array2d(coef_))\",\n \"def putvartypelist(self,subj_,vartype_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(vartype_)\\n elif num_ != len(vartype_):\\n raise IndexError(\\\"Inconsistent length of array vartype\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if vartype_ is None:\\n raise ValueError(\\\"Argument vartype cannot be None\\\")\\n if vartype_ is None:\\n raise ValueError(\\\"Argument vartype may not be None\\\")\\n if vartype_ is not None:\\n _vartype_tmp = (ctypes.c_int32 * len(vartype_))(*vartype_)\\n else:\\n _vartype_tmp = None\\n res = __library__.MSK_XX_putvartypelist(self.__nativep,num_,_subj_tmp,_vartype_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\\n self.coeff[self._core.index_alpha(key[0]),\\n self._core.index_beta(key[1])] = value\",\n \"def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):\\n num_ = None\\n if num_ is None:\\n num_ = len(dims_)\\n elif num_ != len(dims_):\\n raise IndexError(\\\"Inconsistent length of array dims\\\")\\n if num_ is None:\\n num_ = len(nz_)\\n elif num_ != len(nz_):\\n raise IndexError(\\\"Inconsistent length of array nz\\\")\\n if dims_ is None:\\n raise ValueError(\\\"Argument dims cannot be None\\\")\\n if dims_ is None:\\n raise ValueError(\\\"Argument dims may not be None\\\")\\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\\n _dims_copyarray = False\\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif dims_ is not None:\\n _dims_copyarray = True\\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\\n _dims_np_tmp[:] = dims_\\n assert _dims_np_tmp.flags.contiguous\\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _dims_copyarray = False\\n _dims_tmp = None\\n \\n if nz_ is None:\\n raise ValueError(\\\"Argument nz cannot be None\\\")\\n if nz_ is None:\\n raise ValueError(\\\"Argument nz may not be None\\\")\\n if isinstance(nz_, numpy.ndarray) and nz_.dtype is numpy.dtype(numpy.int64) and nz_.flags.contiguous:\\n _nz_copyarray = False\\n _nz_tmp = ctypes.cast(nz_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif nz_ is not None:\\n _nz_copyarray = True\\n _nz_np_tmp = numpy.zeros(len(nz_),numpy.dtype(numpy.int64))\\n _nz_np_tmp[:] = nz_\\n assert _nz_np_tmp.flags.contiguous\\n _nz_tmp = ctypes.cast(_nz_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _nz_copyarray = False\\n _nz_tmp = None\\n \\n _subi_minlength = sum((nz_))\\n if sum((nz_)) > 0 and subi_ is not None and len(subi_) != sum((nz_)):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),sum((nz_))))\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = sum((nz_))\\n if sum((nz_)) > 0 and subj_ is not None and len(subj_) != sum((nz_)):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),sum((nz_))))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _valij_minlength = sum((nz_))\\n if sum((nz_)) > 0 and valij_ is not None and len(valij_) != sum((nz_)):\\n raise ValueError(\\\"Array argument valij is not long enough: Is %d, expected %d\\\" % (len(valij_),sum((nz_))))\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n _idx_minlength = (num_)\\n if (num_) > 0 and idx_ is not None and len(idx_) != (num_):\\n raise ValueError(\\\"Array argument idx is not long enough: Is %d, expected %d\\\" % (len(idx_),(num_)))\\n if isinstance(idx_,numpy.ndarray) and not idx_.flags.writeable:\\n raise ValueError(\\\"Argument idx must be writable\\\")\\n if idx_ is None:\\n raise ValueError(\\\"Argument idx may not be None\\\")\\n if isinstance(idx_, numpy.ndarray) and idx_.dtype is numpy.dtype(numpy.int64) and idx_.flags.contiguous:\\n _idx_copyarray = False\\n _idx_tmp = ctypes.cast(idx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif idx_ is not None:\\n _idx_copyarray = True\\n _idx_np_tmp = numpy.zeros(len(idx_),numpy.dtype(numpy.int64))\\n _idx_np_tmp[:] = idx_\\n assert _idx_np_tmp.flags.contiguous\\n _idx_tmp = ctypes.cast(_idx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _idx_copyarray = False\\n _idx_tmp = None\\n \\n res = __library__.MSK_XX_appendsparsesymmatlist(self.__nativep,num_,_dims_tmp,_nz_tmp,_subi_tmp,_subj_tmp,_valij_tmp,_idx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _idx_copyarray:\\n idx_[:] = _idx_np_tmp\",\n \"def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def store(self, subj):\\n if subj in self.__lst:\\n raise ValueError('Disciplina exista deja')\\n self.__lst.append(subj)\",\n \"def _store_2D_coefficients(hdr, poly_model, coeff_prefix, keeplinear=False):\\n mindeg = int(not keeplinear)\\n degree = poly_model.degree\\n for i in range(0, degree + 1):\\n for j in range(0, degree + 1):\\n if (i + j) > mindeg and (i + j < degree + 1):\\n hdr[f'{coeff_prefix}_{i}_{j}'] = getattr(poly_model, f'c{i}_{j}').value\",\n \"def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi_)\\n elif nz_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj_)\\n elif nz_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij_)\\n elif nz_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n idx_ = ctypes.c_int64()\\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n idx_ = idx_.value\\n _idx_return_value = idx_\\n return (_idx_return_value)\",\n \"def parameters_polynomial(cobj, prop, prop_units, alist, blist):\\n for i, aval in enumerate(alist):\\n if i == 0:\\n param_units = prop_units\\n else:\\n param_units = prop_units / pyunits.K**i\\n\\n coeff = Var(doc=\\\"A parameter for CoolProp polynomial form\\\", units=param_units)\\n cobj.add_component(prop + \\\"_coeff_A\\\" + str(i), coeff)\\n coeff.fix(aval)\\n\\n for i, bval in enumerate(blist):\\n if i == 0:\\n param_units = pyunits.dimensionless\\n else:\\n param_units = pyunits.K**-i\\n\\n coeff = Var(doc=\\\"B parameter for CoolProp exponential form\\\", units=param_units)\\n cobj.add_component(prop + \\\"_coeff_B\\\" + str(i), coeff)\\n coeff.fix(bval)\",\n \"def add_coefficients(self, L, overwrite=False):\\n if not isinstance(L, dict):\\n raise ValueError(\\\"Call with dictionary as argument!\\\")\\n\\n for p in L.keys():\\n c = mpmath.mpmathify(L[p])\\n # print \\\"c=\\\",c\\n cd = ceil(mpmath.log10(abs(c)))\\n if(cd > self.maxdigs):\\n self.maxdigs = cd\\n # print \\\"p=\\\",p\\n if(is_int(p)):\\n (r, n) = rn_from_D(self._space.WR, p)\\n elif(isinstance(p, tuple)):\\n (r, n) = p\\n if r in self._coeffs:\\n if n in self._coeffs[r]:\\n c_old = self._coeffs[r][n]\\n # Try to determine (heuristically) if the new coefficient is really better\\n d1 = dist_from_int(c)[0]\\n d2 = dist_from_int(c_old)[0]\\n if(overwrite):\\n self._coeffs[r][n] = c\\n else:\\n self._coeffs[r][n] = c\\n else:\\n # see if it is a possible index at all\\n if not r < 0 or r > self._space.multiplier().ambient_rank():\\n raise ValueError(\\\"Key {0} corr to (r,n)=({1},{2}) is invalid for the current space!\\\".format(p, r, n))\\n elif r not in self._space.multiplier().D():\\n if self._space._sym_type == -1 and (r == 0 or r == self._space.multiplier().N):\\n # Should be 0 by symmetry\\n if abs(c) > 10**(1 - self.prec):\\n raise ValueError(\\\"Coefficient should be zero by symmetry. Got c({0},{1})={2}!\\\".format(r, n, c))\\n else:\\n self._coeffs[r][n] = 0\\n else:\\n # is equal to +- c(-r,n)\\n mr = 2 * self.multiplier().N - r\\n if mr in self._coeffs:\\n if n in self._coeffs[mr]:\\n c_old = self._coeffs[mr][n]\\n if abs(c - self._space.multiplier()._sym_type * c_old) > 10**(1 - self.prec):\\n st = \\\"Might add an erroneous coefficient! Got c({0},{1})={2}. \\\".format(r, n, c)\\n st += \\\"From previous coefficients should have {0}\\\".format(self._space._sym_type * c_old)\\n raise ValueError(st)\\n if overwrite:\\n self._coeffs[mr][n] = c\\n else:\\n raise ValueError(\\\"Coefficient should be zero by symmetry. Got c({0},{1})={2}!\\\" .format(r, n, c))\",\n \"def perturbe_clist(cl_array,bins,amount):\\n cltt_list=[]\\n for i in range(len(bins)):\\n cl=cl_array.copy()\\n cl[int(bins[i])]=amount*cl_array[int(bins[i])]\\n cltt_list.append(cl)\\n return cltt_list\",\n \"def putslc(self,whichsol_,slc_):\\n _slc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),self.getnumcon()))\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc cannot be None\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_putslc(self.__nativep,whichsol_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __getitem__( self, l ) :\\n\\n return( self.coefficients[l] )\",\n \"def update_coeff(self, **kwargs: float) -> None:\\n for rule_name, coeff in kwargs.items():\\n if rule_name not in self.rules:\\n raise ValueError(f\\\"Behavioral rule {rule_name} does not exist\\\")\\n else:\\n self.rules[getattr(self, rule_name)] = coeff\",\n \"def add_com_jac(ui):\\n global com_jac_list\\n\\n content = content_fk_jac_loops(ui, \\\"com_jac\\\")\\n if content in com_jac_list:\\n return\\n com_jac_list.append(content)\\n ui.listWidget_com_jac.addItem(f\\\"Center of Mass Jacobian \\\"\\n f\\\"{parse_content(content)}\\\")\",\n \"def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def UpdateCostMatrix( self, extraXs ):\\n for x in extraXs:\\n newRow = [ self.EuclideanDistanceSq(x,y) for y in self.Y ]\\n self.C.append(newRow)\",\n \"def setListPunctCmplx(self, list):\\n\\t\\tself.list_punct_cmplx = list\",\n \"def __init__(self, coef_list):\\n assert type(coef_list) is list, 'error message indicating that coef is not a list'\\n self.degree = len(coef_list) - 1\\n self.coefs = []\\n for coef in coef_list:\\n self.coefs.append(coef)\",\n \"def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi)\\n elif nz_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj)\\n elif nz_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij)\\n elif nz_ != len(valij):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if nz_ is None: nz_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valij is None: raise TypeError(\\\"Invalid type for argument valij\\\")\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n \\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n \\n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _idx_return_value = resargs\\n return _idx_return_value\",\n \"def __init__(self, sect_list, coef=1):\\n self.subsectors = copy.deepcopy(sect_list)\\n self.ds = {}\\n self.coef = coef\\n self._UpdateDS()\\n self.domains = []\\n self.excluded_vars = list()\",\n \"def __init__(self, coefficients):\\n self.coefficients = coefficients\",\n \"def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(alphaptrb_)\\n elif num_ != len(alphaptrb_):\\n raise IndexError(\\\"Inconsistent length of array alphaptrb\\\")\\n if num_ is None:\\n num_ = len(alphaptre_)\\n elif num_ != len(alphaptre_):\\n raise IndexError(\\\"Inconsistent length of array alphaptre\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if alphaptrb_ is None:\\n raise ValueError(\\\"Argument alphaptrb cannot be None\\\")\\n if alphaptrb_ is None:\\n raise ValueError(\\\"Argument alphaptrb may not be None\\\")\\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\\n _alphaptrb_copyarray = False\\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif alphaptrb_ is not None:\\n _alphaptrb_copyarray = True\\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\\n _alphaptrb_np_tmp[:] = alphaptrb_\\n assert _alphaptrb_np_tmp.flags.contiguous\\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _alphaptrb_copyarray = False\\n _alphaptrb_tmp = None\\n \\n if alphaptre_ is None:\\n raise ValueError(\\\"Argument alphaptre cannot be None\\\")\\n if alphaptre_ is None:\\n raise ValueError(\\\"Argument alphaptre may not be None\\\")\\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\\n _alphaptre_copyarray = False\\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif alphaptre_ is not None:\\n _alphaptre_copyarray = True\\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\\n _alphaptre_np_tmp[:] = alphaptre_\\n assert _alphaptre_np_tmp.flags.contiguous\\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _alphaptre_copyarray = False\\n _alphaptre_tmp = None\\n \\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx cannot be None\\\")\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx may not be None\\\")\\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\\n _matidx_copyarray = False\\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif matidx_ is not None:\\n _matidx_copyarray = True\\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\\n _matidx_np_tmp[:] = matidx_\\n assert _matidx_np_tmp.flags.contiguous\\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _matidx_copyarray = False\\n _matidx_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvartypelist(self,subj,vartype): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subj)\\n elif num_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(vartype)\\n elif num_ != len(vartype):\\n raise IndexError(\\\"Inconsistent length of array vartype\\\")\\n if num_ is None: num_ = 0\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if vartype is None: raise TypeError(\\\"Invalid type for argument vartype\\\")\\n if vartype is None:\\n vartype_ = None\\n else:\\n try:\\n vartype_ = memoryview(vartype)\\n except TypeError:\\n try:\\n _tmparr_vartype = array.array(\\\"i\\\",vartype)\\n except TypeError:\\n raise TypeError(\\\"Argument vartype has wrong type\\\")\\n else:\\n vartype_ = memoryview(_tmparr_vartype)\\n \\n else:\\n if vartype_.format != \\\"i\\\":\\n vartype_ = memoryview(array.array(\\\"i\\\",vartype))\\n \\n res = self.__obj.putvartypelist(num_,subj_,vartype_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubk_ is None:\\n raise ValueError(\\\"Argument qcsubk cannot be None\\\")\\n if qcsubk_ is None:\\n raise ValueError(\\\"Argument qcsubk may not be None\\\")\\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\\n _qcsubk_copyarray = False\\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubk_ is not None:\\n _qcsubk_copyarray = True\\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\\n _qcsubk_np_tmp[:] = qcsubk_\\n assert _qcsubk_np_tmp.flags.contiguous\\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubk_copyarray = False\\n _qcsubk_tmp = None\\n \\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putacol(self,j_,subj_,valj_):\\n nzj_ = None\\n if nzj_ is None:\\n nzj_ = len(subj_)\\n elif nzj_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nzj_ is None:\\n nzj_ = len(valj_)\\n elif nzj_ != len(valj_):\\n raise IndexError(\\\"Inconsistent length of array valj\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valj_ is None:\\n raise ValueError(\\\"Argument valj cannot be None\\\")\\n if valj_ is None:\\n raise ValueError(\\\"Argument valj may not be None\\\")\\n if isinstance(valj_, numpy.ndarray) and valj_.dtype is numpy.dtype(numpy.float64) and valj_.flags.contiguous:\\n _valj_copyarray = False\\n _valj_tmp = ctypes.cast(valj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valj_ is not None:\\n _valj_copyarray = True\\n _valj_np_tmp = numpy.zeros(len(valj_),numpy.dtype(numpy.float64))\\n _valj_np_tmp[:] = valj_\\n assert _valj_np_tmp.flags.contiguous\\n _valj_tmp = ctypes.cast(_valj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valj_copyarray = False\\n _valj_tmp = None\\n \\n res = __library__.MSK_XX_putacol(self.__nativep,j_,nzj_,_subj_tmp,_valj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def redcloth_list(self, text):\\n pattern = re.compile(r\\\"^([-]+%s[ .].*:=.*)$(?![^-])\\\" % self.lc,\\n re.M | re.U | re.S)\\n return pattern.sub(self.fRCList, text)\",\n \"def SetPRCatConstraint(self, model ) :\\n tot = np.multiply(self.wish, self.dispo)\\n for line in tot :\\n for val in line :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def putacol(self,j_,subj,valj): # 3\\n nzj_ = None\\n if nzj_ is None:\\n nzj_ = len(subj)\\n elif nzj_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nzj_ is None:\\n nzj_ = len(valj)\\n elif nzj_ != len(valj):\\n raise IndexError(\\\"Inconsistent length of array valj\\\")\\n if nzj_ is None: nzj_ = 0\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valj is None: raise TypeError(\\\"Invalid type for argument valj\\\")\\n if valj is None:\\n valj_ = None\\n else:\\n try:\\n valj_ = memoryview(valj)\\n except TypeError:\\n try:\\n _tmparr_valj = array.array(\\\"d\\\",valj)\\n except TypeError:\\n raise TypeError(\\\"Argument valj has wrong type\\\")\\n else:\\n valj_ = memoryview(_tmparr_valj)\\n \\n else:\\n if valj_.format != \\\"d\\\":\\n valj_ = memoryview(array.array(\\\"d\\\",valj))\\n \\n res = self.__obj.putacol(j_,nzj_,subj_,valj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubk is None: raise TypeError(\\\"Invalid type for argument qcsubk\\\")\\n if qcsubk is None:\\n qcsubk_ = None\\n else:\\n try:\\n qcsubk_ = memoryview(qcsubk)\\n except TypeError:\\n try:\\n _tmparr_qcsubk = array.array(\\\"i\\\",qcsubk)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubk has wrong type\\\")\\n else:\\n qcsubk_ = memoryview(_tmparr_qcsubk)\\n \\n else:\\n if qcsubk_.format != \\\"i\\\":\\n qcsubk_ = memoryview(array.array(\\\"i\\\",qcsubk))\\n \\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setCoefficient(self, *args):\\n return _libsbml.FluxObjective_setCoefficient(self, *args)\",\n \"def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if subk is None: raise TypeError(\\\"Invalid type for argument subk\\\")\\n if subk is None:\\n subk_ = None\\n else:\\n try:\\n subk_ = memoryview(subk)\\n except TypeError:\\n try:\\n _tmparr_subk = array.array(\\\"i\\\",subk)\\n except TypeError:\\n raise TypeError(\\\"Argument subk has wrong type\\\")\\n else:\\n subk_ = memoryview(_tmparr_subk)\\n \\n else:\\n if subk_.format != \\\"i\\\":\\n subk_ = memoryview(array.array(\\\"i\\\",subk))\\n \\n if subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk has wrong length\\\")\\n if subl is None: raise TypeError(\\\"Invalid type for argument subl\\\")\\n if subl is None:\\n subl_ = None\\n else:\\n try:\\n subl_ = memoryview(subl)\\n except TypeError:\\n try:\\n _tmparr_subl = array.array(\\\"i\\\",subl)\\n except TypeError:\\n raise TypeError(\\\"Argument subl has wrong type\\\")\\n else:\\n subl_ = memoryview(_tmparr_subl)\\n \\n else:\\n if subl_.format != \\\"i\\\":\\n subl_ = memoryview(array.array(\\\"i\\\",subl))\\n \\n if subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl has wrong length\\\")\\n if valjkl is None: raise TypeError(\\\"Invalid type for argument valjkl\\\")\\n if valjkl is None:\\n valjkl_ = None\\n else:\\n try:\\n valjkl_ = memoryview(valjkl)\\n except TypeError:\\n try:\\n _tmparr_valjkl = array.array(\\\"d\\\",valjkl)\\n except TypeError:\\n raise TypeError(\\\"Argument valjkl has wrong type\\\")\\n else:\\n valjkl_ = memoryview(_tmparr_valjkl)\\n \\n else:\\n if valjkl_.format != \\\"d\\\":\\n valjkl_ = memoryview(array.array(\\\"d\\\",valjkl))\\n \\n if valjkl_ is not None and len(valjkl_) != (num_):\\n raise ValueError(\\\"Array argument valjkl has wrong length\\\")\\n res = self.__obj.putbarcblocktriplet(num_,subj_,subk_,subl_,valjkl_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __set__(self,obj,val):\\n self._check_bounds(val)\\n super(List,self).__set__(obj,val)\",\n \"def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\\n _subj_minlength = (num_)\\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(num_)))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _subk_minlength = (num_)\\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk is not long enough: Is %d, expected %d\\\" % (len(subk_),(num_)))\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk cannot be None\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n _subl_minlength = (num_)\\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl is not long enough: Is %d, expected %d\\\" % (len(subl_),(num_)))\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl cannot be None\\\")\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl may not be None\\\")\\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\\n _subl_copyarray = False\\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subl_ is not None:\\n _subl_copyarray = True\\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\\n _subl_np_tmp[:] = subl_\\n assert _subl_np_tmp.flags.contiguous\\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subl_copyarray = False\\n _subl_tmp = None\\n \\n _valjkl_minlength = (num_)\\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\\n raise ValueError(\\\"Array argument valjkl is not long enough: Is %d, expected %d\\\" % (len(valjkl_),(num_)))\\n if valjkl_ is None:\\n raise ValueError(\\\"Argument valjkl cannot be None\\\")\\n if valjkl_ is None:\\n raise ValueError(\\\"Argument valjkl may not be None\\\")\\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\\n _valjkl_copyarray = False\\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valjkl_ is not None:\\n _valjkl_copyarray = True\\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\\n _valjkl_np_tmp[:] = valjkl_\\n assert _valjkl_np_tmp.flags.contiguous\\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valjkl_copyarray = False\\n _valjkl_tmp = None\\n \\n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def lin_reg_step(phen_vals, sd, cof_chr_pos_list, progress_file_writer=None, plot_prefix=None):\\n import gwaResults as gr\\n import bisect\\n s1 = time.time()\\n lm = LinearModel(phen_vals)\\n\\n h0_rss = lm.get_rss()\\n step_dict = {}\\n step_dict['h0_rss'] = h0_rss\\n\\n chrom_pos_list = sd.get_chr_pos_list()\\n print 'Looking up cofactors'\\n cof_indices = []\\n for chrom_pos in cof_chr_pos_list:\\n i = bisect.bisect(chrom_pos_list, chrom_pos) - 1\\n assert chrom_pos_list[i] == chrom_pos, 'Cofactor missing??'\\n cof_indices.append(i)\\n snps = sd.get_snps()\\n cof_snps = [snps[i] for i in cof_indices]\\n lm.set_factors(cof_snps)\\n\\n if not progress_file_writer == None:\\n progress_file_writer.update_progress_bar(progress=0.20, task_status='Performing linear regression')\\n progress_file_writer.set_step(0.05)\\n\\n\\n print 'Performing linear regression.'\\n r = lm.fast_f_test(snps, progress_file_writer=progress_file_writer)\\n min_pval_i = sp.argmin(r['ps'])\\n step_dict['min_pval_i'] = min_pval_i\\n step_dict['min_pval'] = r['ps'][min_pval_i]\\n step_dict['mahalanobis_rss'] = r['rss'][min_pval_i]\\n step_dict['min_pval_chr_pos'] = chrom_pos_list[min_pval_i]\\n\\n num_snps = len(snps)\\n num_par = lm.X.shape[1] + 1\\n step_dict['num_snps'] = num_snps\\n step_dict['num_par'] = num_par\\n rss = lm.get_rss()\\n ll = lm.get_ll(rss)\\n (bic, extended_bic, modified_bic) = _calc_bic_(ll, num_snps, num_par, lm.n) # Calculate the BICs\\n step_dict['ebic'] = extended_bic\\n step_dict['mbic'] = modified_bic\\n step_dict['bic'] = bic\\n\\n step_dict['rss'] = rss\\n perc_var_expl = 1.0 - (rss / h0_rss)\\n step_dict['perc_var_expl'] = perc_var_expl\\n\\n # Calculate maximum cofactor p-value\\\\\\n print 'Updating the cofactor p-values'\\n cof_pvals = []\\n cof_chrom_pos_pval_list = []\\n for i, snp in enumerate(cof_snps):\\n t_cofactors = cof_snps[:]\\n del t_cofactors[i]\\n lm.set_factors(t_cofactors)\\n res = lm.fast_f_test([snp])\\n cof_pval = res['ps'][0]\\n cof_pvals.append(cof_pval)\\n cof_chrom_pos_pval_list.append((cof_chr_pos_list[i][0], cof_chr_pos_list[i][1], -math.log10(cof_pval)))\\n for i, pval in zip(cof_indices, cof_pvals):\\n r['ps'][i] = pval\\n if len(cof_pvals):\\n step_dict['max_cof_pval'] = max(cof_pvals)\\n else:\\n step_dict['max_cof_pval'] = 0.0\\n secs = time.time() - s1\\n if secs > 60:\\n mins = int(secs) / 60\\n secs = secs - mins * 60\\n print 'Took %d mins and %f seconds.' % (mins, secs)\\n else:\\n print 'Took %f seconds.' % (secs)\\n\\n if plot_prefix != None:\\n res = gr.Result(scores=list(r['ps']), snps_data=sd)\\n png_file_name = '%s_%s_manhattan.png' % (plot_prefix, _cofactors_to_string_(cof_chrom_pos_pval_list))\\n res.neg_log_trans()\\n res.filter_percentile(0.1)\\n res.plot_manhattan(png_file=png_file_name, plot_bonferroni=True, highlight_markers=cof_chrom_pos_pval_list)\\n\\n return {'stats':step_dict, 'res':r}\",\n \"def fixC(self,i,value):\\n if self.coeffPattern[2] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[2] = [None]*m\\n self.coeffPattern[2][i]=value\\n self._updateEstimatorSize(i)\",\n \"def set_coeffs(self, sol):\\n # TODO: look for bugs here!\\n self.log_debug(\\\"Set spline coefficients\\\")\\n\\n # task: find which of the free parameters (coeffs) belong to which spline object\\n sol_bak = sol.copy()\\n subs = dict()\\n\\n # iterate over the OrderedDict {'x1': [cx1_..., ...], 'u1': [cu1_...]}\\n for k, v in list(self.indep_vars.items()):\\n i = len(v)\\n # TODO: improve comment\\n subs[k] = sol[:i] # set numerical value to symbolical value\\n sol = sol[i:] ##:: sol = []\\n \\n if self._parameters['use_chains']:\\n for var in self.sys.states + self.sys.inputs:\\n for ic in self._chains:\\n if var in ic: ##:: ('x1','x2','u1') and ('x3','x4')\\n subs[var] = subs[ic.upper] ##:: elements in the same chain have the same coefficients (number, not symbol).\\n \\n # set numerical coefficients for each spline and derivative\\n # TODO: handle `!!`-comments after talking to yx \\n ##!! spline_key_plus_k = self.splines.keys().append('k')\\n for k in list(self.splines.keys()): ##:: ['x1','x3']\\n self.splines[k].set_coefficients(free_coeffs=subs[k])\\n ##:: self._indep_vars = free_coeffs (self.splines[k]._indep_coeffs=free_coeffs) makes symbols changing into numbers. {'x1': <Spline object>, 'x3': <Spline object>}, Spline._P[k] saves the polynomial.\\n \\n # yet another dictionary for solution and coeffs\\n# ##!! indep_vars['z_par'] = np.array([sp.symbols('k')])\\n# ##!! self.indep_vars = indep_vars\\n\\n coeffs_sol = OrderedDict()\\n\\n # used for indexing\\n i = 0\\n j = 0\\n\\n for k, v in list(self.indep_vars.items()):\\n ##:: ['x1': array([0.12,0.13,...,]), 'x3':...] symbols change into numbers\\n j += len(v)\\n coeffs_sol[k] = sol_bak[i:j]\\n i = j\\n\\n self.coeffs_sol = coeffs_sol\\n ##:: {'x1': array([ 25.94485709, 16.38313857, -35.65010072, ..., 2.28427004, 2.82974712, 1.88490863]), 'x3': array([-34.33884269, 45.13959025, 1.3272378 , -4.15546318,# 5.3863866 , -5.39286006, -8.86559812, -6.11620983, -2.95630206])}\\n\\n ##!! return self.coeffs_sol['z_par'].tolist()\",\n \"def setta_cubi(lst):\\n for i in range(len(lst)):\\n lst[i] = lst[i]**3\",\n \"def __init__(self, valList):\\r\\n self.possibleVals = valList[:]\",\n \"def updateParList(self, parList):\\n counter = 0\\n for modelName in self._modelList:\\n model = self.__modelDict[modelName]\\n modelParDict = model.parFitDict\\n for parName in modelParDict.keys():\\n if modelParDict[parName][\\\"vary\\\"]:\\n modelParDict[parName][\\\"value\\\"] = parList[counter]\\n counter += 1\\n else:\\n pass\",\n \"def set_values(self, value_list, chat_id):\\n coin, value, action = value_list\\n self.clients[chat_id][0].criptos[coin] = []\\n self.clients[chat_id][0].criptos[coin].append(float(value))\\n self.clients[chat_id][0].criptos[coin].append(action)\\n\\n if coin not in self.clients[chat_id][0].pairs:\\n self.clients[chat_id][0].pairs.append(coin)\",\n \"def putconboundlist(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkc_)\\n elif num_ != len(bkc_):\\n raise IndexError(\\\"Inconsistent length of array bkc\\\")\\n if num_ is None:\\n num_ = len(blc_)\\n elif num_ != len(blc_):\\n raise IndexError(\\\"Inconsistent length of array blc\\\")\\n if num_ is None:\\n num_ = len(buc_)\\n elif num_ != len(buc_):\\n raise IndexError(\\\"Inconsistent length of array buc\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc cannot be None\\\")\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc may not be None\\\")\\n if bkc_ is not None:\\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\\n else:\\n _bkc_tmp = None\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc cannot be None\\\")\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc may not be None\\\")\\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\\n _blc_copyarray = False\\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blc_ is not None:\\n _blc_copyarray = True\\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\\n _blc_np_tmp[:] = blc_\\n assert _blc_np_tmp.flags.contiguous\\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blc_copyarray = False\\n _blc_tmp = None\\n \\n if buc_ is None:\\n raise ValueError(\\\"Argument buc cannot be None\\\")\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc may not be None\\\")\\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\\n _buc_copyarray = False\\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif buc_ is not None:\\n _buc_copyarray = True\\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\\n _buc_np_tmp[:] = buc_\\n assert _buc_np_tmp.flags.contiguous\\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _buc_copyarray = False\\n _buc_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __init__(self, valList):\\n self.possibleVals = valList[:]\",\n \"def edit_polynomial_trajectory_condition(ui):\\n\\n if ui.tableWidget_poly_conditions.currentItem() is None:\\n return\\n\\n # Get selected list item .................................................\\n\\n global polynomial_trajectories\\n\\n selection = ui.listWidget_poly.selectedItems()\\n\\n index_list = None\\n for item in selection:\\n index_list = ui.listWidget_poly.row(item)\\n if index_list is None:\\n return\\n\\n # Find the cell to edit ..................................................\\n\\n row = ui.tableWidget_poly_conditions.currentItem().row()\\n col = ui.tableWidget_poly_conditions.currentItem().column()\\n\\n content = ui.tableWidget_poly_conditions.currentItem().text()\\n\\n # Check if the number is correct .........................................\\n\\n content = content.replace(\\\",\\\", \\\".\\\")\\n\\n try:\\n float(content)\\n\\n # It's a symbolic variable\\n except ValueError:\\n\\n # Avoiding non alphanumeric characters ...............................\\n\\n i = 0\\n while i < len(content):\\n char = content[i]\\n # Avoiding whitespaces and -\\n if char in [\\\" \\\", \\\"-\\\", \\\",\\\", \\\";\\\"]:\\n content = content[:i] + \\\"_\\\" + content[i + 1:]\\n i += 1\\n elif not (char.isascii() and char.isalnum() or char == \\\"_\\\"):\\n content = content[:i] + content[i + 1:]\\n else:\\n i += 1\\n\\n # Avoiding name starting by a number .................................\\n\\n if content[0:1].isdigit():\\n letters = [\\\"k\\\", \\\"t\\\", \\\"x\\\"]\\n content = letters[col] + content\\n\\n if col == 0:\\n try:\\n int(content)\\n if int(content) > 0:\\n content = str(int(content))\\n else:\\n content = \\\"0\\\"\\n except ValueError:\\n content = \\\"0\\\"\\n\\n ui.tableWidget_poly_conditions.currentItem().setText(content)\\n\\n if content == \\\"\\\":\\n content = \\\"0\\\"\\n\\n polynomial_trajectories[index_list][\\\"conditions\\\"][row][col] = content\",\n \"def putbararowlist(self,subi_,ptrb_,ptre_,subj_,nummat_,matidx_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(ptrb_)\\n elif num_ != len(ptrb_):\\n raise IndexError(\\\"Inconsistent length of array ptrb\\\")\\n if num_ is None:\\n num_ = len(ptre_)\\n elif num_ != len(ptre_):\\n raise IndexError(\\\"Inconsistent length of array ptre\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb cannot be None\\\")\\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb may not be None\\\")\\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\\n _ptrb_copyarray = False\\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptrb_ is not None:\\n _ptrb_copyarray = True\\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\\n _ptrb_np_tmp[:] = ptrb_\\n assert _ptrb_np_tmp.flags.contiguous\\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptrb_copyarray = False\\n _ptrb_tmp = None\\n \\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre cannot be None\\\")\\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre may not be None\\\")\\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\\n _ptre_copyarray = False\\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptre_ is not None:\\n _ptre_copyarray = True\\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\\n _ptre_np_tmp[:] = ptre_\\n assert _ptre_np_tmp.flags.contiguous\\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptre_copyarray = False\\n _ptre_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _nummat_minlength = len((subj_))\\n if len((subj_)) > 0 and nummat_ is not None and len(nummat_) != len((subj_)):\\n raise ValueError(\\\"Array argument nummat is not long enough: Is %d, expected %d\\\" % (len(nummat_),len((subj_))))\\n if nummat_ is None:\\n raise ValueError(\\\"Argument nummat cannot be None\\\")\\n if nummat_ is None:\\n raise ValueError(\\\"Argument nummat may not be None\\\")\\n if isinstance(nummat_, numpy.ndarray) and nummat_.dtype is numpy.dtype(numpy.int64) and nummat_.flags.contiguous:\\n _nummat_copyarray = False\\n _nummat_tmp = ctypes.cast(nummat_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif nummat_ is not None:\\n _nummat_copyarray = True\\n _nummat_np_tmp = numpy.zeros(len(nummat_),numpy.dtype(numpy.int64))\\n _nummat_np_tmp[:] = nummat_\\n assert _nummat_np_tmp.flags.contiguous\\n _nummat_tmp = ctypes.cast(_nummat_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _nummat_copyarray = False\\n _nummat_tmp = None\\n \\n _matidx_minlength = sum((nummat_))\\n if sum((nummat_)) > 0 and matidx_ is not None and len(matidx_) != sum((nummat_)):\\n raise ValueError(\\\"Array argument matidx is not long enough: Is %d, expected %d\\\" % (len(matidx_),sum((nummat_))))\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx cannot be None\\\")\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx may not be None\\\")\\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\\n _matidx_copyarray = False\\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif matidx_ is not None:\\n _matidx_copyarray = True\\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\\n _matidx_np_tmp[:] = matidx_\\n assert _matidx_np_tmp.flags.contiguous\\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _matidx_copyarray = False\\n _matidx_tmp = None\\n \\n _weights_minlength = sum((nummat_))\\n if sum((nummat_)) > 0 and weights_ is not None and len(weights_) != sum((nummat_)):\\n raise ValueError(\\\"Array argument weights is not long enough: Is %d, expected %d\\\" % (len(weights_),sum((nummat_))))\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbararowlist(self.__nativep,num_,_subi_tmp,_ptrb_tmp,_ptre_tmp,_subj_tmp,_nummat_tmp,_matidx_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __setitem__(self, key, val):\\n self[key][...] = val\",\n \"def add_prim_value(self, var_name, val):\\n if not self.pdv_to_csv: # if not save to csv file, just store the value\\n self.add_other_value(self.prim_var, var_name, val)\\n else: # otherwise, we save the value to a csv file and add the file pos to self.prim_var[var_name]\\n if not var_name in self.prim_var.keys(): # if the var is not established in the dictionary\\n self.prim_var[var_name] = []\\n pos = len(self.prim_var[var_name])\\n f_path = os.path.join(self.root_dir, 'prim_vars', var_name, str(pos) + '.csv')\\n self.add_file(f_path) # we should create the file at first\\n np.savetxt(f_path, np.asmatrix(val), delimiter = ',')\\n self.prim_var[var_name].append(pos)\",\n \"def updateCoeff(self, **args):\\n for par in args:\\n self.rateCoeffMeta[par] = args[par]\\n meta = self.rateCoeffMeta\\n if self.rateCoeffMeta['type'] ==\\\"constant\\\":\\n self.k = cp.k_const(meta['k'])\\n elif self.rateCoeffMeta['type'] ==\\\"Arrhenius\\\":\\n self.k = cp.k_arr(meta['A'], meta['E'], meta['T'], meta['R'])\\n elif self.rateCoeffMeta['type'] ==\\\"modifiedArrhenius\\\":\\n self.k = cp.k_mod_arr(meta['A'], meta['b'], meta['E'], meta['T'], meta['R'])\\n else:\\n # Other type of reaction rate coeff\\n self.k = None # k = cp.newMethodToComputeK(...)\\n return\",\n \"def add_objective(self): \\n \\n if \\\"CSS\\\" in self.algorithm:\\n \\n if self.num_hidden == 0:\\n \\n data_term = self.compute_energy(self.x, self.batch_size)\\n \\n else:\\n \\n data_term = self.compute_free_energy(self.x)\\n \\n normalizer_term = self.add_css_approximation(data_term)\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden ==0:\\n \\n data_term = self.compute_energy(self.x, self.batch_size)\\n \\n normalizer_term = self.compute_energy(self.x_gibbs, \\n self.batch_size)\\n \\n normalizer_term = -T.mean(normalizer_term)\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden > 0:\\n \\n data_term = self.compute_free_energy(self.x)\\n \\n normalizer_term = self.compute_free_energy(self.rbm_cd_samples)\\n \\n normalizer_term = -T.mean(normalizer_term)\\n \\n # cost is negative log likelihood \\n self.cost = T.mean(data_term) + normalizer_term\",\n \"def circ_j_ctrl_multiPauli(reg_target: List[QRegStorage], reg_ctrlling: List[QRegStorage], int_j: int,\\n str_Pauli_term: str, if_minus: bool,\\n reg_borrowed: Optional[List[QRegStorage]] = None) -> None:\\n if reg_borrowed is None:\\n reg_borrowed = []\\n list_str_single_Pauli_term = re.split(r',\\\\s*', str_Pauli_term.upper())\\n # we can transform such j-ctrl gate into multictrl gate by conjugating operation with several X gates\\n for int_k in range(len(reg_ctrlling)):\\n if (int_j >> int_k) % 2 == 0:\\n X(reg_ctrlling[-1 - int_k])\\n for str_single_Pauli_term in list_str_single_Pauli_term:\\n match = re.match(r'([XYZ])([0-9]+)(\\\\w+)', str_single_Pauli_term, flags=re.I)\\n while match:\\n circ_multictrl_Pauli(reg_target[int(match.group(2))], reg_ctrlling, match.group(1).upper(),\\n reg_borrowed=reg_target[:int(match.group(2))] + reg_target[int(match.group(2)) + 1:] +\\n reg_borrowed)\\n str_single_Pauli_term = match.group(3)\\n match = re.match(r'([XYZ])([0-9]+)(\\\\w+)', str_single_Pauli_term, flags=re.I)\\n match = re.match(r'([XYZ])([0-9]+)', str_single_Pauli_term, flags=re.I)\\n if match:\\n circ_multictrl_Pauli(reg_target[int(match.group(2))], reg_ctrlling, match.group(1).upper(),\\n reg_borrowed=reg_target[:int(match.group(2))] + reg_target[int(match.group(2)) + 1:] +\\n reg_borrowed)\\n # We need to implement a multictrl-(-ID) gate,\\n # which just equals to a multictrl-Z gate operated on those ctrlling qubits.\\n if if_minus and len(reg_ctrlling) > 0:\\n circ_multictrl_Z(reg_ctrlling[-1], reg_ctrlling[:-1], reg_borrowed=reg_target + reg_borrowed)\\n # the other part of the conjugating operation with several X gates\\n for int_k in range(len(reg_ctrlling)):\\n if (int_j >> int_k) % 2 == 0:\\n X(reg_ctrlling[-1 - int_k])\",\n \"def __setitem__(self, j, val):\\n\\t\\tself._coords[j] = val\",\n \"def translate_list(self, oov_list, show_progbar=True, n_jobs=1, debug=False):\\n raise NotImplementedError\",\n \"def putslcslice(self,whichsol_,first_,last_,slc_):\\n _slc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),((last_) - (first_))))\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc cannot be None\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _list_coefficients_by_components(self, fd=True, pos=True, neg=True, printimag=False, norm_neg=True, nmin=0, nmax=0, latex=False, nd=0, Lvals=False, prime=False):\\n sig = 1\\n if(self._space.WR.is_dual()):\\n sig = -1\\n maxi = max(self._coeffs.keys())\\n w1 = len(str(maxi))\\n w2 = max(list(map(len, str(self._space.WR.D()).split())))\\n maxn = max(self._coeffs[list(self._coeffs.keys())[0]].keys())\\n w3 = len(str(maxn)) + 1\\n C = self._coeffs\\n mp0 = mpmath.mpf(0)\\n mpold = mpmath.mp.dps\\n N = self._space.WR.N\\n if(mpmath.mp.dps < self.maxdigs):\\n mpmath.mp.dps = self.maxdigs\\n if norm_neg:\\n cnorm = 0\\n tnorm = (0, 0)\\n for j in range(1, 100):\\n t = rn_from_D(self.space.WR, -j * sig)\\n if(t is None):\\n continue\\n if(t[1] + self._space.WR.Qv[t[0]] >= 0):\\n continue\\n c1 = self.get_coefficient(t[0], t[1])\\n if c1 is None:\\n continue\\n if abs(c1) > self._prec:\\n cnorm = c1\\n tnorm = t\\n print(\\\"c1=c({0})={1}\\\".format(tnorm, cnorm))\\n break\\n for r in C.keys():\\n for n in range(min(C[r].keys()), max(C[r].keys()) + 1):\\n if nmin > 0 and abs(n) < nmin:\\n continue\\n if nmax > 0 and abs(n) > nmax:\\n continue\\n nn = n + self._space.WR.Qv[r]\\n if not neg and nn < 0:\\n continue\\n if not pos and nn >= 0:\\n continue\\n D = self._space.D_from_rn((r, n))\\n if(fd):\\n if fd and not is_fundamental_discriminant(D) and D != 1:\\n continue\\n if prime and gcd(D, N) > 1:\\n continue\\n c = self.get_coefficient(r, n)\\n cs = \\\"\\\"\\n if c != 0 and c is not None:\\n if(nn >= 0):\\n ss = \\\"+\\\"\\n if(nn < 0):\\n ss = \\\"-\\\"\\n if(norm_neg):\\n # print \\\"r,n=\\\",r,n\\n # print \\\"cnorm=\\\",cnorm\\n # print \\\"tnorm=\\\",tnorm\\n D = self._space.D_from_rn((r, n))\\n if ((r, n) != tnorm) and cnorm != 0:\\n c = c / cnorm * mpmath.sqrt(mpmath.mpf(abs(D)))\\n if c.real() >= 0:\\n cs = \\\" \\\"\\n if not printimag:\\n if nd > 0:\\n cs = str(c.real()).strip()\\n cs = sci_pretty_print(cs, nd, latex_pow=latex)\\n else:\\n cs = str(c.real())\\n else:\\n cs = cs + str(c)\\n if Lvals and list(self._Lv.keys()).count(D) == 1:\\n ls = \\\"\\\\t\\\" + str(self._Lv[D])\\n else:\\n if latex:\\n ls = \\\"\\\\\\\\\\\\\\\\ \\\\n\\\"\\n else:\\n ls = \\\"\\\"\\n if latex:\\n D = self._space.WR.D()[r]\\n if(is_int(D)):\\n p = numerator(D)\\n q = denominator(D)\\n sr = \\\"\\\\\\\\frac{\\\" + str(p) + \\\"}{\\\" + str(q) + \\\"}\\\"\\n else:\\n sr = str(D)\\n ss = \\\"\\\"\\n print(\\\"$C{0}({1},{2}) $ & $ {3} $ {4}\\\".format(ss, sr.ljust(w1), str(n).ljust(w3), cs, ls))\\n else:\\n print(\\\"C^{0}[{1}][{2}] = {3}\\\".format(ss, str(r).ljust(w1), str(n).ljust(w3), cs + ls))\\n mpmath.mp.dps = mpold\",\n \"def la_coeff(f1, coeff, cov, var_names, digits=3):\\n f1.write(r\\\"\\\\begin{align}\\\" + \\\"\\\\n\\\")\\n\\n for j, co in enumerate(coeff):\\n str_co = \\\"\\\\t\\\" + var_names[j]\\n #var = uc.ufloat(coeff[j]\\n #str_co += \\\" &=& {:L} \\\\\\\\\\\\\\\\\\\".format(var) # uncertainties format\\n str_co += (\\\" &= %.\\\" + str(digits) + \\\"f \\\\\\\\\\\\\\\\\\\")%coeff[j] # specified format withouh errors\\n str_co = em(str_co)\\n f1.write(str_co +\\\"\\\\n\\\")\\n\\n f1.write(\\\"\\\\t\\\\mathrm{cov} &=\\\\n\\\")\\n f1.write(\\\"\\\\t\\\\\\\\begin{pmatrix}\\\\n\\\")\\n for row in cov:\\n str_row = \\\"\\\\t\\\\t\\\"\\n for var in row:\\n str_row += (\\\"%.\\\" + str(1) + \\\"e &\\\")%var\\n str_row = str_row[:-1] + r\\\"\\\\\\\\\\\"\\n str_row = em(str_row)\\n f1.write(str_row + \\\"\\\\n\\\")\\n f1.write(\\\"\\\\t\\\\end{pmatrix} \\\\n\\\")\\n f1.write(\\\"\\\\end{align}\\\\n\\\\n\\\")\\n return 0\",\n \"def score_coefficient(self, value):\\n self.predictor._score_coefficient = value\",\n \"def coefficients(self) :\\n raise NotImplementedError\",\n \"def _update_parallel_coef_constraints(self, x):\\n n_features = x.shape[1]\\n xi_final = np.zeros((n_features, n_features))\\n\\n # Todo: parallelize this for loop with Multiprocessing/joblib\\n if self.model_subset is None:\\n self.model_subset = range(n_features)\\n elif np.max(np.abs(self.model_subset)) >= n_features:\\n raise ValueError(\\n \\\"A value in model_subset is larger than the number \\\"\\n \\\"of features in the candidate library\\\"\\n )\\n for i in self.model_subset:\\n print(\\\"Model \\\", i)\\n xi = cp.Variable(n_features)\\n # Note that norm choice below must be convex,\\n # so thresholder must be L1 or L2\\n if (self.thresholder).lower() in (\\\"l1\\\", \\\"weighted_l1\\\"):\\n if self.thresholds is None:\\n cost = cp.sum_squares(x[:, i] - x @ xi) + self.threshold * cp.norm1(\\n xi\\n )\\n else:\\n cost = cp.sum_squares(x[:, i] - x @ xi) + cp.norm1(\\n self.thresholds[i, :] @ xi\\n )\\n if (self.thresholder).lower() in (\\\"l2\\\", \\\"weighted_l2\\\"):\\n if self.thresholds is None:\\n cost = (\\n cp.sum_squares(x[:, i] - x @ xi)\\n + self.threshold * cp.norm2(xi) ** 2\\n )\\n else:\\n cost = (\\n cp.sum_squares(x[:, i] - x @ xi)\\n + cp.norm2(self.thresholds[i, :] @ xi) ** 2\\n )\\n prob = cp.Problem(\\n cp.Minimize(cost),\\n [xi[i] == 0.0],\\n )\\n try:\\n prob.solve(\\n max_iter=self.max_iter,\\n eps_abs=self.tol,\\n eps_rel=self.tol,\\n verbose=self.verbose_cvxpy,\\n )\\n if xi.value is None:\\n warnings.warn(\\n \\\"Infeasible solve on iteration \\\"\\n + str(i)\\n + \\\", try changing your library\\\",\\n ConvergenceWarning,\\n )\\n xi_final[:, i] = xi.value\\n # Annoying error coming from L2 norm switching to use the ECOS\\n # solver, which uses \\\"max_iters\\\" instead of \\\"max_iter\\\", and\\n # similar semantic changes for the other variables.\\n except TypeError:\\n prob.solve(\\n max_iters=self.max_iter,\\n abstol=self.tol,\\n reltol=self.tol,\\n verbose=self.verbose_cvxpy,\\n )\\n if xi.value is None:\\n warnings.warn(\\n \\\"Infeasible solve on iteration \\\"\\n + str(i)\\n + \\\", try changing your library\\\",\\n ConvergenceWarning,\\n )\\n xi_final[:, i] = xi.value\\n except cp.error.SolverError:\\n print(\\\"Solver failed on model \\\", str(i), \\\", setting coefs to zeros\\\")\\n xi_final[:, i] = np.zeros(n_features)\\n return xi_final\",\n \"def __setSpectrum__(self):\\n \\n self.Ck = []\\n TempCk = []\\n TempOneCk = OneCk()\\n \\n # Process 1st frequency\\n Tb = self.freqs[1].totalJ\\n \\n for b in range(-Tb, Tb, 1):\\n TempOneCk.freq = b*self.freqs[1].Vph\\n TempOneCk.Amp = self.freqs[1].Cjk(b)\\n self.Ck.append(TempOneCk)\\n \\n # Process additional frequencies\\n CkSize = len(self.Ck)\\n Added = FALSE\\n \\n for f in range(2, len(self.freqs), 1):\\n # Reset temporary variables\\n Tb = self.freqs[f].totalJ\\n TempCk = []\\n \\n # Calculate each Ck coefficient\\n for b in range(-Tb, Tb, 1):\\n for k in range(CkSize):\\n TempOneCk.Amp = Ck[k].Amp * self.freq[f].Cjk(b)\\n \\n # Check to see if Amp is big enough to keep\\n if( abs(TempOneCk.Amp) > self.min_Ck ):\\n Added = FALSE\\n TempOneCk.freq = self.Ck[k].freq + b*self.freqs.Vph\\n \\n # If freq is already in Ck, add new value to old,\\n # if not, add new value and freq to spectrum\\n for c in TempCk:\\n if abs(c.freq-TempOneCk.freq < DOUBLE_PRECISION):\\n c.Amp += TempOneCk.Amp\\n Added = TRUE\\n break\\n \\n if (not Added):\\n TempCk.append(TempOneCk)\\n \\n self.Ck = TempCk\\n CkSize = len(self.Ck)\",\n \"def fixA(self,i,j,value):\\n if self.coeffPattern[0] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[0] = [[None]*m for i in range(m)]\\n self.coeffPattern[0][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def objective(self, x):\\n pass\",\n \"def objective(self, x):\\n pass\",\n \"def add(self, *args):\\n return _libsbml.ListWrapperCVTerm_add(self, *args)\",\n \"def objective(self, param):\\n self.__init__(param, self.data)\\n # return self.rmse() + self.penalty()\\n return self.rmse() + self.penalty()\",\n \"def build_coeff_dict(l):\\n coeff_dict = {}\\n for triplet in l:\\n coeff_dict[triplet[0]] = triplet[2]\\n return coeff_dict\",\n \"def __setitem__(self, idx, val):\\n self.rows[idx[0]][idx[1]] = val\",\n \"def put_proj_coeffs(self, dest):\\n if parallel.is_rank_zero():\\n self.put_array(self.proj_coeffs, dest)\\n parallel.barrier()\",\n \"def update_co_occ_mat(hc_ro, i_s_real, co_occs, co_occ_list, idx, nsteps):\\n\\n # Go to next element in circular array.\\n idx = (idx + 1) % nsteps\\n\\n # Remove estimate from left hand side of time window.\\n if len(co_occ_list[idx]):\\n i_s_real_left, hc_ro_left = co_occ_list[idx]\\n co_occs[:, i_s_real_left] -= hc_ro_left\\n\\n # Add current estimate to matrix and list.\\n co_occs[:, i_s_real] += hc_ro\\n co_occ_list[idx] = [i_s_real, hc_ro]\\n\\n # Fix float imprecision.\\n co_occs[(co_occs < 0) & (co_occs > -1e-10)] = 0\\n\\n return idx\",\n \"def getsparsesymmat(self,idx_,subi,subj,valij): # 3\\n maxlen_ = self.getsymmatinfo((idx_))[1]\\n _copyback_subi = False\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n _copyback_subi = True\\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n _copyback_subi = True\\n if subi_ is not None and len(subi_) != (maxlen_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxlen_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n _copyback_valij = False\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n _copyback_valij = True\\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n _copyback_valij = True\\n if valij_ is not None and len(valij_) != (maxlen_):\\n raise ValueError(\\\"Array argument valij has wrong length\\\")\\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_valij:\\n valij[:] = _tmparr_valij\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n if _copyback_subi:\\n subi[:] = _tmparr_subi\",\n \"def lcs(hh, vv):\\n B=LCS.getB(hh,vv)\\n trac=LCS.backtrack(B);\\n cs=[ hh[h-1] for v,h,k in trac if k=='1' ]\\n return cs\",\n \"def enqueue(self, value): ################# <-\\n self.lst = self.lst +[value]\",\n \"def __init__(self, coefficients):\\n \\n if not isinstance(coefficients, list):\\n raise TypeError(\\\"The coefficients variable must be a list of numbers\\\")\\n\\n if sum(coefficients) != 1:\\n raise ValueError(\\\"The sum of coefficients must be equal to 1\\\")\\n\\n self.coef = coefficients\\n self.samples = [0.0] * len(self.coef)\",\n \"def coeffs_from_vec(self, coeffs_vec):\\n coeffs = [self.coeffs[0].copy(), self.coeffs[1].copy()]\\n coeffs[0].iloc[1:] = coeffs_vec[:self.index_ncoeffs[0]].copy()\\n coeffs[1].iloc[1:] = coeffs_vec[self.index_ncoeffs[0]:].copy()\\n return coeffs\",\n \"def update_listed_eqns(self):\\n self.clear_rightside()\\n self.lst_eqns.clear()\\n self.lst_eqns.addItems(list(self.eqn_data[self.cmb_sections.currentText()]))\",\n \"def putboundlist(self,accmode_,sub,bk,bl,bu): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bk)\\n elif num_ != len(bk):\\n raise IndexError(\\\"Inconsistent length of array bk\\\")\\n if num_ is None:\\n num_ = len(bl)\\n elif num_ != len(bl):\\n raise IndexError(\\\"Inconsistent length of array bl\\\")\\n if num_ is None:\\n num_ = len(bu)\\n elif num_ != len(bu):\\n raise IndexError(\\\"Inconsistent length of array bu\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n res = self.__obj.putboundlist(accmode_,num_,sub_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def extend_step_tmp_vals_at_index(self, tmp_vals, index):\\n try:\\n self[index][3].extend(tmp_vals)\\n except IndexError:\\n self[index].append(tmp_vals)\",\n \"def cont_leg(leg_val, tens_list, leg_list, ent_list):\\n New_Tens = tens_list\\n New_Legs = leg_list\\n New_Schm = ent_list\\n # search for desired leg value\\n o1, o2 = leg_find(leg_val,leg_list)\\n # new contracted tensor\\n N_T= np.tensordot(New_Tens[o1[0]],New_Tens[o2[0]],[[o1[1]],[o2[1]]])\\n # remove contracted legs and update legs data\\n New_Legs[o1[0]].remove(leg_val)\\n New_Legs[o2[0]].remove(leg_val)\\n N_L = New_Legs[o1[0]] + New_Legs[o2[0]]\\n m_big = max(o1[0],o2[0])\\n m_small = min(o1[0],o2[0])\\n # delete old tens_list/Legs elements\\n del New_Tens[m_big]\\n del New_Tens[m_small]\\n del New_Legs[m_big]\\n del New_Legs[m_small]\\n # append new tensor with updated leg indicies\\n New_Tens.append(N_T)\\n New_Legs.append(N_L)\\n New_Schm[leg_val] = np.array([0])\\n return New_Tens, New_Legs, New_Schm\",\n \"def add_cost_value(self, var_name, val):\\n self.add_other_value(self.cost, var_name, val)\\n\\n # add the change of cost automatically\\n if len(self.cost[var_name]) > 1: # if the cost is not empty\\n last_val = self.get_last_last_cost_val(var_name)\\n cost_change = abs(val - last_val) / last_val\\n self.add_other_value(self.cost_change, var_name + '_change', cost_change)\",\n \"def fixB(self,i,j,value):\\n if self.coeffPattern[1] == None:\\n m,n=self.m,self.n\\n self.coeffPattern = [[None]*n for i in range(m)]\\n self.coeffPattern[1][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def add_value(trajectories, val_func):\\n for trajectory in trajectories:\\n observes = trajectory['observes']\\n values = val_func.predict(observes)\\n trajectory['values'] = values\",\n \"def getbarcblocktriplet(self,subj,subk,subl,valjkl): # 3\\n maxnum_ = self.getnumbarcblocktriplets()\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxnum_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if subk is None: raise TypeError(\\\"Invalid type for argument subk\\\")\\n _copyback_subk = False\\n if subk is None:\\n subk_ = None\\n else:\\n try:\\n subk_ = memoryview(subk)\\n except TypeError:\\n try:\\n _tmparr_subk = array.array(\\\"i\\\",subk)\\n except TypeError:\\n raise TypeError(\\\"Argument subk has wrong type\\\")\\n else:\\n subk_ = memoryview(_tmparr_subk)\\n _copyback_subk = True\\n else:\\n if subk_.format != \\\"i\\\":\\n subk_ = memoryview(array.array(\\\"i\\\",subk))\\n _copyback_subk = True\\n if subk_ is not None and len(subk_) != (maxnum_):\\n raise ValueError(\\\"Array argument subk has wrong length\\\")\\n if subl is None: raise TypeError(\\\"Invalid type for argument subl\\\")\\n _copyback_subl = False\\n if subl is None:\\n subl_ = None\\n else:\\n try:\\n subl_ = memoryview(subl)\\n except TypeError:\\n try:\\n _tmparr_subl = array.array(\\\"i\\\",subl)\\n except TypeError:\\n raise TypeError(\\\"Argument subl has wrong type\\\")\\n else:\\n subl_ = memoryview(_tmparr_subl)\\n _copyback_subl = True\\n else:\\n if subl_.format != \\\"i\\\":\\n subl_ = memoryview(array.array(\\\"i\\\",subl))\\n _copyback_subl = True\\n if subl_ is not None and len(subl_) != (maxnum_):\\n raise ValueError(\\\"Array argument subl has wrong length\\\")\\n if valjkl is None: raise TypeError(\\\"Invalid type for argument valjkl\\\")\\n _copyback_valjkl = False\\n if valjkl is None:\\n valjkl_ = None\\n else:\\n try:\\n valjkl_ = memoryview(valjkl)\\n except TypeError:\\n try:\\n _tmparr_valjkl = array.array(\\\"d\\\",valjkl)\\n except TypeError:\\n raise TypeError(\\\"Argument valjkl has wrong type\\\")\\n else:\\n valjkl_ = memoryview(_tmparr_valjkl)\\n _copyback_valjkl = True\\n else:\\n if valjkl_.format != \\\"d\\\":\\n valjkl_ = memoryview(array.array(\\\"d\\\",valjkl))\\n _copyback_valjkl = True\\n if valjkl_ is not None and len(valjkl_) != (maxnum_):\\n raise ValueError(\\\"Array argument valjkl has wrong length\\\")\\n res,resargs = self.__obj.getbarcblocktriplet(maxnum_,subj_,subk_,subl_,valjkl_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _num_return_value = resargs\\n if _copyback_valjkl:\\n valjkl[:] = _tmparr_valjkl\\n if _copyback_subl:\\n subl[:] = _tmparr_subl\\n if _copyback_subk:\\n subk[:] = _tmparr_subk\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n return _num_return_value\",\n \"def addObjective(self, *args):\\n return _libsbml.FbcModelPlugin_addObjective(self, *args)\",\n \"def createObjective(self, solver, objective, conflict_vars_d, sched_d, conflicts_d, courses):\\n\\n\\n # Put a little pressure on to not use slots 6 or 7\\n for asl in self.vars_actualslots:\\n if asl[1] == \\\"6\\\":\\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_SLOT_6']) \\n if asl[1] == \\\"7\\\":\\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_SLOT_7']) \\n\\n if asl[0]==\\\"T\\\" and asl[1] in [\\\"4\\\", \\\"5\\\"]:\\n if self.name in non_FAS_instructor:\\n # for non-FAS faculty, favor teaching on Tuesday 3pm-5pm\\n if asl[1] == \\\"4\\\":\\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_FAVOR_COURSES_TU_3_TO_5'])\\n else: \\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_FAVOR_COURSES_TU_3_TO_5']*3)\\n else:\\n # avoid teaching on Tuesday 3pm-5pm for FAS instructors\\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_COURSES_TU_3_TO_5'])\\n\\n if asl[0]==\\\"F\\\":\\n # avoid Friday teaching, to mimic faculty preferences\\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_COURSES_FRIDAY']) \\n \\n \\n \\n # Add to objective function for bad course conflicts\\n for other in conflicts_d.get(self.name, []):\\n assert is_cross_list_canonical(other), other\\n\\n if other not in courses and other not in sched_d:\\n # we're not scheduling the other course, so we can ignore it\\n continue\\n\\n if other in courses and not (self.name < other):\\n # we will let the processing for the other course handle this weight\\n continue\\n\\n # create a variable that indicates if self and other conflict, add add the weight\\n # to the objective function\\n v_conflicts = solver.IntVar(0, 1, self.name + \\\" and \\\" + other + \\\" conflict\\\")\\n objective.SetCoefficient(v_conflicts, PARAMS['WEIGHT_BAD_CONFLICT_FACTOR'] * int(conflicts_d[self.name][other]))\\n\\n # Record the variable so we can look at it later\\n if self.name not in conflict_vars_d:\\n conflict_vars_d[self.name] = {}\\n assert other not in conflict_vars_d[self.name]\\n conflict_vars_d[self.name][other] = v_conflicts\\n \\n if other not in courses:\\n # the other course already has a fixed schedule.\\n # Go through each actual slot and see if it intersects with other course\\n disjuncts = []\\n for s in self.vars_actualslots:\\n my_course_time = ss.meeting_time_to_course_time([s])\\n \\n if sct.courses_conflict([my_course_time], sched_d[other]):\\n # slots s conflicts with the time for other\\n disjuncts.append(self.vars_actualslots[s])\\n\\n if len(disjuncts) > 0:\\n makeDisjunction(solver, v_conflicts, disjuncts)\\n \\n if other in courses:\\n # we are scheduling the other course, so we need to use its variables.\\n # Create a variable for each actual slot that indicates if both self and other use that slot.\\n d = courses[other]\\n vs_d_same_slot = []\\n for asl in self.vars_actualslots:\\n if asl in d.vars_actualslots:\\n # actual slot represented by vas is in both courses self and d.\\n v_both_use_slot = solver.IntVar(0, 1, self.name + \\\" and \\\" + other + \\\" using slot \\\" + asl)\\n makeConjunction(solver, v_both_use_slot, [self.vars_actualslots[asl], d.vars_actualslots[asl]])\\n vs_d_same_slot.append(v_both_use_slot)\\n\\n if len(vs_d_same_slot) > 0:\\n makeDisjunction(solver, v_conflicts, vs_d_same_slot)\",\n \"def __init__(self):\\r\\n self.vals = []\",\n \"def putacollist(self,sub_,ptrb_,ptre_,asub_,aval_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(ptrb_)\\n elif num_ != len(ptrb_):\\n raise IndexError(\\\"Inconsistent length of array ptrb\\\")\\n if num_ is None:\\n num_ = len(ptre_)\\n elif num_ != len(ptre_):\\n raise IndexError(\\\"Inconsistent length of array ptre\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb cannot be None\\\")\\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb may not be None\\\")\\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\\n _ptrb_copyarray = False\\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptrb_ is not None:\\n _ptrb_copyarray = True\\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\\n _ptrb_np_tmp[:] = ptrb_\\n assert _ptrb_np_tmp.flags.contiguous\\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptrb_copyarray = False\\n _ptrb_tmp = None\\n \\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre cannot be None\\\")\\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre may not be None\\\")\\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\\n _ptre_copyarray = False\\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptre_ is not None:\\n _ptre_copyarray = True\\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\\n _ptre_np_tmp[:] = ptre_\\n assert _ptre_np_tmp.flags.contiguous\\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptre_copyarray = False\\n _ptre_tmp = None\\n \\n if asub_ is None:\\n raise ValueError(\\\"Argument asub cannot be None\\\")\\n if asub_ is None:\\n raise ValueError(\\\"Argument asub may not be None\\\")\\n if isinstance(asub_, numpy.ndarray) and asub_.dtype is numpy.dtype(numpy.int32) and asub_.flags.contiguous:\\n _asub_copyarray = False\\n _asub_tmp = ctypes.cast(asub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif asub_ is not None:\\n _asub_copyarray = True\\n _asub_np_tmp = numpy.zeros(len(asub_),numpy.dtype(numpy.int32))\\n _asub_np_tmp[:] = asub_\\n assert _asub_np_tmp.flags.contiguous\\n _asub_tmp = ctypes.cast(_asub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _asub_copyarray = False\\n _asub_tmp = None\\n \\n if aval_ is None:\\n raise ValueError(\\\"Argument aval cannot be None\\\")\\n if aval_ is None:\\n raise ValueError(\\\"Argument aval may not be None\\\")\\n if isinstance(aval_, numpy.ndarray) and aval_.dtype is numpy.dtype(numpy.float64) and aval_.flags.contiguous:\\n _aval_copyarray = False\\n _aval_tmp = ctypes.cast(aval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif aval_ is not None:\\n _aval_copyarray = True\\n _aval_np_tmp = numpy.zeros(len(aval_),numpy.dtype(numpy.float64))\\n _aval_np_tmp[:] = aval_\\n assert _aval_np_tmp.flags.contiguous\\n _aval_tmp = ctypes.cast(_aval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _aval_copyarray = False\\n _aval_tmp = None\\n \\n res = __library__.MSK_XX_putacollist64(self.__nativep,num_,_sub_tmp,_ptrb_tmp,_ptre_tmp,_asub_tmp,_aval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def new_condition_polynomial_trajectory(ui):\\n\\n # Add the row ............................................................\\n\\n pos = ui.tableWidget_poly_conditions.rowCount()\\n ui.tableWidget_poly_conditions.insertRow(pos)\\n\\n ui.tableWidget_poly_conditions.setItem(pos, 0, QTableWidgetItem(\\\"0\\\"))\\n ui.tableWidget_poly_conditions.setItem(pos, 1, QTableWidgetItem(\\\"0\\\"))\\n ui.tableWidget_poly_conditions.setItem(pos, 2, QTableWidgetItem(\\\"0\\\"))\\n\\n # Save to global variable ................................................\\n\\n global polynomial_trajectories\\n\\n selection = ui.listWidget_poly.selectedItems()\\n\\n index = None\\n for item in selection:\\n index = ui.listWidget_poly.row(item)\\n if index is None:\\n return\\n\\n polynomial_trajectories[index][\\\"conditions\\\"].append([\\\"0\\\", \\\"0\\\", \\\"0\\\"])\",\n \"def update_output(self, latent_mat, weight_mat, y_list):\",\n \"def __getitem__(self, idx):\\n return [self.y[idx], self.cont_X[idx], self.cat_X[idx]]\",\n \"def __setitem__(self, name, val):\\n\\n if name in self.vars:\\n l[name].setVal(val)\\n else:\\n l[name] = YPFVal(name, val)\",\n \"def __setitem__(self, key, item):\\n assert isinstance(key,list) and isinstance(item,list) and len(key)==2 and len(item)==2\\n self._data[self.__ptBin(key[0])][self.__etaBin(key[1])] = item\",\n \"def update(self):\\n\\n terms_toRemove = []\\n\\n for termIndex, [term_constantFactor, term_unknowns_attributeAddresses] in enumerate(self.LHS):\\n\\n # Check if coefficient is 0 - then no need to process any of the unknowns since term will be 0 anyways\\n if term_constantFactor == 0:\\n terms_toRemove.append(termIndex)\\n continue # continue to next term, no need to resolve the unknowns of this term since the product will be 0 anyways\\n\\n # Check if any unknowns became known\\n unknowns_toRemove = []\\n for unknown_attributeAddress in term_unknowns_attributeAddresses:\\n attribute = getattr_fromAddress(*unknown_attributeAddress)\\n if isNumeric(attribute):\\n # object.attribute which had previously been identified as unknown now has a value, add it to the constant factor product and remove from the unknowns\\n self.LHS[termIndex][0] *= attribute # multiply it with the constant factor product\\n unknowns_toRemove.append([termIndex, unknown_attributeAddress])\\n for termIndex, unknown_attributeAddress in unknowns_toRemove: # remove unknowns which have become known in the end\\n # removing in the end not to tamper with the iteration of the above loop\\n self.LHS[termIndex][1].remove(unknown_attributeAddress)\\n\\n # Move constants to RHS\\n if self.LHS[termIndex][1] == []:\\n # if term has no unknowns, it is a constant, move to RHS\\n self.RHS -= self.LHS[termIndex][0]\\n self.LHS.pop(termIndex)\\n\\n for termIndex in reversed(terms_toRemove): # reversed - otherwise would tamper with indices of items identified for removal\\n self.LHS.pop(termIndex)\\n\\n self._gatherUnknowns()\",\n \"def putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_):\\n _subi_minlength = (num_)\\n if (num_) > 0 and subi_ is not None and len(subi_) != (num_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(num_)))\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = (num_)\\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(num_)))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _subk_minlength = (num_)\\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk is not long enough: Is %d, expected %d\\\" % (len(subk_),(num_)))\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk cannot be None\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n _subl_minlength = (num_)\\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl is not long enough: Is %d, expected %d\\\" % (len(subl_),(num_)))\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl cannot be None\\\")\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl may not be None\\\")\\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\\n _subl_copyarray = False\\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subl_ is not None:\\n _subl_copyarray = True\\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\\n _subl_np_tmp[:] = subl_\\n assert _subl_np_tmp.flags.contiguous\\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subl_copyarray = False\\n _subl_tmp = None\\n \\n _valijkl_minlength = (num_)\\n if (num_) > 0 and valijkl_ is not None and len(valijkl_) != (num_):\\n raise ValueError(\\\"Array argument valijkl is not long enough: Is %d, expected %d\\\" % (len(valijkl_),(num_)))\\n if valijkl_ is None:\\n raise ValueError(\\\"Argument valijkl cannot be None\\\")\\n if valijkl_ is None:\\n raise ValueError(\\\"Argument valijkl may not be None\\\")\\n if isinstance(valijkl_, numpy.ndarray) and valijkl_.dtype is numpy.dtype(numpy.float64) and valijkl_.flags.contiguous:\\n _valijkl_copyarray = False\\n _valijkl_tmp = ctypes.cast(valijkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valijkl_ is not None:\\n _valijkl_copyarray = True\\n _valijkl_np_tmp = numpy.zeros(len(valijkl_),numpy.dtype(numpy.float64))\\n _valijkl_np_tmp[:] = valijkl_\\n assert _valijkl_np_tmp.flags.contiguous\\n _valijkl_tmp = ctypes.cast(_valijkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valijkl_copyarray = False\\n _valijkl_tmp = None\\n \\n res = __library__.MSK_XX_putbarablocktriplet(self.__nativep,num_,_subi_tmp,_subj_tmp,_subk_tmp,_subl_tmp,_valijkl_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def JCoeff(l, m, s, lp, mp, sp):\\n coeff = int((l == lp) & (m == -mp) & (s == sp))*1j*(-1)**(int(m-1/2))*s\\n return coeff\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7019918","0.6498057","0.565561","0.55103093","0.5359275","0.53481793","0.5285191","0.5283986","0.5262921","0.5168434","0.5103253","0.50901055","0.50725174","0.5070623","0.50672764","0.5047127","0.49618518","0.49521777","0.49174896","0.4880588","0.48753652","0.48740962","0.48617876","0.48550335","0.48400462","0.4826834","0.48260972","0.48197344","0.4815076","0.4808084","0.47998992","0.47765166","0.4767293","0.47611302","0.47538784","0.4746411","0.47420624","0.47377256","0.47360972","0.47330773","0.472207","0.47198024","0.47120842","0.47004274","0.46795207","0.46599013","0.46538848","0.46432704","0.4630936","0.4621859","0.4615977","0.4600688","0.45916957","0.45806122","0.45695227","0.45583287","0.45538825","0.4527702","0.45267162","0.4513764","0.44867027","0.44821045","0.44734555","0.44709474","0.44578832","0.4444981","0.44392622","0.44315287","0.44315287","0.44300073","0.44298846","0.4420721","0.44170466","0.4416982","0.44081524","0.4405915","0.4404982","0.44046745","0.43990064","0.4394834","0.43899322","0.43892628","0.43779123","0.43723932","0.43687364","0.43616116","0.4360089","0.43513185","0.43460265","0.43450636","0.4344955","0.4342226","0.4333398","0.43310404","0.43279743","0.43219942","0.43209913","0.4319135","0.43182144","0.43148458"],"string":"[\n \"0.7019918\",\n \"0.6498057\",\n \"0.565561\",\n \"0.55103093\",\n \"0.5359275\",\n \"0.53481793\",\n \"0.5285191\",\n \"0.5283986\",\n \"0.5262921\",\n \"0.5168434\",\n \"0.5103253\",\n \"0.50901055\",\n \"0.50725174\",\n \"0.5070623\",\n \"0.50672764\",\n \"0.5047127\",\n \"0.49618518\",\n \"0.49521777\",\n \"0.49174896\",\n \"0.4880588\",\n \"0.48753652\",\n \"0.48740962\",\n \"0.48617876\",\n \"0.48550335\",\n \"0.48400462\",\n \"0.4826834\",\n \"0.48260972\",\n \"0.48197344\",\n \"0.4815076\",\n \"0.4808084\",\n \"0.47998992\",\n \"0.47765166\",\n \"0.4767293\",\n \"0.47611302\",\n \"0.47538784\",\n \"0.4746411\",\n \"0.47420624\",\n \"0.47377256\",\n \"0.47360972\",\n \"0.47330773\",\n \"0.472207\",\n \"0.47198024\",\n \"0.47120842\",\n \"0.47004274\",\n \"0.46795207\",\n \"0.46599013\",\n \"0.46538848\",\n \"0.46432704\",\n \"0.4630936\",\n \"0.4621859\",\n \"0.4615977\",\n \"0.4600688\",\n \"0.45916957\",\n \"0.45806122\",\n \"0.45695227\",\n \"0.45583287\",\n \"0.45538825\",\n \"0.4527702\",\n \"0.45267162\",\n \"0.4513764\",\n \"0.44867027\",\n \"0.44821045\",\n \"0.44734555\",\n \"0.44709474\",\n \"0.44578832\",\n \"0.4444981\",\n \"0.44392622\",\n \"0.44315287\",\n \"0.44315287\",\n \"0.44300073\",\n \"0.44298846\",\n \"0.4420721\",\n \"0.44170466\",\n \"0.4416982\",\n \"0.44081524\",\n \"0.4405915\",\n \"0.4404982\",\n \"0.44046745\",\n \"0.43990064\",\n \"0.4394834\",\n \"0.43899322\",\n \"0.43892628\",\n \"0.43779123\",\n \"0.43723932\",\n \"0.43687364\",\n \"0.43616116\",\n \"0.4360089\",\n \"0.43513185\",\n \"0.43460265\",\n \"0.43450636\",\n \"0.4344955\",\n \"0.4342226\",\n \"0.4333398\",\n \"0.43310404\",\n \"0.43279743\",\n \"0.43219942\",\n \"0.43209913\",\n \"0.4319135\",\n \"0.43182144\",\n \"0.43148458\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6732897"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11967,"cells":{"query":{"kind":"string","value":"Modifies a slice of the linear objective coefficients. putcslice(self,first_,last_,slice_)"},"document":{"kind":"string","value":"def putcslice(self,first_,last_,slice_):\n _slice_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slice_ is not None and len(slice_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slice is not long enough: Is %d, expected %d\" % (len(slice_),((last_) - (first_))))\n if slice_ is None:\n raise ValueError(\"Argument slice cannot be None\")\n if slice_ is None:\n raise ValueError(\"Argument slice may not be None\")\n if isinstance(slice_, numpy.ndarray) and slice_.dtype is numpy.dtype(numpy.float64) and slice_.flags.contiguous:\n _slice_copyarray = False\n _slice_tmp = ctypes.cast(slice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slice_ is not None:\n _slice_copyarray = True\n _slice_np_tmp = numpy.zeros(len(slice_),numpy.dtype(numpy.float64))\n _slice_np_tmp[:] = slice_\n assert _slice_np_tmp.flags.contiguous\n _slice_tmp = ctypes.cast(_slice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slice_copyarray = False\n _slice_tmp = None\n \n res = __library__.MSK_XX_putcslice(self.__nativep,first_,last_,_slice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putcslice(self,first_,last_,slice): # 3\n if slice is None: raise TypeError(\"Invalid type for argument slice\")\n if slice is None:\n slice_ = None\n else:\n try:\n slice_ = memoryview(slice)\n except TypeError:\n try:\n _tmparr_slice = array.array(\"d\",slice)\n except TypeError:\n raise TypeError(\"Argument slice has wrong type\")\n else:\n slice_ = memoryview(_tmparr_slice)\n \n else:\n if slice_.format != \"d\":\n slice_ = memoryview(array.array(\"d\",slice))\n \n if slice_ is not None and len(slice_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slice has wrong length\")\n res = self.__obj.putcslice(first_,last_,slice_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskcslice(self,whichsol_,first_,last_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskcslice(whichsol_,first_,last_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putslcslice(self,whichsol_,first_,last_,slc_):\n _slc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),((last_) - (first_))))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putslcslice(self,whichsol_,first_,last_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slc is None: raise TypeError(\"Invalid type for argument slc\")\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.putslcslice(whichsol_,first_,last_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putxcslice(self,whichsol_,first_,last_,xc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xc is None: raise TypeError(\"Invalid type for argument xc\")\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xc_ is not None and len(xc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xc has wrong length\")\n res = self.__obj.putxcslice(whichsol_,first_,last_,xc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putxxslice(self,whichsol_,first_,last_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xx is None: raise TypeError(\"Invalid type for argument xx\")\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.putxxslice(whichsol_,first_,last_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskcslice(self,whichsol_,first_,last_,skc_):\n _skc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),((last_) - (first_))))\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskcslice(self.__nativep,whichsol_,first_,last_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putxcslice(self,whichsol_,first_,last_,xc_):\n _xc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xc_ is not None and len(xc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),((last_) - (first_))))\n if xc_ is None:\n raise ValueError(\"Argument xc cannot be None\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxcslice(self.__nativep,whichsol_,first_,last_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __setslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___setslice__(self, *args)","def putxxslice(self,whichsol_,first_,last_,xx_):\n _xx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),((last_) - (first_))))\n if xx_ is None:\n raise ValueError(\"Argument xx cannot be None\")\n if xx_ is None:\n raise ValueError(\"Argument xx may not be None\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_putxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskxslice(self,whichsol_,first_,last_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskxslice(whichsol_,first_,last_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getslcslice(self,whichsol_,first_,last_,slc_):\n _slc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),((last_) - (first_))))\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\n raise ValueError(\"Argument slc must be writable\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_getslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _slc_copyarray:\n slc_[:] = _slc_np_tmp","def getcslice(self,first_,last_,c): # 3\n _copyback_c = False\n if c is None:\n c_ = None\n else:\n try:\n c_ = memoryview(c)\n except TypeError:\n try:\n _tmparr_c = array.array(\"d\",c)\n except TypeError:\n raise TypeError(\"Argument c has wrong type\")\n else:\n c_ = memoryview(_tmparr_c)\n _copyback_c = True\n else:\n if c_.format != \"d\":\n c_ = memoryview(array.array(\"d\",c))\n _copyback_c = True\n if c_ is not None and len(c_) != ((last_) - (first_)):\n raise ValueError(\"Array argument c has wrong length\")\n res = self.__obj.getcslice(first_,last_,c_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_c:\n c[:] = _tmparr_c","def __setslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___setslice__(self, *args)","def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def test_getslice_setslice2(self):\n class C(object):\n def __getitem__(self, index):\n return 'Ok'\n def __setitem__(self, index, value):\n self.lastCall = 'set'\n def __delitem__(self, index):\n self.lastCall = 'delete'\n\n a = C()\n self.assertEqual(a[5:10], 'Ok')\n\n a.lastCall = ''\n a[5:10] = 'abc'\n self.assertEqual(a.lastCall, 'set')\n\n a.lastCall = ''\n del(a[5:10])\n self.assertEqual(a.lastCall, 'delete')","def slice(self, start=None, end=None, inplace=False):\n if inplace:\n self.data = self.data[start:end]\n else:\n cpy = self.copy()\n\n cpy.data = cpy.data[start:end]\n\n return cpy\n return","def putyslice(self,whichsol_,first_,last_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.putyslice(whichsol_,first_,last_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putyslice(self,whichsol_,first_,last_,y_):\n _y_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),((last_) - (first_))))\n if y_ is None:\n raise ValueError(\"Argument y cannot be None\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_putyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getslcslice(self,whichsol_,first_,last_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.getslcslice(whichsol_,first_,last_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_slc:\n slc[:] = _tmparr_slc","def putsucslice(self,whichsol_,first_,last_,suc_):\n _suc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and suc_ is not None and len(suc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),((last_) - (first_))))\n if suc_ is None:\n raise ValueError(\"Argument suc cannot be None\")\n if suc_ is None:\n raise ValueError(\"Argument suc may not be None\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n res = __library__.MSK_XX_putsucslice(self.__nativep,whichsol_,first_,last_,_suc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskxslice(self,whichsol_,first_,last_,skx_):\n _skx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),((last_) - (first_))))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __setslice__(self, *args):\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___setslice__(self, *args)","def getcslice(self,first_,last_,c_):\n _c_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and c_ is not None and len(c_) != ((last_) - (first_)):\n raise ValueError(\"Array argument c is not long enough: Is %d, expected %d\" % (len(c_),((last_) - (first_))))\n if isinstance(c_,numpy.ndarray) and not c_.flags.writeable:\n raise ValueError(\"Argument c must be writable\")\n if isinstance(c_, numpy.ndarray) and c_.dtype is numpy.dtype(numpy.float64) and c_.flags.contiguous:\n _c_copyarray = False\n _c_tmp = ctypes.cast(c_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif c_ is not None:\n _c_copyarray = True\n _c_np_tmp = numpy.zeros(len(c_),numpy.dtype(numpy.float64))\n _c_np_tmp[:] = c_\n assert _c_np_tmp.flags.contiguous\n _c_tmp = ctypes.cast(_c_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _c_copyarray = False\n _c_tmp = None\n \n res = __library__.MSK_XX_getcslice(self.__nativep,first_,last_,_c_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _c_copyarray:\n c_[:] = _c_np_tmp","def _fix_slice(self, inputs, new_attr):\n begin = new_attr.get('begin')\n end = new_attr.get('end')\n axes = new_attr.get('axis', tuple(range(len(begin))))\n slice_op = mx.sym.slice_axis(inputs[0], axis=axes[0], begin=begin[0], end=end[0])\n if len(axes) > 1:\n for i, axis in enumerate(axes):\n slice_op = mx.sym.slice_axis(slice_op, axis=axis, begin=begin[i], end=end[i])\n return slice_op","def putsucslice(self,whichsol_,first_,last_,suc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if suc is None: raise TypeError(\"Invalid type for argument suc\")\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if suc_ is not None and len(suc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument suc has wrong length\")\n res = self.__obj.putsucslice(whichsol_,first_,last_,suc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __getslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___getslice__(self, *args)","def getxxslice(self,whichsol_,first_,last_,xx_):\n _xx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),((last_) - (first_))))\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\n raise ValueError(\"Argument xx must be writable\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_getxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xx_copyarray:\n xx_[:] = _xx_np_tmp","def getskcslice(self,whichsol_,first_,last_,skc_):\n _skc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),((last_) - (first_))))\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\n raise ValueError(\"Argument skc must be writable\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_getskcslice(self.__nativep,whichsol_,first_,last_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]","def getskcslice(self,whichsol_,first_,last_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.getskcslice(whichsol_,first_,last_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])","def __getslice__(self,i,j):\n return self.x[i:j]","def putslxslice(self,whichsol_,first_,last_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslxslice(whichsol_,first_,last_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getxxslice(self,whichsol_,first_,last_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_xx = False\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n _copyback_xx = True\n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n _copyback_xx = True\n if xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.getxxslice(whichsol_,first_,last_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_xx:\n xx[:] = _tmparr_xx","def getxcslice(self,whichsol_,first_,last_,xc_):\n _xc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xc_ is not None and len(xc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),((last_) - (first_))))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_getxcslice(self.__nativep,whichsol_,first_,last_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp","def slice(self, pred):\n return NotImplemented","def test_slice_setslice_forbidden(self):\n global setVal\n class foo:\n def __setslice__(self, i, j, value):\n global setVal\n setVal = i, j, value\n def __setitem__(self, index, value):\n global setVal\n setVal = index, value\n\n foo()[::] = 23\n self.assertEqual(setVal, (slice(None, None, None), 23))\n foo()[::None] = 23\n self.assertEqual(setVal, (slice(None, None, None), 23))","def __setslice__(self, *args):\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint2___setslice__(self, *args)","def putslxslice(self,whichsol_,first_,last_,slx_):\n _slx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slx_ is not None and len(slx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),((last_) - (first_))))\n if slx_ is None:\n raise ValueError(\"Argument slx cannot be None\")\n if slx_ is None:\n raise ValueError(\"Argument slx may not be None\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n res = __library__.MSK_XX_putslxslice(self.__nativep,whichsol_,first_,last_,_slx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsnxslice(self,whichsol_,first_,last_,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if snx is None: raise TypeError(\"Invalid type for argument snx\")\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n if snx_ is not None and len(snx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument snx has wrong length\")\n res = self.__obj.putsnxslice(whichsol_,first_,last_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getxcslice(self,whichsol_,first_,last_,xc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xc has wrong length\")\n res = self.__obj.getxcslice(whichsol_,first_,last_,xc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_xc:\n xc[:] = _tmparr_xc","def slice(self, s=0, e=0):\r\n self.p = self.p[s:e]\r\n self.r = self.r[s:e]\r\n self.alpha = self.alpha[s:e]\r\n self.v = self.v[s:e]\r\n self.w = self.w[s:e]\r\n self.a = self.a[s:e]","def slice(self, begin, end):\n sliced = self._collection.slice(begin, end)\n return self.set_collection(sliced)","def getbarsslice(self,whichsol_,first_,last_,slicesize_,barsslice_):\n _barsslice_minlength = (slicesize_)\n if (slicesize_) > 0 and barsslice_ is not None and len(barsslice_) != (slicesize_):\n raise ValueError(\"Array argument barsslice is not long enough: Is %d, expected %d\" % (len(barsslice_),(slicesize_)))\n if isinstance(barsslice_,numpy.ndarray) and not barsslice_.flags.writeable:\n raise ValueError(\"Argument barsslice must be writable\")\n if barsslice_ is None:\n raise ValueError(\"Argument barsslice may not be None\")\n if isinstance(barsslice_, numpy.ndarray) and barsslice_.dtype is numpy.dtype(numpy.float64) and barsslice_.flags.contiguous:\n _barsslice_copyarray = False\n _barsslice_tmp = ctypes.cast(barsslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsslice_ is not None:\n _barsslice_copyarray = True\n _barsslice_np_tmp = numpy.zeros(len(barsslice_),numpy.dtype(numpy.float64))\n _barsslice_np_tmp[:] = barsslice_\n assert _barsslice_np_tmp.flags.contiguous\n _barsslice_tmp = ctypes.cast(_barsslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsslice_copyarray = False\n _barsslice_tmp = None\n \n res = __library__.MSK_XX_getbarsslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barsslice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barsslice_copyarray:\n barsslice_[:] = _barsslice_np_tmp","def __delslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___delslice__(self, *args)","def AddSlice(self, data_slice):\n self.slices.append(data_slice)","def __getslice__(self, start, stop):\n return self.__getitem__(slice(start, stop, None))","def getsucslice(self,whichsol_,first_,last_,suc_):\n _suc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and suc_ is not None and len(suc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),((last_) - (first_))))\n if isinstance(suc_,numpy.ndarray) and not suc_.flags.writeable:\n raise ValueError(\"Argument suc must be writable\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n res = __library__.MSK_XX_getsucslice(self.__nativep,whichsol_,first_,last_,_suc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _suc_copyarray:\n suc_[:] = _suc_np_tmp","def putsnxslice(self,whichsol_,first_,last_,snx_):\n _snx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and snx_ is not None and len(snx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument snx is not long enough: Is %d, expected %d\" % (len(snx_),((last_) - (first_))))\n if snx_ is None:\n raise ValueError(\"Argument snx cannot be None\")\n if snx_ is None:\n raise ValueError(\"Argument snx may not be None\")\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsnxslice(self.__nativep,whichsol_,first_,last_,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsuxslice(self,whichsol_,first_,last_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsuxslice(whichsol_,first_,last_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsuxslice(self,whichsol_,first_,last_,sux_):\n _sux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),((last_) - (first_))))\n if sux_ is None:\n raise ValueError(\"Argument sux cannot be None\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_putsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __delslice__(self, start, end):\n # delete the slice from the data\n del self._data[start:end]\n\n # determined the length of the data element\n self._nrows = len(self._data)","def __getslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___getslice__(self, *args)","def getbarxslice(self,whichsol_,first_,last_,slicesize_,barxslice_):\n _barxslice_minlength = (slicesize_)\n if (slicesize_) > 0 and barxslice_ is not None and len(barxslice_) != (slicesize_):\n raise ValueError(\"Array argument barxslice is not long enough: Is %d, expected %d\" % (len(barxslice_),(slicesize_)))\n if isinstance(barxslice_,numpy.ndarray) and not barxslice_.flags.writeable:\n raise ValueError(\"Argument barxslice must be writable\")\n if barxslice_ is None:\n raise ValueError(\"Argument barxslice may not be None\")\n if isinstance(barxslice_, numpy.ndarray) and barxslice_.dtype is numpy.dtype(numpy.float64) and barxslice_.flags.contiguous:\n _barxslice_copyarray = False\n _barxslice_tmp = ctypes.cast(barxslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxslice_ is not None:\n _barxslice_copyarray = True\n _barxslice_np_tmp = numpy.zeros(len(barxslice_),numpy.dtype(numpy.float64))\n _barxslice_np_tmp[:] = barxslice_\n assert _barxslice_np_tmp.flags.contiguous\n _barxslice_tmp = ctypes.cast(_barxslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxslice_copyarray = False\n _barxslice_tmp = None\n \n res = __library__.MSK_XX_getbarxslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barxslice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barxslice_copyarray:\n barxslice_[:] = _barxslice_np_tmp","def slice( # noqa: A003, C901\n self,\n trace: ExecutionTrace,\n slicing_criterion: SlicingCriterion,\n ) -> list[UniqueInstruction]:\n slc = self._setup_slicing_configuration(slicing_criterion, trace)\n\n while True:\n criterion_in_slice = imp_data_dep = False\n include_use = True\n\n # Get last instruction\n last_state = slc.update_state()\n\n if last_state.exception:\n # Stack can not be reliably simulated when an exception occurred\n slc.stack_simulation = False\n if not last_state.last_instr:\n # Reached end of executed instructions -> return slice (and keep order)\n instructions = set()\n slice_instructions = []\n for i in reversed(slc.context.instr_in_slice):\n if i not in instructions:\n instructions.add(i)\n slice_instructions.append(i)\n return slice_instructions\n\n last_unique_instr = self.create_unique_instruction(\n slc.file,\n last_state.last_instr,\n slc.code_object_id,\n slc.basic_block_id,\n slc.offset,\n )\n # Adjust trace position\n last_traced_instr = None\n if last_state.last_instr.opcode in op.TRACED_INSTRUCTIONS:\n last_traced_instr = trace.executed_instructions[slc.trace_position]\n slc.trace_position -= 1\n\n # Stack housekeeping\n prev_import_back_call = self._stack_housekeeping(\n last_state, last_unique_instr, slc\n )\n\n # Control dependency\n control_dependency = self.check_control_dependency(\n slc.context, last_unique_instr, slc.code_object_id\n )\n\n # Data dependencies\n # Explicit data dependency\n (\n exp_data_dep,\n slc.new_attribute_object_uses,\n ) = self.check_explicit_data_dependency(\n slc.context, last_unique_instr, last_traced_instr\n )\n\n # Dependency via method call\n if last_state.call and slc.code_object_dependent:\n imp_data_dep = True\n slc.code_object_dependent = False\n\n if last_state.import_start:\n # We need to include the import statement after determining\n # if one of the instructions executed by the import is included\n # (because IMPORT_NAME is traced afterwards).\n slc.context.instr_in_slice.append(prev_import_back_call)\n num_import_pops = StackEffect.stack_effect(\n prev_import_back_call.opcode, arg=None, jump=False\n )[0]\n slc.trace_stack.update_pop_operations(\n num_import_pops, prev_import_back_call, True\n )\n # Implicit data dependency (over stack)\n if slc.stack_simulation:\n stack_dep, include_use = slc.trace_stack.update_push_operations(\n slc.pushes, last_state.returned\n )\n if stack_dep:\n imp_data_dep = True\n if last_state.returned:\n slc.code_object_dependent = False\n\n if control_dependency or exp_data_dep or imp_data_dep:\n criterion_in_slice = True\n\n if not last_state.call:\n slc.code_object_dependent = True\n\n # Unconditional jumps\n if last_state.jump and last_state.last_instr.is_uncond_jump():\n criterion_in_slice = True\n\n # Housekeeping for execution trace, stack\n self._trace_housekeeping(\n criterion_in_slice,\n include_use,\n last_traced_instr,\n last_unique_instr,\n slc,\n )\n\n # next iteration\n slc.curr_instr = last_state.last_instr\n\n if time.time() > slc.timeout:\n raise SlicingTimeoutException","def simple_slice():\n examples = [\n benchmark.Example(\n inputs=[\n [[12, 34, 56, 78], [-1, -2, -3, -4]],\n -1,\n ],\n output=[[34, 56], [-2, -3]],\n ),\n ]\n constants = []\n description = 'Slice a tensor'\n target_program = 'in1[:, 1:in2]'\n source = 'handwritten task'\n return benchmark.Benchmark(examples=examples,\n constants=constants,\n description=description,\n target_program=target_program,\n source=source,\n name='simple_slice')","def _getitem_slice(self: MultiIndex, slobj: slice) -> MultiIndex:\n sortorder = None\n if slobj.step is None or slobj.step > 0:\n sortorder = self.sortorder\n\n new_codes = [level_codes[slobj] for level_codes in self.codes]\n\n return type(self)(\n levels=self.levels,\n codes=new_codes,\n names=self._names,\n sortorder=sortorder,\n verify_integrity=False,\n )","def __getslice__(self, i, j):\n return self.__getitem__(slice(i,j))","def test_getslice_setslice3(self):\n class C(object):\n def __init__(self):\n self.calls = []\n def __getitem__(self, index):\n self.calls.append('get')\n return 'Ok'\n def __setitem__(self, index, value):\n self.calls.append('set')\n def __delitem__(self, index):\n self.calls.append('delete')\n def __len__(self):\n self.calls.append('len')\n return 5\n\n a = C()\n self.assertEqual(a[3:5], 'Ok')\n self.assertEqual(a.calls, ['get'])\n\n a = C()\n a[3:5] = 'abc'\n self.assertEqual(a.calls, ['set'])\n\n a = C()\n del(a[3:5])\n self.assertEqual(a.calls, ['delete'])\n\n # but call length if it's negative (and we should only call length once)\n a = C()\n self.assertEqual(a[-1:5], 'Ok')\n self.assertEqual(a.calls, ['get'])\n\n a = C()\n self.assertEqual(a[1:-5], 'Ok')\n self.assertEqual(a.calls, ['get'])\n\n a = C()\n self.assertEqual(a[-1:-5], 'Ok')\n self.assertEqual(a.calls, ['get'])\n\n a = C()\n a[-1:5] = 'abc'\n self.assertEqual(a.calls, ['set'])\n\n a = C()\n a[1:-5] = 'abc'\n self.assertEqual(a.calls, ['set'])\n\n a = C()\n a[-1:-5] = 'abc'\n self.assertEqual(a.calls, ['set'])\n\n a = C()\n del(a[-1:5])\n self.assertEqual(a.calls, ['delete'])\n\n a = C()\n del(a[1:-5])\n self.assertEqual(a.calls, ['delete'])\n\n a = C()\n del(a[-1:-5])\n self.assertEqual(a.calls, ['delete'])","def getyslice(self,whichsol_,first_,last_,y_):\n _y_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),((last_) - (first_))))\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\n raise ValueError(\"Argument y must be writable\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_getyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _y_copyarray:\n y_[:] = _y_np_tmp","def getsuxslice(self,whichsol_,first_,last_,sux_):\n _sux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),((last_) - (first_))))\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\n raise ValueError(\"Argument sux must be writable\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_getsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _sux_copyarray:\n sux_[:] = _sux_np_tmp","def _extend_slice(self, sl, extent, dim_max, dim_min=0):\n\n x_start = max(sl.start - extent, dim_min)\n x_end = min(sl.stop + extent, dim_max)\n return np.s_[x_start:x_end]","def __setitem__( self, l, c_l ) :\n\n if( l == len( self ) ) :\n self.coefficients.append( float( c_l ) )\n else :\n self.coefficients[l] = float( c_l )","def __getslice__(self, i, j):\n return self.dtrs[i:j]","def getyslice(self,whichsol_,first_,last_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.getyslice(whichsol_,first_,last_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_y:\n y[:] = _tmparr_y","def __getslice__(self, i, j):\n return self.__getitem__(slice(i, j))","def __setslice__(self, i, j, sequence):\n self.__field.validate(sequence)\n list.__setslice__(self, i, j, sequence)","def chain(self,slices):\n for proj in self.projs[::-1]:\n slices = proj(slices)\n return","def __setslice__(self, i, j, sequence):\n self.__field.validate(sequence)\n list.__setslice__(self, i, j, sequence)","def __getitem__(self, i):\n new_data = super().__getitem__(i)\n if isinstance(i, slice):\n new_data = self.__class__(new_data)\n new_data.global_settings = copy.copy(self.global_settings)\n return new_data","def getsucslice(self,whichsol_,first_,last_,suc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_suc = False\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n _copyback_suc = True\n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n _copyback_suc = True\n if suc_ is not None and len(suc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument suc has wrong length\")\n res = self.__obj.getsucslice(whichsol_,first_,last_,suc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_suc:\n suc[:] = _tmparr_suc","def slice2(self, vs=None,xs=None):\n return self.condition2(vs,xs)","def __setslice__(self, i, j, sequence):\n if self.__pepth__ != 0:\n if '__warned__' not in plist.__setslice__.__dict__:\n qj('Slicing of inner plist elements with negative indices in python 2.7 does not work, and the error cannot be detected or corrected!\\n'\n 'Instead of slicing with one or two arguments: `plist._[-2:]`, use the three argument slice: `plist._[-2::1]`.\\n'\n 'This avoids the broken code path in the python compiler.', 'WARNING!')\n plist.__setslice__.__dict__['__warned__'] = True\n return plist.__getattr__(self, '__setslice__')(i, j, sequence)\n try:\n list.__setslice__(self, i, j, sequence)\n except Exception:\n plist.__setitem__(self, slice(i, j), sequence)\n return self","def __delslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___delslice__(self, *args)","def getskxslice(self,whichsol_,first_,last_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.getskxslice(whichsol_,first_,last_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_skx:\n for __tmp_var_0 in range(len(skx_)): skx[__tmp_var_0] = stakey(_tmparr_skx[__tmp_var_0])","def __getitem__(self, i):\n if isinstance(i, slice):\n return self.v[slice(i.start+self.tau-1, i.stop+self.tau-1, i.step)]\n else:\n return self.v[i+self.tau-1]","def getsuxslice(self,whichsol_,first_,last_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_sux = False\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n _copyback_sux = True\n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n _copyback_sux = True\n if sux_ is not None and len(sux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.getsuxslice(whichsol_,first_,last_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_sux:\n sux[:] = _tmparr_sux","def __getslice__(self, *args):\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___getslice__(self, *args)","def set_next_slice(self, start_idx, end_idx, strip_slice: bool = False):\n si, ei = int(start_idx), int(end_idx)\n if strip_slice:\n si, ei = _strip_slice_of_string(self.string, si, ei)\n self._next_slice = (si, ei)","def putconboundslice(self,first_,last_,bkc_,blc_,buc_):\n _bkc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bkc_ is not None and len(bkc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkc is not long enough: Is %d, expected %d\" % (len(bkc_),((last_) - (first_))))\n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n _blc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blc_ is not None and len(blc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blc is not long enough: Is %d, expected %d\" % (len(blc_),((last_) - (first_))))\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n _buc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and buc_ is not None and len(buc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument buc is not long enough: Is %d, expected %d\" % (len(buc_),((last_) - (first_))))\n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundslice(self.__nativep,first_,last_,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def test_getslice1(self):\n class C(list):\n def __getitem__(self, index):\n return (index.start, index.stop)\n\n a = C()\n self.assertEqual(a[32:197], (32,197))","def special_slice(self, form):\n obj = self.reallyCompile(form[1])\n rest = form[2:]\n if len(rest) == 1:\n return ast.Subscript(obj, 'OP_APPLY', [self.reallyCompile(rest[0])])\n elif len(rest) == 2:\n return ast.Slice(obj, 'OP_APPLY', *self.compileForms(rest))\n elif len(rest) == 3:\n return ast.Subscript(obj, 'OP_APPLY', [ast.Sliceobj(self.compileForms(rest))])\n else:\n raise SyntaxError(\"Too many thingies to slice! %r\" % rest)","def slicer(seq, start=None, stop=None, step=None):\n return seq[start:stop:step]","def getskxslice(self,whichsol_,first_,last_,skx_):\n _skx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),((last_) - (first_))))\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\n raise ValueError(\"Argument skx must be writable\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_getskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]","def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getslxslice(self,whichsol_,first_,last_,slx_):\n _slx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slx_ is not None and len(slx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),((last_) - (first_))))\n if isinstance(slx_,numpy.ndarray) and not slx_.flags.writeable:\n raise ValueError(\"Argument slx must be writable\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n res = __library__.MSK_XX_getslxslice(self.__nativep,whichsol_,first_,last_,_slx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _slx_copyarray:\n slx_[:] = _slx_np_tmp","def cutout(self, slices: tuple):\n if len(slices) == self.ndim - 1:\n slices = (slice(0, self.shape[0]), ) + slices\n internalSlices = self._get_internal_slices(slices)\n arr = self.array[internalSlices]\n global_offset = tuple(s.start for s in slices)\n return Chunk(arr, global_offset=global_offset)","def putconboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putconboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putboundslice(self,con_,first_,last_,bk,bl,bu): # 3\n if not isinstance(con_,accmode): raise TypeError(\"Argument con has wrong type\")\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putboundslice(con_,first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def slice_parameters(self, slice_parameters: SliceParamsIm):\n\n self._slice_parameters = slice_parameters","def _slice_assign(self, batch_element, batch_start, batch_end, is_finished):\n try:\n self.results[batch_start:batch_end] = batch_element\n\n except Exception as e: # pylint: disable=broad-except\n # `_slice_assign` should only be called in threads and exceptions raised\n # in threads do not carry over to the main thread. So instead we perform a\n # a broad catch in the thread and then store the exception to be re-raised\n # in the main thread.\n self._errors.append(e)\n\n finally:\n is_finished.set()","def test_simple_slicing(self):\n class only_slice(object):\n def __getitem__(self, index):\n self.res = 'get', index.start, index.stop\n def __setitem__(self, index, value):\n self.res = 'set', index.start, index.stop, value\n def __delitem__(self, index):\n self.res = 'del', index.start, index.stop\n\n class mixed_slice(object):\n def __getitem__(self, index):\n if isinstance(index, slice):\n self.res = 'get', index.start, index.stop\n else:\n raise Exception()\n def __setitem__(self, index, value):\n if isinstance(index, slice):\n self.res = 'set', index.start, index.stop, value\n else:\n raise Exception()\n def __delitem__(self, index):\n if isinstance(index, slice):\n self.res = 'del', index.start, index.stop\n else:\n raise Exception()\n\n for mytype in [only_slice, mixed_slice]:\n x = mytype()\n x[:]\n self.assertEqual(x.res, ('get', None, None))\n\n x[0:]\n self.assertEqual(x.res, ('get', 0, None))\n\n x[1:]\n self.assertEqual(x.res, ('get', 1, None))\n\n x[:100]\n self.assertEqual(x.res, ('get', None, 100))\n\n x[:] = 2\n self.assertEqual(x.res, ('set', None, None, 2))\n\n x[0:] = 2\n self.assertEqual(x.res, ('set', 0, None, 2))\n\n x[1:] = 2\n self.assertEqual(x.res, ('set', 1, None, 2))\n\n x[:100] = 2\n self.assertEqual(x.res, ('set', None, 100, 2))\n\n del x[:]\n self.assertEqual(x.res, ('del', None, None))\n\n del x[0:]\n self.assertEqual(x.res, ('del', 0, None))\n\n del x[1:]\n self.assertEqual(x.res, ('del', 1, None))\n\n del x[:100]\n self.assertEqual(x.res, ('del', None, 100))","def _slice(tensor, size, i):\n return tensor[:, i * size : (i + 1) * size]","def getSlice(properties=None, **kw):","def __getitem__( self, l ) :\n\n return( self.coefficients[l] )","def __delslice__(self, *args):\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___delslice__(self, *args)","def getslxslice(self,whichsol_,first_,last_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_slx = False\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n _copyback_slx = True\n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n _copyback_slx = True\n if slx_ is not None and len(slx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.getslxslice(whichsol_,first_,last_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_slx:\n slx[:] = _tmparr_slx","def test_slice_delslice_forbidden(self):\n global setVal\n class foo:\n def __delslice__(self, i, j, value):\n global setVal\n setVal = i, j, value\n def __delitem__(self, index):\n global setVal\n setVal = index\n\n del foo()[::]\n self.assertEqual(setVal, slice(None, None, None))\n del foo()[::None]\n self.assertEqual(setVal, slice(None, None, None))","def __setitem__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___setitem__(self, *args)","def add_slice(\n self, input_name, begin=None, end=None, size=None, strides=None, squeeze=[], name=None):\n begin = self._maybe_add_const(begin, \"slice_begin\")\n end = self._maybe_add_const(end, \"slice_end\")\n size = self._maybe_add_const(size, \"slice_size\")\n strides = self._maybe_add_const(strides, \"slice_strides\")\n if end is not None and size is not None:\n raise ValueError(\"end and size parameter in Slice cannot be used simultaneously.\")\n if strides is not None and size is not None:\n raise ValueError(\"stride and size parameter in Slice cannot be used simultaneously.\")\n\n if size is not None:\n return self._build_op('Slice', [input_name, begin, size], name=name)\n else:\n squeeze_mask = 0\n for i in squeeze:\n squeeze_mask += 2 ** i\n return self._build_op(\n 'StridedSlice', [input_name, begin, end, strides],\n attr={'shrink_axis_mask': squeeze_mask},\n name=name)"],"string":"[\n \"def putcslice(self,first_,last_,slice): # 3\\n if slice is None: raise TypeError(\\\"Invalid type for argument slice\\\")\\n if slice is None:\\n slice_ = None\\n else:\\n try:\\n slice_ = memoryview(slice)\\n except TypeError:\\n try:\\n _tmparr_slice = array.array(\\\"d\\\",slice)\\n except TypeError:\\n raise TypeError(\\\"Argument slice has wrong type\\\")\\n else:\\n slice_ = memoryview(_tmparr_slice)\\n \\n else:\\n if slice_.format != \\\"d\\\":\\n slice_ = memoryview(array.array(\\\"d\\\",slice))\\n \\n if slice_ is not None and len(slice_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slice has wrong length\\\")\\n res = self.__obj.putcslice(first_,last_,slice_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskcslice(self,whichsol_,first_,last_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skc_ is not None and len(skc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.putskcslice(whichsol_,first_,last_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putslcslice(self,whichsol_,first_,last_,slc_):\\n _slc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),((last_) - (first_))))\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc cannot be None\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putslcslice(self,whichsol_,first_,last_,slc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slc is None: raise TypeError(\\\"Invalid type for argument slc\\\")\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n \\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n \\n if slc_ is not None and len(slc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n res = self.__obj.putslcslice(whichsol_,first_,last_,slc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putxcslice(self,whichsol_,first_,last_,xc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xc is None: raise TypeError(\\\"Invalid type for argument xc\\\")\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n \\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n \\n if xc_ is not None and len(xc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n res = self.__obj.putxcslice(whichsol_,first_,last_,xc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putxxslice(self,whichsol_,first_,last_,xx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xx is None: raise TypeError(\\\"Invalid type for argument xx\\\")\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n res = self.__obj.putxxslice(whichsol_,first_,last_,xx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskcslice(self,whichsol_,first_,last_,skc_):\\n _skc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and skc_ is not None and len(skc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),((last_) - (first_))))\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_putskcslice(self.__nativep,whichsol_,first_,last_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putxcslice(self,whichsol_,first_,last_,xc_):\\n _xc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and xc_ is not None and len(xc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),((last_) - (first_))))\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc cannot be None\\\")\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc may not be None\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n res = __library__.MSK_XX_putxcslice(self.__nativep,whichsol_,first_,last_,_xc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __setslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___setslice__(self, *args)\",\n \"def putxxslice(self,whichsol_,first_,last_,xx_):\\n _xx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),((last_) - (first_))))\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx cannot be None\\\")\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx may not be None\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n res = __library__.MSK_XX_putxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskxslice(self,whichsol_,first_,last_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skx is None: raise TypeError(\\\"Invalid type for argument skx\\\")\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skx_ is not None and len(skx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.putskxslice(whichsol_,first_,last_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getslcslice(self,whichsol_,first_,last_,slc_):\\n _slc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),((last_) - (first_))))\\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\\n raise ValueError(\\\"Argument slc must be writable\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_getslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _slc_copyarray:\\n slc_[:] = _slc_np_tmp\",\n \"def getcslice(self,first_,last_,c): # 3\\n _copyback_c = False\\n if c is None:\\n c_ = None\\n else:\\n try:\\n c_ = memoryview(c)\\n except TypeError:\\n try:\\n _tmparr_c = array.array(\\\"d\\\",c)\\n except TypeError:\\n raise TypeError(\\\"Argument c has wrong type\\\")\\n else:\\n c_ = memoryview(_tmparr_c)\\n _copyback_c = True\\n else:\\n if c_.format != \\\"d\\\":\\n c_ = memoryview(array.array(\\\"d\\\",c))\\n _copyback_c = True\\n if c_ is not None and len(c_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument c has wrong length\\\")\\n res = self.__obj.getcslice(first_,last_,c_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_c:\\n c[:] = _tmparr_c\",\n \"def __setslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___setslice__(self, *args)\",\n \"def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def test_getslice_setslice2(self):\\n class C(object):\\n def __getitem__(self, index):\\n return 'Ok'\\n def __setitem__(self, index, value):\\n self.lastCall = 'set'\\n def __delitem__(self, index):\\n self.lastCall = 'delete'\\n\\n a = C()\\n self.assertEqual(a[5:10], 'Ok')\\n\\n a.lastCall = ''\\n a[5:10] = 'abc'\\n self.assertEqual(a.lastCall, 'set')\\n\\n a.lastCall = ''\\n del(a[5:10])\\n self.assertEqual(a.lastCall, 'delete')\",\n \"def slice(self, start=None, end=None, inplace=False):\\n if inplace:\\n self.data = self.data[start:end]\\n else:\\n cpy = self.copy()\\n\\n cpy.data = cpy.data[start:end]\\n\\n return cpy\\n return\",\n \"def putyslice(self,whichsol_,first_,last_,y): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if y is None: raise TypeError(\\\"Invalid type for argument y\\\")\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n res = self.__obj.putyslice(whichsol_,first_,last_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putyslice(self,whichsol_,first_,last_,y_):\\n _y_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),((last_) - (first_))))\\n if y_ is None:\\n raise ValueError(\\\"Argument y cannot be None\\\")\\n if y_ is None:\\n raise ValueError(\\\"Argument y may not be None\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n res = __library__.MSK_XX_putyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getslcslice(self,whichsol_,first_,last_,slc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_slc = False\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n _copyback_slc = True\\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n _copyback_slc = True\\n if slc_ is not None and len(slc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n res = self.__obj.getslcslice(whichsol_,first_,last_,slc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_slc:\\n slc[:] = _tmparr_slc\",\n \"def putsucslice(self,whichsol_,first_,last_,suc_):\\n _suc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and suc_ is not None and len(suc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument suc is not long enough: Is %d, expected %d\\\" % (len(suc_),((last_) - (first_))))\\n if suc_ is None:\\n raise ValueError(\\\"Argument suc cannot be None\\\")\\n if suc_ is None:\\n raise ValueError(\\\"Argument suc may not be None\\\")\\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n res = __library__.MSK_XX_putsucslice(self.__nativep,whichsol_,first_,last_,_suc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskxslice(self,whichsol_,first_,last_,skx_):\\n _skx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),((last_) - (first_))))\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx cannot be None\\\")\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx may not be None\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_putskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __setslice__(self, *args):\\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___setslice__(self, *args)\",\n \"def getcslice(self,first_,last_,c_):\\n _c_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and c_ is not None and len(c_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument c is not long enough: Is %d, expected %d\\\" % (len(c_),((last_) - (first_))))\\n if isinstance(c_,numpy.ndarray) and not c_.flags.writeable:\\n raise ValueError(\\\"Argument c must be writable\\\")\\n if isinstance(c_, numpy.ndarray) and c_.dtype is numpy.dtype(numpy.float64) and c_.flags.contiguous:\\n _c_copyarray = False\\n _c_tmp = ctypes.cast(c_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif c_ is not None:\\n _c_copyarray = True\\n _c_np_tmp = numpy.zeros(len(c_),numpy.dtype(numpy.float64))\\n _c_np_tmp[:] = c_\\n assert _c_np_tmp.flags.contiguous\\n _c_tmp = ctypes.cast(_c_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _c_copyarray = False\\n _c_tmp = None\\n \\n res = __library__.MSK_XX_getcslice(self.__nativep,first_,last_,_c_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _c_copyarray:\\n c_[:] = _c_np_tmp\",\n \"def _fix_slice(self, inputs, new_attr):\\n begin = new_attr.get('begin')\\n end = new_attr.get('end')\\n axes = new_attr.get('axis', tuple(range(len(begin))))\\n slice_op = mx.sym.slice_axis(inputs[0], axis=axes[0], begin=begin[0], end=end[0])\\n if len(axes) > 1:\\n for i, axis in enumerate(axes):\\n slice_op = mx.sym.slice_axis(slice_op, axis=axis, begin=begin[i], end=end[i])\\n return slice_op\",\n \"def putsucslice(self,whichsol_,first_,last_,suc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if suc is None: raise TypeError(\\\"Invalid type for argument suc\\\")\\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n \\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n \\n if suc_ is not None and len(suc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument suc has wrong length\\\")\\n res = self.__obj.putsucslice(whichsol_,first_,last_,suc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __getslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___getslice__(self, *args)\",\n \"def getxxslice(self,whichsol_,first_,last_,xx_):\\n _xx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),((last_) - (first_))))\\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\\n raise ValueError(\\\"Argument xx must be writable\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n res = __library__.MSK_XX_getxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _xx_copyarray:\\n xx_[:] = _xx_np_tmp\",\n \"def getskcslice(self,whichsol_,first_,last_,skc_):\\n _skc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and skc_ is not None and len(skc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),((last_) - (first_))))\\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\\n raise ValueError(\\\"Argument skc must be writable\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_getskcslice(self.__nativep,whichsol_,first_,last_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\",\n \"def getskcslice(self,whichsol_,first_,last_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skc = False\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n _copyback_skc = True\\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n _copyback_skc = True\\n if skc_ is not None and len(skc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.getskcslice(whichsol_,first_,last_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_skc:\\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\",\n \"def __getslice__(self,i,j):\\n return self.x[i:j]\",\n \"def putslxslice(self,whichsol_,first_,last_,slx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slx is None: raise TypeError(\\\"Invalid type for argument slx\\\")\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if slx_ is not None and len(slx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n res = self.__obj.putslxslice(whichsol_,first_,last_,slx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getxxslice(self,whichsol_,first_,last_,xx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_xx = False\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n _copyback_xx = True\\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n _copyback_xx = True\\n if xx_ is not None and len(xx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n res = self.__obj.getxxslice(whichsol_,first_,last_,xx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_xx:\\n xx[:] = _tmparr_xx\",\n \"def getxcslice(self,whichsol_,first_,last_,xc_):\\n _xc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and xc_ is not None and len(xc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),((last_) - (first_))))\\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\\n raise ValueError(\\\"Argument xc must be writable\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n res = __library__.MSK_XX_getxcslice(self.__nativep,whichsol_,first_,last_,_xc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _xc_copyarray:\\n xc_[:] = _xc_np_tmp\",\n \"def slice(self, pred):\\n return NotImplemented\",\n \"def test_slice_setslice_forbidden(self):\\n global setVal\\n class foo:\\n def __setslice__(self, i, j, value):\\n global setVal\\n setVal = i, j, value\\n def __setitem__(self, index, value):\\n global setVal\\n setVal = index, value\\n\\n foo()[::] = 23\\n self.assertEqual(setVal, (slice(None, None, None), 23))\\n foo()[::None] = 23\\n self.assertEqual(setVal, (slice(None, None, None), 23))\",\n \"def __setslice__(self, *args):\\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint2___setslice__(self, *args)\",\n \"def putslxslice(self,whichsol_,first_,last_,slx_):\\n _slx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slx_ is not None and len(slx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slx is not long enough: Is %d, expected %d\\\" % (len(slx_),((last_) - (first_))))\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx cannot be None\\\")\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx may not be None\\\")\\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n res = __library__.MSK_XX_putslxslice(self.__nativep,whichsol_,first_,last_,_slx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsnxslice(self,whichsol_,first_,last_,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if snx is None: raise TypeError(\\\"Invalid type for argument snx\\\")\\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n \\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n \\n if snx_ is not None and len(snx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument snx has wrong length\\\")\\n res = self.__obj.putsnxslice(whichsol_,first_,last_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getxcslice(self,whichsol_,first_,last_,xc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_xc = False\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n _copyback_xc = True\\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n _copyback_xc = True\\n if xc_ is not None and len(xc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n res = self.__obj.getxcslice(whichsol_,first_,last_,xc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_xc:\\n xc[:] = _tmparr_xc\",\n \"def slice(self, s=0, e=0):\\r\\n self.p = self.p[s:e]\\r\\n self.r = self.r[s:e]\\r\\n self.alpha = self.alpha[s:e]\\r\\n self.v = self.v[s:e]\\r\\n self.w = self.w[s:e]\\r\\n self.a = self.a[s:e]\",\n \"def slice(self, begin, end):\\n sliced = self._collection.slice(begin, end)\\n return self.set_collection(sliced)\",\n \"def getbarsslice(self,whichsol_,first_,last_,slicesize_,barsslice_):\\n _barsslice_minlength = (slicesize_)\\n if (slicesize_) > 0 and barsslice_ is not None and len(barsslice_) != (slicesize_):\\n raise ValueError(\\\"Array argument barsslice is not long enough: Is %d, expected %d\\\" % (len(barsslice_),(slicesize_)))\\n if isinstance(barsslice_,numpy.ndarray) and not barsslice_.flags.writeable:\\n raise ValueError(\\\"Argument barsslice must be writable\\\")\\n if barsslice_ is None:\\n raise ValueError(\\\"Argument barsslice may not be None\\\")\\n if isinstance(barsslice_, numpy.ndarray) and barsslice_.dtype is numpy.dtype(numpy.float64) and barsslice_.flags.contiguous:\\n _barsslice_copyarray = False\\n _barsslice_tmp = ctypes.cast(barsslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barsslice_ is not None:\\n _barsslice_copyarray = True\\n _barsslice_np_tmp = numpy.zeros(len(barsslice_),numpy.dtype(numpy.float64))\\n _barsslice_np_tmp[:] = barsslice_\\n assert _barsslice_np_tmp.flags.contiguous\\n _barsslice_tmp = ctypes.cast(_barsslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barsslice_copyarray = False\\n _barsslice_tmp = None\\n \\n res = __library__.MSK_XX_getbarsslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barsslice_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _barsslice_copyarray:\\n barsslice_[:] = _barsslice_np_tmp\",\n \"def __delslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___delslice__(self, *args)\",\n \"def AddSlice(self, data_slice):\\n self.slices.append(data_slice)\",\n \"def __getslice__(self, start, stop):\\n return self.__getitem__(slice(start, stop, None))\",\n \"def getsucslice(self,whichsol_,first_,last_,suc_):\\n _suc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and suc_ is not None and len(suc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument suc is not long enough: Is %d, expected %d\\\" % (len(suc_),((last_) - (first_))))\\n if isinstance(suc_,numpy.ndarray) and not suc_.flags.writeable:\\n raise ValueError(\\\"Argument suc must be writable\\\")\\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n res = __library__.MSK_XX_getsucslice(self.__nativep,whichsol_,first_,last_,_suc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _suc_copyarray:\\n suc_[:] = _suc_np_tmp\",\n \"def putsnxslice(self,whichsol_,first_,last_,snx_):\\n _snx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and snx_ is not None and len(snx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument snx is not long enough: Is %d, expected %d\\\" % (len(snx_),((last_) - (first_))))\\n if snx_ is None:\\n raise ValueError(\\\"Argument snx cannot be None\\\")\\n if snx_ is None:\\n raise ValueError(\\\"Argument snx may not be None\\\")\\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_putsnxslice(self.__nativep,whichsol_,first_,last_,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsuxslice(self,whichsol_,first_,last_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if sux is None: raise TypeError(\\\"Invalid type for argument sux\\\")\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.putsuxslice(whichsol_,first_,last_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsuxslice(self,whichsol_,first_,last_,sux_):\\n _sux_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),((last_) - (first_))))\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux cannot be None\\\")\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux may not be None\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_putsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __delslice__(self, start, end):\\n # delete the slice from the data\\n del self._data[start:end]\\n\\n # determined the length of the data element\\n self._nrows = len(self._data)\",\n \"def __getslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___getslice__(self, *args)\",\n \"def getbarxslice(self,whichsol_,first_,last_,slicesize_,barxslice_):\\n _barxslice_minlength = (slicesize_)\\n if (slicesize_) > 0 and barxslice_ is not None and len(barxslice_) != (slicesize_):\\n raise ValueError(\\\"Array argument barxslice is not long enough: Is %d, expected %d\\\" % (len(barxslice_),(slicesize_)))\\n if isinstance(barxslice_,numpy.ndarray) and not barxslice_.flags.writeable:\\n raise ValueError(\\\"Argument barxslice must be writable\\\")\\n if barxslice_ is None:\\n raise ValueError(\\\"Argument barxslice may not be None\\\")\\n if isinstance(barxslice_, numpy.ndarray) and barxslice_.dtype is numpy.dtype(numpy.float64) and barxslice_.flags.contiguous:\\n _barxslice_copyarray = False\\n _barxslice_tmp = ctypes.cast(barxslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barxslice_ is not None:\\n _barxslice_copyarray = True\\n _barxslice_np_tmp = numpy.zeros(len(barxslice_),numpy.dtype(numpy.float64))\\n _barxslice_np_tmp[:] = barxslice_\\n assert _barxslice_np_tmp.flags.contiguous\\n _barxslice_tmp = ctypes.cast(_barxslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barxslice_copyarray = False\\n _barxslice_tmp = None\\n \\n res = __library__.MSK_XX_getbarxslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barxslice_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _barxslice_copyarray:\\n barxslice_[:] = _barxslice_np_tmp\",\n \"def slice( # noqa: A003, C901\\n self,\\n trace: ExecutionTrace,\\n slicing_criterion: SlicingCriterion,\\n ) -> list[UniqueInstruction]:\\n slc = self._setup_slicing_configuration(slicing_criterion, trace)\\n\\n while True:\\n criterion_in_slice = imp_data_dep = False\\n include_use = True\\n\\n # Get last instruction\\n last_state = slc.update_state()\\n\\n if last_state.exception:\\n # Stack can not be reliably simulated when an exception occurred\\n slc.stack_simulation = False\\n if not last_state.last_instr:\\n # Reached end of executed instructions -> return slice (and keep order)\\n instructions = set()\\n slice_instructions = []\\n for i in reversed(slc.context.instr_in_slice):\\n if i not in instructions:\\n instructions.add(i)\\n slice_instructions.append(i)\\n return slice_instructions\\n\\n last_unique_instr = self.create_unique_instruction(\\n slc.file,\\n last_state.last_instr,\\n slc.code_object_id,\\n slc.basic_block_id,\\n slc.offset,\\n )\\n # Adjust trace position\\n last_traced_instr = None\\n if last_state.last_instr.opcode in op.TRACED_INSTRUCTIONS:\\n last_traced_instr = trace.executed_instructions[slc.trace_position]\\n slc.trace_position -= 1\\n\\n # Stack housekeeping\\n prev_import_back_call = self._stack_housekeeping(\\n last_state, last_unique_instr, slc\\n )\\n\\n # Control dependency\\n control_dependency = self.check_control_dependency(\\n slc.context, last_unique_instr, slc.code_object_id\\n )\\n\\n # Data dependencies\\n # Explicit data dependency\\n (\\n exp_data_dep,\\n slc.new_attribute_object_uses,\\n ) = self.check_explicit_data_dependency(\\n slc.context, last_unique_instr, last_traced_instr\\n )\\n\\n # Dependency via method call\\n if last_state.call and slc.code_object_dependent:\\n imp_data_dep = True\\n slc.code_object_dependent = False\\n\\n if last_state.import_start:\\n # We need to include the import statement after determining\\n # if one of the instructions executed by the import is included\\n # (because IMPORT_NAME is traced afterwards).\\n slc.context.instr_in_slice.append(prev_import_back_call)\\n num_import_pops = StackEffect.stack_effect(\\n prev_import_back_call.opcode, arg=None, jump=False\\n )[0]\\n slc.trace_stack.update_pop_operations(\\n num_import_pops, prev_import_back_call, True\\n )\\n # Implicit data dependency (over stack)\\n if slc.stack_simulation:\\n stack_dep, include_use = slc.trace_stack.update_push_operations(\\n slc.pushes, last_state.returned\\n )\\n if stack_dep:\\n imp_data_dep = True\\n if last_state.returned:\\n slc.code_object_dependent = False\\n\\n if control_dependency or exp_data_dep or imp_data_dep:\\n criterion_in_slice = True\\n\\n if not last_state.call:\\n slc.code_object_dependent = True\\n\\n # Unconditional jumps\\n if last_state.jump and last_state.last_instr.is_uncond_jump():\\n criterion_in_slice = True\\n\\n # Housekeeping for execution trace, stack\\n self._trace_housekeeping(\\n criterion_in_slice,\\n include_use,\\n last_traced_instr,\\n last_unique_instr,\\n slc,\\n )\\n\\n # next iteration\\n slc.curr_instr = last_state.last_instr\\n\\n if time.time() > slc.timeout:\\n raise SlicingTimeoutException\",\n \"def simple_slice():\\n examples = [\\n benchmark.Example(\\n inputs=[\\n [[12, 34, 56, 78], [-1, -2, -3, -4]],\\n -1,\\n ],\\n output=[[34, 56], [-2, -3]],\\n ),\\n ]\\n constants = []\\n description = 'Slice a tensor'\\n target_program = 'in1[:, 1:in2]'\\n source = 'handwritten task'\\n return benchmark.Benchmark(examples=examples,\\n constants=constants,\\n description=description,\\n target_program=target_program,\\n source=source,\\n name='simple_slice')\",\n \"def _getitem_slice(self: MultiIndex, slobj: slice) -> MultiIndex:\\n sortorder = None\\n if slobj.step is None or slobj.step > 0:\\n sortorder = self.sortorder\\n\\n new_codes = [level_codes[slobj] for level_codes in self.codes]\\n\\n return type(self)(\\n levels=self.levels,\\n codes=new_codes,\\n names=self._names,\\n sortorder=sortorder,\\n verify_integrity=False,\\n )\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i,j))\",\n \"def test_getslice_setslice3(self):\\n class C(object):\\n def __init__(self):\\n self.calls = []\\n def __getitem__(self, index):\\n self.calls.append('get')\\n return 'Ok'\\n def __setitem__(self, index, value):\\n self.calls.append('set')\\n def __delitem__(self, index):\\n self.calls.append('delete')\\n def __len__(self):\\n self.calls.append('len')\\n return 5\\n\\n a = C()\\n self.assertEqual(a[3:5], 'Ok')\\n self.assertEqual(a.calls, ['get'])\\n\\n a = C()\\n a[3:5] = 'abc'\\n self.assertEqual(a.calls, ['set'])\\n\\n a = C()\\n del(a[3:5])\\n self.assertEqual(a.calls, ['delete'])\\n\\n # but call length if it's negative (and we should only call length once)\\n a = C()\\n self.assertEqual(a[-1:5], 'Ok')\\n self.assertEqual(a.calls, ['get'])\\n\\n a = C()\\n self.assertEqual(a[1:-5], 'Ok')\\n self.assertEqual(a.calls, ['get'])\\n\\n a = C()\\n self.assertEqual(a[-1:-5], 'Ok')\\n self.assertEqual(a.calls, ['get'])\\n\\n a = C()\\n a[-1:5] = 'abc'\\n self.assertEqual(a.calls, ['set'])\\n\\n a = C()\\n a[1:-5] = 'abc'\\n self.assertEqual(a.calls, ['set'])\\n\\n a = C()\\n a[-1:-5] = 'abc'\\n self.assertEqual(a.calls, ['set'])\\n\\n a = C()\\n del(a[-1:5])\\n self.assertEqual(a.calls, ['delete'])\\n\\n a = C()\\n del(a[1:-5])\\n self.assertEqual(a.calls, ['delete'])\\n\\n a = C()\\n del(a[-1:-5])\\n self.assertEqual(a.calls, ['delete'])\",\n \"def getyslice(self,whichsol_,first_,last_,y_):\\n _y_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),((last_) - (first_))))\\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\\n raise ValueError(\\\"Argument y must be writable\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n res = __library__.MSK_XX_getyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _y_copyarray:\\n y_[:] = _y_np_tmp\",\n \"def getsuxslice(self,whichsol_,first_,last_,sux_):\\n _sux_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),((last_) - (first_))))\\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\\n raise ValueError(\\\"Argument sux must be writable\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_getsuxslice(self.__nativep,whichsol_,first_,last_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _sux_copyarray:\\n sux_[:] = _sux_np_tmp\",\n \"def _extend_slice(self, sl, extent, dim_max, dim_min=0):\\n\\n x_start = max(sl.start - extent, dim_min)\\n x_end = min(sl.stop + extent, dim_max)\\n return np.s_[x_start:x_end]\",\n \"def __setitem__( self, l, c_l ) :\\n\\n if( l == len( self ) ) :\\n self.coefficients.append( float( c_l ) )\\n else :\\n self.coefficients[l] = float( c_l )\",\n \"def __getslice__(self, i, j):\\n return self.dtrs[i:j]\",\n \"def getyslice(self,whichsol_,first_,last_,y): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_y = False\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n _copyback_y = True\\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n _copyback_y = True\\n if y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n res = self.__obj.getyslice(whichsol_,first_,last_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_y:\\n y[:] = _tmparr_y\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i, j))\",\n \"def __setslice__(self, i, j, sequence):\\n self.__field.validate(sequence)\\n list.__setslice__(self, i, j, sequence)\",\n \"def chain(self,slices):\\n for proj in self.projs[::-1]:\\n slices = proj(slices)\\n return\",\n \"def __setslice__(self, i, j, sequence):\\n self.__field.validate(sequence)\\n list.__setslice__(self, i, j, sequence)\",\n \"def __getitem__(self, i):\\n new_data = super().__getitem__(i)\\n if isinstance(i, slice):\\n new_data = self.__class__(new_data)\\n new_data.global_settings = copy.copy(self.global_settings)\\n return new_data\",\n \"def getsucslice(self,whichsol_,first_,last_,suc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_suc = False\\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n _copyback_suc = True\\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n _copyback_suc = True\\n if suc_ is not None and len(suc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument suc has wrong length\\\")\\n res = self.__obj.getsucslice(whichsol_,first_,last_,suc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_suc:\\n suc[:] = _tmparr_suc\",\n \"def slice2(self, vs=None,xs=None):\\n return self.condition2(vs,xs)\",\n \"def __setslice__(self, i, j, sequence):\\n if self.__pepth__ != 0:\\n if '__warned__' not in plist.__setslice__.__dict__:\\n qj('Slicing of inner plist elements with negative indices in python 2.7 does not work, and the error cannot be detected or corrected!\\\\n'\\n 'Instead of slicing with one or two arguments: `plist._[-2:]`, use the three argument slice: `plist._[-2::1]`.\\\\n'\\n 'This avoids the broken code path in the python compiler.', 'WARNING!')\\n plist.__setslice__.__dict__['__warned__'] = True\\n return plist.__getattr__(self, '__setslice__')(i, j, sequence)\\n try:\\n list.__setslice__(self, i, j, sequence)\\n except Exception:\\n plist.__setitem__(self, slice(i, j), sequence)\\n return self\",\n \"def __delslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___delslice__(self, *args)\",\n \"def getskxslice(self,whichsol_,first_,last_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skx = False\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n _copyback_skx = True\\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n _copyback_skx = True\\n if skx_ is not None and len(skx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.getskxslice(whichsol_,first_,last_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_skx:\\n for __tmp_var_0 in range(len(skx_)): skx[__tmp_var_0] = stakey(_tmparr_skx[__tmp_var_0])\",\n \"def __getitem__(self, i):\\n if isinstance(i, slice):\\n return self.v[slice(i.start+self.tau-1, i.stop+self.tau-1, i.step)]\\n else:\\n return self.v[i+self.tau-1]\",\n \"def getsuxslice(self,whichsol_,first_,last_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_sux = False\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n _copyback_sux = True\\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n _copyback_sux = True\\n if sux_ is not None and len(sux_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.getsuxslice(whichsol_,first_,last_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_sux:\\n sux[:] = _tmparr_sux\",\n \"def __getslice__(self, *args):\\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___getslice__(self, *args)\",\n \"def set_next_slice(self, start_idx, end_idx, strip_slice: bool = False):\\n si, ei = int(start_idx), int(end_idx)\\n if strip_slice:\\n si, ei = _strip_slice_of_string(self.string, si, ei)\\n self._next_slice = (si, ei)\",\n \"def putconboundslice(self,first_,last_,bkc_,blc_,buc_):\\n _bkc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and bkc_ is not None and len(bkc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bkc is not long enough: Is %d, expected %d\\\" % (len(bkc_),((last_) - (first_))))\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc cannot be None\\\")\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc may not be None\\\")\\n if bkc_ is not None:\\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\\n else:\\n _bkc_tmp = None\\n _blc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and blc_ is not None and len(blc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument blc is not long enough: Is %d, expected %d\\\" % (len(blc_),((last_) - (first_))))\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc cannot be None\\\")\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc may not be None\\\")\\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\\n _blc_copyarray = False\\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blc_ is not None:\\n _blc_copyarray = True\\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\\n _blc_np_tmp[:] = blc_\\n assert _blc_np_tmp.flags.contiguous\\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blc_copyarray = False\\n _blc_tmp = None\\n \\n _buc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and buc_ is not None and len(buc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument buc is not long enough: Is %d, expected %d\\\" % (len(buc_),((last_) - (first_))))\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc cannot be None\\\")\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc may not be None\\\")\\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\\n _buc_copyarray = False\\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif buc_ is not None:\\n _buc_copyarray = True\\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\\n _buc_np_tmp[:] = buc_\\n assert _buc_np_tmp.flags.contiguous\\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _buc_copyarray = False\\n _buc_tmp = None\\n \\n res = __library__.MSK_XX_putconboundslice(self.__nativep,first_,last_,_bkc_tmp,_blc_tmp,_buc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def test_getslice1(self):\\n class C(list):\\n def __getitem__(self, index):\\n return (index.start, index.stop)\\n\\n a = C()\\n self.assertEqual(a[32:197], (32,197))\",\n \"def special_slice(self, form):\\n obj = self.reallyCompile(form[1])\\n rest = form[2:]\\n if len(rest) == 1:\\n return ast.Subscript(obj, 'OP_APPLY', [self.reallyCompile(rest[0])])\\n elif len(rest) == 2:\\n return ast.Slice(obj, 'OP_APPLY', *self.compileForms(rest))\\n elif len(rest) == 3:\\n return ast.Subscript(obj, 'OP_APPLY', [ast.Sliceobj(self.compileForms(rest))])\\n else:\\n raise SyntaxError(\\\"Too many thingies to slice! %r\\\" % rest)\",\n \"def slicer(seq, start=None, stop=None, step=None):\\n return seq[start:stop:step]\",\n \"def getskxslice(self,whichsol_,first_,last_,skx_):\\n _skx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),((last_) - (first_))))\\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\\n raise ValueError(\\\"Argument skx must be writable\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_getskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\",\n \"def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getslxslice(self,whichsol_,first_,last_,slx_):\\n _slx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slx_ is not None and len(slx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slx is not long enough: Is %d, expected %d\\\" % (len(slx_),((last_) - (first_))))\\n if isinstance(slx_,numpy.ndarray) and not slx_.flags.writeable:\\n raise ValueError(\\\"Argument slx must be writable\\\")\\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n res = __library__.MSK_XX_getslxslice(self.__nativep,whichsol_,first_,last_,_slx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _slx_copyarray:\\n slx_[:] = _slx_np_tmp\",\n \"def cutout(self, slices: tuple):\\n if len(slices) == self.ndim - 1:\\n slices = (slice(0, self.shape[0]), ) + slices\\n internalSlices = self._get_internal_slices(slices)\\n arr = self.array[internalSlices]\\n global_offset = tuple(s.start for s in slices)\\n return Chunk(arr, global_offset=global_offset)\",\n \"def putconboundslice(self,first_,last_,bk,bl,bu): # 3\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putconboundslice(first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putboundslice(self,con_,first_,last_,bk,bl,bu): # 3\\n if not isinstance(con_,accmode): raise TypeError(\\\"Argument con has wrong type\\\")\\n if bk is None: raise TypeError(\\\"Invalid type for argument bk\\\")\\n if bk is None:\\n bk_ = None\\n else:\\n try:\\n bk_ = memoryview(bk)\\n except TypeError:\\n try:\\n _tmparr_bk = array.array(\\\"i\\\",bk)\\n except TypeError:\\n raise TypeError(\\\"Argument bk has wrong type\\\")\\n else:\\n bk_ = memoryview(_tmparr_bk)\\n \\n else:\\n if bk_.format != \\\"i\\\":\\n bk_ = memoryview(array.array(\\\"i\\\",bk))\\n \\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bk has wrong length\\\")\\n if bl is None: raise TypeError(\\\"Invalid type for argument bl\\\")\\n if bl is None:\\n bl_ = None\\n else:\\n try:\\n bl_ = memoryview(bl)\\n except TypeError:\\n try:\\n _tmparr_bl = array.array(\\\"d\\\",bl)\\n except TypeError:\\n raise TypeError(\\\"Argument bl has wrong type\\\")\\n else:\\n bl_ = memoryview(_tmparr_bl)\\n \\n else:\\n if bl_.format != \\\"d\\\":\\n bl_ = memoryview(array.array(\\\"d\\\",bl))\\n \\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bl has wrong length\\\")\\n if bu is None: raise TypeError(\\\"Invalid type for argument bu\\\")\\n if bu is None:\\n bu_ = None\\n else:\\n try:\\n bu_ = memoryview(bu)\\n except TypeError:\\n try:\\n _tmparr_bu = array.array(\\\"d\\\",bu)\\n except TypeError:\\n raise TypeError(\\\"Argument bu has wrong type\\\")\\n else:\\n bu_ = memoryview(_tmparr_bu)\\n \\n else:\\n if bu_.format != \\\"d\\\":\\n bu_ = memoryview(array.array(\\\"d\\\",bu))\\n \\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument bu has wrong length\\\")\\n res = self.__obj.putboundslice(con_,first_,last_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def slice_parameters(self, slice_parameters: SliceParamsIm):\\n\\n self._slice_parameters = slice_parameters\",\n \"def _slice_assign(self, batch_element, batch_start, batch_end, is_finished):\\n try:\\n self.results[batch_start:batch_end] = batch_element\\n\\n except Exception as e: # pylint: disable=broad-except\\n # `_slice_assign` should only be called in threads and exceptions raised\\n # in threads do not carry over to the main thread. So instead we perform a\\n # a broad catch in the thread and then store the exception to be re-raised\\n # in the main thread.\\n self._errors.append(e)\\n\\n finally:\\n is_finished.set()\",\n \"def test_simple_slicing(self):\\n class only_slice(object):\\n def __getitem__(self, index):\\n self.res = 'get', index.start, index.stop\\n def __setitem__(self, index, value):\\n self.res = 'set', index.start, index.stop, value\\n def __delitem__(self, index):\\n self.res = 'del', index.start, index.stop\\n\\n class mixed_slice(object):\\n def __getitem__(self, index):\\n if isinstance(index, slice):\\n self.res = 'get', index.start, index.stop\\n else:\\n raise Exception()\\n def __setitem__(self, index, value):\\n if isinstance(index, slice):\\n self.res = 'set', index.start, index.stop, value\\n else:\\n raise Exception()\\n def __delitem__(self, index):\\n if isinstance(index, slice):\\n self.res = 'del', index.start, index.stop\\n else:\\n raise Exception()\\n\\n for mytype in [only_slice, mixed_slice]:\\n x = mytype()\\n x[:]\\n self.assertEqual(x.res, ('get', None, None))\\n\\n x[0:]\\n self.assertEqual(x.res, ('get', 0, None))\\n\\n x[1:]\\n self.assertEqual(x.res, ('get', 1, None))\\n\\n x[:100]\\n self.assertEqual(x.res, ('get', None, 100))\\n\\n x[:] = 2\\n self.assertEqual(x.res, ('set', None, None, 2))\\n\\n x[0:] = 2\\n self.assertEqual(x.res, ('set', 0, None, 2))\\n\\n x[1:] = 2\\n self.assertEqual(x.res, ('set', 1, None, 2))\\n\\n x[:100] = 2\\n self.assertEqual(x.res, ('set', None, 100, 2))\\n\\n del x[:]\\n self.assertEqual(x.res, ('del', None, None))\\n\\n del x[0:]\\n self.assertEqual(x.res, ('del', 0, None))\\n\\n del x[1:]\\n self.assertEqual(x.res, ('del', 1, None))\\n\\n del x[:100]\\n self.assertEqual(x.res, ('del', None, 100))\",\n \"def _slice(tensor, size, i):\\n return tensor[:, i * size : (i + 1) * size]\",\n \"def getSlice(properties=None, **kw):\",\n \"def __getitem__( self, l ) :\\n\\n return( self.coefficients[l] )\",\n \"def __delslice__(self, *args):\\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___delslice__(self, *args)\",\n \"def getslxslice(self,whichsol_,first_,last_,slx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_slx = False\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n _copyback_slx = True\\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n _copyback_slx = True\\n if slx_ is not None and len(slx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n res = self.__obj.getslxslice(whichsol_,first_,last_,slx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_slx:\\n slx[:] = _tmparr_slx\",\n \"def test_slice_delslice_forbidden(self):\\n global setVal\\n class foo:\\n def __delslice__(self, i, j, value):\\n global setVal\\n setVal = i, j, value\\n def __delitem__(self, index):\\n global setVal\\n setVal = index\\n\\n del foo()[::]\\n self.assertEqual(setVal, slice(None, None, None))\\n del foo()[::None]\\n self.assertEqual(setVal, slice(None, None, None))\",\n \"def __setitem__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___setitem__(self, *args)\",\n \"def add_slice(\\n self, input_name, begin=None, end=None, size=None, strides=None, squeeze=[], name=None):\\n begin = self._maybe_add_const(begin, \\\"slice_begin\\\")\\n end = self._maybe_add_const(end, \\\"slice_end\\\")\\n size = self._maybe_add_const(size, \\\"slice_size\\\")\\n strides = self._maybe_add_const(strides, \\\"slice_strides\\\")\\n if end is not None and size is not None:\\n raise ValueError(\\\"end and size parameter in Slice cannot be used simultaneously.\\\")\\n if strides is not None and size is not None:\\n raise ValueError(\\\"stride and size parameter in Slice cannot be used simultaneously.\\\")\\n\\n if size is not None:\\n return self._build_op('Slice', [input_name, begin, size], name=name)\\n else:\\n squeeze_mask = 0\\n for i in squeeze:\\n squeeze_mask += 2 ** i\\n return self._build_op(\\n 'StridedSlice', [input_name, begin, end, strides],\\n attr={'shrink_axis_mask': squeeze_mask},\\n name=name)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7177398","0.64559644","0.64415175","0.6327401","0.63102835","0.62641084","0.6245864","0.6206491","0.61138916","0.60814995","0.597995","0.593315","0.5893596","0.58712506","0.5860539","0.5855395","0.58295006","0.58105403","0.5787265","0.5746839","0.57019055","0.57017094","0.56873286","0.568603","0.568457","0.56585735","0.5654836","0.56272733","0.5614747","0.56144816","0.56123424","0.5600643","0.5589407","0.5578903","0.55597824","0.5524749","0.55112106","0.5493415","0.54862946","0.5445519","0.54451734","0.54324496","0.54259795","0.5425914","0.5421978","0.5420771","0.54167056","0.5416055","0.5415796","0.5354716","0.5341794","0.5326417","0.53050643","0.5301251","0.5263493","0.5236938","0.52345353","0.5231393","0.52074826","0.5204258","0.5194357","0.51917267","0.5183022","0.5179799","0.5173073","0.51715285","0.51448923","0.5140013","0.5123763","0.51115495","0.5110616","0.5109179","0.5106149","0.51019645","0.50963926","0.5091828","0.5090981","0.5075011","0.5046951","0.5040272","0.503743","0.50339544","0.5025724","0.50255114","0.5021206","0.5016748","0.500795","0.4979516","0.49790567","0.49543867","0.49488708","0.4944554","0.49418202","0.49382022","0.49139577","0.491311","0.49106765","0.491018","0.49078307","0.49036178"],"string":"[\n \"0.7177398\",\n \"0.64559644\",\n \"0.64415175\",\n \"0.6327401\",\n \"0.63102835\",\n \"0.62641084\",\n \"0.6245864\",\n \"0.6206491\",\n \"0.61138916\",\n \"0.60814995\",\n \"0.597995\",\n \"0.593315\",\n \"0.5893596\",\n \"0.58712506\",\n \"0.5860539\",\n \"0.5855395\",\n \"0.58295006\",\n \"0.58105403\",\n \"0.5787265\",\n \"0.5746839\",\n \"0.57019055\",\n \"0.57017094\",\n \"0.56873286\",\n \"0.568603\",\n \"0.568457\",\n \"0.56585735\",\n \"0.5654836\",\n \"0.56272733\",\n \"0.5614747\",\n \"0.56144816\",\n \"0.56123424\",\n \"0.5600643\",\n \"0.5589407\",\n \"0.5578903\",\n \"0.55597824\",\n \"0.5524749\",\n \"0.55112106\",\n \"0.5493415\",\n \"0.54862946\",\n \"0.5445519\",\n \"0.54451734\",\n \"0.54324496\",\n \"0.54259795\",\n \"0.5425914\",\n \"0.5421978\",\n \"0.5420771\",\n \"0.54167056\",\n \"0.5416055\",\n \"0.5415796\",\n \"0.5354716\",\n \"0.5341794\",\n \"0.5326417\",\n \"0.53050643\",\n \"0.5301251\",\n \"0.5263493\",\n \"0.5236938\",\n \"0.52345353\",\n \"0.5231393\",\n \"0.52074826\",\n \"0.5204258\",\n \"0.5194357\",\n \"0.51917267\",\n \"0.5183022\",\n \"0.5179799\",\n \"0.5173073\",\n \"0.51715285\",\n \"0.51448923\",\n \"0.5140013\",\n \"0.5123763\",\n \"0.51115495\",\n \"0.5110616\",\n \"0.5109179\",\n \"0.5106149\",\n \"0.51019645\",\n \"0.50963926\",\n \"0.5091828\",\n \"0.5090981\",\n \"0.5075011\",\n \"0.5046951\",\n \"0.5040272\",\n \"0.503743\",\n \"0.50339544\",\n \"0.5025724\",\n \"0.50255114\",\n \"0.5021206\",\n \"0.5016748\",\n \"0.500795\",\n \"0.4979516\",\n \"0.49790567\",\n \"0.49543867\",\n \"0.49488708\",\n \"0.4944554\",\n \"0.49418202\",\n \"0.49382022\",\n \"0.49139577\",\n \"0.491311\",\n \"0.49106765\",\n \"0.491018\",\n \"0.49078307\",\n \"0.49036178\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73769736"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11968,"cells":{"query":{"kind":"string","value":"Changes one element in barc. putbarcj(self,j_,sub_,weights_)"},"document":{"kind":"string","value":"def putbarcj(self,j_,sub_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights_)\n elif num_ != len(weights_):\n raise IndexError(\"Inconsistent length of array weights\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbarcj(self.__nativep,j_,num_,_sub_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putbarcj(self,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexError(\"Inconsistent length of array weights\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n \n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n \n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n \n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbaraij(self,i_,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexError(\"Inconsistent length of array weights\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n \n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n \n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n \n res = self.__obj.putbaraij(i_,j_,num_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbaraij(self,i_,j_,sub_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights_)\n elif num_ != len(weights_):\n raise IndexError(\"Inconsistent length of array weights\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraij(self.__nativep,i_,j_,num_,_sub_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarxj(self,whichsol_,j_,barxj_):\n _barxj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj is not long enough: Is %d, expected %d\" % (len(barxj_),self.getlenbarvarj((j_))))\n if barxj_ is None:\n raise ValueError(\"Argument barxj cannot be None\")\n if barxj_ is None:\n raise ValueError(\"Argument barxj may not be None\")\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\n _barxj_copyarray = False\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxj_ is not None:\n _barxj_copyarray = True\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\n _barxj_np_tmp[:] = barxj_\n assert _barxj_np_tmp.flags.contiguous\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxj_copyarray = False\n _barxj_tmp = None\n \n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(alphaptrb_)\n elif num_ != len(alphaptrb_):\n raise IndexError(\"Inconsistent length of array alphaptrb\")\n if num_ is None:\n num_ = len(alphaptre_)\n elif num_ != len(alphaptre_):\n raise IndexError(\"Inconsistent length of array alphaptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb cannot be None\")\n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb may not be None\")\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptrb_ is not None:\n _alphaptrb_copyarray = True\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\n _alphaptrb_np_tmp[:] = alphaptrb_\n assert _alphaptrb_np_tmp.flags.contiguous\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = None\n \n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre cannot be None\")\n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre may not be None\")\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\n _alphaptre_copyarray = False\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptre_ is not None:\n _alphaptre_copyarray = True\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\n _alphaptre_np_tmp[:] = alphaptre_\n assert _alphaptre_np_tmp.flags.contiguous\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptre_copyarray = False\n _alphaptre_tmp = None\n \n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if barsj_ is None:\n raise ValueError(\"Argument barsj cannot be None\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n \n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n \n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getbarcidx(self,idx_,sub_,weights_):\n maxnum_ = self.getbarcidxinfo((idx_))\n j_ = ctypes.c_int32()\n num_ = ctypes.c_int64()\n _sub_minlength = (maxnum_)\n if (maxnum_) > 0 and sub_ is not None and len(sub_) != (maxnum_):\n raise ValueError(\"Array argument sub is not long enough: Is %d, expected %d\" % (len(sub_),(maxnum_)))\n if isinstance(sub_,numpy.ndarray) and not sub_.flags.writeable:\n raise ValueError(\"Argument sub must be writable\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _weights_minlength = (maxnum_)\n if (maxnum_) > 0 and weights_ is not None and len(weights_) != (maxnum_):\n raise ValueError(\"Array argument weights is not long enough: Is %d, expected %d\" % (len(weights_),(maxnum_)))\n if isinstance(weights_,numpy.ndarray) and not weights_.flags.writeable:\n raise ValueError(\"Argument weights must be writable\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_getbarcidx(self.__nativep,idx_,maxnum_,ctypes.byref(j_),ctypes.byref(num_),_sub_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n j_ = j_.value\n _j_return_value = j_\n num_ = num_.value\n _num_return_value = num_\n if _sub_copyarray:\n sub_[:] = _sub_np_tmp\n if _weights_copyarray:\n weights_[:] = _weights_np_tmp\n return (_j_return_value,_num_return_value)","def getbarcidx(self,idx_,sub,weights): # 3\n maxnum_ = self.getbarcidxinfo((idx_))\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n _copyback_sub = False\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n _copyback_sub = True\n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n _copyback_sub = True\n if sub_ is not None and len(sub_) != (maxnum_):\n raise ValueError(\"Array argument sub has wrong length\")\n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n _copyback_weights = False\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n _copyback_weights = True\n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n _copyback_weights = True\n if weights_ is not None and len(weights_) != (maxnum_):\n raise ValueError(\"Array argument weights has wrong length\")\n res,resargs = self.__obj.getbarcidx(idx_,maxnum_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _j_return_value,_num_return_value = resargs\n if _copyback_weights:\n weights[:] = _tmparr_weights\n if _copyback_sub:\n sub[:] = _tmparr_sub\n return _j_return_value,_num_return_value","def fixB(self,i,j,value):\n if self.coeffPattern[1] == None:\n m,n=self.m,self.n\n self.coeffPattern = [[None]*n for i in range(m)]\n self.coeffPattern[1][i][j]=value\n self._updateEstimatorSize(i)","def _bucket_setitem(self, j, k, v):\n pass","def putbarsj(self,whichsol_,j_,barsj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barsj is None: raise TypeError(\"Invalid type for argument barsj\")\n if barsj is None:\n barsj_ = None\n else:\n try:\n barsj_ = memoryview(barsj)\n except TypeError:\n try:\n _tmparr_barsj = array.array(\"d\",barsj)\n except TypeError:\n raise TypeError(\"Argument barsj has wrong type\")\n else:\n barsj_ = memoryview(_tmparr_barsj)\n \n else:\n if barsj_.format != \"d\":\n barsj_ = memoryview(array.array(\"d\",barsj))\n \n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj has wrong length\")\n res = self.__obj.putbarsj(whichsol_,j_,barsj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getbarxj(self,whichsol_,j_,barxj_):\n _barxj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj is not long enough: Is %d, expected %d\" % (len(barxj_),self.getlenbarvarj((j_))))\n if isinstance(barxj_,numpy.ndarray) and not barxj_.flags.writeable:\n raise ValueError(\"Argument barxj must be writable\")\n if barxj_ is None:\n raise ValueError(\"Argument barxj may not be None\")\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\n _barxj_copyarray = False\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxj_ is not None:\n _barxj_copyarray = True\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\n _barxj_np_tmp[:] = barxj_\n assert _barxj_np_tmp.flags.contiguous\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxj_copyarray = False\n _barxj_tmp = None\n \n res = __library__.MSK_XX_getbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barxj_copyarray:\n barxj_[:] = _barxj_np_tmp","def putbararowlist(self,subi_,ptrb_,ptre_,subj_,nummat_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(ptrb_)\n elif num_ != len(ptrb_):\n raise IndexError(\"Inconsistent length of array ptrb\")\n if num_ is None:\n num_ = len(ptre_)\n elif num_ != len(ptre_):\n raise IndexError(\"Inconsistent length of array ptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if ptrb_ is None:\n raise ValueError(\"Argument ptrb cannot be None\")\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb may not be None\")\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\n _ptrb_copyarray = False\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptrb_ is not None:\n _ptrb_copyarray = True\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\n _ptrb_np_tmp[:] = ptrb_\n assert _ptrb_np_tmp.flags.contiguous\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptrb_copyarray = False\n _ptrb_tmp = None\n \n if ptre_ is None:\n raise ValueError(\"Argument ptre cannot be None\")\n if ptre_ is None:\n raise ValueError(\"Argument ptre may not be None\")\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\n _ptre_copyarray = False\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptre_ is not None:\n _ptre_copyarray = True\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\n _ptre_np_tmp[:] = ptre_\n assert _ptre_np_tmp.flags.contiguous\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptre_copyarray = False\n _ptre_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _nummat_minlength = len((subj_))\n if len((subj_)) > 0 and nummat_ is not None and len(nummat_) != len((subj_)):\n raise ValueError(\"Array argument nummat is not long enough: Is %d, expected %d\" % (len(nummat_),len((subj_))))\n if nummat_ is None:\n raise ValueError(\"Argument nummat cannot be None\")\n if nummat_ is None:\n raise ValueError(\"Argument nummat may not be None\")\n if isinstance(nummat_, numpy.ndarray) and nummat_.dtype is numpy.dtype(numpy.int64) and nummat_.flags.contiguous:\n _nummat_copyarray = False\n _nummat_tmp = ctypes.cast(nummat_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nummat_ is not None:\n _nummat_copyarray = True\n _nummat_np_tmp = numpy.zeros(len(nummat_),numpy.dtype(numpy.int64))\n _nummat_np_tmp[:] = nummat_\n assert _nummat_np_tmp.flags.contiguous\n _nummat_tmp = ctypes.cast(_nummat_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nummat_copyarray = False\n _nummat_tmp = None\n \n _matidx_minlength = sum((nummat_))\n if sum((nummat_)) > 0 and matidx_ is not None and len(matidx_) != sum((nummat_)):\n raise ValueError(\"Array argument matidx is not long enough: Is %d, expected %d\" % (len(matidx_),sum((nummat_))))\n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n _weights_minlength = sum((nummat_))\n if sum((nummat_)) > 0 and weights_ is not None and len(weights_) != sum((nummat_)):\n raise ValueError(\"Array argument weights is not long enough: Is %d, expected %d\" % (len(weights_),sum((nummat_))))\n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbararowlist(self.__nativep,num_,_subi_tmp,_ptrb_tmp,_ptre_tmp,_subj_tmp,_nummat_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if isinstance(barsj_,numpy.ndarray) and not barsj_.flags.writeable:\n raise ValueError(\"Argument barsj must be writable\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_getbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barsj_copyarray:\n barsj_[:] = _barsj_np_tmp","def getbarcidxj(self,idx_): # 3\n res,resargs = self.__obj.getbarcidxj(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _j_return_value = resargs\n return _j_return_value","def UBM_update_double_prime(self, j_ind, k_ind, alpha_1, alpha_2, beta_1, beta_2, Gamma, Lambda, Omega):\n #set nodeType to \"-1,1\"\n self.nodeType = \"-11\"\n\n\n lambda_entry = Lambda[0, 1]\n gamma_entry = Gamma[0, 1]\n #two new hidden nodes\n self.n_h += 2\n\n #store old parameters\n old_b_v = np.copy(self.b_v)\n old_b_h = np.copy(self.b_h)\n\n old_w_hv = np.copy(self.w_hv)\n\n\n #update visible biases\n self.b_v[j_ind] = alpha_1\n self.b_v[k_ind] = alpha_2\n\n #update hidden biases\n self.b_h = np.zeros(self.n_h, dtype=complex)\n self.b_h[:-2] = old_b_h\n self.b_h[-2] = beta_1 + old_b_v[j_ind]\n self.b_h[-1] = beta_2 + old_b_v[k_ind]\n\n #update weight_matrix\n \"here could be the reason why successiive 2-qubit gates don't work! \"\n if hasattr(self, \"updated\"):\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\n\n self.w_hv[:-2, :j_ind] = old_w_hv[:, :j_ind]\n self.w_hv[:-2, j_ind+1:k_ind] = old_w_hv[:, j_ind+1:k_ind]\n self.w_hv[:-2, k_ind+1:] = old_w_hv[:, k_ind+1:]\n\n self.w_hv[-2, :] = self.w_Z[j_ind, :]\n self.w_hv[-1, :] = self.w_Z[k_ind, :]\n #replace the Z-entries by unitary parameters\n self.w_hv[-2, j_ind] = Omega[0, 0]\n self.w_hv[-1, j_ind] = Omega[1, 0]\n self.w_hv[-2, k_ind] = Omega[0, 1]\n self.w_hv[-1, k_ind] = Omega[1, 1]\n else:\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\n self.w_hv[:-2, :j_ind] = old_w_hv[:, :j_ind]\n self.w_hv[:-2, j_ind+1:k_ind] = old_w_hv[:, j_ind+1:k_ind]\n self.w_hv[:-2, k_ind+1:] = old_w_hv[:, k_ind+1:]\n self.w_hv[-2, j_ind] = Omega[0, 0]\n self.w_hv[-1, j_ind] = Omega[1, 0]\n self.w_hv[-2, k_ind] = Omega[0, 1]\n self.w_hv[-1, k_ind] = Omega[1, 1]\n\n\n\n\n\n if hasattr(self, \"updated\"):\n print(\"already updated\")\n old_w_X = np.copy(self.w_X)\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\n self.w_X[-2, :-2] = old_w_hv[:, j_ind].T\n self.w_X[-1, :-2] = old_w_hv[:, k_ind].T\n self.w_X[:-2, -2] = old_w_hv[:, j_ind]\n self.w_X[:-2, -1] = old_w_hv[:, k_ind]\n self.w_X[-1, -2] = gamma_entry + self.w_Z[j_ind, k_ind]\n self.w_X[-2, -1] = gamma_entry + self.w_Z[j_ind, k_ind]\n self.w_X[:-2, :-2] = old_w_X\n\n\n self.w_Z[j_ind, k_ind] = lambda_entry\n self.w_Z[k_ind, j_ind] = lambda_entry\n\n\n else:\n print(\"First RBM update \")\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\n self.w_X[-2, :-2] = old_w_hv[:, j_ind].T\n self.w_X[-1, :-2] = old_w_hv[:, k_ind].T\n self.w_X[:-2, -2] = old_w_hv[:, j_ind]\n self.w_X[:-2, -1] = old_w_hv[:, k_ind]\n self.w_X[-1, -2] = gamma_entry\n self.w_X[-2, -1] = gamma_entry\n\n\n self.w_Z = np.zeros((self.n_v, self.n_v), dtype=complex)\n self.w_Z[j_ind, k_ind] = lambda_entry\n self.w_Z[k_ind, j_ind] = lambda_entry\n\n\n\n\n self.updated = True","def updateWeights(self,weightUpdate):\n\t\n\t\tbranches = self.collectAllBranches()\n\n\t\tfor i in range(self.nBranches):\n\n\t\t\tbranches[i].weight -= weightUpdate[i]","def _bucket_setitem(self, j, k, v):\n if self._table[j] is None:\n self._table[j] = UnsortedTableMap() # create new bucket at index j\n oldSize = len(self._table[j])\n self._table[j][k] = v\n if len(self._table[j]) > oldSize: # key is new to the table\n self._n += 1","def reassignWeights(self,weights):\n\t\n\t\tbranches = self.collectAllBranches()\n\n\t\tfor i in range(self.nBranches):\n\n\t\t\tbranches[i].weight = weights[i]","def UBM_update_double(self, j_ind, k_ind, alpha_1, alpha_2, beta_1, beta_2, Gamma, Lambda, Omega):\n #set nodeType to \"-1,1\"\n self.nodeType = \"-11\"\n\n lambda_entry = Lambda[0, 1]\n gamma_entry = Gamma[0, 1]\n #two new hidden nodes\n self.n_h += 2\n\n #store old parameters\n old_biases_v = np.copy(self.b_v)\n old_b_h = np.copy(self.b_h)\n\n old_weights_h = np.copy(self.w_hv)\n\n #update visible biases\n self.b_v[j_ind] = alpha_1\n self.b_v[k_ind] = alpha_2\n\n #update hidden biases\n self.b_h = np.zeros(self.n_h, dtype=complex)\n self.b_h[:-2] = old_b_h\n self.b_h[-2] = beta_1 + old_biases_v[j_ind]\n self.b_h[-1] = beta_2 + old_biases_v[k_ind]\n\n #update weight_matrix\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\n self.w_hv[:-2, :j_ind] = old_weights_h[:, :j_ind]\n self.w_hv[:-2, j_ind+1:k_ind] = old_weights_h[:, j_ind+1:k_ind]\n self.w_hv[:-2, k_ind+1:] = old_weights_h[:, k_ind+1:]\n self.w_hv[-2, j_ind] = Omega[0, 0]\n self.w_hv[-1, j_ind] = Omega[1, 0]\n self.w_hv[-2, k_ind] = Omega[0, 1]\n self.w_hv[-1, k_ind] = Omega[1, 1]\n\n #introduce X (=h-h matrix)\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\n self.w_X[-2, :-2] = old_weights_h[:, j_ind].T\n self.w_X[-1, :-2] = old_weights_h[:, k_ind].T\n self.w_X[:-2, -2] = old_weights_h[:, j_ind]\n self.w_X[:-2, -1] = old_weights_h[:, k_ind]\n self.w_X[-1, -2] = gamma_entry\n self.w_X[-2, -1] = gamma_entry\n\n #introduce Y (=v-v matrix)\n self.w_Z = np.zeros((self.n_v, self.n_v), dtype=complex)\n self.w_Z[j_ind, k_ind] = lambda_entry\n self.w_Z[k_ind, j_ind] = lambda_entry","def getbarcsparsity(self,idxj_):\n maxnumnz_ = self.getnumbarcnz()\n numnz_ = ctypes.c_int64()\n _idxj_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and idxj_ is not None and len(idxj_) != (maxnumnz_):\n raise ValueError(\"Array argument idxj is not long enough: Is %d, expected %d\" % (len(idxj_),(maxnumnz_)))\n if isinstance(idxj_,numpy.ndarray) and not idxj_.flags.writeable:\n raise ValueError(\"Argument idxj must be writable\")\n if idxj_ is None:\n raise ValueError(\"Argument idxj may not be None\")\n if isinstance(idxj_, numpy.ndarray) and idxj_.dtype is numpy.dtype(numpy.int64) and idxj_.flags.contiguous:\n _idxj_copyarray = False\n _idxj_tmp = ctypes.cast(idxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif idxj_ is not None:\n _idxj_copyarray = True\n _idxj_np_tmp = numpy.zeros(len(idxj_),numpy.dtype(numpy.int64))\n _idxj_np_tmp[:] = idxj_\n assert _idxj_np_tmp.flags.contiguous\n _idxj_tmp = ctypes.cast(_idxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _idxj_copyarray = False\n _idxj_tmp = None\n \n res = __library__.MSK_XX_getbarcsparsity(self.__nativep,maxnumnz_,ctypes.byref(numnz_),_idxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numnz_ = numnz_.value\n _numnz_return_value = numnz_\n if _idxj_copyarray:\n idxj_[:] = _idxj_np_tmp\n return (_numnz_return_value)","def updateBar(self):\n pass","def getbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n _copyback_barxj = False\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n _copyback_barxj = True\n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n _copyback_barxj = True\n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.getbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_barxj:\n barxj[:] = _tmparr_barxj","def getbarsj(self,whichsol_,j_,barsj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barsj is None: raise TypeError(\"Invalid type for argument barsj\")\n _copyback_barsj = False\n if barsj is None:\n barsj_ = None\n else:\n try:\n barsj_ = memoryview(barsj)\n except TypeError:\n try:\n _tmparr_barsj = array.array(\"d\",barsj)\n except TypeError:\n raise TypeError(\"Argument barsj has wrong type\")\n else:\n barsj_ = memoryview(_tmparr_barsj)\n _copyback_barsj = True\n else:\n if barsj_.format != \"d\":\n barsj_ = memoryview(array.array(\"d\",barsj))\n _copyback_barsj = True\n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj has wrong length\")\n res = self.__obj.getbarsj(whichsol_,j_,barsj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_barsj:\n barsj[:] = _tmparr_barsj","def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valjkl_minlength = (num_)\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl is not long enough: Is %d, expected %d\" % (len(valjkl_),(num_)))\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl cannot be None\")\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl may not be None\")\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\n _valjkl_copyarray = False\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valjkl_ is not None:\n _valjkl_copyarray = True\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\n _valjkl_np_tmp[:] = valjkl_\n assert _valjkl_np_tmp.flags.contiguous\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valjkl_copyarray = False\n _valjkl_tmp = None\n \n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def update_bar(self, bar):\n self.count += 1\n if not self.inited and self.count >= self.size:\n self.inited = True\n\n self.open_array[:-1] = self.open_array[1:]\n self.high_array[:-1] = self.high_array[1:]\n self.low_array[:-1] = self.low_array[1:]\n self.close_array[:-1] = self.close_array[1:]\n self.volume_array[:-1] = self.volume_array[1:]\n self.time_array[:-1] = self.time_array[1:]\n self.extra_array[:-1] = self.extra_array[1:]\n self.range_array[:-1] = self.range_array[1:]\n\n self.open_array[-1] = bar.open_price\n self.high_array[-1] = bar.high_price\n self.low_array[-1] = bar.low_price\n self.close_array[-1] = bar.close_price\n self.volume_array[-1] = bar.volume\n self.time_array[-1] = bar.datetime\n self.extra_array[-1] = {\"pattern\":[]}\n if self.count > 1:\n self.range_array[:-1] = self.range_array[1:]\n self.range_array[-1] = round(self.close_array[-1] / self.close_array[-2] - 1, 6)\n else:\n self.range_array[-1] = 0","def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n \n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n \n if subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n \n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n \n if subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valjkl is None: raise TypeError(\"Invalid type for argument valjkl\")\n if valjkl is None:\n valjkl_ = None\n else:\n try:\n valjkl_ = memoryview(valjkl)\n except TypeError:\n try:\n _tmparr_valjkl = array.array(\"d\",valjkl)\n except TypeError:\n raise TypeError(\"Argument valjkl has wrong type\")\n else:\n valjkl_ = memoryview(_tmparr_valjkl)\n \n else:\n if valjkl_.format != \"d\":\n valjkl_ = memoryview(array.array(\"d\",valjkl))\n \n if valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl has wrong length\")\n res = self.__obj.putbarcblocktriplet(num_,subj_,subk_,subl_,valjkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getbarcidxj(self,idx_):\n j_ = ctypes.c_int32()\n res = __library__.MSK_XX_getbarcidxj(self.__nativep,idx_,ctypes.byref(j_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n j_ = j_.value\n _j_return_value = j_\n return (_j_return_value)","def refine(self, ijk):\n if self.cbc is None or not self.sub_block_count:\n raise ValueError(\n \"Cannot refine sub block model without specifying number \"\n \"of parent and sub blocks\"\n )\n try:\n inds = self.ijk_array_to_indices(ijk)\n except ValueError:\n inds = self.ijk_to_index(ijk)\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137","def __setitem__(self, j, val):\n\t\tself._coords[j] = val","def getbarcsparsity(self,idxj): # 3\n maxnumnz_ = self.getnumbarcnz()\n if idxj is None: raise TypeError(\"Invalid type for argument idxj\")\n _copyback_idxj = False\n if idxj is None:\n idxj_ = None\n else:\n try:\n idxj_ = memoryview(idxj)\n except TypeError:\n try:\n _tmparr_idxj = array.array(\"q\",idxj)\n except TypeError:\n raise TypeError(\"Argument idxj has wrong type\")\n else:\n idxj_ = memoryview(_tmparr_idxj)\n _copyback_idxj = True\n else:\n if idxj_.format != \"q\":\n idxj_ = memoryview(array.array(\"q\",idxj))\n _copyback_idxj = True\n if idxj_ is not None and len(idxj_) != (maxnumnz_):\n raise ValueError(\"Array argument idxj has wrong length\")\n res,resargs = self.__obj.getbarcsparsity(maxnumnz_,idxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n if _copyback_idxj:\n idxj[:] = _tmparr_idxj\n return _numnz_return_value","def change_weight(self, new_weight_arr):\n self.weights = new_weight_arr","def cbar( self , y , t = 0 ):\n \n u = self.c( self.zbar, y , self.rbar , t )\n \n return u","def put_B_reduced(self, dest):\n _parallel.call_from_rank_zero(self.put_mat, self.B_reduced, dest)\n _parallel.barrier()","def test_weight_based_ties(self, dim):\r\n graph = nx.barbell_graph(dim, 0)\r\n subgraph = graph.nodes()\r\n weights = [1] * dim + [2] * dim\r\n\r\n c = clique.shrink(subgraph, graph, node_select=weights)\r\n assert c == list(range(dim, 2 * dim))","def update_weight(self,ctr,new_weight):\n self.sum1 -= self.data_set[ctr].weight\n self.data_set[ctr].weight = new_weight\n self.sum1 += new_weight","def set_bcol(self, bond):\n i, j = self.blist[bond]\n c = self.colors[bond]\n #logger.debug(\"bond from %3d to %3d : color %3d\" % (i, j, c) )\n #logger.debug(\"i_conn: %s\" % repr(self.conn[i]) )\n #logger.debug(\"j_conn: %s\" % repr(self.conn[j]) )\n ### set for i\n ### BUG FOR 2x2x2 pcu: a vertex connects twice to the same vertex!!!\n ### TBI: add pconn for topos\n j_ind = self.conn[i].index(j)\n i_ind = self.conn[j].index(i)\n #logger.debug(\"i_ind %6d\" % i_ind)\n #logger.debug(\"j_ind %6d\" % j_ind)\n self.bcolors[i][j_ind] = c\n self.bcolors[j][i_ind] = c\n return","def jacobian(self, c):\n\n raise NotImplementedError","def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def update_weights(self):\n\t\tpass","def fixA(self,i,j,value):\n if self.coeffPattern[0] == None:\n m,n=self.m,self.n\n self.coeffPattern[0] = [[None]*m for i in range(m)]\n self.coeffPattern[0][i][j]=value\n self._updateEstimatorSize(i)","def cb_update(val):\n alpha_update = [sAlpha0.val, sAlpha1.val, sAlpha2.val]\n\n # update Dirichlet's parameters alpha\n dirichlet.set_param(alpha_update)\n draw_pdf_contours(axDirichlet, dirichlet, True) # Draw Dirichlet\n\n # MAP\n lambda_MAP = CatMAP.MAPinfer(x_cat, dirichlet)\n axMAP.cla()\n drawBarGraph( axMAP, \"MAP\", lambda_MAP, bar_y_max, col_MAP ) # Draw Bar graph\n\n # Bayes\n posteriorDirichlet.set_param(alpha_update)\n posteriorDirichlet.calcPosterior(x_cat)\n draw_pdf_contours(axPosteriorDirichlet, posteriorDirichlet) # Draw Posterior Dirichlet\n lambda_Bayes = np.zeros(3)\n for k in range(3):\n lambda_Bayes[k] = posteriorDirichlet.BayesInfer(k)\n\n axBayes.cla()\n drawBarGraph( axBayes, \"Bayes\", lambda_Bayes, bar_y_max, col_Bayes ) # Draw Bar graph\n\n print('Update')\n print('lambda_ML =', lambda_ML)\n print('lambda_MAP =', lambda_MAP)\n print('lambda_Bayes=', lambda_Bayes)\n draw_point(axDirichlet, lambda_ML, col_ML)\n draw_point(axDirichlet, lambda_MAP, col_MAP)\n draw_point(axDirichlet, lambda_Bayes, col_Bayes)\n draw_point(axPosteriorDirichlet, lambda_MAP, col_MAP)\n draw_point(axPosteriorDirichlet, lambda_Bayes, col_Bayes)\n\n fig.canvas.draw_idle()","def reduceBin(bin, size, binLabel):\n print(\"reducing bin [\" + str(binLabel) + \"] (size: \" + str(len(bin)) + \")\")\n np.random.shuffle(bin)\n chosenImages = bin[:size]\n newRatings = open(new_ratings_file_path, 'a')\n for image in chosenImages:\n newRatings.write(getRatingsLine(image[0], image[1]))\n newRatings.close()","def merge_bowl(self):\n self.B += self.Bowl_bS\n self.Bc += self.Bowl_bC\n self.W += self.Bowl_WS\n self.Wc += self.Bowl_WS","def getbarcblocktriplet(self,subj,subk,subl,valjkl): # 3\n maxnum_ = self.getnumbarcblocktriplets()\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxnum_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n _copyback_subk = False\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n _copyback_subk = True\n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n _copyback_subk = True\n if subk_ is not None and len(subk_) != (maxnum_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n _copyback_subl = False\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n _copyback_subl = True\n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n _copyback_subl = True\n if subl_ is not None and len(subl_) != (maxnum_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valjkl is None: raise TypeError(\"Invalid type for argument valjkl\")\n _copyback_valjkl = False\n if valjkl is None:\n valjkl_ = None\n else:\n try:\n valjkl_ = memoryview(valjkl)\n except TypeError:\n try:\n _tmparr_valjkl = array.array(\"d\",valjkl)\n except TypeError:\n raise TypeError(\"Argument valjkl has wrong type\")\n else:\n valjkl_ = memoryview(_tmparr_valjkl)\n _copyback_valjkl = True\n else:\n if valjkl_.format != \"d\":\n valjkl_ = memoryview(array.array(\"d\",valjkl))\n _copyback_valjkl = True\n if valjkl_ is not None and len(valjkl_) != (maxnum_):\n raise ValueError(\"Array argument valjkl has wrong length\")\n res,resargs = self.__obj.getbarcblocktriplet(maxnum_,subj_,subk_,subl_,valjkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n if _copyback_valjkl:\n valjkl[:] = _tmparr_valjkl\n if _copyback_subl:\n subl[:] = _tmparr_subl\n if _copyback_subk:\n subk[:] = _tmparr_subk\n if _copyback_subj:\n subj[:] = _tmparr_subj\n return _num_return_value","def substitute_cost(self, i, j):\n raise NotImplementedError","def update_bars(self):\n raise NotImplementedError(\"Should implement update_bars()\")","def wire(self):\n self.J[self.size[0]//2, self.size[1]//2, :] = 1.0 / self.size[2]","def __update_jacobian(self, x, F):\n old_err = _n.seterr(divide='raise')\n\n try:\n y = F - self.F\n s = x - self.x\n\n zt = None\n if self.update_type == BroydenSolver.UPDATE_ICUM:\n maxi = abs(_n.ravel(y)).argmax()\n zt = _n.transpose(_n.zeros((1,self.n), _n.float_))\n zt[0, maxi] = 1\n elif self.update_type == BroydenSolver.UPDATE_GOOD_BROYDEN:\n # (Good) Broyden update\n zt = _n.dot(_n.transpose(s), self.H)\n elif self.update_type == BroydenSolver.UPDATE_BAD_BROYDEN:\n # (Bad) Broyden update\n zt = _n.transpose(y)\n else:\n raise ValueError(\"Unknown update type %s\" % (self.update_type))\n\n self.H = self.H \\\n + _n.dot(s - _n.dot(self.H, y), zt) / _n.dot(zt, y)\n except FloatingPointError:\n warnings.warn(\"%% Broyden reset: singular\", BroydenWarning)\n self.H = _n.identity(self.n) / self.initial_scale\n\n _n.seterr(**old_err)","def convertJacobiToBeta(self,x):\n u = 0.5*(self.high+self.low)\n s = 0.5*(self.high-self.low)\n return s*x+u","def cbar( self , y , t = 0 ):\n r = self.t2r( t )\n u = self.c( y , r , t )\n \n return u","def SetPRBinCatConstraint( self, model ) :\n tot = np.dot( self.wish.T, self.dispo )\n for val in tot :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def resort_couplings(J,sortIx):\n return","def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def jacobian(self, x):\n pass","def setitem(self, i, j, value):\n # XXX: flint matrices do not support negative indices\n # XXX: They also raise ValueError instead of IndexError\n m, n = self.shape\n if i < 0:\n i += m\n if j < 0:\n j += n\n try:\n self.rep[i, j] = value\n except ValueError:\n raise IndexError(f\"Invalid indices ({i}, {j}) for Matrix of shape {self.shape}\")","def putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_):\n _subi_minlength = (num_)\n if (num_) > 0 and subi_ is not None and len(subi_) != (num_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(num_)))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valijkl_minlength = (num_)\n if (num_) > 0 and valijkl_ is not None and len(valijkl_) != (num_):\n raise ValueError(\"Array argument valijkl is not long enough: Is %d, expected %d\" % (len(valijkl_),(num_)))\n if valijkl_ is None:\n raise ValueError(\"Argument valijkl cannot be None\")\n if valijkl_ is None:\n raise ValueError(\"Argument valijkl may not be None\")\n if isinstance(valijkl_, numpy.ndarray) and valijkl_.dtype is numpy.dtype(numpy.float64) and valijkl_.flags.contiguous:\n _valijkl_copyarray = False\n _valijkl_tmp = ctypes.cast(valijkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valijkl_ is not None:\n _valijkl_copyarray = True\n _valijkl_np_tmp = numpy.zeros(len(valijkl_),numpy.dtype(numpy.float64))\n _valijkl_np_tmp[:] = valijkl_\n assert _valijkl_np_tmp.flags.contiguous\n _valijkl_tmp = ctypes.cast(_valijkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valijkl_copyarray = False\n _valijkl_tmp = None\n \n res = __library__.MSK_XX_putbarablocktriplet(self.__nativep,num_,_subi_tmp,_subj_tmp,_subk_tmp,_subl_tmp,_valijkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_bc(self, problem):\n bcs = problem.bcs\n n_bound = cfg.const['N_GHOST_CELLS']\n # Left X-b.c.\n for i in range(0, self.i_min):\n for j in range(self.j_min, self.j_max):\n for k in range(self.k_min, self.k_max): \n if bcs[0] == 't': \n self.U[i][j][k] = self.U[self.i_min][j][k]\n elif bcs[0] == 'w':\n for num in [0, 2, 3, 4]: # 0 -> 3, 1 -> 2, i_min-1 -> i_min, i_min-2 -> i_min+1\n self.U[i][j][k][num] = self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\n for num in [1]:\n self.U[i][j][k][num] = - self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\n else:\n print(\"Errof field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Right X-b.c.\n for i in range(self.i_max, self.i_max+n_bound):\n for j in range(self.j_min, self.j_max):\n for k in range(self.k_min, self.k_max): \n if bcs[1] == 't':\n self.U[i][j][k] = self.U[self.i_max-1][j][k]\n elif bcs[1] == 'w':\n for num in [0, 2, 3, 4]: # i_max -> i_max-1 , i_max+1-> i_max-2\n self.U[i][j][k][num] = self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\n for num in [1]:\n self.U[i][j][k][num] = - self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Left Y-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(0, self.j_min):\n for k in range(self.k_min, self.k_max): \n if bcs[2] == 't':\n self.U[i][j][k] = self.U[i][self.j_min][k]\n elif bcs[2] == 'w':\n for num in [0, 1, 3, 4]:\n self.U[i][j][k][num] = self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\n for num in [2]:\n self.U[i][j][k][num] = - self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Right Y-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(self.j_max, self.j_max+n_bound):\n for k in range(self.k_min, self.k_max): \n if bcs[3] == 't':\n self.U[i][j][k] = self.U[i][self.j_max-1][k]\n elif bcs[3] == 'w':\n for num in [0, 1, 3, 4]:\n self.U[i][j][k][num] = self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\n for num in [2]:\n self.U[i][j][k][num] = -self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Left Z-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(0, self.j_max+n_bound):\n for k in range(0, self.k_min): \n if bcs[4] == 't':\n self.U[i][j][k] = self.U[i][j][self.k_min]\n elif bcs[4] == 'w':\n for num in [0, 1, 2, 4]:\n self.U[i][j][k][num] = self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\n for num in [3]:\n self.U[i][j][k][num] = - self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")\n # Right Z-b.c.\n for i in range(0, self.i_max+n_bound):\n for j in range(0, self.j_max+n_bound):\n for k in range(self.k_max, self.k_max+n_bound):\n if bcs[5] == 't':\n self.U[i][j][k] = self.U[i][j][self.k_max-1]\n elif bcs[5] == 'w':\n for num in [0, 1, 2, 4]:\n self.U[i][j][k][num] = self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\n for num in [3]:\n self.U[i][j][k][num] = - self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\n else:\n print(\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\")","def doublec(self, j):\n\t\tif j < (self.k0 + 1):\n\t\t\treturn 0\n\t\tif (self.b[j] != self.b[j-1]):\n\t\t\treturn 0\n\t\treturn self.cons(j)","def update(self, i, v):\n # index in BTree is 1 more than index in arr[]\n i += 1\n\n # Traverse to ancestors of BITree[i]\n while i <= self.size:\n self.BITree[i] += v\n\n # Update index to next set bit in binary representation\n i += i & (-i)","def compute_effective_nbar(cat):\n # the total weight\n total_weight = cat['Weight']*cat['FKPWeight']\n\n # effective nbar\n nbar = (total_weight*cat['NZ']).sum() / total_weight.sum()\n\n return cat.compute(nbar)","def _bucket_getitem(self, j, k):\n pass","def bar(self):\n return self.val * 1.0","def _bron_kerbosch(self, acc, possible, excluded, cliques):\n if len(possible) == 0 and len(excluded) == 0:\n cliques.append(acc)\n for v in iter(possible):\n neighbors_of_v = self.neighbors(v)\n self._bron_kerbosch(\n acc.union({v}),\n possible.intersection(neighbors_of_v),\n excluded.intersection(neighbors_of_v),\n cliques)\n possible = possible.difference({v})\n excluded = excluded.union({v})","def histogram(original, updated, bins=None, main=\"\", save=None, log=False):\n #Lengths of score lists must be identical, assume in same order\n assert len(original) == len(original)\n\n #Set up bins:\n if bins is not None and bins > 0:\n imoprovements = {(-1,-1):0}\n for i in xrange(0, len(original), bins):\n improvements[(0,i+bins)] = 0\n else:\n improvements = {(-1,-1):0, (-5,0):0, (0,1):0, (1,25):0, (25,50):0, (50,75):0, (75,100):0, (100,125):0, (125,150):0, (150,200):0, (200,300):0, (300,400):0, (500,10000):0} #defaultdict(int)\n \n #Calcualte improvements\n for o, u in izip(original, updated):\n if o>u: \n improvements[(-1,-1)] += 1\n continue\n for lower, upper in improvements:\n if lower <= int(u-o) < upper:\n improvements[(lower,upper)] += 1\n break\n keys = sorted(improvements.keys(), key=lambda x:x[0])\n values = [improvements[r] for r in keys]\n\n fig, ax = plt.subplots()\n ax.set_title(main)\n ax.set_xlabel(\"Improvement (updated-original) bitscores\")\n ax.set_ylabel(\"log(Frequency)\")\n #ax.set_yscale('log')\n\n width = 1.0\n #ax.set_xticks(np.arange(len(improvements)))\n #ax.set_xticklabels([l for l, u in keys])\n bar(ax, np.arange(len(improvements)), values, log=log,\n annotate=True, grid='y', xticklabels=[l for l, u in keys])\n\n if save is None:\n plt.show()\n else:\n plt.savefig(save)","def putacol(self,j_,subj,valj): # 3\n nzj_ = None\n if nzj_ is None:\n nzj_ = len(subj)\n elif nzj_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nzj_ is None:\n nzj_ = len(valj)\n elif nzj_ != len(valj):\n raise IndexError(\"Inconsistent length of array valj\")\n if nzj_ is None: nzj_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valj is None: raise TypeError(\"Invalid type for argument valj\")\n if valj is None:\n valj_ = None\n else:\n try:\n valj_ = memoryview(valj)\n except TypeError:\n try:\n _tmparr_valj = array.array(\"d\",valj)\n except TypeError:\n raise TypeError(\"Argument valj has wrong type\")\n else:\n valj_ = memoryview(_tmparr_valj)\n \n else:\n if valj_.format != \"d\":\n valj_ = memoryview(array.array(\"d\",valj))\n \n res = self.__obj.putacol(j_,nzj_,subj_,valj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putacol(self,j_,subj_,valj_):\n nzj_ = None\n if nzj_ is None:\n nzj_ = len(subj_)\n elif nzj_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nzj_ is None:\n nzj_ = len(valj_)\n elif nzj_ != len(valj_):\n raise IndexError(\"Inconsistent length of array valj\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valj_ is None:\n raise ValueError(\"Argument valj cannot be None\")\n if valj_ is None:\n raise ValueError(\"Argument valj may not be None\")\n if isinstance(valj_, numpy.ndarray) and valj_.dtype is numpy.dtype(numpy.float64) and valj_.flags.contiguous:\n _valj_copyarray = False\n _valj_tmp = ctypes.cast(valj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valj_ is not None:\n _valj_copyarray = True\n _valj_np_tmp = numpy.zeros(len(valj_),numpy.dtype(numpy.float64))\n _valj_np_tmp[:] = valj_\n assert _valj_np_tmp.flags.contiguous\n _valj_tmp = ctypes.cast(_valj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valj_copyarray = False\n _valj_tmp = None\n \n res = __library__.MSK_XX_putacol(self.__nativep,j_,nzj_,_subj_tmp,_valj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_j(cmd, limb, joints, index, delta):\n joint = joints[index]\n cmd[joint] = delta + limb.joint_angle(joint)","def set_jbcol_from_ibcol(self, vert, bcol, set_arg=False):\n iconn = self.conn[vert]\n for i,iatom in enumerate(iconn):\n j = self.conn[iatom].index(vert)\n self.bcolors[iatom][j] = int(bcol[i])\n if set_arg: self.bcolors[vert] = list(bcol)\n return","def __add_boundary_contrib_prediction(self, bc, b_idx):\n if bc is not None:\n if bc.boundary_condition_type is configuration.BoundaryConditionType.DIRICHLET:\n self.ustar[b_idx] = bc.value(self.time)\n else:\n self.ustar[b_idx] += self.timestep * self.timestep * bc.value(self.time)","def update_M_B(Bt, M, B):\n n,_ = Bt.shape\n for i in range(n):\n g = np.where(Bt[i,:]==1)[0][0]\n # print(\"g=\", g)\n M.remove(g)\n B[i].add(g)\n return M, B","def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\n self.coeff[self._core.index_alpha(key[0]),\n self._core.index_beta(key[1])] = value","def putbarablocktriplet(self,num_,subi,subj,subk,subl,valijkl): # 3\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subi_ is not None and len(subi_) != (num_):\n raise ValueError(\"Array argument subi has wrong length\")\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n \n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n \n if subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n \n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n \n if subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valijkl is None: raise TypeError(\"Invalid type for argument valijkl\")\n if valijkl is None:\n valijkl_ = None\n else:\n try:\n valijkl_ = memoryview(valijkl)\n except TypeError:\n try:\n _tmparr_valijkl = array.array(\"d\",valijkl)\n except TypeError:\n raise TypeError(\"Argument valijkl has wrong type\")\n else:\n valijkl_ = memoryview(_tmparr_valijkl)\n \n else:\n if valijkl_.format != \"d\":\n valijkl_ = memoryview(array.array(\"d\",valijkl))\n \n if valijkl_ is not None and len(valijkl_) != (num_):\n raise ValueError(\"Array argument valijkl has wrong length\")\n res = self.__obj.putbarablocktriplet(num_,subi_,subj_,subk_,subl_,valijkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getbarcblocktriplet(self,subj_,subk_,subl_,valjkl_):\n maxnum_ = self.getnumbarcblocktriplets()\n num_ = ctypes.c_int64()\n _subj_minlength = (maxnum_)\n if (maxnum_) > 0 and subj_ is not None and len(subj_) != (maxnum_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnum_)))\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\n raise ValueError(\"Argument subj must be writable\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (maxnum_)\n if (maxnum_) > 0 and subk_ is not None and len(subk_) != (maxnum_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(maxnum_)))\n if isinstance(subk_,numpy.ndarray) and not subk_.flags.writeable:\n raise ValueError(\"Argument subk must be writable\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (maxnum_)\n if (maxnum_) > 0 and subl_ is not None and len(subl_) != (maxnum_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(maxnum_)))\n if isinstance(subl_,numpy.ndarray) and not subl_.flags.writeable:\n raise ValueError(\"Argument subl must be writable\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valjkl_minlength = (maxnum_)\n if (maxnum_) > 0 and valjkl_ is not None and len(valjkl_) != (maxnum_):\n raise ValueError(\"Array argument valjkl is not long enough: Is %d, expected %d\" % (len(valjkl_),(maxnum_)))\n if isinstance(valjkl_,numpy.ndarray) and not valjkl_.flags.writeable:\n raise ValueError(\"Argument valjkl must be writable\")\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl may not be None\")\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\n _valjkl_copyarray = False\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valjkl_ is not None:\n _valjkl_copyarray = True\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\n _valjkl_np_tmp[:] = valjkl_\n assert _valjkl_np_tmp.flags.contiguous\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valjkl_copyarray = False\n _valjkl_tmp = None\n \n res = __library__.MSK_XX_getbarcblocktriplet(self.__nativep,maxnum_,ctypes.byref(num_),_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n num_ = num_.value\n _num_return_value = num_\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _subk_copyarray:\n subk_[:] = _subk_np_tmp\n if _subl_copyarray:\n subl_[:] = _subl_np_tmp\n if _valjkl_copyarray:\n valjkl_[:] = _valjkl_np_tmp\n return (_num_return_value)","def cut(self,cell):\r\n self.grid[cell[0]][cell[1]] = 1","def bic(self, X):\n raise NotImplementedError","def setSubtreeBF(self, index, subtree):\n if index == 0:\n try:\n self[:] = subtree\n except TypeError:\n del self[1:]\n self[0] = subtree\n return\n \n queue = deque(izip(repeat(self, len(self[1:])), count(1)))\n for i in xrange(index):\n elem = queue.popleft()\n parent = elem[0]\n child = elem[1]\n if isinstance(parent[child], Tree):\n tree = parent[child]\n queue.extend(izip(repeat(tree, len(tree[1:])), count(1)))\n parent[child] = subtree","def put_weights(self, content: ndarray, var_id: int, batch_no: int, block_id: int) -> None:\n pass","def ridge_cd_weight_update(\n x: FloatTensor,\n r: FloatTensor,\n j: int,\n w_j: Union[float, FloatTensor],\n col_l2: float,\n lmb: float,\n) -> float:\n if col_l2 == 0.0:\n return 0.0\n n, d = x.size()\n return w_j + (x[:, j].matmul(r) - n * lmb * w_j) / col_l2","def run(self):\n self.coffee_machine.beans_tank.decrease_weight(self.coffee_machine.chosen_coffee_data.get('beans_weight'))","def update_weight(self, error_matrix, i, j):\n #weight i,j gets error of j\n error = error_matrix[i]\n input = log_deriv(self.input_matrix[i-1][j])\n self.bias[i-1][j] += ALPHA * input * error_matrix[i-1][j]\n self.weights[i][j] = [weight + ALPHA * input * error[i] for i, weight in enumerate(self.weights[i][j])]","def plot_progression(weights, bhs, bvs):\n\tweights_plot = []\n\tfor i in range(40):\n\t\tweights_plot.append(weights[i][0][0])\t# only plots the first value in the matrix every time\n\tplt.plot(weights_plot)\n\n\tplt.show()","def __setitem__(self, key, item):\n assert isinstance(key,list) and isinstance(item,list) and len(key)==2 and len(item)==2\n self._data[self.__ptBin(key[0])][self.__etaBin(key[1])] = item","def changeBarriers(self, value):\r\n\t\tself.numBarriers = value","def modify_cell(self,c,**kws):\n if 'nodes' in kws and self._node_to_cells is not None:\n for n in self.cell_to_nodes(c):\n self._node_to_cells[n].remove(c)\n\n for k,v in six.iteritems(kws):\n if k in ('nodes','edges'):\n self.cells[k][c,:len(v)]=v\n self.cells[k][c,len(v):]=self.UNDEFINED\n else: \n self.cells[k][c]=v\n\n if 'nodes' in kws and self._node_to_cells is not None:\n for n in self.cell_to_nodes(c):\n self._node_to_cells[n].append(c)","def getbarablocktriplet(self,subi,subj,subk,subl,valijkl): # 3\n maxnum_ = self.getnumbarablocktriplets()\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxnum_):\n raise ValueError(\"Array argument subi has wrong length\")\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxnum_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n _copyback_subk = False\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n _copyback_subk = True\n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n _copyback_subk = True\n if subk_ is not None and len(subk_) != (maxnum_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n _copyback_subl = False\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n _copyback_subl = True\n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n _copyback_subl = True\n if subl_ is not None and len(subl_) != (maxnum_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valijkl is None: raise TypeError(\"Invalid type for argument valijkl\")\n _copyback_valijkl = False\n if valijkl is None:\n valijkl_ = None\n else:\n try:\n valijkl_ = memoryview(valijkl)\n except TypeError:\n try:\n _tmparr_valijkl = array.array(\"d\",valijkl)\n except TypeError:\n raise TypeError(\"Argument valijkl has wrong type\")\n else:\n valijkl_ = memoryview(_tmparr_valijkl)\n _copyback_valijkl = True\n else:\n if valijkl_.format != \"d\":\n valijkl_ = memoryview(array.array(\"d\",valijkl))\n _copyback_valijkl = True\n if valijkl_ is not None and len(valijkl_) != (maxnum_):\n raise ValueError(\"Array argument valijkl has wrong length\")\n res,resargs = self.__obj.getbarablocktriplet(maxnum_,subi_,subj_,subk_,subl_,valijkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n if _copyback_valijkl:\n valijkl[:] = _tmparr_valijkl\n if _copyback_subl:\n subl[:] = _tmparr_subl\n if _copyback_subk:\n subk[:] = _tmparr_subk\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi\n return _num_return_value","def putconbound(self,i_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_conjugates(self):\n \n # declare new dict\n self.new_dict = copy.deepcopy(self.dict)\n \n # iterate over items\n for i in range(len(self.dict)):\n for rel_tag, hopping in self.dict[i].items():\n x, y, z, j = rel_tag\n reverse_tag = (-x, -y, -z, i)\n reverse_hopping = np.conjugate(np.transpose(hopping))\n if reverse_tag not in self.new_dict[j]:\n self.new_dict[j][reverse_tag] = reverse_hopping\n \n # done\n self.dict = self.new_dict","def convertBetaToJacobi(self,y):\n u = 0.5*(self.high+self.low)\n s = 0.5*(self.high-self.low)\n return (y-u)/(s)","def test00b(self):\n a = np.arange(1e2)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n b[(1,)] = 10.\n a[(1,)] = 10.\n # print \"b->\", `b`\n assert_array_equal(a, b[:], \"__setitem__ not working correctly\")","def getbarasparsity(self,idxij): # 3\n maxnumnz_ = self.getnumbaranz()\n if idxij is None: raise TypeError(\"Invalid type for argument idxij\")\n _copyback_idxij = False\n if idxij is None:\n idxij_ = None\n else:\n try:\n idxij_ = memoryview(idxij)\n except TypeError:\n try:\n _tmparr_idxij = array.array(\"q\",idxij)\n except TypeError:\n raise TypeError(\"Argument idxij has wrong type\")\n else:\n idxij_ = memoryview(_tmparr_idxij)\n _copyback_idxij = True\n else:\n if idxij_.format != \"q\":\n idxij_ = memoryview(array.array(\"q\",idxij))\n _copyback_idxij = True\n if idxij_ is not None and len(idxij_) != (maxnumnz_):\n raise ValueError(\"Array argument idxij has wrong length\")\n res,resargs = self.__obj.getbarasparsity(maxnumnz_,idxij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n if _copyback_idxij:\n idxij[:] = _tmparr_idxij\n return _numnz_return_value","def update_bin(self, bin_i, conflict, sat_engine):\r\n global gen_debug_info\r\n gen_debug_info.cnt_update += 1\r\n logger.info('update_bin %d' % (bin_i + 1))\r\n logger.info('\\tcnt_update %d' % (gen_debug_info.cnt_update))\r\n\r\n # update var states，因为是引用的形式，所以不用更新\r\n # 只有当没有冲突时才更新，发生冲突的bin是unsat的，不需要\r\n # if conflict is False:\r\n # for i in xrange(sat_engine.local_vars.nv):\r\n # v = self.vars_bins[bin_i][i]\r\n # self.global_vs[v] = sat_engine.local_vars.vs[i]\r\n\r\n if conflict is False:\r\n if logger.level <= logging.INFO:\r\n logger.info(gen_debug_info.bin_clauses(\r\n bin_i,\r\n sat_engine,\r\n self.bin_packer.cbins[bin_i].variables))\r\n if logger.level <= logging.NOTSET:\r\n logger.info(gen_debug_info.bin_clauses_sv(sat_engine))","def update(self, values, weights):\n super().update(values)","def update(self, values, weights):\n super().update(values)","def update(self, values, weights):\n super().update(values)","def update(self, values, weights):\n super().update(values)","def __setitem__(self, choice, weight):\n if choice in self._weights and self._weights[choice] == weight:\n return\n\n self._weights[choice] = weight\n self._generate_p()","def jacobian(self,var,g=None):\n if (g==None):g=self.g\n jac=np.zeros([self.n+1,self.n])\n for i in range(self.n):\n for j in range(self.n):\n if(i==j): jac[i][j]=2.*(var[i]+1.)-g*np.sum([self.XXZ.Z(i,k) for k in range(self.n) if k!=i])\n else: jac[i][j]=g*self.XXZ.Z(i,j)\n for i in range(self.n):\n jac[self.n][i]=1.\n return jac","def getbarasparsity(self,idxij_):\n maxnumnz_ = self.getnumbaranz()\n numnz_ = ctypes.c_int64()\n _idxij_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and idxij_ is not None and len(idxij_) != (maxnumnz_):\n raise ValueError(\"Array argument idxij is not long enough: Is %d, expected %d\" % (len(idxij_),(maxnumnz_)))\n if isinstance(idxij_,numpy.ndarray) and not idxij_.flags.writeable:\n raise ValueError(\"Argument idxij must be writable\")\n if idxij_ is None:\n raise ValueError(\"Argument idxij may not be None\")\n if isinstance(idxij_, numpy.ndarray) and idxij_.dtype is numpy.dtype(numpy.int64) and idxij_.flags.contiguous:\n _idxij_copyarray = False\n _idxij_tmp = ctypes.cast(idxij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif idxij_ is not None:\n _idxij_copyarray = True\n _idxij_np_tmp = numpy.zeros(len(idxij_),numpy.dtype(numpy.int64))\n _idxij_np_tmp[:] = idxij_\n assert _idxij_np_tmp.flags.contiguous\n _idxij_tmp = ctypes.cast(_idxij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _idxij_copyarray = False\n _idxij_tmp = None\n \n res = __library__.MSK_XX_getbarasparsity(self.__nativep,maxnumnz_,ctypes.byref(numnz_),_idxij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numnz_ = numnz_.value\n _numnz_return_value = numnz_\n if _idxij_copyarray:\n idxij_[:] = _idxij_np_tmp\n return (_numnz_return_value)"],"string":"[\n \"def putbarcj(self,j_,sub,weights): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights)\\n elif num_ != len(weights):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"q\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"q\\\":\\n sub_ = memoryview(array.array(\\\"q\\\",sub))\\n \\n if weights is None: raise TypeError(\\\"Invalid type for argument weights\\\")\\n if weights is None:\\n weights_ = None\\n else:\\n try:\\n weights_ = memoryview(weights)\\n except TypeError:\\n try:\\n _tmparr_weights = array.array(\\\"d\\\",weights)\\n except TypeError:\\n raise TypeError(\\\"Argument weights has wrong type\\\")\\n else:\\n weights_ = memoryview(_tmparr_weights)\\n \\n else:\\n if weights_.format != \\\"d\\\":\\n weights_ = memoryview(array.array(\\\"d\\\",weights))\\n \\n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbaraij(self,i_,j_,sub,weights): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights)\\n elif num_ != len(weights):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"q\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"q\\\":\\n sub_ = memoryview(array.array(\\\"q\\\",sub))\\n \\n if weights is None: raise TypeError(\\\"Invalid type for argument weights\\\")\\n if weights is None:\\n weights_ = None\\n else:\\n try:\\n weights_ = memoryview(weights)\\n except TypeError:\\n try:\\n _tmparr_weights = array.array(\\\"d\\\",weights)\\n except TypeError:\\n raise TypeError(\\\"Argument weights has wrong type\\\")\\n else:\\n weights_ = memoryview(_tmparr_weights)\\n \\n else:\\n if weights_.format != \\\"d\\\":\\n weights_ = memoryview(array.array(\\\"d\\\",weights))\\n \\n res = self.__obj.putbaraij(i_,j_,num_,sub_,weights_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbaraij(self,i_,j_,sub_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights_)\\n elif num_ != len(weights_):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbaraij(self.__nativep,i_,j_,num_,_sub_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarxj(self,whichsol_,j_,barxj_):\\n _barxj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj is not long enough: Is %d, expected %d\\\" % (len(barxj_),self.getlenbarvarj((j_))))\\n if barxj_ is None:\\n raise ValueError(\\\"Argument barxj cannot be None\\\")\\n if barxj_ is None:\\n raise ValueError(\\\"Argument barxj may not be None\\\")\\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\\n _barxj_copyarray = False\\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barxj_ is not None:\\n _barxj_copyarray = True\\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\\n _barxj_np_tmp[:] = barxj_\\n assert _barxj_np_tmp.flags.contiguous\\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barxj_copyarray = False\\n _barxj_tmp = None\\n \\n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(alphaptrb_)\\n elif num_ != len(alphaptrb_):\\n raise IndexError(\\\"Inconsistent length of array alphaptrb\\\")\\n if num_ is None:\\n num_ = len(alphaptre_)\\n elif num_ != len(alphaptre_):\\n raise IndexError(\\\"Inconsistent length of array alphaptre\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if alphaptrb_ is None:\\n raise ValueError(\\\"Argument alphaptrb cannot be None\\\")\\n if alphaptrb_ is None:\\n raise ValueError(\\\"Argument alphaptrb may not be None\\\")\\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\\n _alphaptrb_copyarray = False\\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif alphaptrb_ is not None:\\n _alphaptrb_copyarray = True\\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\\n _alphaptrb_np_tmp[:] = alphaptrb_\\n assert _alphaptrb_np_tmp.flags.contiguous\\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _alphaptrb_copyarray = False\\n _alphaptrb_tmp = None\\n \\n if alphaptre_ is None:\\n raise ValueError(\\\"Argument alphaptre cannot be None\\\")\\n if alphaptre_ is None:\\n raise ValueError(\\\"Argument alphaptre may not be None\\\")\\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\\n _alphaptre_copyarray = False\\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif alphaptre_ is not None:\\n _alphaptre_copyarray = True\\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\\n _alphaptre_np_tmp[:] = alphaptre_\\n assert _alphaptre_np_tmp.flags.contiguous\\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _alphaptre_copyarray = False\\n _alphaptre_tmp = None\\n \\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx cannot be None\\\")\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx may not be None\\\")\\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\\n _matidx_copyarray = False\\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif matidx_ is not None:\\n _matidx_copyarray = True\\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\\n _matidx_np_tmp[:] = matidx_\\n assert _matidx_np_tmp.flags.contiguous\\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _matidx_copyarray = False\\n _matidx_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarsj(self,whichsol_,j_,barsj_):\\n _barsj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj is not long enough: Is %d, expected %d\\\" % (len(barsj_),self.getlenbarvarj((j_))))\\n if barsj_ is None:\\n raise ValueError(\\\"Argument barsj cannot be None\\\")\\n if barsj_ is None:\\n raise ValueError(\\\"Argument barsj may not be None\\\")\\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\\n _barsj_copyarray = False\\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barsj_ is not None:\\n _barsj_copyarray = True\\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\\n _barsj_np_tmp[:] = barsj_\\n assert _barsj_np_tmp.flags.contiguous\\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barsj_copyarray = False\\n _barsj_tmp = None\\n \\n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarxj(self,whichsol_,j_,barxj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barxj is None: raise TypeError(\\\"Invalid type for argument barxj\\\")\\n if barxj is None:\\n barxj_ = None\\n else:\\n try:\\n barxj_ = memoryview(barxj)\\n except TypeError:\\n try:\\n _tmparr_barxj = array.array(\\\"d\\\",barxj)\\n except TypeError:\\n raise TypeError(\\\"Argument barxj has wrong type\\\")\\n else:\\n barxj_ = memoryview(_tmparr_barxj)\\n \\n else:\\n if barxj_.format != \\\"d\\\":\\n barxj_ = memoryview(array.array(\\\"d\\\",barxj))\\n \\n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj has wrong length\\\")\\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getbarcidx(self,idx_,sub_,weights_):\\n maxnum_ = self.getbarcidxinfo((idx_))\\n j_ = ctypes.c_int32()\\n num_ = ctypes.c_int64()\\n _sub_minlength = (maxnum_)\\n if (maxnum_) > 0 and sub_ is not None and len(sub_) != (maxnum_):\\n raise ValueError(\\\"Array argument sub is not long enough: Is %d, expected %d\\\" % (len(sub_),(maxnum_)))\\n if isinstance(sub_,numpy.ndarray) and not sub_.flags.writeable:\\n raise ValueError(\\\"Argument sub must be writable\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _weights_minlength = (maxnum_)\\n if (maxnum_) > 0 and weights_ is not None and len(weights_) != (maxnum_):\\n raise ValueError(\\\"Array argument weights is not long enough: Is %d, expected %d\\\" % (len(weights_),(maxnum_)))\\n if isinstance(weights_,numpy.ndarray) and not weights_.flags.writeable:\\n raise ValueError(\\\"Argument weights must be writable\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_getbarcidx(self.__nativep,idx_,maxnum_,ctypes.byref(j_),ctypes.byref(num_),_sub_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n j_ = j_.value\\n _j_return_value = j_\\n num_ = num_.value\\n _num_return_value = num_\\n if _sub_copyarray:\\n sub_[:] = _sub_np_tmp\\n if _weights_copyarray:\\n weights_[:] = _weights_np_tmp\\n return (_j_return_value,_num_return_value)\",\n \"def getbarcidx(self,idx_,sub,weights): # 3\\n maxnum_ = self.getbarcidxinfo((idx_))\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n _copyback_sub = False\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"q\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n _copyback_sub = True\\n else:\\n if sub_.format != \\\"q\\\":\\n sub_ = memoryview(array.array(\\\"q\\\",sub))\\n _copyback_sub = True\\n if sub_ is not None and len(sub_) != (maxnum_):\\n raise ValueError(\\\"Array argument sub has wrong length\\\")\\n if weights is None: raise TypeError(\\\"Invalid type for argument weights\\\")\\n _copyback_weights = False\\n if weights is None:\\n weights_ = None\\n else:\\n try:\\n weights_ = memoryview(weights)\\n except TypeError:\\n try:\\n _tmparr_weights = array.array(\\\"d\\\",weights)\\n except TypeError:\\n raise TypeError(\\\"Argument weights has wrong type\\\")\\n else:\\n weights_ = memoryview(_tmparr_weights)\\n _copyback_weights = True\\n else:\\n if weights_.format != \\\"d\\\":\\n weights_ = memoryview(array.array(\\\"d\\\",weights))\\n _copyback_weights = True\\n if weights_ is not None and len(weights_) != (maxnum_):\\n raise ValueError(\\\"Array argument weights has wrong length\\\")\\n res,resargs = self.__obj.getbarcidx(idx_,maxnum_,sub_,weights_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _j_return_value,_num_return_value = resargs\\n if _copyback_weights:\\n weights[:] = _tmparr_weights\\n if _copyback_sub:\\n sub[:] = _tmparr_sub\\n return _j_return_value,_num_return_value\",\n \"def fixB(self,i,j,value):\\n if self.coeffPattern[1] == None:\\n m,n=self.m,self.n\\n self.coeffPattern = [[None]*n for i in range(m)]\\n self.coeffPattern[1][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def _bucket_setitem(self, j, k, v):\\n pass\",\n \"def putbarsj(self,whichsol_,j_,barsj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barsj is None: raise TypeError(\\\"Invalid type for argument barsj\\\")\\n if barsj is None:\\n barsj_ = None\\n else:\\n try:\\n barsj_ = memoryview(barsj)\\n except TypeError:\\n try:\\n _tmparr_barsj = array.array(\\\"d\\\",barsj)\\n except TypeError:\\n raise TypeError(\\\"Argument barsj has wrong type\\\")\\n else:\\n barsj_ = memoryview(_tmparr_barsj)\\n \\n else:\\n if barsj_.format != \\\"d\\\":\\n barsj_ = memoryview(array.array(\\\"d\\\",barsj))\\n \\n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj has wrong length\\\")\\n res = self.__obj.putbarsj(whichsol_,j_,barsj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getbarxj(self,whichsol_,j_,barxj_):\\n _barxj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj is not long enough: Is %d, expected %d\\\" % (len(barxj_),self.getlenbarvarj((j_))))\\n if isinstance(barxj_,numpy.ndarray) and not barxj_.flags.writeable:\\n raise ValueError(\\\"Argument barxj must be writable\\\")\\n if barxj_ is None:\\n raise ValueError(\\\"Argument barxj may not be None\\\")\\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\\n _barxj_copyarray = False\\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barxj_ is not None:\\n _barxj_copyarray = True\\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\\n _barxj_np_tmp[:] = barxj_\\n assert _barxj_np_tmp.flags.contiguous\\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barxj_copyarray = False\\n _barxj_tmp = None\\n \\n res = __library__.MSK_XX_getbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _barxj_copyarray:\\n barxj_[:] = _barxj_np_tmp\",\n \"def putbararowlist(self,subi_,ptrb_,ptre_,subj_,nummat_,matidx_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(ptrb_)\\n elif num_ != len(ptrb_):\\n raise IndexError(\\\"Inconsistent length of array ptrb\\\")\\n if num_ is None:\\n num_ = len(ptre_)\\n elif num_ != len(ptre_):\\n raise IndexError(\\\"Inconsistent length of array ptre\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb cannot be None\\\")\\n if ptrb_ is None:\\n raise ValueError(\\\"Argument ptrb may not be None\\\")\\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\\n _ptrb_copyarray = False\\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptrb_ is not None:\\n _ptrb_copyarray = True\\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\\n _ptrb_np_tmp[:] = ptrb_\\n assert _ptrb_np_tmp.flags.contiguous\\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptrb_copyarray = False\\n _ptrb_tmp = None\\n \\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre cannot be None\\\")\\n if ptre_ is None:\\n raise ValueError(\\\"Argument ptre may not be None\\\")\\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\\n _ptre_copyarray = False\\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif ptre_ is not None:\\n _ptre_copyarray = True\\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\\n _ptre_np_tmp[:] = ptre_\\n assert _ptre_np_tmp.flags.contiguous\\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _ptre_copyarray = False\\n _ptre_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _nummat_minlength = len((subj_))\\n if len((subj_)) > 0 and nummat_ is not None and len(nummat_) != len((subj_)):\\n raise ValueError(\\\"Array argument nummat is not long enough: Is %d, expected %d\\\" % (len(nummat_),len((subj_))))\\n if nummat_ is None:\\n raise ValueError(\\\"Argument nummat cannot be None\\\")\\n if nummat_ is None:\\n raise ValueError(\\\"Argument nummat may not be None\\\")\\n if isinstance(nummat_, numpy.ndarray) and nummat_.dtype is numpy.dtype(numpy.int64) and nummat_.flags.contiguous:\\n _nummat_copyarray = False\\n _nummat_tmp = ctypes.cast(nummat_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif nummat_ is not None:\\n _nummat_copyarray = True\\n _nummat_np_tmp = numpy.zeros(len(nummat_),numpy.dtype(numpy.int64))\\n _nummat_np_tmp[:] = nummat_\\n assert _nummat_np_tmp.flags.contiguous\\n _nummat_tmp = ctypes.cast(_nummat_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _nummat_copyarray = False\\n _nummat_tmp = None\\n \\n _matidx_minlength = sum((nummat_))\\n if sum((nummat_)) > 0 and matidx_ is not None and len(matidx_) != sum((nummat_)):\\n raise ValueError(\\\"Array argument matidx is not long enough: Is %d, expected %d\\\" % (len(matidx_),sum((nummat_))))\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx cannot be None\\\")\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx may not be None\\\")\\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\\n _matidx_copyarray = False\\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif matidx_ is not None:\\n _matidx_copyarray = True\\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\\n _matidx_np_tmp[:] = matidx_\\n assert _matidx_np_tmp.flags.contiguous\\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _matidx_copyarray = False\\n _matidx_tmp = None\\n \\n _weights_minlength = sum((nummat_))\\n if sum((nummat_)) > 0 and weights_ is not None and len(weights_) != sum((nummat_)):\\n raise ValueError(\\\"Array argument weights is not long enough: Is %d, expected %d\\\" % (len(weights_),sum((nummat_))))\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbararowlist(self.__nativep,num_,_subi_tmp,_ptrb_tmp,_ptre_tmp,_subj_tmp,_nummat_tmp,_matidx_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getbarsj(self,whichsol_,j_,barsj_):\\n _barsj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj is not long enough: Is %d, expected %d\\\" % (len(barsj_),self.getlenbarvarj((j_))))\\n if isinstance(barsj_,numpy.ndarray) and not barsj_.flags.writeable:\\n raise ValueError(\\\"Argument barsj must be writable\\\")\\n if barsj_ is None:\\n raise ValueError(\\\"Argument barsj may not be None\\\")\\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\\n _barsj_copyarray = False\\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barsj_ is not None:\\n _barsj_copyarray = True\\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\\n _barsj_np_tmp[:] = barsj_\\n assert _barsj_np_tmp.flags.contiguous\\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barsj_copyarray = False\\n _barsj_tmp = None\\n \\n res = __library__.MSK_XX_getbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _barsj_copyarray:\\n barsj_[:] = _barsj_np_tmp\",\n \"def getbarcidxj(self,idx_): # 3\\n res,resargs = self.__obj.getbarcidxj(idx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _j_return_value = resargs\\n return _j_return_value\",\n \"def UBM_update_double_prime(self, j_ind, k_ind, alpha_1, alpha_2, beta_1, beta_2, Gamma, Lambda, Omega):\\n #set nodeType to \\\"-1,1\\\"\\n self.nodeType = \\\"-11\\\"\\n\\n\\n lambda_entry = Lambda[0, 1]\\n gamma_entry = Gamma[0, 1]\\n #two new hidden nodes\\n self.n_h += 2\\n\\n #store old parameters\\n old_b_v = np.copy(self.b_v)\\n old_b_h = np.copy(self.b_h)\\n\\n old_w_hv = np.copy(self.w_hv)\\n\\n\\n #update visible biases\\n self.b_v[j_ind] = alpha_1\\n self.b_v[k_ind] = alpha_2\\n\\n #update hidden biases\\n self.b_h = np.zeros(self.n_h, dtype=complex)\\n self.b_h[:-2] = old_b_h\\n self.b_h[-2] = beta_1 + old_b_v[j_ind]\\n self.b_h[-1] = beta_2 + old_b_v[k_ind]\\n\\n #update weight_matrix\\n \\\"here could be the reason why successiive 2-qubit gates don't work! \\\"\\n if hasattr(self, \\\"updated\\\"):\\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\\n\\n self.w_hv[:-2, :j_ind] = old_w_hv[:, :j_ind]\\n self.w_hv[:-2, j_ind+1:k_ind] = old_w_hv[:, j_ind+1:k_ind]\\n self.w_hv[:-2, k_ind+1:] = old_w_hv[:, k_ind+1:]\\n\\n self.w_hv[-2, :] = self.w_Z[j_ind, :]\\n self.w_hv[-1, :] = self.w_Z[k_ind, :]\\n #replace the Z-entries by unitary parameters\\n self.w_hv[-2, j_ind] = Omega[0, 0]\\n self.w_hv[-1, j_ind] = Omega[1, 0]\\n self.w_hv[-2, k_ind] = Omega[0, 1]\\n self.w_hv[-1, k_ind] = Omega[1, 1]\\n else:\\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\\n self.w_hv[:-2, :j_ind] = old_w_hv[:, :j_ind]\\n self.w_hv[:-2, j_ind+1:k_ind] = old_w_hv[:, j_ind+1:k_ind]\\n self.w_hv[:-2, k_ind+1:] = old_w_hv[:, k_ind+1:]\\n self.w_hv[-2, j_ind] = Omega[0, 0]\\n self.w_hv[-1, j_ind] = Omega[1, 0]\\n self.w_hv[-2, k_ind] = Omega[0, 1]\\n self.w_hv[-1, k_ind] = Omega[1, 1]\\n\\n\\n\\n\\n\\n if hasattr(self, \\\"updated\\\"):\\n print(\\\"already updated\\\")\\n old_w_X = np.copy(self.w_X)\\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\\n self.w_X[-2, :-2] = old_w_hv[:, j_ind].T\\n self.w_X[-1, :-2] = old_w_hv[:, k_ind].T\\n self.w_X[:-2, -2] = old_w_hv[:, j_ind]\\n self.w_X[:-2, -1] = old_w_hv[:, k_ind]\\n self.w_X[-1, -2] = gamma_entry + self.w_Z[j_ind, k_ind]\\n self.w_X[-2, -1] = gamma_entry + self.w_Z[j_ind, k_ind]\\n self.w_X[:-2, :-2] = old_w_X\\n\\n\\n self.w_Z[j_ind, k_ind] = lambda_entry\\n self.w_Z[k_ind, j_ind] = lambda_entry\\n\\n\\n else:\\n print(\\\"First RBM update \\\")\\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\\n self.w_X[-2, :-2] = old_w_hv[:, j_ind].T\\n self.w_X[-1, :-2] = old_w_hv[:, k_ind].T\\n self.w_X[:-2, -2] = old_w_hv[:, j_ind]\\n self.w_X[:-2, -1] = old_w_hv[:, k_ind]\\n self.w_X[-1, -2] = gamma_entry\\n self.w_X[-2, -1] = gamma_entry\\n\\n\\n self.w_Z = np.zeros((self.n_v, self.n_v), dtype=complex)\\n self.w_Z[j_ind, k_ind] = lambda_entry\\n self.w_Z[k_ind, j_ind] = lambda_entry\\n\\n\\n\\n\\n self.updated = True\",\n \"def updateWeights(self,weightUpdate):\\n\\t\\n\\t\\tbranches = self.collectAllBranches()\\n\\n\\t\\tfor i in range(self.nBranches):\\n\\n\\t\\t\\tbranches[i].weight -= weightUpdate[i]\",\n \"def _bucket_setitem(self, j, k, v):\\n if self._table[j] is None:\\n self._table[j] = UnsortedTableMap() # create new bucket at index j\\n oldSize = len(self._table[j])\\n self._table[j][k] = v\\n if len(self._table[j]) > oldSize: # key is new to the table\\n self._n += 1\",\n \"def reassignWeights(self,weights):\\n\\t\\n\\t\\tbranches = self.collectAllBranches()\\n\\n\\t\\tfor i in range(self.nBranches):\\n\\n\\t\\t\\tbranches[i].weight = weights[i]\",\n \"def UBM_update_double(self, j_ind, k_ind, alpha_1, alpha_2, beta_1, beta_2, Gamma, Lambda, Omega):\\n #set nodeType to \\\"-1,1\\\"\\n self.nodeType = \\\"-11\\\"\\n\\n lambda_entry = Lambda[0, 1]\\n gamma_entry = Gamma[0, 1]\\n #two new hidden nodes\\n self.n_h += 2\\n\\n #store old parameters\\n old_biases_v = np.copy(self.b_v)\\n old_b_h = np.copy(self.b_h)\\n\\n old_weights_h = np.copy(self.w_hv)\\n\\n #update visible biases\\n self.b_v[j_ind] = alpha_1\\n self.b_v[k_ind] = alpha_2\\n\\n #update hidden biases\\n self.b_h = np.zeros(self.n_h, dtype=complex)\\n self.b_h[:-2] = old_b_h\\n self.b_h[-2] = beta_1 + old_biases_v[j_ind]\\n self.b_h[-1] = beta_2 + old_biases_v[k_ind]\\n\\n #update weight_matrix\\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\\n self.w_hv[:-2, :j_ind] = old_weights_h[:, :j_ind]\\n self.w_hv[:-2, j_ind+1:k_ind] = old_weights_h[:, j_ind+1:k_ind]\\n self.w_hv[:-2, k_ind+1:] = old_weights_h[:, k_ind+1:]\\n self.w_hv[-2, j_ind] = Omega[0, 0]\\n self.w_hv[-1, j_ind] = Omega[1, 0]\\n self.w_hv[-2, k_ind] = Omega[0, 1]\\n self.w_hv[-1, k_ind] = Omega[1, 1]\\n\\n #introduce X (=h-h matrix)\\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\\n self.w_X[-2, :-2] = old_weights_h[:, j_ind].T\\n self.w_X[-1, :-2] = old_weights_h[:, k_ind].T\\n self.w_X[:-2, -2] = old_weights_h[:, j_ind]\\n self.w_X[:-2, -1] = old_weights_h[:, k_ind]\\n self.w_X[-1, -2] = gamma_entry\\n self.w_X[-2, -1] = gamma_entry\\n\\n #introduce Y (=v-v matrix)\\n self.w_Z = np.zeros((self.n_v, self.n_v), dtype=complex)\\n self.w_Z[j_ind, k_ind] = lambda_entry\\n self.w_Z[k_ind, j_ind] = lambda_entry\",\n \"def getbarcsparsity(self,idxj_):\\n maxnumnz_ = self.getnumbarcnz()\\n numnz_ = ctypes.c_int64()\\n _idxj_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and idxj_ is not None and len(idxj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument idxj is not long enough: Is %d, expected %d\\\" % (len(idxj_),(maxnumnz_)))\\n if isinstance(idxj_,numpy.ndarray) and not idxj_.flags.writeable:\\n raise ValueError(\\\"Argument idxj must be writable\\\")\\n if idxj_ is None:\\n raise ValueError(\\\"Argument idxj may not be None\\\")\\n if isinstance(idxj_, numpy.ndarray) and idxj_.dtype is numpy.dtype(numpy.int64) and idxj_.flags.contiguous:\\n _idxj_copyarray = False\\n _idxj_tmp = ctypes.cast(idxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif idxj_ is not None:\\n _idxj_copyarray = True\\n _idxj_np_tmp = numpy.zeros(len(idxj_),numpy.dtype(numpy.int64))\\n _idxj_np_tmp[:] = idxj_\\n assert _idxj_np_tmp.flags.contiguous\\n _idxj_tmp = ctypes.cast(_idxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _idxj_copyarray = False\\n _idxj_tmp = None\\n \\n res = __library__.MSK_XX_getbarcsparsity(self.__nativep,maxnumnz_,ctypes.byref(numnz_),_idxj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numnz_ = numnz_.value\\n _numnz_return_value = numnz_\\n if _idxj_copyarray:\\n idxj_[:] = _idxj_np_tmp\\n return (_numnz_return_value)\",\n \"def updateBar(self):\\n pass\",\n \"def getbarxj(self,whichsol_,j_,barxj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barxj is None: raise TypeError(\\\"Invalid type for argument barxj\\\")\\n _copyback_barxj = False\\n if barxj is None:\\n barxj_ = None\\n else:\\n try:\\n barxj_ = memoryview(barxj)\\n except TypeError:\\n try:\\n _tmparr_barxj = array.array(\\\"d\\\",barxj)\\n except TypeError:\\n raise TypeError(\\\"Argument barxj has wrong type\\\")\\n else:\\n barxj_ = memoryview(_tmparr_barxj)\\n _copyback_barxj = True\\n else:\\n if barxj_.format != \\\"d\\\":\\n barxj_ = memoryview(array.array(\\\"d\\\",barxj))\\n _copyback_barxj = True\\n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj has wrong length\\\")\\n res = self.__obj.getbarxj(whichsol_,j_,barxj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_barxj:\\n barxj[:] = _tmparr_barxj\",\n \"def getbarsj(self,whichsol_,j_,barsj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barsj is None: raise TypeError(\\\"Invalid type for argument barsj\\\")\\n _copyback_barsj = False\\n if barsj is None:\\n barsj_ = None\\n else:\\n try:\\n barsj_ = memoryview(barsj)\\n except TypeError:\\n try:\\n _tmparr_barsj = array.array(\\\"d\\\",barsj)\\n except TypeError:\\n raise TypeError(\\\"Argument barsj has wrong type\\\")\\n else:\\n barsj_ = memoryview(_tmparr_barsj)\\n _copyback_barsj = True\\n else:\\n if barsj_.format != \\\"d\\\":\\n barsj_ = memoryview(array.array(\\\"d\\\",barsj))\\n _copyback_barsj = True\\n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj has wrong length\\\")\\n res = self.__obj.getbarsj(whichsol_,j_,barsj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_barsj:\\n barsj[:] = _tmparr_barsj\",\n \"def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\\n _subj_minlength = (num_)\\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(num_)))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _subk_minlength = (num_)\\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk is not long enough: Is %d, expected %d\\\" % (len(subk_),(num_)))\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk cannot be None\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n _subl_minlength = (num_)\\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl is not long enough: Is %d, expected %d\\\" % (len(subl_),(num_)))\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl cannot be None\\\")\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl may not be None\\\")\\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\\n _subl_copyarray = False\\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subl_ is not None:\\n _subl_copyarray = True\\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\\n _subl_np_tmp[:] = subl_\\n assert _subl_np_tmp.flags.contiguous\\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subl_copyarray = False\\n _subl_tmp = None\\n \\n _valjkl_minlength = (num_)\\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\\n raise ValueError(\\\"Array argument valjkl is not long enough: Is %d, expected %d\\\" % (len(valjkl_),(num_)))\\n if valjkl_ is None:\\n raise ValueError(\\\"Argument valjkl cannot be None\\\")\\n if valjkl_ is None:\\n raise ValueError(\\\"Argument valjkl may not be None\\\")\\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\\n _valjkl_copyarray = False\\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valjkl_ is not None:\\n _valjkl_copyarray = True\\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\\n _valjkl_np_tmp[:] = valjkl_\\n assert _valjkl_np_tmp.flags.contiguous\\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valjkl_copyarray = False\\n _valjkl_tmp = None\\n \\n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def update_bar(self, bar):\\n self.count += 1\\n if not self.inited and self.count >= self.size:\\n self.inited = True\\n\\n self.open_array[:-1] = self.open_array[1:]\\n self.high_array[:-1] = self.high_array[1:]\\n self.low_array[:-1] = self.low_array[1:]\\n self.close_array[:-1] = self.close_array[1:]\\n self.volume_array[:-1] = self.volume_array[1:]\\n self.time_array[:-1] = self.time_array[1:]\\n self.extra_array[:-1] = self.extra_array[1:]\\n self.range_array[:-1] = self.range_array[1:]\\n\\n self.open_array[-1] = bar.open_price\\n self.high_array[-1] = bar.high_price\\n self.low_array[-1] = bar.low_price\\n self.close_array[-1] = bar.close_price\\n self.volume_array[-1] = bar.volume\\n self.time_array[-1] = bar.datetime\\n self.extra_array[-1] = {\\\"pattern\\\":[]}\\n if self.count > 1:\\n self.range_array[:-1] = self.range_array[1:]\\n self.range_array[-1] = round(self.close_array[-1] / self.close_array[-2] - 1, 6)\\n else:\\n self.range_array[-1] = 0\",\n \"def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if subk is None: raise TypeError(\\\"Invalid type for argument subk\\\")\\n if subk is None:\\n subk_ = None\\n else:\\n try:\\n subk_ = memoryview(subk)\\n except TypeError:\\n try:\\n _tmparr_subk = array.array(\\\"i\\\",subk)\\n except TypeError:\\n raise TypeError(\\\"Argument subk has wrong type\\\")\\n else:\\n subk_ = memoryview(_tmparr_subk)\\n \\n else:\\n if subk_.format != \\\"i\\\":\\n subk_ = memoryview(array.array(\\\"i\\\",subk))\\n \\n if subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk has wrong length\\\")\\n if subl is None: raise TypeError(\\\"Invalid type for argument subl\\\")\\n if subl is None:\\n subl_ = None\\n else:\\n try:\\n subl_ = memoryview(subl)\\n except TypeError:\\n try:\\n _tmparr_subl = array.array(\\\"i\\\",subl)\\n except TypeError:\\n raise TypeError(\\\"Argument subl has wrong type\\\")\\n else:\\n subl_ = memoryview(_tmparr_subl)\\n \\n else:\\n if subl_.format != \\\"i\\\":\\n subl_ = memoryview(array.array(\\\"i\\\",subl))\\n \\n if subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl has wrong length\\\")\\n if valjkl is None: raise TypeError(\\\"Invalid type for argument valjkl\\\")\\n if valjkl is None:\\n valjkl_ = None\\n else:\\n try:\\n valjkl_ = memoryview(valjkl)\\n except TypeError:\\n try:\\n _tmparr_valjkl = array.array(\\\"d\\\",valjkl)\\n except TypeError:\\n raise TypeError(\\\"Argument valjkl has wrong type\\\")\\n else:\\n valjkl_ = memoryview(_tmparr_valjkl)\\n \\n else:\\n if valjkl_.format != \\\"d\\\":\\n valjkl_ = memoryview(array.array(\\\"d\\\",valjkl))\\n \\n if valjkl_ is not None and len(valjkl_) != (num_):\\n raise ValueError(\\\"Array argument valjkl has wrong length\\\")\\n res = self.__obj.putbarcblocktriplet(num_,subj_,subk_,subl_,valjkl_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getbarcidxj(self,idx_):\\n j_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getbarcidxj(self.__nativep,idx_,ctypes.byref(j_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n j_ = j_.value\\n _j_return_value = j_\\n return (_j_return_value)\",\n \"def refine(self, ijk):\\n if self.cbc is None or not self.sub_block_count:\\n raise ValueError(\\n \\\"Cannot refine sub block model without specifying number \\\"\\n \\\"of parent and sub blocks\\\"\\n )\\n try:\\n inds = self.ijk_array_to_indices(ijk)\\n except ValueError:\\n inds = self.ijk_to_index(ijk)\\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137\",\n \"def __setitem__(self, j, val):\\n\\t\\tself._coords[j] = val\",\n \"def getbarcsparsity(self,idxj): # 3\\n maxnumnz_ = self.getnumbarcnz()\\n if idxj is None: raise TypeError(\\\"Invalid type for argument idxj\\\")\\n _copyback_idxj = False\\n if idxj is None:\\n idxj_ = None\\n else:\\n try:\\n idxj_ = memoryview(idxj)\\n except TypeError:\\n try:\\n _tmparr_idxj = array.array(\\\"q\\\",idxj)\\n except TypeError:\\n raise TypeError(\\\"Argument idxj has wrong type\\\")\\n else:\\n idxj_ = memoryview(_tmparr_idxj)\\n _copyback_idxj = True\\n else:\\n if idxj_.format != \\\"q\\\":\\n idxj_ = memoryview(array.array(\\\"q\\\",idxj))\\n _copyback_idxj = True\\n if idxj_ is not None and len(idxj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument idxj has wrong length\\\")\\n res,resargs = self.__obj.getbarcsparsity(maxnumnz_,idxj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n if _copyback_idxj:\\n idxj[:] = _tmparr_idxj\\n return _numnz_return_value\",\n \"def change_weight(self, new_weight_arr):\\n self.weights = new_weight_arr\",\n \"def cbar( self , y , t = 0 ):\\n \\n u = self.c( self.zbar, y , self.rbar , t )\\n \\n return u\",\n \"def put_B_reduced(self, dest):\\n _parallel.call_from_rank_zero(self.put_mat, self.B_reduced, dest)\\n _parallel.barrier()\",\n \"def test_weight_based_ties(self, dim):\\r\\n graph = nx.barbell_graph(dim, 0)\\r\\n subgraph = graph.nodes()\\r\\n weights = [1] * dim + [2] * dim\\r\\n\\r\\n c = clique.shrink(subgraph, graph, node_select=weights)\\r\\n assert c == list(range(dim, 2 * dim))\",\n \"def update_weight(self,ctr,new_weight):\\n self.sum1 -= self.data_set[ctr].weight\\n self.data_set[ctr].weight = new_weight\\n self.sum1 += new_weight\",\n \"def set_bcol(self, bond):\\n i, j = self.blist[bond]\\n c = self.colors[bond]\\n #logger.debug(\\\"bond from %3d to %3d : color %3d\\\" % (i, j, c) )\\n #logger.debug(\\\"i_conn: %s\\\" % repr(self.conn[i]) )\\n #logger.debug(\\\"j_conn: %s\\\" % repr(self.conn[j]) )\\n ### set for i\\n ### BUG FOR 2x2x2 pcu: a vertex connects twice to the same vertex!!!\\n ### TBI: add pconn for topos\\n j_ind = self.conn[i].index(j)\\n i_ind = self.conn[j].index(i)\\n #logger.debug(\\\"i_ind %6d\\\" % i_ind)\\n #logger.debug(\\\"j_ind %6d\\\" % j_ind)\\n self.bcolors[i][j_ind] = c\\n self.bcolors[j][i_ind] = c\\n return\",\n \"def jacobian(self, c):\\n\\n raise NotImplementedError\",\n \"def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def update_weights(self):\\n\\t\\tpass\",\n \"def fixA(self,i,j,value):\\n if self.coeffPattern[0] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[0] = [[None]*m for i in range(m)]\\n self.coeffPattern[0][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def cb_update(val):\\n alpha_update = [sAlpha0.val, sAlpha1.val, sAlpha2.val]\\n\\n # update Dirichlet's parameters alpha\\n dirichlet.set_param(alpha_update)\\n draw_pdf_contours(axDirichlet, dirichlet, True) # Draw Dirichlet\\n\\n # MAP\\n lambda_MAP = CatMAP.MAPinfer(x_cat, dirichlet)\\n axMAP.cla()\\n drawBarGraph( axMAP, \\\"MAP\\\", lambda_MAP, bar_y_max, col_MAP ) # Draw Bar graph\\n\\n # Bayes\\n posteriorDirichlet.set_param(alpha_update)\\n posteriorDirichlet.calcPosterior(x_cat)\\n draw_pdf_contours(axPosteriorDirichlet, posteriorDirichlet) # Draw Posterior Dirichlet\\n lambda_Bayes = np.zeros(3)\\n for k in range(3):\\n lambda_Bayes[k] = posteriorDirichlet.BayesInfer(k)\\n\\n axBayes.cla()\\n drawBarGraph( axBayes, \\\"Bayes\\\", lambda_Bayes, bar_y_max, col_Bayes ) # Draw Bar graph\\n\\n print('Update')\\n print('lambda_ML =', lambda_ML)\\n print('lambda_MAP =', lambda_MAP)\\n print('lambda_Bayes=', lambda_Bayes)\\n draw_point(axDirichlet, lambda_ML, col_ML)\\n draw_point(axDirichlet, lambda_MAP, col_MAP)\\n draw_point(axDirichlet, lambda_Bayes, col_Bayes)\\n draw_point(axPosteriorDirichlet, lambda_MAP, col_MAP)\\n draw_point(axPosteriorDirichlet, lambda_Bayes, col_Bayes)\\n\\n fig.canvas.draw_idle()\",\n \"def reduceBin(bin, size, binLabel):\\n print(\\\"reducing bin [\\\" + str(binLabel) + \\\"] (size: \\\" + str(len(bin)) + \\\")\\\")\\n np.random.shuffle(bin)\\n chosenImages = bin[:size]\\n newRatings = open(new_ratings_file_path, 'a')\\n for image in chosenImages:\\n newRatings.write(getRatingsLine(image[0], image[1]))\\n newRatings.close()\",\n \"def merge_bowl(self):\\n self.B += self.Bowl_bS\\n self.Bc += self.Bowl_bC\\n self.W += self.Bowl_WS\\n self.Wc += self.Bowl_WS\",\n \"def getbarcblocktriplet(self,subj,subk,subl,valjkl): # 3\\n maxnum_ = self.getnumbarcblocktriplets()\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxnum_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if subk is None: raise TypeError(\\\"Invalid type for argument subk\\\")\\n _copyback_subk = False\\n if subk is None:\\n subk_ = None\\n else:\\n try:\\n subk_ = memoryview(subk)\\n except TypeError:\\n try:\\n _tmparr_subk = array.array(\\\"i\\\",subk)\\n except TypeError:\\n raise TypeError(\\\"Argument subk has wrong type\\\")\\n else:\\n subk_ = memoryview(_tmparr_subk)\\n _copyback_subk = True\\n else:\\n if subk_.format != \\\"i\\\":\\n subk_ = memoryview(array.array(\\\"i\\\",subk))\\n _copyback_subk = True\\n if subk_ is not None and len(subk_) != (maxnum_):\\n raise ValueError(\\\"Array argument subk has wrong length\\\")\\n if subl is None: raise TypeError(\\\"Invalid type for argument subl\\\")\\n _copyback_subl = False\\n if subl is None:\\n subl_ = None\\n else:\\n try:\\n subl_ = memoryview(subl)\\n except TypeError:\\n try:\\n _tmparr_subl = array.array(\\\"i\\\",subl)\\n except TypeError:\\n raise TypeError(\\\"Argument subl has wrong type\\\")\\n else:\\n subl_ = memoryview(_tmparr_subl)\\n _copyback_subl = True\\n else:\\n if subl_.format != \\\"i\\\":\\n subl_ = memoryview(array.array(\\\"i\\\",subl))\\n _copyback_subl = True\\n if subl_ is not None and len(subl_) != (maxnum_):\\n raise ValueError(\\\"Array argument subl has wrong length\\\")\\n if valjkl is None: raise TypeError(\\\"Invalid type for argument valjkl\\\")\\n _copyback_valjkl = False\\n if valjkl is None:\\n valjkl_ = None\\n else:\\n try:\\n valjkl_ = memoryview(valjkl)\\n except TypeError:\\n try:\\n _tmparr_valjkl = array.array(\\\"d\\\",valjkl)\\n except TypeError:\\n raise TypeError(\\\"Argument valjkl has wrong type\\\")\\n else:\\n valjkl_ = memoryview(_tmparr_valjkl)\\n _copyback_valjkl = True\\n else:\\n if valjkl_.format != \\\"d\\\":\\n valjkl_ = memoryview(array.array(\\\"d\\\",valjkl))\\n _copyback_valjkl = True\\n if valjkl_ is not None and len(valjkl_) != (maxnum_):\\n raise ValueError(\\\"Array argument valjkl has wrong length\\\")\\n res,resargs = self.__obj.getbarcblocktriplet(maxnum_,subj_,subk_,subl_,valjkl_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _num_return_value = resargs\\n if _copyback_valjkl:\\n valjkl[:] = _tmparr_valjkl\\n if _copyback_subl:\\n subl[:] = _tmparr_subl\\n if _copyback_subk:\\n subk[:] = _tmparr_subk\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n return _num_return_value\",\n \"def substitute_cost(self, i, j):\\n raise NotImplementedError\",\n \"def update_bars(self):\\n raise NotImplementedError(\\\"Should implement update_bars()\\\")\",\n \"def wire(self):\\n self.J[self.size[0]//2, self.size[1]//2, :] = 1.0 / self.size[2]\",\n \"def __update_jacobian(self, x, F):\\n old_err = _n.seterr(divide='raise')\\n\\n try:\\n y = F - self.F\\n s = x - self.x\\n\\n zt = None\\n if self.update_type == BroydenSolver.UPDATE_ICUM:\\n maxi = abs(_n.ravel(y)).argmax()\\n zt = _n.transpose(_n.zeros((1,self.n), _n.float_))\\n zt[0, maxi] = 1\\n elif self.update_type == BroydenSolver.UPDATE_GOOD_BROYDEN:\\n # (Good) Broyden update\\n zt = _n.dot(_n.transpose(s), self.H)\\n elif self.update_type == BroydenSolver.UPDATE_BAD_BROYDEN:\\n # (Bad) Broyden update\\n zt = _n.transpose(y)\\n else:\\n raise ValueError(\\\"Unknown update type %s\\\" % (self.update_type))\\n\\n self.H = self.H \\\\\\n + _n.dot(s - _n.dot(self.H, y), zt) / _n.dot(zt, y)\\n except FloatingPointError:\\n warnings.warn(\\\"%% Broyden reset: singular\\\", BroydenWarning)\\n self.H = _n.identity(self.n) / self.initial_scale\\n\\n _n.seterr(**old_err)\",\n \"def convertJacobiToBeta(self,x):\\n u = 0.5*(self.high+self.low)\\n s = 0.5*(self.high-self.low)\\n return s*x+u\",\n \"def cbar( self , y , t = 0 ):\\n r = self.t2r( t )\\n u = self.c( y , r , t )\\n \\n return u\",\n \"def SetPRBinCatConstraint( self, model ) :\\n tot = np.dot( self.wish.T, self.dispo )\\n for val in tot :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def resort_couplings(J,sortIx):\\n return\",\n \"def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def jacobian(self, x):\\n pass\",\n \"def setitem(self, i, j, value):\\n # XXX: flint matrices do not support negative indices\\n # XXX: They also raise ValueError instead of IndexError\\n m, n = self.shape\\n if i < 0:\\n i += m\\n if j < 0:\\n j += n\\n try:\\n self.rep[i, j] = value\\n except ValueError:\\n raise IndexError(f\\\"Invalid indices ({i}, {j}) for Matrix of shape {self.shape}\\\")\",\n \"def putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_):\\n _subi_minlength = (num_)\\n if (num_) > 0 and subi_ is not None and len(subi_) != (num_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(num_)))\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = (num_)\\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(num_)))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _subk_minlength = (num_)\\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk is not long enough: Is %d, expected %d\\\" % (len(subk_),(num_)))\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk cannot be None\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n _subl_minlength = (num_)\\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl is not long enough: Is %d, expected %d\\\" % (len(subl_),(num_)))\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl cannot be None\\\")\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl may not be None\\\")\\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\\n _subl_copyarray = False\\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subl_ is not None:\\n _subl_copyarray = True\\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\\n _subl_np_tmp[:] = subl_\\n assert _subl_np_tmp.flags.contiguous\\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subl_copyarray = False\\n _subl_tmp = None\\n \\n _valijkl_minlength = (num_)\\n if (num_) > 0 and valijkl_ is not None and len(valijkl_) != (num_):\\n raise ValueError(\\\"Array argument valijkl is not long enough: Is %d, expected %d\\\" % (len(valijkl_),(num_)))\\n if valijkl_ is None:\\n raise ValueError(\\\"Argument valijkl cannot be None\\\")\\n if valijkl_ is None:\\n raise ValueError(\\\"Argument valijkl may not be None\\\")\\n if isinstance(valijkl_, numpy.ndarray) and valijkl_.dtype is numpy.dtype(numpy.float64) and valijkl_.flags.contiguous:\\n _valijkl_copyarray = False\\n _valijkl_tmp = ctypes.cast(valijkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valijkl_ is not None:\\n _valijkl_copyarray = True\\n _valijkl_np_tmp = numpy.zeros(len(valijkl_),numpy.dtype(numpy.float64))\\n _valijkl_np_tmp[:] = valijkl_\\n assert _valijkl_np_tmp.flags.contiguous\\n _valijkl_tmp = ctypes.cast(_valijkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valijkl_copyarray = False\\n _valijkl_tmp = None\\n \\n res = __library__.MSK_XX_putbarablocktriplet(self.__nativep,num_,_subi_tmp,_subj_tmp,_subk_tmp,_subl_tmp,_valijkl_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_bc(self, problem):\\n bcs = problem.bcs\\n n_bound = cfg.const['N_GHOST_CELLS']\\n # Left X-b.c.\\n for i in range(0, self.i_min):\\n for j in range(self.j_min, self.j_max):\\n for k in range(self.k_min, self.k_max): \\n if bcs[0] == 't': \\n self.U[i][j][k] = self.U[self.i_min][j][k]\\n elif bcs[0] == 'w':\\n for num in [0, 2, 3, 4]: # 0 -> 3, 1 -> 2, i_min-1 -> i_min, i_min-2 -> i_min+1\\n self.U[i][j][k][num] = self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\\n for num in [1]:\\n self.U[i][j][k][num] = - self.U[self.i_min + (self.i_min - i - 1)][j][k][num]\\n else:\\n print(\\\"Errof field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Right X-b.c.\\n for i in range(self.i_max, self.i_max+n_bound):\\n for j in range(self.j_min, self.j_max):\\n for k in range(self.k_min, self.k_max): \\n if bcs[1] == 't':\\n self.U[i][j][k] = self.U[self.i_max-1][j][k]\\n elif bcs[1] == 'w':\\n for num in [0, 2, 3, 4]: # i_max -> i_max-1 , i_max+1-> i_max-2\\n self.U[i][j][k][num] = self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\\n for num in [1]:\\n self.U[i][j][k][num] = - self.U[self.i_max - (i - self.i_max + 1)][j][k][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Left Y-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(0, self.j_min):\\n for k in range(self.k_min, self.k_max): \\n if bcs[2] == 't':\\n self.U[i][j][k] = self.U[i][self.j_min][k]\\n elif bcs[2] == 'w':\\n for num in [0, 1, 3, 4]:\\n self.U[i][j][k][num] = self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\\n for num in [2]:\\n self.U[i][j][k][num] = - self.U[i][self.j_min + (self.j_min - j - 1)][k][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Right Y-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(self.j_max, self.j_max+n_bound):\\n for k in range(self.k_min, self.k_max): \\n if bcs[3] == 't':\\n self.U[i][j][k] = self.U[i][self.j_max-1][k]\\n elif bcs[3] == 'w':\\n for num in [0, 1, 3, 4]:\\n self.U[i][j][k][num] = self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\\n for num in [2]:\\n self.U[i][j][k][num] = -self.U[i][self.j_max - (j - self.j_max + 1)][k][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Left Z-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(0, self.j_max+n_bound):\\n for k in range(0, self.k_min): \\n if bcs[4] == 't':\\n self.U[i][j][k] = self.U[i][j][self.k_min]\\n elif bcs[4] == 'w':\\n for num in [0, 1, 2, 4]:\\n self.U[i][j][k][num] = self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\\n for num in [3]:\\n self.U[i][j][k][num] = - self.U[i][j][self.k_min + (self.k_min - k - 1)][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\\n # Right Z-b.c.\\n for i in range(0, self.i_max+n_bound):\\n for j in range(0, self.j_max+n_bound):\\n for k in range(self.k_max, self.k_max+n_bound):\\n if bcs[5] == 't':\\n self.U[i][j][k] = self.U[i][j][self.k_max-1]\\n elif bcs[5] == 'w':\\n for num in [0, 1, 2, 4]:\\n self.U[i][j][k][num] = self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\\n for num in [3]:\\n self.U[i][j][k][num] = - self.U[i][j][self.k_max - (k - self.k_max + 1)][num]\\n else:\\n print(\\\"Error field.set_ics(): only wall-type and transmissive boundaries supported! Bye!\\\")\",\n \"def doublec(self, j):\\n\\t\\tif j < (self.k0 + 1):\\n\\t\\t\\treturn 0\\n\\t\\tif (self.b[j] != self.b[j-1]):\\n\\t\\t\\treturn 0\\n\\t\\treturn self.cons(j)\",\n \"def update(self, i, v):\\n # index in BTree is 1 more than index in arr[]\\n i += 1\\n\\n # Traverse to ancestors of BITree[i]\\n while i <= self.size:\\n self.BITree[i] += v\\n\\n # Update index to next set bit in binary representation\\n i += i & (-i)\",\n \"def compute_effective_nbar(cat):\\n # the total weight\\n total_weight = cat['Weight']*cat['FKPWeight']\\n\\n # effective nbar\\n nbar = (total_weight*cat['NZ']).sum() / total_weight.sum()\\n\\n return cat.compute(nbar)\",\n \"def _bucket_getitem(self, j, k):\\n pass\",\n \"def bar(self):\\n return self.val * 1.0\",\n \"def _bron_kerbosch(self, acc, possible, excluded, cliques):\\n if len(possible) == 0 and len(excluded) == 0:\\n cliques.append(acc)\\n for v in iter(possible):\\n neighbors_of_v = self.neighbors(v)\\n self._bron_kerbosch(\\n acc.union({v}),\\n possible.intersection(neighbors_of_v),\\n excluded.intersection(neighbors_of_v),\\n cliques)\\n possible = possible.difference({v})\\n excluded = excluded.union({v})\",\n \"def histogram(original, updated, bins=None, main=\\\"\\\", save=None, log=False):\\n #Lengths of score lists must be identical, assume in same order\\n assert len(original) == len(original)\\n\\n #Set up bins:\\n if bins is not None and bins > 0:\\n imoprovements = {(-1,-1):0}\\n for i in xrange(0, len(original), bins):\\n improvements[(0,i+bins)] = 0\\n else:\\n improvements = {(-1,-1):0, (-5,0):0, (0,1):0, (1,25):0, (25,50):0, (50,75):0, (75,100):0, (100,125):0, (125,150):0, (150,200):0, (200,300):0, (300,400):0, (500,10000):0} #defaultdict(int)\\n \\n #Calcualte improvements\\n for o, u in izip(original, updated):\\n if o>u: \\n improvements[(-1,-1)] += 1\\n continue\\n for lower, upper in improvements:\\n if lower <= int(u-o) < upper:\\n improvements[(lower,upper)] += 1\\n break\\n keys = sorted(improvements.keys(), key=lambda x:x[0])\\n values = [improvements[r] for r in keys]\\n\\n fig, ax = plt.subplots()\\n ax.set_title(main)\\n ax.set_xlabel(\\\"Improvement (updated-original) bitscores\\\")\\n ax.set_ylabel(\\\"log(Frequency)\\\")\\n #ax.set_yscale('log')\\n\\n width = 1.0\\n #ax.set_xticks(np.arange(len(improvements)))\\n #ax.set_xticklabels([l for l, u in keys])\\n bar(ax, np.arange(len(improvements)), values, log=log,\\n annotate=True, grid='y', xticklabels=[l for l, u in keys])\\n\\n if save is None:\\n plt.show()\\n else:\\n plt.savefig(save)\",\n \"def putacol(self,j_,subj,valj): # 3\\n nzj_ = None\\n if nzj_ is None:\\n nzj_ = len(subj)\\n elif nzj_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nzj_ is None:\\n nzj_ = len(valj)\\n elif nzj_ != len(valj):\\n raise IndexError(\\\"Inconsistent length of array valj\\\")\\n if nzj_ is None: nzj_ = 0\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valj is None: raise TypeError(\\\"Invalid type for argument valj\\\")\\n if valj is None:\\n valj_ = None\\n else:\\n try:\\n valj_ = memoryview(valj)\\n except TypeError:\\n try:\\n _tmparr_valj = array.array(\\\"d\\\",valj)\\n except TypeError:\\n raise TypeError(\\\"Argument valj has wrong type\\\")\\n else:\\n valj_ = memoryview(_tmparr_valj)\\n \\n else:\\n if valj_.format != \\\"d\\\":\\n valj_ = memoryview(array.array(\\\"d\\\",valj))\\n \\n res = self.__obj.putacol(j_,nzj_,subj_,valj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putacol(self,j_,subj_,valj_):\\n nzj_ = None\\n if nzj_ is None:\\n nzj_ = len(subj_)\\n elif nzj_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nzj_ is None:\\n nzj_ = len(valj_)\\n elif nzj_ != len(valj_):\\n raise IndexError(\\\"Inconsistent length of array valj\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valj_ is None:\\n raise ValueError(\\\"Argument valj cannot be None\\\")\\n if valj_ is None:\\n raise ValueError(\\\"Argument valj may not be None\\\")\\n if isinstance(valj_, numpy.ndarray) and valj_.dtype is numpy.dtype(numpy.float64) and valj_.flags.contiguous:\\n _valj_copyarray = False\\n _valj_tmp = ctypes.cast(valj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valj_ is not None:\\n _valj_copyarray = True\\n _valj_np_tmp = numpy.zeros(len(valj_),numpy.dtype(numpy.float64))\\n _valj_np_tmp[:] = valj_\\n assert _valj_np_tmp.flags.contiguous\\n _valj_tmp = ctypes.cast(_valj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valj_copyarray = False\\n _valj_tmp = None\\n \\n res = __library__.MSK_XX_putacol(self.__nativep,j_,nzj_,_subj_tmp,_valj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_j(cmd, limb, joints, index, delta):\\n joint = joints[index]\\n cmd[joint] = delta + limb.joint_angle(joint)\",\n \"def set_jbcol_from_ibcol(self, vert, bcol, set_arg=False):\\n iconn = self.conn[vert]\\n for i,iatom in enumerate(iconn):\\n j = self.conn[iatom].index(vert)\\n self.bcolors[iatom][j] = int(bcol[i])\\n if set_arg: self.bcolors[vert] = list(bcol)\\n return\",\n \"def __add_boundary_contrib_prediction(self, bc, b_idx):\\n if bc is not None:\\n if bc.boundary_condition_type is configuration.BoundaryConditionType.DIRICHLET:\\n self.ustar[b_idx] = bc.value(self.time)\\n else:\\n self.ustar[b_idx] += self.timestep * self.timestep * bc.value(self.time)\",\n \"def update_M_B(Bt, M, B):\\n n,_ = Bt.shape\\n for i in range(n):\\n g = np.where(Bt[i,:]==1)[0][0]\\n # print(\\\"g=\\\", g)\\n M.remove(g)\\n B[i].add(g)\\n return M, B\",\n \"def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\\n self.coeff[self._core.index_alpha(key[0]),\\n self._core.index_beta(key[1])] = value\",\n \"def putbarablocktriplet(self,num_,subi,subj,subk,subl,valijkl): # 3\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subi_ is not None and len(subi_) != (num_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if subk is None: raise TypeError(\\\"Invalid type for argument subk\\\")\\n if subk is None:\\n subk_ = None\\n else:\\n try:\\n subk_ = memoryview(subk)\\n except TypeError:\\n try:\\n _tmparr_subk = array.array(\\\"i\\\",subk)\\n except TypeError:\\n raise TypeError(\\\"Argument subk has wrong type\\\")\\n else:\\n subk_ = memoryview(_tmparr_subk)\\n \\n else:\\n if subk_.format != \\\"i\\\":\\n subk_ = memoryview(array.array(\\\"i\\\",subk))\\n \\n if subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk has wrong length\\\")\\n if subl is None: raise TypeError(\\\"Invalid type for argument subl\\\")\\n if subl is None:\\n subl_ = None\\n else:\\n try:\\n subl_ = memoryview(subl)\\n except TypeError:\\n try:\\n _tmparr_subl = array.array(\\\"i\\\",subl)\\n except TypeError:\\n raise TypeError(\\\"Argument subl has wrong type\\\")\\n else:\\n subl_ = memoryview(_tmparr_subl)\\n \\n else:\\n if subl_.format != \\\"i\\\":\\n subl_ = memoryview(array.array(\\\"i\\\",subl))\\n \\n if subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl has wrong length\\\")\\n if valijkl is None: raise TypeError(\\\"Invalid type for argument valijkl\\\")\\n if valijkl is None:\\n valijkl_ = None\\n else:\\n try:\\n valijkl_ = memoryview(valijkl)\\n except TypeError:\\n try:\\n _tmparr_valijkl = array.array(\\\"d\\\",valijkl)\\n except TypeError:\\n raise TypeError(\\\"Argument valijkl has wrong type\\\")\\n else:\\n valijkl_ = memoryview(_tmparr_valijkl)\\n \\n else:\\n if valijkl_.format != \\\"d\\\":\\n valijkl_ = memoryview(array.array(\\\"d\\\",valijkl))\\n \\n if valijkl_ is not None and len(valijkl_) != (num_):\\n raise ValueError(\\\"Array argument valijkl has wrong length\\\")\\n res = self.__obj.putbarablocktriplet(num_,subi_,subj_,subk_,subl_,valijkl_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getbarcblocktriplet(self,subj_,subk_,subl_,valjkl_):\\n maxnum_ = self.getnumbarcblocktriplets()\\n num_ = ctypes.c_int64()\\n _subj_minlength = (maxnum_)\\n if (maxnum_) > 0 and subj_ is not None and len(subj_) != (maxnum_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnum_)))\\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\\n raise ValueError(\\\"Argument subj must be writable\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _subk_minlength = (maxnum_)\\n if (maxnum_) > 0 and subk_ is not None and len(subk_) != (maxnum_):\\n raise ValueError(\\\"Array argument subk is not long enough: Is %d, expected %d\\\" % (len(subk_),(maxnum_)))\\n if isinstance(subk_,numpy.ndarray) and not subk_.flags.writeable:\\n raise ValueError(\\\"Argument subk must be writable\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n _subl_minlength = (maxnum_)\\n if (maxnum_) > 0 and subl_ is not None and len(subl_) != (maxnum_):\\n raise ValueError(\\\"Array argument subl is not long enough: Is %d, expected %d\\\" % (len(subl_),(maxnum_)))\\n if isinstance(subl_,numpy.ndarray) and not subl_.flags.writeable:\\n raise ValueError(\\\"Argument subl must be writable\\\")\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl may not be None\\\")\\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\\n _subl_copyarray = False\\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subl_ is not None:\\n _subl_copyarray = True\\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\\n _subl_np_tmp[:] = subl_\\n assert _subl_np_tmp.flags.contiguous\\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subl_copyarray = False\\n _subl_tmp = None\\n \\n _valjkl_minlength = (maxnum_)\\n if (maxnum_) > 0 and valjkl_ is not None and len(valjkl_) != (maxnum_):\\n raise ValueError(\\\"Array argument valjkl is not long enough: Is %d, expected %d\\\" % (len(valjkl_),(maxnum_)))\\n if isinstance(valjkl_,numpy.ndarray) and not valjkl_.flags.writeable:\\n raise ValueError(\\\"Argument valjkl must be writable\\\")\\n if valjkl_ is None:\\n raise ValueError(\\\"Argument valjkl may not be None\\\")\\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\\n _valjkl_copyarray = False\\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valjkl_ is not None:\\n _valjkl_copyarray = True\\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\\n _valjkl_np_tmp[:] = valjkl_\\n assert _valjkl_np_tmp.flags.contiguous\\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valjkl_copyarray = False\\n _valjkl_tmp = None\\n \\n res = __library__.MSK_XX_getbarcblocktriplet(self.__nativep,maxnum_,ctypes.byref(num_),_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n num_ = num_.value\\n _num_return_value = num_\\n if _subj_copyarray:\\n subj_[:] = _subj_np_tmp\\n if _subk_copyarray:\\n subk_[:] = _subk_np_tmp\\n if _subl_copyarray:\\n subl_[:] = _subl_np_tmp\\n if _valjkl_copyarray:\\n valjkl_[:] = _valjkl_np_tmp\\n return (_num_return_value)\",\n \"def cut(self,cell):\\r\\n self.grid[cell[0]][cell[1]] = 1\",\n \"def bic(self, X):\\n raise NotImplementedError\",\n \"def setSubtreeBF(self, index, subtree):\\n if index == 0:\\n try:\\n self[:] = subtree\\n except TypeError:\\n del self[1:]\\n self[0] = subtree\\n return\\n \\n queue = deque(izip(repeat(self, len(self[1:])), count(1)))\\n for i in xrange(index):\\n elem = queue.popleft()\\n parent = elem[0]\\n child = elem[1]\\n if isinstance(parent[child], Tree):\\n tree = parent[child]\\n queue.extend(izip(repeat(tree, len(tree[1:])), count(1)))\\n parent[child] = subtree\",\n \"def put_weights(self, content: ndarray, var_id: int, batch_no: int, block_id: int) -> None:\\n pass\",\n \"def ridge_cd_weight_update(\\n x: FloatTensor,\\n r: FloatTensor,\\n j: int,\\n w_j: Union[float, FloatTensor],\\n col_l2: float,\\n lmb: float,\\n) -> float:\\n if col_l2 == 0.0:\\n return 0.0\\n n, d = x.size()\\n return w_j + (x[:, j].matmul(r) - n * lmb * w_j) / col_l2\",\n \"def run(self):\\n self.coffee_machine.beans_tank.decrease_weight(self.coffee_machine.chosen_coffee_data.get('beans_weight'))\",\n \"def update_weight(self, error_matrix, i, j):\\n #weight i,j gets error of j\\n error = error_matrix[i]\\n input = log_deriv(self.input_matrix[i-1][j])\\n self.bias[i-1][j] += ALPHA * input * error_matrix[i-1][j]\\n self.weights[i][j] = [weight + ALPHA * input * error[i] for i, weight in enumerate(self.weights[i][j])]\",\n \"def plot_progression(weights, bhs, bvs):\\n\\tweights_plot = []\\n\\tfor i in range(40):\\n\\t\\tweights_plot.append(weights[i][0][0])\\t# only plots the first value in the matrix every time\\n\\tplt.plot(weights_plot)\\n\\n\\tplt.show()\",\n \"def __setitem__(self, key, item):\\n assert isinstance(key,list) and isinstance(item,list) and len(key)==2 and len(item)==2\\n self._data[self.__ptBin(key[0])][self.__etaBin(key[1])] = item\",\n \"def changeBarriers(self, value):\\r\\n\\t\\tself.numBarriers = value\",\n \"def modify_cell(self,c,**kws):\\n if 'nodes' in kws and self._node_to_cells is not None:\\n for n in self.cell_to_nodes(c):\\n self._node_to_cells[n].remove(c)\\n\\n for k,v in six.iteritems(kws):\\n if k in ('nodes','edges'):\\n self.cells[k][c,:len(v)]=v\\n self.cells[k][c,len(v):]=self.UNDEFINED\\n else: \\n self.cells[k][c]=v\\n\\n if 'nodes' in kws and self._node_to_cells is not None:\\n for n in self.cell_to_nodes(c):\\n self._node_to_cells[n].append(c)\",\n \"def getbarablocktriplet(self,subi,subj,subk,subl,valijkl): # 3\\n maxnum_ = self.getnumbarablocktriplets()\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n _copyback_subi = False\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n _copyback_subi = True\\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n _copyback_subi = True\\n if subi_ is not None and len(subi_) != (maxnum_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxnum_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if subk is None: raise TypeError(\\\"Invalid type for argument subk\\\")\\n _copyback_subk = False\\n if subk is None:\\n subk_ = None\\n else:\\n try:\\n subk_ = memoryview(subk)\\n except TypeError:\\n try:\\n _tmparr_subk = array.array(\\\"i\\\",subk)\\n except TypeError:\\n raise TypeError(\\\"Argument subk has wrong type\\\")\\n else:\\n subk_ = memoryview(_tmparr_subk)\\n _copyback_subk = True\\n else:\\n if subk_.format != \\\"i\\\":\\n subk_ = memoryview(array.array(\\\"i\\\",subk))\\n _copyback_subk = True\\n if subk_ is not None and len(subk_) != (maxnum_):\\n raise ValueError(\\\"Array argument subk has wrong length\\\")\\n if subl is None: raise TypeError(\\\"Invalid type for argument subl\\\")\\n _copyback_subl = False\\n if subl is None:\\n subl_ = None\\n else:\\n try:\\n subl_ = memoryview(subl)\\n except TypeError:\\n try:\\n _tmparr_subl = array.array(\\\"i\\\",subl)\\n except TypeError:\\n raise TypeError(\\\"Argument subl has wrong type\\\")\\n else:\\n subl_ = memoryview(_tmparr_subl)\\n _copyback_subl = True\\n else:\\n if subl_.format != \\\"i\\\":\\n subl_ = memoryview(array.array(\\\"i\\\",subl))\\n _copyback_subl = True\\n if subl_ is not None and len(subl_) != (maxnum_):\\n raise ValueError(\\\"Array argument subl has wrong length\\\")\\n if valijkl is None: raise TypeError(\\\"Invalid type for argument valijkl\\\")\\n _copyback_valijkl = False\\n if valijkl is None:\\n valijkl_ = None\\n else:\\n try:\\n valijkl_ = memoryview(valijkl)\\n except TypeError:\\n try:\\n _tmparr_valijkl = array.array(\\\"d\\\",valijkl)\\n except TypeError:\\n raise TypeError(\\\"Argument valijkl has wrong type\\\")\\n else:\\n valijkl_ = memoryview(_tmparr_valijkl)\\n _copyback_valijkl = True\\n else:\\n if valijkl_.format != \\\"d\\\":\\n valijkl_ = memoryview(array.array(\\\"d\\\",valijkl))\\n _copyback_valijkl = True\\n if valijkl_ is not None and len(valijkl_) != (maxnum_):\\n raise ValueError(\\\"Array argument valijkl has wrong length\\\")\\n res,resargs = self.__obj.getbarablocktriplet(maxnum_,subi_,subj_,subk_,subl_,valijkl_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _num_return_value = resargs\\n if _copyback_valijkl:\\n valijkl[:] = _tmparr_valijkl\\n if _copyback_subl:\\n subl[:] = _tmparr_subl\\n if _copyback_subk:\\n subk[:] = _tmparr_subk\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n if _copyback_subi:\\n subi[:] = _tmparr_subi\\n return _num_return_value\",\n \"def putconbound(self,i_,bk_,bl_,bu_): # 3\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_conjugates(self):\\n \\n # declare new dict\\n self.new_dict = copy.deepcopy(self.dict)\\n \\n # iterate over items\\n for i in range(len(self.dict)):\\n for rel_tag, hopping in self.dict[i].items():\\n x, y, z, j = rel_tag\\n reverse_tag = (-x, -y, -z, i)\\n reverse_hopping = np.conjugate(np.transpose(hopping))\\n if reverse_tag not in self.new_dict[j]:\\n self.new_dict[j][reverse_tag] = reverse_hopping\\n \\n # done\\n self.dict = self.new_dict\",\n \"def convertBetaToJacobi(self,y):\\n u = 0.5*(self.high+self.low)\\n s = 0.5*(self.high-self.low)\\n return (y-u)/(s)\",\n \"def test00b(self):\\n a = np.arange(1e2)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n b[(1,)] = 10.\\n a[(1,)] = 10.\\n # print \\\"b->\\\", `b`\\n assert_array_equal(a, b[:], \\\"__setitem__ not working correctly\\\")\",\n \"def getbarasparsity(self,idxij): # 3\\n maxnumnz_ = self.getnumbaranz()\\n if idxij is None: raise TypeError(\\\"Invalid type for argument idxij\\\")\\n _copyback_idxij = False\\n if idxij is None:\\n idxij_ = None\\n else:\\n try:\\n idxij_ = memoryview(idxij)\\n except TypeError:\\n try:\\n _tmparr_idxij = array.array(\\\"q\\\",idxij)\\n except TypeError:\\n raise TypeError(\\\"Argument idxij has wrong type\\\")\\n else:\\n idxij_ = memoryview(_tmparr_idxij)\\n _copyback_idxij = True\\n else:\\n if idxij_.format != \\\"q\\\":\\n idxij_ = memoryview(array.array(\\\"q\\\",idxij))\\n _copyback_idxij = True\\n if idxij_ is not None and len(idxij_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument idxij has wrong length\\\")\\n res,resargs = self.__obj.getbarasparsity(maxnumnz_,idxij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n if _copyback_idxij:\\n idxij[:] = _tmparr_idxij\\n return _numnz_return_value\",\n \"def update_bin(self, bin_i, conflict, sat_engine):\\r\\n global gen_debug_info\\r\\n gen_debug_info.cnt_update += 1\\r\\n logger.info('update_bin %d' % (bin_i + 1))\\r\\n logger.info('\\\\tcnt_update %d' % (gen_debug_info.cnt_update))\\r\\n\\r\\n # update var states，因为是引用的形式，所以不用更新\\r\\n # 只有当没有冲突时才更新，发生冲突的bin是unsat的，不需要\\r\\n # if conflict is False:\\r\\n # for i in xrange(sat_engine.local_vars.nv):\\r\\n # v = self.vars_bins[bin_i][i]\\r\\n # self.global_vs[v] = sat_engine.local_vars.vs[i]\\r\\n\\r\\n if conflict is False:\\r\\n if logger.level <= logging.INFO:\\r\\n logger.info(gen_debug_info.bin_clauses(\\r\\n bin_i,\\r\\n sat_engine,\\r\\n self.bin_packer.cbins[bin_i].variables))\\r\\n if logger.level <= logging.NOTSET:\\r\\n logger.info(gen_debug_info.bin_clauses_sv(sat_engine))\",\n \"def update(self, values, weights):\\n super().update(values)\",\n \"def update(self, values, weights):\\n super().update(values)\",\n \"def update(self, values, weights):\\n super().update(values)\",\n \"def update(self, values, weights):\\n super().update(values)\",\n \"def __setitem__(self, choice, weight):\\n if choice in self._weights and self._weights[choice] == weight:\\n return\\n\\n self._weights[choice] = weight\\n self._generate_p()\",\n \"def jacobian(self,var,g=None):\\n if (g==None):g=self.g\\n jac=np.zeros([self.n+1,self.n])\\n for i in range(self.n):\\n for j in range(self.n):\\n if(i==j): jac[i][j]=2.*(var[i]+1.)-g*np.sum([self.XXZ.Z(i,k) for k in range(self.n) if k!=i])\\n else: jac[i][j]=g*self.XXZ.Z(i,j)\\n for i in range(self.n):\\n jac[self.n][i]=1.\\n return jac\",\n \"def getbarasparsity(self,idxij_):\\n maxnumnz_ = self.getnumbaranz()\\n numnz_ = ctypes.c_int64()\\n _idxij_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and idxij_ is not None and len(idxij_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument idxij is not long enough: Is %d, expected %d\\\" % (len(idxij_),(maxnumnz_)))\\n if isinstance(idxij_,numpy.ndarray) and not idxij_.flags.writeable:\\n raise ValueError(\\\"Argument idxij must be writable\\\")\\n if idxij_ is None:\\n raise ValueError(\\\"Argument idxij may not be None\\\")\\n if isinstance(idxij_, numpy.ndarray) and idxij_.dtype is numpy.dtype(numpy.int64) and idxij_.flags.contiguous:\\n _idxij_copyarray = False\\n _idxij_tmp = ctypes.cast(idxij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif idxij_ is not None:\\n _idxij_copyarray = True\\n _idxij_np_tmp = numpy.zeros(len(idxij_),numpy.dtype(numpy.int64))\\n _idxij_np_tmp[:] = idxij_\\n assert _idxij_np_tmp.flags.contiguous\\n _idxij_tmp = ctypes.cast(_idxij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _idxij_copyarray = False\\n _idxij_tmp = None\\n \\n res = __library__.MSK_XX_getbarasparsity(self.__nativep,maxnumnz_,ctypes.byref(numnz_),_idxij_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numnz_ = numnz_.value\\n _numnz_return_value = numnz_\\n if _idxij_copyarray:\\n idxij_[:] = _idxij_np_tmp\\n return (_numnz_return_value)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8175987","0.73748964","0.7216971","0.62823015","0.62727207","0.6010744","0.58721524","0.5819966","0.57759297","0.5744071","0.56815547","0.5676308","0.55539197","0.54821527","0.53647524","0.53490466","0.52245176","0.52084583","0.5198786","0.5183286","0.5151148","0.51038736","0.5077571","0.5038549","0.5034123","0.5000068","0.4992607","0.49911627","0.49890035","0.4976444","0.49635035","0.4957879","0.49319497","0.4931311","0.48768348","0.48584557","0.48163435","0.4813238","0.47954288","0.47856888","0.4768873","0.47639698","0.47415635","0.4739674","0.47338402","0.47232318","0.47226626","0.47226208","0.4715338","0.47076836","0.4707669","0.47004136","0.46944338","0.4693405","0.46873683","0.46773407","0.46768472","0.46762326","0.4666982","0.46659872","0.4664893","0.4643056","0.46281034","0.4617241","0.46012482","0.45853376","0.4575655","0.45752412","0.45721683","0.4568985","0.45642534","0.45624486","0.4554526","0.45522743","0.45488712","0.45477113","0.45435202","0.45435148","0.4540735","0.4535982","0.4530904","0.45300496","0.4519101","0.45087555","0.4484613","0.4482277","0.44765192","0.44737804","0.44729027","0.44696468","0.4467615","0.44568956","0.44548523","0.44527423","0.44527423","0.44527423","0.44527423","0.44486722","0.44482276","0.44450426"],"string":"[\n \"0.8175987\",\n \"0.73748964\",\n \"0.7216971\",\n \"0.62823015\",\n \"0.62727207\",\n \"0.6010744\",\n \"0.58721524\",\n \"0.5819966\",\n \"0.57759297\",\n \"0.5744071\",\n \"0.56815547\",\n \"0.5676308\",\n \"0.55539197\",\n \"0.54821527\",\n \"0.53647524\",\n \"0.53490466\",\n \"0.52245176\",\n \"0.52084583\",\n \"0.5198786\",\n \"0.5183286\",\n \"0.5151148\",\n \"0.51038736\",\n \"0.5077571\",\n \"0.5038549\",\n \"0.5034123\",\n \"0.5000068\",\n \"0.4992607\",\n \"0.49911627\",\n \"0.49890035\",\n \"0.4976444\",\n \"0.49635035\",\n \"0.4957879\",\n \"0.49319497\",\n \"0.4931311\",\n \"0.48768348\",\n \"0.48584557\",\n \"0.48163435\",\n \"0.4813238\",\n \"0.47954288\",\n \"0.47856888\",\n \"0.4768873\",\n \"0.47639698\",\n \"0.47415635\",\n \"0.4739674\",\n \"0.47338402\",\n \"0.47232318\",\n \"0.47226626\",\n \"0.47226208\",\n \"0.4715338\",\n \"0.47076836\",\n \"0.4707669\",\n \"0.47004136\",\n \"0.46944338\",\n \"0.4693405\",\n \"0.46873683\",\n \"0.46773407\",\n \"0.46768472\",\n \"0.46762326\",\n \"0.4666982\",\n \"0.46659872\",\n \"0.4664893\",\n \"0.4643056\",\n \"0.46281034\",\n \"0.4617241\",\n \"0.46012482\",\n \"0.45853376\",\n \"0.4575655\",\n \"0.45752412\",\n \"0.45721683\",\n \"0.4568985\",\n \"0.45642534\",\n \"0.45624486\",\n \"0.4554526\",\n \"0.45522743\",\n \"0.45488712\",\n \"0.45477113\",\n \"0.45435202\",\n \"0.45435148\",\n \"0.4540735\",\n \"0.4535982\",\n \"0.4530904\",\n \"0.45300496\",\n \"0.4519101\",\n \"0.45087555\",\n \"0.4484613\",\n \"0.4482277\",\n \"0.44765192\",\n \"0.44737804\",\n \"0.44729027\",\n \"0.44696468\",\n \"0.4467615\",\n \"0.44568956\",\n \"0.44548523\",\n \"0.44527423\",\n \"0.44527423\",\n \"0.44527423\",\n \"0.44527423\",\n \"0.44486722\",\n \"0.44482276\",\n \"0.44450426\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77634215"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11969,"cells":{"query":{"kind":"string","value":"Replaces a conic constraint. putcone(self,k_,ct_,conepar_,submem_)"},"document":{"kind":"string","value":"def putcone(self,k_,ct_,conepar_,submem_):\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem_)\n elif nummem_ != len(submem_):\n raise IndexError(\"Inconsistent length of array submem\")\n if submem_ is None:\n raise ValueError(\"Argument submem cannot be None\")\n if submem_ is None:\n raise ValueError(\"Argument submem may not be None\")\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\n _submem_copyarray = False\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif submem_ is not None:\n _submem_copyarray = True\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\n _submem_np_tmp[:] = submem_\n assert _submem_np_tmp.flags.contiguous\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _submem_copyarray = False\n _submem_tmp = None\n \n res = __library__.MSK_XX_putcone(self.__nativep,k_,ct_,conepar_,nummem_,_submem_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putcone(self,k_,ct_,conepar_,submem): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem)\n elif nummem_ != len(submem):\n raise IndexError(\"Inconsistent length of array submem\")\n if nummem_ is None: nummem_ = 0\n if submem is None: raise TypeError(\"Invalid type for argument submem\")\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n \n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n \n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def appendcone(self,ct_,conepar_,submem_):\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem_)\n elif nummem_ != len(submem_):\n raise IndexError(\"Inconsistent length of array submem\")\n if submem_ is None:\n raise ValueError(\"Argument submem cannot be None\")\n if submem_ is None:\n raise ValueError(\"Argument submem may not be None\")\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\n _submem_copyarray = False\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif submem_ is not None:\n _submem_copyarray = True\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\n _submem_np_tmp[:] = submem_\n assert _submem_np_tmp.flags.contiguous\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _submem_copyarray = False\n _submem_tmp = None\n \n res = __library__.MSK_XX_appendcone(self.__nativep,ct_,conepar_,nummem_,_submem_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def appendcone(self,ct_,conepar_,submem): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem)\n elif nummem_ != len(submem):\n raise IndexError(\"Inconsistent length of array submem\")\n if nummem_ is None: nummem_ = 0\n if submem is None: raise TypeError(\"Invalid type for argument submem\")\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n \n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n \n res = self.__obj.appendcone(ct_,conepar_,nummem_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def appendconeseq(self,ct_,conepar_,nummem_,j_):\n res = __library__.MSK_XX_appendconeseq(self.__nativep,ct_,conepar_,nummem_,j_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def appendconeseq(self,ct_,conepar_,nummem_,j_): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n res = self.__obj.appendconeseq(ct_,conepar_,nummem_,j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getcone(self,k_,submem): # 3\n _copyback_submem = False\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n _copyback_submem = True\n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n _copyback_submem = True\n if submem_ is not None and len(submem_) != self.getconeinfo((k_))[2]:\n raise ValueError(\"Array argument submem has wrong length\")\n res,resargs = self.__obj.getcone(k_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ct_return_value,_conepar_return_value,_nummem_return_value = resargs\n if _copyback_submem:\n submem[:] = _tmparr_submem\n _ct_return_value = conetype(_ct_return_value)\n return _ct_return_value,_conepar_return_value,_nummem_return_value","def getcone(self,k_,submem_):\n ct_ = ctypes.c_int32()\n conepar_ = ctypes.c_double()\n nummem_ = ctypes.c_int32()\n _submem_minlength = self.getconeinfo((k_))[2]\n if self.getconeinfo((k_))[2] > 0 and submem_ is not None and len(submem_) != self.getconeinfo((k_))[2]:\n raise ValueError(\"Array argument submem is not long enough: Is %d, expected %d\" % (len(submem_),self.getconeinfo((k_))[2]))\n if isinstance(submem_,numpy.ndarray) and not submem_.flags.writeable:\n raise ValueError(\"Argument submem must be writable\")\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\n _submem_copyarray = False\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif submem_ is not None:\n _submem_copyarray = True\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\n _submem_np_tmp[:] = submem_\n assert _submem_np_tmp.flags.contiguous\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _submem_copyarray = False\n _submem_tmp = None\n \n res = __library__.MSK_XX_getcone(self.__nativep,k_,ctypes.byref(ct_),ctypes.byref(conepar_),ctypes.byref(nummem_),_submem_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ct_return_value = conetype(ct_.value)\n conepar_ = conepar_.value\n _conepar_return_value = conepar_\n nummem_ = nummem_.value\n _nummem_return_value = nummem_\n if _submem_copyarray:\n submem_[:] = _submem_np_tmp\n return (_ct_return_value,_conepar_return_value,_nummem_return_value)","def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _k_coaffine_pair(pair, bound=math.inf):\n g = pair.graph\n sigma = pair.coaffination\n kg = clique_graph(g, bound)\n coaf_k = dict([])\n for q in kg:\n coaf_k[q] = Clique([sigma[x] for x in q])\n return CoaffinePair(kg, coaf_k)","def set_ecuacion_constitutiva(self, param_con, ec_con_id):\n self.param_con = param_con\n self.ecucon_id = ec_con_id\n self.ecuacion_constitutiva = self.ecuaciones_constitutivas(ec_con_id)","def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconbound(self,i_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskc(self,whichsol_,skc_):\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if skc_ is None:\n raise ValueError(\"Argument skc cannot be None\")\n if skc_ is None:\n raise ValueError(\"Argument skc may not be None\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def constraint(self, c):\n self.add_constraint(c)","def cz(control: QubitInput, target: QubitInput) -> Instruction:\n return Instruction(CZ(), target=[control, target])","def cone(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True, degree:\n Union[int, bool]=3, endSweep: Union[float, bool]=2, heightRatio: Union[float,\n bool]=2.0, nodeState: Union[int, bool]=0, pivot: Union[List[float, float, float],\n bool]=None, radius: Union[float, bool]=1.0, sections: Union[int, bool]=8, spans:\n Union[int, bool]=1, startSweep: Union[float, bool]=0, tolerance: Union[float,\n bool]=0.01, useOldInitBehaviour: bool=False, useTolerance: bool=False,\n constructionHistory: bool=True, name: AnyStr=\"\", object: bool=True, polygon: int=0,\n q=True, query=True, e=True, edit=True, **kwargs)->Union[List[AnyStr], Any]:\n pass","def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_conectividad(self, conec):\n self.add_conec_listoflists(conec) # calcula el ne y el je","def con_ceq(x,project):\n \n cons = project.con_ceq(x)\n \n if cons: cons = array(cons)\n else: cons = zeros([0])\n \n return cons","def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubk is None: raise TypeError(\"Invalid type for argument qcsubk\")\n if qcsubk is None:\n qcsubk_ = None\n else:\n try:\n qcsubk_ = memoryview(qcsubk)\n except TypeError:\n try:\n _tmparr_qcsubk = array.array(\"i\",qcsubk)\n except TypeError:\n raise TypeError(\"Argument qcsubk has wrong type\")\n else:\n qcsubk_ = memoryview(_tmparr_qcsubk)\n \n else:\n if qcsubk_.format != \"i\":\n qcsubk_ = memoryview(array.array(\"i\",qcsubk))\n \n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getconeinfo(self,k_): # 3\n res,resargs = self.__obj.getconeinfo(k_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ct_return_value,_conepar_return_value,_nummem_return_value = resargs\n _ct_return_value = conetype(_ct_return_value)\n return _ct_return_value,_conepar_return_value,_nummem_return_value","def transform_coors(self, mtx_t, ref_coors=None):\n if ref_coors is None:\n ref_coors = self.coors\n\n if mtx_t.shape[1] > self.coors.shape[1]:\n self.coors[:] = nm.dot(ref_coors, mtx_t[:,:-1].T) + mtx_t[:,-1]\n else:\n self.coors[:] = nm.dot(ref_coors, mtx_t.T)","def SetPRCatConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for line in tot :\n for val in line :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def c_code_contiguous(self, node, name, inp, out, sub):\r\n raise theano.gof.utils.MethodNotDefined()","def remove_constraint(self, ckey):\n if ckey not in self.constraints:\n raise KeyError(\"Constraints not found on object key: {}\".format(ckey))\n del self.constraints[ckey]","def censor_contig(contig_end, u_contigs, o_dict):\n for c_e in [contig_end, other_end(contig_end)]:\n if c_e in u_contigs:\n u_contigs.remove(c_e)\n if c_e in o_dict:\n o_dic = o_dict[c_e]\n if o_dic != {}:\n overlapped_contig = list(o_dic.keys())[0]\n if overlapped_contig in o_dict: del o_dict[overlapped_contig][c_e]\n del o_dict[c_e]\n return","def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putxc(self,whichsol_,xc_):\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp","def appendconesseq(self,ct_,conepar_,nummem_,j_):\n num_ = None\n if num_ is None:\n num_ = len(ct_)\n elif num_ != len(ct_):\n raise IndexError(\"Inconsistent length of array ct\")\n if num_ is None:\n num_ = len(conepar_)\n elif num_ != len(conepar_):\n raise IndexError(\"Inconsistent length of array conepar\")\n if num_ is None:\n num_ = len(nummem_)\n elif num_ != len(nummem_):\n raise IndexError(\"Inconsistent length of array nummem\")\n if ct_ is None:\n raise ValueError(\"Argument ct cannot be None\")\n if ct_ is None:\n raise ValueError(\"Argument ct may not be None\")\n if ct_ is not None:\n _ct_tmp = (ctypes.c_int32 * len(ct_))(*ct_)\n else:\n _ct_tmp = None\n if conepar_ is None:\n raise ValueError(\"Argument conepar cannot be None\")\n if conepar_ is None:\n raise ValueError(\"Argument conepar may not be None\")\n if isinstance(conepar_, numpy.ndarray) and conepar_.dtype is numpy.dtype(numpy.float64) and conepar_.flags.contiguous:\n _conepar_copyarray = False\n _conepar_tmp = ctypes.cast(conepar_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif conepar_ is not None:\n _conepar_copyarray = True\n _conepar_np_tmp = numpy.zeros(len(conepar_),numpy.dtype(numpy.float64))\n _conepar_np_tmp[:] = conepar_\n assert _conepar_np_tmp.flags.contiguous\n _conepar_tmp = ctypes.cast(_conepar_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _conepar_copyarray = False\n _conepar_tmp = None\n \n if nummem_ is None:\n raise ValueError(\"Argument nummem cannot be None\")\n if nummem_ is None:\n raise ValueError(\"Argument nummem may not be None\")\n if isinstance(nummem_, numpy.ndarray) and nummem_.dtype is numpy.dtype(numpy.int32) and nummem_.flags.contiguous:\n _nummem_copyarray = False\n _nummem_tmp = ctypes.cast(nummem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif nummem_ is not None:\n _nummem_copyarray = True\n _nummem_np_tmp = numpy.zeros(len(nummem_),numpy.dtype(numpy.int32))\n _nummem_np_tmp[:] = nummem_\n assert _nummem_np_tmp.flags.contiguous\n _nummem_tmp = ctypes.cast(_nummem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _nummem_copyarray = False\n _nummem_tmp = None\n \n res = __library__.MSK_XX_appendconesseq(self.__nativep,num_,_ct_tmp,_conepar_tmp,_nummem_tmp,j_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_constraint(self, constraint):\n self._ckey += 1\n self.constraints[self._ckey] = constraint","def put_C_reduced(self, dest):\n _parallel.call_from_rank_zero(self.put_mat, self.C_reduced, dest)\n _parallel.barrier()","def placeConcentrationInto(self, region, conc):\n self._sim.distributeConcentration(self, region, conc)\n return self","def release_cytokine(self) :\n for cy in self.cytokine :\n if self.cytosol.get(cy, False) and self.cytosol[cy] > 0:\n cy(self.pos, tool.rand_2D(1.2))\n self.cytosol[cy] -= 1\n else : \n self.ribosome.mrna(cy, 100)\n self.cytokine.clear()","def poet_known(self, K, tau):\n # check K and tau\n if K < 0 or K >= self.N:\n raise RuntimeError(\"Invalid value for K, number of factors.\")\n if tau < 0 or tau > 1:\n raise RuntimeError(\"Invalid value for tau, which should be between 0 and 1.\")\n \n # mean centering and calculate SVD for pca\n Rc = self.R.T - np.mean(self.R, axis=1)\n u, s, vt = np.linalg.svd(Rc/np.sqrt(self.T-1))\n eigvecs = vt.T\n eigvals = s**2\n \n # decomposition of covariance matrix\n cov_pca = eigvecs[:,:K] @ np.diag(eigvals[:K]) @ eigvecs[:,:K].T\n Rk = self.cov - cov_pca\n \n # thresholding the complement matrix\n rii = np.diag(Rk)\n tauij = np.sqrt(np.outer(rii, rii))*tau\n RkT = Rk*(Rk > tauij)\n \n # combine the two terms\n result = cov_pca + RkT\n return result","def test_kyc_put_legal(self):\n pass","def set_coor(self, new_coor):\n self.__x_coor, self.__y_coor = new_coor","def putconbound(self,i_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def bcc(self, arg):\n\n self.pc += arg if not self.p & const.FLAG_CARRY else 0\n self.pc = c_uint16(self.pc).value","def CZ(self, qubit_expr):\n self.apply_gate_operation(cirq.ops.CZ, qubit_expr)","def move_to_coc(self):\n coc = scale(self.center_of_charge(), -1.0)\n self.translate(coc)","def pk_cs(self, snr=30, headroom = 0):\n # Initialize\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\n # loop over frequencies\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\n # print(self.pk.shape)\n for jf, k0 in enumerate(self.controls.k0):\n # get the scaled version of the propagating directions\n k_vec = k0 * self.dir\n # Form the sensing matrix\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\n H = h_mtx.astype(complex)\n # measured data\n pm = self.pres_s[:,jf].astype(complex)\n # Performing constrained optmization cvxpy\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True)\n # Create the problem\n epsilon = 10**(-(snr-headroom)/10)\n objective = cp.Minimize(cp.pnorm(x_cvx, p=1))\n constraints = [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon]#[H*x == pm]\n # Create the problem and solve\n problem = cp.Problem(objective, constraints)\n problem.solve(solver=cp.SCS, verbose=True) \n self.pk[:,jf] = x_cvx.value\n bar.update(1)\n bar.close()\n return self.pk","def Ev_ccs(ccs_coord, ccs_span, vacancy_index, **kwargs):\n if 'QMInp' not in kwargs:\n qtk.exit(\"kwargs: 'QMInp' is missing.\\n\"\\\n + \"It should be set to QMInp object of \"\\\n + \"system without vacancies.\\n\"\\\n + \"It is necessary for inp settings\")\n base_inp = kwargs['QMInp']\n\n qm_setting = {}\n if 'qm_setting' in kwargs:\n qm_setting = kwargs['qm_setting']\n\n if 'pref' in kwargs and 'vref' in kwargs:\n alchem = True\n perfect_ref = kwargs['pref']\n vacancy_ref = kwargs['vref']\n elif 'pref' not in kwargs and 'vref' not in kwargs:\n alchem = False\n\n freeE = qtk.QMOut('freeAtom/freeAtom.out')\n freeE.inUnit('ev')\n\n if 'threads' in kwargs:\n _threads = kwargs['threads']\n else:\n _threads = 1\n\n inp_wov = qtk.QMInp(ccs_span.generate(**ccs_coord))\n inp_wv = qtk.QMInp(ccs_span.generate(**ccs_coord))\n\n inp_wv.removeAtoms(vacancy_index)\n inp_wv.setChargeMultiplicity(0, 2)\n\n perfect = 'ev_perfect' + str(os.getpid())\n vacancy = 'ev_vacancy' + str(os.getpid())\n perfectinp = perfect + '.inp'\n vacancyinp = vacancy + '.inp'\n inp_wov.molecule.name = perfectinp\n inp_wv.molecule.name = vacancyinp\n\n if os.path.exists(perfect):\n shutil.rmtree(perfect)\n if os.path.exists(vacancy):\n shutil.rmtree(vacancy)\n\n print ccs_coord\n if alchem:\n out_wov = qtk.Al1st(inp_wov, ref_dir=perfect_ref, **qm_setting)\n out_wv = qtk.Al1st(inp_wv, ref_dir=vacancy_ref, **qm_setting)\n else:\n out_wov = inp_wov.run(**qm_setting)\n out_wv = inp_wv.run(**qm_setting)\n try:\n os.remove(perfectinp)\n os.remove(vacancyinp)\n except OSError:\n shutil.rmtree(perfectinp)\n shutil.rmtree(vacancyinp)\n\n out_wov.inUnit('ev')\n out_wv.inUnit('ev')\n\n final = out_wov - out_wv - freeE\n\n msg = str(out_wov.Et) + '-(' + str(out_wv.Et) + \\\n '+' + str(freeE.Et) + ') = ' + str(final.Et)\n qtk.report('trial Ev', msg)\n\n return final.Et","def replace_construct(self, c):\n if self.array_index is not None:\n self.parent_item.construct.args[self.arg_index][self.array_index] = c\n elif self.arg_index is not None:\n self.parent_item.construct.args[self.arg_index] = c\n else:\n raise ValueError(\"Invalid parent\")","def putskc(self,whichsol_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None: raise TypeError(\"Invalid type for argument skc\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskc(whichsol_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def appendconesseq(self,ct,conepar,nummem,j_): # 3\n num_ = None\n if num_ is None:\n num_ = len(ct)\n elif num_ != len(ct):\n raise IndexError(\"Inconsistent length of array ct\")\n if num_ is None:\n num_ = len(conepar)\n elif num_ != len(conepar):\n raise IndexError(\"Inconsistent length of array conepar\")\n if num_ is None:\n num_ = len(nummem)\n elif num_ != len(nummem):\n raise IndexError(\"Inconsistent length of array nummem\")\n if num_ is None: num_ = 0\n if ct is None: raise TypeError(\"Invalid type for argument ct\")\n if ct is None:\n ct_ = None\n else:\n try:\n ct_ = memoryview(ct)\n except TypeError:\n try:\n _tmparr_ct = array.array(\"i\",ct)\n except TypeError:\n raise TypeError(\"Argument ct has wrong type\")\n else:\n ct_ = memoryview(_tmparr_ct)\n \n else:\n if ct_.format != \"i\":\n ct_ = memoryview(array.array(\"i\",ct))\n \n if conepar is None: raise TypeError(\"Invalid type for argument conepar\")\n if conepar is None:\n conepar_ = None\n else:\n try:\n conepar_ = memoryview(conepar)\n except TypeError:\n try:\n _tmparr_conepar = array.array(\"d\",conepar)\n except TypeError:\n raise TypeError(\"Argument conepar has wrong type\")\n else:\n conepar_ = memoryview(_tmparr_conepar)\n \n else:\n if conepar_.format != \"d\":\n conepar_ = memoryview(array.array(\"d\",conepar))\n \n if nummem is None: raise TypeError(\"Invalid type for argument nummem\")\n if nummem is None:\n nummem_ = None\n else:\n try:\n nummem_ = memoryview(nummem)\n except TypeError:\n try:\n _tmparr_nummem = array.array(\"i\",nummem)\n except TypeError:\n raise TypeError(\"Argument nummem has wrong type\")\n else:\n nummem_ = memoryview(_tmparr_nummem)\n \n else:\n if nummem_.format != \"i\":\n nummem_ = memoryview(array.array(\"i\",nummem))\n \n res = self.__obj.appendconesseq(num_,ct_,conepar_,nummem_,j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def pk_constrained(self, snr=30, headroom = 0):\n # Initialize\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\n # loop over frequencies\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\n for jf, k0 in enumerate(self.controls.k0):\n # get the scaled version of the propagating directions\n k_vec = k0 * self.dir\n # Form the sensing matrix\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\n H = h_mtx.astype(complex) # cvxpy does not accept floats, apparently\n # measured data\n pm = self.pres_s[:,jf].astype(complex)\n # Performing constrained optmization cvxpy\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True) # create x variable\n # Create the problem\n epsilon = 10**(-(snr-headroom)/10)\n problem = cp.Problem(cp.Minimize(cp.norm2(x_cvx)**2),\n [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon])\n problem.solve(solver=cp.SCS, verbose=False)\n self.pk[:,jf] = x_cvx.value\n bar.update(1)\n bar.close()","def SetConicMode(self, *args):\n return _ShapeUpgrade.ShapeUpgrade_ConvertCurve3dToBezier_SetConicMode(self, *args)","def ikcon(self, T, q0=None):\n\n if not isinstance(T, SE3):\n T = SE3(T)\n\n trajn = len(T)\n\n try:\n if q0 is not None:\n q0 = getvector(q0, self.n, 'row')\n else:\n q0 = np.zeros((trajn, self.n))\n except ValueError:\n verifymatrix(q0, (trajn, self.n))\n\n # create output variables\n qstar = np.zeros((trajn, self.n))\n error = []\n exitflag = []\n\n omega = np.diag([1, 1, 1, 3 / self.reach])\n\n def cost(q, T, omega):\n return np.sum(\n (\n (np.linalg.pinv(T.A) @ self.fkine(q).A - np.eye(4)) @\n omega) ** 2\n )\n\n bnds = Bounds(self.qlim[0, :], self.qlim[1, :])\n\n for i in range(trajn):\n Ti = T[i]\n res = minimize(\n lambda q: cost(q, Ti, omega),\n q0[i, :], bounds=bnds, options={'gtol': 1e-6})\n qstar[i, :] = res.x\n error.append(res.fun)\n exitflag.append(res.success)\n\n if trajn > 1:\n return qstar, exitflag, error\n else:\n return qstar[0, :], exitflag[0], error[0]","def ALIGNF(km_list, ky):\n n_feat = len(km_list)\n\n #km_list_copy = []\n # center the kernel first\n #for i in range(n_feat):\n # km_list_copy.append(center(km_list[i].copy()))\n #ky_copy = center(ky.copy())\n\n\n a = np.zeros(n_feat)\n for i in range(n_feat):\n a[i] = f_dot(km_list[i], ky)\n\n M = np.zeros((n_feat, n_feat))\n for i in range(n_feat):\n for j in range(i,n_feat):\n M[i,j] = f_dot(km_list[i],km_list[j])\n M[j,i] = M[i,j]\n\n Q = 2*M\n C = -2*a\n\n Q = Q + np.diag(np.ones(n_feat)*1e-8)\n\n ################################################\n # Using mosek to solve the quadratice programming\n\n # Set upper diagonal element to zeros, mosek only accept lower triangle\n iu = np.triu_indices(n_feat,1)\n Q[iu] = 0\n\n # start solving with mosek\n inf = 0.0\n env = mosek.Env()\n env.set_Stream(mosek.streamtype.log, streamprinter)\n\n # Create a task \n task = env.Task()\n task.set_Stream(mosek.streamtype.log, streamprinter)\n\n # Set up bound for variables \n bkx = [mosek.boundkey.lo]* n_feat\n blx = [0.0] * n_feat\n #bkx = [mosek.boundkey.fr]* n_feat\n #blx = [-inf] * n_feat\n bux = [+inf] * n_feat\n\n numvar = len(bkx)\n\n task.appendvars(numvar)\n\n for j in range(numvar):\n task.putcj(j,C[j])\n task.putvarbound(j,bkx[j],blx[j],bux[j])\n\n # Set up quadratic objective \n inds = np.nonzero(Q)\n qsubi = inds[0].tolist()\n qsubj = inds[1].tolist()\n qval = Q[inds].tolist()\n\n # Input quadratic objective \n task.putqobj(qsubi,qsubj,qval)\n\n # Input objective sense (minimize/mximize) \n task.putobjsense(mosek.objsense.minimize)\n\n task.optimize()\n\n # Print a summary containing information \n # about the solution for debugging purposes \n task.solutionsummary(mosek.streamtype.msg)\n\n solsta = task.getsolsta(mosek.soltype.itr)\n if (solsta == mosek.solsta.optimal or\n solsta == mosek.solsta.near_optimal):\n # Output a solution \n xx = np.zeros(numvar, float)\n task.getxx(mosek.soltype.itr, xx)\n #xx = xx/np.linalg.norm(xx)\n return xx\n else:\n print solsta\n xx = np.zeros(numvar, float)\n task.getxx(mosek.soltype.itr, xx)\n #xx = xx/np.linalg.norm(xx)\n return xx","def conj(x, *args, **kwargs):\n raise NotImplementedError","def add_corridor_constraint(self,seg,r,weight=1.0):\n\n constraint_type = \"cylinder\"\n params = dict()\n params['x1'] = np.array([ self.qr_polytraj.waypoints['x'][0,seg],\n self.qr_polytraj.waypoints['y'][0,seg],\n self.qr_polytraj.waypoints['z'][0,seg]])\n params['x2'] = np.array([ self.qr_polytraj.waypoints['x'][0,seg+1],\n self.qr_polytraj.waypoints['y'][0,seg+1],\n self.qr_polytraj.waypoints['z'][0,seg+1]])\n params['der'] = 0\n params['l'] = r # Give the same radius buffer on the end caps\n params['r'] = r\n params['weight'] = weight\n params['keep_out'] = False\n params['active_seg'] = seg\n\n\n self.qr_polytraj.add_constraint(constraint_type,params,dynamic_weighting=False,sum_func=False)","def fixC(self,i,value):\n if self.coeffPattern[2] == None:\n m,n=self.m,self.n\n self.coeffPattern[2] = [None]*m\n self.coeffPattern[2][i]=value\n self._updateEstimatorSize(i)","def cz(self, q0, q1, ctrl=None):\n self.__add_quantum_gate(kind=CONTROLLED_Z, qid=[q0,q1], ctrl=ctrl)\n return self","def _cpEquilibriumFunc(self,cp,k6,kdT,kdI,Kactiv0,Kinhib0,Cactiv0,Cinhib0,E0,X0,masks):\n new_cp = 1\n olderX = [np.zeros(m.shape[1]) for m in masks]\n for layeridx,layer in enumerate(masks):\n layerEq = np.zeros(layer.shape[1])\n if(layeridx==0):\n\n for inpIdx in range(layer.shape[1]):\n if self.k1M[layeridx][inpIdx,inpIdx]*E0*self.TA0[layeridx][inpIdx,inpIdx]/cp == 0:\n #case where there is no polynomial equation\n new_input = X0[inpIdx]\n new_cp += 0\n else:\n bOnA = X0[inpIdx]-self.TA0[layeridx][inpIdx,inpIdx]- cp/(self.k1M[layeridx][inpIdx,inpIdx]*E0)\n # BE CAREFUL, HERE ONE SHOULD USE THE GREATER ROOT of the polynomial (only positive)\n new_input = 1/2*(bOnA + (bOnA**2+4*cp*X0[inpIdx]/(self.k1M[layeridx][inpIdx,inpIdx]*E0))**0.5)\n # update for fixed point:\n new_cp += self.k1M[layeridx][inpIdx,inpIdx]*new_input*self.TA0[layeridx][inpIdx,inpIdx]/(1+self.k1M[layeridx][inpIdx,inpIdx]*new_input*E0/cp)\n layerEq[inpIdx] = new_input\n olderX[layeridx] = layerEq\n\n else:\n for inpIdx in range(layer.shape[1]):\n #compute of Cactivs,Cinhibs, the denominator marks the template's variation from equilibrium\n #Terms for the previous layers\n CactivsOld = np.where(masks[layeridx-1][inpIdx,:]>0,Cactiv0[layeridx-1][inpIdx]/(1+self.k1M[layeridx-1][inpIdx]*olderX[layeridx-1]*self.E0/cp),0)\n CinhibsOld = np.where(masks[layeridx-1][inpIdx,:]<0,Cinhib0[layeridx-1][inpIdx]/(1+self.k3M[layeridx-1][inpIdx]*olderX[layeridx-1]*self.E0/cp),0)\n Inhib = np.sum(CinhibsOld*olderX[layeridx-1]/kdT[layeridx-1][inpIdx])\n #computing of new equilibrium\n x_eq = np.sum(CactivsOld*olderX[layeridx-1]/(kdI[layeridx-1][inpIdx]*cp+Inhib/cp))\n # print(np.sum(CactivsOld*olderX[layeridx-1])/(kdI[layeridx-1][inpIdx,0]*cp+Inhib/cp))\n # print(x_eq)\n # assert np.sum(CactivsOld*olderX[layeridx-1])/(kdI[layeridx-1][inpIdx,0]*cp+Inhib/cp) == x_eq\n\n layerEq[inpIdx] = x_eq\n\n\n #compute of Kactivs,Kinhibs, for the current layer:\n Kactivs = np.where(layer[:,inpIdx]>0,Kactiv0[layeridx][:,inpIdx],0)\n Kinhibs = np.where(layer[:,inpIdx]<0,Kinhib0[layeridx][:,inpIdx],0)\n #Adding, to the competition over enzyme, the complex formed in this layer by this input.\n firstComplex = np.sum(np.where(layer[:,inpIdx]>0,Kactivs*x_eq/(1+self.k1M[layeridx][:,inpIdx]*x_eq*self.E0/cp),np.where(layer[:,inpIdx]<0,Kinhibs*x_eq/(1+self.k3M[layeridx][:,inpIdx]*x_eq*self.E0/cp),0)))\n if layeridx>1:\n #We must also add the effect of pseudoTempalte enzymatic complex in the previous layers which can't be computed previously because we missed x_eq\n Inhib2 = np.sum(CinhibsOld*olderX[layeridx-1]/(kdT[layeridx-1][inpIdx]*k6[layeridx-1][inpIdx]))\n new_cp += Inhib2/(E0*cp)*x_eq\n new_cp += firstComplex\n\n olderX[layeridx] = layerEq\n\n #Finally we must add the effect of pseudoTemplate enzymatic complex in the last layer\n for outputsIdx in range(masks[-1].shape[0]):\n Cactivs = np.where(masks[-1][outputsIdx,:]>0,Cactiv0[-1][outputsIdx]/(1+self.k1M[-1][outputsIdx]*olderX[-1]*self.E0/cp),0)\n Cinhibs = np.where(masks[-1][outputsIdx,:]<0,Cinhib0[-1][outputsIdx]/(1+self.k3M[-1][outputsIdx]*olderX[-1]*self.E0/cp),0)\n\n Inhib = np.sum(Cinhibs*olderX[-1]/kdT[-1][outputsIdx])\n x_eq = np.sum(Cactivs*olderX[-1]/(kdI[-1][outputsIdx]*cp+Inhib/cp))\n Inhib2 = np.sum(Cinhibs*olderX[-1]/(kdT[-1][outputsIdx]*k6[-1][outputsIdx]))\n new_cp += Inhib2/(E0*cp)*x_eq\n\n\n return cp - new_cp","def con_cieq(x,project):\n \n cons = project.con_cieq(x)\n \n if cons: cons = array(cons)\n else: cons = zeros([0])\n \n return -cons","def _ReplaceCompound(self, from_id, to_id):\n if from_id == to_id:\n return\n \n # set the coefficient of the original compound to 0\n i = self._FindCompoundIndex(from_id)\n if i is None:\n return\n how_many = self.reactants[i].coeff\n self.reactants[i].coeff = 0\n\n # create a new compound with the new kegg_id and the same coefficient\n # or add the number to the coefficient if it already is a reactant\n j = self._FindCompoundIndex(to_id)\n if j is None:\n self.reactants[i] = CompoundWithCoeff.FromId(how_many, to_id)\n else:\n self.reactants[j].coeff += how_many\n self._Dedup()\n\n # clear the cache since the reaction has changed\n self._catalyzing_enzymes = None","def bcs(self, arg):\n\n self.pc += arg if self.p & const.FLAG_CARRY else 0\n self.pc = c_uint16(self.pc).value","def put(self, pvname, value: Union[np.ndarray, float]) -> None:\n if self.protocol == \"ca\":\n caput(pvname, value)\n\n elif self.protocol == \"pva\":\n self.context.put(pvname, value)","def mapKeypointsToCOCO(self, cmu_keypoint):\n coco_keypoint = np.zeros((4, 17), np.float)\n for cmu_coco_idx in CMU_TO_COCO_JOINT_LABEL.items():\n cmu_idx = cmu_coco_idx[0]\n coco_idx = cmu_coco_idx[1]\n coco_keypoint[:, coco_idx] = cmu_keypoint[:, cmu_idx]\n\n return coco_keypoint","def covar(fx,cx):\n \n fx = np.array(fx)\n cx = np.array(cx)\n \n shape_fx = fx.shape\n shape_cx = cx.shape\n \n \n if shape_fx[1] != shape_cx[0]:\n print('-----------------------------------------')\n print(\"Shapes of fx and cx cannot be multiplied:\")\n print(shape_fx,\"x\",shape_cx)\n print('-----------------------------------------')\n raise ValueError('Input matrices are not compliant')\n \n cy = np.dot(np.dot(fx,cx),fx.T)\n \n print(\"Size of Cy matrix: \",np.shape(cy))\n \n return cy","def weak_repulsion_boundary(Cents,a,k, CV_matrix,n_c,n_C):\n CCW = np.dstack((roll_reverse(Cents[:,:,0]),roll_reverse(Cents[:,:,1])))#np.column_stack((Cents[:,1:3],Cents[:,0].reshape(-1,1,2)))\n CCW_displacement = Cents - CCW\n rij = np.sqrt(CCW_displacement[:,:,0]**2 + CCW_displacement[:,:,1]**2)\n norm_disp = (CCW_displacement.T/rij.T).T\n V_soft_mag = -k*(rij - 2*a)*(rij<2*a)\n V_soft_CCW = (V_soft_mag.T*norm_disp.T).T\n V_soft_CW = -(roll_forward(V_soft_mag).T*norm_disp.T).T\n V_soft = V_soft_CW + V_soft_CCW\n F_soft = np.zeros((n_c, 2))\n for i in range(3):\n F_soft += np.asfortranarray(CV_matrix[:, :, i])@np.asfortranarray(V_soft[:, i])\n F_soft[n_C:] = 0\n return F_soft","def con_minimize(\n fun, bounds, constr=(), x0=None, args=(), callback=None, options={}, workers=None\n):\n # Wrap cost function with constraints\n def fun_soft_con(x, *augmented_args):\n # Unpack constraints and arguments\n fcore = augmented_args[0] # Function to be minimized\n fcons = augmented_args[1] # Constraints\n user_args = augmented_args[2:] # Arguments\n\n # Evaluate core function\n ycore = fcore(x, *user_args)\n\n # Initialize penalty\n penalty = 0.0\n\n # Update penalty\n # (the more negative fcon() is, the higher penalty)\n for f in fcons:\n ycon = np.max([f(x, *user_args) * -1.0, 0.0])\n pscale = ycore / (ycon + 1e-6)\n penalty += ycon * pscale\n\n return ycore + penalty\n\n # Run minimization\n augmented_args = (fun, constr, *args)\n\n res = modestga.minimize(\n fun=fun_soft_con,\n bounds=bounds,\n x0=x0,\n args=augmented_args,\n callback=callback,\n options=options,\n workers=workers,\n )\n\n # Extend result with contraint violation info\n res.constr = [fcon(res.x, *args) for fcon in constr]\n\n return res","def kspace_cholesky_solve(self, other, complx = False, n_points = None):\n if n_points is None:\n \n m1n = np.max(np.abs(self.coords), axis = 0)\n m2n = np.max(np.abs(other.coords), axis = 0)\n \n n_points = np.max([m1n,m2n], axis = 0) \n\n nx,ny,nz = 2*n_points + 1\n\n #print(\"kspace_svd_solve: \", nx,ny,nz)\n m1x,m1y = self.blocks.shape[1], self.blocks.shape[2]\n m2x,m2y = other.blocks.shape[1], other.blocks.shape[2]\n \n coords = np.roll(lattice_coords(n_points).reshape(nx,ny,nz, 3), -n_points, axis = (0,1,2)).reshape(nx*ny*nz, 3)\n\n #print(coords)\n\n \n m1r = self.cget(coords).reshape(nx,ny,nz,m1x,m1y)\n m2r = other.cget(coords).reshape(nx,ny,nz,m2x,m2y)\n M1 = np.fft.fftn(m1r, axes = (0,1,2))\n M2 = np.fft.fftn(m2r, axes = (0,1,2))\n M3 = np.zeros((nx,ny,nz,m1x, m2y),dtype = np.complex128)\n\n for c in coords:\n \n Mk = np.linalg.cholesky(M1[c[0], c[1], c[2]])\n yk = np.linalg.solve(Mk, M2[c[0], c[1], c[2]])\n\n\n\n \n\n M3[c[0], c[1], c[2]] = np.linalg.solve(Mk.conj().T, yk)\n\n\n\n \n\n ret = tmat()\n if complx:\n ret.load_nparray(np.fft.ifftn(M3.reshape(nx,ny,nz,m1x,m2y), axes = (0,1,2)).reshape(coords.shape[0], m1x,m2y), coords)\n else:\n ret.load_nparray(np.fft.ifftn(M3.reshape(nx,ny,nz,m1x,m2y), axes = (0,1,2)).real.reshape(coords.shape[0], m1x,m2y), coords)\n return ret","def custom_constr(x, qr, inverse, depth):\n qc = QuantumCircuit(qr)\n maxi, mini = max(x), min(x)\n n = x.shape[0]\n #qc_wv = Wavelets(n).construct_circuit(register=qr)\n for _ in range(depth):\n qc.h(qr)\n for i in range(n):\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini), 2*np.pi*(x[i]-mini)/(maxi-mini), qr[i])\n for i in range(n):\n qc.cx(qr[i], qr[(i + 1) % n])\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini),\n ((2*np.pi)**2*(x[i]-mini)*(x[(i+1) % n]-mini)/(maxi-mini)**2) % 2*np.pi,\n qr[(i + 1) % n])\n qc.cx(qr[i], qr[(i + 1) % n])\n #qc = qc + qc_wv\n if inverse:\n return qc.inverse()\n return qc","def _update_tarsqidoc(self, cp):\n self.tarsqidoc.remove_tlinks()\n for n1, rest in cp.graph.edges.items():\n for n2, edge in cp.graph.edges[n1].items():\n if edge.constraint is not None:\n if edge.constraint.has_simple_relation():\n self._add_constraint_to_tarsqidoc(edge)","def set(self, opts, popsize=None, ccovfac=1, verbose=True):\r\n\r\n alpha_cc = 1.0 # cc-correction for mueff, was zero before\r\n\r\n def cone(df, mu, N, alphacov=2.0):\r\n \"\"\"rank one update learning rate, ``df`` is disregarded and obsolete, reduce alphacov on noisy problems, say to 0.5\"\"\"\r\n return alphacov / ((N + 1.3)**2 + mu)\r\n\r\n def cmu(df, mu, alphamu=0.0, alphacov=2.0):\r\n \"\"\"rank mu learning rate, disregarding the constrant cmu <= 1 - cone\"\"\"\r\n c = alphacov * (alphamu + mu - 2 + 1/mu) / ((N + 2)**2 + alphacov * mu / 2)\r\n # c = alphacov * (alphamu + mu - 2 + 1/mu) / (2 * (N + 2)**1.5 + alphacov * mu / 2)\r\n # print 'cmu =', c\r\n return c\r\n\r\n def conedf(df, mu, N):\r\n \"\"\"used for computing separable learning rate\"\"\"\r\n return 1. / (df + 2.*sqrt(df) + float(mu)/N)\r\n\r\n def cmudf(df, mu, alphamu):\r\n \"\"\"used for computing separable learning rate\"\"\"\r\n return (alphamu + mu - 2. + 1./mu) / (df + 4.*sqrt(df) + mu/2.)\r\n\r\n sp = self\r\n N = sp.N\r\n if popsize:\r\n opts.evalall({'N':N, 'popsize':popsize})\r\n else:\r\n popsize = opts.evalall({'N':N})['popsize'] # the default popsize is computed in Options()\r\n sp.popsize = popsize\r\n if opts['CMA_mirrors'] < 0.5:\r\n sp.lam_mirr = int(0.5 + opts['CMA_mirrors'] * popsize)\r\n elif opts['CMA_mirrors'] > 1:\r\n sp.lam_mirr = int(0.5 + opts['CMA_mirrors'])\r\n else:\r\n sp.lam_mirr = int(0.5 + 0.16 * min((popsize, 2 * N + 2)) + 0.29) # 0.158650... * popsize is optimal\r\n # lam = arange(2,22)\r\n # mirr = 0.16 + 0.29/lam\r\n # print(lam); print([int(0.5 + l) for l in mirr*lam])\r\n # [ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21]\r\n # [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4]\r\n\r\n sp.mu_f = sp.popsize / 2.0 # float value of mu\r\n if opts['CMA_mu'] is not None:\r\n sp.mu_f = opts['CMA_mu']\r\n sp.mu = int(sp.mu_f + 0.499999) # round down for x.5\r\n # in principle we have mu_opt = popsize/2 + lam_mirr/2,\r\n # which means in particular weights should only be negative for q > 0.5+mirr_frac/2\r\n if sp.mu > sp.popsize - 2 * sp.lam_mirr + 1:\r\n print(\"WARNING: pairwise selection is not implemented, therefore \" +\r\n \" mu = %d > %d = %d - 2*%d + 1 = popsize - 2*mirr + 1 can produce a bias\" % (\r\n sp.mu, sp.popsize - 2 * sp.lam_mirr + 1, sp.popsize, sp.lam_mirr))\r\n if sp.lam_mirr > sp.popsize // 2:\r\n raise _Error(\"fraction of mirrors in the population as read from option CMA_mirrors cannot be larger 0.5, \" +\r\n \"theoretically optimal is 0.159\")\r\n sp.weights = log(max([sp.mu, sp.popsize / 2.0]) + 0.5) - log(1 + np.arange(sp.mu))\r\n if 11 < 3: # equal recombination weights\r\n sp.mu = sp.popsize // 4\r\n sp.weights = np.ones(sp.mu)\r\n print(sp.weights[:10])\r\n sp.weights /= sum(sp.weights)\r\n sp.mueff = 1 / sum(sp.weights**2)\r\n sp.cs = (sp.mueff + 2) / (N + sp.mueff + 3)\r\n # TODO: clean up (here the cumulation constant is shorter if sigma_vec is used)\r\n sp.dampsvec = opts['CMA_dampsvec_fac'] * (N + 2) if opts['CMA_dampsvec_fac'] else np.Inf\r\n sp.dampsvec_fading = opts['CMA_dampsvec_fade']\r\n if np.isfinite(sp.dampsvec):\r\n sp.cs = ((sp.mueff + 2) / (N + sp.mueff + 3))**0.5\r\n # sp.cs = (sp.mueff + 2) / (N + 1.5*sp.mueff + 1)\r\n sp.cc = (4 + alpha_cc * sp.mueff / N) / (N + 4 + alpha_cc * 2 * sp.mueff / N)\r\n sp.cc_sep = (1 + 1/N + alpha_cc * sp.mueff / N) / (N**0.5 + 1/N + alpha_cc * 2 * sp.mueff / N) # \\not\\gg\\cc\r\n sp.rankmualpha = opts['CMA_rankmualpha']\r\n # sp.rankmualpha = _evalOption(opts['CMA_rankmualpha'], 0.3)\r\n sp.c1 = ccovfac * min(1, sp.popsize/6) * cone((N**2 + N) / 2, sp.mueff, N) # 2. / ((N+1.3)**2 + sp.mucov)\r\n sp.c1_sep = ccovfac * conedf(N, sp.mueff, N)\r\n if 11 < 3:\r\n sp.c1 = 0.\r\n print('c1 is zero')\r\n if opts['CMA_rankmu'] != 0: # also empty\r\n sp.cmu = min(1 - sp.c1, ccovfac * cmu((N**2+N)/2, sp.mueff, sp.rankmualpha))\r\n sp.cmu_sep = min(1 - sp.c1_sep, ccovfac * cmudf(N, sp.mueff, sp.rankmualpha))\r\n else:\r\n sp.cmu = sp.cmu_sep = 0\r\n\r\n sp.neg = BlancClass()\r\n if opts['CMA_active']:\r\n # in principle we have mu_opt = popsize/2 + lam_mirr/2,\r\n # which means in particular weights should only be negative for q > 0.5+mirr_frac/2\r\n sp.neg.mu_f = popsize - (popsize + sp.lam_mirr) / 2 if popsize > 2 else 1\r\n sp.neg.weights = log(sp.mu_f + 0.5) - log(1 + np.arange(sp.popsize - int(sp.neg.mu_f), sp.popsize))\r\n sp.neg.mu = len(sp.neg.weights) # maybe never useful?\r\n sp.neg.weights /= sum(sp.neg.weights)\r\n sp.neg.mueff = 1 / sum(sp.neg.weights**2)\r\n sp.neg.cmuexp = opts['CMA_activefac'] * 0.25 * sp.neg.mueff / ((N+2)**1.5 + 2 * sp.neg.mueff)\r\n assert sp.neg.mu >= sp.lam_mirr # not really necessary\r\n # sp.neg.minresidualvariance = 0.66 # not it use, keep at least 0.66 in all directions, small popsize is most critical\r\n else:\r\n sp.neg.cmuexp = 0\r\n\r\n sp.CMA_on = sp.c1 + sp.cmu > 0\r\n # print(sp.c1_sep / sp.cc_sep)\r\n\r\n if not opts['CMA_on'] and opts['CMA_on'] not in (None,[],(),''):\r\n sp.CMA_on = False\r\n # sp.c1 = sp.cmu = sp.c1_sep = sp.cmu_sep = 0\r\n\r\n sp.damps = opts['CMA_dampfac'] * (0.5 +\r\n 0.5 * min([1, (sp.lam_mirr/(0.159*sp.popsize) - 1)**2])**1 +\r\n 2 * max([0, ((sp.mueff-1) / (N+1))**0.5 - 1]) + sp.cs\r\n )\r\n if 11 < 3:\r\n # this is worse than damps = 1 + sp.cs for the (1,10000)-ES on 40D parabolic ridge\r\n sp.damps = 0.3 + 2 * max([sp.mueff/sp.popsize, ((sp.mueff-1)/(N+1))**0.5 - 1]) + sp.cs\r\n if 11 < 3:\r\n # this does not work for lambda = 4*N^2 on the parabolic ridge\r\n sp.damps = opts['CMA_dampfac'] * (2 - 0*sp.lam_mirr/sp.popsize) * sp.mueff/sp.popsize + 0.3 + sp.cs # nicer future setting\r\n print('damps =', sp.damps)\r\n if 11 < 3:\r\n sp.damps = 10 * sp.damps # 1e99 # (1 + 2*max(0,sqrt((sp.mueff-1)/(N+1))-1)) + sp.cs;\r\n # sp.damps = 20 # 1. + 20 * sp.cs**-1 # 1e99 # (1 + 2*max(0,sqrt((sp.mueff-1)/(N+1))-1)) + sp.cs;\r\n print('damps is %f' % (sp.damps))\r\n\r\n sp.cmean = float(opts['CMA_cmean'])\r\n # sp.kappa = 1 # 4-D, lam=16, rank1, kappa < 4 does not influence convergence rate\r\n # in larger dim it does, 15-D with defaults, kappa=8 factor 2\r\n if sp.cmean != 1:\r\n print(' cmean = %f' % (sp.cmean))\r\n\r\n if verbose:\r\n if not sp.CMA_on:\r\n print('covariance matrix adaptation turned off')\r\n if opts['CMA_mu'] != None:\r\n print('mu = %f' % (sp.mu_f))\r\n\r\n # return self # the constructor returns itself\r","def _AddCompound(self, kegg_id, how_many):\n i = self._FindCompoundIndex(kegg_id)\n if i is not None:\n self.reactants[i].coeff += how_many\n else:\n self.reactants += [CompoundWithCoeff.FromId(how_many, kegg_id)]\n \n # clear the cache since the reaction has changed\n self._catalyzing_enzymes = None","def putconboundlist(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkc_)\n elif num_ != len(bkc_):\n raise IndexError(\"Inconsistent length of array bkc\")\n if num_ is None:\n num_ = len(blc_)\n elif num_ != len(blc_):\n raise IndexError(\"Inconsistent length of array blc\")\n if num_ is None:\n num_ = len(buc_)\n elif num_ != len(buc_):\n raise IndexError(\"Inconsistent length of array buc\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def update_coe_cluster(self, name_or_id, **kwargs):\n self.list_coe_clusters.invalidate(self)\n cluster = self.get_coe_cluster(name_or_id)\n if not cluster:\n raise exc.OpenStackCloudException(\n \"COE cluster %s not found.\" % name_or_id\n )\n\n cluster = self.container_infrastructure_management.update_cluster(\n cluster, **kwargs\n )\n\n return cluster","def refine(self, ijk):\n if self.cbc is None or not self.sub_block_count:\n raise ValueError(\n \"Cannot refine sub block model without specifying number \"\n \"of parent and sub blocks\"\n )\n try:\n inds = self.ijk_array_to_indices(ijk)\n except ValueError:\n inds = self.ijk_to_index(ijk)\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137","def test_simple_cc():\n hs_c = LocalSpace('c', dimension=3)\n hs_q = LocalSpace('q1', basis=('g', 'e'))\n Delta_1 = Symbol('Delta_1')\n Omega_1 = Symbol('Omega_1')\n g_1 = Symbol('g_1')\n a = Destroy(hs=hs_c)\n a_dag = Create(hs=hs_c)\n sig_p = LocalSigma('e', 'g', hs=hs_q)\n sig_m = LocalSigma('g', 'e', hs=hs_q)\n coeff = (-I / 2) * (Omega_1 * g_1 / Delta_1)\n jc_expr = coeff * (a * sig_p - a_dag * sig_m)\n\n simplified = rewrite_with_operator_pm_cc(jc_expr)\n assert simplified == coeff * OperatorPlusMinusCC(a * sig_p, sign=-1)\n assert (\n srepr(simplified.term)\n == \"OperatorPlusMinusCC(OperatorTimes(Destroy(hs=LocalSpace('c', \"\n \"dimension=3)), LocalSigma('e', 'g', hs=LocalSpace('q1', \"\n \"basis=('g', 'e')))), sign=-1)\"\n )\n expanded = simplified.doit()\n assert expanded == jc_expr","def convex(self, *args, **kwargs) -> Any:\n pass","def SetConstraint(self, model) :\n if 'pp' in self.__type : self.SetPPConstraint( model )\n elif self.__type == 'prBin' and self.bound!=0 : self.SetPRBinConstraint( model )\n elif self.__type == 'prCat' and self.bound != 0 : self.SetPRCatConstraint(model)\n elif self.__type == 'prBinCat' and self.bound != 0 : self.SetPRBinCatConstraint(model)\n elif self.bound == 0 : return\n else : raise RuntimeError( 'SetConstraint : Unknown type for Constraint : ', self.__type )","def transfer_parameters_call_optimization(self, mainCl, mainPt, consCall=None, consPt=None):","def constrain(*args, barrier: bool=True, damping: Union[float, bool]=0.0, directionalHinge:\n bool=True, hinge: bool=True, interpenetrate: bool=True, nail: bool=True, name:\n Union[AnyStr, bool]=\"\", orientation: Union[List[float, float, float], bool]=None,\n pinConstraint: bool=True, position: Union[List[float, float, float], bool]=None,\n restLength: Union[float, bool]=0.0, spring: bool=True, stiffness: Union[float,\n bool]=0.0, q=True, query=True, e=True, edit=True, **kwargs)->Union[None, Any]:\n pass","def SetPRBinCatConstraint( self, model ) :\n tot = np.dot( self.wish.T, self.dispo )\n for val in tot :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def de_cic(engine, deconvolved, d_k):\n code = CodeSegment(engine)\n def tf(k):\n kny = [numpy.sinc(k[i]*engine.pm.BoxSize[i]/(2*numpy.pi*engine.pm.Nmesh[i])) for i in range(3)]\n wts = (kny[0]*kny[1]*kny[2])**-2\n return wts\n \n #kny = numpy.pi*engine.pm.Nmesh[0]/engine.pm.BoxSize[0]\n code.assign(x='d_k', y='tmp')\n code.transfer(complex='tmp', tf=tf)\n code.c2r(real=deconvolved, complex='tmp')\n return code","def CX(self, qubit_expr):\n self.apply_gate_operation(cirq.ops.CX, qubit_expr)","def getqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n maxnumqcnz_ = self.getnumqconknz((k_))\n numqcnz_ = ctypes.c_int64()\n _qcsubi_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi is not long enough: Is %d, expected %d\" % (len(qcsubi_),self.getnumqconknz((k_))))\n if isinstance(qcsubi_,numpy.ndarray) and not qcsubi_.flags.writeable:\n raise ValueError(\"Argument qcsubi must be writable\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n _qcsubj_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj is not long enough: Is %d, expected %d\" % (len(qcsubj_),self.getnumqconknz((k_))))\n if isinstance(qcsubj_,numpy.ndarray) and not qcsubj_.flags.writeable:\n raise ValueError(\"Argument qcsubj must be writable\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n _qcval_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval is not long enough: Is %d, expected %d\" % (len(qcval_),self.getnumqconknz((k_))))\n if isinstance(qcval_,numpy.ndarray) and not qcval_.flags.writeable:\n raise ValueError(\"Argument qcval must be writable\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n qcsurp_ = ctypes.c_int64(_qcsubi_minlength)\n res = __library__.MSK_XX_getqconk64(self.__nativep,k_,maxnumqcnz_,ctypes.byref(qcsurp_),ctypes.byref(numqcnz_),_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqcnz_ = numqcnz_.value\n _numqcnz_return_value = numqcnz_\n if _qcsubi_copyarray:\n qcsubi_[:] = _qcsubi_np_tmp\n if _qcsubj_copyarray:\n qcsubj_[:] = _qcsubj_np_tmp\n if _qcval_copyarray:\n qcval_[:] = _qcval_np_tmp\n return (_numqcnz_return_value)","def test_scalar_coeff_cc():\n hs_1 = LocalSpace('q1', basis=('g', 'e'))\n hs_2 = LocalSpace('q2', basis=('g', 'e'))\n kappa = Symbol('kappa', real=True)\n a1 = Destroy(hs=hs_1)\n a2 = Destroy(hs=hs_2)\n\n jc_expr = (\n I / 2 * (2 * kappa * (a1.dag() * a2) - 2 * kappa * (a1 * a2.dag()))\n )\n\n simplified = rewrite_with_operator_pm_cc(jc_expr)\n assert simplified == I * kappa * OperatorPlusMinusCC(\n a1.dag() * a2, sign=-1\n )\n expanded = simplified.doit()\n assert expanded == I * kappa * (a1.dag() * a2 - a1 * a2.dag())","def addConstrs(self, constrs, name=''):\n ...","def putxc(self,whichsol_,xc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xc is None: raise TypeError(\"Invalid type for argument xc\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n res = self.__obj.putxc(whichsol_,xc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_xc:\n xc[:] = _tmparr_xc","def covariance(self, cond, include_obs=False, parametric_only=False, pad=1e-8):\n X1 = self.standardize_input_array(cond)\n m = X1.shape[0]\n\n Kstar = self.get_query_K(X1)\n if not parametric_only:\n tmp = self.Kinv_sp_tri * Kstar\n qf = np.dot(Kstar.T, tmp)\n k = self.kernel(X1,X1, identical=include_obs)\n gp_cov = k - qf\n else:\n gp_cov = np.zeros((m,m))\n\n R = self.query_R\n tmp = np.dot(self.invc, R)\n mean_cov = np.dot(tmp.T, tmp)\n gp_cov += mean_cov\n\n gp_cov += pad * np.eye(gp_cov.shape[0])\n return gp_cov","def cca_loss(outdim_size, use_all_singular_values):\n def inner_cca_objective(y_true, y_pred):\n \"\"\"\n It is the loss function of CCA as introduced in the original paper. There can be other formulations.\n It is implemented by Theano tensor operations, and does not work on Tensorflow backend\n y_true is just ignored\n \"\"\"\n\n r1 = 1e-4\n r2 = 1e-4\n eps = 1e-12\n o1 = o2 = y_pred.shape[1]//2\n\n # unpack (separate) the output of networks for view 1 and view 2\n H1 = tf.transpose(y_pred[:, 0:o1])\n H2 = tf.transpose(y_pred[:, o1:o1+o2])\n\n m = H1.shape[1]\n\n H1bar = H1 - (tf.math.divide(1, m)) * tf.dot(H1, tf.ones([m, m]))\n H2bar = H2 - (tf.math.divide(1, m)) * tf.dot(H2, tf.ones([m, m]))\n\n SigmaHat12 = (tf.math.divide(1, m-1)) * \\\n tf.dot(H1bar, tf.transpose(H2bar))\n SigmaHat11 = (tf.math.divide(1, m-1)) * tf.dot(H1bar,\n tf.transpose(H1bar)) + r1 * tf.eye(o1)\n SigmaHat22 = (tf.math.divide(1, m-1)) * tf.dot(H2bar,\n tf.transpose(H2bar)) + r2 * tf.eye(o2)\n\n # Calculating the root inverse of covariance matrices by using eigen decomposition\n [D1, V1] = tf.nlinalg.eigh(SigmaHat11)\n [D2, V2] = tf.nlinalg.eigh(SigmaHat22)\n\n # Added to increase stability\n posInd1 = tf.gt(D1, eps).nonzero()[0]\n D1 = D1[posInd1]\n V1 = V1[:, posInd1]\n posInd2 = tf.gt(D2, eps).nonzero()[0]\n D2 = D2[posInd2]\n V2 = V2[:, posInd2]\n\n SigmaHat11RootInv = tf.dot(\n tf.dot(V1, tf.nlinalg.diag(D1 ** -0.5)), tf.transpose(V1))\n SigmaHat22RootInv = tf.dot(\n tf.dot(V2, tf.nlinalg.diag(D2 ** -0.5)), tf.transpose(V2))\n\n Tval = tf.dot(tf.dot(SigmaHat11RootInv, SigmaHat12), SigmaHat22RootInv)\n\n if use_all_singular_values:\n # all singular values are used to calculate the correlation\n corr = tf.sqrt(tf.nlinalg.trace(tf.dot(tf.transpose(Tval), Tval)))\n else:\n # just the top outdim_size singular values are used\n [U, V] = tf.nlinalg.eigh(T.dot(tf.transpose(Tval), Tval))\n U = U[tf.gt(U, eps).nonzero()[0]]\n U = U.sort()\n corr = tf.sum(tf.sqrt(U[0:outdim_size]))\n\n return -corr\n\n return inner_cca_objective","def reset_parameters(self, p: Dict[str, ArrayType]):\n super().reset_parameters(p)\n if self.method == \"trust-constr\":\n if self.opt.nk:\n self._constraints[\"k\"].A = csc_matrix(self.opt.M(self.p).toarray())\n self._constraints[\"k\"].lb = -self.opt.c(self.p).toarray().flatten()\n if self.opt.na:\n eq = -self.opt.b(self.p).toarray().flatten()\n self._constraints[\"a\"].A = csc_matrix(self.opt.A(self.p).toarray())\n self._constraints[\"a\"].lb = eq\n self._constraints[\"a\"].ub = eq\n if self._constraints:\n self.minimize_input[\"constraints\"] = list(self._constraints.values())","def match_contract_to_charter_constraints(contract, charter, charter_constraints, charity_constraints):\n\n r_quotes = []\n r_vector = []\n\n quote_slice = slice(0, 17)\n\n if 'subj' not in contract.sections:\n raise ValueError(\"contract has no subject section\")\n\n subj = contract.sections['subj'].body\n print(subj.untokenize_cc())\n print('------')\n if subj.embeddings is None:\n print(\"Subj embeddings are gone, restoring...\")\n subj.embeddings = contract.embeddings[subj.start:subj.end]\n # subj.tokens = doc.tokens[subj.start:subj.end]\n # subj.tokens_cc = doc.tokens_cc[subj.start:subj.end]\n # subj.embedd( GLOBALS__['CharterAnlysingContext'].pattern_factory )\n print('\\t\\t sample:', subj.embeddings[0][1:10])\n\n for head_type in charter_constraints:\n\n ##charity:\n if head_type in charity_constraints:\n print(f'{head_type} has charity constrinats')\n \n charity_constraints_by_head = charity_constraints[head_type]\n charity_constraints_by_head_new = []\n \n charity_constraints['new.'+head_type] = charity_constraints_by_head_new\n \n for i in range(len(charity_constraints_by_head)):\n _tuple = charity_constraints_by_head[i] \n# for cc in charity_constraints[head_type]:\n _slice = _tuple[0]\n emb_charter = charter.sections[head_type].body.embeddings[_slice]\n \n distance = 1 - DF(emb_charter, subj.embeddings[5:])\n \n# cc.add['subj_correlation'] = distance\n \n# detupling\n charity_constraints_by_head_new.append ( {\n 'slice':_slice,\n 'subj_correlation': distance,\n 'confidence': _tuple[1],\n 'sum': _tuple[2]\n })\n \n print('\\t'*4, 'cc=', charity_constraints_by_head_new[i])\n \n # print('\\t\\t---CC', cc[0])\n \n\n # GLOBALS__['CharterAnlysingContext'].doc.sections['head.directors'].body.embeddings[_slice]\n\n ##------------------------charity end\n print(f'measuring {head_type} constraints...'.upper())\n cc = charter_constraints[head_type]\n quotes = cc['sentences']\n for quote in quotes:\n print()\n _q = untokenize(quote['subdoc'].tokens_cc[quote_slice])\n print(_q)\n\n distance = 1 - DF(quote['subdoc'].embeddings[quote_slice],\n subj.embeddings[5:])\n\n quote['subj_correlation'] = distance\n\n print(f'distance = {distance:.4f}')\n\n r_quotes.append(_q)\n r_vector.append(distance)\n r_quotes.append('\\n')\n r_vector.append(distance)\n\n GLOBALS__['renderer'].render_color_text(r_quotes, r_vector)\n print(r_vector)\n print(r_quotes)","def pointCurveConstraint(*args, caching: bool=True, nodeState: Union[int, bool]=0,\n pointConstraintUVW: Union[List[float, float, float], bool]=None,\n pointWeight: Union[float, bool]=1.0, position: List[float, float,\n float]=None, weight: float=0.0, constructionHistory: bool=True, name:\n AnyStr=\"\", object: bool=True, replaceOriginal: bool=True, q=True,\n query=True, e=True, edit=True, **kwargs)->Union[List[AnyStr], Any]:\n pass","def conc_after(V, C_e, Q, t, t_e):\n C = 0.21-(0.21-C_e)*math.e**-(abs(Q)/V*(t-t_e))\n return C","def _set_constraint(self):\n pass","def local_inplace_setsubtensor(node):\r\n if isinstance(node.op, IncSubtensor) and not node.op.inplace:\r\n new_op = node.op.__class__(\r\n node.op.idx_list, inplace=True,\r\n set_instead_of_inc=node.op.set_instead_of_inc,\r\n destroyhandler_tolerate_aliased=node.op.destroyhandler_tolerate_aliased)\r\n new_node = new_op(*node.inputs)\r\n return [new_node]\r\n return False","def csw(self, q0, q1, q2, ctrl=None):\n self.cx(q2,q1,ctrl=ctrl).ccx(q0,q1,q2,ctrl=ctrl).cx(q2,q1,ctrl=ctrl)\n return self","def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def PCO(A, K, alpha):\n A=np.array(A, dtype=float)\n \n #The adjacency matrix A is first normalized\n A=normalize(A) \n \n n=A.shape[0]\n \n I=np.eye(n)\n \n P=I\n M=np.zeros((n, n))\n \n for i in range(K):\n P = alpha*np.dot(P,A) + (1-alpha)*I\n M = M+P\n \n return(M)","def attach_CA(self):\n n = self.pC\n self.A[n] = self._mps_CA(self.C, self.A[n])","def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def get_KE_constraints(self):\n C2 = np.eye(self.m)\n C2 = C2[:self.m - 2, :]\n to_be_deleted = []\n for idx_vij_1 in range(self.m - 2):\n idx_vij_2 = idx_vij_1 + 1\n C2[idx_vij_1, idx_vij_2] = -1\n i1 = np.where(self.C[idx_vij_1, :] == 1)[0][0]\n i2 = np.where(self.C[idx_vij_2, :] == 1)[0][0]\n j = np.where(self.C[idx_vij_1, :] == -1)[0][0]\n if i1 == i2:\n i = i1\n k = np.where(self.C[idx_vij_2, :] == -1)[0][0]\n i_indices = self.C[:, j] == 1\n j_indices = self.C[:, k] == -1\n idx_vij_3 = np.where(np.bitwise_and(\n i_indices, j_indices))[0][0]\n C2[idx_vij_1, idx_vij_3] = 1\n else:\n to_be_deleted.append(idx_vij_1)\n C2 = np.delete(C2, to_be_deleted, axis=0)\n b = np.zeros((C2.shape[0], 1))\n return C2, b","def conj(self, o): \n return (o.inv()) * self * o"],"string":"[\n \"def putcone(self,k_,ct_,conepar_,submem): # 3\\n if not isinstance(ct_,conetype): raise TypeError(\\\"Argument ct has wrong type\\\")\\n nummem_ = None\\n if nummem_ is None:\\n nummem_ = len(submem)\\n elif nummem_ != len(submem):\\n raise IndexError(\\\"Inconsistent length of array submem\\\")\\n if nummem_ is None: nummem_ = 0\\n if submem is None: raise TypeError(\\\"Invalid type for argument submem\\\")\\n if submem is None:\\n submem_ = None\\n else:\\n try:\\n submem_ = memoryview(submem)\\n except TypeError:\\n try:\\n _tmparr_submem = array.array(\\\"i\\\",submem)\\n except TypeError:\\n raise TypeError(\\\"Argument submem has wrong type\\\")\\n else:\\n submem_ = memoryview(_tmparr_submem)\\n \\n else:\\n if submem_.format != \\\"i\\\":\\n submem_ = memoryview(array.array(\\\"i\\\",submem))\\n \\n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def appendcone(self,ct_,conepar_,submem_):\\n nummem_ = None\\n if nummem_ is None:\\n nummem_ = len(submem_)\\n elif nummem_ != len(submem_):\\n raise IndexError(\\\"Inconsistent length of array submem\\\")\\n if submem_ is None:\\n raise ValueError(\\\"Argument submem cannot be None\\\")\\n if submem_ is None:\\n raise ValueError(\\\"Argument submem may not be None\\\")\\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\\n _submem_copyarray = False\\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif submem_ is not None:\\n _submem_copyarray = True\\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\\n _submem_np_tmp[:] = submem_\\n assert _submem_np_tmp.flags.contiguous\\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _submem_copyarray = False\\n _submem_tmp = None\\n \\n res = __library__.MSK_XX_appendcone(self.__nativep,ct_,conepar_,nummem_,_submem_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def appendcone(self,ct_,conepar_,submem): # 3\\n if not isinstance(ct_,conetype): raise TypeError(\\\"Argument ct has wrong type\\\")\\n nummem_ = None\\n if nummem_ is None:\\n nummem_ = len(submem)\\n elif nummem_ != len(submem):\\n raise IndexError(\\\"Inconsistent length of array submem\\\")\\n if nummem_ is None: nummem_ = 0\\n if submem is None: raise TypeError(\\\"Invalid type for argument submem\\\")\\n if submem is None:\\n submem_ = None\\n else:\\n try:\\n submem_ = memoryview(submem)\\n except TypeError:\\n try:\\n _tmparr_submem = array.array(\\\"i\\\",submem)\\n except TypeError:\\n raise TypeError(\\\"Argument submem has wrong type\\\")\\n else:\\n submem_ = memoryview(_tmparr_submem)\\n \\n else:\\n if submem_.format != \\\"i\\\":\\n submem_ = memoryview(array.array(\\\"i\\\",submem))\\n \\n res = self.__obj.appendcone(ct_,conepar_,nummem_,submem_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def appendconeseq(self,ct_,conepar_,nummem_,j_):\\n res = __library__.MSK_XX_appendconeseq(self.__nativep,ct_,conepar_,nummem_,j_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def appendconeseq(self,ct_,conepar_,nummem_,j_): # 3\\n if not isinstance(ct_,conetype): raise TypeError(\\\"Argument ct has wrong type\\\")\\n res = self.__obj.appendconeseq(ct_,conepar_,nummem_,j_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getcone(self,k_,submem): # 3\\n _copyback_submem = False\\n if submem is None:\\n submem_ = None\\n else:\\n try:\\n submem_ = memoryview(submem)\\n except TypeError:\\n try:\\n _tmparr_submem = array.array(\\\"i\\\",submem)\\n except TypeError:\\n raise TypeError(\\\"Argument submem has wrong type\\\")\\n else:\\n submem_ = memoryview(_tmparr_submem)\\n _copyback_submem = True\\n else:\\n if submem_.format != \\\"i\\\":\\n submem_ = memoryview(array.array(\\\"i\\\",submem))\\n _copyback_submem = True\\n if submem_ is not None and len(submem_) != self.getconeinfo((k_))[2]:\\n raise ValueError(\\\"Array argument submem has wrong length\\\")\\n res,resargs = self.__obj.getcone(k_,submem_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _ct_return_value,_conepar_return_value,_nummem_return_value = resargs\\n if _copyback_submem:\\n submem[:] = _tmparr_submem\\n _ct_return_value = conetype(_ct_return_value)\\n return _ct_return_value,_conepar_return_value,_nummem_return_value\",\n \"def getcone(self,k_,submem_):\\n ct_ = ctypes.c_int32()\\n conepar_ = ctypes.c_double()\\n nummem_ = ctypes.c_int32()\\n _submem_minlength = self.getconeinfo((k_))[2]\\n if self.getconeinfo((k_))[2] > 0 and submem_ is not None and len(submem_) != self.getconeinfo((k_))[2]:\\n raise ValueError(\\\"Array argument submem is not long enough: Is %d, expected %d\\\" % (len(submem_),self.getconeinfo((k_))[2]))\\n if isinstance(submem_,numpy.ndarray) and not submem_.flags.writeable:\\n raise ValueError(\\\"Argument submem must be writable\\\")\\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\\n _submem_copyarray = False\\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif submem_ is not None:\\n _submem_copyarray = True\\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\\n _submem_np_tmp[:] = submem_\\n assert _submem_np_tmp.flags.contiguous\\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _submem_copyarray = False\\n _submem_tmp = None\\n \\n res = __library__.MSK_XX_getcone(self.__nativep,k_,ctypes.byref(ct_),ctypes.byref(conepar_),ctypes.byref(nummem_),_submem_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _ct_return_value = conetype(ct_.value)\\n conepar_ = conepar_.value\\n _conepar_return_value = conepar_\\n nummem_ = nummem_.value\\n _nummem_return_value = nummem_\\n if _submem_copyarray:\\n submem_[:] = _submem_np_tmp\\n return (_ct_return_value,_conepar_return_value,_nummem_return_value)\",\n \"def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _k_coaffine_pair(pair, bound=math.inf):\\n g = pair.graph\\n sigma = pair.coaffination\\n kg = clique_graph(g, bound)\\n coaf_k = dict([])\\n for q in kg:\\n coaf_k[q] = Clique([sigma[x] for x in q])\\n return CoaffinePair(kg, coaf_k)\",\n \"def set_ecuacion_constitutiva(self, param_con, ec_con_id):\\n self.param_con = param_con\\n self.ecucon_id = ec_con_id\\n self.ecuacion_constitutiva = self.ecuaciones_constitutivas(ec_con_id)\",\n \"def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubk_ is None:\\n raise ValueError(\\\"Argument qcsubk cannot be None\\\")\\n if qcsubk_ is None:\\n raise ValueError(\\\"Argument qcsubk may not be None\\\")\\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\\n _qcsubk_copyarray = False\\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubk_ is not None:\\n _qcsubk_copyarray = True\\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\\n _qcsubk_np_tmp[:] = qcsubk_\\n assert _qcsubk_np_tmp.flags.contiguous\\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubk_copyarray = False\\n _qcsubk_tmp = None\\n \\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconbound(self,i_,bkc_,blc_,buc_):\\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskc(self,whichsol_,skc_):\\n _skc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),self.getnumcon()))\\n if skc_ is None:\\n raise ValueError(\\\"Argument skc cannot be None\\\")\\n if skc_ is None:\\n raise ValueError(\\\"Argument skc may not be None\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def constraint(self, c):\\n self.add_constraint(c)\",\n \"def cz(control: QubitInput, target: QubitInput) -> Instruction:\\n return Instruction(CZ(), target=[control, target])\",\n \"def cone(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True, degree:\\n Union[int, bool]=3, endSweep: Union[float, bool]=2, heightRatio: Union[float,\\n bool]=2.0, nodeState: Union[int, bool]=0, pivot: Union[List[float, float, float],\\n bool]=None, radius: Union[float, bool]=1.0, sections: Union[int, bool]=8, spans:\\n Union[int, bool]=1, startSweep: Union[float, bool]=0, tolerance: Union[float,\\n bool]=0.01, useOldInitBehaviour: bool=False, useTolerance: bool=False,\\n constructionHistory: bool=True, name: AnyStr=\\\"\\\", object: bool=True, polygon: int=0,\\n q=True, query=True, e=True, edit=True, **kwargs)->Union[List[AnyStr], Any]:\\n pass\",\n \"def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcone(self,maxnumcone_):\\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_conectividad(self, conec):\\n self.add_conec_listoflists(conec) # calcula el ne y el je\",\n \"def con_ceq(x,project):\\n \\n cons = project.con_ceq(x)\\n \\n if cons: cons = array(cons)\\n else: cons = zeros([0])\\n \\n return cons\",\n \"def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubk is None: raise TypeError(\\\"Invalid type for argument qcsubk\\\")\\n if qcsubk is None:\\n qcsubk_ = None\\n else:\\n try:\\n qcsubk_ = memoryview(qcsubk)\\n except TypeError:\\n try:\\n _tmparr_qcsubk = array.array(\\\"i\\\",qcsubk)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubk has wrong type\\\")\\n else:\\n qcsubk_ = memoryview(_tmparr_qcsubk)\\n \\n else:\\n if qcsubk_.format != \\\"i\\\":\\n qcsubk_ = memoryview(array.array(\\\"i\\\",qcsubk))\\n \\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getconeinfo(self,k_): # 3\\n res,resargs = self.__obj.getconeinfo(k_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _ct_return_value,_conepar_return_value,_nummem_return_value = resargs\\n _ct_return_value = conetype(_ct_return_value)\\n return _ct_return_value,_conepar_return_value,_nummem_return_value\",\n \"def transform_coors(self, mtx_t, ref_coors=None):\\n if ref_coors is None:\\n ref_coors = self.coors\\n\\n if mtx_t.shape[1] > self.coors.shape[1]:\\n self.coors[:] = nm.dot(ref_coors, mtx_t[:,:-1].T) + mtx_t[:,-1]\\n else:\\n self.coors[:] = nm.dot(ref_coors, mtx_t.T)\",\n \"def SetPRCatConstraint(self, model ) :\\n tot = np.multiply(self.wish, self.dispo)\\n for line in tot :\\n for val in line :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def c_code_contiguous(self, node, name, inp, out, sub):\\r\\n raise theano.gof.utils.MethodNotDefined()\",\n \"def remove_constraint(self, ckey):\\n if ckey not in self.constraints:\\n raise KeyError(\\\"Constraints not found on object key: {}\\\".format(ckey))\\n del self.constraints[ckey]\",\n \"def censor_contig(contig_end, u_contigs, o_dict):\\n for c_e in [contig_end, other_end(contig_end)]:\\n if c_e in u_contigs:\\n u_contigs.remove(c_e)\\n if c_e in o_dict:\\n o_dic = o_dict[c_e]\\n if o_dic != {}:\\n overlapped_contig = list(o_dic.keys())[0]\\n if overlapped_contig in o_dict: del o_dict[overlapped_contig][c_e]\\n del o_dict[c_e]\\n return\",\n \"def putmaxnumcone(self,maxnumcone_): # 3\\n res = self.__obj.putmaxnumcone(maxnumcone_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putxc(self,whichsol_,xc_):\\n _xc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),self.getnumcon()))\\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\\n raise ValueError(\\\"Argument xc must be writable\\\")\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc may not be None\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _xc_copyarray:\\n xc_[:] = _xc_np_tmp\",\n \"def appendconesseq(self,ct_,conepar_,nummem_,j_):\\n num_ = None\\n if num_ is None:\\n num_ = len(ct_)\\n elif num_ != len(ct_):\\n raise IndexError(\\\"Inconsistent length of array ct\\\")\\n if num_ is None:\\n num_ = len(conepar_)\\n elif num_ != len(conepar_):\\n raise IndexError(\\\"Inconsistent length of array conepar\\\")\\n if num_ is None:\\n num_ = len(nummem_)\\n elif num_ != len(nummem_):\\n raise IndexError(\\\"Inconsistent length of array nummem\\\")\\n if ct_ is None:\\n raise ValueError(\\\"Argument ct cannot be None\\\")\\n if ct_ is None:\\n raise ValueError(\\\"Argument ct may not be None\\\")\\n if ct_ is not None:\\n _ct_tmp = (ctypes.c_int32 * len(ct_))(*ct_)\\n else:\\n _ct_tmp = None\\n if conepar_ is None:\\n raise ValueError(\\\"Argument conepar cannot be None\\\")\\n if conepar_ is None:\\n raise ValueError(\\\"Argument conepar may not be None\\\")\\n if isinstance(conepar_, numpy.ndarray) and conepar_.dtype is numpy.dtype(numpy.float64) and conepar_.flags.contiguous:\\n _conepar_copyarray = False\\n _conepar_tmp = ctypes.cast(conepar_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif conepar_ is not None:\\n _conepar_copyarray = True\\n _conepar_np_tmp = numpy.zeros(len(conepar_),numpy.dtype(numpy.float64))\\n _conepar_np_tmp[:] = conepar_\\n assert _conepar_np_tmp.flags.contiguous\\n _conepar_tmp = ctypes.cast(_conepar_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _conepar_copyarray = False\\n _conepar_tmp = None\\n \\n if nummem_ is None:\\n raise ValueError(\\\"Argument nummem cannot be None\\\")\\n if nummem_ is None:\\n raise ValueError(\\\"Argument nummem may not be None\\\")\\n if isinstance(nummem_, numpy.ndarray) and nummem_.dtype is numpy.dtype(numpy.int32) and nummem_.flags.contiguous:\\n _nummem_copyarray = False\\n _nummem_tmp = ctypes.cast(nummem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif nummem_ is not None:\\n _nummem_copyarray = True\\n _nummem_np_tmp = numpy.zeros(len(nummem_),numpy.dtype(numpy.int32))\\n _nummem_np_tmp[:] = nummem_\\n assert _nummem_np_tmp.flags.contiguous\\n _nummem_tmp = ctypes.cast(_nummem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _nummem_copyarray = False\\n _nummem_tmp = None\\n \\n res = __library__.MSK_XX_appendconesseq(self.__nativep,num_,_ct_tmp,_conepar_tmp,_nummem_tmp,j_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_constraint(self, constraint):\\n self._ckey += 1\\n self.constraints[self._ckey] = constraint\",\n \"def put_C_reduced(self, dest):\\n _parallel.call_from_rank_zero(self.put_mat, self.C_reduced, dest)\\n _parallel.barrier()\",\n \"def placeConcentrationInto(self, region, conc):\\n self._sim.distributeConcentration(self, region, conc)\\n return self\",\n \"def release_cytokine(self) :\\n for cy in self.cytokine :\\n if self.cytosol.get(cy, False) and self.cytosol[cy] > 0:\\n cy(self.pos, tool.rand_2D(1.2))\\n self.cytosol[cy] -= 1\\n else : \\n self.ribosome.mrna(cy, 100)\\n self.cytokine.clear()\",\n \"def poet_known(self, K, tau):\\n # check K and tau\\n if K < 0 or K >= self.N:\\n raise RuntimeError(\\\"Invalid value for K, number of factors.\\\")\\n if tau < 0 or tau > 1:\\n raise RuntimeError(\\\"Invalid value for tau, which should be between 0 and 1.\\\")\\n \\n # mean centering and calculate SVD for pca\\n Rc = self.R.T - np.mean(self.R, axis=1)\\n u, s, vt = np.linalg.svd(Rc/np.sqrt(self.T-1))\\n eigvecs = vt.T\\n eigvals = s**2\\n \\n # decomposition of covariance matrix\\n cov_pca = eigvecs[:,:K] @ np.diag(eigvals[:K]) @ eigvecs[:,:K].T\\n Rk = self.cov - cov_pca\\n \\n # thresholding the complement matrix\\n rii = np.diag(Rk)\\n tauij = np.sqrt(np.outer(rii, rii))*tau\\n RkT = Rk*(Rk > tauij)\\n \\n # combine the two terms\\n result = cov_pca + RkT\\n return result\",\n \"def test_kyc_put_legal(self):\\n pass\",\n \"def set_coor(self, new_coor):\\n self.__x_coor, self.__y_coor = new_coor\",\n \"def putconbound(self,i_,bk_,bl_,bu_): # 3\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def bcc(self, arg):\\n\\n self.pc += arg if not self.p & const.FLAG_CARRY else 0\\n self.pc = c_uint16(self.pc).value\",\n \"def CZ(self, qubit_expr):\\n self.apply_gate_operation(cirq.ops.CZ, qubit_expr)\",\n \"def move_to_coc(self):\\n coc = scale(self.center_of_charge(), -1.0)\\n self.translate(coc)\",\n \"def pk_cs(self, snr=30, headroom = 0):\\n # Initialize\\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\\n # loop over frequencies\\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\\n # print(self.pk.shape)\\n for jf, k0 in enumerate(self.controls.k0):\\n # get the scaled version of the propagating directions\\n k_vec = k0 * self.dir\\n # Form the sensing matrix\\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\\n H = h_mtx.astype(complex)\\n # measured data\\n pm = self.pres_s[:,jf].astype(complex)\\n # Performing constrained optmization cvxpy\\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True)\\n # Create the problem\\n epsilon = 10**(-(snr-headroom)/10)\\n objective = cp.Minimize(cp.pnorm(x_cvx, p=1))\\n constraints = [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon]#[H*x == pm]\\n # Create the problem and solve\\n problem = cp.Problem(objective, constraints)\\n problem.solve(solver=cp.SCS, verbose=True) \\n self.pk[:,jf] = x_cvx.value\\n bar.update(1)\\n bar.close()\\n return self.pk\",\n \"def Ev_ccs(ccs_coord, ccs_span, vacancy_index, **kwargs):\\n if 'QMInp' not in kwargs:\\n qtk.exit(\\\"kwargs: 'QMInp' is missing.\\\\n\\\"\\\\\\n + \\\"It should be set to QMInp object of \\\"\\\\\\n + \\\"system without vacancies.\\\\n\\\"\\\\\\n + \\\"It is necessary for inp settings\\\")\\n base_inp = kwargs['QMInp']\\n\\n qm_setting = {}\\n if 'qm_setting' in kwargs:\\n qm_setting = kwargs['qm_setting']\\n\\n if 'pref' in kwargs and 'vref' in kwargs:\\n alchem = True\\n perfect_ref = kwargs['pref']\\n vacancy_ref = kwargs['vref']\\n elif 'pref' not in kwargs and 'vref' not in kwargs:\\n alchem = False\\n\\n freeE = qtk.QMOut('freeAtom/freeAtom.out')\\n freeE.inUnit('ev')\\n\\n if 'threads' in kwargs:\\n _threads = kwargs['threads']\\n else:\\n _threads = 1\\n\\n inp_wov = qtk.QMInp(ccs_span.generate(**ccs_coord))\\n inp_wv = qtk.QMInp(ccs_span.generate(**ccs_coord))\\n\\n inp_wv.removeAtoms(vacancy_index)\\n inp_wv.setChargeMultiplicity(0, 2)\\n\\n perfect = 'ev_perfect' + str(os.getpid())\\n vacancy = 'ev_vacancy' + str(os.getpid())\\n perfectinp = perfect + '.inp'\\n vacancyinp = vacancy + '.inp'\\n inp_wov.molecule.name = perfectinp\\n inp_wv.molecule.name = vacancyinp\\n\\n if os.path.exists(perfect):\\n shutil.rmtree(perfect)\\n if os.path.exists(vacancy):\\n shutil.rmtree(vacancy)\\n\\n print ccs_coord\\n if alchem:\\n out_wov = qtk.Al1st(inp_wov, ref_dir=perfect_ref, **qm_setting)\\n out_wv = qtk.Al1st(inp_wv, ref_dir=vacancy_ref, **qm_setting)\\n else:\\n out_wov = inp_wov.run(**qm_setting)\\n out_wv = inp_wv.run(**qm_setting)\\n try:\\n os.remove(perfectinp)\\n os.remove(vacancyinp)\\n except OSError:\\n shutil.rmtree(perfectinp)\\n shutil.rmtree(vacancyinp)\\n\\n out_wov.inUnit('ev')\\n out_wv.inUnit('ev')\\n\\n final = out_wov - out_wv - freeE\\n\\n msg = str(out_wov.Et) + '-(' + str(out_wv.Et) + \\\\\\n '+' + str(freeE.Et) + ') = ' + str(final.Et)\\n qtk.report('trial Ev', msg)\\n\\n return final.Et\",\n \"def replace_construct(self, c):\\n if self.array_index is not None:\\n self.parent_item.construct.args[self.arg_index][self.array_index] = c\\n elif self.arg_index is not None:\\n self.parent_item.construct.args[self.arg_index] = c\\n else:\\n raise ValueError(\\\"Invalid parent\\\")\",\n \"def putskc(self,whichsol_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None: raise TypeError(\\\"Invalid type for argument skc\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.putskc(whichsol_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def appendconesseq(self,ct,conepar,nummem,j_): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(ct)\\n elif num_ != len(ct):\\n raise IndexError(\\\"Inconsistent length of array ct\\\")\\n if num_ is None:\\n num_ = len(conepar)\\n elif num_ != len(conepar):\\n raise IndexError(\\\"Inconsistent length of array conepar\\\")\\n if num_ is None:\\n num_ = len(nummem)\\n elif num_ != len(nummem):\\n raise IndexError(\\\"Inconsistent length of array nummem\\\")\\n if num_ is None: num_ = 0\\n if ct is None: raise TypeError(\\\"Invalid type for argument ct\\\")\\n if ct is None:\\n ct_ = None\\n else:\\n try:\\n ct_ = memoryview(ct)\\n except TypeError:\\n try:\\n _tmparr_ct = array.array(\\\"i\\\",ct)\\n except TypeError:\\n raise TypeError(\\\"Argument ct has wrong type\\\")\\n else:\\n ct_ = memoryview(_tmparr_ct)\\n \\n else:\\n if ct_.format != \\\"i\\\":\\n ct_ = memoryview(array.array(\\\"i\\\",ct))\\n \\n if conepar is None: raise TypeError(\\\"Invalid type for argument conepar\\\")\\n if conepar is None:\\n conepar_ = None\\n else:\\n try:\\n conepar_ = memoryview(conepar)\\n except TypeError:\\n try:\\n _tmparr_conepar = array.array(\\\"d\\\",conepar)\\n except TypeError:\\n raise TypeError(\\\"Argument conepar has wrong type\\\")\\n else:\\n conepar_ = memoryview(_tmparr_conepar)\\n \\n else:\\n if conepar_.format != \\\"d\\\":\\n conepar_ = memoryview(array.array(\\\"d\\\",conepar))\\n \\n if nummem is None: raise TypeError(\\\"Invalid type for argument nummem\\\")\\n if nummem is None:\\n nummem_ = None\\n else:\\n try:\\n nummem_ = memoryview(nummem)\\n except TypeError:\\n try:\\n _tmparr_nummem = array.array(\\\"i\\\",nummem)\\n except TypeError:\\n raise TypeError(\\\"Argument nummem has wrong type\\\")\\n else:\\n nummem_ = memoryview(_tmparr_nummem)\\n \\n else:\\n if nummem_.format != \\\"i\\\":\\n nummem_ = memoryview(array.array(\\\"i\\\",nummem))\\n \\n res = self.__obj.appendconesseq(num_,ct_,conepar_,nummem_,j_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def pk_constrained(self, snr=30, headroom = 0):\\n # Initialize\\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\\n # loop over frequencies\\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\\n for jf, k0 in enumerate(self.controls.k0):\\n # get the scaled version of the propagating directions\\n k_vec = k0 * self.dir\\n # Form the sensing matrix\\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\\n H = h_mtx.astype(complex) # cvxpy does not accept floats, apparently\\n # measured data\\n pm = self.pres_s[:,jf].astype(complex)\\n # Performing constrained optmization cvxpy\\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True) # create x variable\\n # Create the problem\\n epsilon = 10**(-(snr-headroom)/10)\\n problem = cp.Problem(cp.Minimize(cp.norm2(x_cvx)**2),\\n [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon])\\n problem.solve(solver=cp.SCS, verbose=False)\\n self.pk[:,jf] = x_cvx.value\\n bar.update(1)\\n bar.close()\",\n \"def SetConicMode(self, *args):\\n return _ShapeUpgrade.ShapeUpgrade_ConvertCurve3dToBezier_SetConicMode(self, *args)\",\n \"def ikcon(self, T, q0=None):\\n\\n if not isinstance(T, SE3):\\n T = SE3(T)\\n\\n trajn = len(T)\\n\\n try:\\n if q0 is not None:\\n q0 = getvector(q0, self.n, 'row')\\n else:\\n q0 = np.zeros((trajn, self.n))\\n except ValueError:\\n verifymatrix(q0, (trajn, self.n))\\n\\n # create output variables\\n qstar = np.zeros((trajn, self.n))\\n error = []\\n exitflag = []\\n\\n omega = np.diag([1, 1, 1, 3 / self.reach])\\n\\n def cost(q, T, omega):\\n return np.sum(\\n (\\n (np.linalg.pinv(T.A) @ self.fkine(q).A - np.eye(4)) @\\n omega) ** 2\\n )\\n\\n bnds = Bounds(self.qlim[0, :], self.qlim[1, :])\\n\\n for i in range(trajn):\\n Ti = T[i]\\n res = minimize(\\n lambda q: cost(q, Ti, omega),\\n q0[i, :], bounds=bnds, options={'gtol': 1e-6})\\n qstar[i, :] = res.x\\n error.append(res.fun)\\n exitflag.append(res.success)\\n\\n if trajn > 1:\\n return qstar, exitflag, error\\n else:\\n return qstar[0, :], exitflag[0], error[0]\",\n \"def ALIGNF(km_list, ky):\\n n_feat = len(km_list)\\n\\n #km_list_copy = []\\n # center the kernel first\\n #for i in range(n_feat):\\n # km_list_copy.append(center(km_list[i].copy()))\\n #ky_copy = center(ky.copy())\\n\\n\\n a = np.zeros(n_feat)\\n for i in range(n_feat):\\n a[i] = f_dot(km_list[i], ky)\\n\\n M = np.zeros((n_feat, n_feat))\\n for i in range(n_feat):\\n for j in range(i,n_feat):\\n M[i,j] = f_dot(km_list[i],km_list[j])\\n M[j,i] = M[i,j]\\n\\n Q = 2*M\\n C = -2*a\\n\\n Q = Q + np.diag(np.ones(n_feat)*1e-8)\\n\\n ################################################\\n # Using mosek to solve the quadratice programming\\n\\n # Set upper diagonal element to zeros, mosek only accept lower triangle\\n iu = np.triu_indices(n_feat,1)\\n Q[iu] = 0\\n\\n # start solving with mosek\\n inf = 0.0\\n env = mosek.Env()\\n env.set_Stream(mosek.streamtype.log, streamprinter)\\n\\n # Create a task \\n task = env.Task()\\n task.set_Stream(mosek.streamtype.log, streamprinter)\\n\\n # Set up bound for variables \\n bkx = [mosek.boundkey.lo]* n_feat\\n blx = [0.0] * n_feat\\n #bkx = [mosek.boundkey.fr]* n_feat\\n #blx = [-inf] * n_feat\\n bux = [+inf] * n_feat\\n\\n numvar = len(bkx)\\n\\n task.appendvars(numvar)\\n\\n for j in range(numvar):\\n task.putcj(j,C[j])\\n task.putvarbound(j,bkx[j],blx[j],bux[j])\\n\\n # Set up quadratic objective \\n inds = np.nonzero(Q)\\n qsubi = inds[0].tolist()\\n qsubj = inds[1].tolist()\\n qval = Q[inds].tolist()\\n\\n # Input quadratic objective \\n task.putqobj(qsubi,qsubj,qval)\\n\\n # Input objective sense (minimize/mximize) \\n task.putobjsense(mosek.objsense.minimize)\\n\\n task.optimize()\\n\\n # Print a summary containing information \\n # about the solution for debugging purposes \\n task.solutionsummary(mosek.streamtype.msg)\\n\\n solsta = task.getsolsta(mosek.soltype.itr)\\n if (solsta == mosek.solsta.optimal or\\n solsta == mosek.solsta.near_optimal):\\n # Output a solution \\n xx = np.zeros(numvar, float)\\n task.getxx(mosek.soltype.itr, xx)\\n #xx = xx/np.linalg.norm(xx)\\n return xx\\n else:\\n print solsta\\n xx = np.zeros(numvar, float)\\n task.getxx(mosek.soltype.itr, xx)\\n #xx = xx/np.linalg.norm(xx)\\n return xx\",\n \"def conj(x, *args, **kwargs):\\n raise NotImplementedError\",\n \"def add_corridor_constraint(self,seg,r,weight=1.0):\\n\\n constraint_type = \\\"cylinder\\\"\\n params = dict()\\n params['x1'] = np.array([ self.qr_polytraj.waypoints['x'][0,seg],\\n self.qr_polytraj.waypoints['y'][0,seg],\\n self.qr_polytraj.waypoints['z'][0,seg]])\\n params['x2'] = np.array([ self.qr_polytraj.waypoints['x'][0,seg+1],\\n self.qr_polytraj.waypoints['y'][0,seg+1],\\n self.qr_polytraj.waypoints['z'][0,seg+1]])\\n params['der'] = 0\\n params['l'] = r # Give the same radius buffer on the end caps\\n params['r'] = r\\n params['weight'] = weight\\n params['keep_out'] = False\\n params['active_seg'] = seg\\n\\n\\n self.qr_polytraj.add_constraint(constraint_type,params,dynamic_weighting=False,sum_func=False)\",\n \"def fixC(self,i,value):\\n if self.coeffPattern[2] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[2] = [None]*m\\n self.coeffPattern[2][i]=value\\n self._updateEstimatorSize(i)\",\n \"def cz(self, q0, q1, ctrl=None):\\n self.__add_quantum_gate(kind=CONTROLLED_Z, qid=[q0,q1], ctrl=ctrl)\\n return self\",\n \"def _cpEquilibriumFunc(self,cp,k6,kdT,kdI,Kactiv0,Kinhib0,Cactiv0,Cinhib0,E0,X0,masks):\\n new_cp = 1\\n olderX = [np.zeros(m.shape[1]) for m in masks]\\n for layeridx,layer in enumerate(masks):\\n layerEq = np.zeros(layer.shape[1])\\n if(layeridx==0):\\n\\n for inpIdx in range(layer.shape[1]):\\n if self.k1M[layeridx][inpIdx,inpIdx]*E0*self.TA0[layeridx][inpIdx,inpIdx]/cp == 0:\\n #case where there is no polynomial equation\\n new_input = X0[inpIdx]\\n new_cp += 0\\n else:\\n bOnA = X0[inpIdx]-self.TA0[layeridx][inpIdx,inpIdx]- cp/(self.k1M[layeridx][inpIdx,inpIdx]*E0)\\n # BE CAREFUL, HERE ONE SHOULD USE THE GREATER ROOT of the polynomial (only positive)\\n new_input = 1/2*(bOnA + (bOnA**2+4*cp*X0[inpIdx]/(self.k1M[layeridx][inpIdx,inpIdx]*E0))**0.5)\\n # update for fixed point:\\n new_cp += self.k1M[layeridx][inpIdx,inpIdx]*new_input*self.TA0[layeridx][inpIdx,inpIdx]/(1+self.k1M[layeridx][inpIdx,inpIdx]*new_input*E0/cp)\\n layerEq[inpIdx] = new_input\\n olderX[layeridx] = layerEq\\n\\n else:\\n for inpIdx in range(layer.shape[1]):\\n #compute of Cactivs,Cinhibs, the denominator marks the template's variation from equilibrium\\n #Terms for the previous layers\\n CactivsOld = np.where(masks[layeridx-1][inpIdx,:]>0,Cactiv0[layeridx-1][inpIdx]/(1+self.k1M[layeridx-1][inpIdx]*olderX[layeridx-1]*self.E0/cp),0)\\n CinhibsOld = np.where(masks[layeridx-1][inpIdx,:]<0,Cinhib0[layeridx-1][inpIdx]/(1+self.k3M[layeridx-1][inpIdx]*olderX[layeridx-1]*self.E0/cp),0)\\n Inhib = np.sum(CinhibsOld*olderX[layeridx-1]/kdT[layeridx-1][inpIdx])\\n #computing of new equilibrium\\n x_eq = np.sum(CactivsOld*olderX[layeridx-1]/(kdI[layeridx-1][inpIdx]*cp+Inhib/cp))\\n # print(np.sum(CactivsOld*olderX[layeridx-1])/(kdI[layeridx-1][inpIdx,0]*cp+Inhib/cp))\\n # print(x_eq)\\n # assert np.sum(CactivsOld*olderX[layeridx-1])/(kdI[layeridx-1][inpIdx,0]*cp+Inhib/cp) == x_eq\\n\\n layerEq[inpIdx] = x_eq\\n\\n\\n #compute of Kactivs,Kinhibs, for the current layer:\\n Kactivs = np.where(layer[:,inpIdx]>0,Kactiv0[layeridx][:,inpIdx],0)\\n Kinhibs = np.where(layer[:,inpIdx]<0,Kinhib0[layeridx][:,inpIdx],0)\\n #Adding, to the competition over enzyme, the complex formed in this layer by this input.\\n firstComplex = np.sum(np.where(layer[:,inpIdx]>0,Kactivs*x_eq/(1+self.k1M[layeridx][:,inpIdx]*x_eq*self.E0/cp),np.where(layer[:,inpIdx]<0,Kinhibs*x_eq/(1+self.k3M[layeridx][:,inpIdx]*x_eq*self.E0/cp),0)))\\n if layeridx>1:\\n #We must also add the effect of pseudoTempalte enzymatic complex in the previous layers which can't be computed previously because we missed x_eq\\n Inhib2 = np.sum(CinhibsOld*olderX[layeridx-1]/(kdT[layeridx-1][inpIdx]*k6[layeridx-1][inpIdx]))\\n new_cp += Inhib2/(E0*cp)*x_eq\\n new_cp += firstComplex\\n\\n olderX[layeridx] = layerEq\\n\\n #Finally we must add the effect of pseudoTemplate enzymatic complex in the last layer\\n for outputsIdx in range(masks[-1].shape[0]):\\n Cactivs = np.where(masks[-1][outputsIdx,:]>0,Cactiv0[-1][outputsIdx]/(1+self.k1M[-1][outputsIdx]*olderX[-1]*self.E0/cp),0)\\n Cinhibs = np.where(masks[-1][outputsIdx,:]<0,Cinhib0[-1][outputsIdx]/(1+self.k3M[-1][outputsIdx]*olderX[-1]*self.E0/cp),0)\\n\\n Inhib = np.sum(Cinhibs*olderX[-1]/kdT[-1][outputsIdx])\\n x_eq = np.sum(Cactivs*olderX[-1]/(kdI[-1][outputsIdx]*cp+Inhib/cp))\\n Inhib2 = np.sum(Cinhibs*olderX[-1]/(kdT[-1][outputsIdx]*k6[-1][outputsIdx]))\\n new_cp += Inhib2/(E0*cp)*x_eq\\n\\n\\n return cp - new_cp\",\n \"def con_cieq(x,project):\\n \\n cons = project.con_cieq(x)\\n \\n if cons: cons = array(cons)\\n else: cons = zeros([0])\\n \\n return -cons\",\n \"def _ReplaceCompound(self, from_id, to_id):\\n if from_id == to_id:\\n return\\n \\n # set the coefficient of the original compound to 0\\n i = self._FindCompoundIndex(from_id)\\n if i is None:\\n return\\n how_many = self.reactants[i].coeff\\n self.reactants[i].coeff = 0\\n\\n # create a new compound with the new kegg_id and the same coefficient\\n # or add the number to the coefficient if it already is a reactant\\n j = self._FindCompoundIndex(to_id)\\n if j is None:\\n self.reactants[i] = CompoundWithCoeff.FromId(how_many, to_id)\\n else:\\n self.reactants[j].coeff += how_many\\n self._Dedup()\\n\\n # clear the cache since the reaction has changed\\n self._catalyzing_enzymes = None\",\n \"def bcs(self, arg):\\n\\n self.pc += arg if self.p & const.FLAG_CARRY else 0\\n self.pc = c_uint16(self.pc).value\",\n \"def put(self, pvname, value: Union[np.ndarray, float]) -> None:\\n if self.protocol == \\\"ca\\\":\\n caput(pvname, value)\\n\\n elif self.protocol == \\\"pva\\\":\\n self.context.put(pvname, value)\",\n \"def mapKeypointsToCOCO(self, cmu_keypoint):\\n coco_keypoint = np.zeros((4, 17), np.float)\\n for cmu_coco_idx in CMU_TO_COCO_JOINT_LABEL.items():\\n cmu_idx = cmu_coco_idx[0]\\n coco_idx = cmu_coco_idx[1]\\n coco_keypoint[:, coco_idx] = cmu_keypoint[:, cmu_idx]\\n\\n return coco_keypoint\",\n \"def covar(fx,cx):\\n \\n fx = np.array(fx)\\n cx = np.array(cx)\\n \\n shape_fx = fx.shape\\n shape_cx = cx.shape\\n \\n \\n if shape_fx[1] != shape_cx[0]:\\n print('-----------------------------------------')\\n print(\\\"Shapes of fx and cx cannot be multiplied:\\\")\\n print(shape_fx,\\\"x\\\",shape_cx)\\n print('-----------------------------------------')\\n raise ValueError('Input matrices are not compliant')\\n \\n cy = np.dot(np.dot(fx,cx),fx.T)\\n \\n print(\\\"Size of Cy matrix: \\\",np.shape(cy))\\n \\n return cy\",\n \"def weak_repulsion_boundary(Cents,a,k, CV_matrix,n_c,n_C):\\n CCW = np.dstack((roll_reverse(Cents[:,:,0]),roll_reverse(Cents[:,:,1])))#np.column_stack((Cents[:,1:3],Cents[:,0].reshape(-1,1,2)))\\n CCW_displacement = Cents - CCW\\n rij = np.sqrt(CCW_displacement[:,:,0]**2 + CCW_displacement[:,:,1]**2)\\n norm_disp = (CCW_displacement.T/rij.T).T\\n V_soft_mag = -k*(rij - 2*a)*(rij<2*a)\\n V_soft_CCW = (V_soft_mag.T*norm_disp.T).T\\n V_soft_CW = -(roll_forward(V_soft_mag).T*norm_disp.T).T\\n V_soft = V_soft_CW + V_soft_CCW\\n F_soft = np.zeros((n_c, 2))\\n for i in range(3):\\n F_soft += np.asfortranarray(CV_matrix[:, :, i])@np.asfortranarray(V_soft[:, i])\\n F_soft[n_C:] = 0\\n return F_soft\",\n \"def con_minimize(\\n fun, bounds, constr=(), x0=None, args=(), callback=None, options={}, workers=None\\n):\\n # Wrap cost function with constraints\\n def fun_soft_con(x, *augmented_args):\\n # Unpack constraints and arguments\\n fcore = augmented_args[0] # Function to be minimized\\n fcons = augmented_args[1] # Constraints\\n user_args = augmented_args[2:] # Arguments\\n\\n # Evaluate core function\\n ycore = fcore(x, *user_args)\\n\\n # Initialize penalty\\n penalty = 0.0\\n\\n # Update penalty\\n # (the more negative fcon() is, the higher penalty)\\n for f in fcons:\\n ycon = np.max([f(x, *user_args) * -1.0, 0.0])\\n pscale = ycore / (ycon + 1e-6)\\n penalty += ycon * pscale\\n\\n return ycore + penalty\\n\\n # Run minimization\\n augmented_args = (fun, constr, *args)\\n\\n res = modestga.minimize(\\n fun=fun_soft_con,\\n bounds=bounds,\\n x0=x0,\\n args=augmented_args,\\n callback=callback,\\n options=options,\\n workers=workers,\\n )\\n\\n # Extend result with contraint violation info\\n res.constr = [fcon(res.x, *args) for fcon in constr]\\n\\n return res\",\n \"def kspace_cholesky_solve(self, other, complx = False, n_points = None):\\n if n_points is None:\\n \\n m1n = np.max(np.abs(self.coords), axis = 0)\\n m2n = np.max(np.abs(other.coords), axis = 0)\\n \\n n_points = np.max([m1n,m2n], axis = 0) \\n\\n nx,ny,nz = 2*n_points + 1\\n\\n #print(\\\"kspace_svd_solve: \\\", nx,ny,nz)\\n m1x,m1y = self.blocks.shape[1], self.blocks.shape[2]\\n m2x,m2y = other.blocks.shape[1], other.blocks.shape[2]\\n \\n coords = np.roll(lattice_coords(n_points).reshape(nx,ny,nz, 3), -n_points, axis = (0,1,2)).reshape(nx*ny*nz, 3)\\n\\n #print(coords)\\n\\n \\n m1r = self.cget(coords).reshape(nx,ny,nz,m1x,m1y)\\n m2r = other.cget(coords).reshape(nx,ny,nz,m2x,m2y)\\n M1 = np.fft.fftn(m1r, axes = (0,1,2))\\n M2 = np.fft.fftn(m2r, axes = (0,1,2))\\n M3 = np.zeros((nx,ny,nz,m1x, m2y),dtype = np.complex128)\\n\\n for c in coords:\\n \\n Mk = np.linalg.cholesky(M1[c[0], c[1], c[2]])\\n yk = np.linalg.solve(Mk, M2[c[0], c[1], c[2]])\\n\\n\\n\\n \\n\\n M3[c[0], c[1], c[2]] = np.linalg.solve(Mk.conj().T, yk)\\n\\n\\n\\n \\n\\n ret = tmat()\\n if complx:\\n ret.load_nparray(np.fft.ifftn(M3.reshape(nx,ny,nz,m1x,m2y), axes = (0,1,2)).reshape(coords.shape[0], m1x,m2y), coords)\\n else:\\n ret.load_nparray(np.fft.ifftn(M3.reshape(nx,ny,nz,m1x,m2y), axes = (0,1,2)).real.reshape(coords.shape[0], m1x,m2y), coords)\\n return ret\",\n \"def custom_constr(x, qr, inverse, depth):\\n qc = QuantumCircuit(qr)\\n maxi, mini = max(x), min(x)\\n n = x.shape[0]\\n #qc_wv = Wavelets(n).construct_circuit(register=qr)\\n for _ in range(depth):\\n qc.h(qr)\\n for i in range(n):\\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini), 2*np.pi*(x[i]-mini)/(maxi-mini), qr[i])\\n for i in range(n):\\n qc.cx(qr[i], qr[(i + 1) % n])\\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini),\\n ((2*np.pi)**2*(x[i]-mini)*(x[(i+1) % n]-mini)/(maxi-mini)**2) % 2*np.pi,\\n qr[(i + 1) % n])\\n qc.cx(qr[i], qr[(i + 1) % n])\\n #qc = qc + qc_wv\\n if inverse:\\n return qc.inverse()\\n return qc\",\n \"def _update_tarsqidoc(self, cp):\\n self.tarsqidoc.remove_tlinks()\\n for n1, rest in cp.graph.edges.items():\\n for n2, edge in cp.graph.edges[n1].items():\\n if edge.constraint is not None:\\n if edge.constraint.has_simple_relation():\\n self._add_constraint_to_tarsqidoc(edge)\",\n \"def set(self, opts, popsize=None, ccovfac=1, verbose=True):\\r\\n\\r\\n alpha_cc = 1.0 # cc-correction for mueff, was zero before\\r\\n\\r\\n def cone(df, mu, N, alphacov=2.0):\\r\\n \\\"\\\"\\\"rank one update learning rate, ``df`` is disregarded and obsolete, reduce alphacov on noisy problems, say to 0.5\\\"\\\"\\\"\\r\\n return alphacov / ((N + 1.3)**2 + mu)\\r\\n\\r\\n def cmu(df, mu, alphamu=0.0, alphacov=2.0):\\r\\n \\\"\\\"\\\"rank mu learning rate, disregarding the constrant cmu <= 1 - cone\\\"\\\"\\\"\\r\\n c = alphacov * (alphamu + mu - 2 + 1/mu) / ((N + 2)**2 + alphacov * mu / 2)\\r\\n # c = alphacov * (alphamu + mu - 2 + 1/mu) / (2 * (N + 2)**1.5 + alphacov * mu / 2)\\r\\n # print 'cmu =', c\\r\\n return c\\r\\n\\r\\n def conedf(df, mu, N):\\r\\n \\\"\\\"\\\"used for computing separable learning rate\\\"\\\"\\\"\\r\\n return 1. / (df + 2.*sqrt(df) + float(mu)/N)\\r\\n\\r\\n def cmudf(df, mu, alphamu):\\r\\n \\\"\\\"\\\"used for computing separable learning rate\\\"\\\"\\\"\\r\\n return (alphamu + mu - 2. + 1./mu) / (df + 4.*sqrt(df) + mu/2.)\\r\\n\\r\\n sp = self\\r\\n N = sp.N\\r\\n if popsize:\\r\\n opts.evalall({'N':N, 'popsize':popsize})\\r\\n else:\\r\\n popsize = opts.evalall({'N':N})['popsize'] # the default popsize is computed in Options()\\r\\n sp.popsize = popsize\\r\\n if opts['CMA_mirrors'] < 0.5:\\r\\n sp.lam_mirr = int(0.5 + opts['CMA_mirrors'] * popsize)\\r\\n elif opts['CMA_mirrors'] > 1:\\r\\n sp.lam_mirr = int(0.5 + opts['CMA_mirrors'])\\r\\n else:\\r\\n sp.lam_mirr = int(0.5 + 0.16 * min((popsize, 2 * N + 2)) + 0.29) # 0.158650... * popsize is optimal\\r\\n # lam = arange(2,22)\\r\\n # mirr = 0.16 + 0.29/lam\\r\\n # print(lam); print([int(0.5 + l) for l in mirr*lam])\\r\\n # [ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21]\\r\\n # [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4]\\r\\n\\r\\n sp.mu_f = sp.popsize / 2.0 # float value of mu\\r\\n if opts['CMA_mu'] is not None:\\r\\n sp.mu_f = opts['CMA_mu']\\r\\n sp.mu = int(sp.mu_f + 0.499999) # round down for x.5\\r\\n # in principle we have mu_opt = popsize/2 + lam_mirr/2,\\r\\n # which means in particular weights should only be negative for q > 0.5+mirr_frac/2\\r\\n if sp.mu > sp.popsize - 2 * sp.lam_mirr + 1:\\r\\n print(\\\"WARNING: pairwise selection is not implemented, therefore \\\" +\\r\\n \\\" mu = %d > %d = %d - 2*%d + 1 = popsize - 2*mirr + 1 can produce a bias\\\" % (\\r\\n sp.mu, sp.popsize - 2 * sp.lam_mirr + 1, sp.popsize, sp.lam_mirr))\\r\\n if sp.lam_mirr > sp.popsize // 2:\\r\\n raise _Error(\\\"fraction of mirrors in the population as read from option CMA_mirrors cannot be larger 0.5, \\\" +\\r\\n \\\"theoretically optimal is 0.159\\\")\\r\\n sp.weights = log(max([sp.mu, sp.popsize / 2.0]) + 0.5) - log(1 + np.arange(sp.mu))\\r\\n if 11 < 3: # equal recombination weights\\r\\n sp.mu = sp.popsize // 4\\r\\n sp.weights = np.ones(sp.mu)\\r\\n print(sp.weights[:10])\\r\\n sp.weights /= sum(sp.weights)\\r\\n sp.mueff = 1 / sum(sp.weights**2)\\r\\n sp.cs = (sp.mueff + 2) / (N + sp.mueff + 3)\\r\\n # TODO: clean up (here the cumulation constant is shorter if sigma_vec is used)\\r\\n sp.dampsvec = opts['CMA_dampsvec_fac'] * (N + 2) if opts['CMA_dampsvec_fac'] else np.Inf\\r\\n sp.dampsvec_fading = opts['CMA_dampsvec_fade']\\r\\n if np.isfinite(sp.dampsvec):\\r\\n sp.cs = ((sp.mueff + 2) / (N + sp.mueff + 3))**0.5\\r\\n # sp.cs = (sp.mueff + 2) / (N + 1.5*sp.mueff + 1)\\r\\n sp.cc = (4 + alpha_cc * sp.mueff / N) / (N + 4 + alpha_cc * 2 * sp.mueff / N)\\r\\n sp.cc_sep = (1 + 1/N + alpha_cc * sp.mueff / N) / (N**0.5 + 1/N + alpha_cc * 2 * sp.mueff / N) # \\\\not\\\\gg\\\\cc\\r\\n sp.rankmualpha = opts['CMA_rankmualpha']\\r\\n # sp.rankmualpha = _evalOption(opts['CMA_rankmualpha'], 0.3)\\r\\n sp.c1 = ccovfac * min(1, sp.popsize/6) * cone((N**2 + N) / 2, sp.mueff, N) # 2. / ((N+1.3)**2 + sp.mucov)\\r\\n sp.c1_sep = ccovfac * conedf(N, sp.mueff, N)\\r\\n if 11 < 3:\\r\\n sp.c1 = 0.\\r\\n print('c1 is zero')\\r\\n if opts['CMA_rankmu'] != 0: # also empty\\r\\n sp.cmu = min(1 - sp.c1, ccovfac * cmu((N**2+N)/2, sp.mueff, sp.rankmualpha))\\r\\n sp.cmu_sep = min(1 - sp.c1_sep, ccovfac * cmudf(N, sp.mueff, sp.rankmualpha))\\r\\n else:\\r\\n sp.cmu = sp.cmu_sep = 0\\r\\n\\r\\n sp.neg = BlancClass()\\r\\n if opts['CMA_active']:\\r\\n # in principle we have mu_opt = popsize/2 + lam_mirr/2,\\r\\n # which means in particular weights should only be negative for q > 0.5+mirr_frac/2\\r\\n sp.neg.mu_f = popsize - (popsize + sp.lam_mirr) / 2 if popsize > 2 else 1\\r\\n sp.neg.weights = log(sp.mu_f + 0.5) - log(1 + np.arange(sp.popsize - int(sp.neg.mu_f), sp.popsize))\\r\\n sp.neg.mu = len(sp.neg.weights) # maybe never useful?\\r\\n sp.neg.weights /= sum(sp.neg.weights)\\r\\n sp.neg.mueff = 1 / sum(sp.neg.weights**2)\\r\\n sp.neg.cmuexp = opts['CMA_activefac'] * 0.25 * sp.neg.mueff / ((N+2)**1.5 + 2 * sp.neg.mueff)\\r\\n assert sp.neg.mu >= sp.lam_mirr # not really necessary\\r\\n # sp.neg.minresidualvariance = 0.66 # not it use, keep at least 0.66 in all directions, small popsize is most critical\\r\\n else:\\r\\n sp.neg.cmuexp = 0\\r\\n\\r\\n sp.CMA_on = sp.c1 + sp.cmu > 0\\r\\n # print(sp.c1_sep / sp.cc_sep)\\r\\n\\r\\n if not opts['CMA_on'] and opts['CMA_on'] not in (None,[],(),''):\\r\\n sp.CMA_on = False\\r\\n # sp.c1 = sp.cmu = sp.c1_sep = sp.cmu_sep = 0\\r\\n\\r\\n sp.damps = opts['CMA_dampfac'] * (0.5 +\\r\\n 0.5 * min([1, (sp.lam_mirr/(0.159*sp.popsize) - 1)**2])**1 +\\r\\n 2 * max([0, ((sp.mueff-1) / (N+1))**0.5 - 1]) + sp.cs\\r\\n )\\r\\n if 11 < 3:\\r\\n # this is worse than damps = 1 + sp.cs for the (1,10000)-ES on 40D parabolic ridge\\r\\n sp.damps = 0.3 + 2 * max([sp.mueff/sp.popsize, ((sp.mueff-1)/(N+1))**0.5 - 1]) + sp.cs\\r\\n if 11 < 3:\\r\\n # this does not work for lambda = 4*N^2 on the parabolic ridge\\r\\n sp.damps = opts['CMA_dampfac'] * (2 - 0*sp.lam_mirr/sp.popsize) * sp.mueff/sp.popsize + 0.3 + sp.cs # nicer future setting\\r\\n print('damps =', sp.damps)\\r\\n if 11 < 3:\\r\\n sp.damps = 10 * sp.damps # 1e99 # (1 + 2*max(0,sqrt((sp.mueff-1)/(N+1))-1)) + sp.cs;\\r\\n # sp.damps = 20 # 1. + 20 * sp.cs**-1 # 1e99 # (1 + 2*max(0,sqrt((sp.mueff-1)/(N+1))-1)) + sp.cs;\\r\\n print('damps is %f' % (sp.damps))\\r\\n\\r\\n sp.cmean = float(opts['CMA_cmean'])\\r\\n # sp.kappa = 1 # 4-D, lam=16, rank1, kappa < 4 does not influence convergence rate\\r\\n # in larger dim it does, 15-D with defaults, kappa=8 factor 2\\r\\n if sp.cmean != 1:\\r\\n print(' cmean = %f' % (sp.cmean))\\r\\n\\r\\n if verbose:\\r\\n if not sp.CMA_on:\\r\\n print('covariance matrix adaptation turned off')\\r\\n if opts['CMA_mu'] != None:\\r\\n print('mu = %f' % (sp.mu_f))\\r\\n\\r\\n # return self # the constructor returns itself\\r\",\n \"def _AddCompound(self, kegg_id, how_many):\\n i = self._FindCompoundIndex(kegg_id)\\n if i is not None:\\n self.reactants[i].coeff += how_many\\n else:\\n self.reactants += [CompoundWithCoeff.FromId(how_many, kegg_id)]\\n \\n # clear the cache since the reaction has changed\\n self._catalyzing_enzymes = None\",\n \"def putconboundlist(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkc_)\\n elif num_ != len(bkc_):\\n raise IndexError(\\\"Inconsistent length of array bkc\\\")\\n if num_ is None:\\n num_ = len(blc_)\\n elif num_ != len(blc_):\\n raise IndexError(\\\"Inconsistent length of array blc\\\")\\n if num_ is None:\\n num_ = len(buc_)\\n elif num_ != len(buc_):\\n raise IndexError(\\\"Inconsistent length of array buc\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc cannot be None\\\")\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc may not be None\\\")\\n if bkc_ is not None:\\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\\n else:\\n _bkc_tmp = None\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc cannot be None\\\")\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc may not be None\\\")\\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\\n _blc_copyarray = False\\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blc_ is not None:\\n _blc_copyarray = True\\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\\n _blc_np_tmp[:] = blc_\\n assert _blc_np_tmp.flags.contiguous\\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blc_copyarray = False\\n _blc_tmp = None\\n \\n if buc_ is None:\\n raise ValueError(\\\"Argument buc cannot be None\\\")\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc may not be None\\\")\\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\\n _buc_copyarray = False\\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif buc_ is not None:\\n _buc_copyarray = True\\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\\n _buc_np_tmp[:] = buc_\\n assert _buc_np_tmp.flags.contiguous\\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _buc_copyarray = False\\n _buc_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def update_coe_cluster(self, name_or_id, **kwargs):\\n self.list_coe_clusters.invalidate(self)\\n cluster = self.get_coe_cluster(name_or_id)\\n if not cluster:\\n raise exc.OpenStackCloudException(\\n \\\"COE cluster %s not found.\\\" % name_or_id\\n )\\n\\n cluster = self.container_infrastructure_management.update_cluster(\\n cluster, **kwargs\\n )\\n\\n return cluster\",\n \"def refine(self, ijk):\\n if self.cbc is None or not self.sub_block_count:\\n raise ValueError(\\n \\\"Cannot refine sub block model without specifying number \\\"\\n \\\"of parent and sub blocks\\\"\\n )\\n try:\\n inds = self.ijk_array_to_indices(ijk)\\n except ValueError:\\n inds = self.ijk_to_index(ijk)\\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137\",\n \"def test_simple_cc():\\n hs_c = LocalSpace('c', dimension=3)\\n hs_q = LocalSpace('q1', basis=('g', 'e'))\\n Delta_1 = Symbol('Delta_1')\\n Omega_1 = Symbol('Omega_1')\\n g_1 = Symbol('g_1')\\n a = Destroy(hs=hs_c)\\n a_dag = Create(hs=hs_c)\\n sig_p = LocalSigma('e', 'g', hs=hs_q)\\n sig_m = LocalSigma('g', 'e', hs=hs_q)\\n coeff = (-I / 2) * (Omega_1 * g_1 / Delta_1)\\n jc_expr = coeff * (a * sig_p - a_dag * sig_m)\\n\\n simplified = rewrite_with_operator_pm_cc(jc_expr)\\n assert simplified == coeff * OperatorPlusMinusCC(a * sig_p, sign=-1)\\n assert (\\n srepr(simplified.term)\\n == \\\"OperatorPlusMinusCC(OperatorTimes(Destroy(hs=LocalSpace('c', \\\"\\n \\\"dimension=3)), LocalSigma('e', 'g', hs=LocalSpace('q1', \\\"\\n \\\"basis=('g', 'e')))), sign=-1)\\\"\\n )\\n expanded = simplified.doit()\\n assert expanded == jc_expr\",\n \"def convex(self, *args, **kwargs) -> Any:\\n pass\",\n \"def SetConstraint(self, model) :\\n if 'pp' in self.__type : self.SetPPConstraint( model )\\n elif self.__type == 'prBin' and self.bound!=0 : self.SetPRBinConstraint( model )\\n elif self.__type == 'prCat' and self.bound != 0 : self.SetPRCatConstraint(model)\\n elif self.__type == 'prBinCat' and self.bound != 0 : self.SetPRBinCatConstraint(model)\\n elif self.bound == 0 : return\\n else : raise RuntimeError( 'SetConstraint : Unknown type for Constraint : ', self.__type )\",\n \"def transfer_parameters_call_optimization(self, mainCl, mainPt, consCall=None, consPt=None):\",\n \"def constrain(*args, barrier: bool=True, damping: Union[float, bool]=0.0, directionalHinge:\\n bool=True, hinge: bool=True, interpenetrate: bool=True, nail: bool=True, name:\\n Union[AnyStr, bool]=\\\"\\\", orientation: Union[List[float, float, float], bool]=None,\\n pinConstraint: bool=True, position: Union[List[float, float, float], bool]=None,\\n restLength: Union[float, bool]=0.0, spring: bool=True, stiffness: Union[float,\\n bool]=0.0, q=True, query=True, e=True, edit=True, **kwargs)->Union[None, Any]:\\n pass\",\n \"def SetPRBinCatConstraint( self, model ) :\\n tot = np.dot( self.wish.T, self.dispo )\\n for val in tot :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def de_cic(engine, deconvolved, d_k):\\n code = CodeSegment(engine)\\n def tf(k):\\n kny = [numpy.sinc(k[i]*engine.pm.BoxSize[i]/(2*numpy.pi*engine.pm.Nmesh[i])) for i in range(3)]\\n wts = (kny[0]*kny[1]*kny[2])**-2\\n return wts\\n \\n #kny = numpy.pi*engine.pm.Nmesh[0]/engine.pm.BoxSize[0]\\n code.assign(x='d_k', y='tmp')\\n code.transfer(complex='tmp', tf=tf)\\n code.c2r(real=deconvolved, complex='tmp')\\n return code\",\n \"def CX(self, qubit_expr):\\n self.apply_gate_operation(cirq.ops.CX, qubit_expr)\",\n \"def getqconk(self,k_,qcsubi_,qcsubj_,qcval_):\\n maxnumqcnz_ = self.getnumqconknz((k_))\\n numqcnz_ = ctypes.c_int64()\\n _qcsubi_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubi is not long enough: Is %d, expected %d\\\" % (len(qcsubi_),self.getnumqconknz((k_))))\\n if isinstance(qcsubi_,numpy.ndarray) and not qcsubi_.flags.writeable:\\n raise ValueError(\\\"Argument qcsubi must be writable\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n _qcsubj_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubj is not long enough: Is %d, expected %d\\\" % (len(qcsubj_),self.getnumqconknz((k_))))\\n if isinstance(qcsubj_,numpy.ndarray) and not qcsubj_.flags.writeable:\\n raise ValueError(\\\"Argument qcsubj must be writable\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n _qcval_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcval is not long enough: Is %d, expected %d\\\" % (len(qcval_),self.getnumqconknz((k_))))\\n if isinstance(qcval_,numpy.ndarray) and not qcval_.flags.writeable:\\n raise ValueError(\\\"Argument qcval must be writable\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n qcsurp_ = ctypes.c_int64(_qcsubi_minlength)\\n res = __library__.MSK_XX_getqconk64(self.__nativep,k_,maxnumqcnz_,ctypes.byref(qcsurp_),ctypes.byref(numqcnz_),_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numqcnz_ = numqcnz_.value\\n _numqcnz_return_value = numqcnz_\\n if _qcsubi_copyarray:\\n qcsubi_[:] = _qcsubi_np_tmp\\n if _qcsubj_copyarray:\\n qcsubj_[:] = _qcsubj_np_tmp\\n if _qcval_copyarray:\\n qcval_[:] = _qcval_np_tmp\\n return (_numqcnz_return_value)\",\n \"def test_scalar_coeff_cc():\\n hs_1 = LocalSpace('q1', basis=('g', 'e'))\\n hs_2 = LocalSpace('q2', basis=('g', 'e'))\\n kappa = Symbol('kappa', real=True)\\n a1 = Destroy(hs=hs_1)\\n a2 = Destroy(hs=hs_2)\\n\\n jc_expr = (\\n I / 2 * (2 * kappa * (a1.dag() * a2) - 2 * kappa * (a1 * a2.dag()))\\n )\\n\\n simplified = rewrite_with_operator_pm_cc(jc_expr)\\n assert simplified == I * kappa * OperatorPlusMinusCC(\\n a1.dag() * a2, sign=-1\\n )\\n expanded = simplified.doit()\\n assert expanded == I * kappa * (a1.dag() * a2 - a1 * a2.dag())\",\n \"def addConstrs(self, constrs, name=''):\\n ...\",\n \"def putxc(self,whichsol_,xc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xc is None: raise TypeError(\\\"Invalid type for argument xc\\\")\\n _copyback_xc = False\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n _copyback_xc = True\\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n _copyback_xc = True\\n if xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n res = self.__obj.putxc(whichsol_,xc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_xc:\\n xc[:] = _tmparr_xc\",\n \"def covariance(self, cond, include_obs=False, parametric_only=False, pad=1e-8):\\n X1 = self.standardize_input_array(cond)\\n m = X1.shape[0]\\n\\n Kstar = self.get_query_K(X1)\\n if not parametric_only:\\n tmp = self.Kinv_sp_tri * Kstar\\n qf = np.dot(Kstar.T, tmp)\\n k = self.kernel(X1,X1, identical=include_obs)\\n gp_cov = k - qf\\n else:\\n gp_cov = np.zeros((m,m))\\n\\n R = self.query_R\\n tmp = np.dot(self.invc, R)\\n mean_cov = np.dot(tmp.T, tmp)\\n gp_cov += mean_cov\\n\\n gp_cov += pad * np.eye(gp_cov.shape[0])\\n return gp_cov\",\n \"def cca_loss(outdim_size, use_all_singular_values):\\n def inner_cca_objective(y_true, y_pred):\\n \\\"\\\"\\\"\\n It is the loss function of CCA as introduced in the original paper. There can be other formulations.\\n It is implemented by Theano tensor operations, and does not work on Tensorflow backend\\n y_true is just ignored\\n \\\"\\\"\\\"\\n\\n r1 = 1e-4\\n r2 = 1e-4\\n eps = 1e-12\\n o1 = o2 = y_pred.shape[1]//2\\n\\n # unpack (separate) the output of networks for view 1 and view 2\\n H1 = tf.transpose(y_pred[:, 0:o1])\\n H2 = tf.transpose(y_pred[:, o1:o1+o2])\\n\\n m = H1.shape[1]\\n\\n H1bar = H1 - (tf.math.divide(1, m)) * tf.dot(H1, tf.ones([m, m]))\\n H2bar = H2 - (tf.math.divide(1, m)) * tf.dot(H2, tf.ones([m, m]))\\n\\n SigmaHat12 = (tf.math.divide(1, m-1)) * \\\\\\n tf.dot(H1bar, tf.transpose(H2bar))\\n SigmaHat11 = (tf.math.divide(1, m-1)) * tf.dot(H1bar,\\n tf.transpose(H1bar)) + r1 * tf.eye(o1)\\n SigmaHat22 = (tf.math.divide(1, m-1)) * tf.dot(H2bar,\\n tf.transpose(H2bar)) + r2 * tf.eye(o2)\\n\\n # Calculating the root inverse of covariance matrices by using eigen decomposition\\n [D1, V1] = tf.nlinalg.eigh(SigmaHat11)\\n [D2, V2] = tf.nlinalg.eigh(SigmaHat22)\\n\\n # Added to increase stability\\n posInd1 = tf.gt(D1, eps).nonzero()[0]\\n D1 = D1[posInd1]\\n V1 = V1[:, posInd1]\\n posInd2 = tf.gt(D2, eps).nonzero()[0]\\n D2 = D2[posInd2]\\n V2 = V2[:, posInd2]\\n\\n SigmaHat11RootInv = tf.dot(\\n tf.dot(V1, tf.nlinalg.diag(D1 ** -0.5)), tf.transpose(V1))\\n SigmaHat22RootInv = tf.dot(\\n tf.dot(V2, tf.nlinalg.diag(D2 ** -0.5)), tf.transpose(V2))\\n\\n Tval = tf.dot(tf.dot(SigmaHat11RootInv, SigmaHat12), SigmaHat22RootInv)\\n\\n if use_all_singular_values:\\n # all singular values are used to calculate the correlation\\n corr = tf.sqrt(tf.nlinalg.trace(tf.dot(tf.transpose(Tval), Tval)))\\n else:\\n # just the top outdim_size singular values are used\\n [U, V] = tf.nlinalg.eigh(T.dot(tf.transpose(Tval), Tval))\\n U = U[tf.gt(U, eps).nonzero()[0]]\\n U = U.sort()\\n corr = tf.sum(tf.sqrt(U[0:outdim_size]))\\n\\n return -corr\\n\\n return inner_cca_objective\",\n \"def reset_parameters(self, p: Dict[str, ArrayType]):\\n super().reset_parameters(p)\\n if self.method == \\\"trust-constr\\\":\\n if self.opt.nk:\\n self._constraints[\\\"k\\\"].A = csc_matrix(self.opt.M(self.p).toarray())\\n self._constraints[\\\"k\\\"].lb = -self.opt.c(self.p).toarray().flatten()\\n if self.opt.na:\\n eq = -self.opt.b(self.p).toarray().flatten()\\n self._constraints[\\\"a\\\"].A = csc_matrix(self.opt.A(self.p).toarray())\\n self._constraints[\\\"a\\\"].lb = eq\\n self._constraints[\\\"a\\\"].ub = eq\\n if self._constraints:\\n self.minimize_input[\\\"constraints\\\"] = list(self._constraints.values())\",\n \"def match_contract_to_charter_constraints(contract, charter, charter_constraints, charity_constraints):\\n\\n r_quotes = []\\n r_vector = []\\n\\n quote_slice = slice(0, 17)\\n\\n if 'subj' not in contract.sections:\\n raise ValueError(\\\"contract has no subject section\\\")\\n\\n subj = contract.sections['subj'].body\\n print(subj.untokenize_cc())\\n print('------')\\n if subj.embeddings is None:\\n print(\\\"Subj embeddings are gone, restoring...\\\")\\n subj.embeddings = contract.embeddings[subj.start:subj.end]\\n # subj.tokens = doc.tokens[subj.start:subj.end]\\n # subj.tokens_cc = doc.tokens_cc[subj.start:subj.end]\\n # subj.embedd( GLOBALS__['CharterAnlysingContext'].pattern_factory )\\n print('\\\\t\\\\t sample:', subj.embeddings[0][1:10])\\n\\n for head_type in charter_constraints:\\n\\n ##charity:\\n if head_type in charity_constraints:\\n print(f'{head_type} has charity constrinats')\\n \\n charity_constraints_by_head = charity_constraints[head_type]\\n charity_constraints_by_head_new = []\\n \\n charity_constraints['new.'+head_type] = charity_constraints_by_head_new\\n \\n for i in range(len(charity_constraints_by_head)):\\n _tuple = charity_constraints_by_head[i] \\n# for cc in charity_constraints[head_type]:\\n _slice = _tuple[0]\\n emb_charter = charter.sections[head_type].body.embeddings[_slice]\\n \\n distance = 1 - DF(emb_charter, subj.embeddings[5:])\\n \\n# cc.add['subj_correlation'] = distance\\n \\n# detupling\\n charity_constraints_by_head_new.append ( {\\n 'slice':_slice,\\n 'subj_correlation': distance,\\n 'confidence': _tuple[1],\\n 'sum': _tuple[2]\\n })\\n \\n print('\\\\t'*4, 'cc=', charity_constraints_by_head_new[i])\\n \\n # print('\\\\t\\\\t---CC', cc[0])\\n \\n\\n # GLOBALS__['CharterAnlysingContext'].doc.sections['head.directors'].body.embeddings[_slice]\\n\\n ##------------------------charity end\\n print(f'measuring {head_type} constraints...'.upper())\\n cc = charter_constraints[head_type]\\n quotes = cc['sentences']\\n for quote in quotes:\\n print()\\n _q = untokenize(quote['subdoc'].tokens_cc[quote_slice])\\n print(_q)\\n\\n distance = 1 - DF(quote['subdoc'].embeddings[quote_slice],\\n subj.embeddings[5:])\\n\\n quote['subj_correlation'] = distance\\n\\n print(f'distance = {distance:.4f}')\\n\\n r_quotes.append(_q)\\n r_vector.append(distance)\\n r_quotes.append('\\\\n')\\n r_vector.append(distance)\\n\\n GLOBALS__['renderer'].render_color_text(r_quotes, r_vector)\\n print(r_vector)\\n print(r_quotes)\",\n \"def pointCurveConstraint(*args, caching: bool=True, nodeState: Union[int, bool]=0,\\n pointConstraintUVW: Union[List[float, float, float], bool]=None,\\n pointWeight: Union[float, bool]=1.0, position: List[float, float,\\n float]=None, weight: float=0.0, constructionHistory: bool=True, name:\\n AnyStr=\\\"\\\", object: bool=True, replaceOriginal: bool=True, q=True,\\n query=True, e=True, edit=True, **kwargs)->Union[List[AnyStr], Any]:\\n pass\",\n \"def conc_after(V, C_e, Q, t, t_e):\\n C = 0.21-(0.21-C_e)*math.e**-(abs(Q)/V*(t-t_e))\\n return C\",\n \"def _set_constraint(self):\\n pass\",\n \"def local_inplace_setsubtensor(node):\\r\\n if isinstance(node.op, IncSubtensor) and not node.op.inplace:\\r\\n new_op = node.op.__class__(\\r\\n node.op.idx_list, inplace=True,\\r\\n set_instead_of_inc=node.op.set_instead_of_inc,\\r\\n destroyhandler_tolerate_aliased=node.op.destroyhandler_tolerate_aliased)\\r\\n new_node = new_op(*node.inputs)\\r\\n return [new_node]\\r\\n return False\",\n \"def csw(self, q0, q1, q2, ctrl=None):\\n self.cx(q2,q1,ctrl=ctrl).ccx(q0,q1,q2,ctrl=ctrl).cx(q2,q1,ctrl=ctrl)\\n return self\",\n \"def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def PCO(A, K, alpha):\\n A=np.array(A, dtype=float)\\n \\n #The adjacency matrix A is first normalized\\n A=normalize(A) \\n \\n n=A.shape[0]\\n \\n I=np.eye(n)\\n \\n P=I\\n M=np.zeros((n, n))\\n \\n for i in range(K):\\n P = alpha*np.dot(P,A) + (1-alpha)*I\\n M = M+P\\n \\n return(M)\",\n \"def attach_CA(self):\\n n = self.pC\\n self.A[n] = self._mps_CA(self.C, self.A[n])\",\n \"def putnaintparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnaintparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def get_KE_constraints(self):\\n C2 = np.eye(self.m)\\n C2 = C2[:self.m - 2, :]\\n to_be_deleted = []\\n for idx_vij_1 in range(self.m - 2):\\n idx_vij_2 = idx_vij_1 + 1\\n C2[idx_vij_1, idx_vij_2] = -1\\n i1 = np.where(self.C[idx_vij_1, :] == 1)[0][0]\\n i2 = np.where(self.C[idx_vij_2, :] == 1)[0][0]\\n j = np.where(self.C[idx_vij_1, :] == -1)[0][0]\\n if i1 == i2:\\n i = i1\\n k = np.where(self.C[idx_vij_2, :] == -1)[0][0]\\n i_indices = self.C[:, j] == 1\\n j_indices = self.C[:, k] == -1\\n idx_vij_3 = np.where(np.bitwise_and(\\n i_indices, j_indices))[0][0]\\n C2[idx_vij_1, idx_vij_3] = 1\\n else:\\n to_be_deleted.append(idx_vij_1)\\n C2 = np.delete(C2, to_be_deleted, axis=0)\\n b = np.zeros((C2.shape[0], 1))\\n return C2, b\",\n \"def conj(self, o): \\n return (o.inv()) * self * o\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79132587","0.65889823","0.6287175","0.60342395","0.58452046","0.584365","0.54893136","0.53622764","0.5344895","0.52238137","0.5210514","0.50818294","0.50687397","0.5066322","0.5015226","0.50098133","0.4991474","0.49618068","0.49253765","0.4921023","0.48985314","0.48561004","0.48323688","0.48116612","0.48082897","0.48021096","0.4792872","0.477442","0.47744092","0.4772568","0.47696766","0.47647592","0.4763589","0.4746776","0.47409284","0.4713596","0.47104383","0.4692478","0.46862158","0.46785918","0.46715313","0.46691284","0.46665114","0.46588054","0.465154","0.46498045","0.46387643","0.46306854","0.4629863","0.4614445","0.46019438","0.4582472","0.45742217","0.45732304","0.45624983","0.45590216","0.45376152","0.45316333","0.45314953","0.45261085","0.45248058","0.45230204","0.4516117","0.45115563","0.45086357","0.44876742","0.4482614","0.44808656","0.44760528","0.44727984","0.44711286","0.4468459","0.44652364","0.4456949","0.44502288","0.44467482","0.44405597","0.44402263","0.44360667","0.44360337","0.4433005","0.44304958","0.4413931","0.44133306","0.44125226","0.4411762","0.44104716","0.44056877","0.44018635","0.43993044","0.4387345","0.43836537","0.43818623","0.4378405","0.43636456","0.43598914","0.43557307","0.43499884","0.43494466","0.43473616"],"string":"[\n \"0.79132587\",\n \"0.65889823\",\n \"0.6287175\",\n \"0.60342395\",\n \"0.58452046\",\n \"0.584365\",\n \"0.54893136\",\n \"0.53622764\",\n \"0.5344895\",\n \"0.52238137\",\n \"0.5210514\",\n \"0.50818294\",\n \"0.50687397\",\n \"0.5066322\",\n \"0.5015226\",\n \"0.50098133\",\n \"0.4991474\",\n \"0.49618068\",\n \"0.49253765\",\n \"0.4921023\",\n \"0.48985314\",\n \"0.48561004\",\n \"0.48323688\",\n \"0.48116612\",\n \"0.48082897\",\n \"0.48021096\",\n \"0.4792872\",\n \"0.477442\",\n \"0.47744092\",\n \"0.4772568\",\n \"0.47696766\",\n \"0.47647592\",\n \"0.4763589\",\n \"0.4746776\",\n \"0.47409284\",\n \"0.4713596\",\n \"0.47104383\",\n \"0.4692478\",\n \"0.46862158\",\n \"0.46785918\",\n \"0.46715313\",\n \"0.46691284\",\n \"0.46665114\",\n \"0.46588054\",\n \"0.465154\",\n \"0.46498045\",\n \"0.46387643\",\n \"0.46306854\",\n \"0.4629863\",\n \"0.4614445\",\n \"0.46019438\",\n \"0.4582472\",\n \"0.45742217\",\n \"0.45732304\",\n \"0.45624983\",\n \"0.45590216\",\n \"0.45376152\",\n \"0.45316333\",\n \"0.45314953\",\n \"0.45261085\",\n \"0.45248058\",\n \"0.45230204\",\n \"0.4516117\",\n \"0.45115563\",\n \"0.45086357\",\n \"0.44876742\",\n \"0.4482614\",\n \"0.44808656\",\n \"0.44760528\",\n \"0.44727984\",\n \"0.44711286\",\n \"0.4468459\",\n \"0.44652364\",\n \"0.4456949\",\n \"0.44502288\",\n \"0.44467482\",\n \"0.44405597\",\n \"0.44402263\",\n \"0.44360667\",\n \"0.44360337\",\n \"0.4433005\",\n \"0.44304958\",\n \"0.4413931\",\n \"0.44133306\",\n \"0.44125226\",\n \"0.4411762\",\n \"0.44104716\",\n \"0.44056877\",\n \"0.44018635\",\n \"0.43993044\",\n \"0.4387345\",\n \"0.43836537\",\n \"0.43818623\",\n \"0.4378405\",\n \"0.43636456\",\n \"0.43598914\",\n \"0.43557307\",\n \"0.43499884\",\n \"0.43494466\",\n \"0.43473616\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78817594"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11970,"cells":{"query":{"kind":"string","value":"Appends a general sparse symmetric matrix to the storage of symmetric matrices. appendsparsesymmat(self,dim_,subi_,subj_,valij_)"},"document":{"kind":"string","value":"def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij)\n elif nz_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if nz_ is None: nz_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _idx_return_value = resargs\n return _idx_return_value","def getsparsesymmat(self,idx_,subi_,subj_,valij_):\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _subi_minlength = (maxlen_)\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxlen_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (maxlen_)\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxlen_)))\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\n raise ValueError(\"Argument subj must be writable\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = (maxlen_)\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),(maxlen_)))\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\n raise ValueError(\"Argument valij must be writable\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valij_copyarray:\n valij_[:] = _valij_np_tmp","def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_valij = False\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n _copyback_valij = True\n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n _copyback_valij = True\n if valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij has wrong length\")\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_valij:\n valij[:] = _tmparr_valij\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi","def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):\n num_ = None\n if num_ is None:\n num_ = len(dims_)\n elif num_ != len(dims_):\n raise IndexError(\"Inconsistent length of array dims\")\n if num_ is None:\n num_ = len(nz_)\n elif num_ != len(nz_):\n raise IndexError(\"Inconsistent length of array nz\")\n if dims_ is None:\n raise ValueError(\"Argument dims cannot be None\")\n if dims_ is None:\n raise ValueError(\"Argument dims may not be None\")\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\n _dims_copyarray = False\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dims_ is not None:\n _dims_copyarray = True\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\n _dims_np_tmp[:] = dims_\n assert _dims_np_tmp.flags.contiguous\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dims_copyarray = False\n _dims_tmp = None\n \n if nz_ is None:\n raise ValueError(\"Argument nz cannot be None\")\n if nz_ is None:\n raise ValueError(\"Argument nz may not be None\")\n if isinstance(nz_, numpy.ndarray) and nz_.dtype is numpy.dtype(numpy.int64) and nz_.flags.contiguous:\n _nz_copyarray = False\n _nz_tmp = ctypes.cast(nz_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nz_ is not None:\n _nz_copyarray = True\n _nz_np_tmp = numpy.zeros(len(nz_),numpy.dtype(numpy.int64))\n _nz_np_tmp[:] = nz_\n assert _nz_np_tmp.flags.contiguous\n _nz_tmp = ctypes.cast(_nz_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nz_copyarray = False\n _nz_tmp = None\n \n _subi_minlength = sum((nz_))\n if sum((nz_)) > 0 and subi_ is not None and len(subi_) != sum((nz_)):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),sum((nz_))))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = sum((nz_))\n if sum((nz_)) > 0 and subj_ is not None and len(subj_) != sum((nz_)):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),sum((nz_))))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = sum((nz_))\n if sum((nz_)) > 0 and valij_ is not None and len(valij_) != sum((nz_)):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),sum((nz_))))\n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n _idx_minlength = (num_)\n if (num_) > 0 and idx_ is not None and len(idx_) != (num_):\n raise ValueError(\"Array argument idx is not long enough: Is %d, expected %d\" % (len(idx_),(num_)))\n if isinstance(idx_,numpy.ndarray) and not idx_.flags.writeable:\n raise ValueError(\"Argument idx must be writable\")\n if idx_ is None:\n raise ValueError(\"Argument idx may not be None\")\n if isinstance(idx_, numpy.ndarray) and idx_.dtype is numpy.dtype(numpy.int64) and idx_.flags.contiguous:\n _idx_copyarray = False\n _idx_tmp = ctypes.cast(idx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif idx_ is not None:\n _idx_copyarray = True\n _idx_np_tmp = numpy.zeros(len(idx_),numpy.dtype(numpy.int64))\n _idx_np_tmp[:] = idx_\n assert _idx_np_tmp.flags.contiguous\n _idx_tmp = ctypes.cast(_idx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _idx_copyarray = False\n _idx_tmp = None\n \n res = __library__.MSK_XX_appendsparsesymmatlist(self.__nativep,num_,_dims_tmp,_nz_tmp,_subi_tmp,_subj_tmp,_valij_tmp,_idx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _idx_copyarray:\n idx_[:] = _idx_np_tmp","def add_sparse(self, key, element):\n self.add(self._sparse2seq(key), element)","def matrix_add_symmetric(M, M_sym):\n M[0, 0] += M_sym[0]\n M[0, 1] += M_sym[1]\n M[1, 0] += M_sym[1]\n M[0, 2] += M_sym[2]\n M[2, 0] += M_sym[2]\n\n M[1, 1] += M_sym[3]\n M[1, 2] += M_sym[4]\n M[2, 1] += M_sym[4]\n\n M[2, 2] += M_sym[5]\n\n return M","def _build_sparse(self, name, wrt, consize, param_vals, sub_param_conns,\n full_param_conns, rels):\n\n jac = None\n\n # Additional sparsity for index connections\n for param in wrt:\n\n sub_conns = sub_param_conns.get(param)\n if not sub_conns:\n continue\n\n # If we have a simultaneous full connection, then we move on\n full_conns = full_param_conns.get(param)\n if full_conns.intersection(rels):\n continue\n\n rel_idx = set()\n for target, idx in iteritems(sub_conns):\n\n # If a target of the indexed desvar connection is\n # in the relevant path for this constraint, then\n # those indices are relevant.\n if target in rels:\n rel_idx.update(idx)\n\n nrel = len(rel_idx)\n if nrel > 0:\n\n if jac is None:\n jac = {}\n\n if param not in jac:\n # A coo matrix for the Jacobian\n # mat = {'coo':[row, col, data],\n # 'shape':[nrow, ncols]}\n coo = {}\n coo['shape'] = [consize, len(param_vals[param])]\n jac[param] = coo\n\n row = []\n col = []\n for i in range(consize):\n row.extend([i]*nrel)\n col.extend(rel_idx)\n data = np.ones((len(row), ))\n\n jac[param]['coo'] = [np.array(row), np.array(col), data]\n\n if name not in self.sub_sparsity:\n self.sub_sparsity[name] = {}\n self.sub_sparsity[name][param] = np.array(list(rel_idx))\n\n return jac","def setDominantSparseSymeig(A, Aadjoint_to_gadjoint):\n global DominantSparseSymeig \n from .CG import setCGSubspaceSparse\n setCGSubspaceSparse(A, Aadjoint_to_gadjoint)\n from .CG import CGSubspaceSparse\n @staticmethod\n def forward(ctx, g, k, dim):\n eigval, eigvector = symeigLanczos(A, k, extreme=\"min\", sparse=True, dim=dim)\n ctx.save_for_backward(g, eigval, eigvector)\n return eigval, eigvector\n @staticmethod\n def backward(ctx, grad_eigval, grad_eigvector):\n cg = CGSubspaceSparse.apply\n g, eigval, eigvector = ctx.saved_tensors\n b = grad_eigvector - torch.matmul(eigvector, grad_eigvector) * eigvector\n lambda0 = cg(g, eigval, b, eigvector)\n grad_A = grad_eigval * eigvector - lambda0, eigvector\n v1, v2 = grad_A\n grad_g = Aadjoint_to_gadjoint(v1, v2)\n grad_k = grad_dim = None\n return grad_g, grad_k, grad_dim\n DominantSparseSymeig = type(\"DominantSparseSymeig\", (torch.autograd.Function, ), \n {\"forward\": forward, \"backward\": backward})","def set_sparse_signals(self):\n\t\n\t\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\n\t\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\n\t\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\tparams_dSs,\tseed=self.seed_dSs)\n\t\t\n\t\t# Replace components with conflicting background odor \n\t\tif self.Kk_split is not None and self.Kk_split != 0:\n\t\t\tassert 0 <= self.Kk_split <= self.Kk, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires Kk_split\" \\\n\t\t\t\t\" to be non-negative and less than or equal to Kk.\"\n\t\t\tassert self.mu_dSs_2 is not None \\\n\t\t\t\tand self.sigma_dSs_2 is not None, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires that\" \\\n\t\t\t\t\" mu_dSs_2 and sigma_dSs_2 are set.\"\n\n\t\t\tsp.random.seed(self.seed_dSs)\n\t\t\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \n\t\t\t\t\t\t\t\t\t\t\treplace=False)\n\t\t\tfor idx_2 in self.idxs_2:\n\t\t\t\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \n\t\t\t\t\t\t\t\t\t\t\t\t\tself.sigma_dSs_2)\n\t\telse:\n\t\t\tself.idxs_2 = []\n\t\t\tself.Kk_split = 0\n\t\t\t\n\t\t# Ss0 is the ideal (learned) background stimulus without noise\n\t\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.idxs, params_Ss0,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tseed=self.seed_Ss0)\n\t\t\n\t\tself.Ss = self.dSs + self.Ss0_noisy","def SymmetriseMatrix(adjmatrix):\n\n if galib.metrics.Reciprocity(adjmatrix) == 1:\n # if Reciprocity(adjmatrix) == 1:\n return adjmatrix\n else:\n return 0.5 * (adjmatrix + adjmatrix.T)","def dict2sparseMatrix(wDict,std=0,diag=0):\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\n nAreas = len(list(wDict.keys()))\n for i in wDict:\n data[i,i] = diag\n ne = len(wDict[i])+ diag\n for j in wDict[i]:\n if std:\n data[i,j] = 1 / float(ne)\n else:\n data[i,j] = 1\n return data","def eval_sparse(self, array_in, array_out, sp_matrix=None):\n if sp_matrix is None:\n sp_matrix = self.to_sparse_matrix(array_in.shape, \"csc\")\n # print(\"usually:\", sp_matrix.todense())\n array_out[:] = sp_matrix.dot(array_in.reshape(-1)).reshape(array_out.shape)","def sparse_matrix(data, stype=\"csr\", dtype=complex):\n return _SPARSE_CONSTRUCTORS[stype](data, dtype=dtype)","def set_sparsity(self,use_sparse):\n \n if hasattr(self.problem,'sparse_jac'):\n self.use_sparse = use_sparse\n else:\n raise KINSOL_Exception(\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\")","def sparse_matlab(i, j, v, m, n):\n return csr_matrix((v, (i, j)), shape=(m, n))","def make_sparse(self, fmt='csc', make_method=None):\n if make_method:\n self.sparse = make_method(self.hamiltonian)\n else:\n self.sparse = self.hamiltonian.to_matrix(sparse=fmt)","def sparsify(W,conn):\n \n N = W.shape[0]\n W_sparse = sparse.lil_matrix((N,N)) \n for row, weights in itertools.izip(conn, W):\n W_sparse[row[0],row[1:]] = weights[1:]\n return W_sparse","def normalize_adj( adj : np.ndarray, \n sparse : bool = False\n ) -> Union[np.ndarray, sp.spmatrix]:\n if sparse:\n adj = sp.coo_matrix(adj) # [N,N]\n rowsum = np.array(adj.sum(1)) # [N,]\n \n d_inv_sqrt = np.power(rowsum, -0.5) # [N,], may issue runtime warnings (div by zero)\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. # []\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) if sparse else np.diag(d_inv_sqrt) #[N,N]\n \n if sparse:\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n else:\n return ((adj @ d_mat_inv_sqrt).transpose() @ d_mat_inv_sqrt) # not quite sure why this order = D^T A^T D, D^T = D, A^T = A - the transpose is unncessary?!","def sparse_matrix(shape, integer=False):\n dtype = numpy.int_ if integer else numpy.float_\n return scipy.sparse.lil_matrix(shape, dtype=dtype)","def append(self, symmData: list) -> None:\n newSymm = SymmetryElement(symmData)\n if not newSymm in self._symmcards:\n self._symmcards.append(newSymm)","def matrix_add():","def store(self, subj):\n if subj in self.__lst:\n raise ValueError('Disciplina exista deja')\n self.__lst.append(subj)","def save_sparse_matrix(self,\n artifact_type: str,\n params: Dict[str, Any],\n sparse_matrix: sp.csr_matrix,\n ignore_duplicate: bool = False) -> str:\n ppr_idx = None\n if \"ppr_idx\" in params.keys() and not isinstance(params[\"ppr_idx\"], int):\n ppr_idx = np.array(params[\"ppr_idx\"])\n params[\"ppr_idx\"] = hash(frozenset(params[\"ppr_idx\"]))\n\n if ignore_duplicate:\n # check there's no entry with the exact same config already present\n ids = Storage.locked_call(\n lambda: self._find_meta_by_exact_params(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n if len(ids) > 0:\n logging.info(f\"Ignoring duplicate save in save_sparse_matrix call\")\n return self._build_artifact_path(artifact_type, ids[0].doc_id).replace(\".pt\", \".npz\")\n\n ids = Storage.locked_call(\n lambda: self._upsert_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout,\n )\n if len(ids) != 1:\n raise RuntimeError(f'The index contains duplicates (artifact_type={artifact_type}, params={params})')\n\n try:\n path = self._build_artifact_path(artifact_type, ids[0]).replace(\".pt\", \".npz\")\n sp.save_npz(path, sparse_matrix)\n logging.info(f\"Saved sparse matrix to storage\")\n if ppr_idx is not None:\n ppr_path = path.replace(\".npz\", \"idx.npy\")\n np.save(ppr_path, ppr_idx)\n logging.info(f\"Saved ppr index to storage\")\n return path\n except: # noqa: E722\n Storage.locked_call(\n lambda: self._remove_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n raise","def sparse_matrix (base_type=float):\n return defaultdict (lambda: sparse_vector (base_type))","def _identity_sparse(d, stype=\"csr\", dtype=complex):\n return sp.eye(d, dtype=dtype, format=stype)","def create_sparse_matrix(self, matrix_df):\n\n print('creating sparse matrix...')\n sparse_seg_tmp_df = matrix_df.groupby(['segment_id','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n sparse_rt_tmp_df = matrix_df.groupby(['road_type','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n time_seg_df = sparse_seg_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n time_rt_df = sparse_rt_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n #time_seg_df['time_id'] = time_seg_df.index\n #time_rt_df['time_id'] = time_rt_df.index\n times = list(range(24*60/self.args['time_resolution']))\n full_time_idx = pd.DataFrame([i * 30 for i in times],columns = ['time_idx'])\n full_time_idx['key'] = 1\n full_day_of_week = pd.DataFrame(list(range(7)), columns = ['day_of_week'])\n full_day_of_week['key'] = 1\n full_times = pd.merge(full_time_idx, full_day_of_week, on='key')\n full_times['time_id'] = full_times.index\n time_seg_df = pd.merge(time_seg_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\n time_rt_df = pd.merge(time_rt_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\n \n matrix_seg_keys_df = pd.merge(sparse_seg_tmp_df, time_seg_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\n matrix_rt_keys_df = pd.merge(sparse_rt_tmp_df, time_rt_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\n\n time_seg_array = np.array(matrix_seg_keys_df['time_id'])\n time_rt_array = np.array(matrix_rt_keys_df['time_id'])\n segment_array = np.array(matrix_seg_keys_df['segment_id'])\n rt_array = np.array(matrix_rt_keys_df['road_type'])\n\n uniquesegments = np.array(list(set(segment_array)))\n uniquerts = np.array(list(set(rt_array)))\n keyuniquesegments = np.array(range(len(uniquesegments)))\n keyuniquerts = np.array(range(len(uniquerts)))\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\n uniquerts_df = pd.DataFrame({'roadtypekey':keyuniquerts, 'road_type':uniquerts})\n\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\n rt_df = pd.DataFrame(rt_array, columns = ['road_type'])\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\n rt_keys_df = pd.merge(rt_df, uniquerts_df, how='left', on=['road_type'])\n segmentkeys = np.array(segments_keys_df['segmentskey'])\n rtkeys = np.array(rt_keys_df['road_type'])\n\n level_array_seg = np.array(matrix_seg_keys_df['level_max'])\n sparse_matrix_s = csr_matrix((level_array_seg, (segmentkeys,time_seg_array))).toarray()\n sparse_matrix_seg = preprocessing.scale(sparse_matrix_s)\n level_array_rt = np.array(matrix_rt_keys_df['level_max'])\n sparse_matrix_r = csr_matrix((level_array_rt, (rtkeys,time_rt_array))).toarray()\n sparse_matrix_rt = preprocessing.scale(sparse_matrix_r)\n \n if self.args['perform_pca']:\n sparse_matrix_seg, self.pca_model = self.run_PCA(sparse_matrix_seg)\n sparse_matrix_rt, self.pca_model = self.run_PCA(sparse_matrix_rt)\n else:\n sparse_matrix_seg = sparse_matrix_seg\n sparse_matrix_rt = sparse_matrix_rt\n \n sparse_matrix_withsegkey = pd.DataFrame(sparse_matrix_seg)\n sparse_matrix_withrtkey = pd.DataFrame(sparse_matrix_rt)\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\n sparse_matrix_withrtkey['roadtypekey'] = sparse_matrix_withrtkey.index\n sparse_matrix_withrt = pd.merge(uniquerts_df, sparse_matrix_withrtkey, on=['roadtypekey'])\n \n # write sparse_matrix to database as 'clustering' table\n print('writing sparse matrix to db...')\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n if self.split_type == 'random':\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_random', con=engine, if_exists='replace')\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_random', con=engine, if_exists='replace')\n elif self.split_type == 'date':\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_date', con=engine, if_exists='replace')\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_date', con=engine, if_exists='replace')\n \n print('returning train sparse matrix...')\n return (uniquesegments_df, sparse_matrix_seg)","def coregionalization_sparse(optimize=True, plot=True):\r\n #fetch the data from the non sparse examples\r\n m = coregionalization_toy2(optimize=False, plot=False)\r\n X, Y = m.X, m.likelihood.Y\r\n\r\n #construct a model\r\n m = GPy.models.SparseGPRegression(X,Y)\r\n m.constrain_fixed('iip_\\d+_1') # don't optimize the inducing input indexes\r\n\r\n if optimize:\r\n m.optimize('bfgs', max_iters=100, messages=1)\r\n\r\n if plot:\r\n m.plot(fixed_inputs=[(1,0)])\r\n m.plot(fixed_inputs=[(1,1)], ax=pb.gca())\r\n\r\n return m","def putaijlist(self,subi_,subj_,valij_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij_)\n elif num_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def build_lhs_matrix(self):\n j=3\n diags1 = self.build_linear_diags()\n diags1 += self.build_dispersion_diags()\n\n # Ones down primary diagonal\n diags2 = np.zeros_like(diags1)\n diags2[j,:] = 1.\n\n cff = self.dt*(1+self.c_im)*0.5 \n diags = diags2 - cff*diags1\n \n # Build the sparse matrix\n cols = [ii for ii in range(-self._j, self._j+1)]\n M = sparse.spdiags(diags, cols, self.N, self.N)\n\n return M, diags","def __add__(self, other):\n h, w = self.size\n col_indices = self.col_indices + [w + i for i in other.col_indices]\n row_indices = self.row_indices + other.row_indices\n values = self.values + other.values\n oh, ow = other.size\n size = [max(h, oh), w + ow]\n return Sparse(size, row_indices, col_indices, values)","def to_sparse(self, method='csr_matrix'):\r\n data = self.data.values\r\n if method == 'csr_matrix':\r\n data_sp = sps.csr_matrix(data)\r\n elif method == 'bsr_matrix':\r\n data_sp = sps.bsr_matrix(data)\r\n elif method == 'coo_matrix':\r\n data_sp = sps.coo_matrix(data)\r\n elif method == 'csc_matrix':\r\n data_sp = sps.csc_matrix(data)\r\n elif method == 'dia_matrix':\r\n data_sp = sps.dia_matrix(data)\r\n elif method == 'dok_matrix':\r\n data_sp = sps.dok_matrix(data)\r\n elif method == 'lil_matrix':\r\n data_sp = sps.lil_matrix(data)\r\n else:\r\n raise ValueError('The method does not exist in scipy.sparse')\r\n return data_sp","def append_arr(self, num_seg, g_pix, intercept, slope, sig_intercept,\n sig_slope, inv_var, save_opt):\n self.slope_2d[num_seg[g_pix], g_pix] = slope[g_pix]\n if save_opt:\n self.interc_2d[num_seg[g_pix], g_pix] = intercept[g_pix]\n self.siginterc_2d[num_seg[g_pix], g_pix] = sig_intercept[g_pix]\n self.sigslope_2d[num_seg[g_pix], g_pix] = sig_slope[g_pix]\n self.inv_var_2d[num_seg[g_pix], g_pix] = inv_var[g_pix]","def _append_sparse_tensor_value(target, to_append):\n # Make sure the sparse tensors are of the same size (except for the 0th dim).\n if len(target.dense_shape) != len(to_append.dense_shape):\n raise RuntimeError(\n 'Unable to concatenate %s and %s. The inner dense shapes do not '\n 'have the same number of dimensions (%s vs %s)' %\n (target, to_append, target.dense_shape, to_append.dense_shape))\n\n if target.dense_shape[1:] != to_append.dense_shape[1:]:\n raise RuntimeError(\n 'Unable to concatenate %s and %s. The inner dense shapes do not '\n 'match inner dimensions (%s vs %s)' %\n (target, to_append, target.dense_shape[1:], to_append.dense_shape[1:]))\n\n # Add the to_append indices to target, updating the 0th value, and keeping\n # track of the maximum so we know the final dense_shape of this tensor.\n base_dim0_value = target.dense_shape[0]\n max_dim0_value = target.dense_shape[0]\n new_indices = target.indices\n for index in to_append.indices:\n # Here, we iterate through the sparse indices of the tensor to append. For\n # each index, we update its zeroth value (the batch index) by adding the\n # number of batch items in the tensor we are appending to (so an index\n # of [0, 0, 1] for a value that is being appended to a tensor with 0th dim\n # size 3 would become [3, 0, 1].)\n index[0] += base_dim0_value\n max_dim0_value = max(max_dim0_value, index[0])\n new_indices = np.append(new_indices, [index], axis=0)\n\n # Extend the values array to contain all of the appended values. These will\n # be in the same order as the indices added above.\n new_values = np.concatenate((target.values, to_append.values), axis=0)\n\n # Create a new dense shape by replacing the value for the 0th dimension\n # with the new max dim0 value.\n new_dense_shape = list(target.dense_shape)\n new_dense_shape[0] = max_dim0_value + 1\n new_dense_shape = tuple(new_dense_shape)\n\n return sparse_tensor.SparseTensorValue(\n indices=new_indices, values=new_values, dense_shape=new_dense_shape)","def putaijlist(self,subi,subj,valij): # 3\n num_ = None\n if num_ is None:\n num_ = len(subi)\n elif num_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij)\n elif num_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if num_ is None: num_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res = self.__obj.putaijlist64(num_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def sym_adj(adj):\n adj = ss.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = ss.diags(d_inv_sqrt)\n return np.array(adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense())","def to_s_matrix(w,v):\n pass","def to_sparse(self):\n from divisi2.sparse import SparseMatrix\n return SparseMatrix(self, self.row_labels, self.col_labels)","def sym_adj(adj):\n adj = sp.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense()","def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\"<i4\")\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\"<f8\")\n size = j_obj.getSize()\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()","def sparse(cls, a_ndarray, i_ndarray, shape, bigdl_type=\"float\"):\n if a_ndarray is None:\n return None\n invalidInputError(isinstance(a_ndarray, np.ndarray),\n f\"input should be a np.ndarray, not ${type(a_ndarray)}\")\n invalidInputError(isinstance(i_ndarray, np.ndarray),\n f\"indices should be a np.ndarray, not ${type(i_ndarray)}\")\n invalidInputError(i_ndarray.size == a_ndarray.size * shape.size,\n f\"size of values ${a_ndarray.size * shape.size} and\"\n f\" indices ${i_ndarray.size} should match\")\n return cls(a_ndarray,\n shape,\n bigdl_type,\n i_ndarray)","def parse_subgraph(subgraph_nodes, graph, ass_matrix):\n\n sg = graph.subgraph(subgraph_nodes)\n sg_adj = nx.to_scipy_sparse_matrix(sg, format='csc') # New adjacency matrix.\n sg_ass = ass_matrix[list(sg.nodes)]\n\n return sg_adj, sg_ass","def drawSparseMatrix(ax, mat, **kwargs):\n row = kwargs.pop('rowOffset', 0)\n col = kwargs.pop('colOffset', 0)\n color = kwargs.pop('color', None)\n\n mat = pg.utils.sparseMatrix2coo(mat)\n mat.row += row\n mat.col += col\n gci = ax.spy(mat, color=color, **kwargs)\n\n ax.autoscale(enable=True, axis='both', tight=True)\n return gci","def wrapDBMatrix(self,mat):\n return mat.todense()","def sparse_esn_reservoir(size, spectral_radius, density, symmetric):\n rvs = stats.uniform(loc=-1., scale=2.).rvs\n matrix = sparse.random(size, size, density=density, data_rvs=rvs)\n matrix = matrix.tocsr()\n if symmetric:\n matrix = sparse.triu(matrix)\n tril = sparse.tril(matrix.transpose(), k=-1)\n matrix = matrix + tril\n # calc eigenvalues with scipy's lanczos implementation:\n eig, _ = sparse.linalg.eigsh(matrix, k=2, tol=1e-4)\n else:\n eig, _ = sparse.linalg.eigs(matrix, k=2, tol=1e-4)\n\n rho = np.abs(eig).max()\n matrix = matrix.multiply(1. / rho)\n matrix = matrix.multiply(spectral_radius)\n return matrix","def create_adjust_matrix(self, is_sub: bool):\n matrix = None\n if not is_sub:\n ratings = np.ones_like(self._user, dtype=np.float32)\n matrix = sp.csr_matrix(\n (ratings, (self._user, self._item + self.n_users)),\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\n )\n else:\n if self.type == \"ND\":\n drop_user = self.rand_sample(\n self.n_users,\n size=int(self.n_users * self.drop_ratio),\n replace=False,\n )\n drop_item = self.rand_sample(\n self.n_items,\n size=int(self.n_items * self.drop_ratio),\n replace=False,\n )\n R_user = np.ones(self.n_users, dtype=np.float32)\n R_user[drop_user] = 0.0\n R_item = np.ones(self.n_items, dtype=np.float32)\n R_item[drop_item] = 0.0\n R_user = sp.diags(R_user)\n R_item = sp.diags(R_item)\n R_G = sp.csr_matrix(\n (\n np.ones_like(self._user, dtype=np.float32),\n (self._user, self._item),\n ),\n shape=(self.n_users, self.n_items),\n )\n res = R_user.dot(R_G)\n res = res.dot(R_item)\n\n user, item = res.nonzero()\n ratings = res.data\n matrix = sp.csr_matrix(\n (ratings, (user, item + self.n_users)),\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\n )\n\n elif self.type == \"ED\" or self.type == \"RW\":\n keep_item = self.rand_sample(\n len(self._user),\n size=int(len(self._user) * (1 - self.drop_ratio)),\n replace=False,\n )\n user = self._user[keep_item]\n item = self._item[keep_item]\n\n matrix = sp.csr_matrix(\n (np.ones_like(user), (user, item + self.n_users)),\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\n )\n\n matrix = matrix + matrix.T\n D = np.array(matrix.sum(axis=1)) + 1e-7\n D = np.power(D, -0.5).flatten()\n D = sp.diags(D)\n return D.dot(matrix).dot(D)","def save_feature_matrix(tgt, feat_mat):\n smat_util.save_matrix(tgt, feat_mat)","def write_matfile(Sint_df, outputfilepath='Sint_no_cofactor_20160831.mat'):\n # convert dataframe to matrix\n Smat = Sint_df.as_matrix()\n\n # get all indices for non-zero elements in Smat (row, col)\n Smat_nzr, Smat_nzc = np.nonzero(Smat)\n\n # get all non-zero elements from Smat\n Smat_nze = Smat[Smat_nzr, Smat_nzc]\n\n # Adjust for matlab coordinate\n Smat_nzr = Smat_nzr + 1\n Smat_nzc = Smat_nzc + 1\n\n # This final line gives the size of the S matrix in matlab\n nr, nc = Smat.shape\n\n # Create a 2D array\n sparseMat = np.vstack((Smat_nzr, Smat_nzc, Smat_nze)).T\n sparseMat = np.vstack((sparseMat, np.array([[nr, nc, 0]])))\n\n # Create a numpy object array from dataframe index\n reactionList = Sint_df.columns.ravel()\n\n # Write only one matlab .mat file\n scipy.io.savemat(outputfilepath,\n mdict={'Sint_sparse': sparseMat,\n 'reactionList': np.array(reactionList)}\n )\n\n return sparseMat, reactionList","def expand_conn_mat(conn_mat, nr_of_traps):\n\n r, c = conn_mat._shape\n new_indptr = np.hstack((conn_mat.indptr, np.asarray([conn_mat.indptr[-1]] * nr_of_traps)))\n expanded_mat = csr_matrix((conn_mat.data, conn_mat.indices, new_indptr),\n shape=(r + nr_of_traps, c + nr_of_traps))\n\n return expanded_mat","def sparse_GP_regression_2D(num_samples=400, num_inducing=50, max_iters=100, optimize=True, plot=True):\r\n X = np.random.uniform(-3., 3., (num_samples, 2))\r\n Y = np.sin(X[:, 0:1]) * np.sin(X[:, 1:2]) + np.random.randn(num_samples, 1) * 0.05\r\n\r\n # construct kernel\r\n rbf = GPy.kern.rbf(2)\r\n\r\n # create simple GP Model\r\n m = GPy.models.SparseGPRegression(X, Y, kernel=rbf, num_inducing=num_inducing)\r\n\r\n # contrain all parameters to be positive (but not inducing inputs)\r\n m['.*len'] = 2.\r\n\r\n m.checkgrad()\r\n\r\n # optimize\r\n if optimize:\r\n m.optimize('tnc', messages=1, max_iters=max_iters)\r\n\r\n # plot\r\n if plot:\r\n m.plot()\r\n\r\n print m\r\n return m","def add_ts_to_matrix(self, i_ts, norm_flag, add_x=True, add_y=True):\n\n # ts are not overwritten, only normalization constants\n if norm_flag:\n ts, norm_div, norm_subt = _normalize_ts(self.ts[i_ts].s, self.ts[i_ts].name)\n self.ts[i_ts] = TsMiniStruct(self.ts[i_ts].s, norm_div, norm_subt, self.ts[i_ts].name, self.ts[i_ts].index)\n ts = TsMiniStruct(ts, norm_div, norm_subt, self.ts[i_ts].name, self.ts[i_ts].index)\n else:\n ts = self.ts[i_ts]\n\n n_hist = self.n_hist_points[i_ts]\n n_req = self.n_req_points[i_ts]\n n_rows = self.X.shape[0]\n\n # reverse time series, so that the top row is always to be forecasted first\n time = np.flipud(ts.index)\n ts = np.flipud(ts.s)\n \n idxX, idxY = matrix_idx(n_hist, n_req, n_rows)\n self.idxY[i_ts] = idxY\n if add_y:\n self.Y = np.hstack((self.Y, ts[idxY]))\n if add_x:\n self.X = np.hstack((self.X, ts[idxX]))\n\n return time[idxY[:, -1]], time[idxX[:, 0]]","def fit_from_scipy_sparse_matrix(self,\n adj,\n attr,\n spatially_extensive_attr,\n threshold,\n solver=\"cbc\",\n metric=\"euclidean\"):\n self.metric = get_metric_function(metric)\n check_solver(solver)\n\n prob = LpProblem(\"Max-p-Regions\", LpMinimize)\n\n # Parameters of the optimization problem\n n_areas = adj.shape[0]\n I = list(range(n_areas)) # index for areas\n II = [(i, j) for i in I for j in I]\n II_upper_triangle = [(i, j) for i, j in II if i < j]\n # index of potential regions, called k in [DAR2012]_:\n K = range(n_areas)\n # index of contiguity order, called c in [DAR2012]_:\n O = range(n_areas)\n d = {(i, j): self.metric(attr[i].reshape(1, -1), attr[j].reshape(\n 1, -1))\n for i, j in II_upper_triangle}\n h = 1 + floor(log10(sum(d[(i, j)] for i, j in II_upper_triangle)))\n\n # Decision variables\n t = LpVariable.dicts(\n \"t\", ((i, j) for i, j in II_upper_triangle),\n lowBound=0,\n upBound=1,\n cat=LpInteger)\n x = LpVariable.dicts(\n \"x\", ((i, k, o) for i in I for k in K for o in O),\n lowBound=0,\n upBound=1,\n cat=LpInteger)\n\n # Objective function\n # (1) in Duque et al. (2012): \"The Max-p-Regions Problem\"\n prob += -10**h * lpSum(x[i, k, 0] for k in K for i in I) \\\n + lpSum(d[i, j] * t[i, j] for i, j in II_upper_triangle)\n\n # Constraints\n # (2) in Duque et al. (2012): \"The Max-p-Regions Problem\"\n for k in K:\n prob += lpSum(x[i, k, 0] for i in I) <= 1\n # (3) in Duque et al. (2012): \"The Max-p-Regions Problem\"\n for i in I:\n prob += lpSum(x[i, k, o] for k in K for o in O) == 1\n # (4) in Duque et al. (2012): \"The Max-p-Regions Problem\"\n for i in I:\n for k in K:\n for o in range(1, len(O)):\n prob += x[i, k, o] <= lpSum(x[j, k, o - 1]\n for j in neighbors(adj, i))\n # (5) in Duque et al. (2012): \"The Max-p-Regions Problem\"\n if isinstance(spatially_extensive_attr[I[0]], numbers.Real):\n for k in K:\n lhs = lpSum(x[i, k, o] * spatially_extensive_attr[i] for i in I\n for o in O)\n prob += lhs >= threshold * lpSum(x[i, k, 0] for i in I)\n elif isinstance(spatially_extensive_attr[I[0]], collections.Iterable):\n for el in range(len(spatially_extensive_attr[I[0]])):\n for k in K:\n lhs = lpSum(x[i, k, o] * spatially_extensive_attr[i][el]\n for i in I for o in O)\n if isinstance(threshold, numbers.Real):\n rhs = threshold * lpSum(x[i, k, 0] for i in I)\n prob += lhs >= rhs\n elif isinstance(threshold, np.ndarray):\n rhs = threshold[el] * lpSum(x[i, k, 0] for i in I)\n prob += lhs >= rhs\n # (6) in Duque et al. (2012): \"The Max-p-Regions Problem\"\n for i, j in II_upper_triangle:\n for k in K:\n prob += t[i, j] >= \\\n lpSum(x[i, k, o] + x[j, k, o] for o in O) - 1\n # (7) in Duque et al. (2012): \"The Max-p-Regions Problem\"\n # already in LpVariable-definition\n # (8) in Duque et al. (2012): \"The Max-p-Regions Problem\"\n # already in LpVariable-definition\n\n # additional constraint for speedup (p. 405 in [DAR2012]_)\n for o in O:\n prob += x[I[0], K[0], o] == (1 if o == 0 else 0)\n\n # Solve the optimization problem\n solver = get_solver_instance(solver)\n prob.solve(solver=solver)\n result = np.zeros(n_areas)\n for i in I:\n for k in K:\n for o in O:\n if x[i, k, o].varValue == 1:\n result[i] = k\n self.labels_ = result\n self.solver = solver","def putacol(self,j_,subj,valj): # 3\n nzj_ = None\n if nzj_ is None:\n nzj_ = len(subj)\n elif nzj_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nzj_ is None:\n nzj_ = len(valj)\n elif nzj_ != len(valj):\n raise IndexError(\"Inconsistent length of array valj\")\n if nzj_ is None: nzj_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valj is None: raise TypeError(\"Invalid type for argument valj\")\n if valj is None:\n valj_ = None\n else:\n try:\n valj_ = memoryview(valj)\n except TypeError:\n try:\n _tmparr_valj = array.array(\"d\",valj)\n except TypeError:\n raise TypeError(\"Argument valj has wrong type\")\n else:\n valj_ = memoryview(_tmparr_valj)\n \n else:\n if valj_.format != \"d\":\n valj_ = memoryview(array.array(\"d\",valj))\n \n res = self.__obj.putacol(j_,nzj_,subj_,valj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _buildMatrix(self, SparseMatrix, Ncells, MaxFaces, coeff):\n return (0, 0)","def apply_stiffness_assembling(fe_space, param, stiffness):\n if stiffness.format is 'lil':\n for ie in range(fe_space.get_nelem()):\n local_stiffness = fe_space.apply_dbasis_diag_dbasis(fe_space.eval_at_quadrature_pnts(\n lambda k, s:\n fe_space.get_quadrature_weight(k) * param(fe_space.get_coord(ie, s)) / fe_space.get_elem_length(ie)))\n locals_to_globals = fe_space.locals_to_globals(ie)\n stiffness[np.array(locals_to_globals)[:, None], locals_to_globals] += local_stiffness\n else:\n raise ValueError(\"Assembling procedure are expecting lil format sparse matrices.\")","def create_sparse_matrix(self, filename, matrix_length, density):\n pos = 0\n aux_pos = 0\n matrix = []\n pointerB = []\n pointerE = []\n columns = []\n values = []\n\n for i in range(0, matrix_length):\n row = []\n pointerB.append(pos)\n aux_pos = pos\n for j in range(0, matrix_length):\n probability = random.random()\n if probability < density:\n pos += 1\n val = random.randint(1, 10)\n values.append(val)\n columns.append(j)\n else:\n val = 0\n row.append(val)\n matrix.append(row)\n pointerE.append(pos)\n vector_b = SparseMatrix.gen_vector(matrix_length)\n matrix_A = np.matrix(matrix)\n vector_res = np.dot(matrix_A, vector_b).reshape(matrix_length, 1)\n data = {\"values\": values, \"columns\": columns, \"pointerB\": pointerB, \"pointerE\": pointerE}\n CSR_A = json.dumps(data)\n '''\n print(\"x: \", vector_x)\n print(\"A: \", matrix_A)\n print(\"b: \", vector_b)\n data = {\"values\": values, \"columns\": columns, \"pointerB\": pointerB, \"pointerE\": pointerE}\n data_json = json.dumps(data)\n file = open(filename, 'w')\n file.write(data_json)\n file.close()\n np.savetxt(\"vector.txt\", vector_x, fmt=\"%1.9f\", delimiter=\" \")\n '''\n return matrix_A, CSR_A, vector_b, vector_res","def expand_csr_adj(adj, count:int):\n r,c = adj.shape\n \n adj = sp.vstack(\n [adj, sp.csr_matrix(np.zeros((count, c)))])\n adj = sp.hstack(\n [adj, sp.csr_matrix(np.zeros((r + count, count)))])\n \n return adj","def test_import_sparse_type_mat():\n x = sps.csr_matrix(np.random.rand(3, 2))\n export_data('/tmp/test.mat', x)\n assert x.dtype == import_data('/tmp/test.mat').dtype","def putacol(self,j_,subj_,valj_):\n nzj_ = None\n if nzj_ is None:\n nzj_ = len(subj_)\n elif nzj_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nzj_ is None:\n nzj_ = len(valj_)\n elif nzj_ != len(valj_):\n raise IndexError(\"Inconsistent length of array valj\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valj_ is None:\n raise ValueError(\"Argument valj cannot be None\")\n if valj_ is None:\n raise ValueError(\"Argument valj may not be None\")\n if isinstance(valj_, numpy.ndarray) and valj_.dtype is numpy.dtype(numpy.float64) and valj_.flags.contiguous:\n _valj_copyarray = False\n _valj_tmp = ctypes.cast(valj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valj_ is not None:\n _valj_copyarray = True\n _valj_np_tmp = numpy.zeros(len(valj_),numpy.dtype(numpy.float64))\n _valj_np_tmp[:] = valj_\n assert _valj_np_tmp.flags.contiguous\n _valj_tmp = ctypes.cast(_valj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valj_copyarray = False\n _valj_tmp = None\n \n res = __library__.MSK_XX_putacol(self.__nativep,j_,nzj_,_subj_tmp,_valj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def to_sparse(self):\n if self.rep.fmt == 'sparse':\n return self\n\n return self.from_rep(self.rep.to_sdm())","def calc_sparsity (data): \n matrix_size = data.shape[0]*data.shape[1] # Number of possible interactions in the matrix\n num_purchases = len(data.nonzero()[0]) # Number of items interacted with\n sparsity = 100*(1 - (num_purchases/matrix_size))\n print('{:.2f} % of the user interaction matrix is sparse'.format(sparsity,2))","def __init__(self, size):\n _PysparseMatrixFromShape.__init__(self, rows=size, cols=size, bandwidth = 1)\n ids = numerix.arange(size)\n self.put(numerix.ones(size, 'd'), ids, ids)","def calc_sparse(self, calc_aw=False, vector_calc=True):\n # Local copies of instance attributes for faster access\n b = self.b\n qs = self.q_sim\n qo = self.q_obs\n maxlead = self.max_lead\n maxlag = self.max_lag\n measure = self.measure\n time = self.time\n\n # Prepare sparse matrices with only cells around diagonal defined.\n # padds 1 extra cell on each side to avoid false zeros later\n n = len(self.q_obs)\n w = self.max_lag + self.max_lead + 3\n data = np.array([np.full(n, np.inf)]).repeat(w, axis=0)\n offsets = np.arange(-self.max_lead-1, self.max_lag + 1.5)\n\n # Calculate work between obs and sim pairs, not needed anymore.\n if calc_aw:\n aw = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\n st = pd.Timestamp.now()\n # print('Start aw loops')\n # iterate through all flow observations\n for i_o in range(len(qo)):\n # Check only simulations within the allowed window\n for i_s in range(max(0, i_o - self.max_lead),\n min(len(qs), i_o + maxlag + 1)):\n if measure in ('nse', 'square'):\n aw[i_s, i_o] = (qs[i_s] - qo[i_o]) ** 2 + b ** 2 * (i_s - i_o) ** 2\n elif measure in ('mae', 'abs'):\n aw[i_s, i_o] = np.abs(qs[i_s] - qo[i_o]) + b * np.abs(i_s - i_o)\n # print('End aw loops', (pd.Timestamp.now()-st)/pd.Timedelta('1s'))\n self.aw = aw\n\n # Old debugging code: run original implementation alongside\n # Applies to all variables appended with _orig\n # aw_orig = np.full(shape=(len(self.q_obs), len(self.q_sim)), fill_value=np.nan)\n # # iterate through all flow observations\n # for i_o in range(len(self.q_obs)):\n # # Check only simulations within the allowed window\n # for i_s in range(max(0, i_o - self.max_lead),\n # min(len(self.q_sim), i_o + self.max_lag + 1)):\n # aw_orig[i_s, i_o] = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + self.b ** 2 * (i_s - i_o) ** 2\n\n # Calculate cumulative work along possible paths\n cw0 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\n cw1 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\n\n # Old debugging code: run original implementation (with dense arrays) alongside\n # Applies to all variables marked _orig\n # # Calculate cumulative work along possible paths\n # cw_orig = np.ones(shape=aw_orig.shape + (2,)) * np.nan\n # # Populate first column\n # cw_orig[:, 0, 0] = aw_orig[:, 0]\n\n if self.keep_internals:\n # self.aw_orig = aw_orig\n self.cw0 = cw0\n self.cw1 = cw1\n # self.cw_orig = cw_orig\n\n # Populate other columns\n for i_o in range(n):\n iss = max(0, i_o - maxlead) # sim index start\n ise = min(n, i_o + maxlag + 1) # sim index end\n isv = np.arange(iss, ise) # sim index as vector\n if time is not None:\n dt = time[isv] - time[i_o]\n # Heavily penalize sim-obs combinations outside allowed window\n # so that they are not selected\n dt[(dt < -maxlag) | (dt > maxlead)] = np.inf\n else:\n dt = isv - i_o\n if vector_calc:\n # Vectorized version of the calculation.\n # For understanding it is best to take a look at the else clause below\n # Calculate the work for each (sim, obs_i) pair\n if measure in ('nse', 'square'):\n e = (qs[iss:ise] - qo[i_o]) ** 2 + b ** 2 * dt ** 2\n elif measure in ('mae', 'abs'):\n e = np.abs(qs[iss:ise] - qo[i_o]) + b * np.abs(dt)\n if i_o == 0:\n # Only populate first column of cw0 and move to i_o = 1\n cw0[iss:ise, 0] = e.reshape(len(e), 1)\n continue\n # Repeat the same simulation point\n d = cw0[iss:ise, i_o - 1].toarray()\n d[d == 0] = np.nan\n cw1[iss:ise, i_o] = e.reshape(len(e), 1) + d\n\n # Find the 'cheapest' available preceding sim point\n points = np.full((len(isv), 4), np.inf)\n # Use the previous simulation point\n st = max(0, iss - 1)\n end = min(n + 1, ise - 1)\n l = end - st\n points[-l:, 0] = cw0[st:end, i_o - 1].toarray().squeeze()\n points[-l:, 1] = cw1[st:end, i_o - 1].toarray().squeeze()\n # Skip a simulation point\n st = max(0, iss - 2)\n end = min(n + 1, ise - 2)\n l = end - st\n points[-l:, 2] = cw0[st:end, i_o - 1].toarray().squeeze()\n points[-l:, 3] = cw1[st:end, i_o - 1].toarray().squeeze()\n # points[points == 0] = np.nan\n cw0[iss:ise, i_o] = (e + np.min(points, axis=1)).reshape(len(e), 1)\n else:\n # Old, non-vectorized code. Slower.\n # Left in place since it is easier to understand than vectorized code above\n def zero_to_nan(x):\n if x == 0:\n return np.nan\n else:\n return x\n\n for i_s in range(max(0, i_o - self.max_lead),\n min(n, i_o + self.max_lag + 1)):\n # print(i_o, i_s, sep='\\t')\n # e = aw[i_s, i_o]\n if time:\n dt = time[isv] - time[i_o]\n # Heavily penalize sim-obs combinations outside allowed window\n # so that they are not selected\n dt[dt > maxlead] = 999999999\n dt[dt < -maxlag] = 999999999\n else:\n dt = isv - i_o\n if measure in ('nse', 'square'):\n e = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + b ** 2 * dt ** 2\n elif measure in ('mae', 'abs'):\n e = np.abs(self.q_sim[i_s] - self.q_obs[i_o]) + np.abs(b * dt)\n # e_orig = aw_orig[i_s, i_o]\n # if e != e_orig:\n # print('ediff', e, e_orig, sep='\\t')\n\n # Repeat the same simulation point\n cw1[i_s, i_o] = e + zero_to_nan(cw0[i_s, i_o - 1])\n # cw_orig[i_s, i_o, 1] = e + cw_orig[i_s, i_o - 1, 0]\n # if cw1[i_s, i_o] != cw_orig[i_s, i_o, 1]:\n # print('cw1diff', cw1[i_s, i_o], cw_orig[i_s, i_o, 1], sep='\\t')\n\n if i_s == 0:\n continue\n # Else:\n # Find the 'cheapest' available preceding point\n # Use the previous simulation point\n points = [zero_to_nan(cw0[i_s - 1, i_o - 1]),\n zero_to_nan(cw1[i_s - 1, i_o - 1])]\n # m1 = np.nanmin(cw_orig[i_s - 1, i_o - 1, :])\n # m2 = np.inf\n if i_s > 1:\n # Skip a simulation point\n points += [cw0[i_s - 2, i_o - 1], cw1[i_s - 2, i_o - 1]]\n # m2 = np.nanmin(cw_orig[i_s - 2, i_o - 1, :])\n # m = min(m1, m2)\n try:\n cp = min([p for p in points if p > 0])\n except ValueError:\n cp = np.nan\n # if cp != m:\n # print('cpdiff', cp, m, sep='\\t')\n # print('\\torig', cw_orig[i_s - 1, i_o - 1, :], cw_orig[i_s - 2, i_o - 1, :])\n # print('\\tspar', points)\n cw0[i_s, i_o] = e + cp\n # cw_orig[i_s, i_o, 0] = e + m\n # print('')\n # Find the cheapest point in the last column, i.e. end of cheapest path, i.e. optimum score\n # nz0 = cw0[cw0[:, -1].nonzero()[0], -1].toarray().min()\n # nz1 = cw1[cw1[:, -1].nonzero()[0], -1].toarray().min()\n nz0 = cw0[-self.max_lag:, -1].toarray()\n nz1 = cw1[-self.max_lag:, -1].toarray()\n self.opt_score = min(nz0[nz0 != 0].min(), nz1[nz1 != 0].min())\n\n self.of = 1 - self.opt_score / self.fbench\n\n if self.calc_rays:\n self._do_calc_rays(cw0, cw1)\n\n return self.of","def _extend_sparse_traj_field(self, run_idx, traj_idx, field_path, values, sparse_idxs):\n\n field = self.h5['{}/{}/{}/{}/{}'.format(RUNS, run_idx, TRAJECTORIES, traj_idx, field_path)]\n\n field_data = field[DATA]\n field_sparse_idxs = field[SPARSE_IDXS]\n\n # number of new frames\n n_new_frames = values.shape[0]\n\n # if this sparse_field has been initialized empty we need to resize\n if all([i == 0 for i in field_data.shape]):\n\n\n # check the feature shape against the maxshape which gives\n # the feature dimensions for an empty dataset\n assert values.shape[1:] == field_data.maxshape[1:], \\\n \"input value features have shape {}, expected {}\".format(\n values.shape[1:], field_data.maxshape[1:])\n\n # if it is empty resize it to make an array the size of\n # the new values with the maxshape for the feature\n # dimensions\n feature_dims = field_data.maxshape[1:]\n field_data.resize( (n_new_frames, *feature_dims) )\n\n # set the new data to this\n field_data[0:, ...] = values\n\n else:\n\n # make sure the new data has the right dimensions\n assert values.shape[1:] == field_data.shape[1:], \\\n \"field feature dimensions must be the same, i.e. all but the first dimension\"\n\n # append to the dataset on the first dimension, keeping the\n # others the same, these must be feature vectors and therefore\n # must exist\n field_data.resize( (field_data.shape[0] + n_new_frames, *field_data.shape[1:]) )\n # add the new data\n field_data[-n_new_frames:, ...] = values\n\n # add the sparse idxs in the same way\n field_sparse_idxs.resize( (field_sparse_idxs.shape[0] + n_new_frames,\n *field_sparse_idxs.shape[1:]) )\n # add the new data\n field_sparse_idxs[-n_new_frames:, ...] = sparse_idxs","def SparseEmbedding(data=None, weight=None, input_dim=_Null, output_dim=_Null, dtype=_Null, out=None, name=None, **kwargs):\n return (0,)","def create_jac_sens(x_sp,sensitivity_sp,diffeq_params, integration_params,\n SDerivSymbolicJacParamsLambFun,SDerivSymbolicJacConcLambFun):\n\n # create state variables\n allVars = np.concatenate((x_sp,sensitivity_sp))\n\n #create RHS\n dSensSym = sp.Matrix(dSens(0,allVars,diffeq_params, integration_params,\n SDerivSymbolicJacParamsLambFun, SDerivSymbolicJacConcLambFun))\n dSensSymJac = dSensSym.jacobian(allVars)\n\n # generate jacobian\n dSensSymJacDenseMatLam = sp.lambdify(allVars,dSensSymJac)\n dSensSymJacSparseMatLamFun = lambda t,xs: sparse.csr_matrix(dSensSymJacDenseMatLam(*xs))\n\n return dSensSymJacSparseMatLamFun","def test_import_sparse_values_mat():\n x = sps.csr_matrix(np.random.rand(3, 2))\n export_data('/tmp/test.mat', x)\n assert np.array_equal(x.toarray(), import_data('/tmp/test.mat').toarray())","def scipy_sparse_to_spmatrix(A):\n coo = A.tocoo()\n SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)\n return SP","def build_scoring_matrix(alphabet,diag_score, off_diag_score, dash_score):\n scoring_matrix = dict()\n #add \"-\" to alphabet\n alphabet_ = alphabet.union(\"-\")\n\n for letter1 in alphabet_:\n #for every letter in alphabet build appropriate dictionary of scores\n col_dict = dict()\n \n for letter2 in alphabet_:\n if letter1 == \"-\" or letter2 == \"-\":\n score = dash_score\n elif letter1 == letter2:\n score = diag_score\n else: \n score = off_diag_score\n\n col_dict[letter2] = score\n \n scoring_matrix[letter1] = col_dict\n\n return scoring_matrix","def _weight_jac_mat_prod(self, module, g_inp, g_out, mat):\n raise NotImplementedError","def create_nonsparse_matrix(self, matrix_df):\n\n print('creating nonsparse matrix...')\n clustering_df_tmp = matrix_df[['date','time','date_idx', 'time_idx', 'day_of_week', 'segment_id', 'road_type', 'lat1', 'lat2', 'lon1', 'lon2', 'level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']]\n seg_averages = clustering_df_tmp.groupby(['segment_id', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']].mean().reset_index()\n rt_averages = clustering_df_tmp.groupby(['road_type', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']].mean().reset_index()\n \n # write nonsparse_matrix_db to database as 'clustering' table\n print('writing nonsparse matrix to db...')\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n if self.split_type == 'random':\n seg_averages.to_sql(name='clust_nonsparse_avebysegment_random', con=engine, if_exists='replace')\n rt_averages.to_sql(name='clust_nonsparse_avebyrt_random', con=engine, if_exists='replace')\n elif self.split_type == 'date':\n seg_averages.to_sql(name='clust_nonsparse_avebysegment_date', con=engine, if_exists='replace')\n rt_averages.to_sql(name='clust_nonsparse_avebyrt_date', con=engine, if_exists='replace')\n \n print('returning train nonsparse matrix...')\n train_averages = clustering_df_tmp.groupby(['segment_id', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count','level_binary']].mean().reset_index()\n train_averages.columns = ['segment_id', 'day_of_week', 'time_idx', 'ave_level_min', 'ave_level_max', 'ave_level_mean', 'ave_level_count', 'ave_level_binary']\n nonsparse_matrix = pd.merge(clustering_df_tmp, train_averages, how='left', on=['segment_id', 'day_of_week', 'time_idx'])\n segtimes_df = nonsparse_matrix[['segment_id', 'date', 'time']]\n nonsparse_matrix_final = nonsparse_matrix.drop(columns=['segment_id', 'date','time', 'level_min', 'level_max', 'level_mean', 'level_count', 'level_binary'])\n \n return (segtimes_df, nonsparse_matrix_final)","def to_amat(self, node_list=None, sparse=False) -> np.ndarray:\n if not node_list:\n node_list = sorted(self._nodes)\n node2ix = {node: i for i, node in enumerate(node_list)}\n\n if sparse:\n raise NotImplementedError\n # js, ks = [], []\n # for j, k in self._edges:\n # js.append(j)\n # ks.append(k)\n # js.append(k)\n # ks.append(j)\n # return spmatrix(1, js, ks)\n amat = np.zeros([self.num_nodes, self.num_nodes], dtype=int)\n\n for i, j in self._edges:\n amat[node2ix[i], node2ix[j]] = True\n amat[node2ix[j], node2ix[i]] = True\n return amat","def r_diag_dot_sparse(mat, diag):\n return mat @ sp.diags(diag)","def makesparse(matrix):\n n = matrix[0].size\n elements = []\n for i in range(n):\n for j in range(n):\n if matrix[i][j] != 0 :\n temp = MatrixElement(i, j, matrix[i][j])\n elements.append(temp)\n return SparseMatrix(n, elements)","def add_sparse_pwd(a, b, neg1_fmt=True):\n \n a_coo = a.tocoo()\n b_coo = b.tocoo()\n nr, nc = a_coo.shape\n \n ind_a = a_coo.row.astype(np.int64) * nc + a_coo.col\n ind_b = b_coo.row.astype(np.int64) * nc + b_coo.col\n # ind_ab = np.array(tuple(set(ind_a).intersection(set(ind_b))))\n ind_ab = np.intersect1d(ind_a, ind_b, assume_unique=True)\n keep_a = np.in1d(ind_a, ind_ab, assume_unique=True)\n keep_b = np.in1d(ind_b, ind_ab, assume_unique=True)\n if neg1_fmt:\n a_data = a_coo.data[keep_a]\n a_data[a_data == -1] = 0\n b_data = b_coo.data[keep_b]\n b_data[b_data == -1] = 0\n tot = a_data + b_data\n else:\n tot = a_coo.data[keep_a] + b_coo.data[keep_b]\n \"\"\"Need to add -1 as placeholder upon creation of new sparse matrix\"\"\"\n tot[tot == 0] = -1\n aplusb = sparse.coo_matrix((tot,\n (a_coo.row[keep_a], a_coo.col[keep_a])), shape=(nr, nc))\n aplusb = aplusb.tocsr()\n if not neg1_fmt:\n \"\"\"Convert it back to 0 = true zero distance\"\"\"\n aplusb.data[aplusb.data == -1] = 0\n return aplusb","def stiffnessMatrix (bsp, knotlist,p, nbquad):\n\n\tS = np.zeros((bsp.N-2, bsp.N-2))\n\t\n\tfor line in range(1, bsp.N-1):\n\t\t\n\t\tfor column in range(1, bsp.N-1):\n\t\t\n\t\t\tfor iknot in range(len(knotlist)-1):\n\t\t\t\tS[line-1, column-1] = S[line-1, column-1] + legendreGauss(bilinearForm, nbquad, knotlist[iknot], knotlist[iknot+1], line, bsp, ind2=column)\n\t\n\treturn S;","def test_csm_sparser(self):\r\n sp_types = {'csc': sp.csc_matrix,\r\n 'csr': sp.csr_matrix}\r\n\r\n for format in ['csc', 'csr']:\r\n for dtype in ['float32', 'float64']:\r\n x = tensor.tensor(dtype=dtype, broadcastable=(False,))\r\n y = tensor.ivector()\r\n z = tensor.ivector()\r\n s = tensor.ivector()\r\n\r\n a = as_sparse_variable(sp_types[format](random_lil((4, 3),\r\n dtype, 1)))\r\n\r\n f = theano.function([x, y, z, s],\r\n tensor.grad(dense_from_sparse(\r\n a * CSM(format)(x, y, z, s)).sum(), x))\r\n\r\n spmat = sp_types[format](random_lil((4, 3), dtype, 3))\r\n\r\n res = f(spmat.data, spmat.indices, spmat.indptr,\r\n numpy.asarray(spmat.shape, 'int32'))\r\n\r\n assert len(spmat.data) == len(res)","def build_scoring_matrix(alphabet, diag_score, off_diag_score, dash_score):\n scoring_matrix = {}\n\n alphabet = list(alphabet) + [\"-\"]\n\n for letter1 in alphabet:\n scoring_matrix[letter1] = {}\n for letter2 in alphabet:\n if letter1 == \"-\" or letter2 == \"-\":\n scoring_matrix[letter1][letter2] = dash_score\n elif letter1 == letter2:\n scoring_matrix[letter1][letter2] = diag_score\n else:\n scoring_matrix[letter1][letter2] = off_diag_score\n\n #for key,val in scoring_matrix.items():\n # print key,val\n\n return scoring_matrix","def create_attach_sparsifier(model, **sparse_config):\n data_norm_sparsifier = DataNormSparsifier(**sparse_config)\n for name, parameter in model.named_parameters():\n if 'emb_l' in name:\n valid_name = get_valid_name(name)\n data_norm_sparsifier.add_data(name=valid_name, data=parameter)\n return data_norm_sparsifier","def create_sparseDB():\n datas = data.Kmercount_to_matrix()\n datas.run()\n print('***Sparse matrix created***')","def test_bind_init(self, sp_in):\n\n bind = DatasetSparseArray(sp_in).bind\n\n dset_sp = DatasetSparseArray(bind=bind)\n\n assert dset_sp.info[\"type_id\"] == \"sparse_array\"\n assert dset_sp.info[\"py_type\"] == f\"scipy.sparse.{type(sp_in).__qualname__}\"\n assert dset_sp.sparse_array_class is type(sp_in)\n\n sp_out = dset_sp.get_value()\n assert isinstance(sp_out, type(sp_in))\n\n assert (sp_in.todense() == sp_out.todense()).all()","def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(alphaptrb_)\n elif num_ != len(alphaptrb_):\n raise IndexError(\"Inconsistent length of array alphaptrb\")\n if num_ is None:\n num_ = len(alphaptre_)\n elif num_ != len(alphaptre_):\n raise IndexError(\"Inconsistent length of array alphaptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb cannot be None\")\n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb may not be None\")\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptrb_ is not None:\n _alphaptrb_copyarray = True\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\n _alphaptrb_np_tmp[:] = alphaptrb_\n assert _alphaptrb_np_tmp.flags.contiguous\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = None\n \n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre cannot be None\")\n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre may not be None\")\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\n _alphaptre_copyarray = False\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptre_ is not None:\n _alphaptre_copyarray = True\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\n _alphaptre_np_tmp[:] = alphaptre_\n assert _alphaptre_np_tmp.flags.contiguous\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptre_copyarray = False\n _alphaptre_tmp = None\n \n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def SSC_test():\n \n # generating data\n\n D = 40 # Dimension of ambient space\n k = 4 # Number of subspaces\n d = 1\n N = 90 # Number of points in subspaces\n # Generating N points in d dim. subspaces\n Xi = [np.random.randn(D, d).dot(np.random.randn(d, N))\n for _ in range(k)]\n X = np.concatenate(Xi, axis=1)\n\n # Generating the ground-truth for evaluating clustering results\n lbls = [i * np.ones([1, N]) for i in range(k)]\n s = np.concatenate(lbls, axis=1)\n r = 0 # Enter the projection dimension e.g. r = d*n, enter r = 0 to not project\n Cst = 0 # Enter 1 to use the additional affine constraint sum(c) == 1\n OptM = 'L1Noise' # OptM can be {'L1Perfect','L1Noise','Lasso','L1ED'}\n lmbda = 0.001 # Regularization parameter in 'Lasso' or the noise level for 'L1Noise'\n # Number of top coefficients to build the similarity graph, enter K=0 for using the whole coefficients\n K = 0 #max(d1, d2)\n if Cst == 1:\n K = d + 1 # For affine subspaces, the number of coefficients to pick is dimension + 1\n\n Xp = DataProjection(X, r, 'NormalProj')\n \n # testing clustering\n \n '''\n # using wrapper function\n Grps, sc = sparseSubspaceClustering(\n Xp.T, n, ground_truth=s, affine=Cst, OptM=OptM, lam=lmbda)\n Grps = BestMap(sc, Grps)\n Missrate = float(np.sum(sc != Grps)) / sc.size\n print(\"\\n\\nMisclassification rate: {:.4f} %\\n\\n\".format(Missrate * 100))\n '''\n \n '''\n # calling internal tools\n CMat = SparseCoefRecovery(Xp, Cst, OptM, lmbda)\n # Make small values 0\n eps = np.finfo(float).eps\n CMat[np.abs(CMat) < eps] = 0\n\n CMatC, sc, OutlierIndx, Fail = OutlierDetection(CMat, s)\n\n if Fail == False:\n CKSym = BuildAdjacency(CMatC, K)\n Grps = SpectralClustering(CKSym, n)\n Grps = BestMap(sc, Grps)\n Missrate = float(np.sum(sc != Grps)) / sc.size\n print(\"\\n\\nMisclassification rate: {:.4f} %\\n\\n\".format(Missrate * 100))\n else:\n print(\"Something failed\")\n '''\n \n labels, _, __ = KSubspaces(Xp.T, k, d)\n labels = BestMap(s, labels)\n Missrate = float(np.sum(s != labels)) / s.size\n print(\"\\n\\nMisclassification rate: {:.4f} %\\n\\n\".format(Missrate * 100))","def convert_sparse_network(weights: Dict[str, np.ndarray],\n metadata: Dict[str, Any],\n hypers: Dict[str, Any],\n is_msp: bool):\n # List to hold all variables\n components: List[str] = []\n\n # Start the input units at the input shape\n input_size = metadata[INPUT_SHAPE][-1]\n\n for i, output_size in enumerate(hypers['hidden_units']):\n\n # Convert the sparse variable\n layer_name = 'hidden_{0}'.format(i)\n\n sparse_layer = convert_sparse_layer(layer_name=layer_name,\n weights=weights,\n input_size=input_size,\n output_size=output_size,\n coordinates=metadata['sparse_indices'][layer_name],\n is_msp=is_msp)\n\n components.append(sparse_layer)\n\n # Reset the input size as we progress\n input_size = output_size\n\n # Include the output layer\n output_kernel_name = 'output/kernel:0'\n output_kernel = convert_matrix(name=output_kernel_name,\n mat=weights[output_kernel_name],\n precision=PRECISION,\n width=WIDTH,\n is_msp=is_msp)\n components.append(output_kernel)\n\n output_bias_name = 'output/bias:0'\n output_bias = convert_matrix(name=output_bias_name,\n mat=weights[output_bias_name],\n precision=PRECISION,\n width=WIDTH,\n is_msp=is_msp)\n components.append(output_bias)\n\n return '\\n'.join(components)","def Problem2(n):\n diag_entries = np.empty((3,n))\n diag_entries[0] = np.ones(n)*(-1)\n diag_entries[1] = np.ones(n)*2\n diag_entries[2] = np.ones(n)*(-1)\n A = sparse.spdiags(diag_entries, [-1,0,1],n,n,format=\"csr\")\n return A","def set_sparse_backend(sparse_backend, warn=True):\n if 'biom.table' in modules:\n if warn:\n print (\"Warning: biom.table has already been loaded. This call to \"\n \"biom.set_sparse_backend() has no effect. It must be \"\n \"called before biom.table is imported for the first time.\")\n else:\n biom_config['python_code_sparse_backend'] = sparse_backend","def __init__(self, rows, cols, bandwidth=0, sizeHint=None, matrix=None, storeZeros=True):\n sizeHint = sizeHint or max(rows, cols) * bandwidth\n if matrix is None:\n tmpMatrix = spmatrix.ll_mat(1, 1, 1)\n if hasattr(tmpMatrix, 'storeZeros'):\n matrix = spmatrix.ll_mat(rows, cols, sizeHint, storeZeros)\n else:\n matrix = spmatrix.ll_mat(rows, cols, sizeHint)\n\n _PysparseMatrix.__init__(self, matrix=matrix)","def __init_matrix(self, adjacencyMatrix, **optional):\n\t\t# Error handling\n if adjacencyMatrix.ndim!=2 or adjacencyMatrix.shape[0]!=adjacencyMatrix.shape[1]:\n raise TypeError(\"\\'adjacencyMatrix\\' has to be a square matrix.\")\n elif np.any(adjacencyMatrix<0):\n raise ValueError(\"The weights have to be positive.\")\n elif not (str(adjacencyMatrix.dtype).startswith(\"float\") or str(adjacencyMatrix.dtype).startswith(\"int\")):\n raise TypeError(\"Weights have to be of the type \\'float*\\' or \\'int*\\'.\")\n elif np.any(np.diagonal(adjacencyMatrix)):\n raise ValueError(\"The graph is not allowed to contain circles of length 1.\")\n\t\t# Save shape and type\n self.__size = adjacencyMatrix.shape[0]\n self.__type = adjacencyMatrix.dtype\n if \"vertexNames\" in optional:\n # List of names\n self.__name_list = optional[\"vertexNames\"]\n if len(self.__name_list)!=self.__size:\n raise ValueError(\"There have not been specified as many names in \\'vertexNames\\' as there are nodes.\")\n else:\n if len(set(self.__name_list)) != len(self.__name_list):\n raise ValueError(\"Vertex names are not unique.\")\n else:\n # Hashmap name->index\n self.__names = {self.__name_list[i]:i for i in range(0,self.__size)}\n else:\n warn(Warning(\"If node names in adjacency matrix representation are not explicitly set with the parameter \\'vertexNames\\', partial and supergraphs cannot be recognized reliably.\"))\n self.__name_list = [i for i in range(0,self.__size)]\n self.__names = {self.__name_list[i]:i for i in range(0,self.__size)}\n \n # Adjacency matrix\n self.__mat = adjacencyMatrix\n # Adjacency matrix\n self.__adj = [[(i,self.__mat[vertices][i]) for i in np.arange(self.__size) if self.__mat[vertices][i]>0] for vertices in np.arange(0,self.__size)]\n \n # List of known supergraphs\n self.__supergraph = []","def create_sparse_matrix_long(self, matrix_df):\n\n time_df_tmp = matrix_df.groupby(['date_idx','time_idx']).count().reset_index()\n time_df = time_df_tmp[['date_idx','time_idx']]\n time_df['time_id'] = time_df.index.copy()\n matrix_df_tid = pd.merge(matrix_df, time_df, how='left', on=['date_idx','time_idx'])\n\n time_array = np.array(matrix_df_tid['time_id'])\n segment_array = np.array(matrix_df_tid['segment_id'])\n\n uniquesegments = np.array(list(set(segment_array)))\n keyuniquesegments = np.array(range(len(uniquesegments)))\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\n\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\n segmentkeys = np.array(segments_keys_df['segmentskey'])\n\n level_array = np.array(matrix_df_tid[self.args['cluster_variable']])\n sparse_matrix = csr_matrix((level_array, (segmentkeys,time_array))).toarray()\n sparse_matrix = preprocessing.scale(sparse_matrix)\n \n if self.args['perform_pca']:\n data_to_cluster, self.pca_model = self.run_PCA_long(sparse_matrix)\n else:\n data_to_cluster = sparse_matrix\n\n sparse_matrix_withsegkey = pd.DataFrame(data_to_cluster)\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\n \n print('writing sparse matrix_long to db...')\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n if self.split_type == 'random':\n sparse_matrix_withseg.to_sql(name='clust_sparse_long_avebysegment_random', con=engine, if_exists='replace')\n elif self.split_type == 'date':\n sparse_matrix_withseg.to_sql(name='clust_sparse_long_avebysegment_date', con=engine, if_exists='replace')\n \n print('returning train sparse_long matrix...')\n return (uniquesegments_df, data_to_cluster)","def __add__(self, other):\n\n if other == 0:\n return self\n else:\n L = self.matrix.copy()\n L.shift(1, other.matrix)\n return _PysparseMatrix(matrix=L)","def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def test_assign_clusters_sparse(self, new_data, filename):\n\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n \n print('creating test sparse matrix...')\n if self.split_type == 'random':\n averages_seg = pd.read_sql('SELECT * FROM clust_sparse_avebysegment_random',con=engine)\n averages_rt = pd.read_sql('SELECT * FROM clust_sparse_avebyrt_random',con=engine)\n if self.split_type == 'date':\n averages_seg = pd.read_sql('SELECT * FROM clust_sparse_avebysegment_date',con=engine)\n averages_rt = pd.read_sql('SELECT * FROM clust_sparse_avebyrt_date',con=engine)\n\n averages_seg['exists'] = 1\n test_data_exists = pd.merge(new_data, averages_seg[['segment_id', 'exists']], on=['segment_id'])\n test_exists = test_data_exists[test_data_exists['exists']==1]\n test_notexists = test_data_exists[test_data_exists['exists']!=1] \n \n test_matrix_exists = pd.merge(test_exists[['segment_id', 'road_type']], averages_seg, how='left', on=['segment_id'])\n test_matrix_notexists = pd.merge(test_notexists[['segment_id', 'road_type']], averages_rt, how='left', on=['road_type'])\n test_matrix = pd.concat([test_matrix_exists, test_matrix_notexists])\n test_matrix = test_matrix.fillna(0) \n \n test_sparse_matrix = test_matrix.drop(columns = ['segment_id', 'road_type', 'exists', 'index', 'roadtypekey', 'segmentskey'])\n num = list(range(len(list(averages_seg))-4))\n columns = [str(item) for item in num]\n test_sparse_matrix = test_sparse_matrix[columns] \n \n print('clustering new data...')\n cluster_model = joblib.load(filename)\n cluster_predictions = cluster_model.predict(test_sparse_matrix)\n \n clusterdf = pd.DataFrame(cluster_predictions,columns = ['cluster_sparse'])\n clusterdf['index'] = clusterdf.index\n segmentdf = test_matrix['segment_id'].to_frame()\n segmentdf['index'] = segmentdf.index\n test_cluster_df_sparse = pd.merge(clusterdf, segmentdf, on=['index'])\n test_cluster_df_sparse = test_cluster_df_sparse[['segment_id','cluster_sparse']].groupby(['segment_id','cluster_sparse']).count()\n \n return test_cluster_df_sparse.reset_index()","def _sparse_projection(self,mat,sparsity=1,random_seed=10):\n [n,_] = mat.shape\n sketch = np.zeros((self.rp_dim ,n),dtype=float)\n for i in range(n):\n nnz_loc = np.random.choice(self.rp_dim ,size=sparsity,replace=False)\n nnz_sign = np.random.choice([-1,1],size=sparsity,replace=True)\n sketch[nnz_loc,i] = nnz_sign\n self.sketch_mat = sketch\n return (1./np.sqrt(sparsity))*sketch@mat","def symmeterize(self):\n A = self.to_coo_matrix()\n symg = wgraph_from_adjacency((A + A.T) / 2)\n self.E = symg.E\n self.edges = symg.edges\n self.weights = symg.weights\n return self","def qubit_operator_sparse(qubit_operator, n_qubits=None):\n if n_qubits is None:\n n_qubits = count_qubits(qubit_operator)\n if n_qubits < count_qubits(qubit_operator):\n raise ValueError('Invalid number of qubits specified.')\n\n # Construct the Scipy sparse matrix.\n n_hilbert = 2**n_qubits\n values_list = [[]]\n row_list = [[]]\n column_list = [[]]\n\n # Loop through the terms.\n for qubit_term in qubit_operator.terms:\n tensor_factor = 0\n coefficient = qubit_operator.terms[qubit_term]\n sparse_operators = [coefficient]\n for pauli_operator in qubit_term:\n\n # Grow space for missing identity operators.\n if pauli_operator[0] > tensor_factor:\n identity_qubits = pauli_operator[0] - tensor_factor\n identity = scipy.sparse.identity(2**identity_qubits,\n dtype=complex,\n format='csc')\n sparse_operators += [identity]\n\n # Add actual operator to the list.\n sparse_operators += [pauli_matrix_map[pauli_operator[1]]]\n tensor_factor = pauli_operator[0] + 1\n\n # Grow space at end of string unless operator acted on final qubit.\n if tensor_factor < n_qubits or not qubit_term:\n identity_qubits = n_qubits - tensor_factor\n identity = scipy.sparse.identity(2**identity_qubits,\n dtype=complex,\n format='csc')\n sparse_operators += [identity]\n\n # Extract triplets from sparse_term.\n sparse_matrix = kronecker_operators(sparse_operators)\n values_list.append(sparse_matrix.tocoo(copy=False).data)\n (column, row) = sparse_matrix.nonzero()\n column_list.append(column)\n row_list.append(row)\n\n # Create sparse operator.\n values_list = numpy.concatenate(values_list)\n row_list = numpy.concatenate(row_list)\n column_list = numpy.concatenate(column_list)\n sparse_operator = scipy.sparse.coo_matrix(\n (values_list, (row_list, column_list)),\n shape=(n_hilbert, n_hilbert)).tocsc(copy=False)\n sparse_operator.eliminate_zeros()\n return sparse_operator","def to_sparse_matrix(self, grid, format=None):\n S = self.centered_stencil()\n # print(\"grid :\")\n\n grid = tuple(grid)\n # print(grid)\n if not (np.asarray(S.shape) % 2 == 1).all():\n raise ValueError('all stencil dimensions must be odd')\n\n assert_condition(len(grid) == np.rank(S), ValueError,\n 'stencil rank must equal number of grid dimensions')\n assert_condition(min(grid) >= 1, ValueError,\n 'grid dimensions must be positive')\n\n N_v = np.prod(grid) # number of vertices in the mesh\n N_s = (S != 0).sum() # number of nonzero stencil entries\n\n # diagonal offsets\n diags = np.zeros(N_s, dtype=int)\n\n # compute index offset of each dof within the stencil\n strides = np.cumprod([1] + list(reversed(grid)))[:-1]\n indices = tuple(i.copy() for i in S.nonzero())\n for i,s in zip(indices,S.shape):\n i -= s // 2\n for stride,coords in zip(strides, reversed(indices)):\n diags += stride * coords\n\n #\n data = S[S != 0].repeat(N_v).reshape(N_s, N_v)\n indices = np.vstack(indices).T\n\n # zero boundary connections\n for index,diag in zip(indices,data):\n diag = diag.reshape(grid)\n for n,i in enumerate(index):\n if i > 0:\n s = [ slice(None) ]*len(grid)\n s[n] = slice(0,i)\n diag[s] = 0\n elif i < 0:\n s = [ slice(None) ]*len(grid)\n s[n] = slice(i,None)\n diag[s] = 0\n\n # remove diagonals that lie outside matrix\n mask = abs(diags) < N_v\n if not mask.all():\n diags = diags[mask]\n data = data[mask]\n\n # sum duplicate diagonals\n if len(np.unique(diags)) != len(diags):\n new_diags = np.unique(diags)\n new_data = np.zeros( (len(new_diags),data.shape[1]), dtype=data.dtype)\n for dia,dat in zip(diags,data):\n n = np.searchsorted(new_diags,dia)\n new_data[n,:] += dat\n\n diags = new_diags\n data = new_data\n\n return sprs.dia_matrix((data,diags), shape=(N_v, N_v)).asformat(format)","def close_signal_from_subjects_pad(self, geom):\n self.subjectspad_geometry_memory = geom","def build_mat(self):\n for row, s in enumerate(self.S):\n for col, t in enumerate(self.T):\n\n if self.symmetric and row > col:\n pass\n\n else:\n self.mat[row, col] = self.kernel(s, t, self.n)\n\n if self.symmetric:\n self.mat = self.symmetrize(self.mat)\n else:\n for idx, s in enumerate(self.S):\n self.test_normalization[idx] = self.kernel(s, s, self.n)","def test_import_type_sparse():\n x = sps.csr_matrix(np.random.rand(7, 11))\n export_data('/tmp/test.sparse', x)\n assert x.dtype == import_data('/tmp/test.sparse').dtype","def test_00_create_sparse_1d_array(self):\n ncells = 100\n sparsity = 3.0 # 1 / density\n _, err = _iquery(\"create array SPARSE <v:int64>[i=0:{0}:0:5]\".format(\n ncells - 1))\n assert not err, err\n self._array_cleanups.append('SPARSE')\n _, err = _iquery(\"\"\"\n insert(\n redimension(\n apply(\n build(<i:int64>[fud=0:{0}], {1}*fud),\n (v, 1)),\n SPARSE),\n SPARSE)\"\"\".format(int(ncells / sparsity) - 1,\n int(sparsity)))\n assert not err, err\n check_v_sum('SPARSE')\n nchunks = chunk_count(vaid_of('SPARSE'))\n prt(\"SPARSE has\", nchunks, \"chunks\")","def test_sparsity(self):\n\n self.validator.adata.X = self.validator.adata.X.toarray()\n self.validator.validate_adata()\n self.assertEqual(\n self.validator.warnings,\n [\n \"WARNING: Sparsity of 'X' is 0.875 which is greater than 0.5, \"\n \"and it is not a 'scipy.sparse.csr_matrix'. It is \"\n \"STRONGLY RECOMMENDED to use this type of matrix for \"\n \"the given sparsity.\"\n ],\n )"],"string":"[\n \"def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi)\\n elif nz_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj)\\n elif nz_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij)\\n elif nz_ != len(valij):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if nz_ is None: nz_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valij is None: raise TypeError(\\\"Invalid type for argument valij\\\")\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n \\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n \\n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _idx_return_value = resargs\\n return _idx_return_value\",\n \"def getsparsesymmat(self,idx_,subi_,subj_,valij_):\\n maxlen_ = self.getsymmatinfo((idx_))[1]\\n _subi_minlength = (maxlen_)\\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxlen_)))\\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\\n raise ValueError(\\\"Argument subi must be writable\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = (maxlen_)\\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxlen_)))\\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\\n raise ValueError(\\\"Argument subj must be writable\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _valij_minlength = (maxlen_)\\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\\n raise ValueError(\\\"Array argument valij is not long enough: Is %d, expected %d\\\" % (len(valij_),(maxlen_)))\\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\\n raise ValueError(\\\"Argument valij must be writable\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _subi_copyarray:\\n subi_[:] = _subi_np_tmp\\n if _subj_copyarray:\\n subj_[:] = _subj_np_tmp\\n if _valij_copyarray:\\n valij_[:] = _valij_np_tmp\",\n \"def getsparsesymmat(self,idx_,subi,subj,valij): # 3\\n maxlen_ = self.getsymmatinfo((idx_))[1]\\n _copyback_subi = False\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n _copyback_subi = True\\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n _copyback_subi = True\\n if subi_ is not None and len(subi_) != (maxlen_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxlen_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n _copyback_valij = False\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n _copyback_valij = True\\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n _copyback_valij = True\\n if valij_ is not None and len(valij_) != (maxlen_):\\n raise ValueError(\\\"Array argument valij has wrong length\\\")\\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_valij:\\n valij[:] = _tmparr_valij\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n if _copyback_subi:\\n subi[:] = _tmparr_subi\",\n \"def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):\\n num_ = None\\n if num_ is None:\\n num_ = len(dims_)\\n elif num_ != len(dims_):\\n raise IndexError(\\\"Inconsistent length of array dims\\\")\\n if num_ is None:\\n num_ = len(nz_)\\n elif num_ != len(nz_):\\n raise IndexError(\\\"Inconsistent length of array nz\\\")\\n if dims_ is None:\\n raise ValueError(\\\"Argument dims cannot be None\\\")\\n if dims_ is None:\\n raise ValueError(\\\"Argument dims may not be None\\\")\\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\\n _dims_copyarray = False\\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif dims_ is not None:\\n _dims_copyarray = True\\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\\n _dims_np_tmp[:] = dims_\\n assert _dims_np_tmp.flags.contiguous\\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _dims_copyarray = False\\n _dims_tmp = None\\n \\n if nz_ is None:\\n raise ValueError(\\\"Argument nz cannot be None\\\")\\n if nz_ is None:\\n raise ValueError(\\\"Argument nz may not be None\\\")\\n if isinstance(nz_, numpy.ndarray) and nz_.dtype is numpy.dtype(numpy.int64) and nz_.flags.contiguous:\\n _nz_copyarray = False\\n _nz_tmp = ctypes.cast(nz_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif nz_ is not None:\\n _nz_copyarray = True\\n _nz_np_tmp = numpy.zeros(len(nz_),numpy.dtype(numpy.int64))\\n _nz_np_tmp[:] = nz_\\n assert _nz_np_tmp.flags.contiguous\\n _nz_tmp = ctypes.cast(_nz_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _nz_copyarray = False\\n _nz_tmp = None\\n \\n _subi_minlength = sum((nz_))\\n if sum((nz_)) > 0 and subi_ is not None and len(subi_) != sum((nz_)):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),sum((nz_))))\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = sum((nz_))\\n if sum((nz_)) > 0 and subj_ is not None and len(subj_) != sum((nz_)):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),sum((nz_))))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _valij_minlength = sum((nz_))\\n if sum((nz_)) > 0 and valij_ is not None and len(valij_) != sum((nz_)):\\n raise ValueError(\\\"Array argument valij is not long enough: Is %d, expected %d\\\" % (len(valij_),sum((nz_))))\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n _idx_minlength = (num_)\\n if (num_) > 0 and idx_ is not None and len(idx_) != (num_):\\n raise ValueError(\\\"Array argument idx is not long enough: Is %d, expected %d\\\" % (len(idx_),(num_)))\\n if isinstance(idx_,numpy.ndarray) and not idx_.flags.writeable:\\n raise ValueError(\\\"Argument idx must be writable\\\")\\n if idx_ is None:\\n raise ValueError(\\\"Argument idx may not be None\\\")\\n if isinstance(idx_, numpy.ndarray) and idx_.dtype is numpy.dtype(numpy.int64) and idx_.flags.contiguous:\\n _idx_copyarray = False\\n _idx_tmp = ctypes.cast(idx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif idx_ is not None:\\n _idx_copyarray = True\\n _idx_np_tmp = numpy.zeros(len(idx_),numpy.dtype(numpy.int64))\\n _idx_np_tmp[:] = idx_\\n assert _idx_np_tmp.flags.contiguous\\n _idx_tmp = ctypes.cast(_idx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _idx_copyarray = False\\n _idx_tmp = None\\n \\n res = __library__.MSK_XX_appendsparsesymmatlist(self.__nativep,num_,_dims_tmp,_nz_tmp,_subi_tmp,_subj_tmp,_valij_tmp,_idx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _idx_copyarray:\\n idx_[:] = _idx_np_tmp\",\n \"def add_sparse(self, key, element):\\n self.add(self._sparse2seq(key), element)\",\n \"def matrix_add_symmetric(M, M_sym):\\n M[0, 0] += M_sym[0]\\n M[0, 1] += M_sym[1]\\n M[1, 0] += M_sym[1]\\n M[0, 2] += M_sym[2]\\n M[2, 0] += M_sym[2]\\n\\n M[1, 1] += M_sym[3]\\n M[1, 2] += M_sym[4]\\n M[2, 1] += M_sym[4]\\n\\n M[2, 2] += M_sym[5]\\n\\n return M\",\n \"def _build_sparse(self, name, wrt, consize, param_vals, sub_param_conns,\\n full_param_conns, rels):\\n\\n jac = None\\n\\n # Additional sparsity for index connections\\n for param in wrt:\\n\\n sub_conns = sub_param_conns.get(param)\\n if not sub_conns:\\n continue\\n\\n # If we have a simultaneous full connection, then we move on\\n full_conns = full_param_conns.get(param)\\n if full_conns.intersection(rels):\\n continue\\n\\n rel_idx = set()\\n for target, idx in iteritems(sub_conns):\\n\\n # If a target of the indexed desvar connection is\\n # in the relevant path for this constraint, then\\n # those indices are relevant.\\n if target in rels:\\n rel_idx.update(idx)\\n\\n nrel = len(rel_idx)\\n if nrel > 0:\\n\\n if jac is None:\\n jac = {}\\n\\n if param not in jac:\\n # A coo matrix for the Jacobian\\n # mat = {'coo':[row, col, data],\\n # 'shape':[nrow, ncols]}\\n coo = {}\\n coo['shape'] = [consize, len(param_vals[param])]\\n jac[param] = coo\\n\\n row = []\\n col = []\\n for i in range(consize):\\n row.extend([i]*nrel)\\n col.extend(rel_idx)\\n data = np.ones((len(row), ))\\n\\n jac[param]['coo'] = [np.array(row), np.array(col), data]\\n\\n if name not in self.sub_sparsity:\\n self.sub_sparsity[name] = {}\\n self.sub_sparsity[name][param] = np.array(list(rel_idx))\\n\\n return jac\",\n \"def setDominantSparseSymeig(A, Aadjoint_to_gadjoint):\\n global DominantSparseSymeig \\n from .CG import setCGSubspaceSparse\\n setCGSubspaceSparse(A, Aadjoint_to_gadjoint)\\n from .CG import CGSubspaceSparse\\n @staticmethod\\n def forward(ctx, g, k, dim):\\n eigval, eigvector = symeigLanczos(A, k, extreme=\\\"min\\\", sparse=True, dim=dim)\\n ctx.save_for_backward(g, eigval, eigvector)\\n return eigval, eigvector\\n @staticmethod\\n def backward(ctx, grad_eigval, grad_eigvector):\\n cg = CGSubspaceSparse.apply\\n g, eigval, eigvector = ctx.saved_tensors\\n b = grad_eigvector - torch.matmul(eigvector, grad_eigvector) * eigvector\\n lambda0 = cg(g, eigval, b, eigvector)\\n grad_A = grad_eigval * eigvector - lambda0, eigvector\\n v1, v2 = grad_A\\n grad_g = Aadjoint_to_gadjoint(v1, v2)\\n grad_k = grad_dim = None\\n return grad_g, grad_k, grad_dim\\n DominantSparseSymeig = type(\\\"DominantSparseSymeig\\\", (torch.autograd.Function, ), \\n {\\\"forward\\\": forward, \\\"backward\\\": backward})\",\n \"def set_sparse_signals(self):\\n\\t\\n\\t\\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\\n\\t\\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\\n\\t\\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tparams_dSs,\\tseed=self.seed_dSs)\\n\\t\\t\\n\\t\\t# Replace components with conflicting background odor \\n\\t\\tif self.Kk_split is not None and self.Kk_split != 0:\\n\\t\\t\\tassert 0 <= self.Kk_split <= self.Kk, \\\\\\n\\t\\t\\t\\t\\\"Splitting sparse signal into two levels requires Kk_split\\\" \\\\\\n\\t\\t\\t\\t\\\" to be non-negative and less than or equal to Kk.\\\"\\n\\t\\t\\tassert self.mu_dSs_2 is not None \\\\\\n\\t\\t\\t\\tand self.sigma_dSs_2 is not None, \\\\\\n\\t\\t\\t\\t\\\"Splitting sparse signal into two levels requires that\\\" \\\\\\n\\t\\t\\t\\t\\\" mu_dSs_2 and sigma_dSs_2 are set.\\\"\\n\\n\\t\\t\\tsp.random.seed(self.seed_dSs)\\n\\t\\t\\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\treplace=False)\\n\\t\\t\\tfor idx_2 in self.idxs_2:\\n\\t\\t\\t\\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.sigma_dSs_2)\\n\\t\\telse:\\n\\t\\t\\tself.idxs_2 = []\\n\\t\\t\\tself.Kk_split = 0\\n\\t\\t\\t\\n\\t\\t# Ss0 is the ideal (learned) background stimulus without noise\\n\\t\\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.idxs, params_Ss0,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Ss0)\\n\\t\\t\\n\\t\\tself.Ss = self.dSs + self.Ss0_noisy\",\n \"def SymmetriseMatrix(adjmatrix):\\n\\n if galib.metrics.Reciprocity(adjmatrix) == 1:\\n # if Reciprocity(adjmatrix) == 1:\\n return adjmatrix\\n else:\\n return 0.5 * (adjmatrix + adjmatrix.T)\",\n \"def dict2sparseMatrix(wDict,std=0,diag=0):\\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\\n nAreas = len(list(wDict.keys()))\\n for i in wDict:\\n data[i,i] = diag\\n ne = len(wDict[i])+ diag\\n for j in wDict[i]:\\n if std:\\n data[i,j] = 1 / float(ne)\\n else:\\n data[i,j] = 1\\n return data\",\n \"def eval_sparse(self, array_in, array_out, sp_matrix=None):\\n if sp_matrix is None:\\n sp_matrix = self.to_sparse_matrix(array_in.shape, \\\"csc\\\")\\n # print(\\\"usually:\\\", sp_matrix.todense())\\n array_out[:] = sp_matrix.dot(array_in.reshape(-1)).reshape(array_out.shape)\",\n \"def sparse_matrix(data, stype=\\\"csr\\\", dtype=complex):\\n return _SPARSE_CONSTRUCTORS[stype](data, dtype=dtype)\",\n \"def set_sparsity(self,use_sparse):\\n \\n if hasattr(self.problem,'sparse_jac'):\\n self.use_sparse = use_sparse\\n else:\\n raise KINSOL_Exception(\\\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\\\")\",\n \"def sparse_matlab(i, j, v, m, n):\\n return csr_matrix((v, (i, j)), shape=(m, n))\",\n \"def make_sparse(self, fmt='csc', make_method=None):\\n if make_method:\\n self.sparse = make_method(self.hamiltonian)\\n else:\\n self.sparse = self.hamiltonian.to_matrix(sparse=fmt)\",\n \"def sparsify(W,conn):\\n \\n N = W.shape[0]\\n W_sparse = sparse.lil_matrix((N,N)) \\n for row, weights in itertools.izip(conn, W):\\n W_sparse[row[0],row[1:]] = weights[1:]\\n return W_sparse\",\n \"def normalize_adj( adj : np.ndarray, \\n sparse : bool = False\\n ) -> Union[np.ndarray, sp.spmatrix]:\\n if sparse:\\n adj = sp.coo_matrix(adj) # [N,N]\\n rowsum = np.array(adj.sum(1)) # [N,]\\n \\n d_inv_sqrt = np.power(rowsum, -0.5) # [N,], may issue runtime warnings (div by zero)\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. # []\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) if sparse else np.diag(d_inv_sqrt) #[N,N]\\n \\n if sparse:\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\\n else:\\n return ((adj @ d_mat_inv_sqrt).transpose() @ d_mat_inv_sqrt) # not quite sure why this order = D^T A^T D, D^T = D, A^T = A - the transpose is unncessary?!\",\n \"def sparse_matrix(shape, integer=False):\\n dtype = numpy.int_ if integer else numpy.float_\\n return scipy.sparse.lil_matrix(shape, dtype=dtype)\",\n \"def append(self, symmData: list) -> None:\\n newSymm = SymmetryElement(symmData)\\n if not newSymm in self._symmcards:\\n self._symmcards.append(newSymm)\",\n \"def matrix_add():\",\n \"def store(self, subj):\\n if subj in self.__lst:\\n raise ValueError('Disciplina exista deja')\\n self.__lst.append(subj)\",\n \"def save_sparse_matrix(self,\\n artifact_type: str,\\n params: Dict[str, Any],\\n sparse_matrix: sp.csr_matrix,\\n ignore_duplicate: bool = False) -> str:\\n ppr_idx = None\\n if \\\"ppr_idx\\\" in params.keys() and not isinstance(params[\\\"ppr_idx\\\"], int):\\n ppr_idx = np.array(params[\\\"ppr_idx\\\"])\\n params[\\\"ppr_idx\\\"] = hash(frozenset(params[\\\"ppr_idx\\\"]))\\n\\n if ignore_duplicate:\\n # check there's no entry with the exact same config already present\\n ids = Storage.locked_call(\\n lambda: self._find_meta_by_exact_params(artifact_type, params),\\n self._get_lock_path(artifact_type),\\n self.lock_timeout\\n )\\n if len(ids) > 0:\\n logging.info(f\\\"Ignoring duplicate save in save_sparse_matrix call\\\")\\n return self._build_artifact_path(artifact_type, ids[0].doc_id).replace(\\\".pt\\\", \\\".npz\\\")\\n\\n ids = Storage.locked_call(\\n lambda: self._upsert_meta(artifact_type, params),\\n self._get_lock_path(artifact_type),\\n self.lock_timeout,\\n )\\n if len(ids) != 1:\\n raise RuntimeError(f'The index contains duplicates (artifact_type={artifact_type}, params={params})')\\n\\n try:\\n path = self._build_artifact_path(artifact_type, ids[0]).replace(\\\".pt\\\", \\\".npz\\\")\\n sp.save_npz(path, sparse_matrix)\\n logging.info(f\\\"Saved sparse matrix to storage\\\")\\n if ppr_idx is not None:\\n ppr_path = path.replace(\\\".npz\\\", \\\"idx.npy\\\")\\n np.save(ppr_path, ppr_idx)\\n logging.info(f\\\"Saved ppr index to storage\\\")\\n return path\\n except: # noqa: E722\\n Storage.locked_call(\\n lambda: self._remove_meta(artifact_type, params),\\n self._get_lock_path(artifact_type),\\n self.lock_timeout\\n )\\n raise\",\n \"def sparse_matrix (base_type=float):\\n return defaultdict (lambda: sparse_vector (base_type))\",\n \"def _identity_sparse(d, stype=\\\"csr\\\", dtype=complex):\\n return sp.eye(d, dtype=dtype, format=stype)\",\n \"def create_sparse_matrix(self, matrix_df):\\n\\n print('creating sparse matrix...')\\n sparse_seg_tmp_df = matrix_df.groupby(['segment_id','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n sparse_rt_tmp_df = matrix_df.groupby(['road_type','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n time_seg_df = sparse_seg_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n time_rt_df = sparse_rt_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n #time_seg_df['time_id'] = time_seg_df.index\\n #time_rt_df['time_id'] = time_rt_df.index\\n times = list(range(24*60/self.args['time_resolution']))\\n full_time_idx = pd.DataFrame([i * 30 for i in times],columns = ['time_idx'])\\n full_time_idx['key'] = 1\\n full_day_of_week = pd.DataFrame(list(range(7)), columns = ['day_of_week'])\\n full_day_of_week['key'] = 1\\n full_times = pd.merge(full_time_idx, full_day_of_week, on='key')\\n full_times['time_id'] = full_times.index\\n time_seg_df = pd.merge(time_seg_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\\n time_rt_df = pd.merge(time_rt_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\\n \\n matrix_seg_keys_df = pd.merge(sparse_seg_tmp_df, time_seg_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\\n matrix_rt_keys_df = pd.merge(sparse_rt_tmp_df, time_rt_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\\n\\n time_seg_array = np.array(matrix_seg_keys_df['time_id'])\\n time_rt_array = np.array(matrix_rt_keys_df['time_id'])\\n segment_array = np.array(matrix_seg_keys_df['segment_id'])\\n rt_array = np.array(matrix_rt_keys_df['road_type'])\\n\\n uniquesegments = np.array(list(set(segment_array)))\\n uniquerts = np.array(list(set(rt_array)))\\n keyuniquesegments = np.array(range(len(uniquesegments)))\\n keyuniquerts = np.array(range(len(uniquerts)))\\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\\n uniquerts_df = pd.DataFrame({'roadtypekey':keyuniquerts, 'road_type':uniquerts})\\n\\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\\n rt_df = pd.DataFrame(rt_array, columns = ['road_type'])\\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\\n rt_keys_df = pd.merge(rt_df, uniquerts_df, how='left', on=['road_type'])\\n segmentkeys = np.array(segments_keys_df['segmentskey'])\\n rtkeys = np.array(rt_keys_df['road_type'])\\n\\n level_array_seg = np.array(matrix_seg_keys_df['level_max'])\\n sparse_matrix_s = csr_matrix((level_array_seg, (segmentkeys,time_seg_array))).toarray()\\n sparse_matrix_seg = preprocessing.scale(sparse_matrix_s)\\n level_array_rt = np.array(matrix_rt_keys_df['level_max'])\\n sparse_matrix_r = csr_matrix((level_array_rt, (rtkeys,time_rt_array))).toarray()\\n sparse_matrix_rt = preprocessing.scale(sparse_matrix_r)\\n \\n if self.args['perform_pca']:\\n sparse_matrix_seg, self.pca_model = self.run_PCA(sparse_matrix_seg)\\n sparse_matrix_rt, self.pca_model = self.run_PCA(sparse_matrix_rt)\\n else:\\n sparse_matrix_seg = sparse_matrix_seg\\n sparse_matrix_rt = sparse_matrix_rt\\n \\n sparse_matrix_withsegkey = pd.DataFrame(sparse_matrix_seg)\\n sparse_matrix_withrtkey = pd.DataFrame(sparse_matrix_rt)\\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\\n sparse_matrix_withrtkey['roadtypekey'] = sparse_matrix_withrtkey.index\\n sparse_matrix_withrt = pd.merge(uniquerts_df, sparse_matrix_withrtkey, on=['roadtypekey'])\\n \\n # write sparse_matrix to database as 'clustering' table\\n print('writing sparse matrix to db...')\\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\\n engine = create_engine(sqlalchemy_conn_str)\\n if self.split_type == 'random':\\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_random', con=engine, if_exists='replace')\\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_random', con=engine, if_exists='replace')\\n elif self.split_type == 'date':\\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_date', con=engine, if_exists='replace')\\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_date', con=engine, if_exists='replace')\\n \\n print('returning train sparse matrix...')\\n return (uniquesegments_df, sparse_matrix_seg)\",\n \"def coregionalization_sparse(optimize=True, plot=True):\\r\\n #fetch the data from the non sparse examples\\r\\n m = coregionalization_toy2(optimize=False, plot=False)\\r\\n X, Y = m.X, m.likelihood.Y\\r\\n\\r\\n #construct a model\\r\\n m = GPy.models.SparseGPRegression(X,Y)\\r\\n m.constrain_fixed('iip_\\\\d+_1') # don't optimize the inducing input indexes\\r\\n\\r\\n if optimize:\\r\\n m.optimize('bfgs', max_iters=100, messages=1)\\r\\n\\r\\n if plot:\\r\\n m.plot(fixed_inputs=[(1,0)])\\r\\n m.plot(fixed_inputs=[(1,1)], ax=pb.gca())\\r\\n\\r\\n return m\",\n \"def putaijlist(self,subi_,subj_,valij_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(valij_)\\n elif num_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def build_lhs_matrix(self):\\n j=3\\n diags1 = self.build_linear_diags()\\n diags1 += self.build_dispersion_diags()\\n\\n # Ones down primary diagonal\\n diags2 = np.zeros_like(diags1)\\n diags2[j,:] = 1.\\n\\n cff = self.dt*(1+self.c_im)*0.5 \\n diags = diags2 - cff*diags1\\n \\n # Build the sparse matrix\\n cols = [ii for ii in range(-self._j, self._j+1)]\\n M = sparse.spdiags(diags, cols, self.N, self.N)\\n\\n return M, diags\",\n \"def __add__(self, other):\\n h, w = self.size\\n col_indices = self.col_indices + [w + i for i in other.col_indices]\\n row_indices = self.row_indices + other.row_indices\\n values = self.values + other.values\\n oh, ow = other.size\\n size = [max(h, oh), w + ow]\\n return Sparse(size, row_indices, col_indices, values)\",\n \"def to_sparse(self, method='csr_matrix'):\\r\\n data = self.data.values\\r\\n if method == 'csr_matrix':\\r\\n data_sp = sps.csr_matrix(data)\\r\\n elif method == 'bsr_matrix':\\r\\n data_sp = sps.bsr_matrix(data)\\r\\n elif method == 'coo_matrix':\\r\\n data_sp = sps.coo_matrix(data)\\r\\n elif method == 'csc_matrix':\\r\\n data_sp = sps.csc_matrix(data)\\r\\n elif method == 'dia_matrix':\\r\\n data_sp = sps.dia_matrix(data)\\r\\n elif method == 'dok_matrix':\\r\\n data_sp = sps.dok_matrix(data)\\r\\n elif method == 'lil_matrix':\\r\\n data_sp = sps.lil_matrix(data)\\r\\n else:\\r\\n raise ValueError('The method does not exist in scipy.sparse')\\r\\n return data_sp\",\n \"def append_arr(self, num_seg, g_pix, intercept, slope, sig_intercept,\\n sig_slope, inv_var, save_opt):\\n self.slope_2d[num_seg[g_pix], g_pix] = slope[g_pix]\\n if save_opt:\\n self.interc_2d[num_seg[g_pix], g_pix] = intercept[g_pix]\\n self.siginterc_2d[num_seg[g_pix], g_pix] = sig_intercept[g_pix]\\n self.sigslope_2d[num_seg[g_pix], g_pix] = sig_slope[g_pix]\\n self.inv_var_2d[num_seg[g_pix], g_pix] = inv_var[g_pix]\",\n \"def _append_sparse_tensor_value(target, to_append):\\n # Make sure the sparse tensors are of the same size (except for the 0th dim).\\n if len(target.dense_shape) != len(to_append.dense_shape):\\n raise RuntimeError(\\n 'Unable to concatenate %s and %s. The inner dense shapes do not '\\n 'have the same number of dimensions (%s vs %s)' %\\n (target, to_append, target.dense_shape, to_append.dense_shape))\\n\\n if target.dense_shape[1:] != to_append.dense_shape[1:]:\\n raise RuntimeError(\\n 'Unable to concatenate %s and %s. The inner dense shapes do not '\\n 'match inner dimensions (%s vs %s)' %\\n (target, to_append, target.dense_shape[1:], to_append.dense_shape[1:]))\\n\\n # Add the to_append indices to target, updating the 0th value, and keeping\\n # track of the maximum so we know the final dense_shape of this tensor.\\n base_dim0_value = target.dense_shape[0]\\n max_dim0_value = target.dense_shape[0]\\n new_indices = target.indices\\n for index in to_append.indices:\\n # Here, we iterate through the sparse indices of the tensor to append. For\\n # each index, we update its zeroth value (the batch index) by adding the\\n # number of batch items in the tensor we are appending to (so an index\\n # of [0, 0, 1] for a value that is being appended to a tensor with 0th dim\\n # size 3 would become [3, 0, 1].)\\n index[0] += base_dim0_value\\n max_dim0_value = max(max_dim0_value, index[0])\\n new_indices = np.append(new_indices, [index], axis=0)\\n\\n # Extend the values array to contain all of the appended values. These will\\n # be in the same order as the indices added above.\\n new_values = np.concatenate((target.values, to_append.values), axis=0)\\n\\n # Create a new dense shape by replacing the value for the 0th dimension\\n # with the new max dim0 value.\\n new_dense_shape = list(target.dense_shape)\\n new_dense_shape[0] = max_dim0_value + 1\\n new_dense_shape = tuple(new_dense_shape)\\n\\n return sparse_tensor.SparseTensorValue(\\n indices=new_indices, values=new_values, dense_shape=new_dense_shape)\",\n \"def putaijlist(self,subi,subj,valij): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subi)\\n elif num_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj)\\n elif num_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(valij)\\n elif num_ != len(valij):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if num_ is None: num_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valij is None: raise TypeError(\\\"Invalid type for argument valij\\\")\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n \\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n \\n res = self.__obj.putaijlist64(num_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def sym_adj(adj):\\n adj = ss.coo_matrix(adj)\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = ss.diags(d_inv_sqrt)\\n return np.array(adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense())\",\n \"def to_s_matrix(w,v):\\n pass\",\n \"def to_sparse(self):\\n from divisi2.sparse import SparseMatrix\\n return SparseMatrix(self, self.row_labels, self.col_labels)\",\n \"def sym_adj(adj):\\n adj = sp.coo_matrix(adj)\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense()\",\n \"def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\\\"<i4\\\")\\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\\\"<f8\\\")\\n size = j_obj.getSize()\\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()\",\n \"def sparse(cls, a_ndarray, i_ndarray, shape, bigdl_type=\\\"float\\\"):\\n if a_ndarray is None:\\n return None\\n invalidInputError(isinstance(a_ndarray, np.ndarray),\\n f\\\"input should be a np.ndarray, not ${type(a_ndarray)}\\\")\\n invalidInputError(isinstance(i_ndarray, np.ndarray),\\n f\\\"indices should be a np.ndarray, not ${type(i_ndarray)}\\\")\\n invalidInputError(i_ndarray.size == a_ndarray.size * shape.size,\\n f\\\"size of values ${a_ndarray.size * shape.size} and\\\"\\n f\\\" indices ${i_ndarray.size} should match\\\")\\n return cls(a_ndarray,\\n shape,\\n bigdl_type,\\n i_ndarray)\",\n \"def parse_subgraph(subgraph_nodes, graph, ass_matrix):\\n\\n sg = graph.subgraph(subgraph_nodes)\\n sg_adj = nx.to_scipy_sparse_matrix(sg, format='csc') # New adjacency matrix.\\n sg_ass = ass_matrix[list(sg.nodes)]\\n\\n return sg_adj, sg_ass\",\n \"def drawSparseMatrix(ax, mat, **kwargs):\\n row = kwargs.pop('rowOffset', 0)\\n col = kwargs.pop('colOffset', 0)\\n color = kwargs.pop('color', None)\\n\\n mat = pg.utils.sparseMatrix2coo(mat)\\n mat.row += row\\n mat.col += col\\n gci = ax.spy(mat, color=color, **kwargs)\\n\\n ax.autoscale(enable=True, axis='both', tight=True)\\n return gci\",\n \"def wrapDBMatrix(self,mat):\\n return mat.todense()\",\n \"def sparse_esn_reservoir(size, spectral_radius, density, symmetric):\\n rvs = stats.uniform(loc=-1., scale=2.).rvs\\n matrix = sparse.random(size, size, density=density, data_rvs=rvs)\\n matrix = matrix.tocsr()\\n if symmetric:\\n matrix = sparse.triu(matrix)\\n tril = sparse.tril(matrix.transpose(), k=-1)\\n matrix = matrix + tril\\n # calc eigenvalues with scipy's lanczos implementation:\\n eig, _ = sparse.linalg.eigsh(matrix, k=2, tol=1e-4)\\n else:\\n eig, _ = sparse.linalg.eigs(matrix, k=2, tol=1e-4)\\n\\n rho = np.abs(eig).max()\\n matrix = matrix.multiply(1. / rho)\\n matrix = matrix.multiply(spectral_radius)\\n return matrix\",\n \"def create_adjust_matrix(self, is_sub: bool):\\n matrix = None\\n if not is_sub:\\n ratings = np.ones_like(self._user, dtype=np.float32)\\n matrix = sp.csr_matrix(\\n (ratings, (self._user, self._item + self.n_users)),\\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\\n )\\n else:\\n if self.type == \\\"ND\\\":\\n drop_user = self.rand_sample(\\n self.n_users,\\n size=int(self.n_users * self.drop_ratio),\\n replace=False,\\n )\\n drop_item = self.rand_sample(\\n self.n_items,\\n size=int(self.n_items * self.drop_ratio),\\n replace=False,\\n )\\n R_user = np.ones(self.n_users, dtype=np.float32)\\n R_user[drop_user] = 0.0\\n R_item = np.ones(self.n_items, dtype=np.float32)\\n R_item[drop_item] = 0.0\\n R_user = sp.diags(R_user)\\n R_item = sp.diags(R_item)\\n R_G = sp.csr_matrix(\\n (\\n np.ones_like(self._user, dtype=np.float32),\\n (self._user, self._item),\\n ),\\n shape=(self.n_users, self.n_items),\\n )\\n res = R_user.dot(R_G)\\n res = res.dot(R_item)\\n\\n user, item = res.nonzero()\\n ratings = res.data\\n matrix = sp.csr_matrix(\\n (ratings, (user, item + self.n_users)),\\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\\n )\\n\\n elif self.type == \\\"ED\\\" or self.type == \\\"RW\\\":\\n keep_item = self.rand_sample(\\n len(self._user),\\n size=int(len(self._user) * (1 - self.drop_ratio)),\\n replace=False,\\n )\\n user = self._user[keep_item]\\n item = self._item[keep_item]\\n\\n matrix = sp.csr_matrix(\\n (np.ones_like(user), (user, item + self.n_users)),\\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\\n )\\n\\n matrix = matrix + matrix.T\\n D = np.array(matrix.sum(axis=1)) + 1e-7\\n D = np.power(D, -0.5).flatten()\\n D = sp.diags(D)\\n return D.dot(matrix).dot(D)\",\n \"def save_feature_matrix(tgt, feat_mat):\\n smat_util.save_matrix(tgt, feat_mat)\",\n \"def write_matfile(Sint_df, outputfilepath='Sint_no_cofactor_20160831.mat'):\\n # convert dataframe to matrix\\n Smat = Sint_df.as_matrix()\\n\\n # get all indices for non-zero elements in Smat (row, col)\\n Smat_nzr, Smat_nzc = np.nonzero(Smat)\\n\\n # get all non-zero elements from Smat\\n Smat_nze = Smat[Smat_nzr, Smat_nzc]\\n\\n # Adjust for matlab coordinate\\n Smat_nzr = Smat_nzr + 1\\n Smat_nzc = Smat_nzc + 1\\n\\n # This final line gives the size of the S matrix in matlab\\n nr, nc = Smat.shape\\n\\n # Create a 2D array\\n sparseMat = np.vstack((Smat_nzr, Smat_nzc, Smat_nze)).T\\n sparseMat = np.vstack((sparseMat, np.array([[nr, nc, 0]])))\\n\\n # Create a numpy object array from dataframe index\\n reactionList = Sint_df.columns.ravel()\\n\\n # Write only one matlab .mat file\\n scipy.io.savemat(outputfilepath,\\n mdict={'Sint_sparse': sparseMat,\\n 'reactionList': np.array(reactionList)}\\n )\\n\\n return sparseMat, reactionList\",\n \"def expand_conn_mat(conn_mat, nr_of_traps):\\n\\n r, c = conn_mat._shape\\n new_indptr = np.hstack((conn_mat.indptr, np.asarray([conn_mat.indptr[-1]] * nr_of_traps)))\\n expanded_mat = csr_matrix((conn_mat.data, conn_mat.indices, new_indptr),\\n shape=(r + nr_of_traps, c + nr_of_traps))\\n\\n return expanded_mat\",\n \"def sparse_GP_regression_2D(num_samples=400, num_inducing=50, max_iters=100, optimize=True, plot=True):\\r\\n X = np.random.uniform(-3., 3., (num_samples, 2))\\r\\n Y = np.sin(X[:, 0:1]) * np.sin(X[:, 1:2]) + np.random.randn(num_samples, 1) * 0.05\\r\\n\\r\\n # construct kernel\\r\\n rbf = GPy.kern.rbf(2)\\r\\n\\r\\n # create simple GP Model\\r\\n m = GPy.models.SparseGPRegression(X, Y, kernel=rbf, num_inducing=num_inducing)\\r\\n\\r\\n # contrain all parameters to be positive (but not inducing inputs)\\r\\n m['.*len'] = 2.\\r\\n\\r\\n m.checkgrad()\\r\\n\\r\\n # optimize\\r\\n if optimize:\\r\\n m.optimize('tnc', messages=1, max_iters=max_iters)\\r\\n\\r\\n # plot\\r\\n if plot:\\r\\n m.plot()\\r\\n\\r\\n print m\\r\\n return m\",\n \"def add_ts_to_matrix(self, i_ts, norm_flag, add_x=True, add_y=True):\\n\\n # ts are not overwritten, only normalization constants\\n if norm_flag:\\n ts, norm_div, norm_subt = _normalize_ts(self.ts[i_ts].s, self.ts[i_ts].name)\\n self.ts[i_ts] = TsMiniStruct(self.ts[i_ts].s, norm_div, norm_subt, self.ts[i_ts].name, self.ts[i_ts].index)\\n ts = TsMiniStruct(ts, norm_div, norm_subt, self.ts[i_ts].name, self.ts[i_ts].index)\\n else:\\n ts = self.ts[i_ts]\\n\\n n_hist = self.n_hist_points[i_ts]\\n n_req = self.n_req_points[i_ts]\\n n_rows = self.X.shape[0]\\n\\n # reverse time series, so that the top row is always to be forecasted first\\n time = np.flipud(ts.index)\\n ts = np.flipud(ts.s)\\n \\n idxX, idxY = matrix_idx(n_hist, n_req, n_rows)\\n self.idxY[i_ts] = idxY\\n if add_y:\\n self.Y = np.hstack((self.Y, ts[idxY]))\\n if add_x:\\n self.X = np.hstack((self.X, ts[idxX]))\\n\\n return time[idxY[:, -1]], time[idxX[:, 0]]\",\n \"def fit_from_scipy_sparse_matrix(self,\\n adj,\\n attr,\\n spatially_extensive_attr,\\n threshold,\\n solver=\\\"cbc\\\",\\n metric=\\\"euclidean\\\"):\\n self.metric = get_metric_function(metric)\\n check_solver(solver)\\n\\n prob = LpProblem(\\\"Max-p-Regions\\\", LpMinimize)\\n\\n # Parameters of the optimization problem\\n n_areas = adj.shape[0]\\n I = list(range(n_areas)) # index for areas\\n II = [(i, j) for i in I for j in I]\\n II_upper_triangle = [(i, j) for i, j in II if i < j]\\n # index of potential regions, called k in [DAR2012]_:\\n K = range(n_areas)\\n # index of contiguity order, called c in [DAR2012]_:\\n O = range(n_areas)\\n d = {(i, j): self.metric(attr[i].reshape(1, -1), attr[j].reshape(\\n 1, -1))\\n for i, j in II_upper_triangle}\\n h = 1 + floor(log10(sum(d[(i, j)] for i, j in II_upper_triangle)))\\n\\n # Decision variables\\n t = LpVariable.dicts(\\n \\\"t\\\", ((i, j) for i, j in II_upper_triangle),\\n lowBound=0,\\n upBound=1,\\n cat=LpInteger)\\n x = LpVariable.dicts(\\n \\\"x\\\", ((i, k, o) for i in I for k in K for o in O),\\n lowBound=0,\\n upBound=1,\\n cat=LpInteger)\\n\\n # Objective function\\n # (1) in Duque et al. (2012): \\\"The Max-p-Regions Problem\\\"\\n prob += -10**h * lpSum(x[i, k, 0] for k in K for i in I) \\\\\\n + lpSum(d[i, j] * t[i, j] for i, j in II_upper_triangle)\\n\\n # Constraints\\n # (2) in Duque et al. (2012): \\\"The Max-p-Regions Problem\\\"\\n for k in K:\\n prob += lpSum(x[i, k, 0] for i in I) <= 1\\n # (3) in Duque et al. (2012): \\\"The Max-p-Regions Problem\\\"\\n for i in I:\\n prob += lpSum(x[i, k, o] for k in K for o in O) == 1\\n # (4) in Duque et al. (2012): \\\"The Max-p-Regions Problem\\\"\\n for i in I:\\n for k in K:\\n for o in range(1, len(O)):\\n prob += x[i, k, o] <= lpSum(x[j, k, o - 1]\\n for j in neighbors(adj, i))\\n # (5) in Duque et al. (2012): \\\"The Max-p-Regions Problem\\\"\\n if isinstance(spatially_extensive_attr[I[0]], numbers.Real):\\n for k in K:\\n lhs = lpSum(x[i, k, o] * spatially_extensive_attr[i] for i in I\\n for o in O)\\n prob += lhs >= threshold * lpSum(x[i, k, 0] for i in I)\\n elif isinstance(spatially_extensive_attr[I[0]], collections.Iterable):\\n for el in range(len(spatially_extensive_attr[I[0]])):\\n for k in K:\\n lhs = lpSum(x[i, k, o] * spatially_extensive_attr[i][el]\\n for i in I for o in O)\\n if isinstance(threshold, numbers.Real):\\n rhs = threshold * lpSum(x[i, k, 0] for i in I)\\n prob += lhs >= rhs\\n elif isinstance(threshold, np.ndarray):\\n rhs = threshold[el] * lpSum(x[i, k, 0] for i in I)\\n prob += lhs >= rhs\\n # (6) in Duque et al. (2012): \\\"The Max-p-Regions Problem\\\"\\n for i, j in II_upper_triangle:\\n for k in K:\\n prob += t[i, j] >= \\\\\\n lpSum(x[i, k, o] + x[j, k, o] for o in O) - 1\\n # (7) in Duque et al. (2012): \\\"The Max-p-Regions Problem\\\"\\n # already in LpVariable-definition\\n # (8) in Duque et al. (2012): \\\"The Max-p-Regions Problem\\\"\\n # already in LpVariable-definition\\n\\n # additional constraint for speedup (p. 405 in [DAR2012]_)\\n for o in O:\\n prob += x[I[0], K[0], o] == (1 if o == 0 else 0)\\n\\n # Solve the optimization problem\\n solver = get_solver_instance(solver)\\n prob.solve(solver=solver)\\n result = np.zeros(n_areas)\\n for i in I:\\n for k in K:\\n for o in O:\\n if x[i, k, o].varValue == 1:\\n result[i] = k\\n self.labels_ = result\\n self.solver = solver\",\n \"def putacol(self,j_,subj,valj): # 3\\n nzj_ = None\\n if nzj_ is None:\\n nzj_ = len(subj)\\n elif nzj_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nzj_ is None:\\n nzj_ = len(valj)\\n elif nzj_ != len(valj):\\n raise IndexError(\\\"Inconsistent length of array valj\\\")\\n if nzj_ is None: nzj_ = 0\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valj is None: raise TypeError(\\\"Invalid type for argument valj\\\")\\n if valj is None:\\n valj_ = None\\n else:\\n try:\\n valj_ = memoryview(valj)\\n except TypeError:\\n try:\\n _tmparr_valj = array.array(\\\"d\\\",valj)\\n except TypeError:\\n raise TypeError(\\\"Argument valj has wrong type\\\")\\n else:\\n valj_ = memoryview(_tmparr_valj)\\n \\n else:\\n if valj_.format != \\\"d\\\":\\n valj_ = memoryview(array.array(\\\"d\\\",valj))\\n \\n res = self.__obj.putacol(j_,nzj_,subj_,valj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _buildMatrix(self, SparseMatrix, Ncells, MaxFaces, coeff):\\n return (0, 0)\",\n \"def apply_stiffness_assembling(fe_space, param, stiffness):\\n if stiffness.format is 'lil':\\n for ie in range(fe_space.get_nelem()):\\n local_stiffness = fe_space.apply_dbasis_diag_dbasis(fe_space.eval_at_quadrature_pnts(\\n lambda k, s:\\n fe_space.get_quadrature_weight(k) * param(fe_space.get_coord(ie, s)) / fe_space.get_elem_length(ie)))\\n locals_to_globals = fe_space.locals_to_globals(ie)\\n stiffness[np.array(locals_to_globals)[:, None], locals_to_globals] += local_stiffness\\n else:\\n raise ValueError(\\\"Assembling procedure are expecting lil format sparse matrices.\\\")\",\n \"def create_sparse_matrix(self, filename, matrix_length, density):\\n pos = 0\\n aux_pos = 0\\n matrix = []\\n pointerB = []\\n pointerE = []\\n columns = []\\n values = []\\n\\n for i in range(0, matrix_length):\\n row = []\\n pointerB.append(pos)\\n aux_pos = pos\\n for j in range(0, matrix_length):\\n probability = random.random()\\n if probability < density:\\n pos += 1\\n val = random.randint(1, 10)\\n values.append(val)\\n columns.append(j)\\n else:\\n val = 0\\n row.append(val)\\n matrix.append(row)\\n pointerE.append(pos)\\n vector_b = SparseMatrix.gen_vector(matrix_length)\\n matrix_A = np.matrix(matrix)\\n vector_res = np.dot(matrix_A, vector_b).reshape(matrix_length, 1)\\n data = {\\\"values\\\": values, \\\"columns\\\": columns, \\\"pointerB\\\": pointerB, \\\"pointerE\\\": pointerE}\\n CSR_A = json.dumps(data)\\n '''\\n print(\\\"x: \\\", vector_x)\\n print(\\\"A: \\\", matrix_A)\\n print(\\\"b: \\\", vector_b)\\n data = {\\\"values\\\": values, \\\"columns\\\": columns, \\\"pointerB\\\": pointerB, \\\"pointerE\\\": pointerE}\\n data_json = json.dumps(data)\\n file = open(filename, 'w')\\n file.write(data_json)\\n file.close()\\n np.savetxt(\\\"vector.txt\\\", vector_x, fmt=\\\"%1.9f\\\", delimiter=\\\" \\\")\\n '''\\n return matrix_A, CSR_A, vector_b, vector_res\",\n \"def expand_csr_adj(adj, count:int):\\n r,c = adj.shape\\n \\n adj = sp.vstack(\\n [adj, sp.csr_matrix(np.zeros((count, c)))])\\n adj = sp.hstack(\\n [adj, sp.csr_matrix(np.zeros((r + count, count)))])\\n \\n return adj\",\n \"def test_import_sparse_type_mat():\\n x = sps.csr_matrix(np.random.rand(3, 2))\\n export_data('/tmp/test.mat', x)\\n assert x.dtype == import_data('/tmp/test.mat').dtype\",\n \"def putacol(self,j_,subj_,valj_):\\n nzj_ = None\\n if nzj_ is None:\\n nzj_ = len(subj_)\\n elif nzj_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nzj_ is None:\\n nzj_ = len(valj_)\\n elif nzj_ != len(valj_):\\n raise IndexError(\\\"Inconsistent length of array valj\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valj_ is None:\\n raise ValueError(\\\"Argument valj cannot be None\\\")\\n if valj_ is None:\\n raise ValueError(\\\"Argument valj may not be None\\\")\\n if isinstance(valj_, numpy.ndarray) and valj_.dtype is numpy.dtype(numpy.float64) and valj_.flags.contiguous:\\n _valj_copyarray = False\\n _valj_tmp = ctypes.cast(valj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valj_ is not None:\\n _valj_copyarray = True\\n _valj_np_tmp = numpy.zeros(len(valj_),numpy.dtype(numpy.float64))\\n _valj_np_tmp[:] = valj_\\n assert _valj_np_tmp.flags.contiguous\\n _valj_tmp = ctypes.cast(_valj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valj_copyarray = False\\n _valj_tmp = None\\n \\n res = __library__.MSK_XX_putacol(self.__nativep,j_,nzj_,_subj_tmp,_valj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def to_sparse(self):\\n if self.rep.fmt == 'sparse':\\n return self\\n\\n return self.from_rep(self.rep.to_sdm())\",\n \"def calc_sparsity (data): \\n matrix_size = data.shape[0]*data.shape[1] # Number of possible interactions in the matrix\\n num_purchases = len(data.nonzero()[0]) # Number of items interacted with\\n sparsity = 100*(1 - (num_purchases/matrix_size))\\n print('{:.2f} % of the user interaction matrix is sparse'.format(sparsity,2))\",\n \"def __init__(self, size):\\n _PysparseMatrixFromShape.__init__(self, rows=size, cols=size, bandwidth = 1)\\n ids = numerix.arange(size)\\n self.put(numerix.ones(size, 'd'), ids, ids)\",\n \"def calc_sparse(self, calc_aw=False, vector_calc=True):\\n # Local copies of instance attributes for faster access\\n b = self.b\\n qs = self.q_sim\\n qo = self.q_obs\\n maxlead = self.max_lead\\n maxlag = self.max_lag\\n measure = self.measure\\n time = self.time\\n\\n # Prepare sparse matrices with only cells around diagonal defined.\\n # padds 1 extra cell on each side to avoid false zeros later\\n n = len(self.q_obs)\\n w = self.max_lag + self.max_lead + 3\\n data = np.array([np.full(n, np.inf)]).repeat(w, axis=0)\\n offsets = np.arange(-self.max_lead-1, self.max_lag + 1.5)\\n\\n # Calculate work between obs and sim pairs, not needed anymore.\\n if calc_aw:\\n aw = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\\n st = pd.Timestamp.now()\\n # print('Start aw loops')\\n # iterate through all flow observations\\n for i_o in range(len(qo)):\\n # Check only simulations within the allowed window\\n for i_s in range(max(0, i_o - self.max_lead),\\n min(len(qs), i_o + maxlag + 1)):\\n if measure in ('nse', 'square'):\\n aw[i_s, i_o] = (qs[i_s] - qo[i_o]) ** 2 + b ** 2 * (i_s - i_o) ** 2\\n elif measure in ('mae', 'abs'):\\n aw[i_s, i_o] = np.abs(qs[i_s] - qo[i_o]) + b * np.abs(i_s - i_o)\\n # print('End aw loops', (pd.Timestamp.now()-st)/pd.Timedelta('1s'))\\n self.aw = aw\\n\\n # Old debugging code: run original implementation alongside\\n # Applies to all variables appended with _orig\\n # aw_orig = np.full(shape=(len(self.q_obs), len(self.q_sim)), fill_value=np.nan)\\n # # iterate through all flow observations\\n # for i_o in range(len(self.q_obs)):\\n # # Check only simulations within the allowed window\\n # for i_s in range(max(0, i_o - self.max_lead),\\n # min(len(self.q_sim), i_o + self.max_lag + 1)):\\n # aw_orig[i_s, i_o] = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + self.b ** 2 * (i_s - i_o) ** 2\\n\\n # Calculate cumulative work along possible paths\\n cw0 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\\n cw1 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\\n\\n # Old debugging code: run original implementation (with dense arrays) alongside\\n # Applies to all variables marked _orig\\n # # Calculate cumulative work along possible paths\\n # cw_orig = np.ones(shape=aw_orig.shape + (2,)) * np.nan\\n # # Populate first column\\n # cw_orig[:, 0, 0] = aw_orig[:, 0]\\n\\n if self.keep_internals:\\n # self.aw_orig = aw_orig\\n self.cw0 = cw0\\n self.cw1 = cw1\\n # self.cw_orig = cw_orig\\n\\n # Populate other columns\\n for i_o in range(n):\\n iss = max(0, i_o - maxlead) # sim index start\\n ise = min(n, i_o + maxlag + 1) # sim index end\\n isv = np.arange(iss, ise) # sim index as vector\\n if time is not None:\\n dt = time[isv] - time[i_o]\\n # Heavily penalize sim-obs combinations outside allowed window\\n # so that they are not selected\\n dt[(dt < -maxlag) | (dt > maxlead)] = np.inf\\n else:\\n dt = isv - i_o\\n if vector_calc:\\n # Vectorized version of the calculation.\\n # For understanding it is best to take a look at the else clause below\\n # Calculate the work for each (sim, obs_i) pair\\n if measure in ('nse', 'square'):\\n e = (qs[iss:ise] - qo[i_o]) ** 2 + b ** 2 * dt ** 2\\n elif measure in ('mae', 'abs'):\\n e = np.abs(qs[iss:ise] - qo[i_o]) + b * np.abs(dt)\\n if i_o == 0:\\n # Only populate first column of cw0 and move to i_o = 1\\n cw0[iss:ise, 0] = e.reshape(len(e), 1)\\n continue\\n # Repeat the same simulation point\\n d = cw0[iss:ise, i_o - 1].toarray()\\n d[d == 0] = np.nan\\n cw1[iss:ise, i_o] = e.reshape(len(e), 1) + d\\n\\n # Find the 'cheapest' available preceding sim point\\n points = np.full((len(isv), 4), np.inf)\\n # Use the previous simulation point\\n st = max(0, iss - 1)\\n end = min(n + 1, ise - 1)\\n l = end - st\\n points[-l:, 0] = cw0[st:end, i_o - 1].toarray().squeeze()\\n points[-l:, 1] = cw1[st:end, i_o - 1].toarray().squeeze()\\n # Skip a simulation point\\n st = max(0, iss - 2)\\n end = min(n + 1, ise - 2)\\n l = end - st\\n points[-l:, 2] = cw0[st:end, i_o - 1].toarray().squeeze()\\n points[-l:, 3] = cw1[st:end, i_o - 1].toarray().squeeze()\\n # points[points == 0] = np.nan\\n cw0[iss:ise, i_o] = (e + np.min(points, axis=1)).reshape(len(e), 1)\\n else:\\n # Old, non-vectorized code. Slower.\\n # Left in place since it is easier to understand than vectorized code above\\n def zero_to_nan(x):\\n if x == 0:\\n return np.nan\\n else:\\n return x\\n\\n for i_s in range(max(0, i_o - self.max_lead),\\n min(n, i_o + self.max_lag + 1)):\\n # print(i_o, i_s, sep='\\\\t')\\n # e = aw[i_s, i_o]\\n if time:\\n dt = time[isv] - time[i_o]\\n # Heavily penalize sim-obs combinations outside allowed window\\n # so that they are not selected\\n dt[dt > maxlead] = 999999999\\n dt[dt < -maxlag] = 999999999\\n else:\\n dt = isv - i_o\\n if measure in ('nse', 'square'):\\n e = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + b ** 2 * dt ** 2\\n elif measure in ('mae', 'abs'):\\n e = np.abs(self.q_sim[i_s] - self.q_obs[i_o]) + np.abs(b * dt)\\n # e_orig = aw_orig[i_s, i_o]\\n # if e != e_orig:\\n # print('ediff', e, e_orig, sep='\\\\t')\\n\\n # Repeat the same simulation point\\n cw1[i_s, i_o] = e + zero_to_nan(cw0[i_s, i_o - 1])\\n # cw_orig[i_s, i_o, 1] = e + cw_orig[i_s, i_o - 1, 0]\\n # if cw1[i_s, i_o] != cw_orig[i_s, i_o, 1]:\\n # print('cw1diff', cw1[i_s, i_o], cw_orig[i_s, i_o, 1], sep='\\\\t')\\n\\n if i_s == 0:\\n continue\\n # Else:\\n # Find the 'cheapest' available preceding point\\n # Use the previous simulation point\\n points = [zero_to_nan(cw0[i_s - 1, i_o - 1]),\\n zero_to_nan(cw1[i_s - 1, i_o - 1])]\\n # m1 = np.nanmin(cw_orig[i_s - 1, i_o - 1, :])\\n # m2 = np.inf\\n if i_s > 1:\\n # Skip a simulation point\\n points += [cw0[i_s - 2, i_o - 1], cw1[i_s - 2, i_o - 1]]\\n # m2 = np.nanmin(cw_orig[i_s - 2, i_o - 1, :])\\n # m = min(m1, m2)\\n try:\\n cp = min([p for p in points if p > 0])\\n except ValueError:\\n cp = np.nan\\n # if cp != m:\\n # print('cpdiff', cp, m, sep='\\\\t')\\n # print('\\\\torig', cw_orig[i_s - 1, i_o - 1, :], cw_orig[i_s - 2, i_o - 1, :])\\n # print('\\\\tspar', points)\\n cw0[i_s, i_o] = e + cp\\n # cw_orig[i_s, i_o, 0] = e + m\\n # print('')\\n # Find the cheapest point in the last column, i.e. end of cheapest path, i.e. optimum score\\n # nz0 = cw0[cw0[:, -1].nonzero()[0], -1].toarray().min()\\n # nz1 = cw1[cw1[:, -1].nonzero()[0], -1].toarray().min()\\n nz0 = cw0[-self.max_lag:, -1].toarray()\\n nz1 = cw1[-self.max_lag:, -1].toarray()\\n self.opt_score = min(nz0[nz0 != 0].min(), nz1[nz1 != 0].min())\\n\\n self.of = 1 - self.opt_score / self.fbench\\n\\n if self.calc_rays:\\n self._do_calc_rays(cw0, cw1)\\n\\n return self.of\",\n \"def _extend_sparse_traj_field(self, run_idx, traj_idx, field_path, values, sparse_idxs):\\n\\n field = self.h5['{}/{}/{}/{}/{}'.format(RUNS, run_idx, TRAJECTORIES, traj_idx, field_path)]\\n\\n field_data = field[DATA]\\n field_sparse_idxs = field[SPARSE_IDXS]\\n\\n # number of new frames\\n n_new_frames = values.shape[0]\\n\\n # if this sparse_field has been initialized empty we need to resize\\n if all([i == 0 for i in field_data.shape]):\\n\\n\\n # check the feature shape against the maxshape which gives\\n # the feature dimensions for an empty dataset\\n assert values.shape[1:] == field_data.maxshape[1:], \\\\\\n \\\"input value features have shape {}, expected {}\\\".format(\\n values.shape[1:], field_data.maxshape[1:])\\n\\n # if it is empty resize it to make an array the size of\\n # the new values with the maxshape for the feature\\n # dimensions\\n feature_dims = field_data.maxshape[1:]\\n field_data.resize( (n_new_frames, *feature_dims) )\\n\\n # set the new data to this\\n field_data[0:, ...] = values\\n\\n else:\\n\\n # make sure the new data has the right dimensions\\n assert values.shape[1:] == field_data.shape[1:], \\\\\\n \\\"field feature dimensions must be the same, i.e. all but the first dimension\\\"\\n\\n # append to the dataset on the first dimension, keeping the\\n # others the same, these must be feature vectors and therefore\\n # must exist\\n field_data.resize( (field_data.shape[0] + n_new_frames, *field_data.shape[1:]) )\\n # add the new data\\n field_data[-n_new_frames:, ...] = values\\n\\n # add the sparse idxs in the same way\\n field_sparse_idxs.resize( (field_sparse_idxs.shape[0] + n_new_frames,\\n *field_sparse_idxs.shape[1:]) )\\n # add the new data\\n field_sparse_idxs[-n_new_frames:, ...] = sparse_idxs\",\n \"def SparseEmbedding(data=None, weight=None, input_dim=_Null, output_dim=_Null, dtype=_Null, out=None, name=None, **kwargs):\\n return (0,)\",\n \"def create_jac_sens(x_sp,sensitivity_sp,diffeq_params, integration_params,\\n SDerivSymbolicJacParamsLambFun,SDerivSymbolicJacConcLambFun):\\n\\n # create state variables\\n allVars = np.concatenate((x_sp,sensitivity_sp))\\n\\n #create RHS\\n dSensSym = sp.Matrix(dSens(0,allVars,diffeq_params, integration_params,\\n SDerivSymbolicJacParamsLambFun, SDerivSymbolicJacConcLambFun))\\n dSensSymJac = dSensSym.jacobian(allVars)\\n\\n # generate jacobian\\n dSensSymJacDenseMatLam = sp.lambdify(allVars,dSensSymJac)\\n dSensSymJacSparseMatLamFun = lambda t,xs: sparse.csr_matrix(dSensSymJacDenseMatLam(*xs))\\n\\n return dSensSymJacSparseMatLamFun\",\n \"def test_import_sparse_values_mat():\\n x = sps.csr_matrix(np.random.rand(3, 2))\\n export_data('/tmp/test.mat', x)\\n assert np.array_equal(x.toarray(), import_data('/tmp/test.mat').toarray())\",\n \"def scipy_sparse_to_spmatrix(A):\\n coo = A.tocoo()\\n SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)\\n return SP\",\n \"def build_scoring_matrix(alphabet,diag_score, off_diag_score, dash_score):\\n scoring_matrix = dict()\\n #add \\\"-\\\" to alphabet\\n alphabet_ = alphabet.union(\\\"-\\\")\\n\\n for letter1 in alphabet_:\\n #for every letter in alphabet build appropriate dictionary of scores\\n col_dict = dict()\\n \\n for letter2 in alphabet_:\\n if letter1 == \\\"-\\\" or letter2 == \\\"-\\\":\\n score = dash_score\\n elif letter1 == letter2:\\n score = diag_score\\n else: \\n score = off_diag_score\\n\\n col_dict[letter2] = score\\n \\n scoring_matrix[letter1] = col_dict\\n\\n return scoring_matrix\",\n \"def _weight_jac_mat_prod(self, module, g_inp, g_out, mat):\\n raise NotImplementedError\",\n \"def create_nonsparse_matrix(self, matrix_df):\\n\\n print('creating nonsparse matrix...')\\n clustering_df_tmp = matrix_df[['date','time','date_idx', 'time_idx', 'day_of_week', 'segment_id', 'road_type', 'lat1', 'lat2', 'lon1', 'lon2', 'level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']]\\n seg_averages = clustering_df_tmp.groupby(['segment_id', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']].mean().reset_index()\\n rt_averages = clustering_df_tmp.groupby(['road_type', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']].mean().reset_index()\\n \\n # write nonsparse_matrix_db to database as 'clustering' table\\n print('writing nonsparse matrix to db...')\\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\\n engine = create_engine(sqlalchemy_conn_str)\\n if self.split_type == 'random':\\n seg_averages.to_sql(name='clust_nonsparse_avebysegment_random', con=engine, if_exists='replace')\\n rt_averages.to_sql(name='clust_nonsparse_avebyrt_random', con=engine, if_exists='replace')\\n elif self.split_type == 'date':\\n seg_averages.to_sql(name='clust_nonsparse_avebysegment_date', con=engine, if_exists='replace')\\n rt_averages.to_sql(name='clust_nonsparse_avebyrt_date', con=engine, if_exists='replace')\\n \\n print('returning train nonsparse matrix...')\\n train_averages = clustering_df_tmp.groupby(['segment_id', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count','level_binary']].mean().reset_index()\\n train_averages.columns = ['segment_id', 'day_of_week', 'time_idx', 'ave_level_min', 'ave_level_max', 'ave_level_mean', 'ave_level_count', 'ave_level_binary']\\n nonsparse_matrix = pd.merge(clustering_df_tmp, train_averages, how='left', on=['segment_id', 'day_of_week', 'time_idx'])\\n segtimes_df = nonsparse_matrix[['segment_id', 'date', 'time']]\\n nonsparse_matrix_final = nonsparse_matrix.drop(columns=['segment_id', 'date','time', 'level_min', 'level_max', 'level_mean', 'level_count', 'level_binary'])\\n \\n return (segtimes_df, nonsparse_matrix_final)\",\n \"def to_amat(self, node_list=None, sparse=False) -> np.ndarray:\\n if not node_list:\\n node_list = sorted(self._nodes)\\n node2ix = {node: i for i, node in enumerate(node_list)}\\n\\n if sparse:\\n raise NotImplementedError\\n # js, ks = [], []\\n # for j, k in self._edges:\\n # js.append(j)\\n # ks.append(k)\\n # js.append(k)\\n # ks.append(j)\\n # return spmatrix(1, js, ks)\\n amat = np.zeros([self.num_nodes, self.num_nodes], dtype=int)\\n\\n for i, j in self._edges:\\n amat[node2ix[i], node2ix[j]] = True\\n amat[node2ix[j], node2ix[i]] = True\\n return amat\",\n \"def r_diag_dot_sparse(mat, diag):\\n return mat @ sp.diags(diag)\",\n \"def makesparse(matrix):\\n n = matrix[0].size\\n elements = []\\n for i in range(n):\\n for j in range(n):\\n if matrix[i][j] != 0 :\\n temp = MatrixElement(i, j, matrix[i][j])\\n elements.append(temp)\\n return SparseMatrix(n, elements)\",\n \"def add_sparse_pwd(a, b, neg1_fmt=True):\\n \\n a_coo = a.tocoo()\\n b_coo = b.tocoo()\\n nr, nc = a_coo.shape\\n \\n ind_a = a_coo.row.astype(np.int64) * nc + a_coo.col\\n ind_b = b_coo.row.astype(np.int64) * nc + b_coo.col\\n # ind_ab = np.array(tuple(set(ind_a).intersection(set(ind_b))))\\n ind_ab = np.intersect1d(ind_a, ind_b, assume_unique=True)\\n keep_a = np.in1d(ind_a, ind_ab, assume_unique=True)\\n keep_b = np.in1d(ind_b, ind_ab, assume_unique=True)\\n if neg1_fmt:\\n a_data = a_coo.data[keep_a]\\n a_data[a_data == -1] = 0\\n b_data = b_coo.data[keep_b]\\n b_data[b_data == -1] = 0\\n tot = a_data + b_data\\n else:\\n tot = a_coo.data[keep_a] + b_coo.data[keep_b]\\n \\\"\\\"\\\"Need to add -1 as placeholder upon creation of new sparse matrix\\\"\\\"\\\"\\n tot[tot == 0] = -1\\n aplusb = sparse.coo_matrix((tot,\\n (a_coo.row[keep_a], a_coo.col[keep_a])), shape=(nr, nc))\\n aplusb = aplusb.tocsr()\\n if not neg1_fmt:\\n \\\"\\\"\\\"Convert it back to 0 = true zero distance\\\"\\\"\\\"\\n aplusb.data[aplusb.data == -1] = 0\\n return aplusb\",\n \"def stiffnessMatrix (bsp, knotlist,p, nbquad):\\n\\n\\tS = np.zeros((bsp.N-2, bsp.N-2))\\n\\t\\n\\tfor line in range(1, bsp.N-1):\\n\\t\\t\\n\\t\\tfor column in range(1, bsp.N-1):\\n\\t\\t\\n\\t\\t\\tfor iknot in range(len(knotlist)-1):\\n\\t\\t\\t\\tS[line-1, column-1] = S[line-1, column-1] + legendreGauss(bilinearForm, nbquad, knotlist[iknot], knotlist[iknot+1], line, bsp, ind2=column)\\n\\t\\n\\treturn S;\",\n \"def test_csm_sparser(self):\\r\\n sp_types = {'csc': sp.csc_matrix,\\r\\n 'csr': sp.csr_matrix}\\r\\n\\r\\n for format in ['csc', 'csr']:\\r\\n for dtype in ['float32', 'float64']:\\r\\n x = tensor.tensor(dtype=dtype, broadcastable=(False,))\\r\\n y = tensor.ivector()\\r\\n z = tensor.ivector()\\r\\n s = tensor.ivector()\\r\\n\\r\\n a = as_sparse_variable(sp_types[format](random_lil((4, 3),\\r\\n dtype, 1)))\\r\\n\\r\\n f = theano.function([x, y, z, s],\\r\\n tensor.grad(dense_from_sparse(\\r\\n a * CSM(format)(x, y, z, s)).sum(), x))\\r\\n\\r\\n spmat = sp_types[format](random_lil((4, 3), dtype, 3))\\r\\n\\r\\n res = f(spmat.data, spmat.indices, spmat.indptr,\\r\\n numpy.asarray(spmat.shape, 'int32'))\\r\\n\\r\\n assert len(spmat.data) == len(res)\",\n \"def build_scoring_matrix(alphabet, diag_score, off_diag_score, dash_score):\\n scoring_matrix = {}\\n\\n alphabet = list(alphabet) + [\\\"-\\\"]\\n\\n for letter1 in alphabet:\\n scoring_matrix[letter1] = {}\\n for letter2 in alphabet:\\n if letter1 == \\\"-\\\" or letter2 == \\\"-\\\":\\n scoring_matrix[letter1][letter2] = dash_score\\n elif letter1 == letter2:\\n scoring_matrix[letter1][letter2] = diag_score\\n else:\\n scoring_matrix[letter1][letter2] = off_diag_score\\n\\n #for key,val in scoring_matrix.items():\\n # print key,val\\n\\n return scoring_matrix\",\n \"def create_attach_sparsifier(model, **sparse_config):\\n data_norm_sparsifier = DataNormSparsifier(**sparse_config)\\n for name, parameter in model.named_parameters():\\n if 'emb_l' in name:\\n valid_name = get_valid_name(name)\\n data_norm_sparsifier.add_data(name=valid_name, data=parameter)\\n return data_norm_sparsifier\",\n \"def create_sparseDB():\\n datas = data.Kmercount_to_matrix()\\n datas.run()\\n print('***Sparse matrix created***')\",\n \"def test_bind_init(self, sp_in):\\n\\n bind = DatasetSparseArray(sp_in).bind\\n\\n dset_sp = DatasetSparseArray(bind=bind)\\n\\n assert dset_sp.info[\\\"type_id\\\"] == \\\"sparse_array\\\"\\n assert dset_sp.info[\\\"py_type\\\"] == f\\\"scipy.sparse.{type(sp_in).__qualname__}\\\"\\n assert dset_sp.sparse_array_class is type(sp_in)\\n\\n sp_out = dset_sp.get_value()\\n assert isinstance(sp_out, type(sp_in))\\n\\n assert (sp_in.todense() == sp_out.todense()).all()\",\n \"def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(alphaptrb_)\\n elif num_ != len(alphaptrb_):\\n raise IndexError(\\\"Inconsistent length of array alphaptrb\\\")\\n if num_ is None:\\n num_ = len(alphaptre_)\\n elif num_ != len(alphaptre_):\\n raise IndexError(\\\"Inconsistent length of array alphaptre\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if alphaptrb_ is None:\\n raise ValueError(\\\"Argument alphaptrb cannot be None\\\")\\n if alphaptrb_ is None:\\n raise ValueError(\\\"Argument alphaptrb may not be None\\\")\\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\\n _alphaptrb_copyarray = False\\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif alphaptrb_ is not None:\\n _alphaptrb_copyarray = True\\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\\n _alphaptrb_np_tmp[:] = alphaptrb_\\n assert _alphaptrb_np_tmp.flags.contiguous\\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _alphaptrb_copyarray = False\\n _alphaptrb_tmp = None\\n \\n if alphaptre_ is None:\\n raise ValueError(\\\"Argument alphaptre cannot be None\\\")\\n if alphaptre_ is None:\\n raise ValueError(\\\"Argument alphaptre may not be None\\\")\\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\\n _alphaptre_copyarray = False\\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif alphaptre_ is not None:\\n _alphaptre_copyarray = True\\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\\n _alphaptre_np_tmp[:] = alphaptre_\\n assert _alphaptre_np_tmp.flags.contiguous\\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _alphaptre_copyarray = False\\n _alphaptre_tmp = None\\n \\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx cannot be None\\\")\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx may not be None\\\")\\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\\n _matidx_copyarray = False\\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif matidx_ is not None:\\n _matidx_copyarray = True\\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\\n _matidx_np_tmp[:] = matidx_\\n assert _matidx_np_tmp.flags.contiguous\\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _matidx_copyarray = False\\n _matidx_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def SSC_test():\\n \\n # generating data\\n\\n D = 40 # Dimension of ambient space\\n k = 4 # Number of subspaces\\n d = 1\\n N = 90 # Number of points in subspaces\\n # Generating N points in d dim. subspaces\\n Xi = [np.random.randn(D, d).dot(np.random.randn(d, N))\\n for _ in range(k)]\\n X = np.concatenate(Xi, axis=1)\\n\\n # Generating the ground-truth for evaluating clustering results\\n lbls = [i * np.ones([1, N]) for i in range(k)]\\n s = np.concatenate(lbls, axis=1)\\n r = 0 # Enter the projection dimension e.g. r = d*n, enter r = 0 to not project\\n Cst = 0 # Enter 1 to use the additional affine constraint sum(c) == 1\\n OptM = 'L1Noise' # OptM can be {'L1Perfect','L1Noise','Lasso','L1ED'}\\n lmbda = 0.001 # Regularization parameter in 'Lasso' or the noise level for 'L1Noise'\\n # Number of top coefficients to build the similarity graph, enter K=0 for using the whole coefficients\\n K = 0 #max(d1, d2)\\n if Cst == 1:\\n K = d + 1 # For affine subspaces, the number of coefficients to pick is dimension + 1\\n\\n Xp = DataProjection(X, r, 'NormalProj')\\n \\n # testing clustering\\n \\n '''\\n # using wrapper function\\n Grps, sc = sparseSubspaceClustering(\\n Xp.T, n, ground_truth=s, affine=Cst, OptM=OptM, lam=lmbda)\\n Grps = BestMap(sc, Grps)\\n Missrate = float(np.sum(sc != Grps)) / sc.size\\n print(\\\"\\\\n\\\\nMisclassification rate: {:.4f} %\\\\n\\\\n\\\".format(Missrate * 100))\\n '''\\n \\n '''\\n # calling internal tools\\n CMat = SparseCoefRecovery(Xp, Cst, OptM, lmbda)\\n # Make small values 0\\n eps = np.finfo(float).eps\\n CMat[np.abs(CMat) < eps] = 0\\n\\n CMatC, sc, OutlierIndx, Fail = OutlierDetection(CMat, s)\\n\\n if Fail == False:\\n CKSym = BuildAdjacency(CMatC, K)\\n Grps = SpectralClustering(CKSym, n)\\n Grps = BestMap(sc, Grps)\\n Missrate = float(np.sum(sc != Grps)) / sc.size\\n print(\\\"\\\\n\\\\nMisclassification rate: {:.4f} %\\\\n\\\\n\\\".format(Missrate * 100))\\n else:\\n print(\\\"Something failed\\\")\\n '''\\n \\n labels, _, __ = KSubspaces(Xp.T, k, d)\\n labels = BestMap(s, labels)\\n Missrate = float(np.sum(s != labels)) / s.size\\n print(\\\"\\\\n\\\\nMisclassification rate: {:.4f} %\\\\n\\\\n\\\".format(Missrate * 100))\",\n \"def convert_sparse_network(weights: Dict[str, np.ndarray],\\n metadata: Dict[str, Any],\\n hypers: Dict[str, Any],\\n is_msp: bool):\\n # List to hold all variables\\n components: List[str] = []\\n\\n # Start the input units at the input shape\\n input_size = metadata[INPUT_SHAPE][-1]\\n\\n for i, output_size in enumerate(hypers['hidden_units']):\\n\\n # Convert the sparse variable\\n layer_name = 'hidden_{0}'.format(i)\\n\\n sparse_layer = convert_sparse_layer(layer_name=layer_name,\\n weights=weights,\\n input_size=input_size,\\n output_size=output_size,\\n coordinates=metadata['sparse_indices'][layer_name],\\n is_msp=is_msp)\\n\\n components.append(sparse_layer)\\n\\n # Reset the input size as we progress\\n input_size = output_size\\n\\n # Include the output layer\\n output_kernel_name = 'output/kernel:0'\\n output_kernel = convert_matrix(name=output_kernel_name,\\n mat=weights[output_kernel_name],\\n precision=PRECISION,\\n width=WIDTH,\\n is_msp=is_msp)\\n components.append(output_kernel)\\n\\n output_bias_name = 'output/bias:0'\\n output_bias = convert_matrix(name=output_bias_name,\\n mat=weights[output_bias_name],\\n precision=PRECISION,\\n width=WIDTH,\\n is_msp=is_msp)\\n components.append(output_bias)\\n\\n return '\\\\n'.join(components)\",\n \"def Problem2(n):\\n diag_entries = np.empty((3,n))\\n diag_entries[0] = np.ones(n)*(-1)\\n diag_entries[1] = np.ones(n)*2\\n diag_entries[2] = np.ones(n)*(-1)\\n A = sparse.spdiags(diag_entries, [-1,0,1],n,n,format=\\\"csr\\\")\\n return A\",\n \"def set_sparse_backend(sparse_backend, warn=True):\\n if 'biom.table' in modules:\\n if warn:\\n print (\\\"Warning: biom.table has already been loaded. This call to \\\"\\n \\\"biom.set_sparse_backend() has no effect. It must be \\\"\\n \\\"called before biom.table is imported for the first time.\\\")\\n else:\\n biom_config['python_code_sparse_backend'] = sparse_backend\",\n \"def __init__(self, rows, cols, bandwidth=0, sizeHint=None, matrix=None, storeZeros=True):\\n sizeHint = sizeHint or max(rows, cols) * bandwidth\\n if matrix is None:\\n tmpMatrix = spmatrix.ll_mat(1, 1, 1)\\n if hasattr(tmpMatrix, 'storeZeros'):\\n matrix = spmatrix.ll_mat(rows, cols, sizeHint, storeZeros)\\n else:\\n matrix = spmatrix.ll_mat(rows, cols, sizeHint)\\n\\n _PysparseMatrix.__init__(self, matrix=matrix)\",\n \"def __init_matrix(self, adjacencyMatrix, **optional):\\n\\t\\t# Error handling\\n if adjacencyMatrix.ndim!=2 or adjacencyMatrix.shape[0]!=adjacencyMatrix.shape[1]:\\n raise TypeError(\\\"\\\\'adjacencyMatrix\\\\' has to be a square matrix.\\\")\\n elif np.any(adjacencyMatrix<0):\\n raise ValueError(\\\"The weights have to be positive.\\\")\\n elif not (str(adjacencyMatrix.dtype).startswith(\\\"float\\\") or str(adjacencyMatrix.dtype).startswith(\\\"int\\\")):\\n raise TypeError(\\\"Weights have to be of the type \\\\'float*\\\\' or \\\\'int*\\\\'.\\\")\\n elif np.any(np.diagonal(adjacencyMatrix)):\\n raise ValueError(\\\"The graph is not allowed to contain circles of length 1.\\\")\\n\\t\\t# Save shape and type\\n self.__size = adjacencyMatrix.shape[0]\\n self.__type = adjacencyMatrix.dtype\\n if \\\"vertexNames\\\" in optional:\\n # List of names\\n self.__name_list = optional[\\\"vertexNames\\\"]\\n if len(self.__name_list)!=self.__size:\\n raise ValueError(\\\"There have not been specified as many names in \\\\'vertexNames\\\\' as there are nodes.\\\")\\n else:\\n if len(set(self.__name_list)) != len(self.__name_list):\\n raise ValueError(\\\"Vertex names are not unique.\\\")\\n else:\\n # Hashmap name->index\\n self.__names = {self.__name_list[i]:i for i in range(0,self.__size)}\\n else:\\n warn(Warning(\\\"If node names in adjacency matrix representation are not explicitly set with the parameter \\\\'vertexNames\\\\', partial and supergraphs cannot be recognized reliably.\\\"))\\n self.__name_list = [i for i in range(0,self.__size)]\\n self.__names = {self.__name_list[i]:i for i in range(0,self.__size)}\\n \\n # Adjacency matrix\\n self.__mat = adjacencyMatrix\\n # Adjacency matrix\\n self.__adj = [[(i,self.__mat[vertices][i]) for i in np.arange(self.__size) if self.__mat[vertices][i]>0] for vertices in np.arange(0,self.__size)]\\n \\n # List of known supergraphs\\n self.__supergraph = []\",\n \"def create_sparse_matrix_long(self, matrix_df):\\n\\n time_df_tmp = matrix_df.groupby(['date_idx','time_idx']).count().reset_index()\\n time_df = time_df_tmp[['date_idx','time_idx']]\\n time_df['time_id'] = time_df.index.copy()\\n matrix_df_tid = pd.merge(matrix_df, time_df, how='left', on=['date_idx','time_idx'])\\n\\n time_array = np.array(matrix_df_tid['time_id'])\\n segment_array = np.array(matrix_df_tid['segment_id'])\\n\\n uniquesegments = np.array(list(set(segment_array)))\\n keyuniquesegments = np.array(range(len(uniquesegments)))\\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\\n\\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\\n segmentkeys = np.array(segments_keys_df['segmentskey'])\\n\\n level_array = np.array(matrix_df_tid[self.args['cluster_variable']])\\n sparse_matrix = csr_matrix((level_array, (segmentkeys,time_array))).toarray()\\n sparse_matrix = preprocessing.scale(sparse_matrix)\\n \\n if self.args['perform_pca']:\\n data_to_cluster, self.pca_model = self.run_PCA_long(sparse_matrix)\\n else:\\n data_to_cluster = sparse_matrix\\n\\n sparse_matrix_withsegkey = pd.DataFrame(data_to_cluster)\\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\\n \\n print('writing sparse matrix_long to db...')\\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\\n engine = create_engine(sqlalchemy_conn_str)\\n if self.split_type == 'random':\\n sparse_matrix_withseg.to_sql(name='clust_sparse_long_avebysegment_random', con=engine, if_exists='replace')\\n elif self.split_type == 'date':\\n sparse_matrix_withseg.to_sql(name='clust_sparse_long_avebysegment_date', con=engine, if_exists='replace')\\n \\n print('returning train sparse_long matrix...')\\n return (uniquesegments_df, data_to_cluster)\",\n \"def __add__(self, other):\\n\\n if other == 0:\\n return self\\n else:\\n L = self.matrix.copy()\\n L.shift(1, other.matrix)\\n return _PysparseMatrix(matrix=L)\",\n \"def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def test_assign_clusters_sparse(self, new_data, filename):\\n\\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\\n engine = create_engine(sqlalchemy_conn_str)\\n \\n print('creating test sparse matrix...')\\n if self.split_type == 'random':\\n averages_seg = pd.read_sql('SELECT * FROM clust_sparse_avebysegment_random',con=engine)\\n averages_rt = pd.read_sql('SELECT * FROM clust_sparse_avebyrt_random',con=engine)\\n if self.split_type == 'date':\\n averages_seg = pd.read_sql('SELECT * FROM clust_sparse_avebysegment_date',con=engine)\\n averages_rt = pd.read_sql('SELECT * FROM clust_sparse_avebyrt_date',con=engine)\\n\\n averages_seg['exists'] = 1\\n test_data_exists = pd.merge(new_data, averages_seg[['segment_id', 'exists']], on=['segment_id'])\\n test_exists = test_data_exists[test_data_exists['exists']==1]\\n test_notexists = test_data_exists[test_data_exists['exists']!=1] \\n \\n test_matrix_exists = pd.merge(test_exists[['segment_id', 'road_type']], averages_seg, how='left', on=['segment_id'])\\n test_matrix_notexists = pd.merge(test_notexists[['segment_id', 'road_type']], averages_rt, how='left', on=['road_type'])\\n test_matrix = pd.concat([test_matrix_exists, test_matrix_notexists])\\n test_matrix = test_matrix.fillna(0) \\n \\n test_sparse_matrix = test_matrix.drop(columns = ['segment_id', 'road_type', 'exists', 'index', 'roadtypekey', 'segmentskey'])\\n num = list(range(len(list(averages_seg))-4))\\n columns = [str(item) for item in num]\\n test_sparse_matrix = test_sparse_matrix[columns] \\n \\n print('clustering new data...')\\n cluster_model = joblib.load(filename)\\n cluster_predictions = cluster_model.predict(test_sparse_matrix)\\n \\n clusterdf = pd.DataFrame(cluster_predictions,columns = ['cluster_sparse'])\\n clusterdf['index'] = clusterdf.index\\n segmentdf = test_matrix['segment_id'].to_frame()\\n segmentdf['index'] = segmentdf.index\\n test_cluster_df_sparse = pd.merge(clusterdf, segmentdf, on=['index'])\\n test_cluster_df_sparse = test_cluster_df_sparse[['segment_id','cluster_sparse']].groupby(['segment_id','cluster_sparse']).count()\\n \\n return test_cluster_df_sparse.reset_index()\",\n \"def _sparse_projection(self,mat,sparsity=1,random_seed=10):\\n [n,_] = mat.shape\\n sketch = np.zeros((self.rp_dim ,n),dtype=float)\\n for i in range(n):\\n nnz_loc = np.random.choice(self.rp_dim ,size=sparsity,replace=False)\\n nnz_sign = np.random.choice([-1,1],size=sparsity,replace=True)\\n sketch[nnz_loc,i] = nnz_sign\\n self.sketch_mat = sketch\\n return (1./np.sqrt(sparsity))*sketch@mat\",\n \"def symmeterize(self):\\n A = self.to_coo_matrix()\\n symg = wgraph_from_adjacency((A + A.T) / 2)\\n self.E = symg.E\\n self.edges = symg.edges\\n self.weights = symg.weights\\n return self\",\n \"def qubit_operator_sparse(qubit_operator, n_qubits=None):\\n if n_qubits is None:\\n n_qubits = count_qubits(qubit_operator)\\n if n_qubits < count_qubits(qubit_operator):\\n raise ValueError('Invalid number of qubits specified.')\\n\\n # Construct the Scipy sparse matrix.\\n n_hilbert = 2**n_qubits\\n values_list = [[]]\\n row_list = [[]]\\n column_list = [[]]\\n\\n # Loop through the terms.\\n for qubit_term in qubit_operator.terms:\\n tensor_factor = 0\\n coefficient = qubit_operator.terms[qubit_term]\\n sparse_operators = [coefficient]\\n for pauli_operator in qubit_term:\\n\\n # Grow space for missing identity operators.\\n if pauli_operator[0] > tensor_factor:\\n identity_qubits = pauli_operator[0] - tensor_factor\\n identity = scipy.sparse.identity(2**identity_qubits,\\n dtype=complex,\\n format='csc')\\n sparse_operators += [identity]\\n\\n # Add actual operator to the list.\\n sparse_operators += [pauli_matrix_map[pauli_operator[1]]]\\n tensor_factor = pauli_operator[0] + 1\\n\\n # Grow space at end of string unless operator acted on final qubit.\\n if tensor_factor < n_qubits or not qubit_term:\\n identity_qubits = n_qubits - tensor_factor\\n identity = scipy.sparse.identity(2**identity_qubits,\\n dtype=complex,\\n format='csc')\\n sparse_operators += [identity]\\n\\n # Extract triplets from sparse_term.\\n sparse_matrix = kronecker_operators(sparse_operators)\\n values_list.append(sparse_matrix.tocoo(copy=False).data)\\n (column, row) = sparse_matrix.nonzero()\\n column_list.append(column)\\n row_list.append(row)\\n\\n # Create sparse operator.\\n values_list = numpy.concatenate(values_list)\\n row_list = numpy.concatenate(row_list)\\n column_list = numpy.concatenate(column_list)\\n sparse_operator = scipy.sparse.coo_matrix(\\n (values_list, (row_list, column_list)),\\n shape=(n_hilbert, n_hilbert)).tocsc(copy=False)\\n sparse_operator.eliminate_zeros()\\n return sparse_operator\",\n \"def to_sparse_matrix(self, grid, format=None):\\n S = self.centered_stencil()\\n # print(\\\"grid :\\\")\\n\\n grid = tuple(grid)\\n # print(grid)\\n if not (np.asarray(S.shape) % 2 == 1).all():\\n raise ValueError('all stencil dimensions must be odd')\\n\\n assert_condition(len(grid) == np.rank(S), ValueError,\\n 'stencil rank must equal number of grid dimensions')\\n assert_condition(min(grid) >= 1, ValueError,\\n 'grid dimensions must be positive')\\n\\n N_v = np.prod(grid) # number of vertices in the mesh\\n N_s = (S != 0).sum() # number of nonzero stencil entries\\n\\n # diagonal offsets\\n diags = np.zeros(N_s, dtype=int)\\n\\n # compute index offset of each dof within the stencil\\n strides = np.cumprod([1] + list(reversed(grid)))[:-1]\\n indices = tuple(i.copy() for i in S.nonzero())\\n for i,s in zip(indices,S.shape):\\n i -= s // 2\\n for stride,coords in zip(strides, reversed(indices)):\\n diags += stride * coords\\n\\n #\\n data = S[S != 0].repeat(N_v).reshape(N_s, N_v)\\n indices = np.vstack(indices).T\\n\\n # zero boundary connections\\n for index,diag in zip(indices,data):\\n diag = diag.reshape(grid)\\n for n,i in enumerate(index):\\n if i > 0:\\n s = [ slice(None) ]*len(grid)\\n s[n] = slice(0,i)\\n diag[s] = 0\\n elif i < 0:\\n s = [ slice(None) ]*len(grid)\\n s[n] = slice(i,None)\\n diag[s] = 0\\n\\n # remove diagonals that lie outside matrix\\n mask = abs(diags) < N_v\\n if not mask.all():\\n diags = diags[mask]\\n data = data[mask]\\n\\n # sum duplicate diagonals\\n if len(np.unique(diags)) != len(diags):\\n new_diags = np.unique(diags)\\n new_data = np.zeros( (len(new_diags),data.shape[1]), dtype=data.dtype)\\n for dia,dat in zip(diags,data):\\n n = np.searchsorted(new_diags,dia)\\n new_data[n,:] += dat\\n\\n diags = new_diags\\n data = new_data\\n\\n return sprs.dia_matrix((data,diags), shape=(N_v, N_v)).asformat(format)\",\n \"def close_signal_from_subjects_pad(self, geom):\\n self.subjectspad_geometry_memory = geom\",\n \"def build_mat(self):\\n for row, s in enumerate(self.S):\\n for col, t in enumerate(self.T):\\n\\n if self.symmetric and row > col:\\n pass\\n\\n else:\\n self.mat[row, col] = self.kernel(s, t, self.n)\\n\\n if self.symmetric:\\n self.mat = self.symmetrize(self.mat)\\n else:\\n for idx, s in enumerate(self.S):\\n self.test_normalization[idx] = self.kernel(s, s, self.n)\",\n \"def test_import_type_sparse():\\n x = sps.csr_matrix(np.random.rand(7, 11))\\n export_data('/tmp/test.sparse', x)\\n assert x.dtype == import_data('/tmp/test.sparse').dtype\",\n \"def test_00_create_sparse_1d_array(self):\\n ncells = 100\\n sparsity = 3.0 # 1 / density\\n _, err = _iquery(\\\"create array SPARSE <v:int64>[i=0:{0}:0:5]\\\".format(\\n ncells - 1))\\n assert not err, err\\n self._array_cleanups.append('SPARSE')\\n _, err = _iquery(\\\"\\\"\\\"\\n insert(\\n redimension(\\n apply(\\n build(<i:int64>[fud=0:{0}], {1}*fud),\\n (v, 1)),\\n SPARSE),\\n SPARSE)\\\"\\\"\\\".format(int(ncells / sparsity) - 1,\\n int(sparsity)))\\n assert not err, err\\n check_v_sum('SPARSE')\\n nchunks = chunk_count(vaid_of('SPARSE'))\\n prt(\\\"SPARSE has\\\", nchunks, \\\"chunks\\\")\",\n \"def test_sparsity(self):\\n\\n self.validator.adata.X = self.validator.adata.X.toarray()\\n self.validator.validate_adata()\\n self.assertEqual(\\n self.validator.warnings,\\n [\\n \\\"WARNING: Sparsity of 'X' is 0.875 which is greater than 0.5, \\\"\\n \\\"and it is not a 'scipy.sparse.csr_matrix'. It is \\\"\\n \\\"STRONGLY RECOMMENDED to use this type of matrix for \\\"\\n \\\"the given sparsity.\\\"\\n ],\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7391125","0.6855938","0.64830375","0.6458727","0.54127806","0.5294181","0.51218337","0.5078287","0.5072397","0.5031775","0.5025988","0.50101835","0.50026613","0.49249858","0.49118322","0.48946965","0.4886756","0.4860377","0.48591778","0.4854623","0.4850984","0.48319915","0.4809785","0.48010013","0.47763708","0.47703344","0.47443148","0.47364503","0.47254178","0.47132152","0.46455756","0.46218073","0.4613709","0.4606712","0.46037117","0.45994765","0.45783442","0.45644858","0.45517448","0.454974","0.45476148","0.45373037","0.4535936","0.45345187","0.45314997","0.45197886","0.45080033","0.45062867","0.4493317","0.44706875","0.44678858","0.44666848","0.44542378","0.44431302","0.44414693","0.44391268","0.44270012","0.44187972","0.44079137","0.44066605","0.44055983","0.4404289","0.4402205","0.43812224","0.43797237","0.43731046","0.43663508","0.43575218","0.43557423","0.43531626","0.43502504","0.43488374","0.4348294","0.43448898","0.4341819","0.4328665","0.43254402","0.43214116","0.4321266","0.4321127","0.4319563","0.43175372","0.4308312","0.42948797","0.42935288","0.4293294","0.4278059","0.42757162","0.42738816","0.4264462","0.42641625","0.42625076","0.42589784","0.42552525","0.42490587","0.42485875","0.42466706","0.42426318","0.4238065","0.42355618"],"string":"[\n \"0.7391125\",\n \"0.6855938\",\n \"0.64830375\",\n \"0.6458727\",\n \"0.54127806\",\n \"0.5294181\",\n \"0.51218337\",\n \"0.5078287\",\n \"0.5072397\",\n \"0.5031775\",\n \"0.5025988\",\n \"0.50101835\",\n \"0.50026613\",\n \"0.49249858\",\n \"0.49118322\",\n \"0.48946965\",\n \"0.4886756\",\n \"0.4860377\",\n \"0.48591778\",\n \"0.4854623\",\n \"0.4850984\",\n \"0.48319915\",\n \"0.4809785\",\n \"0.48010013\",\n \"0.47763708\",\n \"0.47703344\",\n \"0.47443148\",\n \"0.47364503\",\n \"0.47254178\",\n \"0.47132152\",\n \"0.46455756\",\n \"0.46218073\",\n \"0.4613709\",\n \"0.4606712\",\n \"0.46037117\",\n \"0.45994765\",\n \"0.45783442\",\n \"0.45644858\",\n \"0.45517448\",\n \"0.454974\",\n \"0.45476148\",\n \"0.45373037\",\n \"0.4535936\",\n \"0.45345187\",\n \"0.45314997\",\n \"0.45197886\",\n \"0.45080033\",\n \"0.45062867\",\n \"0.4493317\",\n \"0.44706875\",\n \"0.44678858\",\n \"0.44666848\",\n \"0.44542378\",\n \"0.44431302\",\n \"0.44414693\",\n \"0.44391268\",\n \"0.44270012\",\n \"0.44187972\",\n \"0.44079137\",\n \"0.44066605\",\n \"0.44055983\",\n \"0.4404289\",\n \"0.4402205\",\n \"0.43812224\",\n \"0.43797237\",\n \"0.43731046\",\n \"0.43663508\",\n \"0.43575218\",\n \"0.43557423\",\n \"0.43531626\",\n \"0.43502504\",\n \"0.43488374\",\n \"0.4348294\",\n \"0.43448898\",\n \"0.4341819\",\n \"0.4328665\",\n \"0.43254402\",\n \"0.43214116\",\n \"0.4321266\",\n \"0.4321127\",\n \"0.4319563\",\n \"0.43175372\",\n \"0.4308312\",\n \"0.42948797\",\n \"0.42935288\",\n \"0.4293294\",\n \"0.4278059\",\n \"0.42757162\",\n \"0.42738816\",\n \"0.4264462\",\n \"0.42641625\",\n \"0.42625076\",\n \"0.42589784\",\n \"0.42552525\",\n \"0.42490587\",\n \"0.42485875\",\n \"0.42466706\",\n \"0.42426318\",\n \"0.4238065\",\n \"0.42355618\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7588713"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11971,"cells":{"query":{"kind":"string","value":"Appends a general sparse symmetric matrix to the storage of symmetric matrices. appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_)"},"document":{"kind":"string","value":"def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):\n num_ = None\n if num_ is None:\n num_ = len(dims_)\n elif num_ != len(dims_):\n raise IndexError(\"Inconsistent length of array dims\")\n if num_ is None:\n num_ = len(nz_)\n elif num_ != len(nz_):\n raise IndexError(\"Inconsistent length of array nz\")\n if dims_ is None:\n raise ValueError(\"Argument dims cannot be None\")\n if dims_ is None:\n raise ValueError(\"Argument dims may not be None\")\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\n _dims_copyarray = False\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dims_ is not None:\n _dims_copyarray = True\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\n _dims_np_tmp[:] = dims_\n assert _dims_np_tmp.flags.contiguous\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dims_copyarray = False\n _dims_tmp = None\n \n if nz_ is None:\n raise ValueError(\"Argument nz cannot be None\")\n if nz_ is None:\n raise ValueError(\"Argument nz may not be None\")\n if isinstance(nz_, numpy.ndarray) and nz_.dtype is numpy.dtype(numpy.int64) and nz_.flags.contiguous:\n _nz_copyarray = False\n _nz_tmp = ctypes.cast(nz_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nz_ is not None:\n _nz_copyarray = True\n _nz_np_tmp = numpy.zeros(len(nz_),numpy.dtype(numpy.int64))\n _nz_np_tmp[:] = nz_\n assert _nz_np_tmp.flags.contiguous\n _nz_tmp = ctypes.cast(_nz_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nz_copyarray = False\n _nz_tmp = None\n \n _subi_minlength = sum((nz_))\n if sum((nz_)) > 0 and subi_ is not None and len(subi_) != sum((nz_)):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),sum((nz_))))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = sum((nz_))\n if sum((nz_)) > 0 and subj_ is not None and len(subj_) != sum((nz_)):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),sum((nz_))))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = sum((nz_))\n if sum((nz_)) > 0 and valij_ is not None and len(valij_) != sum((nz_)):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),sum((nz_))))\n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n _idx_minlength = (num_)\n if (num_) > 0 and idx_ is not None and len(idx_) != (num_):\n raise ValueError(\"Array argument idx is not long enough: Is %d, expected %d\" % (len(idx_),(num_)))\n if isinstance(idx_,numpy.ndarray) and not idx_.flags.writeable:\n raise ValueError(\"Argument idx must be writable\")\n if idx_ is None:\n raise ValueError(\"Argument idx may not be None\")\n if isinstance(idx_, numpy.ndarray) and idx_.dtype is numpy.dtype(numpy.int64) and idx_.flags.contiguous:\n _idx_copyarray = False\n _idx_tmp = ctypes.cast(idx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif idx_ is not None:\n _idx_copyarray = True\n _idx_np_tmp = numpy.zeros(len(idx_),numpy.dtype(numpy.int64))\n _idx_np_tmp[:] = idx_\n assert _idx_np_tmp.flags.contiguous\n _idx_tmp = ctypes.cast(_idx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _idx_copyarray = False\n _idx_tmp = None\n \n res = __library__.MSK_XX_appendsparsesymmatlist(self.__nativep,num_,_dims_tmp,_nz_tmp,_subi_tmp,_subj_tmp,_valij_tmp,_idx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _idx_copyarray:\n idx_[:] = _idx_np_tmp"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)","def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij)\n elif nz_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if nz_ is None: nz_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _idx_return_value = resargs\n return _idx_return_value","def getsparsesymmat(self,idx_,subi_,subj_,valij_):\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _subi_minlength = (maxlen_)\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxlen_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (maxlen_)\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxlen_)))\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\n raise ValueError(\"Argument subj must be writable\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = (maxlen_)\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),(maxlen_)))\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\n raise ValueError(\"Argument valij must be writable\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valij_copyarray:\n valij_[:] = _valij_np_tmp","def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_valij = False\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n _copyback_valij = True\n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n _copyback_valij = True\n if valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij has wrong length\")\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_valij:\n valij[:] = _tmparr_valij\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi","def add_sparse(self, key, element):\n self.add(self._sparse2seq(key), element)","def _build_sparse(self, name, wrt, consize, param_vals, sub_param_conns,\n full_param_conns, rels):\n\n jac = None\n\n # Additional sparsity for index connections\n for param in wrt:\n\n sub_conns = sub_param_conns.get(param)\n if not sub_conns:\n continue\n\n # If we have a simultaneous full connection, then we move on\n full_conns = full_param_conns.get(param)\n if full_conns.intersection(rels):\n continue\n\n rel_idx = set()\n for target, idx in iteritems(sub_conns):\n\n # If a target of the indexed desvar connection is\n # in the relevant path for this constraint, then\n # those indices are relevant.\n if target in rels:\n rel_idx.update(idx)\n\n nrel = len(rel_idx)\n if nrel > 0:\n\n if jac is None:\n jac = {}\n\n if param not in jac:\n # A coo matrix for the Jacobian\n # mat = {'coo':[row, col, data],\n # 'shape':[nrow, ncols]}\n coo = {}\n coo['shape'] = [consize, len(param_vals[param])]\n jac[param] = coo\n\n row = []\n col = []\n for i in range(consize):\n row.extend([i]*nrel)\n col.extend(rel_idx)\n data = np.ones((len(row), ))\n\n jac[param]['coo'] = [np.array(row), np.array(col), data]\n\n if name not in self.sub_sparsity:\n self.sub_sparsity[name] = {}\n self.sub_sparsity[name][param] = np.array(list(rel_idx))\n\n return jac","def append(self, symmData: list) -> None:\n newSymm = SymmetryElement(symmData)\n if not newSymm in self._symmcards:\n self._symmcards.append(newSymm)","def matrix_add_symmetric(M, M_sym):\n M[0, 0] += M_sym[0]\n M[0, 1] += M_sym[1]\n M[1, 0] += M_sym[1]\n M[0, 2] += M_sym[2]\n M[2, 0] += M_sym[2]\n\n M[1, 1] += M_sym[3]\n M[1, 2] += M_sym[4]\n M[2, 1] += M_sym[4]\n\n M[2, 2] += M_sym[5]\n\n return M","def sparsify(W,conn):\n \n N = W.shape[0]\n W_sparse = sparse.lil_matrix((N,N)) \n for row, weights in itertools.izip(conn, W):\n W_sparse[row[0],row[1:]] = weights[1:]\n return W_sparse","def putaijlist(self,subi_,subj_,valij_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij_)\n elif num_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def store(self, subj):\n if subj in self.__lst:\n raise ValueError('Disciplina exista deja')\n self.__lst.append(subj)","def set_sparse_signals(self):\n\t\n\t\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\n\t\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\n\t\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\tparams_dSs,\tseed=self.seed_dSs)\n\t\t\n\t\t# Replace components with conflicting background odor \n\t\tif self.Kk_split is not None and self.Kk_split != 0:\n\t\t\tassert 0 <= self.Kk_split <= self.Kk, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires Kk_split\" \\\n\t\t\t\t\" to be non-negative and less than or equal to Kk.\"\n\t\t\tassert self.mu_dSs_2 is not None \\\n\t\t\t\tand self.sigma_dSs_2 is not None, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires that\" \\\n\t\t\t\t\" mu_dSs_2 and sigma_dSs_2 are set.\"\n\n\t\t\tsp.random.seed(self.seed_dSs)\n\t\t\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \n\t\t\t\t\t\t\t\t\t\t\treplace=False)\n\t\t\tfor idx_2 in self.idxs_2:\n\t\t\t\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \n\t\t\t\t\t\t\t\t\t\t\t\t\tself.sigma_dSs_2)\n\t\telse:\n\t\t\tself.idxs_2 = []\n\t\t\tself.Kk_split = 0\n\t\t\t\n\t\t# Ss0 is the ideal (learned) background stimulus without noise\n\t\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.idxs, params_Ss0,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tseed=self.seed_Ss0)\n\t\t\n\t\tself.Ss = self.dSs + self.Ss0_noisy","def save_sparse_matrix(self,\n artifact_type: str,\n params: Dict[str, Any],\n sparse_matrix: sp.csr_matrix,\n ignore_duplicate: bool = False) -> str:\n ppr_idx = None\n if \"ppr_idx\" in params.keys() and not isinstance(params[\"ppr_idx\"], int):\n ppr_idx = np.array(params[\"ppr_idx\"])\n params[\"ppr_idx\"] = hash(frozenset(params[\"ppr_idx\"]))\n\n if ignore_duplicate:\n # check there's no entry with the exact same config already present\n ids = Storage.locked_call(\n lambda: self._find_meta_by_exact_params(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n if len(ids) > 0:\n logging.info(f\"Ignoring duplicate save in save_sparse_matrix call\")\n return self._build_artifact_path(artifact_type, ids[0].doc_id).replace(\".pt\", \".npz\")\n\n ids = Storage.locked_call(\n lambda: self._upsert_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout,\n )\n if len(ids) != 1:\n raise RuntimeError(f'The index contains duplicates (artifact_type={artifact_type}, params={params})')\n\n try:\n path = self._build_artifact_path(artifact_type, ids[0]).replace(\".pt\", \".npz\")\n sp.save_npz(path, sparse_matrix)\n logging.info(f\"Saved sparse matrix to storage\")\n if ppr_idx is not None:\n ppr_path = path.replace(\".npz\", \"idx.npy\")\n np.save(ppr_path, ppr_idx)\n logging.info(f\"Saved ppr index to storage\")\n return path\n except: # noqa: E722\n Storage.locked_call(\n lambda: self._remove_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n raise","def dict2sparseMatrix(wDict,std=0,diag=0):\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\n nAreas = len(list(wDict.keys()))\n for i in wDict:\n data[i,i] = diag\n ne = len(wDict[i])+ diag\n for j in wDict[i]:\n if std:\n data[i,j] = 1 / float(ne)\n else:\n data[i,j] = 1\n return data","def putaijlist(self,subi,subj,valij): # 3\n num_ = None\n if num_ is None:\n num_ = len(subi)\n elif num_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij)\n elif num_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if num_ is None: num_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res = self.__obj.putaijlist64(num_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_ss(self, subjects_list, i):\n sub_added = subjects_list[i+1]\n #random.seed(42)\n surface = random.randint(0, len(self.surfaces)-1)\n\n if os.path.isdir(self.data_dir + str(sub_added)):\n skel_file = os.path.join(self.data_dir, str(sub_added), self.cpt_skel_1,\n self.side + self.cpt_skel_2 + str(sub_added) + self.cpt_skel_3)\n self.skel = aims.read(skel_file)\n bck_map = self.surfaces[surface]['aims_ss']\n for voxel in bck_map[0].keys():\n if self.skel.value(voxel[0], voxel[1], voxel[2])!=11:\n self.skel.setValue(60, voxel[0], voxel[1], voxel[2])\n\n bck_map_bottom = self.surfaces[surface]['aims_bottom']\n for voxel in bck_map_bottom[0].keys():\n if self.skel.value(voxel[0], voxel[1], voxel[2])!=11:\n self.skel.setValue(60, voxel[0], voxel[1], voxel[2])\n\n save_subject = sub_added\n return save_subject","def sparse_matlab(i, j, v, m, n):\n return csr_matrix((v, (i, j)), shape=(m, n))","def eval_sparse(self, array_in, array_out, sp_matrix=None):\n if sp_matrix is None:\n sp_matrix = self.to_sparse_matrix(array_in.shape, \"csc\")\n # print(\"usually:\", sp_matrix.todense())\n array_out[:] = sp_matrix.dot(array_in.reshape(-1)).reshape(array_out.shape)","def to_amat(self, node_list=None, sparse=False) -> np.ndarray:\n if not node_list:\n node_list = sorted(self._nodes)\n node2ix = {node: i for i, node in enumerate(node_list)}\n\n if sparse:\n raise NotImplementedError\n # js, ks = [], []\n # for j, k in self._edges:\n # js.append(j)\n # ks.append(k)\n # js.append(k)\n # ks.append(j)\n # return spmatrix(1, js, ks)\n amat = np.zeros([self.num_nodes, self.num_nodes], dtype=int)\n\n for i, j in self._edges:\n amat[node2ix[i], node2ix[j]] = True\n amat[node2ix[j], node2ix[i]] = True\n return amat","def build_lhs_matrix(self):\n j=3\n diags1 = self.build_linear_diags()\n diags1 += self.build_dispersion_diags()\n\n # Ones down primary diagonal\n diags2 = np.zeros_like(diags1)\n diags2[j,:] = 1.\n\n cff = self.dt*(1+self.c_im)*0.5 \n diags = diags2 - cff*diags1\n \n # Build the sparse matrix\n cols = [ii for ii in range(-self._j, self._j+1)]\n M = sparse.spdiags(diags, cols, self.N, self.N)\n\n return M, diags","def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\"<i4\")\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\"<f8\")\n size = j_obj.getSize()\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()","def sparse_matrix(data, stype=\"csr\", dtype=complex):\n return _SPARSE_CONSTRUCTORS[stype](data, dtype=dtype)","def make_sparse(self, fmt='csc', make_method=None):\n if make_method:\n self.sparse = make_method(self.hamiltonian)\n else:\n self.sparse = self.hamiltonian.to_matrix(sparse=fmt)","def sparse_matrix(shape, integer=False):\n dtype = numpy.int_ if integer else numpy.float_\n return scipy.sparse.lil_matrix(shape, dtype=dtype)","def create_sparse_matrix(self, matrix_df):\n\n print('creating sparse matrix...')\n sparse_seg_tmp_df = matrix_df.groupby(['segment_id','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n sparse_rt_tmp_df = matrix_df.groupby(['road_type','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n time_seg_df = sparse_seg_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n time_rt_df = sparse_rt_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n #time_seg_df['time_id'] = time_seg_df.index\n #time_rt_df['time_id'] = time_rt_df.index\n times = list(range(24*60/self.args['time_resolution']))\n full_time_idx = pd.DataFrame([i * 30 for i in times],columns = ['time_idx'])\n full_time_idx['key'] = 1\n full_day_of_week = pd.DataFrame(list(range(7)), columns = ['day_of_week'])\n full_day_of_week['key'] = 1\n full_times = pd.merge(full_time_idx, full_day_of_week, on='key')\n full_times['time_id'] = full_times.index\n time_seg_df = pd.merge(time_seg_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\n time_rt_df = pd.merge(time_rt_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\n \n matrix_seg_keys_df = pd.merge(sparse_seg_tmp_df, time_seg_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\n matrix_rt_keys_df = pd.merge(sparse_rt_tmp_df, time_rt_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\n\n time_seg_array = np.array(matrix_seg_keys_df['time_id'])\n time_rt_array = np.array(matrix_rt_keys_df['time_id'])\n segment_array = np.array(matrix_seg_keys_df['segment_id'])\n rt_array = np.array(matrix_rt_keys_df['road_type'])\n\n uniquesegments = np.array(list(set(segment_array)))\n uniquerts = np.array(list(set(rt_array)))\n keyuniquesegments = np.array(range(len(uniquesegments)))\n keyuniquerts = np.array(range(len(uniquerts)))\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\n uniquerts_df = pd.DataFrame({'roadtypekey':keyuniquerts, 'road_type':uniquerts})\n\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\n rt_df = pd.DataFrame(rt_array, columns = ['road_type'])\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\n rt_keys_df = pd.merge(rt_df, uniquerts_df, how='left', on=['road_type'])\n segmentkeys = np.array(segments_keys_df['segmentskey'])\n rtkeys = np.array(rt_keys_df['road_type'])\n\n level_array_seg = np.array(matrix_seg_keys_df['level_max'])\n sparse_matrix_s = csr_matrix((level_array_seg, (segmentkeys,time_seg_array))).toarray()\n sparse_matrix_seg = preprocessing.scale(sparse_matrix_s)\n level_array_rt = np.array(matrix_rt_keys_df['level_max'])\n sparse_matrix_r = csr_matrix((level_array_rt, (rtkeys,time_rt_array))).toarray()\n sparse_matrix_rt = preprocessing.scale(sparse_matrix_r)\n \n if self.args['perform_pca']:\n sparse_matrix_seg, self.pca_model = self.run_PCA(sparse_matrix_seg)\n sparse_matrix_rt, self.pca_model = self.run_PCA(sparse_matrix_rt)\n else:\n sparse_matrix_seg = sparse_matrix_seg\n sparse_matrix_rt = sparse_matrix_rt\n \n sparse_matrix_withsegkey = pd.DataFrame(sparse_matrix_seg)\n sparse_matrix_withrtkey = pd.DataFrame(sparse_matrix_rt)\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\n sparse_matrix_withrtkey['roadtypekey'] = sparse_matrix_withrtkey.index\n sparse_matrix_withrt = pd.merge(uniquerts_df, sparse_matrix_withrtkey, on=['roadtypekey'])\n \n # write sparse_matrix to database as 'clustering' table\n print('writing sparse matrix to db...')\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n if self.split_type == 'random':\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_random', con=engine, if_exists='replace')\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_random', con=engine, if_exists='replace')\n elif self.split_type == 'date':\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_date', con=engine, if_exists='replace')\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_date', con=engine, if_exists='replace')\n \n print('returning train sparse matrix...')\n return (uniquesegments_df, sparse_matrix_seg)","def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(alphaptrb_)\n elif num_ != len(alphaptrb_):\n raise IndexError(\"Inconsistent length of array alphaptrb\")\n if num_ is None:\n num_ = len(alphaptre_)\n elif num_ != len(alphaptre_):\n raise IndexError(\"Inconsistent length of array alphaptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb cannot be None\")\n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb may not be None\")\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptrb_ is not None:\n _alphaptrb_copyarray = True\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\n _alphaptrb_np_tmp[:] = alphaptrb_\n assert _alphaptrb_np_tmp.flags.contiguous\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = None\n \n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre cannot be None\")\n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre may not be None\")\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\n _alphaptre_copyarray = False\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptre_ is not None:\n _alphaptre_copyarray = True\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\n _alphaptre_np_tmp[:] = alphaptre_\n assert _alphaptre_np_tmp.flags.contiguous\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptre_copyarray = False\n _alphaptre_tmp = None\n \n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def SymmetriseMatrix(adjmatrix):\n\n if galib.metrics.Reciprocity(adjmatrix) == 1:\n # if Reciprocity(adjmatrix) == 1:\n return adjmatrix\n else:\n return 0.5 * (adjmatrix + adjmatrix.T)","def create_attach_sparsifier(model, **sparse_config):\n data_norm_sparsifier = DataNormSparsifier(**sparse_config)\n for name, parameter in model.named_parameters():\n if 'emb_l' in name:\n valid_name = get_valid_name(name)\n data_norm_sparsifier.add_data(name=valid_name, data=parameter)\n return data_norm_sparsifier","def write_matfile(Sint_df, outputfilepath='Sint_no_cofactor_20160831.mat'):\n # convert dataframe to matrix\n Smat = Sint_df.as_matrix()\n\n # get all indices for non-zero elements in Smat (row, col)\n Smat_nzr, Smat_nzc = np.nonzero(Smat)\n\n # get all non-zero elements from Smat\n Smat_nze = Smat[Smat_nzr, Smat_nzc]\n\n # Adjust for matlab coordinate\n Smat_nzr = Smat_nzr + 1\n Smat_nzc = Smat_nzc + 1\n\n # This final line gives the size of the S matrix in matlab\n nr, nc = Smat.shape\n\n # Create a 2D array\n sparseMat = np.vstack((Smat_nzr, Smat_nzc, Smat_nze)).T\n sparseMat = np.vstack((sparseMat, np.array([[nr, nc, 0]])))\n\n # Create a numpy object array from dataframe index\n reactionList = Sint_df.columns.ravel()\n\n # Write only one matlab .mat file\n scipy.io.savemat(outputfilepath,\n mdict={'Sint_sparse': sparseMat,\n 'reactionList': np.array(reactionList)}\n )\n\n return sparseMat, reactionList","def sparse_matrix (base_type=float):\n return defaultdict (lambda: sparse_vector (base_type))","def matrix_add():","def set_sparsity(self,use_sparse):\n \n if hasattr(self.problem,'sparse_jac'):\n self.use_sparse = use_sparse\n else:\n raise KINSOL_Exception(\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\")","def stiffnessMatrix (bsp, knotlist,p, nbquad):\n\n\tS = np.zeros((bsp.N-2, bsp.N-2))\n\t\n\tfor line in range(1, bsp.N-1):\n\t\t\n\t\tfor column in range(1, bsp.N-1):\n\t\t\n\t\t\tfor iknot in range(len(knotlist)-1):\n\t\t\t\tS[line-1, column-1] = S[line-1, column-1] + legendreGauss(bilinearForm, nbquad, knotlist[iknot], knotlist[iknot+1], line, bsp, ind2=column)\n\t\n\treturn S;","def normalize_adj( adj : np.ndarray, \n sparse : bool = False\n ) -> Union[np.ndarray, sp.spmatrix]:\n if sparse:\n adj = sp.coo_matrix(adj) # [N,N]\n rowsum = np.array(adj.sum(1)) # [N,]\n \n d_inv_sqrt = np.power(rowsum, -0.5) # [N,], may issue runtime warnings (div by zero)\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. # []\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) if sparse else np.diag(d_inv_sqrt) #[N,N]\n \n if sparse:\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n else:\n return ((adj @ d_mat_inv_sqrt).transpose() @ d_mat_inv_sqrt) # not quite sure why this order = D^T A^T D, D^T = D, A^T = A - the transpose is unncessary?!","def _append_sparse_tensor_value(target, to_append):\n # Make sure the sparse tensors are of the same size (except for the 0th dim).\n if len(target.dense_shape) != len(to_append.dense_shape):\n raise RuntimeError(\n 'Unable to concatenate %s and %s. The inner dense shapes do not '\n 'have the same number of dimensions (%s vs %s)' %\n (target, to_append, target.dense_shape, to_append.dense_shape))\n\n if target.dense_shape[1:] != to_append.dense_shape[1:]:\n raise RuntimeError(\n 'Unable to concatenate %s and %s. The inner dense shapes do not '\n 'match inner dimensions (%s vs %s)' %\n (target, to_append, target.dense_shape[1:], to_append.dense_shape[1:]))\n\n # Add the to_append indices to target, updating the 0th value, and keeping\n # track of the maximum so we know the final dense_shape of this tensor.\n base_dim0_value = target.dense_shape[0]\n max_dim0_value = target.dense_shape[0]\n new_indices = target.indices\n for index in to_append.indices:\n # Here, we iterate through the sparse indices of the tensor to append. For\n # each index, we update its zeroth value (the batch index) by adding the\n # number of batch items in the tensor we are appending to (so an index\n # of [0, 0, 1] for a value that is being appended to a tensor with 0th dim\n # size 3 would become [3, 0, 1].)\n index[0] += base_dim0_value\n max_dim0_value = max(max_dim0_value, index[0])\n new_indices = np.append(new_indices, [index], axis=0)\n\n # Extend the values array to contain all of the appended values. These will\n # be in the same order as the indices added above.\n new_values = np.concatenate((target.values, to_append.values), axis=0)\n\n # Create a new dense shape by replacing the value for the 0th dimension\n # with the new max dim0 value.\n new_dense_shape = list(target.dense_shape)\n new_dense_shape[0] = max_dim0_value + 1\n new_dense_shape = tuple(new_dense_shape)\n\n return sparse_tensor.SparseTensorValue(\n indices=new_indices, values=new_values, dense_shape=new_dense_shape)","def sparse_arrays(self):\n return self._sparse_arrays","def add_ts_to_matrix(self, i_ts, norm_flag, add_x=True, add_y=True):\n\n # ts are not overwritten, only normalization constants\n if norm_flag:\n ts, norm_div, norm_subt = _normalize_ts(self.ts[i_ts].s, self.ts[i_ts].name)\n self.ts[i_ts] = TsMiniStruct(self.ts[i_ts].s, norm_div, norm_subt, self.ts[i_ts].name, self.ts[i_ts].index)\n ts = TsMiniStruct(ts, norm_div, norm_subt, self.ts[i_ts].name, self.ts[i_ts].index)\n else:\n ts = self.ts[i_ts]\n\n n_hist = self.n_hist_points[i_ts]\n n_req = self.n_req_points[i_ts]\n n_rows = self.X.shape[0]\n\n # reverse time series, so that the top row is always to be forecasted first\n time = np.flipud(ts.index)\n ts = np.flipud(ts.s)\n \n idxX, idxY = matrix_idx(n_hist, n_req, n_rows)\n self.idxY[i_ts] = idxY\n if add_y:\n self.Y = np.hstack((self.Y, ts[idxY]))\n if add_x:\n self.X = np.hstack((self.X, ts[idxX]))\n\n return time[idxY[:, -1]], time[idxX[:, 0]]","def edges_to_matrix(edge_list: List[Tuple[int, int]], add_reverse_edges: bool,\n shape: Tuple[int, int], dtype: TypeVar=bool, sparse: bool=True):\n matrix = scipy.sparse.csc_matrix(\n (numpy.ones(len(edge_list)), zip(*edge_list)), dtype=dtype, shape=shape,\n )\n\n if add_reverse_edges:\n matrix = (matrix + matrix.T) > 0\n matrix = matrix.astype(dtype)\n\n if not sparse:\n matrix = matrix.toarray()\n\n return matrix","def _buildMatrix(self, SparseMatrix, Ncells, MaxFaces, coeff):\n return (0, 0)","def setDominantSparseSymeig(A, Aadjoint_to_gadjoint):\n global DominantSparseSymeig \n from .CG import setCGSubspaceSparse\n setCGSubspaceSparse(A, Aadjoint_to_gadjoint)\n from .CG import CGSubspaceSparse\n @staticmethod\n def forward(ctx, g, k, dim):\n eigval, eigvector = symeigLanczos(A, k, extreme=\"min\", sparse=True, dim=dim)\n ctx.save_for_backward(g, eigval, eigvector)\n return eigval, eigvector\n @staticmethod\n def backward(ctx, grad_eigval, grad_eigvector):\n cg = CGSubspaceSparse.apply\n g, eigval, eigvector = ctx.saved_tensors\n b = grad_eigvector - torch.matmul(eigvector, grad_eigvector) * eigvector\n lambda0 = cg(g, eigval, b, eigvector)\n grad_A = grad_eigval * eigvector - lambda0, eigvector\n v1, v2 = grad_A\n grad_g = Aadjoint_to_gadjoint(v1, v2)\n grad_k = grad_dim = None\n return grad_g, grad_k, grad_dim\n DominantSparseSymeig = type(\"DominantSparseSymeig\", (torch.autograd.Function, ), \n {\"forward\": forward, \"backward\": backward})","def sparse_esn_reservoir(size, spectral_radius, density, symmetric):\n rvs = stats.uniform(loc=-1., scale=2.).rvs\n matrix = sparse.random(size, size, density=density, data_rvs=rvs)\n matrix = matrix.tocsr()\n if symmetric:\n matrix = sparse.triu(matrix)\n tril = sparse.tril(matrix.transpose(), k=-1)\n matrix = matrix + tril\n # calc eigenvalues with scipy's lanczos implementation:\n eig, _ = sparse.linalg.eigsh(matrix, k=2, tol=1e-4)\n else:\n eig, _ = sparse.linalg.eigs(matrix, k=2, tol=1e-4)\n\n rho = np.abs(eig).max()\n matrix = matrix.multiply(1. / rho)\n matrix = matrix.multiply(spectral_radius)\n return matrix","def split_poly_scale_join(matrices, dummy_idx, poly, scale):\n\n adjusted_matrices = []\n\n for matrix in matrices:\n if dummy_idx:\n # Split the matrix into numerical and dummies\n matrix, matrix_dummies = np.split(matrix, [dummy_idx], axis=1)\n\n # add polynomial features to numerical attributes\n if poly:\n matrix = (PolynomialFeatures(poly).\n fit_transform(matrix))\n # scale numerical attributes\n if scale:\n matrix = (StandardScaler().\n fit_transform(matrix))\n if dummy_idx:\n # Join numerical and dummy attributes together again\n matrix = np.concatenate((matrix, matrix_dummies), axis=1)\n adjusted_matrices.append(matrix)\n\n return adjusted_matrices","def append_direct_component_matrices(technique_data: TransitiveTechniqueData):\n for technique in technique_data.technique.get_component_techniques():\n direct_calculator = DirectTechniqueCalculator(technique.definition)\n similarity_matrix = direct_calculator.calculate_technique_data(\n technique_data.dataset\n ).similarity_matrix\n technique_data.transitive_matrices.append(similarity_matrix)","def convert_sparse_network(weights: Dict[str, np.ndarray],\n metadata: Dict[str, Any],\n hypers: Dict[str, Any],\n is_msp: bool):\n # List to hold all variables\n components: List[str] = []\n\n # Start the input units at the input shape\n input_size = metadata[INPUT_SHAPE][-1]\n\n for i, output_size in enumerate(hypers['hidden_units']):\n\n # Convert the sparse variable\n layer_name = 'hidden_{0}'.format(i)\n\n sparse_layer = convert_sparse_layer(layer_name=layer_name,\n weights=weights,\n input_size=input_size,\n output_size=output_size,\n coordinates=metadata['sparse_indices'][layer_name],\n is_msp=is_msp)\n\n components.append(sparse_layer)\n\n # Reset the input size as we progress\n input_size = output_size\n\n # Include the output layer\n output_kernel_name = 'output/kernel:0'\n output_kernel = convert_matrix(name=output_kernel_name,\n mat=weights[output_kernel_name],\n precision=PRECISION,\n width=WIDTH,\n is_msp=is_msp)\n components.append(output_kernel)\n\n output_bias_name = 'output/bias:0'\n output_bias = convert_matrix(name=output_bias_name,\n mat=weights[output_bias_name],\n precision=PRECISION,\n width=WIDTH,\n is_msp=is_msp)\n components.append(output_bias)\n\n return '\\n'.join(components)","def to_s_matrix(w,v):\n pass","def apply_stiffness_assembling(fe_space, param, stiffness):\n if stiffness.format is 'lil':\n for ie in range(fe_space.get_nelem()):\n local_stiffness = fe_space.apply_dbasis_diag_dbasis(fe_space.eval_at_quadrature_pnts(\n lambda k, s:\n fe_space.get_quadrature_weight(k) * param(fe_space.get_coord(ie, s)) / fe_space.get_elem_length(ie)))\n locals_to_globals = fe_space.locals_to_globals(ie)\n stiffness[np.array(locals_to_globals)[:, None], locals_to_globals] += local_stiffness\n else:\n raise ValueError(\"Assembling procedure are expecting lil format sparse matrices.\")","def create_jac_sens(x_sp,sensitivity_sp,diffeq_params, integration_params,\n SDerivSymbolicJacParamsLambFun,SDerivSymbolicJacConcLambFun):\n\n # create state variables\n allVars = np.concatenate((x_sp,sensitivity_sp))\n\n #create RHS\n dSensSym = sp.Matrix(dSens(0,allVars,diffeq_params, integration_params,\n SDerivSymbolicJacParamsLambFun, SDerivSymbolicJacConcLambFun))\n dSensSymJac = dSensSym.jacobian(allVars)\n\n # generate jacobian\n dSensSymJacDenseMatLam = sp.lambdify(allVars,dSensSymJac)\n dSensSymJacSparseMatLamFun = lambda t,xs: sparse.csr_matrix(dSensSymJacDenseMatLam(*xs))\n\n return dSensSymJacSparseMatLamFun","def __add__(self, other):\n h, w = self.size\n col_indices = self.col_indices + [w + i for i in other.col_indices]\n row_indices = self.row_indices + other.row_indices\n values = self.values + other.values\n oh, ow = other.size\n size = [max(h, oh), w + ow]\n return Sparse(size, row_indices, col_indices, values)","def coregionalization_sparse(optimize=True, plot=True):\r\n #fetch the data from the non sparse examples\r\n m = coregionalization_toy2(optimize=False, plot=False)\r\n X, Y = m.X, m.likelihood.Y\r\n\r\n #construct a model\r\n m = GPy.models.SparseGPRegression(X,Y)\r\n m.constrain_fixed('iip_\\d+_1') # don't optimize the inducing input indexes\r\n\r\n if optimize:\r\n m.optimize('bfgs', max_iters=100, messages=1)\r\n\r\n if plot:\r\n m.plot(fixed_inputs=[(1,0)])\r\n m.plot(fixed_inputs=[(1,1)], ax=pb.gca())\r\n\r\n return m","def to_sparse(self):\n from divisi2.sparse import SparseMatrix\n return SparseMatrix(self, self.row_labels, self.col_labels)","def qubit_operator_sparse(qubit_operator, n_qubits=None):\n if n_qubits is None:\n n_qubits = count_qubits(qubit_operator)\n if n_qubits < count_qubits(qubit_operator):\n raise ValueError('Invalid number of qubits specified.')\n\n # Construct the Scipy sparse matrix.\n n_hilbert = 2**n_qubits\n values_list = [[]]\n row_list = [[]]\n column_list = [[]]\n\n # Loop through the terms.\n for qubit_term in qubit_operator.terms:\n tensor_factor = 0\n coefficient = qubit_operator.terms[qubit_term]\n sparse_operators = [coefficient]\n for pauli_operator in qubit_term:\n\n # Grow space for missing identity operators.\n if pauli_operator[0] > tensor_factor:\n identity_qubits = pauli_operator[0] - tensor_factor\n identity = scipy.sparse.identity(2**identity_qubits,\n dtype=complex,\n format='csc')\n sparse_operators += [identity]\n\n # Add actual operator to the list.\n sparse_operators += [pauli_matrix_map[pauli_operator[1]]]\n tensor_factor = pauli_operator[0] + 1\n\n # Grow space at end of string unless operator acted on final qubit.\n if tensor_factor < n_qubits or not qubit_term:\n identity_qubits = n_qubits - tensor_factor\n identity = scipy.sparse.identity(2**identity_qubits,\n dtype=complex,\n format='csc')\n sparse_operators += [identity]\n\n # Extract triplets from sparse_term.\n sparse_matrix = kronecker_operators(sparse_operators)\n values_list.append(sparse_matrix.tocoo(copy=False).data)\n (column, row) = sparse_matrix.nonzero()\n column_list.append(column)\n row_list.append(row)\n\n # Create sparse operator.\n values_list = numpy.concatenate(values_list)\n row_list = numpy.concatenate(row_list)\n column_list = numpy.concatenate(column_list)\n sparse_operator = scipy.sparse.coo_matrix(\n (values_list, (row_list, column_list)),\n shape=(n_hilbert, n_hilbert)).tocsc(copy=False)\n sparse_operator.eliminate_zeros()\n return sparse_operator","def calc_sparse(self, calc_aw=False, vector_calc=True):\n # Local copies of instance attributes for faster access\n b = self.b\n qs = self.q_sim\n qo = self.q_obs\n maxlead = self.max_lead\n maxlag = self.max_lag\n measure = self.measure\n time = self.time\n\n # Prepare sparse matrices with only cells around diagonal defined.\n # padds 1 extra cell on each side to avoid false zeros later\n n = len(self.q_obs)\n w = self.max_lag + self.max_lead + 3\n data = np.array([np.full(n, np.inf)]).repeat(w, axis=0)\n offsets = np.arange(-self.max_lead-1, self.max_lag + 1.5)\n\n # Calculate work between obs and sim pairs, not needed anymore.\n if calc_aw:\n aw = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\n st = pd.Timestamp.now()\n # print('Start aw loops')\n # iterate through all flow observations\n for i_o in range(len(qo)):\n # Check only simulations within the allowed window\n for i_s in range(max(0, i_o - self.max_lead),\n min(len(qs), i_o + maxlag + 1)):\n if measure in ('nse', 'square'):\n aw[i_s, i_o] = (qs[i_s] - qo[i_o]) ** 2 + b ** 2 * (i_s - i_o) ** 2\n elif measure in ('mae', 'abs'):\n aw[i_s, i_o] = np.abs(qs[i_s] - qo[i_o]) + b * np.abs(i_s - i_o)\n # print('End aw loops', (pd.Timestamp.now()-st)/pd.Timedelta('1s'))\n self.aw = aw\n\n # Old debugging code: run original implementation alongside\n # Applies to all variables appended with _orig\n # aw_orig = np.full(shape=(len(self.q_obs), len(self.q_sim)), fill_value=np.nan)\n # # iterate through all flow observations\n # for i_o in range(len(self.q_obs)):\n # # Check only simulations within the allowed window\n # for i_s in range(max(0, i_o - self.max_lead),\n # min(len(self.q_sim), i_o + self.max_lag + 1)):\n # aw_orig[i_s, i_o] = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + self.b ** 2 * (i_s - i_o) ** 2\n\n # Calculate cumulative work along possible paths\n cw0 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\n cw1 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\n\n # Old debugging code: run original implementation (with dense arrays) alongside\n # Applies to all variables marked _orig\n # # Calculate cumulative work along possible paths\n # cw_orig = np.ones(shape=aw_orig.shape + (2,)) * np.nan\n # # Populate first column\n # cw_orig[:, 0, 0] = aw_orig[:, 0]\n\n if self.keep_internals:\n # self.aw_orig = aw_orig\n self.cw0 = cw0\n self.cw1 = cw1\n # self.cw_orig = cw_orig\n\n # Populate other columns\n for i_o in range(n):\n iss = max(0, i_o - maxlead) # sim index start\n ise = min(n, i_o + maxlag + 1) # sim index end\n isv = np.arange(iss, ise) # sim index as vector\n if time is not None:\n dt = time[isv] - time[i_o]\n # Heavily penalize sim-obs combinations outside allowed window\n # so that they are not selected\n dt[(dt < -maxlag) | (dt > maxlead)] = np.inf\n else:\n dt = isv - i_o\n if vector_calc:\n # Vectorized version of the calculation.\n # For understanding it is best to take a look at the else clause below\n # Calculate the work for each (sim, obs_i) pair\n if measure in ('nse', 'square'):\n e = (qs[iss:ise] - qo[i_o]) ** 2 + b ** 2 * dt ** 2\n elif measure in ('mae', 'abs'):\n e = np.abs(qs[iss:ise] - qo[i_o]) + b * np.abs(dt)\n if i_o == 0:\n # Only populate first column of cw0 and move to i_o = 1\n cw0[iss:ise, 0] = e.reshape(len(e), 1)\n continue\n # Repeat the same simulation point\n d = cw0[iss:ise, i_o - 1].toarray()\n d[d == 0] = np.nan\n cw1[iss:ise, i_o] = e.reshape(len(e), 1) + d\n\n # Find the 'cheapest' available preceding sim point\n points = np.full((len(isv), 4), np.inf)\n # Use the previous simulation point\n st = max(0, iss - 1)\n end = min(n + 1, ise - 1)\n l = end - st\n points[-l:, 0] = cw0[st:end, i_o - 1].toarray().squeeze()\n points[-l:, 1] = cw1[st:end, i_o - 1].toarray().squeeze()\n # Skip a simulation point\n st = max(0, iss - 2)\n end = min(n + 1, ise - 2)\n l = end - st\n points[-l:, 2] = cw0[st:end, i_o - 1].toarray().squeeze()\n points[-l:, 3] = cw1[st:end, i_o - 1].toarray().squeeze()\n # points[points == 0] = np.nan\n cw0[iss:ise, i_o] = (e + np.min(points, axis=1)).reshape(len(e), 1)\n else:\n # Old, non-vectorized code. Slower.\n # Left in place since it is easier to understand than vectorized code above\n def zero_to_nan(x):\n if x == 0:\n return np.nan\n else:\n return x\n\n for i_s in range(max(0, i_o - self.max_lead),\n min(n, i_o + self.max_lag + 1)):\n # print(i_o, i_s, sep='\\t')\n # e = aw[i_s, i_o]\n if time:\n dt = time[isv] - time[i_o]\n # Heavily penalize sim-obs combinations outside allowed window\n # so that they are not selected\n dt[dt > maxlead] = 999999999\n dt[dt < -maxlag] = 999999999\n else:\n dt = isv - i_o\n if measure in ('nse', 'square'):\n e = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + b ** 2 * dt ** 2\n elif measure in ('mae', 'abs'):\n e = np.abs(self.q_sim[i_s] - self.q_obs[i_o]) + np.abs(b * dt)\n # e_orig = aw_orig[i_s, i_o]\n # if e != e_orig:\n # print('ediff', e, e_orig, sep='\\t')\n\n # Repeat the same simulation point\n cw1[i_s, i_o] = e + zero_to_nan(cw0[i_s, i_o - 1])\n # cw_orig[i_s, i_o, 1] = e + cw_orig[i_s, i_o - 1, 0]\n # if cw1[i_s, i_o] != cw_orig[i_s, i_o, 1]:\n # print('cw1diff', cw1[i_s, i_o], cw_orig[i_s, i_o, 1], sep='\\t')\n\n if i_s == 0:\n continue\n # Else:\n # Find the 'cheapest' available preceding point\n # Use the previous simulation point\n points = [zero_to_nan(cw0[i_s - 1, i_o - 1]),\n zero_to_nan(cw1[i_s - 1, i_o - 1])]\n # m1 = np.nanmin(cw_orig[i_s - 1, i_o - 1, :])\n # m2 = np.inf\n if i_s > 1:\n # Skip a simulation point\n points += [cw0[i_s - 2, i_o - 1], cw1[i_s - 2, i_o - 1]]\n # m2 = np.nanmin(cw_orig[i_s - 2, i_o - 1, :])\n # m = min(m1, m2)\n try:\n cp = min([p for p in points if p > 0])\n except ValueError:\n cp = np.nan\n # if cp != m:\n # print('cpdiff', cp, m, sep='\\t')\n # print('\\torig', cw_orig[i_s - 1, i_o - 1, :], cw_orig[i_s - 2, i_o - 1, :])\n # print('\\tspar', points)\n cw0[i_s, i_o] = e + cp\n # cw_orig[i_s, i_o, 0] = e + m\n # print('')\n # Find the cheapest point in the last column, i.e. end of cheapest path, i.e. optimum score\n # nz0 = cw0[cw0[:, -1].nonzero()[0], -1].toarray().min()\n # nz1 = cw1[cw1[:, -1].nonzero()[0], -1].toarray().min()\n nz0 = cw0[-self.max_lag:, -1].toarray()\n nz1 = cw1[-self.max_lag:, -1].toarray()\n self.opt_score = min(nz0[nz0 != 0].min(), nz1[nz1 != 0].min())\n\n self.of = 1 - self.opt_score / self.fbench\n\n if self.calc_rays:\n self._do_calc_rays(cw0, cw1)\n\n return self.of","def to_sparse(self, method='csr_matrix'):\r\n data = self.data.values\r\n if method == 'csr_matrix':\r\n data_sp = sps.csr_matrix(data)\r\n elif method == 'bsr_matrix':\r\n data_sp = sps.bsr_matrix(data)\r\n elif method == 'coo_matrix':\r\n data_sp = sps.coo_matrix(data)\r\n elif method == 'csc_matrix':\r\n data_sp = sps.csc_matrix(data)\r\n elif method == 'dia_matrix':\r\n data_sp = sps.dia_matrix(data)\r\n elif method == 'dok_matrix':\r\n data_sp = sps.dok_matrix(data)\r\n elif method == 'lil_matrix':\r\n data_sp = sps.lil_matrix(data)\r\n else:\r\n raise ValueError('The method does not exist in scipy.sparse')\r\n return data_sp","def _extend_sparse_traj_field(self, run_idx, traj_idx, field_path, values, sparse_idxs):\n\n field = self.h5['{}/{}/{}/{}/{}'.format(RUNS, run_idx, TRAJECTORIES, traj_idx, field_path)]\n\n field_data = field[DATA]\n field_sparse_idxs = field[SPARSE_IDXS]\n\n # number of new frames\n n_new_frames = values.shape[0]\n\n # if this sparse_field has been initialized empty we need to resize\n if all([i == 0 for i in field_data.shape]):\n\n\n # check the feature shape against the maxshape which gives\n # the feature dimensions for an empty dataset\n assert values.shape[1:] == field_data.maxshape[1:], \\\n \"input value features have shape {}, expected {}\".format(\n values.shape[1:], field_data.maxshape[1:])\n\n # if it is empty resize it to make an array the size of\n # the new values with the maxshape for the feature\n # dimensions\n feature_dims = field_data.maxshape[1:]\n field_data.resize( (n_new_frames, *feature_dims) )\n\n # set the new data to this\n field_data[0:, ...] = values\n\n else:\n\n # make sure the new data has the right dimensions\n assert values.shape[1:] == field_data.shape[1:], \\\n \"field feature dimensions must be the same, i.e. all but the first dimension\"\n\n # append to the dataset on the first dimension, keeping the\n # others the same, these must be feature vectors and therefore\n # must exist\n field_data.resize( (field_data.shape[0] + n_new_frames, *field_data.shape[1:]) )\n # add the new data\n field_data[-n_new_frames:, ...] = values\n\n # add the sparse idxs in the same way\n field_sparse_idxs.resize( (field_sparse_idxs.shape[0] + n_new_frames,\n *field_sparse_idxs.shape[1:]) )\n # add the new data\n field_sparse_idxs[-n_new_frames:, ...] = sparse_idxs","def visit_list(self, sylist):\n def index(sytable):\n try:\n return sytable.get_column(self.input_index)\n except:\n return np.array([0] * sytable.number_of_rows(), dtype=int)\n\n def column_or_nan(sytable, column, length):\n try:\n return sytable.get_column(column)\n except:\n return int(length)\n\n def fill(columns):\n test = []\n for column in columns:\n try:\n if column.size:\n test.append(column[:0])\n except:\n pass\n test = [[]] if not test else test\n dtype = np.hstack(test).dtype\n kind = dtype.kind\n\n if kind in ['S', 'U']:\n filler = lambda x: np.zeros(x, dtype='S1')\n else:\n filler = lambda x: np.zeros(x, dtype='f4') * np.nan\n\n result = [filler(column)\n if isinstance(column, int)\n else column for column in columns]\n if not result:\n result.append([])\n\n return result\n\n common_columns = set()\n all_columns = []\n order = OrderedDict()\n offset = 0\n indices = [[]]\n lengths = []\n\n # Pre-compute columns etc.\n for item in sylist:\n item_columns = item.columns()\n all_columns.append(item_columns)\n order.update(OrderedDict.fromkeys(item_columns))\n current_index = index(item)\n length = item.number_of_rows()\n minimum = np.min(current_index) if length else 0\n indices.append(current_index + (offset - minimum))\n\n offset += (np.max(current_index) - minimum + 1\n if length else\n self.minimum_increment)\n\n lengths.append(length)\n\n # Keep only common columns except when in fill mode.\n if not self.fill:\n try:\n common_columns.update(order.keys())\n for current_columns in all_columns:\n if current_columns:\n common_columns = common_columns.intersection(\n current_columns)\n except:\n pass\n order = OrderedDict.fromkeys(\n [key for key in order.keys() if key in common_columns])\n\n # VJoin columns and attributes.\n for column in order:\n data = []\n attrs = {}\n\n if self.fill:\n for i, item in enumerate(sylist):\n data.append(column_or_nan(item, column, lengths[i]))\n if column in all_columns[i]:\n attrs.update(item.get_column_attributes(column).get())\n else:\n for length, item in izip(lengths, sylist):\n if length:\n data.append(item.get_column(column))\n try:\n attrs.update(item.get_column_attributes(column).get())\n except KeyError:\n pass\n self.current.set_column(column, np.hstack(fill(data)))\n self.current.get_column_attributes(column).set(attrs)\n\n # VJoin index column.\n if self.current.number_of_columns() and self.output_index:\n self.current.set_column(self.output_index, np.hstack(indices))","def so_sparse(nspins):\n sigma_x = sparse.COO(np.array([[0, 1 / 2], [1 / 2, 0]]))\n sigma_y = sparse.COO(np.array([[0, -1j / 2], [1j / 2, 0]]))\n sigma_z = sparse.COO(np.array([[1 / 2, 0], [0, -1 / 2]]))\n unit = sparse.COO(np.array([[1, 0], [0, 1]]))\n\n L = np.empty((3, nspins, 2 ** nspins, 2 ** nspins), dtype=np.complex128) # consider other dtype?\n # Lxs = []\n # Lys = []\n # Lzs = []\n\n for n in range(nspins):\n Lx_current = 1\n Ly_current = 1\n Lz_current = 1\n\n for k in range(nspins):\n if k == n:\n Lx_current = sparse.kron(Lx_current, sigma_x)\n Ly_current = sparse.kron(Ly_current, sigma_y)\n Lz_current = sparse.kron(Lz_current, sigma_z)\n else:\n Lx_current = sparse.kron(Lx_current, unit)\n Ly_current = sparse.kron(Ly_current, unit)\n Lz_current = sparse.kron(Lz_current, unit)\n\n # Lxs[n] = Lx_current\n # Lys[n] = Ly_current\n # Lzs[n] = Lz_current\n # print(Lx_current.todense())\n L[0][n] = Lx_current.todense()\n L[1][n] = Ly_current.todense()\n L[2][n] = Lz_current.todense()\n Lz_sparse = sparse.COO(L[2])\n L_T = L.transpose(1, 0, 2, 3)\n L_sparse = sparse.COO(L)\n L_T_sparse = sparse.COO(L_T)\n Lproduct = sparse.tensordot(L_T_sparse, L_sparse, axes=((1, 3), (0, 2))).swapaxes(1, 2)\n # Lz_sparse = sparse.COO(L[2])\n Lproduct_sparse = sparse.COO(Lproduct)\n\n return Lz_sparse, Lproduct_sparse","def __init__(self, size):\n _PysparseMatrixFromShape.__init__(self, rows=size, cols=size, bandwidth = 1)\n ids = numerix.arange(size)\n self.put(numerix.ones(size, 'd'), ids, ids)","def scipy_sparse_to_spmatrix(A):\n coo = A.tocoo()\n SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)\n return SP","def expand_conn_mat(conn_mat, nr_of_traps):\n\n r, c = conn_mat._shape\n new_indptr = np.hstack((conn_mat.indptr, np.asarray([conn_mat.indptr[-1]] * nr_of_traps)))\n expanded_mat = csr_matrix((conn_mat.data, conn_mat.indices, new_indptr),\n shape=(r + nr_of_traps, c + nr_of_traps))\n\n return expanded_mat","def add_index(self, idx, subproblem_shape):\n self.indices.append(int(idx))\n self.subproblem_shapes.append(subproblem_shape)","def create_sparse_matrix(self, filename, matrix_length, density):\n pos = 0\n aux_pos = 0\n matrix = []\n pointerB = []\n pointerE = []\n columns = []\n values = []\n\n for i in range(0, matrix_length):\n row = []\n pointerB.append(pos)\n aux_pos = pos\n for j in range(0, matrix_length):\n probability = random.random()\n if probability < density:\n pos += 1\n val = random.randint(1, 10)\n values.append(val)\n columns.append(j)\n else:\n val = 0\n row.append(val)\n matrix.append(row)\n pointerE.append(pos)\n vector_b = SparseMatrix.gen_vector(matrix_length)\n matrix_A = np.matrix(matrix)\n vector_res = np.dot(matrix_A, vector_b).reshape(matrix_length, 1)\n data = {\"values\": values, \"columns\": columns, \"pointerB\": pointerB, \"pointerE\": pointerE}\n CSR_A = json.dumps(data)\n '''\n print(\"x: \", vector_x)\n print(\"A: \", matrix_A)\n print(\"b: \", vector_b)\n data = {\"values\": values, \"columns\": columns, \"pointerB\": pointerB, \"pointerE\": pointerE}\n data_json = json.dumps(data)\n file = open(filename, 'w')\n file.write(data_json)\n file.close()\n np.savetxt(\"vector.txt\", vector_x, fmt=\"%1.9f\", delimiter=\" \")\n '''\n return matrix_A, CSR_A, vector_b, vector_res","def expand_csr_adj(adj, count:int):\n r,c = adj.shape\n \n adj = sp.vstack(\n [adj, sp.csr_matrix(np.zeros((count, c)))])\n adj = sp.hstack(\n [adj, sp.csr_matrix(np.zeros((r + count, count)))])\n \n return adj","def create_nonsparse_matrix(self, matrix_df):\n\n print('creating nonsparse matrix...')\n clustering_df_tmp = matrix_df[['date','time','date_idx', 'time_idx', 'day_of_week', 'segment_id', 'road_type', 'lat1', 'lat2', 'lon1', 'lon2', 'level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']]\n seg_averages = clustering_df_tmp.groupby(['segment_id', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']].mean().reset_index()\n rt_averages = clustering_df_tmp.groupby(['road_type', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']].mean().reset_index()\n \n # write nonsparse_matrix_db to database as 'clustering' table\n print('writing nonsparse matrix to db...')\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n if self.split_type == 'random':\n seg_averages.to_sql(name='clust_nonsparse_avebysegment_random', con=engine, if_exists='replace')\n rt_averages.to_sql(name='clust_nonsparse_avebyrt_random', con=engine, if_exists='replace')\n elif self.split_type == 'date':\n seg_averages.to_sql(name='clust_nonsparse_avebysegment_date', con=engine, if_exists='replace')\n rt_averages.to_sql(name='clust_nonsparse_avebyrt_date', con=engine, if_exists='replace')\n \n print('returning train nonsparse matrix...')\n train_averages = clustering_df_tmp.groupby(['segment_id', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count','level_binary']].mean().reset_index()\n train_averages.columns = ['segment_id', 'day_of_week', 'time_idx', 'ave_level_min', 'ave_level_max', 'ave_level_mean', 'ave_level_count', 'ave_level_binary']\n nonsparse_matrix = pd.merge(clustering_df_tmp, train_averages, how='left', on=['segment_id', 'day_of_week', 'time_idx'])\n segtimes_df = nonsparse_matrix[['segment_id', 'date', 'time']]\n nonsparse_matrix_final = nonsparse_matrix.drop(columns=['segment_id', 'date','time', 'level_min', 'level_max', 'level_mean', 'level_count', 'level_binary'])\n \n return (segtimes_df, nonsparse_matrix_final)","def sparse_GP_regression_2D(num_samples=400, num_inducing=50, max_iters=100, optimize=True, plot=True):\r\n X = np.random.uniform(-3., 3., (num_samples, 2))\r\n Y = np.sin(X[:, 0:1]) * np.sin(X[:, 1:2]) + np.random.randn(num_samples, 1) * 0.05\r\n\r\n # construct kernel\r\n rbf = GPy.kern.rbf(2)\r\n\r\n # create simple GP Model\r\n m = GPy.models.SparseGPRegression(X, Y, kernel=rbf, num_inducing=num_inducing)\r\n\r\n # contrain all parameters to be positive (but not inducing inputs)\r\n m['.*len'] = 2.\r\n\r\n m.checkgrad()\r\n\r\n # optimize\r\n if optimize:\r\n m.optimize('tnc', messages=1, max_iters=max_iters)\r\n\r\n # plot\r\n if plot:\r\n m.plot()\r\n\r\n print m\r\n return m","def ipu_sparse_ops(scope=\"session\"):\n build_path = Path(\n public_examples_dir,\n \"applications\",\n \"tensorflow\",\n \"dynamic_sparsity\"\n )\n completed = run(['python3-config', '--extension-suffix'], stdout=PIPE)\n extension = completed.stdout.decode().replace('\\n', '')\n shared_libs = [f'host_utils{extension}', 'libsparse_matmul.so']\n paths = [Path(build_path, \"ipu_sparse_ops\", f) for f in shared_libs]\n\n # Use exclusive lockfile to avoid race conditions on the build:\n lock_path = Path(build_path, \".ipu_sparse_ops.pytest.build.lockfile\")\n\n @ExecuteOncePerFS(lockfile=lock_path, file_list=paths, timeout=120, retries=20)\n def build_dynsparse():\n run(['make', 'clean'], cwd=build_path)\n run(['make', '-j'], cwd=build_path)\n\n build_dynsparse()","def UpdateCostMatrix( self, extraXs ):\n for x in extraXs:\n newRow = [ self.EuclideanDistanceSq(x,y) for y in self.Y ]\n self.C.append(newRow)","def _identity_sparse(d, stype=\"csr\", dtype=complex):\n return sp.eye(d, dtype=dtype, format=stype)","def _create_stoic_mat(ns, nr, name_list, stoic_tuple, species_names):\n stoic_mat = np.zeros([ns, nr], dtype=np.int)\n for index, (names, stoics) in enumerate(zip(name_list, stoic_tuple)):\n for a_name, a_stoic in zip(names, stoics):\n species_index = species_names.index(a_name)\n stoic_mat[species_index, index] += int(a_stoic)\n return stoic_mat","def append_arr(self, num_seg, g_pix, intercept, slope, sig_intercept,\n sig_slope, inv_var, save_opt):\n self.slope_2d[num_seg[g_pix], g_pix] = slope[g_pix]\n if save_opt:\n self.interc_2d[num_seg[g_pix], g_pix] = intercept[g_pix]\n self.siginterc_2d[num_seg[g_pix], g_pix] = sig_intercept[g_pix]\n self.sigslope_2d[num_seg[g_pix], g_pix] = sig_slope[g_pix]\n self.inv_var_2d[num_seg[g_pix], g_pix] = inv_var[g_pix]","def putvartypelist(self,subj_,vartype_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(vartype_)\n elif num_ != len(vartype_):\n raise IndexError(\"Inconsistent length of array vartype\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if vartype_ is None:\n raise ValueError(\"Argument vartype cannot be None\")\n if vartype_ is None:\n raise ValueError(\"Argument vartype may not be None\")\n if vartype_ is not None:\n _vartype_tmp = (ctypes.c_int32 * len(vartype_))(*vartype_)\n else:\n _vartype_tmp = None\n res = __library__.MSK_XX_putvartypelist(self.__nativep,num_,_subj_tmp,_vartype_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def test_bind_init(self, sp_in):\n\n bind = DatasetSparseArray(sp_in).bind\n\n dset_sp = DatasetSparseArray(bind=bind)\n\n assert dset_sp.info[\"type_id\"] == \"sparse_array\"\n assert dset_sp.info[\"py_type\"] == f\"scipy.sparse.{type(sp_in).__qualname__}\"\n assert dset_sp.sparse_array_class is type(sp_in)\n\n sp_out = dset_sp.get_value()\n assert isinstance(sp_out, type(sp_in))\n\n assert (sp_in.todense() == sp_out.todense()).all()","def test_00_create_sparse_1d_array(self):\n ncells = 100\n sparsity = 3.0 # 1 / density\n _, err = _iquery(\"create array SPARSE <v:int64>[i=0:{0}:0:5]\".format(\n ncells - 1))\n assert not err, err\n self._array_cleanups.append('SPARSE')\n _, err = _iquery(\"\"\"\n insert(\n redimension(\n apply(\n build(<i:int64>[fud=0:{0}], {1}*fud),\n (v, 1)),\n SPARSE),\n SPARSE)\"\"\".format(int(ncells / sparsity) - 1,\n int(sparsity)))\n assert not err, err\n check_v_sum('SPARSE')\n nchunks = chunk_count(vaid_of('SPARSE'))\n prt(\"SPARSE has\", nchunks, \"chunks\")","def _store(self):\n if not self._is_supported_matrix(self._data):\n return super(SparseParameter, self)._store()\n else:\n store_dict = {}\n serial_string = self._serialize_matrix(self._data)\n store_dict[\"data%s\" % SparseParameter.IDENTIFIER] = serial_string\n\n if self.f_has_range():\n # # Supports smart storage by hashing\n smart_dict = {}\n\n store_dict[\"explored_data\" + SparseParameter.IDENTIFIER] = ObjectTable(\n columns=[\"idx\"], index=list(range(len(self)))\n )\n\n count = 0\n for idx, elem in enumerate(self._explored_range):\n\n serial_string = self._serialize_matrix(elem)\n\n # Use the hash_tuple as a key for the smart_dict\n if serial_string in smart_dict:\n name_idx = smart_dict[serial_string]\n add = False\n else:\n name_idx = count\n add = True\n\n store_dict[\"explored_data\" + SparseParameter.IDENTIFIER][\"idx\"][\n idx\n ] = name_idx\n\n if add:\n\n store_dict[\n \"xspm%s%08d\" % (SparseParameter.IDENTIFIER, name_idx)\n ] = serial_string\n\n smart_dict[serial_string] = name_idx\n count += 1\n\n self._locked = True\n\n return store_dict","def to_sparse_matrix(self, grid, format=None):\n S = self.centered_stencil()\n # print(\"grid :\")\n\n grid = tuple(grid)\n # print(grid)\n if not (np.asarray(S.shape) % 2 == 1).all():\n raise ValueError('all stencil dimensions must be odd')\n\n assert_condition(len(grid) == np.rank(S), ValueError,\n 'stencil rank must equal number of grid dimensions')\n assert_condition(min(grid) >= 1, ValueError,\n 'grid dimensions must be positive')\n\n N_v = np.prod(grid) # number of vertices in the mesh\n N_s = (S != 0).sum() # number of nonzero stencil entries\n\n # diagonal offsets\n diags = np.zeros(N_s, dtype=int)\n\n # compute index offset of each dof within the stencil\n strides = np.cumprod([1] + list(reversed(grid)))[:-1]\n indices = tuple(i.copy() for i in S.nonzero())\n for i,s in zip(indices,S.shape):\n i -= s // 2\n for stride,coords in zip(strides, reversed(indices)):\n diags += stride * coords\n\n #\n data = S[S != 0].repeat(N_v).reshape(N_s, N_v)\n indices = np.vstack(indices).T\n\n # zero boundary connections\n for index,diag in zip(indices,data):\n diag = diag.reshape(grid)\n for n,i in enumerate(index):\n if i > 0:\n s = [ slice(None) ]*len(grid)\n s[n] = slice(0,i)\n diag[s] = 0\n elif i < 0:\n s = [ slice(None) ]*len(grid)\n s[n] = slice(i,None)\n diag[s] = 0\n\n # remove diagonals that lie outside matrix\n mask = abs(diags) < N_v\n if not mask.all():\n diags = diags[mask]\n data = data[mask]\n\n # sum duplicate diagonals\n if len(np.unique(diags)) != len(diags):\n new_diags = np.unique(diags)\n new_data = np.zeros( (len(new_diags),data.shape[1]), dtype=data.dtype)\n for dia,dat in zip(diags,data):\n n = np.searchsorted(new_diags,dia)\n new_data[n,:] += dat\n\n diags = new_diags\n data = new_data\n\n return sprs.dia_matrix((data,diags), shape=(N_v, N_v)).asformat(format)","def wrapDBMatrix(self,mat):\n return mat.todense()","def __init__(self, rows, cols, bandwidth=0, sizeHint=None, matrix=None, storeZeros=True):\n sizeHint = sizeHint or max(rows, cols) * bandwidth\n if matrix is None:\n tmpMatrix = spmatrix.ll_mat(1, 1, 1)\n if hasattr(tmpMatrix, 'storeZeros'):\n matrix = spmatrix.ll_mat(rows, cols, sizeHint, storeZeros)\n else:\n matrix = spmatrix.ll_mat(rows, cols, sizeHint)\n\n _PysparseMatrix.__init__(self, matrix=matrix)","def to_sparse(self):\n if self.rep.fmt == 'sparse':\n return self\n\n return self.from_rep(self.rep.to_sdm())","def set_sparse_backend(sparse_backend, warn=True):\n if 'biom.table' in modules:\n if warn:\n print (\"Warning: biom.table has already been loaded. This call to \"\n \"biom.set_sparse_backend() has no effect. It must be \"\n \"called before biom.table is imported for the first time.\")\n else:\n biom_config['python_code_sparse_backend'] = sparse_backend","def get_cvxopt_sparse_intf():\n import cvxpy.interface.cvxopt_interface.sparse_matrix_interface as smi\n return smi.SparseMatrixInterface()","def build_mat_scikit_strandOriented(sralist, scikit_data):\n\n scikit_mat = {}\n seq_codons = {}\n seq_aa = {}\n\n for geneID in scikit_data[sralist[0]][0].keys():\n for ix, dataset in enumerate(sralist):\n\n if geneID in scikit_data[dataset][0].keys():\n current_profile = scikit_data[dataset][0].get(geneID, np.nan)\n current_ribo = current_profile[0]\n current_ribo = current_ribo[8:-8]\n N = len(sralist)\n M = len(current_ribo)\n print(geneID, M)\n\n if ix == 0:\n current_matrix = np.zeros((N,M)) * np.nan\n\n current_seq_codons = current_profile[1]\n current_seq_codons = current_seq_codons[8:-8]\n\n current_seq_aa = current_profile[2]\n current_seq_aa = current_seq_aa[8:-8]\n\n if strand_by_geneID_dict.get(geneID, \"NA\") == \"+\":\n seq_codons[geneID] = current_seq_codons\n seq_aa[geneID] = current_seq_aa\n\n elif strand_by_geneID_dict.get(geneID, \"NA\") == \"-\":\n seq_codons[geneID] = current_seq_codons[::-1]\n seq_aa[geneID] = current_seq_aa[::-1]\n \n \n if strand_by_geneID_dict.get(geneID, \"NA\") == \"+\":\n current_matrix[ix,:] = current_ribo\n\n elif strand_by_geneID_dict.get(geneID, \"NA\") == \"-\":\n current_matrix[ix,:] = current_ribo[::-1]\n \n if np.sum(current_matrix) > 0: \n scikit_mat[geneID] = current_matrix\n\n# scikit_df = pd.DataFrame(values_list, columns=columns_list)\n\n return scikit_mat, seq_codons, seq_aa","def test_csm_sparser(self):\r\n sp_types = {'csc': sp.csc_matrix,\r\n 'csr': sp.csr_matrix}\r\n\r\n for format in ['csc', 'csr']:\r\n for dtype in ['float32', 'float64']:\r\n x = tensor.tensor(dtype=dtype, broadcastable=(False,))\r\n y = tensor.ivector()\r\n z = tensor.ivector()\r\n s = tensor.ivector()\r\n\r\n a = as_sparse_variable(sp_types[format](random_lil((4, 3),\r\n dtype, 1)))\r\n\r\n f = theano.function([x, y, z, s],\r\n tensor.grad(dense_from_sparse(\r\n a * CSM(format)(x, y, z, s)).sum(), x))\r\n\r\n spmat = sp_types[format](random_lil((4, 3), dtype, 3))\r\n\r\n res = f(spmat.data, spmat.indices, spmat.indptr,\r\n numpy.asarray(spmat.shape, 'int32'))\r\n\r\n assert len(spmat.data) == len(res)","def visit_list(self, sylist):\n if len(self.current) == 0:\n self.current.extend(sylist)\n\n for item, other_item in izip(self.current, sylist):\n hjoin(item, other_item)","def test_assign_clusters_sparse(self, new_data, filename):\n\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n \n print('creating test sparse matrix...')\n if self.split_type == 'random':\n averages_seg = pd.read_sql('SELECT * FROM clust_sparse_avebysegment_random',con=engine)\n averages_rt = pd.read_sql('SELECT * FROM clust_sparse_avebyrt_random',con=engine)\n if self.split_type == 'date':\n averages_seg = pd.read_sql('SELECT * FROM clust_sparse_avebysegment_date',con=engine)\n averages_rt = pd.read_sql('SELECT * FROM clust_sparse_avebyrt_date',con=engine)\n\n averages_seg['exists'] = 1\n test_data_exists = pd.merge(new_data, averages_seg[['segment_id', 'exists']], on=['segment_id'])\n test_exists = test_data_exists[test_data_exists['exists']==1]\n test_notexists = test_data_exists[test_data_exists['exists']!=1] \n \n test_matrix_exists = pd.merge(test_exists[['segment_id', 'road_type']], averages_seg, how='left', on=['segment_id'])\n test_matrix_notexists = pd.merge(test_notexists[['segment_id', 'road_type']], averages_rt, how='left', on=['road_type'])\n test_matrix = pd.concat([test_matrix_exists, test_matrix_notexists])\n test_matrix = test_matrix.fillna(0) \n \n test_sparse_matrix = test_matrix.drop(columns = ['segment_id', 'road_type', 'exists', 'index', 'roadtypekey', 'segmentskey'])\n num = list(range(len(list(averages_seg))-4))\n columns = [str(item) for item in num]\n test_sparse_matrix = test_sparse_matrix[columns] \n \n print('clustering new data...')\n cluster_model = joblib.load(filename)\n cluster_predictions = cluster_model.predict(test_sparse_matrix)\n \n clusterdf = pd.DataFrame(cluster_predictions,columns = ['cluster_sparse'])\n clusterdf['index'] = clusterdf.index\n segmentdf = test_matrix['segment_id'].to_frame()\n segmentdf['index'] = segmentdf.index\n test_cluster_df_sparse = pd.merge(clusterdf, segmentdf, on=['index'])\n test_cluster_df_sparse = test_cluster_df_sparse[['segment_id','cluster_sparse']].groupby(['segment_id','cluster_sparse']).count()\n \n return test_cluster_df_sparse.reset_index()","def _apply_sparse(self, grad, var):\n return self._apply_sparse_shared(\n grad.values,\n var,\n grad.indices,\n lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda\n x,\n i,\n v,\n use_locking=self._use_locking))","def __call__(self, sparse_matrix: PipelinedRDD):\n rows = sparse_matrix.collect()\n\n mat_index, mat_weights = zip(*rows)\n mat_row, mat_col = zip(*mat_index)\n tokens_num = len(self.tokens_list)\n\n self._log.info(\"Building matrix...\")\n matrix = sparse.coo_matrix((mat_weights, (mat_row, mat_col)),\n shape=(tokens_num, tokens_num))\n Cooccurrences() \\\n .construct(self.tokens_list, matrix) \\\n .save(output=self.output, series=\"id2vec\", deps=(self.df_model,))","def create_adjust_matrix(self, is_sub: bool):\n matrix = None\n if not is_sub:\n ratings = np.ones_like(self._user, dtype=np.float32)\n matrix = sp.csr_matrix(\n (ratings, (self._user, self._item + self.n_users)),\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\n )\n else:\n if self.type == \"ND\":\n drop_user = self.rand_sample(\n self.n_users,\n size=int(self.n_users * self.drop_ratio),\n replace=False,\n )\n drop_item = self.rand_sample(\n self.n_items,\n size=int(self.n_items * self.drop_ratio),\n replace=False,\n )\n R_user = np.ones(self.n_users, dtype=np.float32)\n R_user[drop_user] = 0.0\n R_item = np.ones(self.n_items, dtype=np.float32)\n R_item[drop_item] = 0.0\n R_user = sp.diags(R_user)\n R_item = sp.diags(R_item)\n R_G = sp.csr_matrix(\n (\n np.ones_like(self._user, dtype=np.float32),\n (self._user, self._item),\n ),\n shape=(self.n_users, self.n_items),\n )\n res = R_user.dot(R_G)\n res = res.dot(R_item)\n\n user, item = res.nonzero()\n ratings = res.data\n matrix = sp.csr_matrix(\n (ratings, (user, item + self.n_users)),\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\n )\n\n elif self.type == \"ED\" or self.type == \"RW\":\n keep_item = self.rand_sample(\n len(self._user),\n size=int(len(self._user) * (1 - self.drop_ratio)),\n replace=False,\n )\n user = self._user[keep_item]\n item = self._item[keep_item]\n\n matrix = sp.csr_matrix(\n (np.ones_like(user), (user, item + self.n_users)),\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\n )\n\n matrix = matrix + matrix.T\n D = np.array(matrix.sum(axis=1)) + 1e-7\n D = np.power(D, -0.5).flatten()\n D = sp.diags(D)\n return D.dot(matrix).dot(D)","def sparse(cls, a_ndarray, i_ndarray, shape, bigdl_type=\"float\"):\n if a_ndarray is None:\n return None\n invalidInputError(isinstance(a_ndarray, np.ndarray),\n f\"input should be a np.ndarray, not ${type(a_ndarray)}\")\n invalidInputError(isinstance(i_ndarray, np.ndarray),\n f\"indices should be a np.ndarray, not ${type(i_ndarray)}\")\n invalidInputError(i_ndarray.size == a_ndarray.size * shape.size,\n f\"size of values ${a_ndarray.size * shape.size} and\"\n f\" indices ${i_ndarray.size} should match\")\n return cls(a_ndarray,\n shape,\n bigdl_type,\n i_ndarray)","def _check_scipy_index_typing(sparse_matrix):\n\n int_max = np.iinfo(MKL.MKL_INT_NUMPY).max\n if (sparse_matrix.nnz > int_max) or (max(sparse_matrix.shape) > int_max):\n msg = \"MKL interface is {t} and cannot hold matrix {m}\\n\".format(m=repr(sparse_matrix), t=MKL.MKL_INT_NUMPY)\n msg += \"Try changing MKL to int64 with the environment variable MKL_INTERFACE_LAYER=ILP64\"\n raise ValueError(msg)\n\n # Cast indexes to MKL_INT type\n if sparse_matrix.indptr.dtype != MKL.MKL_INT_NUMPY:\n sparse_matrix.indptr = sparse_matrix.indptr.astype(MKL.MKL_INT_NUMPY)\n if sparse_matrix.indices.dtype != MKL.MKL_INT_NUMPY:\n sparse_matrix.indices = sparse_matrix.indices.astype(MKL.MKL_INT_NUMPY)","def get_sparse_backend():\n backend = biom_config['python_code_sparse_backend']\n if backend is None:\n backend = 'CSMat'\n\n if backend not in sparse_backends:\n raise InvalidSparseBackendException(\"Unrecognized sparse backend \"\n \"'%s'. Choose from %s.\" % (backend,\n ', '.join(sparse_backends)))\n\n valid_backend = False\n if backend == 'ScipySparseMat':\n try:\n from biom.backends.scipysparse import ScipySparseMat, to_scipy, \\\n dict_to_scipy, list_dict_to_scipy, list_nparray_to_scipy, \\\n nparray_to_scipy, list_list_to_scipy\n SparseObj = ScipySparseMat\n to_sparse = to_scipy\n dict_to_sparseobj = dict_to_scipy\n list_dict_to_sparseobj = list_dict_to_scipy\n list_nparray_to_sparseobj = list_nparray_to_scipy\n nparray_to_sparseobj = nparray_to_scipy\n list_list_to_sparseobj = list_list_to_scipy\n valid_backend = True\n except ImportError:\n valid_backend = False\n stderr.write(\"Cannot load ScipySparseMat (requires that scipy is \"\n \"installed). Using CSMat sparse backend.\\n\")\n\n if backend == 'CSMat' or (not valid_backend):\n try:\n from biom.backends.csmat import CSMat, to_csmat, dict_to_csmat, \\\n list_dict_to_csmat, list_nparray_to_csmat, nparray_to_csmat, \\\n list_list_to_csmat\n SparseObj = CSMat\n to_sparse = to_csmat\n dict_to_sparseobj = dict_to_csmat\n list_dict_to_sparseobj = list_dict_to_csmat\n list_nparray_to_sparseobj = list_nparray_to_csmat\n nparray_to_sparseobj = nparray_to_csmat\n list_list_to_sparseobj = list_list_to_csmat\n valid_backend = True\n except ImportError:\n valid_backend = False\n stderr.write('Cannot load CSMat sparse backend.\\n')\n\n if not valid_backend:\n raise InvalidSparseBackendException(\"The sparse matrix backend '%s' \"\n \"could not be loaded. Please check your biom-format \"\n \"installation.\" % backend)\n\n return SparseObj, to_sparse, dict_to_sparseobj, list_dict_to_sparseobj, \\\n list_nparray_to_sparseobj, nparray_to_sparseobj, \\\n list_list_to_sparseobj","def makesparse(matrix):\n n = matrix[0].size\n elements = []\n for i in range(n):\n for j in range(n):\n if matrix[i][j] != 0 :\n temp = MatrixElement(i, j, matrix[i][j])\n elements.append(temp)\n return SparseMatrix(n, elements)","def append_rss(A):\n rss = np.sqrt(np.diag(A.dot(A.transpose()))) \n out = np.hstack((A, np.atleast_2d(rss).transpose())) \n return out","def _apply_sparse(self, grad, var):\n\n return self._apply_sparse_shared(\n grad.values,\n var,\n grad.indices,\n lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda\n x,\n i,\n v,\n use_locking=self._use_locking))","def putclist(self,subj_,val_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(val_)\n elif num_ != len(val_):\n raise IndexError(\"Inconsistent length of array val\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if val_ is None:\n raise ValueError(\"Argument val cannot be None\")\n if val_ is None:\n raise ValueError(\"Argument val may not be None\")\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\n _val_copyarray = False\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif val_ is not None:\n _val_copyarray = True\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\n _val_np_tmp[:] = val_\n assert _val_np_tmp.flags.contiguous\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _val_copyarray = False\n _val_tmp = None\n \n res = __library__.MSK_XX_putclist(self.__nativep,num_,_subj_tmp,_val_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def create_sparse_matrix_long(self, matrix_df):\n\n time_df_tmp = matrix_df.groupby(['date_idx','time_idx']).count().reset_index()\n time_df = time_df_tmp[['date_idx','time_idx']]\n time_df['time_id'] = time_df.index.copy()\n matrix_df_tid = pd.merge(matrix_df, time_df, how='left', on=['date_idx','time_idx'])\n\n time_array = np.array(matrix_df_tid['time_id'])\n segment_array = np.array(matrix_df_tid['segment_id'])\n\n uniquesegments = np.array(list(set(segment_array)))\n keyuniquesegments = np.array(range(len(uniquesegments)))\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\n\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\n segmentkeys = np.array(segments_keys_df['segmentskey'])\n\n level_array = np.array(matrix_df_tid[self.args['cluster_variable']])\n sparse_matrix = csr_matrix((level_array, (segmentkeys,time_array))).toarray()\n sparse_matrix = preprocessing.scale(sparse_matrix)\n \n if self.args['perform_pca']:\n data_to_cluster, self.pca_model = self.run_PCA_long(sparse_matrix)\n else:\n data_to_cluster = sparse_matrix\n\n sparse_matrix_withsegkey = pd.DataFrame(data_to_cluster)\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\n \n print('writing sparse matrix_long to db...')\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n if self.split_type == 'random':\n sparse_matrix_withseg.to_sql(name='clust_sparse_long_avebysegment_random', con=engine, if_exists='replace')\n elif self.split_type == 'date':\n sparse_matrix_withseg.to_sql(name='clust_sparse_long_avebysegment_date', con=engine, if_exists='replace')\n \n print('returning train sparse_long matrix...')\n return (uniquesegments_df, data_to_cluster)","def add_sparse_pwd(a, b, neg1_fmt=True):\n \n a_coo = a.tocoo()\n b_coo = b.tocoo()\n nr, nc = a_coo.shape\n \n ind_a = a_coo.row.astype(np.int64) * nc + a_coo.col\n ind_b = b_coo.row.astype(np.int64) * nc + b_coo.col\n # ind_ab = np.array(tuple(set(ind_a).intersection(set(ind_b))))\n ind_ab = np.intersect1d(ind_a, ind_b, assume_unique=True)\n keep_a = np.in1d(ind_a, ind_ab, assume_unique=True)\n keep_b = np.in1d(ind_b, ind_ab, assume_unique=True)\n if neg1_fmt:\n a_data = a_coo.data[keep_a]\n a_data[a_data == -1] = 0\n b_data = b_coo.data[keep_b]\n b_data[b_data == -1] = 0\n tot = a_data + b_data\n else:\n tot = a_coo.data[keep_a] + b_coo.data[keep_b]\n \"\"\"Need to add -1 as placeholder upon creation of new sparse matrix\"\"\"\n tot[tot == 0] = -1\n aplusb = sparse.coo_matrix((tot,\n (a_coo.row[keep_a], a_coo.col[keep_a])), shape=(nr, nc))\n aplusb = aplusb.tocsr()\n if not neg1_fmt:\n \"\"\"Convert it back to 0 = true zero distance\"\"\"\n aplusb.data[aplusb.data == -1] = 0\n return aplusb","def create_preference_weighted_similarity_matrix(self):\n # We only need to compare the distances to points within the epsilon shell (to determine if a point is a core point)\n # Since the subspace scaling factor kappa is >>1 (and not <1), no distances to other points will be needed for \n # the core point evaluation\n\n # get points in epsilon shell: attententio point itself is not in neigh_ind list\n _, neigh_ind = self.neigbors_clf.radius_neighbors(radius=self.epsilon)\n row, col, pwsim = [], [], []\n for i, ith_neigh_ind in enumerate(neigh_ind):\n # Calculate preference weighted similarity measure with point and neighbors in eps shell\n sq_diffs = np.square(self.data[ith_neigh_ind,:] - self.data[i,:])\n sum_weighted_sq_diffs = np.inner(self.subspace_preference_vectors[i,:], sq_diffs)\n pwsim_ith = np.sqrt(sum_weighted_sq_diffs)\n \n # Info for sparse matrix\n pwsim.extend(pwsim_ith.tolist()) # Data\n row.extend([i]*(pwsim_ith.shape[0])) # ith Row \n col.extend(ith_neigh_ind.tolist()) # column info\n\n # Construct sparse matrix with data, row, and column info\n A = csr_matrix((pwsim, (row, col)), shape=(self.nb_points, self.nb_points))\n # Create symmetric version: take the elementwise maximum of A and its transpose A.T\n transpose_is_bigger = A.T>A\n A = A - A.multiply(transpose_is_bigger) + (A.T).multiply(transpose_is_bigger)\n \n return A","def extend_traj(self, run_idx, traj_idx, data, weights=None):\n\n if self._wepy_mode == 'c-':\n assert self._append_flags[dataset_key], \"dataset is not available for appending to\"\n\n # convenient alias\n traj_data = data\n\n # number of frames to add\n n_new_frames = traj_data[POSITIONS].shape[0]\n\n n_frames = self.num_traj_frames(run_idx, traj_idx)\n\n # calculate the new sparse idxs for sparse fields that may be\n # being added\n sparse_idxs = np.array(range(n_frames, n_frames + n_new_frames))\n\n # get the trajectory group\n traj_grp = self._h5['{}/{}/{}/{}'.format(RUNS, run_idx, TRAJECTORIES, traj_idx)]\n\n ## weights\n\n # if weights are None then we assume they are 1.0\n if weights is None:\n weights = np.ones((n_new_frames, 1), dtype=float)\n else:\n assert isinstance(weights, np.ndarray), \"weights must be a numpy.ndarray\"\n assert weights.shape[0] == n_new_frames,\\\n \"weights and the number of frames must be the same length\"\n\n # add the weights\n weights_ds = traj_grp[WEIGHTS]\n\n # append to the dataset on the first dimension, keeping the\n # others the same, if they exist\n if len(weights_ds.shape) > 1:\n weights_ds.resize( (weights_ds.shape[0] + n_new_frames, *weights_ds.shape[1:]) )\n else:\n weights_ds.resize( (weights_ds.shape[0] + n_new_frames, ) )\n\n # add the new data\n weights_ds[-n_new_frames:, ...] = weights\n\n\n # add the other fields\n for field_path, field_data in traj_data.items():\n\n # if the field hasn't been initialized yet initialize it,\n # unless we are in SWMR mode\n if not field_path in traj_grp:\n\n # if in SWMR mode you cannot create groups so if we\n # are in SWMR mode raise a warning that the data won't\n # be recorded\n if self.swmr_mode:\n warn(\"New datasets cannot be created while in SWMR mode. The field {} will\"\n \"not be saved. If you want to save this it must be\"\n \"previously created\".format(field_path))\n else:\n\n feature_shape = field_data.shape[1:]\n feature_dtype = field_data.dtype\n\n # not specified as sparse_field, no settings\n if (not field_path in self.field_feature_shapes) and \\\n (not field_path in self.field_feature_dtypes) and \\\n not field_path in self.sparse_fields:\n # only save if it is an observable\n is_observable = False\n if '/' in field_path:\n group_name = field_path.split('/')[0]\n if group_name == OBSERVABLES:\n is_observable = True\n if is_observable:\n warn(\"the field '{}' was received but not previously specified\"\n \" but is being added because it is in observables.\".format(field_path))\n # save sparse_field flag, shape, and dtype\n self._add_sparse_field_flag(field_path)\n self._set_field_feature_shape(field_path, feature_shape)\n self._set_field_feature_dtype(field_path, feature_dtype)\n else:\n raise ValueError(\"the field '{}' was received but not previously specified\"\n \"it is being ignored because it is not an observable.\".format(field_path))\n\n # specified as sparse_field but no settings given\n elif (self.field_feature_shapes[field_path] is None and\n self.field_feature_dtypes[field_path] is None) and \\\n field_path in self.sparse_fields:\n # set the feature shape and dtype since these\n # should be 0 in the settings\n self._set_field_feature_shape(field_path, feature_shape)\n\n self._set_field_feature_dtype(field_path, feature_dtype)\n\n # initialize\n self._init_traj_field(run_idx, traj_idx, field_path, feature_shape, feature_dtype)\n\n # extend it either as a sparse field or a contiguous field\n if field_path in self.sparse_fields:\n self._extend_sparse_traj_field(run_idx, traj_idx, field_path, field_data, sparse_idxs)\n else:\n self._extend_contiguous_traj_field(run_idx, traj_idx, field_path, field_data)","def update_mass_matrix(self, sim, joint_index):\n mass_matrix = np.ndarray(shape=(len(sim.data.qvel) ** 2,), dtype=np.float64, order='C')\n # print(self.current_joint_position)\n # print(mass_matrix)\n mujoco_py.cymj._mj_fullM(sim.model, mass_matrix, sim.data.qM)\n mass_matrix = np.reshape(mass_matrix, (len(sim.data.qvel), len(sim.data.qvel)))\n self.mass_matrix = mass_matrix[joint_index, :][:, joint_index]\n \n # self.mass_matrix = np.array([[ 2.25726539e+00, -2.62750718e-06, 1.60412060e+00,\n # 2.62750718e-06, -5.43221706e-01, 2.62750718e-06,\n # -1.98486117e-01],\n # [-2.62750718e-06, 2.28637709e+00, -6.58357876e-06,\n # -1.16381227e+00, -4.11331754e-06, -4.00056093e-01,\n # 2.53844900e-18],\n # [ 1.60412060e+00, -6.58357876e-06, 1.60070894e+00,\n # 6.58357876e-06, -5.02987276e-01, 6.58357876e-06,\n # -1.89718057e-01],\n # [ 2.62750718e-06, -1.16381227e+00, 6.58357876e-06,\n # 1.43272976e+00, 4.11331754e-06, 4.01913080e-01,\n # -2.53381454e-18],\n # [-5.43221706e-01, -4.11331754e-06, -5.02987276e-01,\n # 4.11331754e-06, 5.11565111e-01, 4.11331754e-06,\n # 1.96115254e-01],\n # [ 2.62750718e-06, -4.00056093e-01, 6.58357876e-06,\n # 4.01913080e-01, 4.11331754e-06, 3.22014223e-01,\n # -2.53991699e-18],\n # [-1.98486117e-01, 2.53844900e-18, -1.89718057e-01,\n # -2.53381454e-18, 1.96115254e-01, -2.53991699e-18,\n # 2.00104011e-01]])","def __init__(self, link_matrix, users, index_id_map, is_sparse=False):\n self.__is_sparse = is_sparse\n self.__link_matrix = link_matrix\n self.__link_matrix_tr = link_matrix.transpose()\n self.__n = self.__link_matrix.shape[0]\n self.__hubs = np.ones(self.__n)\n self.__auths = np.ones(self.__n)\n self.__size = 30\n self.__names = [users[index_id_map[i]]['screen_name'] for i in range(0, self.__size)]\n self.__index_id_map = index_id_map\n self.__users = users\n self.all_hubs = []\n self.all_auths = []","def append(self, solf):\n exist_map = {'vertex_index': len(self.get('vertices', [])),\n 'texcoord_index': len(self.get('texcoords', [])),\n 'normal_index': len(self.get('normals', [])),\n 'param_index': len(self.get('params', []))}\n exist_map.update(points=exist_map['vertex_index'],\n curve2Ds=exist_map['param_index'])\n # Vertex fields\n for k in ['vertices', 'texcoords', 'normals', 'params']:\n if k in solf:\n if k not in self:\n self[k] = []\n self[k] += solf[k]\n # Points/2D curves\n for k in ['points', 'curve2Ds']:\n if k in solf:\n if k not in self:\n self[k] = []\n for x in solf[k]:\n self[k].append([v + exist_map[k] for v in x])\n # Face/line fields\n for k in ['lines', 'faces']:\n if k in solf:\n if k not in self:\n self[k] = []\n for x in solf[k]:\n iele = [{ik: v[ik] + exist_map[ik] for ik in v.keys()} for v in x]\n self[k].append(iele)\n # Curves\n k = 'curves'\n if k in solf:\n if k not in self:\n self[k] = []\n for x in solf[k]:\n iele = copy.deepcopy(x)\n iele['vertex_indices'] = [v + exist_map['vertex_index']\n for v in x['vertex_indices']]\n # Surfaces\n k = 'surfaces'\n if k in solf:\n if k not in self:\n self[k] = []\n for x in solf[k]:\n iele = copy.deepcopy(x)\n iele['vertex_indices'] = [{ik: v[ik] + exist_map[ik] for ik in v.keys()}\n for v in x['vertex_indices']]\n # Merge material using first in list\n material = None\n for x in [self, solf]:\n if x.get('material', None) is not None:\n material = x['material']\n break\n if material is not None:\n self['material'] = material\n return self"],"string":"[\n \"def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi_)\\n elif nz_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj_)\\n elif nz_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij_)\\n elif nz_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n idx_ = ctypes.c_int64()\\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n idx_ = idx_.value\\n _idx_return_value = idx_\\n return (_idx_return_value)\",\n \"def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi)\\n elif nz_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj)\\n elif nz_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij)\\n elif nz_ != len(valij):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if nz_ is None: nz_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valij is None: raise TypeError(\\\"Invalid type for argument valij\\\")\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n \\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n \\n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _idx_return_value = resargs\\n return _idx_return_value\",\n \"def getsparsesymmat(self,idx_,subi_,subj_,valij_):\\n maxlen_ = self.getsymmatinfo((idx_))[1]\\n _subi_minlength = (maxlen_)\\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxlen_)))\\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\\n raise ValueError(\\\"Argument subi must be writable\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = (maxlen_)\\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxlen_)))\\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\\n raise ValueError(\\\"Argument subj must be writable\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _valij_minlength = (maxlen_)\\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\\n raise ValueError(\\\"Array argument valij is not long enough: Is %d, expected %d\\\" % (len(valij_),(maxlen_)))\\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\\n raise ValueError(\\\"Argument valij must be writable\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _subi_copyarray:\\n subi_[:] = _subi_np_tmp\\n if _subj_copyarray:\\n subj_[:] = _subj_np_tmp\\n if _valij_copyarray:\\n valij_[:] = _valij_np_tmp\",\n \"def getsparsesymmat(self,idx_,subi,subj,valij): # 3\\n maxlen_ = self.getsymmatinfo((idx_))[1]\\n _copyback_subi = False\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n _copyback_subi = True\\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n _copyback_subi = True\\n if subi_ is not None and len(subi_) != (maxlen_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxlen_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n _copyback_valij = False\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n _copyback_valij = True\\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n _copyback_valij = True\\n if valij_ is not None and len(valij_) != (maxlen_):\\n raise ValueError(\\\"Array argument valij has wrong length\\\")\\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_valij:\\n valij[:] = _tmparr_valij\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n if _copyback_subi:\\n subi[:] = _tmparr_subi\",\n \"def add_sparse(self, key, element):\\n self.add(self._sparse2seq(key), element)\",\n \"def _build_sparse(self, name, wrt, consize, param_vals, sub_param_conns,\\n full_param_conns, rels):\\n\\n jac = None\\n\\n # Additional sparsity for index connections\\n for param in wrt:\\n\\n sub_conns = sub_param_conns.get(param)\\n if not sub_conns:\\n continue\\n\\n # If we have a simultaneous full connection, then we move on\\n full_conns = full_param_conns.get(param)\\n if full_conns.intersection(rels):\\n continue\\n\\n rel_idx = set()\\n for target, idx in iteritems(sub_conns):\\n\\n # If a target of the indexed desvar connection is\\n # in the relevant path for this constraint, then\\n # those indices are relevant.\\n if target in rels:\\n rel_idx.update(idx)\\n\\n nrel = len(rel_idx)\\n if nrel > 0:\\n\\n if jac is None:\\n jac = {}\\n\\n if param not in jac:\\n # A coo matrix for the Jacobian\\n # mat = {'coo':[row, col, data],\\n # 'shape':[nrow, ncols]}\\n coo = {}\\n coo['shape'] = [consize, len(param_vals[param])]\\n jac[param] = coo\\n\\n row = []\\n col = []\\n for i in range(consize):\\n row.extend([i]*nrel)\\n col.extend(rel_idx)\\n data = np.ones((len(row), ))\\n\\n jac[param]['coo'] = [np.array(row), np.array(col), data]\\n\\n if name not in self.sub_sparsity:\\n self.sub_sparsity[name] = {}\\n self.sub_sparsity[name][param] = np.array(list(rel_idx))\\n\\n return jac\",\n \"def append(self, symmData: list) -> None:\\n newSymm = SymmetryElement(symmData)\\n if not newSymm in self._symmcards:\\n self._symmcards.append(newSymm)\",\n \"def matrix_add_symmetric(M, M_sym):\\n M[0, 0] += M_sym[0]\\n M[0, 1] += M_sym[1]\\n M[1, 0] += M_sym[1]\\n M[0, 2] += M_sym[2]\\n M[2, 0] += M_sym[2]\\n\\n M[1, 1] += M_sym[3]\\n M[1, 2] += M_sym[4]\\n M[2, 1] += M_sym[4]\\n\\n M[2, 2] += M_sym[5]\\n\\n return M\",\n \"def sparsify(W,conn):\\n \\n N = W.shape[0]\\n W_sparse = sparse.lil_matrix((N,N)) \\n for row, weights in itertools.izip(conn, W):\\n W_sparse[row[0],row[1:]] = weights[1:]\\n return W_sparse\",\n \"def putaijlist(self,subi_,subj_,valij_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(valij_)\\n elif num_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def store(self, subj):\\n if subj in self.__lst:\\n raise ValueError('Disciplina exista deja')\\n self.__lst.append(subj)\",\n \"def set_sparse_signals(self):\\n\\t\\n\\t\\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\\n\\t\\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\\n\\t\\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tparams_dSs,\\tseed=self.seed_dSs)\\n\\t\\t\\n\\t\\t# Replace components with conflicting background odor \\n\\t\\tif self.Kk_split is not None and self.Kk_split != 0:\\n\\t\\t\\tassert 0 <= self.Kk_split <= self.Kk, \\\\\\n\\t\\t\\t\\t\\\"Splitting sparse signal into two levels requires Kk_split\\\" \\\\\\n\\t\\t\\t\\t\\\" to be non-negative and less than or equal to Kk.\\\"\\n\\t\\t\\tassert self.mu_dSs_2 is not None \\\\\\n\\t\\t\\t\\tand self.sigma_dSs_2 is not None, \\\\\\n\\t\\t\\t\\t\\\"Splitting sparse signal into two levels requires that\\\" \\\\\\n\\t\\t\\t\\t\\\" mu_dSs_2 and sigma_dSs_2 are set.\\\"\\n\\n\\t\\t\\tsp.random.seed(self.seed_dSs)\\n\\t\\t\\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\treplace=False)\\n\\t\\t\\tfor idx_2 in self.idxs_2:\\n\\t\\t\\t\\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.sigma_dSs_2)\\n\\t\\telse:\\n\\t\\t\\tself.idxs_2 = []\\n\\t\\t\\tself.Kk_split = 0\\n\\t\\t\\t\\n\\t\\t# Ss0 is the ideal (learned) background stimulus without noise\\n\\t\\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.idxs, params_Ss0,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Ss0)\\n\\t\\t\\n\\t\\tself.Ss = self.dSs + self.Ss0_noisy\",\n \"def save_sparse_matrix(self,\\n artifact_type: str,\\n params: Dict[str, Any],\\n sparse_matrix: sp.csr_matrix,\\n ignore_duplicate: bool = False) -> str:\\n ppr_idx = None\\n if \\\"ppr_idx\\\" in params.keys() and not isinstance(params[\\\"ppr_idx\\\"], int):\\n ppr_idx = np.array(params[\\\"ppr_idx\\\"])\\n params[\\\"ppr_idx\\\"] = hash(frozenset(params[\\\"ppr_idx\\\"]))\\n\\n if ignore_duplicate:\\n # check there's no entry with the exact same config already present\\n ids = Storage.locked_call(\\n lambda: self._find_meta_by_exact_params(artifact_type, params),\\n self._get_lock_path(artifact_type),\\n self.lock_timeout\\n )\\n if len(ids) > 0:\\n logging.info(f\\\"Ignoring duplicate save in save_sparse_matrix call\\\")\\n return self._build_artifact_path(artifact_type, ids[0].doc_id).replace(\\\".pt\\\", \\\".npz\\\")\\n\\n ids = Storage.locked_call(\\n lambda: self._upsert_meta(artifact_type, params),\\n self._get_lock_path(artifact_type),\\n self.lock_timeout,\\n )\\n if len(ids) != 1:\\n raise RuntimeError(f'The index contains duplicates (artifact_type={artifact_type}, params={params})')\\n\\n try:\\n path = self._build_artifact_path(artifact_type, ids[0]).replace(\\\".pt\\\", \\\".npz\\\")\\n sp.save_npz(path, sparse_matrix)\\n logging.info(f\\\"Saved sparse matrix to storage\\\")\\n if ppr_idx is not None:\\n ppr_path = path.replace(\\\".npz\\\", \\\"idx.npy\\\")\\n np.save(ppr_path, ppr_idx)\\n logging.info(f\\\"Saved ppr index to storage\\\")\\n return path\\n except: # noqa: E722\\n Storage.locked_call(\\n lambda: self._remove_meta(artifact_type, params),\\n self._get_lock_path(artifact_type),\\n self.lock_timeout\\n )\\n raise\",\n \"def dict2sparseMatrix(wDict,std=0,diag=0):\\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\\n nAreas = len(list(wDict.keys()))\\n for i in wDict:\\n data[i,i] = diag\\n ne = len(wDict[i])+ diag\\n for j in wDict[i]:\\n if std:\\n data[i,j] = 1 / float(ne)\\n else:\\n data[i,j] = 1\\n return data\",\n \"def putaijlist(self,subi,subj,valij): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subi)\\n elif num_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj)\\n elif num_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(valij)\\n elif num_ != len(valij):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if num_ is None: num_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valij is None: raise TypeError(\\\"Invalid type for argument valij\\\")\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n \\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n \\n res = self.__obj.putaijlist64(num_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_ss(self, subjects_list, i):\\n sub_added = subjects_list[i+1]\\n #random.seed(42)\\n surface = random.randint(0, len(self.surfaces)-1)\\n\\n if os.path.isdir(self.data_dir + str(sub_added)):\\n skel_file = os.path.join(self.data_dir, str(sub_added), self.cpt_skel_1,\\n self.side + self.cpt_skel_2 + str(sub_added) + self.cpt_skel_3)\\n self.skel = aims.read(skel_file)\\n bck_map = self.surfaces[surface]['aims_ss']\\n for voxel in bck_map[0].keys():\\n if self.skel.value(voxel[0], voxel[1], voxel[2])!=11:\\n self.skel.setValue(60, voxel[0], voxel[1], voxel[2])\\n\\n bck_map_bottom = self.surfaces[surface]['aims_bottom']\\n for voxel in bck_map_bottom[0].keys():\\n if self.skel.value(voxel[0], voxel[1], voxel[2])!=11:\\n self.skel.setValue(60, voxel[0], voxel[1], voxel[2])\\n\\n save_subject = sub_added\\n return save_subject\",\n \"def sparse_matlab(i, j, v, m, n):\\n return csr_matrix((v, (i, j)), shape=(m, n))\",\n \"def eval_sparse(self, array_in, array_out, sp_matrix=None):\\n if sp_matrix is None:\\n sp_matrix = self.to_sparse_matrix(array_in.shape, \\\"csc\\\")\\n # print(\\\"usually:\\\", sp_matrix.todense())\\n array_out[:] = sp_matrix.dot(array_in.reshape(-1)).reshape(array_out.shape)\",\n \"def to_amat(self, node_list=None, sparse=False) -> np.ndarray:\\n if not node_list:\\n node_list = sorted(self._nodes)\\n node2ix = {node: i for i, node in enumerate(node_list)}\\n\\n if sparse:\\n raise NotImplementedError\\n # js, ks = [], []\\n # for j, k in self._edges:\\n # js.append(j)\\n # ks.append(k)\\n # js.append(k)\\n # ks.append(j)\\n # return spmatrix(1, js, ks)\\n amat = np.zeros([self.num_nodes, self.num_nodes], dtype=int)\\n\\n for i, j in self._edges:\\n amat[node2ix[i], node2ix[j]] = True\\n amat[node2ix[j], node2ix[i]] = True\\n return amat\",\n \"def build_lhs_matrix(self):\\n j=3\\n diags1 = self.build_linear_diags()\\n diags1 += self.build_dispersion_diags()\\n\\n # Ones down primary diagonal\\n diags2 = np.zeros_like(diags1)\\n diags2[j,:] = 1.\\n\\n cff = self.dt*(1+self.c_im)*0.5 \\n diags = diags2 - cff*diags1\\n \\n # Build the sparse matrix\\n cols = [ii for ii in range(-self._j, self._j+1)]\\n M = sparse.spdiags(diags, cols, self.N, self.N)\\n\\n return M, diags\",\n \"def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\\\"<i4\\\")\\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\\\"<f8\\\")\\n size = j_obj.getSize()\\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()\",\n \"def sparse_matrix(data, stype=\\\"csr\\\", dtype=complex):\\n return _SPARSE_CONSTRUCTORS[stype](data, dtype=dtype)\",\n \"def make_sparse(self, fmt='csc', make_method=None):\\n if make_method:\\n self.sparse = make_method(self.hamiltonian)\\n else:\\n self.sparse = self.hamiltonian.to_matrix(sparse=fmt)\",\n \"def sparse_matrix(shape, integer=False):\\n dtype = numpy.int_ if integer else numpy.float_\\n return scipy.sparse.lil_matrix(shape, dtype=dtype)\",\n \"def create_sparse_matrix(self, matrix_df):\\n\\n print('creating sparse matrix...')\\n sparse_seg_tmp_df = matrix_df.groupby(['segment_id','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n sparse_rt_tmp_df = matrix_df.groupby(['road_type','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n time_seg_df = sparse_seg_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n time_rt_df = sparse_rt_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\\n #time_seg_df['time_id'] = time_seg_df.index\\n #time_rt_df['time_id'] = time_rt_df.index\\n times = list(range(24*60/self.args['time_resolution']))\\n full_time_idx = pd.DataFrame([i * 30 for i in times],columns = ['time_idx'])\\n full_time_idx['key'] = 1\\n full_day_of_week = pd.DataFrame(list(range(7)), columns = ['day_of_week'])\\n full_day_of_week['key'] = 1\\n full_times = pd.merge(full_time_idx, full_day_of_week, on='key')\\n full_times['time_id'] = full_times.index\\n time_seg_df = pd.merge(time_seg_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\\n time_rt_df = pd.merge(time_rt_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\\n \\n matrix_seg_keys_df = pd.merge(sparse_seg_tmp_df, time_seg_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\\n matrix_rt_keys_df = pd.merge(sparse_rt_tmp_df, time_rt_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\\n\\n time_seg_array = np.array(matrix_seg_keys_df['time_id'])\\n time_rt_array = np.array(matrix_rt_keys_df['time_id'])\\n segment_array = np.array(matrix_seg_keys_df['segment_id'])\\n rt_array = np.array(matrix_rt_keys_df['road_type'])\\n\\n uniquesegments = np.array(list(set(segment_array)))\\n uniquerts = np.array(list(set(rt_array)))\\n keyuniquesegments = np.array(range(len(uniquesegments)))\\n keyuniquerts = np.array(range(len(uniquerts)))\\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\\n uniquerts_df = pd.DataFrame({'roadtypekey':keyuniquerts, 'road_type':uniquerts})\\n\\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\\n rt_df = pd.DataFrame(rt_array, columns = ['road_type'])\\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\\n rt_keys_df = pd.merge(rt_df, uniquerts_df, how='left', on=['road_type'])\\n segmentkeys = np.array(segments_keys_df['segmentskey'])\\n rtkeys = np.array(rt_keys_df['road_type'])\\n\\n level_array_seg = np.array(matrix_seg_keys_df['level_max'])\\n sparse_matrix_s = csr_matrix((level_array_seg, (segmentkeys,time_seg_array))).toarray()\\n sparse_matrix_seg = preprocessing.scale(sparse_matrix_s)\\n level_array_rt = np.array(matrix_rt_keys_df['level_max'])\\n sparse_matrix_r = csr_matrix((level_array_rt, (rtkeys,time_rt_array))).toarray()\\n sparse_matrix_rt = preprocessing.scale(sparse_matrix_r)\\n \\n if self.args['perform_pca']:\\n sparse_matrix_seg, self.pca_model = self.run_PCA(sparse_matrix_seg)\\n sparse_matrix_rt, self.pca_model = self.run_PCA(sparse_matrix_rt)\\n else:\\n sparse_matrix_seg = sparse_matrix_seg\\n sparse_matrix_rt = sparse_matrix_rt\\n \\n sparse_matrix_withsegkey = pd.DataFrame(sparse_matrix_seg)\\n sparse_matrix_withrtkey = pd.DataFrame(sparse_matrix_rt)\\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\\n sparse_matrix_withrtkey['roadtypekey'] = sparse_matrix_withrtkey.index\\n sparse_matrix_withrt = pd.merge(uniquerts_df, sparse_matrix_withrtkey, on=['roadtypekey'])\\n \\n # write sparse_matrix to database as 'clustering' table\\n print('writing sparse matrix to db...')\\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\\n engine = create_engine(sqlalchemy_conn_str)\\n if self.split_type == 'random':\\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_random', con=engine, if_exists='replace')\\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_random', con=engine, if_exists='replace')\\n elif self.split_type == 'date':\\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_date', con=engine, if_exists='replace')\\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_date', con=engine, if_exists='replace')\\n \\n print('returning train sparse matrix...')\\n return (uniquesegments_df, sparse_matrix_seg)\",\n \"def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(alphaptrb_)\\n elif num_ != len(alphaptrb_):\\n raise IndexError(\\\"Inconsistent length of array alphaptrb\\\")\\n if num_ is None:\\n num_ = len(alphaptre_)\\n elif num_ != len(alphaptre_):\\n raise IndexError(\\\"Inconsistent length of array alphaptre\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if alphaptrb_ is None:\\n raise ValueError(\\\"Argument alphaptrb cannot be None\\\")\\n if alphaptrb_ is None:\\n raise ValueError(\\\"Argument alphaptrb may not be None\\\")\\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\\n _alphaptrb_copyarray = False\\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif alphaptrb_ is not None:\\n _alphaptrb_copyarray = True\\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\\n _alphaptrb_np_tmp[:] = alphaptrb_\\n assert _alphaptrb_np_tmp.flags.contiguous\\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _alphaptrb_copyarray = False\\n _alphaptrb_tmp = None\\n \\n if alphaptre_ is None:\\n raise ValueError(\\\"Argument alphaptre cannot be None\\\")\\n if alphaptre_ is None:\\n raise ValueError(\\\"Argument alphaptre may not be None\\\")\\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\\n _alphaptre_copyarray = False\\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif alphaptre_ is not None:\\n _alphaptre_copyarray = True\\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\\n _alphaptre_np_tmp[:] = alphaptre_\\n assert _alphaptre_np_tmp.flags.contiguous\\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _alphaptre_copyarray = False\\n _alphaptre_tmp = None\\n \\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx cannot be None\\\")\\n if matidx_ is None:\\n raise ValueError(\\\"Argument matidx may not be None\\\")\\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\\n _matidx_copyarray = False\\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif matidx_ is not None:\\n _matidx_copyarray = True\\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\\n _matidx_np_tmp[:] = matidx_\\n assert _matidx_np_tmp.flags.contiguous\\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _matidx_copyarray = False\\n _matidx_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def SymmetriseMatrix(adjmatrix):\\n\\n if galib.metrics.Reciprocity(adjmatrix) == 1:\\n # if Reciprocity(adjmatrix) == 1:\\n return adjmatrix\\n else:\\n return 0.5 * (adjmatrix + adjmatrix.T)\",\n \"def create_attach_sparsifier(model, **sparse_config):\\n data_norm_sparsifier = DataNormSparsifier(**sparse_config)\\n for name, parameter in model.named_parameters():\\n if 'emb_l' in name:\\n valid_name = get_valid_name(name)\\n data_norm_sparsifier.add_data(name=valid_name, data=parameter)\\n return data_norm_sparsifier\",\n \"def write_matfile(Sint_df, outputfilepath='Sint_no_cofactor_20160831.mat'):\\n # convert dataframe to matrix\\n Smat = Sint_df.as_matrix()\\n\\n # get all indices for non-zero elements in Smat (row, col)\\n Smat_nzr, Smat_nzc = np.nonzero(Smat)\\n\\n # get all non-zero elements from Smat\\n Smat_nze = Smat[Smat_nzr, Smat_nzc]\\n\\n # Adjust for matlab coordinate\\n Smat_nzr = Smat_nzr + 1\\n Smat_nzc = Smat_nzc + 1\\n\\n # This final line gives the size of the S matrix in matlab\\n nr, nc = Smat.shape\\n\\n # Create a 2D array\\n sparseMat = np.vstack((Smat_nzr, Smat_nzc, Smat_nze)).T\\n sparseMat = np.vstack((sparseMat, np.array([[nr, nc, 0]])))\\n\\n # Create a numpy object array from dataframe index\\n reactionList = Sint_df.columns.ravel()\\n\\n # Write only one matlab .mat file\\n scipy.io.savemat(outputfilepath,\\n mdict={'Sint_sparse': sparseMat,\\n 'reactionList': np.array(reactionList)}\\n )\\n\\n return sparseMat, reactionList\",\n \"def sparse_matrix (base_type=float):\\n return defaultdict (lambda: sparse_vector (base_type))\",\n \"def matrix_add():\",\n \"def set_sparsity(self,use_sparse):\\n \\n if hasattr(self.problem,'sparse_jac'):\\n self.use_sparse = use_sparse\\n else:\\n raise KINSOL_Exception(\\\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\\\")\",\n \"def stiffnessMatrix (bsp, knotlist,p, nbquad):\\n\\n\\tS = np.zeros((bsp.N-2, bsp.N-2))\\n\\t\\n\\tfor line in range(1, bsp.N-1):\\n\\t\\t\\n\\t\\tfor column in range(1, bsp.N-1):\\n\\t\\t\\n\\t\\t\\tfor iknot in range(len(knotlist)-1):\\n\\t\\t\\t\\tS[line-1, column-1] = S[line-1, column-1] + legendreGauss(bilinearForm, nbquad, knotlist[iknot], knotlist[iknot+1], line, bsp, ind2=column)\\n\\t\\n\\treturn S;\",\n \"def normalize_adj( adj : np.ndarray, \\n sparse : bool = False\\n ) -> Union[np.ndarray, sp.spmatrix]:\\n if sparse:\\n adj = sp.coo_matrix(adj) # [N,N]\\n rowsum = np.array(adj.sum(1)) # [N,]\\n \\n d_inv_sqrt = np.power(rowsum, -0.5) # [N,], may issue runtime warnings (div by zero)\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. # []\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) if sparse else np.diag(d_inv_sqrt) #[N,N]\\n \\n if sparse:\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\\n else:\\n return ((adj @ d_mat_inv_sqrt).transpose() @ d_mat_inv_sqrt) # not quite sure why this order = D^T A^T D, D^T = D, A^T = A - the transpose is unncessary?!\",\n \"def _append_sparse_tensor_value(target, to_append):\\n # Make sure the sparse tensors are of the same size (except for the 0th dim).\\n if len(target.dense_shape) != len(to_append.dense_shape):\\n raise RuntimeError(\\n 'Unable to concatenate %s and %s. The inner dense shapes do not '\\n 'have the same number of dimensions (%s vs %s)' %\\n (target, to_append, target.dense_shape, to_append.dense_shape))\\n\\n if target.dense_shape[1:] != to_append.dense_shape[1:]:\\n raise RuntimeError(\\n 'Unable to concatenate %s and %s. The inner dense shapes do not '\\n 'match inner dimensions (%s vs %s)' %\\n (target, to_append, target.dense_shape[1:], to_append.dense_shape[1:]))\\n\\n # Add the to_append indices to target, updating the 0th value, and keeping\\n # track of the maximum so we know the final dense_shape of this tensor.\\n base_dim0_value = target.dense_shape[0]\\n max_dim0_value = target.dense_shape[0]\\n new_indices = target.indices\\n for index in to_append.indices:\\n # Here, we iterate through the sparse indices of the tensor to append. For\\n # each index, we update its zeroth value (the batch index) by adding the\\n # number of batch items in the tensor we are appending to (so an index\\n # of [0, 0, 1] for a value that is being appended to a tensor with 0th dim\\n # size 3 would become [3, 0, 1].)\\n index[0] += base_dim0_value\\n max_dim0_value = max(max_dim0_value, index[0])\\n new_indices = np.append(new_indices, [index], axis=0)\\n\\n # Extend the values array to contain all of the appended values. These will\\n # be in the same order as the indices added above.\\n new_values = np.concatenate((target.values, to_append.values), axis=0)\\n\\n # Create a new dense shape by replacing the value for the 0th dimension\\n # with the new max dim0 value.\\n new_dense_shape = list(target.dense_shape)\\n new_dense_shape[0] = max_dim0_value + 1\\n new_dense_shape = tuple(new_dense_shape)\\n\\n return sparse_tensor.SparseTensorValue(\\n indices=new_indices, values=new_values, dense_shape=new_dense_shape)\",\n \"def sparse_arrays(self):\\n return self._sparse_arrays\",\n \"def add_ts_to_matrix(self, i_ts, norm_flag, add_x=True, add_y=True):\\n\\n # ts are not overwritten, only normalization constants\\n if norm_flag:\\n ts, norm_div, norm_subt = _normalize_ts(self.ts[i_ts].s, self.ts[i_ts].name)\\n self.ts[i_ts] = TsMiniStruct(self.ts[i_ts].s, norm_div, norm_subt, self.ts[i_ts].name, self.ts[i_ts].index)\\n ts = TsMiniStruct(ts, norm_div, norm_subt, self.ts[i_ts].name, self.ts[i_ts].index)\\n else:\\n ts = self.ts[i_ts]\\n\\n n_hist = self.n_hist_points[i_ts]\\n n_req = self.n_req_points[i_ts]\\n n_rows = self.X.shape[0]\\n\\n # reverse time series, so that the top row is always to be forecasted first\\n time = np.flipud(ts.index)\\n ts = np.flipud(ts.s)\\n \\n idxX, idxY = matrix_idx(n_hist, n_req, n_rows)\\n self.idxY[i_ts] = idxY\\n if add_y:\\n self.Y = np.hstack((self.Y, ts[idxY]))\\n if add_x:\\n self.X = np.hstack((self.X, ts[idxX]))\\n\\n return time[idxY[:, -1]], time[idxX[:, 0]]\",\n \"def edges_to_matrix(edge_list: List[Tuple[int, int]], add_reverse_edges: bool,\\n shape: Tuple[int, int], dtype: TypeVar=bool, sparse: bool=True):\\n matrix = scipy.sparse.csc_matrix(\\n (numpy.ones(len(edge_list)), zip(*edge_list)), dtype=dtype, shape=shape,\\n )\\n\\n if add_reverse_edges:\\n matrix = (matrix + matrix.T) > 0\\n matrix = matrix.astype(dtype)\\n\\n if not sparse:\\n matrix = matrix.toarray()\\n\\n return matrix\",\n \"def _buildMatrix(self, SparseMatrix, Ncells, MaxFaces, coeff):\\n return (0, 0)\",\n \"def setDominantSparseSymeig(A, Aadjoint_to_gadjoint):\\n global DominantSparseSymeig \\n from .CG import setCGSubspaceSparse\\n setCGSubspaceSparse(A, Aadjoint_to_gadjoint)\\n from .CG import CGSubspaceSparse\\n @staticmethod\\n def forward(ctx, g, k, dim):\\n eigval, eigvector = symeigLanczos(A, k, extreme=\\\"min\\\", sparse=True, dim=dim)\\n ctx.save_for_backward(g, eigval, eigvector)\\n return eigval, eigvector\\n @staticmethod\\n def backward(ctx, grad_eigval, grad_eigvector):\\n cg = CGSubspaceSparse.apply\\n g, eigval, eigvector = ctx.saved_tensors\\n b = grad_eigvector - torch.matmul(eigvector, grad_eigvector) * eigvector\\n lambda0 = cg(g, eigval, b, eigvector)\\n grad_A = grad_eigval * eigvector - lambda0, eigvector\\n v1, v2 = grad_A\\n grad_g = Aadjoint_to_gadjoint(v1, v2)\\n grad_k = grad_dim = None\\n return grad_g, grad_k, grad_dim\\n DominantSparseSymeig = type(\\\"DominantSparseSymeig\\\", (torch.autograd.Function, ), \\n {\\\"forward\\\": forward, \\\"backward\\\": backward})\",\n \"def sparse_esn_reservoir(size, spectral_radius, density, symmetric):\\n rvs = stats.uniform(loc=-1., scale=2.).rvs\\n matrix = sparse.random(size, size, density=density, data_rvs=rvs)\\n matrix = matrix.tocsr()\\n if symmetric:\\n matrix = sparse.triu(matrix)\\n tril = sparse.tril(matrix.transpose(), k=-1)\\n matrix = matrix + tril\\n # calc eigenvalues with scipy's lanczos implementation:\\n eig, _ = sparse.linalg.eigsh(matrix, k=2, tol=1e-4)\\n else:\\n eig, _ = sparse.linalg.eigs(matrix, k=2, tol=1e-4)\\n\\n rho = np.abs(eig).max()\\n matrix = matrix.multiply(1. / rho)\\n matrix = matrix.multiply(spectral_radius)\\n return matrix\",\n \"def split_poly_scale_join(matrices, dummy_idx, poly, scale):\\n\\n adjusted_matrices = []\\n\\n for matrix in matrices:\\n if dummy_idx:\\n # Split the matrix into numerical and dummies\\n matrix, matrix_dummies = np.split(matrix, [dummy_idx], axis=1)\\n\\n # add polynomial features to numerical attributes\\n if poly:\\n matrix = (PolynomialFeatures(poly).\\n fit_transform(matrix))\\n # scale numerical attributes\\n if scale:\\n matrix = (StandardScaler().\\n fit_transform(matrix))\\n if dummy_idx:\\n # Join numerical and dummy attributes together again\\n matrix = np.concatenate((matrix, matrix_dummies), axis=1)\\n adjusted_matrices.append(matrix)\\n\\n return adjusted_matrices\",\n \"def append_direct_component_matrices(technique_data: TransitiveTechniqueData):\\n for technique in technique_data.technique.get_component_techniques():\\n direct_calculator = DirectTechniqueCalculator(technique.definition)\\n similarity_matrix = direct_calculator.calculate_technique_data(\\n technique_data.dataset\\n ).similarity_matrix\\n technique_data.transitive_matrices.append(similarity_matrix)\",\n \"def convert_sparse_network(weights: Dict[str, np.ndarray],\\n metadata: Dict[str, Any],\\n hypers: Dict[str, Any],\\n is_msp: bool):\\n # List to hold all variables\\n components: List[str] = []\\n\\n # Start the input units at the input shape\\n input_size = metadata[INPUT_SHAPE][-1]\\n\\n for i, output_size in enumerate(hypers['hidden_units']):\\n\\n # Convert the sparse variable\\n layer_name = 'hidden_{0}'.format(i)\\n\\n sparse_layer = convert_sparse_layer(layer_name=layer_name,\\n weights=weights,\\n input_size=input_size,\\n output_size=output_size,\\n coordinates=metadata['sparse_indices'][layer_name],\\n is_msp=is_msp)\\n\\n components.append(sparse_layer)\\n\\n # Reset the input size as we progress\\n input_size = output_size\\n\\n # Include the output layer\\n output_kernel_name = 'output/kernel:0'\\n output_kernel = convert_matrix(name=output_kernel_name,\\n mat=weights[output_kernel_name],\\n precision=PRECISION,\\n width=WIDTH,\\n is_msp=is_msp)\\n components.append(output_kernel)\\n\\n output_bias_name = 'output/bias:0'\\n output_bias = convert_matrix(name=output_bias_name,\\n mat=weights[output_bias_name],\\n precision=PRECISION,\\n width=WIDTH,\\n is_msp=is_msp)\\n components.append(output_bias)\\n\\n return '\\\\n'.join(components)\",\n \"def to_s_matrix(w,v):\\n pass\",\n \"def apply_stiffness_assembling(fe_space, param, stiffness):\\n if stiffness.format is 'lil':\\n for ie in range(fe_space.get_nelem()):\\n local_stiffness = fe_space.apply_dbasis_diag_dbasis(fe_space.eval_at_quadrature_pnts(\\n lambda k, s:\\n fe_space.get_quadrature_weight(k) * param(fe_space.get_coord(ie, s)) / fe_space.get_elem_length(ie)))\\n locals_to_globals = fe_space.locals_to_globals(ie)\\n stiffness[np.array(locals_to_globals)[:, None], locals_to_globals] += local_stiffness\\n else:\\n raise ValueError(\\\"Assembling procedure are expecting lil format sparse matrices.\\\")\",\n \"def create_jac_sens(x_sp,sensitivity_sp,diffeq_params, integration_params,\\n SDerivSymbolicJacParamsLambFun,SDerivSymbolicJacConcLambFun):\\n\\n # create state variables\\n allVars = np.concatenate((x_sp,sensitivity_sp))\\n\\n #create RHS\\n dSensSym = sp.Matrix(dSens(0,allVars,diffeq_params, integration_params,\\n SDerivSymbolicJacParamsLambFun, SDerivSymbolicJacConcLambFun))\\n dSensSymJac = dSensSym.jacobian(allVars)\\n\\n # generate jacobian\\n dSensSymJacDenseMatLam = sp.lambdify(allVars,dSensSymJac)\\n dSensSymJacSparseMatLamFun = lambda t,xs: sparse.csr_matrix(dSensSymJacDenseMatLam(*xs))\\n\\n return dSensSymJacSparseMatLamFun\",\n \"def __add__(self, other):\\n h, w = self.size\\n col_indices = self.col_indices + [w + i for i in other.col_indices]\\n row_indices = self.row_indices + other.row_indices\\n values = self.values + other.values\\n oh, ow = other.size\\n size = [max(h, oh), w + ow]\\n return Sparse(size, row_indices, col_indices, values)\",\n \"def coregionalization_sparse(optimize=True, plot=True):\\r\\n #fetch the data from the non sparse examples\\r\\n m = coregionalization_toy2(optimize=False, plot=False)\\r\\n X, Y = m.X, m.likelihood.Y\\r\\n\\r\\n #construct a model\\r\\n m = GPy.models.SparseGPRegression(X,Y)\\r\\n m.constrain_fixed('iip_\\\\d+_1') # don't optimize the inducing input indexes\\r\\n\\r\\n if optimize:\\r\\n m.optimize('bfgs', max_iters=100, messages=1)\\r\\n\\r\\n if plot:\\r\\n m.plot(fixed_inputs=[(1,0)])\\r\\n m.plot(fixed_inputs=[(1,1)], ax=pb.gca())\\r\\n\\r\\n return m\",\n \"def to_sparse(self):\\n from divisi2.sparse import SparseMatrix\\n return SparseMatrix(self, self.row_labels, self.col_labels)\",\n \"def qubit_operator_sparse(qubit_operator, n_qubits=None):\\n if n_qubits is None:\\n n_qubits = count_qubits(qubit_operator)\\n if n_qubits < count_qubits(qubit_operator):\\n raise ValueError('Invalid number of qubits specified.')\\n\\n # Construct the Scipy sparse matrix.\\n n_hilbert = 2**n_qubits\\n values_list = [[]]\\n row_list = [[]]\\n column_list = [[]]\\n\\n # Loop through the terms.\\n for qubit_term in qubit_operator.terms:\\n tensor_factor = 0\\n coefficient = qubit_operator.terms[qubit_term]\\n sparse_operators = [coefficient]\\n for pauli_operator in qubit_term:\\n\\n # Grow space for missing identity operators.\\n if pauli_operator[0] > tensor_factor:\\n identity_qubits = pauli_operator[0] - tensor_factor\\n identity = scipy.sparse.identity(2**identity_qubits,\\n dtype=complex,\\n format='csc')\\n sparse_operators += [identity]\\n\\n # Add actual operator to the list.\\n sparse_operators += [pauli_matrix_map[pauli_operator[1]]]\\n tensor_factor = pauli_operator[0] + 1\\n\\n # Grow space at end of string unless operator acted on final qubit.\\n if tensor_factor < n_qubits or not qubit_term:\\n identity_qubits = n_qubits - tensor_factor\\n identity = scipy.sparse.identity(2**identity_qubits,\\n dtype=complex,\\n format='csc')\\n sparse_operators += [identity]\\n\\n # Extract triplets from sparse_term.\\n sparse_matrix = kronecker_operators(sparse_operators)\\n values_list.append(sparse_matrix.tocoo(copy=False).data)\\n (column, row) = sparse_matrix.nonzero()\\n column_list.append(column)\\n row_list.append(row)\\n\\n # Create sparse operator.\\n values_list = numpy.concatenate(values_list)\\n row_list = numpy.concatenate(row_list)\\n column_list = numpy.concatenate(column_list)\\n sparse_operator = scipy.sparse.coo_matrix(\\n (values_list, (row_list, column_list)),\\n shape=(n_hilbert, n_hilbert)).tocsc(copy=False)\\n sparse_operator.eliminate_zeros()\\n return sparse_operator\",\n \"def calc_sparse(self, calc_aw=False, vector_calc=True):\\n # Local copies of instance attributes for faster access\\n b = self.b\\n qs = self.q_sim\\n qo = self.q_obs\\n maxlead = self.max_lead\\n maxlag = self.max_lag\\n measure = self.measure\\n time = self.time\\n\\n # Prepare sparse matrices with only cells around diagonal defined.\\n # padds 1 extra cell on each side to avoid false zeros later\\n n = len(self.q_obs)\\n w = self.max_lag + self.max_lead + 3\\n data = np.array([np.full(n, np.inf)]).repeat(w, axis=0)\\n offsets = np.arange(-self.max_lead-1, self.max_lag + 1.5)\\n\\n # Calculate work between obs and sim pairs, not needed anymore.\\n if calc_aw:\\n aw = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\\n st = pd.Timestamp.now()\\n # print('Start aw loops')\\n # iterate through all flow observations\\n for i_o in range(len(qo)):\\n # Check only simulations within the allowed window\\n for i_s in range(max(0, i_o - self.max_lead),\\n min(len(qs), i_o + maxlag + 1)):\\n if measure in ('nse', 'square'):\\n aw[i_s, i_o] = (qs[i_s] - qo[i_o]) ** 2 + b ** 2 * (i_s - i_o) ** 2\\n elif measure in ('mae', 'abs'):\\n aw[i_s, i_o] = np.abs(qs[i_s] - qo[i_o]) + b * np.abs(i_s - i_o)\\n # print('End aw loops', (pd.Timestamp.now()-st)/pd.Timedelta('1s'))\\n self.aw = aw\\n\\n # Old debugging code: run original implementation alongside\\n # Applies to all variables appended with _orig\\n # aw_orig = np.full(shape=(len(self.q_obs), len(self.q_sim)), fill_value=np.nan)\\n # # iterate through all flow observations\\n # for i_o in range(len(self.q_obs)):\\n # # Check only simulations within the allowed window\\n # for i_s in range(max(0, i_o - self.max_lead),\\n # min(len(self.q_sim), i_o + self.max_lag + 1)):\\n # aw_orig[i_s, i_o] = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + self.b ** 2 * (i_s - i_o) ** 2\\n\\n # Calculate cumulative work along possible paths\\n cw0 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\\n cw1 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\\n\\n # Old debugging code: run original implementation (with dense arrays) alongside\\n # Applies to all variables marked _orig\\n # # Calculate cumulative work along possible paths\\n # cw_orig = np.ones(shape=aw_orig.shape + (2,)) * np.nan\\n # # Populate first column\\n # cw_orig[:, 0, 0] = aw_orig[:, 0]\\n\\n if self.keep_internals:\\n # self.aw_orig = aw_orig\\n self.cw0 = cw0\\n self.cw1 = cw1\\n # self.cw_orig = cw_orig\\n\\n # Populate other columns\\n for i_o in range(n):\\n iss = max(0, i_o - maxlead) # sim index start\\n ise = min(n, i_o + maxlag + 1) # sim index end\\n isv = np.arange(iss, ise) # sim index as vector\\n if time is not None:\\n dt = time[isv] - time[i_o]\\n # Heavily penalize sim-obs combinations outside allowed window\\n # so that they are not selected\\n dt[(dt < -maxlag) | (dt > maxlead)] = np.inf\\n else:\\n dt = isv - i_o\\n if vector_calc:\\n # Vectorized version of the calculation.\\n # For understanding it is best to take a look at the else clause below\\n # Calculate the work for each (sim, obs_i) pair\\n if measure in ('nse', 'square'):\\n e = (qs[iss:ise] - qo[i_o]) ** 2 + b ** 2 * dt ** 2\\n elif measure in ('mae', 'abs'):\\n e = np.abs(qs[iss:ise] - qo[i_o]) + b * np.abs(dt)\\n if i_o == 0:\\n # Only populate first column of cw0 and move to i_o = 1\\n cw0[iss:ise, 0] = e.reshape(len(e), 1)\\n continue\\n # Repeat the same simulation point\\n d = cw0[iss:ise, i_o - 1].toarray()\\n d[d == 0] = np.nan\\n cw1[iss:ise, i_o] = e.reshape(len(e), 1) + d\\n\\n # Find the 'cheapest' available preceding sim point\\n points = np.full((len(isv), 4), np.inf)\\n # Use the previous simulation point\\n st = max(0, iss - 1)\\n end = min(n + 1, ise - 1)\\n l = end - st\\n points[-l:, 0] = cw0[st:end, i_o - 1].toarray().squeeze()\\n points[-l:, 1] = cw1[st:end, i_o - 1].toarray().squeeze()\\n # Skip a simulation point\\n st = max(0, iss - 2)\\n end = min(n + 1, ise - 2)\\n l = end - st\\n points[-l:, 2] = cw0[st:end, i_o - 1].toarray().squeeze()\\n points[-l:, 3] = cw1[st:end, i_o - 1].toarray().squeeze()\\n # points[points == 0] = np.nan\\n cw0[iss:ise, i_o] = (e + np.min(points, axis=1)).reshape(len(e), 1)\\n else:\\n # Old, non-vectorized code. Slower.\\n # Left in place since it is easier to understand than vectorized code above\\n def zero_to_nan(x):\\n if x == 0:\\n return np.nan\\n else:\\n return x\\n\\n for i_s in range(max(0, i_o - self.max_lead),\\n min(n, i_o + self.max_lag + 1)):\\n # print(i_o, i_s, sep='\\\\t')\\n # e = aw[i_s, i_o]\\n if time:\\n dt = time[isv] - time[i_o]\\n # Heavily penalize sim-obs combinations outside allowed window\\n # so that they are not selected\\n dt[dt > maxlead] = 999999999\\n dt[dt < -maxlag] = 999999999\\n else:\\n dt = isv - i_o\\n if measure in ('nse', 'square'):\\n e = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + b ** 2 * dt ** 2\\n elif measure in ('mae', 'abs'):\\n e = np.abs(self.q_sim[i_s] - self.q_obs[i_o]) + np.abs(b * dt)\\n # e_orig = aw_orig[i_s, i_o]\\n # if e != e_orig:\\n # print('ediff', e, e_orig, sep='\\\\t')\\n\\n # Repeat the same simulation point\\n cw1[i_s, i_o] = e + zero_to_nan(cw0[i_s, i_o - 1])\\n # cw_orig[i_s, i_o, 1] = e + cw_orig[i_s, i_o - 1, 0]\\n # if cw1[i_s, i_o] != cw_orig[i_s, i_o, 1]:\\n # print('cw1diff', cw1[i_s, i_o], cw_orig[i_s, i_o, 1], sep='\\\\t')\\n\\n if i_s == 0:\\n continue\\n # Else:\\n # Find the 'cheapest' available preceding point\\n # Use the previous simulation point\\n points = [zero_to_nan(cw0[i_s - 1, i_o - 1]),\\n zero_to_nan(cw1[i_s - 1, i_o - 1])]\\n # m1 = np.nanmin(cw_orig[i_s - 1, i_o - 1, :])\\n # m2 = np.inf\\n if i_s > 1:\\n # Skip a simulation point\\n points += [cw0[i_s - 2, i_o - 1], cw1[i_s - 2, i_o - 1]]\\n # m2 = np.nanmin(cw_orig[i_s - 2, i_o - 1, :])\\n # m = min(m1, m2)\\n try:\\n cp = min([p for p in points if p > 0])\\n except ValueError:\\n cp = np.nan\\n # if cp != m:\\n # print('cpdiff', cp, m, sep='\\\\t')\\n # print('\\\\torig', cw_orig[i_s - 1, i_o - 1, :], cw_orig[i_s - 2, i_o - 1, :])\\n # print('\\\\tspar', points)\\n cw0[i_s, i_o] = e + cp\\n # cw_orig[i_s, i_o, 0] = e + m\\n # print('')\\n # Find the cheapest point in the last column, i.e. end of cheapest path, i.e. optimum score\\n # nz0 = cw0[cw0[:, -1].nonzero()[0], -1].toarray().min()\\n # nz1 = cw1[cw1[:, -1].nonzero()[0], -1].toarray().min()\\n nz0 = cw0[-self.max_lag:, -1].toarray()\\n nz1 = cw1[-self.max_lag:, -1].toarray()\\n self.opt_score = min(nz0[nz0 != 0].min(), nz1[nz1 != 0].min())\\n\\n self.of = 1 - self.opt_score / self.fbench\\n\\n if self.calc_rays:\\n self._do_calc_rays(cw0, cw1)\\n\\n return self.of\",\n \"def to_sparse(self, method='csr_matrix'):\\r\\n data = self.data.values\\r\\n if method == 'csr_matrix':\\r\\n data_sp = sps.csr_matrix(data)\\r\\n elif method == 'bsr_matrix':\\r\\n data_sp = sps.bsr_matrix(data)\\r\\n elif method == 'coo_matrix':\\r\\n data_sp = sps.coo_matrix(data)\\r\\n elif method == 'csc_matrix':\\r\\n data_sp = sps.csc_matrix(data)\\r\\n elif method == 'dia_matrix':\\r\\n data_sp = sps.dia_matrix(data)\\r\\n elif method == 'dok_matrix':\\r\\n data_sp = sps.dok_matrix(data)\\r\\n elif method == 'lil_matrix':\\r\\n data_sp = sps.lil_matrix(data)\\r\\n else:\\r\\n raise ValueError('The method does not exist in scipy.sparse')\\r\\n return data_sp\",\n \"def _extend_sparse_traj_field(self, run_idx, traj_idx, field_path, values, sparse_idxs):\\n\\n field = self.h5['{}/{}/{}/{}/{}'.format(RUNS, run_idx, TRAJECTORIES, traj_idx, field_path)]\\n\\n field_data = field[DATA]\\n field_sparse_idxs = field[SPARSE_IDXS]\\n\\n # number of new frames\\n n_new_frames = values.shape[0]\\n\\n # if this sparse_field has been initialized empty we need to resize\\n if all([i == 0 for i in field_data.shape]):\\n\\n\\n # check the feature shape against the maxshape which gives\\n # the feature dimensions for an empty dataset\\n assert values.shape[1:] == field_data.maxshape[1:], \\\\\\n \\\"input value features have shape {}, expected {}\\\".format(\\n values.shape[1:], field_data.maxshape[1:])\\n\\n # if it is empty resize it to make an array the size of\\n # the new values with the maxshape for the feature\\n # dimensions\\n feature_dims = field_data.maxshape[1:]\\n field_data.resize( (n_new_frames, *feature_dims) )\\n\\n # set the new data to this\\n field_data[0:, ...] = values\\n\\n else:\\n\\n # make sure the new data has the right dimensions\\n assert values.shape[1:] == field_data.shape[1:], \\\\\\n \\\"field feature dimensions must be the same, i.e. all but the first dimension\\\"\\n\\n # append to the dataset on the first dimension, keeping the\\n # others the same, these must be feature vectors and therefore\\n # must exist\\n field_data.resize( (field_data.shape[0] + n_new_frames, *field_data.shape[1:]) )\\n # add the new data\\n field_data[-n_new_frames:, ...] = values\\n\\n # add the sparse idxs in the same way\\n field_sparse_idxs.resize( (field_sparse_idxs.shape[0] + n_new_frames,\\n *field_sparse_idxs.shape[1:]) )\\n # add the new data\\n field_sparse_idxs[-n_new_frames:, ...] = sparse_idxs\",\n \"def visit_list(self, sylist):\\n def index(sytable):\\n try:\\n return sytable.get_column(self.input_index)\\n except:\\n return np.array([0] * sytable.number_of_rows(), dtype=int)\\n\\n def column_or_nan(sytable, column, length):\\n try:\\n return sytable.get_column(column)\\n except:\\n return int(length)\\n\\n def fill(columns):\\n test = []\\n for column in columns:\\n try:\\n if column.size:\\n test.append(column[:0])\\n except:\\n pass\\n test = [[]] if not test else test\\n dtype = np.hstack(test).dtype\\n kind = dtype.kind\\n\\n if kind in ['S', 'U']:\\n filler = lambda x: np.zeros(x, dtype='S1')\\n else:\\n filler = lambda x: np.zeros(x, dtype='f4') * np.nan\\n\\n result = [filler(column)\\n if isinstance(column, int)\\n else column for column in columns]\\n if not result:\\n result.append([])\\n\\n return result\\n\\n common_columns = set()\\n all_columns = []\\n order = OrderedDict()\\n offset = 0\\n indices = [[]]\\n lengths = []\\n\\n # Pre-compute columns etc.\\n for item in sylist:\\n item_columns = item.columns()\\n all_columns.append(item_columns)\\n order.update(OrderedDict.fromkeys(item_columns))\\n current_index = index(item)\\n length = item.number_of_rows()\\n minimum = np.min(current_index) if length else 0\\n indices.append(current_index + (offset - minimum))\\n\\n offset += (np.max(current_index) - minimum + 1\\n if length else\\n self.minimum_increment)\\n\\n lengths.append(length)\\n\\n # Keep only common columns except when in fill mode.\\n if not self.fill:\\n try:\\n common_columns.update(order.keys())\\n for current_columns in all_columns:\\n if current_columns:\\n common_columns = common_columns.intersection(\\n current_columns)\\n except:\\n pass\\n order = OrderedDict.fromkeys(\\n [key for key in order.keys() if key in common_columns])\\n\\n # VJoin columns and attributes.\\n for column in order:\\n data = []\\n attrs = {}\\n\\n if self.fill:\\n for i, item in enumerate(sylist):\\n data.append(column_or_nan(item, column, lengths[i]))\\n if column in all_columns[i]:\\n attrs.update(item.get_column_attributes(column).get())\\n else:\\n for length, item in izip(lengths, sylist):\\n if length:\\n data.append(item.get_column(column))\\n try:\\n attrs.update(item.get_column_attributes(column).get())\\n except KeyError:\\n pass\\n self.current.set_column(column, np.hstack(fill(data)))\\n self.current.get_column_attributes(column).set(attrs)\\n\\n # VJoin index column.\\n if self.current.number_of_columns() and self.output_index:\\n self.current.set_column(self.output_index, np.hstack(indices))\",\n \"def so_sparse(nspins):\\n sigma_x = sparse.COO(np.array([[0, 1 / 2], [1 / 2, 0]]))\\n sigma_y = sparse.COO(np.array([[0, -1j / 2], [1j / 2, 0]]))\\n sigma_z = sparse.COO(np.array([[1 / 2, 0], [0, -1 / 2]]))\\n unit = sparse.COO(np.array([[1, 0], [0, 1]]))\\n\\n L = np.empty((3, nspins, 2 ** nspins, 2 ** nspins), dtype=np.complex128) # consider other dtype?\\n # Lxs = []\\n # Lys = []\\n # Lzs = []\\n\\n for n in range(nspins):\\n Lx_current = 1\\n Ly_current = 1\\n Lz_current = 1\\n\\n for k in range(nspins):\\n if k == n:\\n Lx_current = sparse.kron(Lx_current, sigma_x)\\n Ly_current = sparse.kron(Ly_current, sigma_y)\\n Lz_current = sparse.kron(Lz_current, sigma_z)\\n else:\\n Lx_current = sparse.kron(Lx_current, unit)\\n Ly_current = sparse.kron(Ly_current, unit)\\n Lz_current = sparse.kron(Lz_current, unit)\\n\\n # Lxs[n] = Lx_current\\n # Lys[n] = Ly_current\\n # Lzs[n] = Lz_current\\n # print(Lx_current.todense())\\n L[0][n] = Lx_current.todense()\\n L[1][n] = Ly_current.todense()\\n L[2][n] = Lz_current.todense()\\n Lz_sparse = sparse.COO(L[2])\\n L_T = L.transpose(1, 0, 2, 3)\\n L_sparse = sparse.COO(L)\\n L_T_sparse = sparse.COO(L_T)\\n Lproduct = sparse.tensordot(L_T_sparse, L_sparse, axes=((1, 3), (0, 2))).swapaxes(1, 2)\\n # Lz_sparse = sparse.COO(L[2])\\n Lproduct_sparse = sparse.COO(Lproduct)\\n\\n return Lz_sparse, Lproduct_sparse\",\n \"def __init__(self, size):\\n _PysparseMatrixFromShape.__init__(self, rows=size, cols=size, bandwidth = 1)\\n ids = numerix.arange(size)\\n self.put(numerix.ones(size, 'd'), ids, ids)\",\n \"def scipy_sparse_to_spmatrix(A):\\n coo = A.tocoo()\\n SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)\\n return SP\",\n \"def expand_conn_mat(conn_mat, nr_of_traps):\\n\\n r, c = conn_mat._shape\\n new_indptr = np.hstack((conn_mat.indptr, np.asarray([conn_mat.indptr[-1]] * nr_of_traps)))\\n expanded_mat = csr_matrix((conn_mat.data, conn_mat.indices, new_indptr),\\n shape=(r + nr_of_traps, c + nr_of_traps))\\n\\n return expanded_mat\",\n \"def add_index(self, idx, subproblem_shape):\\n self.indices.append(int(idx))\\n self.subproblem_shapes.append(subproblem_shape)\",\n \"def create_sparse_matrix(self, filename, matrix_length, density):\\n pos = 0\\n aux_pos = 0\\n matrix = []\\n pointerB = []\\n pointerE = []\\n columns = []\\n values = []\\n\\n for i in range(0, matrix_length):\\n row = []\\n pointerB.append(pos)\\n aux_pos = pos\\n for j in range(0, matrix_length):\\n probability = random.random()\\n if probability < density:\\n pos += 1\\n val = random.randint(1, 10)\\n values.append(val)\\n columns.append(j)\\n else:\\n val = 0\\n row.append(val)\\n matrix.append(row)\\n pointerE.append(pos)\\n vector_b = SparseMatrix.gen_vector(matrix_length)\\n matrix_A = np.matrix(matrix)\\n vector_res = np.dot(matrix_A, vector_b).reshape(matrix_length, 1)\\n data = {\\\"values\\\": values, \\\"columns\\\": columns, \\\"pointerB\\\": pointerB, \\\"pointerE\\\": pointerE}\\n CSR_A = json.dumps(data)\\n '''\\n print(\\\"x: \\\", vector_x)\\n print(\\\"A: \\\", matrix_A)\\n print(\\\"b: \\\", vector_b)\\n data = {\\\"values\\\": values, \\\"columns\\\": columns, \\\"pointerB\\\": pointerB, \\\"pointerE\\\": pointerE}\\n data_json = json.dumps(data)\\n file = open(filename, 'w')\\n file.write(data_json)\\n file.close()\\n np.savetxt(\\\"vector.txt\\\", vector_x, fmt=\\\"%1.9f\\\", delimiter=\\\" \\\")\\n '''\\n return matrix_A, CSR_A, vector_b, vector_res\",\n \"def expand_csr_adj(adj, count:int):\\n r,c = adj.shape\\n \\n adj = sp.vstack(\\n [adj, sp.csr_matrix(np.zeros((count, c)))])\\n adj = sp.hstack(\\n [adj, sp.csr_matrix(np.zeros((r + count, count)))])\\n \\n return adj\",\n \"def create_nonsparse_matrix(self, matrix_df):\\n\\n print('creating nonsparse matrix...')\\n clustering_df_tmp = matrix_df[['date','time','date_idx', 'time_idx', 'day_of_week', 'segment_id', 'road_type', 'lat1', 'lat2', 'lon1', 'lon2', 'level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']]\\n seg_averages = clustering_df_tmp.groupby(['segment_id', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']].mean().reset_index()\\n rt_averages = clustering_df_tmp.groupby(['road_type', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count', 'level_binary']].mean().reset_index()\\n \\n # write nonsparse_matrix_db to database as 'clustering' table\\n print('writing nonsparse matrix to db...')\\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\\n engine = create_engine(sqlalchemy_conn_str)\\n if self.split_type == 'random':\\n seg_averages.to_sql(name='clust_nonsparse_avebysegment_random', con=engine, if_exists='replace')\\n rt_averages.to_sql(name='clust_nonsparse_avebyrt_random', con=engine, if_exists='replace')\\n elif self.split_type == 'date':\\n seg_averages.to_sql(name='clust_nonsparse_avebysegment_date', con=engine, if_exists='replace')\\n rt_averages.to_sql(name='clust_nonsparse_avebyrt_date', con=engine, if_exists='replace')\\n \\n print('returning train nonsparse matrix...')\\n train_averages = clustering_df_tmp.groupby(['segment_id', 'day_of_week', 'time_idx'])[['level_min', 'level_max', 'level_mean', 'level_count','level_binary']].mean().reset_index()\\n train_averages.columns = ['segment_id', 'day_of_week', 'time_idx', 'ave_level_min', 'ave_level_max', 'ave_level_mean', 'ave_level_count', 'ave_level_binary']\\n nonsparse_matrix = pd.merge(clustering_df_tmp, train_averages, how='left', on=['segment_id', 'day_of_week', 'time_idx'])\\n segtimes_df = nonsparse_matrix[['segment_id', 'date', 'time']]\\n nonsparse_matrix_final = nonsparse_matrix.drop(columns=['segment_id', 'date','time', 'level_min', 'level_max', 'level_mean', 'level_count', 'level_binary'])\\n \\n return (segtimes_df, nonsparse_matrix_final)\",\n \"def sparse_GP_regression_2D(num_samples=400, num_inducing=50, max_iters=100, optimize=True, plot=True):\\r\\n X = np.random.uniform(-3., 3., (num_samples, 2))\\r\\n Y = np.sin(X[:, 0:1]) * np.sin(X[:, 1:2]) + np.random.randn(num_samples, 1) * 0.05\\r\\n\\r\\n # construct kernel\\r\\n rbf = GPy.kern.rbf(2)\\r\\n\\r\\n # create simple GP Model\\r\\n m = GPy.models.SparseGPRegression(X, Y, kernel=rbf, num_inducing=num_inducing)\\r\\n\\r\\n # contrain all parameters to be positive (but not inducing inputs)\\r\\n m['.*len'] = 2.\\r\\n\\r\\n m.checkgrad()\\r\\n\\r\\n # optimize\\r\\n if optimize:\\r\\n m.optimize('tnc', messages=1, max_iters=max_iters)\\r\\n\\r\\n # plot\\r\\n if plot:\\r\\n m.plot()\\r\\n\\r\\n print m\\r\\n return m\",\n \"def ipu_sparse_ops(scope=\\\"session\\\"):\\n build_path = Path(\\n public_examples_dir,\\n \\\"applications\\\",\\n \\\"tensorflow\\\",\\n \\\"dynamic_sparsity\\\"\\n )\\n completed = run(['python3-config', '--extension-suffix'], stdout=PIPE)\\n extension = completed.stdout.decode().replace('\\\\n', '')\\n shared_libs = [f'host_utils{extension}', 'libsparse_matmul.so']\\n paths = [Path(build_path, \\\"ipu_sparse_ops\\\", f) for f in shared_libs]\\n\\n # Use exclusive lockfile to avoid race conditions on the build:\\n lock_path = Path(build_path, \\\".ipu_sparse_ops.pytest.build.lockfile\\\")\\n\\n @ExecuteOncePerFS(lockfile=lock_path, file_list=paths, timeout=120, retries=20)\\n def build_dynsparse():\\n run(['make', 'clean'], cwd=build_path)\\n run(['make', '-j'], cwd=build_path)\\n\\n build_dynsparse()\",\n \"def UpdateCostMatrix( self, extraXs ):\\n for x in extraXs:\\n newRow = [ self.EuclideanDistanceSq(x,y) for y in self.Y ]\\n self.C.append(newRow)\",\n \"def _identity_sparse(d, stype=\\\"csr\\\", dtype=complex):\\n return sp.eye(d, dtype=dtype, format=stype)\",\n \"def _create_stoic_mat(ns, nr, name_list, stoic_tuple, species_names):\\n stoic_mat = np.zeros([ns, nr], dtype=np.int)\\n for index, (names, stoics) in enumerate(zip(name_list, stoic_tuple)):\\n for a_name, a_stoic in zip(names, stoics):\\n species_index = species_names.index(a_name)\\n stoic_mat[species_index, index] += int(a_stoic)\\n return stoic_mat\",\n \"def append_arr(self, num_seg, g_pix, intercept, slope, sig_intercept,\\n sig_slope, inv_var, save_opt):\\n self.slope_2d[num_seg[g_pix], g_pix] = slope[g_pix]\\n if save_opt:\\n self.interc_2d[num_seg[g_pix], g_pix] = intercept[g_pix]\\n self.siginterc_2d[num_seg[g_pix], g_pix] = sig_intercept[g_pix]\\n self.sigslope_2d[num_seg[g_pix], g_pix] = sig_slope[g_pix]\\n self.inv_var_2d[num_seg[g_pix], g_pix] = inv_var[g_pix]\",\n \"def putvartypelist(self,subj_,vartype_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(vartype_)\\n elif num_ != len(vartype_):\\n raise IndexError(\\\"Inconsistent length of array vartype\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if vartype_ is None:\\n raise ValueError(\\\"Argument vartype cannot be None\\\")\\n if vartype_ is None:\\n raise ValueError(\\\"Argument vartype may not be None\\\")\\n if vartype_ is not None:\\n _vartype_tmp = (ctypes.c_int32 * len(vartype_))(*vartype_)\\n else:\\n _vartype_tmp = None\\n res = __library__.MSK_XX_putvartypelist(self.__nativep,num_,_subj_tmp,_vartype_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def test_bind_init(self, sp_in):\\n\\n bind = DatasetSparseArray(sp_in).bind\\n\\n dset_sp = DatasetSparseArray(bind=bind)\\n\\n assert dset_sp.info[\\\"type_id\\\"] == \\\"sparse_array\\\"\\n assert dset_sp.info[\\\"py_type\\\"] == f\\\"scipy.sparse.{type(sp_in).__qualname__}\\\"\\n assert dset_sp.sparse_array_class is type(sp_in)\\n\\n sp_out = dset_sp.get_value()\\n assert isinstance(sp_out, type(sp_in))\\n\\n assert (sp_in.todense() == sp_out.todense()).all()\",\n \"def test_00_create_sparse_1d_array(self):\\n ncells = 100\\n sparsity = 3.0 # 1 / density\\n _, err = _iquery(\\\"create array SPARSE <v:int64>[i=0:{0}:0:5]\\\".format(\\n ncells - 1))\\n assert not err, err\\n self._array_cleanups.append('SPARSE')\\n _, err = _iquery(\\\"\\\"\\\"\\n insert(\\n redimension(\\n apply(\\n build(<i:int64>[fud=0:{0}], {1}*fud),\\n (v, 1)),\\n SPARSE),\\n SPARSE)\\\"\\\"\\\".format(int(ncells / sparsity) - 1,\\n int(sparsity)))\\n assert not err, err\\n check_v_sum('SPARSE')\\n nchunks = chunk_count(vaid_of('SPARSE'))\\n prt(\\\"SPARSE has\\\", nchunks, \\\"chunks\\\")\",\n \"def _store(self):\\n if not self._is_supported_matrix(self._data):\\n return super(SparseParameter, self)._store()\\n else:\\n store_dict = {}\\n serial_string = self._serialize_matrix(self._data)\\n store_dict[\\\"data%s\\\" % SparseParameter.IDENTIFIER] = serial_string\\n\\n if self.f_has_range():\\n # # Supports smart storage by hashing\\n smart_dict = {}\\n\\n store_dict[\\\"explored_data\\\" + SparseParameter.IDENTIFIER] = ObjectTable(\\n columns=[\\\"idx\\\"], index=list(range(len(self)))\\n )\\n\\n count = 0\\n for idx, elem in enumerate(self._explored_range):\\n\\n serial_string = self._serialize_matrix(elem)\\n\\n # Use the hash_tuple as a key for the smart_dict\\n if serial_string in smart_dict:\\n name_idx = smart_dict[serial_string]\\n add = False\\n else:\\n name_idx = count\\n add = True\\n\\n store_dict[\\\"explored_data\\\" + SparseParameter.IDENTIFIER][\\\"idx\\\"][\\n idx\\n ] = name_idx\\n\\n if add:\\n\\n store_dict[\\n \\\"xspm%s%08d\\\" % (SparseParameter.IDENTIFIER, name_idx)\\n ] = serial_string\\n\\n smart_dict[serial_string] = name_idx\\n count += 1\\n\\n self._locked = True\\n\\n return store_dict\",\n \"def to_sparse_matrix(self, grid, format=None):\\n S = self.centered_stencil()\\n # print(\\\"grid :\\\")\\n\\n grid = tuple(grid)\\n # print(grid)\\n if not (np.asarray(S.shape) % 2 == 1).all():\\n raise ValueError('all stencil dimensions must be odd')\\n\\n assert_condition(len(grid) == np.rank(S), ValueError,\\n 'stencil rank must equal number of grid dimensions')\\n assert_condition(min(grid) >= 1, ValueError,\\n 'grid dimensions must be positive')\\n\\n N_v = np.prod(grid) # number of vertices in the mesh\\n N_s = (S != 0).sum() # number of nonzero stencil entries\\n\\n # diagonal offsets\\n diags = np.zeros(N_s, dtype=int)\\n\\n # compute index offset of each dof within the stencil\\n strides = np.cumprod([1] + list(reversed(grid)))[:-1]\\n indices = tuple(i.copy() for i in S.nonzero())\\n for i,s in zip(indices,S.shape):\\n i -= s // 2\\n for stride,coords in zip(strides, reversed(indices)):\\n diags += stride * coords\\n\\n #\\n data = S[S != 0].repeat(N_v).reshape(N_s, N_v)\\n indices = np.vstack(indices).T\\n\\n # zero boundary connections\\n for index,diag in zip(indices,data):\\n diag = diag.reshape(grid)\\n for n,i in enumerate(index):\\n if i > 0:\\n s = [ slice(None) ]*len(grid)\\n s[n] = slice(0,i)\\n diag[s] = 0\\n elif i < 0:\\n s = [ slice(None) ]*len(grid)\\n s[n] = slice(i,None)\\n diag[s] = 0\\n\\n # remove diagonals that lie outside matrix\\n mask = abs(diags) < N_v\\n if not mask.all():\\n diags = diags[mask]\\n data = data[mask]\\n\\n # sum duplicate diagonals\\n if len(np.unique(diags)) != len(diags):\\n new_diags = np.unique(diags)\\n new_data = np.zeros( (len(new_diags),data.shape[1]), dtype=data.dtype)\\n for dia,dat in zip(diags,data):\\n n = np.searchsorted(new_diags,dia)\\n new_data[n,:] += dat\\n\\n diags = new_diags\\n data = new_data\\n\\n return sprs.dia_matrix((data,diags), shape=(N_v, N_v)).asformat(format)\",\n \"def wrapDBMatrix(self,mat):\\n return mat.todense()\",\n \"def __init__(self, rows, cols, bandwidth=0, sizeHint=None, matrix=None, storeZeros=True):\\n sizeHint = sizeHint or max(rows, cols) * bandwidth\\n if matrix is None:\\n tmpMatrix = spmatrix.ll_mat(1, 1, 1)\\n if hasattr(tmpMatrix, 'storeZeros'):\\n matrix = spmatrix.ll_mat(rows, cols, sizeHint, storeZeros)\\n else:\\n matrix = spmatrix.ll_mat(rows, cols, sizeHint)\\n\\n _PysparseMatrix.__init__(self, matrix=matrix)\",\n \"def to_sparse(self):\\n if self.rep.fmt == 'sparse':\\n return self\\n\\n return self.from_rep(self.rep.to_sdm())\",\n \"def set_sparse_backend(sparse_backend, warn=True):\\n if 'biom.table' in modules:\\n if warn:\\n print (\\\"Warning: biom.table has already been loaded. This call to \\\"\\n \\\"biom.set_sparse_backend() has no effect. It must be \\\"\\n \\\"called before biom.table is imported for the first time.\\\")\\n else:\\n biom_config['python_code_sparse_backend'] = sparse_backend\",\n \"def get_cvxopt_sparse_intf():\\n import cvxpy.interface.cvxopt_interface.sparse_matrix_interface as smi\\n return smi.SparseMatrixInterface()\",\n \"def build_mat_scikit_strandOriented(sralist, scikit_data):\\n\\n scikit_mat = {}\\n seq_codons = {}\\n seq_aa = {}\\n\\n for geneID in scikit_data[sralist[0]][0].keys():\\n for ix, dataset in enumerate(sralist):\\n\\n if geneID in scikit_data[dataset][0].keys():\\n current_profile = scikit_data[dataset][0].get(geneID, np.nan)\\n current_ribo = current_profile[0]\\n current_ribo = current_ribo[8:-8]\\n N = len(sralist)\\n M = len(current_ribo)\\n print(geneID, M)\\n\\n if ix == 0:\\n current_matrix = np.zeros((N,M)) * np.nan\\n\\n current_seq_codons = current_profile[1]\\n current_seq_codons = current_seq_codons[8:-8]\\n\\n current_seq_aa = current_profile[2]\\n current_seq_aa = current_seq_aa[8:-8]\\n\\n if strand_by_geneID_dict.get(geneID, \\\"NA\\\") == \\\"+\\\":\\n seq_codons[geneID] = current_seq_codons\\n seq_aa[geneID] = current_seq_aa\\n\\n elif strand_by_geneID_dict.get(geneID, \\\"NA\\\") == \\\"-\\\":\\n seq_codons[geneID] = current_seq_codons[::-1]\\n seq_aa[geneID] = current_seq_aa[::-1]\\n \\n \\n if strand_by_geneID_dict.get(geneID, \\\"NA\\\") == \\\"+\\\":\\n current_matrix[ix,:] = current_ribo\\n\\n elif strand_by_geneID_dict.get(geneID, \\\"NA\\\") == \\\"-\\\":\\n current_matrix[ix,:] = current_ribo[::-1]\\n \\n if np.sum(current_matrix) > 0: \\n scikit_mat[geneID] = current_matrix\\n\\n# scikit_df = pd.DataFrame(values_list, columns=columns_list)\\n\\n return scikit_mat, seq_codons, seq_aa\",\n \"def test_csm_sparser(self):\\r\\n sp_types = {'csc': sp.csc_matrix,\\r\\n 'csr': sp.csr_matrix}\\r\\n\\r\\n for format in ['csc', 'csr']:\\r\\n for dtype in ['float32', 'float64']:\\r\\n x = tensor.tensor(dtype=dtype, broadcastable=(False,))\\r\\n y = tensor.ivector()\\r\\n z = tensor.ivector()\\r\\n s = tensor.ivector()\\r\\n\\r\\n a = as_sparse_variable(sp_types[format](random_lil((4, 3),\\r\\n dtype, 1)))\\r\\n\\r\\n f = theano.function([x, y, z, s],\\r\\n tensor.grad(dense_from_sparse(\\r\\n a * CSM(format)(x, y, z, s)).sum(), x))\\r\\n\\r\\n spmat = sp_types[format](random_lil((4, 3), dtype, 3))\\r\\n\\r\\n res = f(spmat.data, spmat.indices, spmat.indptr,\\r\\n numpy.asarray(spmat.shape, 'int32'))\\r\\n\\r\\n assert len(spmat.data) == len(res)\",\n \"def visit_list(self, sylist):\\n if len(self.current) == 0:\\n self.current.extend(sylist)\\n\\n for item, other_item in izip(self.current, sylist):\\n hjoin(item, other_item)\",\n \"def test_assign_clusters_sparse(self, new_data, filename):\\n\\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\\n engine = create_engine(sqlalchemy_conn_str)\\n \\n print('creating test sparse matrix...')\\n if self.split_type == 'random':\\n averages_seg = pd.read_sql('SELECT * FROM clust_sparse_avebysegment_random',con=engine)\\n averages_rt = pd.read_sql('SELECT * FROM clust_sparse_avebyrt_random',con=engine)\\n if self.split_type == 'date':\\n averages_seg = pd.read_sql('SELECT * FROM clust_sparse_avebysegment_date',con=engine)\\n averages_rt = pd.read_sql('SELECT * FROM clust_sparse_avebyrt_date',con=engine)\\n\\n averages_seg['exists'] = 1\\n test_data_exists = pd.merge(new_data, averages_seg[['segment_id', 'exists']], on=['segment_id'])\\n test_exists = test_data_exists[test_data_exists['exists']==1]\\n test_notexists = test_data_exists[test_data_exists['exists']!=1] \\n \\n test_matrix_exists = pd.merge(test_exists[['segment_id', 'road_type']], averages_seg, how='left', on=['segment_id'])\\n test_matrix_notexists = pd.merge(test_notexists[['segment_id', 'road_type']], averages_rt, how='left', on=['road_type'])\\n test_matrix = pd.concat([test_matrix_exists, test_matrix_notexists])\\n test_matrix = test_matrix.fillna(0) \\n \\n test_sparse_matrix = test_matrix.drop(columns = ['segment_id', 'road_type', 'exists', 'index', 'roadtypekey', 'segmentskey'])\\n num = list(range(len(list(averages_seg))-4))\\n columns = [str(item) for item in num]\\n test_sparse_matrix = test_sparse_matrix[columns] \\n \\n print('clustering new data...')\\n cluster_model = joblib.load(filename)\\n cluster_predictions = cluster_model.predict(test_sparse_matrix)\\n \\n clusterdf = pd.DataFrame(cluster_predictions,columns = ['cluster_sparse'])\\n clusterdf['index'] = clusterdf.index\\n segmentdf = test_matrix['segment_id'].to_frame()\\n segmentdf['index'] = segmentdf.index\\n test_cluster_df_sparse = pd.merge(clusterdf, segmentdf, on=['index'])\\n test_cluster_df_sparse = test_cluster_df_sparse[['segment_id','cluster_sparse']].groupby(['segment_id','cluster_sparse']).count()\\n \\n return test_cluster_df_sparse.reset_index()\",\n \"def _apply_sparse(self, grad, var):\\n return self._apply_sparse_shared(\\n grad.values,\\n var,\\n grad.indices,\\n lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda\\n x,\\n i,\\n v,\\n use_locking=self._use_locking))\",\n \"def __call__(self, sparse_matrix: PipelinedRDD):\\n rows = sparse_matrix.collect()\\n\\n mat_index, mat_weights = zip(*rows)\\n mat_row, mat_col = zip(*mat_index)\\n tokens_num = len(self.tokens_list)\\n\\n self._log.info(\\\"Building matrix...\\\")\\n matrix = sparse.coo_matrix((mat_weights, (mat_row, mat_col)),\\n shape=(tokens_num, tokens_num))\\n Cooccurrences() \\\\\\n .construct(self.tokens_list, matrix) \\\\\\n .save(output=self.output, series=\\\"id2vec\\\", deps=(self.df_model,))\",\n \"def create_adjust_matrix(self, is_sub: bool):\\n matrix = None\\n if not is_sub:\\n ratings = np.ones_like(self._user, dtype=np.float32)\\n matrix = sp.csr_matrix(\\n (ratings, (self._user, self._item + self.n_users)),\\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\\n )\\n else:\\n if self.type == \\\"ND\\\":\\n drop_user = self.rand_sample(\\n self.n_users,\\n size=int(self.n_users * self.drop_ratio),\\n replace=False,\\n )\\n drop_item = self.rand_sample(\\n self.n_items,\\n size=int(self.n_items * self.drop_ratio),\\n replace=False,\\n )\\n R_user = np.ones(self.n_users, dtype=np.float32)\\n R_user[drop_user] = 0.0\\n R_item = np.ones(self.n_items, dtype=np.float32)\\n R_item[drop_item] = 0.0\\n R_user = sp.diags(R_user)\\n R_item = sp.diags(R_item)\\n R_G = sp.csr_matrix(\\n (\\n np.ones_like(self._user, dtype=np.float32),\\n (self._user, self._item),\\n ),\\n shape=(self.n_users, self.n_items),\\n )\\n res = R_user.dot(R_G)\\n res = res.dot(R_item)\\n\\n user, item = res.nonzero()\\n ratings = res.data\\n matrix = sp.csr_matrix(\\n (ratings, (user, item + self.n_users)),\\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\\n )\\n\\n elif self.type == \\\"ED\\\" or self.type == \\\"RW\\\":\\n keep_item = self.rand_sample(\\n len(self._user),\\n size=int(len(self._user) * (1 - self.drop_ratio)),\\n replace=False,\\n )\\n user = self._user[keep_item]\\n item = self._item[keep_item]\\n\\n matrix = sp.csr_matrix(\\n (np.ones_like(user), (user, item + self.n_users)),\\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\\n )\\n\\n matrix = matrix + matrix.T\\n D = np.array(matrix.sum(axis=1)) + 1e-7\\n D = np.power(D, -0.5).flatten()\\n D = sp.diags(D)\\n return D.dot(matrix).dot(D)\",\n \"def sparse(cls, a_ndarray, i_ndarray, shape, bigdl_type=\\\"float\\\"):\\n if a_ndarray is None:\\n return None\\n invalidInputError(isinstance(a_ndarray, np.ndarray),\\n f\\\"input should be a np.ndarray, not ${type(a_ndarray)}\\\")\\n invalidInputError(isinstance(i_ndarray, np.ndarray),\\n f\\\"indices should be a np.ndarray, not ${type(i_ndarray)}\\\")\\n invalidInputError(i_ndarray.size == a_ndarray.size * shape.size,\\n f\\\"size of values ${a_ndarray.size * shape.size} and\\\"\\n f\\\" indices ${i_ndarray.size} should match\\\")\\n return cls(a_ndarray,\\n shape,\\n bigdl_type,\\n i_ndarray)\",\n \"def _check_scipy_index_typing(sparse_matrix):\\n\\n int_max = np.iinfo(MKL.MKL_INT_NUMPY).max\\n if (sparse_matrix.nnz > int_max) or (max(sparse_matrix.shape) > int_max):\\n msg = \\\"MKL interface is {t} and cannot hold matrix {m}\\\\n\\\".format(m=repr(sparse_matrix), t=MKL.MKL_INT_NUMPY)\\n msg += \\\"Try changing MKL to int64 with the environment variable MKL_INTERFACE_LAYER=ILP64\\\"\\n raise ValueError(msg)\\n\\n # Cast indexes to MKL_INT type\\n if sparse_matrix.indptr.dtype != MKL.MKL_INT_NUMPY:\\n sparse_matrix.indptr = sparse_matrix.indptr.astype(MKL.MKL_INT_NUMPY)\\n if sparse_matrix.indices.dtype != MKL.MKL_INT_NUMPY:\\n sparse_matrix.indices = sparse_matrix.indices.astype(MKL.MKL_INT_NUMPY)\",\n \"def get_sparse_backend():\\n backend = biom_config['python_code_sparse_backend']\\n if backend is None:\\n backend = 'CSMat'\\n\\n if backend not in sparse_backends:\\n raise InvalidSparseBackendException(\\\"Unrecognized sparse backend \\\"\\n \\\"'%s'. Choose from %s.\\\" % (backend,\\n ', '.join(sparse_backends)))\\n\\n valid_backend = False\\n if backend == 'ScipySparseMat':\\n try:\\n from biom.backends.scipysparse import ScipySparseMat, to_scipy, \\\\\\n dict_to_scipy, list_dict_to_scipy, list_nparray_to_scipy, \\\\\\n nparray_to_scipy, list_list_to_scipy\\n SparseObj = ScipySparseMat\\n to_sparse = to_scipy\\n dict_to_sparseobj = dict_to_scipy\\n list_dict_to_sparseobj = list_dict_to_scipy\\n list_nparray_to_sparseobj = list_nparray_to_scipy\\n nparray_to_sparseobj = nparray_to_scipy\\n list_list_to_sparseobj = list_list_to_scipy\\n valid_backend = True\\n except ImportError:\\n valid_backend = False\\n stderr.write(\\\"Cannot load ScipySparseMat (requires that scipy is \\\"\\n \\\"installed). Using CSMat sparse backend.\\\\n\\\")\\n\\n if backend == 'CSMat' or (not valid_backend):\\n try:\\n from biom.backends.csmat import CSMat, to_csmat, dict_to_csmat, \\\\\\n list_dict_to_csmat, list_nparray_to_csmat, nparray_to_csmat, \\\\\\n list_list_to_csmat\\n SparseObj = CSMat\\n to_sparse = to_csmat\\n dict_to_sparseobj = dict_to_csmat\\n list_dict_to_sparseobj = list_dict_to_csmat\\n list_nparray_to_sparseobj = list_nparray_to_csmat\\n nparray_to_sparseobj = nparray_to_csmat\\n list_list_to_sparseobj = list_list_to_csmat\\n valid_backend = True\\n except ImportError:\\n valid_backend = False\\n stderr.write('Cannot load CSMat sparse backend.\\\\n')\\n\\n if not valid_backend:\\n raise InvalidSparseBackendException(\\\"The sparse matrix backend '%s' \\\"\\n \\\"could not be loaded. Please check your biom-format \\\"\\n \\\"installation.\\\" % backend)\\n\\n return SparseObj, to_sparse, dict_to_sparseobj, list_dict_to_sparseobj, \\\\\\n list_nparray_to_sparseobj, nparray_to_sparseobj, \\\\\\n list_list_to_sparseobj\",\n \"def makesparse(matrix):\\n n = matrix[0].size\\n elements = []\\n for i in range(n):\\n for j in range(n):\\n if matrix[i][j] != 0 :\\n temp = MatrixElement(i, j, matrix[i][j])\\n elements.append(temp)\\n return SparseMatrix(n, elements)\",\n \"def append_rss(A):\\n rss = np.sqrt(np.diag(A.dot(A.transpose()))) \\n out = np.hstack((A, np.atleast_2d(rss).transpose())) \\n return out\",\n \"def _apply_sparse(self, grad, var):\\n\\n return self._apply_sparse_shared(\\n grad.values,\\n var,\\n grad.indices,\\n lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda\\n x,\\n i,\\n v,\\n use_locking=self._use_locking))\",\n \"def putclist(self,subj_,val_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(val_)\\n elif num_ != len(val_):\\n raise IndexError(\\\"Inconsistent length of array val\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if val_ is None:\\n raise ValueError(\\\"Argument val cannot be None\\\")\\n if val_ is None:\\n raise ValueError(\\\"Argument val may not be None\\\")\\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\\n _val_copyarray = False\\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif val_ is not None:\\n _val_copyarray = True\\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\\n _val_np_tmp[:] = val_\\n assert _val_np_tmp.flags.contiguous\\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _val_copyarray = False\\n _val_tmp = None\\n \\n res = __library__.MSK_XX_putclist(self.__nativep,num_,_subj_tmp,_val_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def create_sparse_matrix_long(self, matrix_df):\\n\\n time_df_tmp = matrix_df.groupby(['date_idx','time_idx']).count().reset_index()\\n time_df = time_df_tmp[['date_idx','time_idx']]\\n time_df['time_id'] = time_df.index.copy()\\n matrix_df_tid = pd.merge(matrix_df, time_df, how='left', on=['date_idx','time_idx'])\\n\\n time_array = np.array(matrix_df_tid['time_id'])\\n segment_array = np.array(matrix_df_tid['segment_id'])\\n\\n uniquesegments = np.array(list(set(segment_array)))\\n keyuniquesegments = np.array(range(len(uniquesegments)))\\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\\n\\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\\n segmentkeys = np.array(segments_keys_df['segmentskey'])\\n\\n level_array = np.array(matrix_df_tid[self.args['cluster_variable']])\\n sparse_matrix = csr_matrix((level_array, (segmentkeys,time_array))).toarray()\\n sparse_matrix = preprocessing.scale(sparse_matrix)\\n \\n if self.args['perform_pca']:\\n data_to_cluster, self.pca_model = self.run_PCA_long(sparse_matrix)\\n else:\\n data_to_cluster = sparse_matrix\\n\\n sparse_matrix_withsegkey = pd.DataFrame(data_to_cluster)\\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\\n \\n print('writing sparse matrix_long to db...')\\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\\n engine = create_engine(sqlalchemy_conn_str)\\n if self.split_type == 'random':\\n sparse_matrix_withseg.to_sql(name='clust_sparse_long_avebysegment_random', con=engine, if_exists='replace')\\n elif self.split_type == 'date':\\n sparse_matrix_withseg.to_sql(name='clust_sparse_long_avebysegment_date', con=engine, if_exists='replace')\\n \\n print('returning train sparse_long matrix...')\\n return (uniquesegments_df, data_to_cluster)\",\n \"def add_sparse_pwd(a, b, neg1_fmt=True):\\n \\n a_coo = a.tocoo()\\n b_coo = b.tocoo()\\n nr, nc = a_coo.shape\\n \\n ind_a = a_coo.row.astype(np.int64) * nc + a_coo.col\\n ind_b = b_coo.row.astype(np.int64) * nc + b_coo.col\\n # ind_ab = np.array(tuple(set(ind_a).intersection(set(ind_b))))\\n ind_ab = np.intersect1d(ind_a, ind_b, assume_unique=True)\\n keep_a = np.in1d(ind_a, ind_ab, assume_unique=True)\\n keep_b = np.in1d(ind_b, ind_ab, assume_unique=True)\\n if neg1_fmt:\\n a_data = a_coo.data[keep_a]\\n a_data[a_data == -1] = 0\\n b_data = b_coo.data[keep_b]\\n b_data[b_data == -1] = 0\\n tot = a_data + b_data\\n else:\\n tot = a_coo.data[keep_a] + b_coo.data[keep_b]\\n \\\"\\\"\\\"Need to add -1 as placeholder upon creation of new sparse matrix\\\"\\\"\\\"\\n tot[tot == 0] = -1\\n aplusb = sparse.coo_matrix((tot,\\n (a_coo.row[keep_a], a_coo.col[keep_a])), shape=(nr, nc))\\n aplusb = aplusb.tocsr()\\n if not neg1_fmt:\\n \\\"\\\"\\\"Convert it back to 0 = true zero distance\\\"\\\"\\\"\\n aplusb.data[aplusb.data == -1] = 0\\n return aplusb\",\n \"def create_preference_weighted_similarity_matrix(self):\\n # We only need to compare the distances to points within the epsilon shell (to determine if a point is a core point)\\n # Since the subspace scaling factor kappa is >>1 (and not <1), no distances to other points will be needed for \\n # the core point evaluation\\n\\n # get points in epsilon shell: attententio point itself is not in neigh_ind list\\n _, neigh_ind = self.neigbors_clf.radius_neighbors(radius=self.epsilon)\\n row, col, pwsim = [], [], []\\n for i, ith_neigh_ind in enumerate(neigh_ind):\\n # Calculate preference weighted similarity measure with point and neighbors in eps shell\\n sq_diffs = np.square(self.data[ith_neigh_ind,:] - self.data[i,:])\\n sum_weighted_sq_diffs = np.inner(self.subspace_preference_vectors[i,:], sq_diffs)\\n pwsim_ith = np.sqrt(sum_weighted_sq_diffs)\\n \\n # Info for sparse matrix\\n pwsim.extend(pwsim_ith.tolist()) # Data\\n row.extend([i]*(pwsim_ith.shape[0])) # ith Row \\n col.extend(ith_neigh_ind.tolist()) # column info\\n\\n # Construct sparse matrix with data, row, and column info\\n A = csr_matrix((pwsim, (row, col)), shape=(self.nb_points, self.nb_points))\\n # Create symmetric version: take the elementwise maximum of A and its transpose A.T\\n transpose_is_bigger = A.T>A\\n A = A - A.multiply(transpose_is_bigger) + (A.T).multiply(transpose_is_bigger)\\n \\n return A\",\n \"def extend_traj(self, run_idx, traj_idx, data, weights=None):\\n\\n if self._wepy_mode == 'c-':\\n assert self._append_flags[dataset_key], \\\"dataset is not available for appending to\\\"\\n\\n # convenient alias\\n traj_data = data\\n\\n # number of frames to add\\n n_new_frames = traj_data[POSITIONS].shape[0]\\n\\n n_frames = self.num_traj_frames(run_idx, traj_idx)\\n\\n # calculate the new sparse idxs for sparse fields that may be\\n # being added\\n sparse_idxs = np.array(range(n_frames, n_frames + n_new_frames))\\n\\n # get the trajectory group\\n traj_grp = self._h5['{}/{}/{}/{}'.format(RUNS, run_idx, TRAJECTORIES, traj_idx)]\\n\\n ## weights\\n\\n # if weights are None then we assume they are 1.0\\n if weights is None:\\n weights = np.ones((n_new_frames, 1), dtype=float)\\n else:\\n assert isinstance(weights, np.ndarray), \\\"weights must be a numpy.ndarray\\\"\\n assert weights.shape[0] == n_new_frames,\\\\\\n \\\"weights and the number of frames must be the same length\\\"\\n\\n # add the weights\\n weights_ds = traj_grp[WEIGHTS]\\n\\n # append to the dataset on the first dimension, keeping the\\n # others the same, if they exist\\n if len(weights_ds.shape) > 1:\\n weights_ds.resize( (weights_ds.shape[0] + n_new_frames, *weights_ds.shape[1:]) )\\n else:\\n weights_ds.resize( (weights_ds.shape[0] + n_new_frames, ) )\\n\\n # add the new data\\n weights_ds[-n_new_frames:, ...] = weights\\n\\n\\n # add the other fields\\n for field_path, field_data in traj_data.items():\\n\\n # if the field hasn't been initialized yet initialize it,\\n # unless we are in SWMR mode\\n if not field_path in traj_grp:\\n\\n # if in SWMR mode you cannot create groups so if we\\n # are in SWMR mode raise a warning that the data won't\\n # be recorded\\n if self.swmr_mode:\\n warn(\\\"New datasets cannot be created while in SWMR mode. The field {} will\\\"\\n \\\"not be saved. If you want to save this it must be\\\"\\n \\\"previously created\\\".format(field_path))\\n else:\\n\\n feature_shape = field_data.shape[1:]\\n feature_dtype = field_data.dtype\\n\\n # not specified as sparse_field, no settings\\n if (not field_path in self.field_feature_shapes) and \\\\\\n (not field_path in self.field_feature_dtypes) and \\\\\\n not field_path in self.sparse_fields:\\n # only save if it is an observable\\n is_observable = False\\n if '/' in field_path:\\n group_name = field_path.split('/')[0]\\n if group_name == OBSERVABLES:\\n is_observable = True\\n if is_observable:\\n warn(\\\"the field '{}' was received but not previously specified\\\"\\n \\\" but is being added because it is in observables.\\\".format(field_path))\\n # save sparse_field flag, shape, and dtype\\n self._add_sparse_field_flag(field_path)\\n self._set_field_feature_shape(field_path, feature_shape)\\n self._set_field_feature_dtype(field_path, feature_dtype)\\n else:\\n raise ValueError(\\\"the field '{}' was received but not previously specified\\\"\\n \\\"it is being ignored because it is not an observable.\\\".format(field_path))\\n\\n # specified as sparse_field but no settings given\\n elif (self.field_feature_shapes[field_path] is None and\\n self.field_feature_dtypes[field_path] is None) and \\\\\\n field_path in self.sparse_fields:\\n # set the feature shape and dtype since these\\n # should be 0 in the settings\\n self._set_field_feature_shape(field_path, feature_shape)\\n\\n self._set_field_feature_dtype(field_path, feature_dtype)\\n\\n # initialize\\n self._init_traj_field(run_idx, traj_idx, field_path, feature_shape, feature_dtype)\\n\\n # extend it either as a sparse field or a contiguous field\\n if field_path in self.sparse_fields:\\n self._extend_sparse_traj_field(run_idx, traj_idx, field_path, field_data, sparse_idxs)\\n else:\\n self._extend_contiguous_traj_field(run_idx, traj_idx, field_path, field_data)\",\n \"def update_mass_matrix(self, sim, joint_index):\\n mass_matrix = np.ndarray(shape=(len(sim.data.qvel) ** 2,), dtype=np.float64, order='C')\\n # print(self.current_joint_position)\\n # print(mass_matrix)\\n mujoco_py.cymj._mj_fullM(sim.model, mass_matrix, sim.data.qM)\\n mass_matrix = np.reshape(mass_matrix, (len(sim.data.qvel), len(sim.data.qvel)))\\n self.mass_matrix = mass_matrix[joint_index, :][:, joint_index]\\n \\n # self.mass_matrix = np.array([[ 2.25726539e+00, -2.62750718e-06, 1.60412060e+00,\\n # 2.62750718e-06, -5.43221706e-01, 2.62750718e-06,\\n # -1.98486117e-01],\\n # [-2.62750718e-06, 2.28637709e+00, -6.58357876e-06,\\n # -1.16381227e+00, -4.11331754e-06, -4.00056093e-01,\\n # 2.53844900e-18],\\n # [ 1.60412060e+00, -6.58357876e-06, 1.60070894e+00,\\n # 6.58357876e-06, -5.02987276e-01, 6.58357876e-06,\\n # -1.89718057e-01],\\n # [ 2.62750718e-06, -1.16381227e+00, 6.58357876e-06,\\n # 1.43272976e+00, 4.11331754e-06, 4.01913080e-01,\\n # -2.53381454e-18],\\n # [-5.43221706e-01, -4.11331754e-06, -5.02987276e-01,\\n # 4.11331754e-06, 5.11565111e-01, 4.11331754e-06,\\n # 1.96115254e-01],\\n # [ 2.62750718e-06, -4.00056093e-01, 6.58357876e-06,\\n # 4.01913080e-01, 4.11331754e-06, 3.22014223e-01,\\n # -2.53991699e-18],\\n # [-1.98486117e-01, 2.53844900e-18, -1.89718057e-01,\\n # -2.53381454e-18, 1.96115254e-01, -2.53991699e-18,\\n # 2.00104011e-01]])\",\n \"def __init__(self, link_matrix, users, index_id_map, is_sparse=False):\\n self.__is_sparse = is_sparse\\n self.__link_matrix = link_matrix\\n self.__link_matrix_tr = link_matrix.transpose()\\n self.__n = self.__link_matrix.shape[0]\\n self.__hubs = np.ones(self.__n)\\n self.__auths = np.ones(self.__n)\\n self.__size = 30\\n self.__names = [users[index_id_map[i]]['screen_name'] for i in range(0, self.__size)]\\n self.__index_id_map = index_id_map\\n self.__users = users\\n self.all_hubs = []\\n self.all_auths = []\",\n \"def append(self, solf):\\n exist_map = {'vertex_index': len(self.get('vertices', [])),\\n 'texcoord_index': len(self.get('texcoords', [])),\\n 'normal_index': len(self.get('normals', [])),\\n 'param_index': len(self.get('params', []))}\\n exist_map.update(points=exist_map['vertex_index'],\\n curve2Ds=exist_map['param_index'])\\n # Vertex fields\\n for k in ['vertices', 'texcoords', 'normals', 'params']:\\n if k in solf:\\n if k not in self:\\n self[k] = []\\n self[k] += solf[k]\\n # Points/2D curves\\n for k in ['points', 'curve2Ds']:\\n if k in solf:\\n if k not in self:\\n self[k] = []\\n for x in solf[k]:\\n self[k].append([v + exist_map[k] for v in x])\\n # Face/line fields\\n for k in ['lines', 'faces']:\\n if k in solf:\\n if k not in self:\\n self[k] = []\\n for x in solf[k]:\\n iele = [{ik: v[ik] + exist_map[ik] for ik in v.keys()} for v in x]\\n self[k].append(iele)\\n # Curves\\n k = 'curves'\\n if k in solf:\\n if k not in self:\\n self[k] = []\\n for x in solf[k]:\\n iele = copy.deepcopy(x)\\n iele['vertex_indices'] = [v + exist_map['vertex_index']\\n for v in x['vertex_indices']]\\n # Surfaces\\n k = 'surfaces'\\n if k in solf:\\n if k not in self:\\n self[k] = []\\n for x in solf[k]:\\n iele = copy.deepcopy(x)\\n iele['vertex_indices'] = [{ik: v[ik] + exist_map[ik] for ik in v.keys()}\\n for v in x['vertex_indices']]\\n # Merge material using first in list\\n material = None\\n for x in [self, solf]:\\n if x.get('material', None) is not None:\\n material = x['material']\\n break\\n if material is not None:\\n self['material'] = material\\n return self\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70590025","0.6795725","0.63318455","0.6023266","0.52935266","0.5206711","0.5191596","0.5079819","0.49437016","0.49408752","0.4925097","0.4889433","0.4782237","0.4736962","0.4685731","0.467853","0.46744376","0.46646318","0.46621722","0.46508092","0.46442583","0.4643347","0.46405804","0.46150428","0.4612372","0.4607801","0.46047214","0.45903334","0.45872107","0.45837086","0.45785424","0.45764354","0.4555788","0.45488515","0.45159706","0.45139104","0.44964892","0.44807267","0.44712678","0.44393444","0.44309452","0.44194344","0.4417237","0.4375561","0.4365556","0.43583483","0.43500918","0.43402392","0.4338407","0.43381467","0.43332547","0.43271706","0.43258074","0.43234935","0.4323038","0.43214342","0.4319089","0.4315451","0.43151405","0.43139064","0.43061668","0.43030354","0.43001854","0.4295493","0.4290629","0.4274826","0.42678317","0.42636567","0.42633718","0.42624348","0.4259142","0.42564547","0.4249585","0.42389852","0.4237563","0.4232643","0.42303932","0.42271006","0.4227094","0.42248556","0.422391","0.42225993","0.42193666","0.421713","0.42030013","0.42028138","0.4201643","0.42014086","0.4200151","0.4198251","0.41981345","0.41935793","0.41844428","0.41841802","0.41818702","0.4181208","0.41724068","0.41658443","0.4162868","0.41572142"],"string":"[\n \"0.70590025\",\n \"0.6795725\",\n \"0.63318455\",\n \"0.6023266\",\n \"0.52935266\",\n \"0.5206711\",\n \"0.5191596\",\n \"0.5079819\",\n \"0.49437016\",\n \"0.49408752\",\n \"0.4925097\",\n \"0.4889433\",\n \"0.4782237\",\n \"0.4736962\",\n \"0.4685731\",\n \"0.467853\",\n \"0.46744376\",\n \"0.46646318\",\n \"0.46621722\",\n \"0.46508092\",\n \"0.46442583\",\n \"0.4643347\",\n \"0.46405804\",\n \"0.46150428\",\n \"0.4612372\",\n \"0.4607801\",\n \"0.46047214\",\n \"0.45903334\",\n \"0.45872107\",\n \"0.45837086\",\n \"0.45785424\",\n \"0.45764354\",\n \"0.4555788\",\n \"0.45488515\",\n \"0.45159706\",\n \"0.45139104\",\n \"0.44964892\",\n \"0.44807267\",\n \"0.44712678\",\n \"0.44393444\",\n \"0.44309452\",\n \"0.44194344\",\n \"0.4417237\",\n \"0.4375561\",\n \"0.4365556\",\n \"0.43583483\",\n \"0.43500918\",\n \"0.43402392\",\n \"0.4338407\",\n \"0.43381467\",\n \"0.43332547\",\n \"0.43271706\",\n \"0.43258074\",\n \"0.43234935\",\n \"0.4323038\",\n \"0.43214342\",\n \"0.4319089\",\n \"0.4315451\",\n \"0.43151405\",\n \"0.43139064\",\n \"0.43061668\",\n \"0.43030354\",\n \"0.43001854\",\n \"0.4295493\",\n \"0.4290629\",\n \"0.4274826\",\n \"0.42678317\",\n \"0.42636567\",\n \"0.42633718\",\n \"0.42624348\",\n \"0.4259142\",\n \"0.42564547\",\n \"0.4249585\",\n \"0.42389852\",\n \"0.4237563\",\n \"0.4232643\",\n \"0.42303932\",\n \"0.42271006\",\n \"0.4227094\",\n \"0.42248556\",\n \"0.422391\",\n \"0.42225993\",\n \"0.42193666\",\n \"0.421713\",\n \"0.42030013\",\n \"0.42028138\",\n \"0.4201643\",\n \"0.42014086\",\n \"0.4200151\",\n \"0.4198251\",\n \"0.41981345\",\n \"0.41935793\",\n \"0.41844428\",\n \"0.41841802\",\n \"0.41818702\",\n \"0.4181208\",\n \"0.41724068\",\n \"0.41658443\",\n \"0.4162868\",\n \"0.41572142\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74942684"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11972,"cells":{"query":{"kind":"string","value":"Obtains information about a matrix from the symmetric matrix storage. getsymmatinfo(self,idx_)"},"document":{"kind":"string","value":"def getsymmatinfo(self,idx_):\n dim_ = ctypes.c_int32()\n nz_ = ctypes.c_int64()\n type_ = ctypes.c_int32()\n res = __library__.MSK_XX_getsymmatinfo(self.__nativep,idx_,ctypes.byref(dim_),ctypes.byref(nz_),ctypes.byref(type_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n dim_ = dim_.value\n _dim_return_value = dim_\n nz_ = nz_.value\n _nz_return_value = nz_\n _type_return_value = symmattype(type_.value)\n return (_dim_return_value,_nz_return_value,_type_return_value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getsymmatinfo(self,idx_): # 3\n res,resargs = self.__obj.getsymmatinfo(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dim_return_value,_nz_return_value,_type_return_value = resargs\n _type_return_value = symmattype(_type_return_value)\n return _dim_return_value,_nz_return_value,_type_return_value","def getnumsymmat(self):\n num_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumsymmat(self.__nativep,ctypes.byref(num_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n num_ = num_.value\n _num_return_value = num_\n return (_num_return_value)","def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_valij = False\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n _copyback_valij = True\n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n _copyback_valij = True\n if valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij has wrong length\")\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_valij:\n valij[:] = _tmparr_valij\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi","def getsparsesymmat(self,idx_,subi_,subj_,valij_):\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _subi_minlength = (maxlen_)\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxlen_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (maxlen_)\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxlen_)))\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\n raise ValueError(\"Argument subj must be writable\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = (maxlen_)\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),(maxlen_)))\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\n raise ValueError(\"Argument valij must be writable\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valij_copyarray:\n valij_[:] = _valij_np_tmp","def getnumsymmat(self): # 3\n res,resargs = self.__obj.getnumsymmat()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n return _num_return_value","def dctSymUserInfo(pdct, symIndex):\n return _dctmcc.dctSymUserInfo(pdct, symIndex)","def csr_info(mat, name=\"\", non_empy=False):\n if non_empy:\n print(\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\" % (\n name, mat.shape[0], \n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \n for i in range(mat.shape[0])), \n mat.shape[1], len(np.unique(mat.indices)), \n len(mat.data)))\n else:\n print( \"%s [nrows %d, ncols %d, nnz %d]\" % (name, \n mat.shape[0], mat.shape[1], len(mat.data)) )","def csr_info(mat, name=\"\", non_empy=False):\n if non_empy:\n print(\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\" % (\n name, mat.shape[0], \n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \n for i in range(mat.shape[0])), \n mat.shape[1], len(np.unique(mat.indices)), \n len(mat.data)))\n else:\n print( \"%s [nrows %d, ncols %d, nnz %d]\" % (name, \n mat.shape[0], mat.shape[1], len(mat.data)) )","def csr_info(mat, name=\"\", non_empy=False):\n if non_empy:\n print(\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\" % (\n name, mat.shape[0], \n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \n for i in range(mat.shape[0])), \n mat.shape[1], len(np.unique(mat.indices)), \n len(mat.data)))\n else:\n print( \"%s [nrows %d, ncols %d, nnz %d]\" % (name, \n mat.shape[0], mat.shape[1], len(mat.data)) )","def csr_info(mat, name=\"\", non_empy=False):\n if non_empy:\n print(\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\" % (\n name, mat.shape[0], \n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \n for i in range(mat.shape[0])), \n mat.shape[1], len(np.unique(mat.indices)), \n len(mat.data)))\n else:\n print( \"%s [nrows %d, ncols %d, nnz %d]\" % (name, \n mat.shape[0], mat.shape[1], len(mat.data)) )","def get_sym_matrix(self):\n temp_T = Matrix.eye(3)\n for i in range(len(self.lengths)):\n angle_mat = self.T_a.subs(self.q,self.angles[i]).evalf()\n len_mat = self.T_x.subs(self.l,self.lengths[i]).evalf()\n temp_T = temp_T * angle_mat * len_mat\n \n return temp_T","def SymmetriseMatrix(adjmatrix):\n\n if galib.metrics.Reciprocity(adjmatrix) == 1:\n # if Reciprocity(adjmatrix) == 1:\n return adjmatrix\n else:\n return 0.5 * (adjmatrix + adjmatrix.T)","def sym_adj(adj):\n adj = sp.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense()","def sym_adj(adj):\n adj = ss.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = ss.diags(d_inv_sqrt)\n return np.array(adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense())","def symbolic_mutual_information(symX, symY):\n\n if len(symX) != len(symY):\n raise ValueError('All arrays must have same length')\n \n symX = np.array(symX)\n symY = np.array(symY)\n \n symbols = np.unique(np.concatenate((symX,symY))).tolist()\n \n jp = symbolic_joint_probabilities(symX, symY)\n pX = symbolic_probabilities(symX)\n pY = symbolic_probabilities(symY)\n \n MI = 0\n\n for yi, b in pY.items():\n for xi, a in pX.items():\n try:\n c = jp[yi,xi]\n MI += c * np.log(c /(a * b)) / np.log(len(symbols))\n except KeyError:\n continue\n except:\n print(\"Unexpected Error\")\n raise\n \n return MI","def _print_matrix_info(mtrx, name):\r\n pr = lambda t: print(\"ht3_solver:\\t\" + t)\r\n pr(\"MATRIX INFO:\")\r\n pr(\"Matrix:\\t\" + name)\r\n pr(\"Description:\\t\" + str(mtrx.description))\r\n pr(\"Shape:\\t\" + str(mtrx.shape))","def dctSymIndex(pdct, symName):\n return _dctmcc.dctSymIndex(pdct, symName)","def find_symmetry(self):\n from spglib import get_spacegroup\n cell = ( self.lattice, self.fractional_coordinates, self.atomic_nos )\n self.spacegroup = get_spacegroup(cell, symmprec=1e-5)\n print(\"Symmetry space group is\", self.spacegroup)","def getMibSymbol(self):\n if self.__state & self.stClean:\n return self.__modName, self.__symName, self.__indices\n else:\n raise SmiError('%s object not fully initialized' % self.__class__.__name__)","def information_matrix(self):\n return self._cov.inv()","def get_connection_mat(self, idx):\n\n try:\n return self.weights[idx]\n except:\n print(\"\"\"Could not find layer {0} in network.\\nNetwork has {1} layers.\"\"\".format(idx, self.size))","def get_stain_matrix(I):","def sym(self) -> np.ndarray:\n if self._sym is None:\n self._sym = symmetrize_discrete_vector_field(self.F, mode=\"sym\")\n return self._sym","def dctSymEntries(pdct, symIndex):\n return _dctmcc.dctSymEntries(pdct, symIndex)","def get_sol_mat(self, clust):\r\n\r\n return self.__model_data[clust]","def jmat(ind: int):\n return _jm[ind - 1]","def get_solver_mats(self, x_nd, rot_coef):\n raise NotImplementedError","def symmeterize(self):\n A = self.to_coo_matrix()\n symg = wgraph_from_adjacency((A + A.T) / 2)\n self.E = symg.E\n self.edges = symg.edges\n self.weights = symg.weights\n return self","def get_symbolic_model(self):\n return self.sym_func","def get_symbolic_model(self):\n return self.sym_func","def getbaraidxinfo(self,idx_):\n num_ = ctypes.c_int64()\n res = __library__.MSK_XX_getbaraidxinfo(self.__nativep,idx_,ctypes.byref(num_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n num_ = num_.value\n _num_return_value = num_\n return (_num_return_value)","def get_info(atom):\n return [atom.GetIdx(), atom.GetNeighbors()[0].GetIdx()]","def is_symmetric(mat):\n return np.allclose(mat.T, mat)","def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) -> None:\n is_unity = validate_unity(symmetry[0])\n if len(symmetry) == 1 and is_unity:\n return\n build_symmetry_operations(symmetry)\n validate_matrix_symmetry(mat, symmetry)","def sim_info(self) -> SimInfo:\n return self._sim_info","def return_adjacencyMatrix(self):\n return self.__mat","def _general_get_information(self, k=None):\n ## Format k\n ks = self.get_k(k)\n idx_ks = self._get_k_indices(ks)\n ## Get iss\n iss = self.iss\n ## Format idxs\n assert(type(idx_ks) == list)\n neighs = self.get_neighs(idx_ks)\n sp_relative_pos = self.get_sp_rel_pos(idx_ks)\n self.check_output_standards(neighs, sp_relative_pos, ks, iss)\n# print '3'*50, neighs, sp_relative_pos, ks, iss\n return neighs, sp_relative_pos, ks, iss","def dctSymDomIdx(pdct, symIndex):\n return _dctmcc.dctSymDomIdx(pdct, symIndex)","def symmetric(matrix):\n return sp.allclose(matrix, matrix.T)","def get_matrix_info(matrix, n):\n\n ones_positions = np.asarray(np.where(matrix == 1))\n ones_count = np.shape(ones_positions)[1]\n\n one_position_in_row = np.full(n, -1, dtype=int)\n np.put(one_position_in_row, ones_positions[0], ones_positions[1])\n has_one_in_row = np.select([one_position_in_row != -1], [1]).astype(bool)\n zero_rows = np.asarray(np.where(np.invert(has_one_in_row))).squeeze(0)\n\n return ones_count, ones_positions, has_one_in_row, one_position_in_row, zero_rows","def dctSymType(pdct, symIndex):\n return _dctmcc.dctSymType(pdct, symIndex)","def adj_mat(self):\n return self._adj_mat","def get_cvxopt_sparse_intf():\n import cvxpy.interface.cvxopt_interface.sparse_matrix_interface as smi\n return smi.SparseMatrixInterface()","def _symbolic_system(self):\n system = sym.Matrix(self._symbolic_equations)\n return system.subs(self._symbolic_change_of_vars)","def matrix_add_symmetric(M, M_sym):\n M[0, 0] += M_sym[0]\n M[0, 1] += M_sym[1]\n M[1, 0] += M_sym[1]\n M[0, 2] += M_sym[2]\n M[2, 0] += M_sym[2]\n\n M[1, 1] += M_sym[3]\n M[1, 2] += M_sym[4]\n M[2, 1] += M_sym[4]\n\n M[2, 2] += M_sym[5]\n\n return M","def is_symmetric(mat, eps=None):\n if eps is None:\n eps = np.finfo(mat.dtype).eps\n\n assert mat.ndim == 2\n if mat.shape[0] != mat.shape[1]:\n return False\n\n return np.allclose(mat, mat.T, atol=eps)","def get_symmetry_mates(self, model=None, altloc=\"first\",\n extra_fields=[], include_bonds=False):\n # Get base structure\n structure = self.get_structure(\n model,\n altloc,\n extra_fields,\n include_bonds,\n )\n # Get lines containing transformations for crystallographic symmetry\n remark_lines = self.get_remark(290)\n if remark_lines is None:\n raise InvalidFileError(\n \"File does not contain crystallographic symmetry \"\n \"information (REMARK 350)\"\n )\n transform_lines = [\n line for line in remark_lines if line.startswith(\" SMTRY\")\n ]\n rotations, translations = _parse_transformations(\n transform_lines\n )\n return _apply_transformations(\n structure, rotations, translations\n )","def get_mat(self,diag_entries=False):\n if self._mat is None or (diag_entries and not self._diag_entries):\n self.build_mat(diag_entries=diag_entries)\n return self._mat","def with_matsym(*simplifies):\n from sympy.matrices import MatrixSymbol\n from symplus.setplus import AbstractSet\n def simplify_with_matsym(expr, *args, **kwargs):\n # expand MatrixSymbol as Matrix: A -> [ A[0,0] ,..]\n mats = list(expr.atoms(MatrixSymbol))\n agents = list(Dummy(str(mat)) for mat in mats)\n def protect_var(var, expr):\n return AbstractSet(var.xreplace(dict(zip(mats, agents))), expr)\n expr = expr.replace(AbstractSet, protect_var)\n expr = expr.xreplace(dict((mat, mat.as_explicit()) for mat in mats))\n expr = expr.xreplace(dict(zip(agents, mats)))\n\n # replace MatrixElement as Symbol: A[i,j] -> Aij\n elems = tuple(elem for mat in mats for elem in mat)\n syms = tuple(Dummy(str(e)) for e in elems)\n expr = expr.xreplace(dict(zip(elems, syms)))\n\n # simplify expression\n for simp in simplifies:\n expr = simp(expr, *args, **kwargs)\n\n # replace Symbol as MatrixElement: Aij -> A[i,j]\n expr = expr.xreplace(dict(zip(syms, elems)))\n\n return expr\n return simplify_with_matsym","def dctSymDim(pdct, symIndex):\n return _dctmcc.dctSymDim(pdct, symIndex)","def test_sympmat(n):\n I = np.identity(n)\n O = np.zeros_like(I)\n X = np.block([[O, I], [-I, O]])\n res = symplectic.sympmat(n)\n assert np.all(X == res)","def dctSymDomNames(pdct, symIndex):\n return _dctmcc.dctSymDomNames(pdct, symIndex)","def dctSymName(pdct, symIndex, symName_i):\n return _dctmcc.dctSymName(pdct, symIndex, symName_i)","def getSparseAdjacencyMatrix( graph, attribute=None, transposed=False ):\n if (attribute is not None) and (attribute not in graph.es.attribute_names()):\n raise ValueError( \"Attribute does not exists.\" )\n \n row = []\n col = []\n data = []\n \n if attribute is None:\n if transposed:\n for edge in graph.es():\n s,t = edge.tuple\n row.append(t)\n col.append(s)\n else:\n for edge in graph.es():\n s,t = edge.tuple\n row.append(s)\n col.append(t)\n data = np.ones(len(graph.es()))\n else:\n if transposed:\n for edge in graph.es():\n s,t = edge.tuple\n row.append(t)\n col.append(s)\n else:\n for edge in graph.es():\n s,t = edge.tuple\n row.append(s)\n col.append(t)\n data = np.array(graph.es()[attribute])\n\n return sparse.coo_matrix((data, (row, col)) , shape=(len(graph.vs), len(graph.vs))).tocsr()","def sym_K(self):\n raise NotImplementedError","def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\"<i4\")\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\"<f8\")\n size = j_obj.getSize()\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()","def _index_key(self, sig, codegen):\n return (sig, codegen.magic_tuple())","def getMatrixMap(self):\n return self.M_array","def info(self):\r\n\r\n return self.sim_info","def index_condensed_matrix(n, i, j):\n if i == j:\n main_warning(\"Diagonal elements (i=j) are not stored in condensed matrices.\")\n return None\n elif i > j:\n i, j = j, i\n return int(i * (n - (i + 3) * 0.5) + j - 1)","def write_matfile(Sint_df, outputfilepath='Sint_no_cofactor_20160831.mat'):\n # convert dataframe to matrix\n Smat = Sint_df.as_matrix()\n\n # get all indices for non-zero elements in Smat (row, col)\n Smat_nzr, Smat_nzc = np.nonzero(Smat)\n\n # get all non-zero elements from Smat\n Smat_nze = Smat[Smat_nzr, Smat_nzc]\n\n # Adjust for matlab coordinate\n Smat_nzr = Smat_nzr + 1\n Smat_nzc = Smat_nzc + 1\n\n # This final line gives the size of the S matrix in matlab\n nr, nc = Smat.shape\n\n # Create a 2D array\n sparseMat = np.vstack((Smat_nzr, Smat_nzc, Smat_nze)).T\n sparseMat = np.vstack((sparseMat, np.array([[nr, nc, 0]])))\n\n # Create a numpy object array from dataframe index\n reactionList = Sint_df.columns.ravel()\n\n # Write only one matlab .mat file\n scipy.io.savemat(outputfilepath,\n mdict={'Sint_sparse': sparseMat,\n 'reactionList': np.array(reactionList)}\n )\n\n return sparseMat, reactionList","def build_lhs_matrix(self):\n j=3\n diags1 = self.build_linear_diags()\n diags1 += self.build_dispersion_diags()\n\n # Ones down primary diagonal\n diags2 = np.zeros_like(diags1)\n diags2[j,:] = 1.\n\n cff = self.dt*(1+self.c_im)*0.5 \n diags = diags2 - cff*diags1\n \n # Build the sparse matrix\n cols = [ii for ii in range(-self._j, self._j+1)]\n M = sparse.spdiags(diags, cols, self.N, self.N)\n\n return M, diags","def m_symb(self):\n return self._m_symb","def decode_to_matrix_cell(index, matrix):\n row = index / matrix.cols\n col = index - (matrix.cols * row)\n return row, col,","def matrix(self) -> sympy.Matrix:\n raise NotImplementedError()","def dctSymOffset(pdct, symIndex):\n return _dctmcc.dctSymOffset(pdct, symIndex)","def get_symmetry(self):\n\n # used to build the symmetry string\n symmetry = self.get_atoms()[0].get_symmetry_class()\n\n # used to count how many atoms there are of the current symmetry\n symmetric_atom_count = 1\n\n for atom in self.get_atoms()[1:]:\n\n # if this atom has a different symmetry than the one before it\n if atom.get_symmetry_class() != symmetry[-1]:\n\n # record the number of atoms with the previous symmetry\n symmetry += str(symmetric_atom_count) + atom.get_symmetry_class()\n\n # reset the atom counter\n symmetric_atom_count = 1\n\n # if this atom has the same symmetry than the one before it\n else:\n\n # record this atom in the atom count\n symmetric_atom_count += 1\n\n # record the number of atoms with the last symmetry\n symmetry += str(symmetric_atom_count)\n\n return symmetry","def mutual_info_matrix(time_series, num_of_bins):\n num_of_rafts, interval_width = time_series.shape\n mi_matrix = np.zeros((num_of_rafts, num_of_rafts))\n\n for i in range(num_of_rafts):\n for j in range(i + 1):\n i0 = time_series[i, :].copy()\n j0 = time_series[j, :].copy()\n c_xy = np.histogram2d(i0, j0, num_of_bins)[0]\n mi = mutual_info_score(None, None, contingency=c_xy) * np.log2(np.e)\n # in unit of bits, * np.log2(np.e) to convert nats to bits\n mi_matrix[i, j] = mi\n mi_matrix[j, i] = mi\n\n return mi_matrix","def trace(mat):\n return _trace_sparse(mat) if issparse(mat) else _trace_dense(mat)","def dctSymText(pdct, symIndex, q_mut, symTxt_i):\n return _dctmcc.dctSymText(pdct, symIndex, q_mut, symTxt_i)","def getbarcidxinfo(self,idx_):\n num_ = ctypes.c_int64()\n res = __library__.MSK_XX_getbarcidxinfo(self.__nativep,idx_,ctypes.byref(num_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n num_ = num_.value\n _num_return_value = num_\n return (_num_return_value)","def get_sudoku_matrix(self):\n return self.sudoku_matrix","def sparse_matlab(i, j, v, m, n):\n return csr_matrix((v, (i, j)), shape=(m, n))","def _symmetric_matrix_square_root(mat, eps=1e-10):\n # Unlike numpy, tensorflow's return order is (s, u, v)\n s, u, v = linalg_ops.svd(mat)\n # sqrt is unstable around 0, just use 0 in such case\n si = array_ops.where(math_ops.less(s, eps), s, math_ops.sqrt(s))\n # Note that the v returned by Tensorflow is v = V\n # (when referencing the equation A = U S V^T)\n # This is unlike Numpy which returns v = V^T\n return math_ops.matmul(\n math_ops.matmul(u, array_ops.diag(si)), v, transpose_b=True)","def mat_id(self):\n\n return self._mat_id","def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None):\r\n cols = list(self.columns)\r\n cols.pop(cols.index(\"SHAPE\"))\r\n return self[cols].info(verbose, buf, max_cols,\r\n memory_usage, null_counts)","def get_sparse_adj(ent_num, triples):\n adj, degree = get_mat(ent_num, triples)\n\n indices = []\n values = []\n\n for fir, sec in adj:\n indices.append((sec, fir))\n values.append(adj[(fir, sec)] / math.sqrt(degree[fir]) / math.sqrt(degree[sec]))\n\n indices = torch.tensor(indices).t()\n\n adj = torch.sparse_coo_tensor(indices=indices, values=values, size=[ent_num, ent_num])\n\n return adj, degree","def matrix_intf(self):\n return intf.DEFAULT_SPARSE_INTF","def convert_sparse_to_coo(s_mat):\n return np.vstack(find(s_mat)).T","def Dmat(self):\n return self._Dmat_cache","def _apply_symmetry(self):\r\n self.command_stack.do(model.structure.ApplySymmetry(self._structure))","def _GetSymbol(atom):\n ks = atom.keys()\n if 'sym' in ks:\n return atom['sym']\n\n for k in ks:\n if k not in PROTECTED_KEYS and isinstance(atom[k], list):\n if len(atom[k]) == 3:\n return k\n\n raise ValueError","def get_info_format(self):\n return self.session.api.get_index(self)","def csm_indptr(csm):\r\n return csm_properties(csm)[2]","def term_to_matrix_sparse(spin_operator):\n X = sparse.csr_matrix(np.array([[0, 1], [1, 0]]))\n Y = sparse.csr_matrix(np.array([[0, -1j], [1j, 0]]))\n Z = sparse.csr_matrix(np.diag([1, -1]))\n I = sparse.csr_matrix(np.diag([1, 1]))\n dic_Pauli = {\"I\": I, \"X\": X, \"Y\": Y, \"Z\": Z}\n matrix_final = 0\n nbqbits = spin_operator.nbqbits\n for term in spin_operator.terms:\n result_one_term = 0\n char_term = [char for char in term.op]\n qb_term = term.qbits\n dic_op = {}\n for n in range(nbqbits):\n dic_op[n] = I\n for n in range(len(term.qbits)):\n dic_op[qb_term[n]] = dic_Pauli[char_term[n]]\n matrix = 0\n for d in dic_op:\n if type(matrix) == int:\n matrix = dic_op[d]\n else:\n matrix = scipy.sparse.kron(matrix, dic_op[d])\n result_one_term = sparse.csr_matrix(matrix * term.coeff)\n matrix_final += result_one_term\n return matrix_final","def get_dct_matrix(N):\r\n dct_m = np.eye(N)\r\n for k in np.arange(N):\r\n for i in np.arange(N):\r\n w = np.sqrt(2 / N)\r\n if k == 0:\r\n w = np.sqrt(1 / N)\r\n dct_m[k, i] = w * np.cos(np.pi * (i + 1 / 2) * k / N)\r\n idct_m = np.linalg.inv(dct_m)\r\n return dct_m, idct_m","def mat(self): # pylint: disable=C0103\n if self._mat is None:\n object.__setattr__(\n self, \"_mat\", np.diag(self._disp_over_m) + self.potential\n )\n return self._mat","def get_symbol_by_index(self, index):\n return self[self._index[index]]","def drawSparseMatrix(ax, mat, **kwargs):\n row = kwargs.pop('rowOffset', 0)\n col = kwargs.pop('colOffset', 0)\n color = kwargs.pop('color', None)\n\n mat = pg.utils.sparseMatrix2coo(mat)\n mat.row += row\n mat.col += col\n gci = ax.spy(mat, color=color, **kwargs)\n\n ax.autoscale(enable=True, axis='both', tight=True)\n return gci","def get_atom_info(self):\n return","def get_weight_matrix(self):\n\n\t\treturn self.weights.get_value(borrow=True)","def symbols_details(self):\n pass","def getstate(self):\n out = []\n for row in self.a:\n out.append([])\n for item in row:\n out[-1].append(itemstate(item))\n return (\"matrix\", out, self.y, self.x, self.converter, self.onlydiag())","def _check_scipy_index_typing(sparse_matrix):\n\n int_max = np.iinfo(MKL.MKL_INT_NUMPY).max\n if (sparse_matrix.nnz > int_max) or (max(sparse_matrix.shape) > int_max):\n msg = \"MKL interface is {t} and cannot hold matrix {m}\\n\".format(m=repr(sparse_matrix), t=MKL.MKL_INT_NUMPY)\n msg += \"Try changing MKL to int64 with the environment variable MKL_INTERFACE_LAYER=ILP64\"\n raise ValueError(msg)\n\n # Cast indexes to MKL_INT type\n if sparse_matrix.indptr.dtype != MKL.MKL_INT_NUMPY:\n sparse_matrix.indptr = sparse_matrix.indptr.astype(MKL.MKL_INT_NUMPY)\n if sparse_matrix.indices.dtype != MKL.MKL_INT_NUMPY:\n sparse_matrix.indices = sparse_matrix.indices.astype(MKL.MKL_INT_NUMPY)","def _calc_matrix(self):\n\t\tz = self.zoom\n\t\talloc = self.allocation\n\t\tif self.image:\n\t\t\tiw, ih = self.image.get_width(), self.image.get_height()\n\t\telse:\n\t\t\tiw, ih = 0, 0\n#\t\tif __debug__: print self._vadj.lower, self._vadj.value, self._vadj.upper\n\t\t\n\t\ti2w = cairo.Matrix(\n\t\t\tz,0,\n\t\t\t0,z,\n\t\t\t-self._hadj.value if alloc.width < iw*z else (alloc.width - iw*z)/2, \n\t\t\t-self._vadj.value if alloc.height < ih*z else (alloc.height - ih*z)/2,\n\t\t\t)\n\t\t\n\t\tself._i2w_matrix = i2w\n\t\t\n\t\tw2i = cairo.Matrix(*i2w) #copy\n\t\tw2i.invert()\n\t\tself._w2i_matrix = w2i","def find_symBinding(self, filleridx):\n symBinding = dict()\n for i, filler in enumerate(filleridx):\n binding = self.index2filler[int(filler)] + \"/\" + self.index2role[i]\n symBinding[binding] = self.find_bindings(binding)\n return symBinding","def getSymmetries(self, board, pi):\n return [(board, pi), (board[:, ::-1], pi[::-1])]","def getsolutioninfo(self,whichsol_):\n pobj_ = ctypes.c_double()\n pviolcon_ = ctypes.c_double()\n pviolvar_ = ctypes.c_double()\n pviolbarvar_ = ctypes.c_double()\n pviolcone_ = ctypes.c_double()\n pviolitg_ = ctypes.c_double()\n dobj_ = ctypes.c_double()\n dviolcon_ = ctypes.c_double()\n dviolvar_ = ctypes.c_double()\n dviolbarvar_ = ctypes.c_double()\n dviolcone_ = ctypes.c_double()\n res = __library__.MSK_XX_getsolutioninfo(self.__nativep,whichsol_,ctypes.byref(pobj_),ctypes.byref(pviolcon_),ctypes.byref(pviolvar_),ctypes.byref(pviolbarvar_),ctypes.byref(pviolcone_),ctypes.byref(pviolitg_),ctypes.byref(dobj_),ctypes.byref(dviolcon_),ctypes.byref(dviolvar_),ctypes.byref(dviolbarvar_),ctypes.byref(dviolcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n pobj_ = pobj_.value\n _pobj_return_value = pobj_\n pviolcon_ = pviolcon_.value\n _pviolcon_return_value = pviolcon_\n pviolvar_ = pviolvar_.value\n _pviolvar_return_value = pviolvar_\n pviolbarvar_ = pviolbarvar_.value\n _pviolbarvar_return_value = pviolbarvar_\n pviolcone_ = pviolcone_.value\n _pviolcone_return_value = pviolcone_\n pviolitg_ = pviolitg_.value\n _pviolitg_return_value = pviolitg_\n dobj_ = dobj_.value\n _dobj_return_value = dobj_\n dviolcon_ = dviolcon_.value\n _dviolcon_return_value = dviolcon_\n dviolvar_ = dviolvar_.value\n _dviolvar_return_value = dviolvar_\n dviolbarvar_ = dviolbarvar_.value\n _dviolbarvar_return_value = dviolbarvar_\n dviolcone_ = dviolcone_.value\n _dviolcone_return_value = dviolcone_\n return (_pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value)","def getDistanceMatrix(self):\n return self.distmat.as_matrix()","def list_syms():\n\tSymStringVec=[];\n\tSymStringVec.append(\"CSYM\");\n\tSymStringVec.append(\"DSYM\");\n\tSymStringVec.append(\"TET_SYM\");\n\tSymStringVec.append(\"OCT_SYM\");\n\tSymStringVec.append(\"ICOS_SYM\");\n\tSymStringVec.append(\"ISYM\");\n\treturn SymStringVec"],"string":"[\n \"def getsymmatinfo(self,idx_): # 3\\n res,resargs = self.__obj.getsymmatinfo(idx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _dim_return_value,_nz_return_value,_type_return_value = resargs\\n _type_return_value = symmattype(_type_return_value)\\n return _dim_return_value,_nz_return_value,_type_return_value\",\n \"def getnumsymmat(self):\\n num_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getnumsymmat(self.__nativep,ctypes.byref(num_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n num_ = num_.value\\n _num_return_value = num_\\n return (_num_return_value)\",\n \"def getsparsesymmat(self,idx_,subi,subj,valij): # 3\\n maxlen_ = self.getsymmatinfo((idx_))[1]\\n _copyback_subi = False\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n _copyback_subi = True\\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n _copyback_subi = True\\n if subi_ is not None and len(subi_) != (maxlen_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxlen_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n _copyback_valij = False\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n _copyback_valij = True\\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n _copyback_valij = True\\n if valij_ is not None and len(valij_) != (maxlen_):\\n raise ValueError(\\\"Array argument valij has wrong length\\\")\\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_valij:\\n valij[:] = _tmparr_valij\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n if _copyback_subi:\\n subi[:] = _tmparr_subi\",\n \"def getsparsesymmat(self,idx_,subi_,subj_,valij_):\\n maxlen_ = self.getsymmatinfo((idx_))[1]\\n _subi_minlength = (maxlen_)\\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxlen_)))\\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\\n raise ValueError(\\\"Argument subi must be writable\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = (maxlen_)\\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxlen_)))\\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\\n raise ValueError(\\\"Argument subj must be writable\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _valij_minlength = (maxlen_)\\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\\n raise ValueError(\\\"Array argument valij is not long enough: Is %d, expected %d\\\" % (len(valij_),(maxlen_)))\\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\\n raise ValueError(\\\"Argument valij must be writable\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _subi_copyarray:\\n subi_[:] = _subi_np_tmp\\n if _subj_copyarray:\\n subj_[:] = _subj_np_tmp\\n if _valij_copyarray:\\n valij_[:] = _valij_np_tmp\",\n \"def getnumsymmat(self): # 3\\n res,resargs = self.__obj.getnumsymmat()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _num_return_value = resargs\\n return _num_return_value\",\n \"def dctSymUserInfo(pdct, symIndex):\\n return _dctmcc.dctSymUserInfo(pdct, symIndex)\",\n \"def csr_info(mat, name=\\\"\\\", non_empy=False):\\n if non_empy:\\n print(\\\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\\\" % (\\n name, mat.shape[0], \\n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \\n for i in range(mat.shape[0])), \\n mat.shape[1], len(np.unique(mat.indices)), \\n len(mat.data)))\\n else:\\n print( \\\"%s [nrows %d, ncols %d, nnz %d]\\\" % (name, \\n mat.shape[0], mat.shape[1], len(mat.data)) )\",\n \"def csr_info(mat, name=\\\"\\\", non_empy=False):\\n if non_empy:\\n print(\\\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\\\" % (\\n name, mat.shape[0], \\n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \\n for i in range(mat.shape[0])), \\n mat.shape[1], len(np.unique(mat.indices)), \\n len(mat.data)))\\n else:\\n print( \\\"%s [nrows %d, ncols %d, nnz %d]\\\" % (name, \\n mat.shape[0], mat.shape[1], len(mat.data)) )\",\n \"def csr_info(mat, name=\\\"\\\", non_empy=False):\\n if non_empy:\\n print(\\\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\\\" % (\\n name, mat.shape[0], \\n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \\n for i in range(mat.shape[0])), \\n mat.shape[1], len(np.unique(mat.indices)), \\n len(mat.data)))\\n else:\\n print( \\\"%s [nrows %d, ncols %d, nnz %d]\\\" % (name, \\n mat.shape[0], mat.shape[1], len(mat.data)) )\",\n \"def csr_info(mat, name=\\\"\\\", non_empy=False):\\n if non_empy:\\n print(\\\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\\\" % (\\n name, mat.shape[0], \\n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \\n for i in range(mat.shape[0])), \\n mat.shape[1], len(np.unique(mat.indices)), \\n len(mat.data)))\\n else:\\n print( \\\"%s [nrows %d, ncols %d, nnz %d]\\\" % (name, \\n mat.shape[0], mat.shape[1], len(mat.data)) )\",\n \"def get_sym_matrix(self):\\n temp_T = Matrix.eye(3)\\n for i in range(len(self.lengths)):\\n angle_mat = self.T_a.subs(self.q,self.angles[i]).evalf()\\n len_mat = self.T_x.subs(self.l,self.lengths[i]).evalf()\\n temp_T = temp_T * angle_mat * len_mat\\n \\n return temp_T\",\n \"def SymmetriseMatrix(adjmatrix):\\n\\n if galib.metrics.Reciprocity(adjmatrix) == 1:\\n # if Reciprocity(adjmatrix) == 1:\\n return adjmatrix\\n else:\\n return 0.5 * (adjmatrix + adjmatrix.T)\",\n \"def sym_adj(adj):\\n adj = sp.coo_matrix(adj)\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense()\",\n \"def sym_adj(adj):\\n adj = ss.coo_matrix(adj)\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = ss.diags(d_inv_sqrt)\\n return np.array(adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense())\",\n \"def symbolic_mutual_information(symX, symY):\\n\\n if len(symX) != len(symY):\\n raise ValueError('All arrays must have same length')\\n \\n symX = np.array(symX)\\n symY = np.array(symY)\\n \\n symbols = np.unique(np.concatenate((symX,symY))).tolist()\\n \\n jp = symbolic_joint_probabilities(symX, symY)\\n pX = symbolic_probabilities(symX)\\n pY = symbolic_probabilities(symY)\\n \\n MI = 0\\n\\n for yi, b in pY.items():\\n for xi, a in pX.items():\\n try:\\n c = jp[yi,xi]\\n MI += c * np.log(c /(a * b)) / np.log(len(symbols))\\n except KeyError:\\n continue\\n except:\\n print(\\\"Unexpected Error\\\")\\n raise\\n \\n return MI\",\n \"def _print_matrix_info(mtrx, name):\\r\\n pr = lambda t: print(\\\"ht3_solver:\\\\t\\\" + t)\\r\\n pr(\\\"MATRIX INFO:\\\")\\r\\n pr(\\\"Matrix:\\\\t\\\" + name)\\r\\n pr(\\\"Description:\\\\t\\\" + str(mtrx.description))\\r\\n pr(\\\"Shape:\\\\t\\\" + str(mtrx.shape))\",\n \"def dctSymIndex(pdct, symName):\\n return _dctmcc.dctSymIndex(pdct, symName)\",\n \"def find_symmetry(self):\\n from spglib import get_spacegroup\\n cell = ( self.lattice, self.fractional_coordinates, self.atomic_nos )\\n self.spacegroup = get_spacegroup(cell, symmprec=1e-5)\\n print(\\\"Symmetry space group is\\\", self.spacegroup)\",\n \"def getMibSymbol(self):\\n if self.__state & self.stClean:\\n return self.__modName, self.__symName, self.__indices\\n else:\\n raise SmiError('%s object not fully initialized' % self.__class__.__name__)\",\n \"def information_matrix(self):\\n return self._cov.inv()\",\n \"def get_connection_mat(self, idx):\\n\\n try:\\n return self.weights[idx]\\n except:\\n print(\\\"\\\"\\\"Could not find layer {0} in network.\\\\nNetwork has {1} layers.\\\"\\\"\\\".format(idx, self.size))\",\n \"def get_stain_matrix(I):\",\n \"def sym(self) -> np.ndarray:\\n if self._sym is None:\\n self._sym = symmetrize_discrete_vector_field(self.F, mode=\\\"sym\\\")\\n return self._sym\",\n \"def dctSymEntries(pdct, symIndex):\\n return _dctmcc.dctSymEntries(pdct, symIndex)\",\n \"def get_sol_mat(self, clust):\\r\\n\\r\\n return self.__model_data[clust]\",\n \"def jmat(ind: int):\\n return _jm[ind - 1]\",\n \"def get_solver_mats(self, x_nd, rot_coef):\\n raise NotImplementedError\",\n \"def symmeterize(self):\\n A = self.to_coo_matrix()\\n symg = wgraph_from_adjacency((A + A.T) / 2)\\n self.E = symg.E\\n self.edges = symg.edges\\n self.weights = symg.weights\\n return self\",\n \"def get_symbolic_model(self):\\n return self.sym_func\",\n \"def get_symbolic_model(self):\\n return self.sym_func\",\n \"def getbaraidxinfo(self,idx_):\\n num_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getbaraidxinfo(self.__nativep,idx_,ctypes.byref(num_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n num_ = num_.value\\n _num_return_value = num_\\n return (_num_return_value)\",\n \"def get_info(atom):\\n return [atom.GetIdx(), atom.GetNeighbors()[0].GetIdx()]\",\n \"def is_symmetric(mat):\\n return np.allclose(mat.T, mat)\",\n \"def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) -> None:\\n is_unity = validate_unity(symmetry[0])\\n if len(symmetry) == 1 and is_unity:\\n return\\n build_symmetry_operations(symmetry)\\n validate_matrix_symmetry(mat, symmetry)\",\n \"def sim_info(self) -> SimInfo:\\n return self._sim_info\",\n \"def return_adjacencyMatrix(self):\\n return self.__mat\",\n \"def _general_get_information(self, k=None):\\n ## Format k\\n ks = self.get_k(k)\\n idx_ks = self._get_k_indices(ks)\\n ## Get iss\\n iss = self.iss\\n ## Format idxs\\n assert(type(idx_ks) == list)\\n neighs = self.get_neighs(idx_ks)\\n sp_relative_pos = self.get_sp_rel_pos(idx_ks)\\n self.check_output_standards(neighs, sp_relative_pos, ks, iss)\\n# print '3'*50, neighs, sp_relative_pos, ks, iss\\n return neighs, sp_relative_pos, ks, iss\",\n \"def dctSymDomIdx(pdct, symIndex):\\n return _dctmcc.dctSymDomIdx(pdct, symIndex)\",\n \"def symmetric(matrix):\\n return sp.allclose(matrix, matrix.T)\",\n \"def get_matrix_info(matrix, n):\\n\\n ones_positions = np.asarray(np.where(matrix == 1))\\n ones_count = np.shape(ones_positions)[1]\\n\\n one_position_in_row = np.full(n, -1, dtype=int)\\n np.put(one_position_in_row, ones_positions[0], ones_positions[1])\\n has_one_in_row = np.select([one_position_in_row != -1], [1]).astype(bool)\\n zero_rows = np.asarray(np.where(np.invert(has_one_in_row))).squeeze(0)\\n\\n return ones_count, ones_positions, has_one_in_row, one_position_in_row, zero_rows\",\n \"def dctSymType(pdct, symIndex):\\n return _dctmcc.dctSymType(pdct, symIndex)\",\n \"def adj_mat(self):\\n return self._adj_mat\",\n \"def get_cvxopt_sparse_intf():\\n import cvxpy.interface.cvxopt_interface.sparse_matrix_interface as smi\\n return smi.SparseMatrixInterface()\",\n \"def _symbolic_system(self):\\n system = sym.Matrix(self._symbolic_equations)\\n return system.subs(self._symbolic_change_of_vars)\",\n \"def matrix_add_symmetric(M, M_sym):\\n M[0, 0] += M_sym[0]\\n M[0, 1] += M_sym[1]\\n M[1, 0] += M_sym[1]\\n M[0, 2] += M_sym[2]\\n M[2, 0] += M_sym[2]\\n\\n M[1, 1] += M_sym[3]\\n M[1, 2] += M_sym[4]\\n M[2, 1] += M_sym[4]\\n\\n M[2, 2] += M_sym[5]\\n\\n return M\",\n \"def is_symmetric(mat, eps=None):\\n if eps is None:\\n eps = np.finfo(mat.dtype).eps\\n\\n assert mat.ndim == 2\\n if mat.shape[0] != mat.shape[1]:\\n return False\\n\\n return np.allclose(mat, mat.T, atol=eps)\",\n \"def get_symmetry_mates(self, model=None, altloc=\\\"first\\\",\\n extra_fields=[], include_bonds=False):\\n # Get base structure\\n structure = self.get_structure(\\n model,\\n altloc,\\n extra_fields,\\n include_bonds,\\n )\\n # Get lines containing transformations for crystallographic symmetry\\n remark_lines = self.get_remark(290)\\n if remark_lines is None:\\n raise InvalidFileError(\\n \\\"File does not contain crystallographic symmetry \\\"\\n \\\"information (REMARK 350)\\\"\\n )\\n transform_lines = [\\n line for line in remark_lines if line.startswith(\\\" SMTRY\\\")\\n ]\\n rotations, translations = _parse_transformations(\\n transform_lines\\n )\\n return _apply_transformations(\\n structure, rotations, translations\\n )\",\n \"def get_mat(self,diag_entries=False):\\n if self._mat is None or (diag_entries and not self._diag_entries):\\n self.build_mat(diag_entries=diag_entries)\\n return self._mat\",\n \"def with_matsym(*simplifies):\\n from sympy.matrices import MatrixSymbol\\n from symplus.setplus import AbstractSet\\n def simplify_with_matsym(expr, *args, **kwargs):\\n # expand MatrixSymbol as Matrix: A -> [ A[0,0] ,..]\\n mats = list(expr.atoms(MatrixSymbol))\\n agents = list(Dummy(str(mat)) for mat in mats)\\n def protect_var(var, expr):\\n return AbstractSet(var.xreplace(dict(zip(mats, agents))), expr)\\n expr = expr.replace(AbstractSet, protect_var)\\n expr = expr.xreplace(dict((mat, mat.as_explicit()) for mat in mats))\\n expr = expr.xreplace(dict(zip(agents, mats)))\\n\\n # replace MatrixElement as Symbol: A[i,j] -> Aij\\n elems = tuple(elem for mat in mats for elem in mat)\\n syms = tuple(Dummy(str(e)) for e in elems)\\n expr = expr.xreplace(dict(zip(elems, syms)))\\n\\n # simplify expression\\n for simp in simplifies:\\n expr = simp(expr, *args, **kwargs)\\n\\n # replace Symbol as MatrixElement: Aij -> A[i,j]\\n expr = expr.xreplace(dict(zip(syms, elems)))\\n\\n return expr\\n return simplify_with_matsym\",\n \"def dctSymDim(pdct, symIndex):\\n return _dctmcc.dctSymDim(pdct, symIndex)\",\n \"def test_sympmat(n):\\n I = np.identity(n)\\n O = np.zeros_like(I)\\n X = np.block([[O, I], [-I, O]])\\n res = symplectic.sympmat(n)\\n assert np.all(X == res)\",\n \"def dctSymDomNames(pdct, symIndex):\\n return _dctmcc.dctSymDomNames(pdct, symIndex)\",\n \"def dctSymName(pdct, symIndex, symName_i):\\n return _dctmcc.dctSymName(pdct, symIndex, symName_i)\",\n \"def getSparseAdjacencyMatrix( graph, attribute=None, transposed=False ):\\n if (attribute is not None) and (attribute not in graph.es.attribute_names()):\\n raise ValueError( \\\"Attribute does not exists.\\\" )\\n \\n row = []\\n col = []\\n data = []\\n \\n if attribute is None:\\n if transposed:\\n for edge in graph.es():\\n s,t = edge.tuple\\n row.append(t)\\n col.append(s)\\n else:\\n for edge in graph.es():\\n s,t = edge.tuple\\n row.append(s)\\n col.append(t)\\n data = np.ones(len(graph.es()))\\n else:\\n if transposed:\\n for edge in graph.es():\\n s,t = edge.tuple\\n row.append(t)\\n col.append(s)\\n else:\\n for edge in graph.es():\\n s,t = edge.tuple\\n row.append(s)\\n col.append(t)\\n data = np.array(graph.es()[attribute])\\n\\n return sparse.coo_matrix((data, (row, col)) , shape=(len(graph.vs), len(graph.vs))).tocsr()\",\n \"def sym_K(self):\\n raise NotImplementedError\",\n \"def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\\\"<i4\\\")\\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\\\"<f8\\\")\\n size = j_obj.getSize()\\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()\",\n \"def _index_key(self, sig, codegen):\\n return (sig, codegen.magic_tuple())\",\n \"def getMatrixMap(self):\\n return self.M_array\",\n \"def info(self):\\r\\n\\r\\n return self.sim_info\",\n \"def index_condensed_matrix(n, i, j):\\n if i == j:\\n main_warning(\\\"Diagonal elements (i=j) are not stored in condensed matrices.\\\")\\n return None\\n elif i > j:\\n i, j = j, i\\n return int(i * (n - (i + 3) * 0.5) + j - 1)\",\n \"def write_matfile(Sint_df, outputfilepath='Sint_no_cofactor_20160831.mat'):\\n # convert dataframe to matrix\\n Smat = Sint_df.as_matrix()\\n\\n # get all indices for non-zero elements in Smat (row, col)\\n Smat_nzr, Smat_nzc = np.nonzero(Smat)\\n\\n # get all non-zero elements from Smat\\n Smat_nze = Smat[Smat_nzr, Smat_nzc]\\n\\n # Adjust for matlab coordinate\\n Smat_nzr = Smat_nzr + 1\\n Smat_nzc = Smat_nzc + 1\\n\\n # This final line gives the size of the S matrix in matlab\\n nr, nc = Smat.shape\\n\\n # Create a 2D array\\n sparseMat = np.vstack((Smat_nzr, Smat_nzc, Smat_nze)).T\\n sparseMat = np.vstack((sparseMat, np.array([[nr, nc, 0]])))\\n\\n # Create a numpy object array from dataframe index\\n reactionList = Sint_df.columns.ravel()\\n\\n # Write only one matlab .mat file\\n scipy.io.savemat(outputfilepath,\\n mdict={'Sint_sparse': sparseMat,\\n 'reactionList': np.array(reactionList)}\\n )\\n\\n return sparseMat, reactionList\",\n \"def build_lhs_matrix(self):\\n j=3\\n diags1 = self.build_linear_diags()\\n diags1 += self.build_dispersion_diags()\\n\\n # Ones down primary diagonal\\n diags2 = np.zeros_like(diags1)\\n diags2[j,:] = 1.\\n\\n cff = self.dt*(1+self.c_im)*0.5 \\n diags = diags2 - cff*diags1\\n \\n # Build the sparse matrix\\n cols = [ii for ii in range(-self._j, self._j+1)]\\n M = sparse.spdiags(diags, cols, self.N, self.N)\\n\\n return M, diags\",\n \"def m_symb(self):\\n return self._m_symb\",\n \"def decode_to_matrix_cell(index, matrix):\\n row = index / matrix.cols\\n col = index - (matrix.cols * row)\\n return row, col,\",\n \"def matrix(self) -> sympy.Matrix:\\n raise NotImplementedError()\",\n \"def dctSymOffset(pdct, symIndex):\\n return _dctmcc.dctSymOffset(pdct, symIndex)\",\n \"def get_symmetry(self):\\n\\n # used to build the symmetry string\\n symmetry = self.get_atoms()[0].get_symmetry_class()\\n\\n # used to count how many atoms there are of the current symmetry\\n symmetric_atom_count = 1\\n\\n for atom in self.get_atoms()[1:]:\\n\\n # if this atom has a different symmetry than the one before it\\n if atom.get_symmetry_class() != symmetry[-1]:\\n\\n # record the number of atoms with the previous symmetry\\n symmetry += str(symmetric_atom_count) + atom.get_symmetry_class()\\n\\n # reset the atom counter\\n symmetric_atom_count = 1\\n\\n # if this atom has the same symmetry than the one before it\\n else:\\n\\n # record this atom in the atom count\\n symmetric_atom_count += 1\\n\\n # record the number of atoms with the last symmetry\\n symmetry += str(symmetric_atom_count)\\n\\n return symmetry\",\n \"def mutual_info_matrix(time_series, num_of_bins):\\n num_of_rafts, interval_width = time_series.shape\\n mi_matrix = np.zeros((num_of_rafts, num_of_rafts))\\n\\n for i in range(num_of_rafts):\\n for j in range(i + 1):\\n i0 = time_series[i, :].copy()\\n j0 = time_series[j, :].copy()\\n c_xy = np.histogram2d(i0, j0, num_of_bins)[0]\\n mi = mutual_info_score(None, None, contingency=c_xy) * np.log2(np.e)\\n # in unit of bits, * np.log2(np.e) to convert nats to bits\\n mi_matrix[i, j] = mi\\n mi_matrix[j, i] = mi\\n\\n return mi_matrix\",\n \"def trace(mat):\\n return _trace_sparse(mat) if issparse(mat) else _trace_dense(mat)\",\n \"def dctSymText(pdct, symIndex, q_mut, symTxt_i):\\n return _dctmcc.dctSymText(pdct, symIndex, q_mut, symTxt_i)\",\n \"def getbarcidxinfo(self,idx_):\\n num_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getbarcidxinfo(self.__nativep,idx_,ctypes.byref(num_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n num_ = num_.value\\n _num_return_value = num_\\n return (_num_return_value)\",\n \"def get_sudoku_matrix(self):\\n return self.sudoku_matrix\",\n \"def sparse_matlab(i, j, v, m, n):\\n return csr_matrix((v, (i, j)), shape=(m, n))\",\n \"def _symmetric_matrix_square_root(mat, eps=1e-10):\\n # Unlike numpy, tensorflow's return order is (s, u, v)\\n s, u, v = linalg_ops.svd(mat)\\n # sqrt is unstable around 0, just use 0 in such case\\n si = array_ops.where(math_ops.less(s, eps), s, math_ops.sqrt(s))\\n # Note that the v returned by Tensorflow is v = V\\n # (when referencing the equation A = U S V^T)\\n # This is unlike Numpy which returns v = V^T\\n return math_ops.matmul(\\n math_ops.matmul(u, array_ops.diag(si)), v, transpose_b=True)\",\n \"def mat_id(self):\\n\\n return self._mat_id\",\n \"def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None):\\r\\n cols = list(self.columns)\\r\\n cols.pop(cols.index(\\\"SHAPE\\\"))\\r\\n return self[cols].info(verbose, buf, max_cols,\\r\\n memory_usage, null_counts)\",\n \"def get_sparse_adj(ent_num, triples):\\n adj, degree = get_mat(ent_num, triples)\\n\\n indices = []\\n values = []\\n\\n for fir, sec in adj:\\n indices.append((sec, fir))\\n values.append(adj[(fir, sec)] / math.sqrt(degree[fir]) / math.sqrt(degree[sec]))\\n\\n indices = torch.tensor(indices).t()\\n\\n adj = torch.sparse_coo_tensor(indices=indices, values=values, size=[ent_num, ent_num])\\n\\n return adj, degree\",\n \"def matrix_intf(self):\\n return intf.DEFAULT_SPARSE_INTF\",\n \"def convert_sparse_to_coo(s_mat):\\n return np.vstack(find(s_mat)).T\",\n \"def Dmat(self):\\n return self._Dmat_cache\",\n \"def _apply_symmetry(self):\\r\\n self.command_stack.do(model.structure.ApplySymmetry(self._structure))\",\n \"def _GetSymbol(atom):\\n ks = atom.keys()\\n if 'sym' in ks:\\n return atom['sym']\\n\\n for k in ks:\\n if k not in PROTECTED_KEYS and isinstance(atom[k], list):\\n if len(atom[k]) == 3:\\n return k\\n\\n raise ValueError\",\n \"def get_info_format(self):\\n return self.session.api.get_index(self)\",\n \"def csm_indptr(csm):\\r\\n return csm_properties(csm)[2]\",\n \"def term_to_matrix_sparse(spin_operator):\\n X = sparse.csr_matrix(np.array([[0, 1], [1, 0]]))\\n Y = sparse.csr_matrix(np.array([[0, -1j], [1j, 0]]))\\n Z = sparse.csr_matrix(np.diag([1, -1]))\\n I = sparse.csr_matrix(np.diag([1, 1]))\\n dic_Pauli = {\\\"I\\\": I, \\\"X\\\": X, \\\"Y\\\": Y, \\\"Z\\\": Z}\\n matrix_final = 0\\n nbqbits = spin_operator.nbqbits\\n for term in spin_operator.terms:\\n result_one_term = 0\\n char_term = [char for char in term.op]\\n qb_term = term.qbits\\n dic_op = {}\\n for n in range(nbqbits):\\n dic_op[n] = I\\n for n in range(len(term.qbits)):\\n dic_op[qb_term[n]] = dic_Pauli[char_term[n]]\\n matrix = 0\\n for d in dic_op:\\n if type(matrix) == int:\\n matrix = dic_op[d]\\n else:\\n matrix = scipy.sparse.kron(matrix, dic_op[d])\\n result_one_term = sparse.csr_matrix(matrix * term.coeff)\\n matrix_final += result_one_term\\n return matrix_final\",\n \"def get_dct_matrix(N):\\r\\n dct_m = np.eye(N)\\r\\n for k in np.arange(N):\\r\\n for i in np.arange(N):\\r\\n w = np.sqrt(2 / N)\\r\\n if k == 0:\\r\\n w = np.sqrt(1 / N)\\r\\n dct_m[k, i] = w * np.cos(np.pi * (i + 1 / 2) * k / N)\\r\\n idct_m = np.linalg.inv(dct_m)\\r\\n return dct_m, idct_m\",\n \"def mat(self): # pylint: disable=C0103\\n if self._mat is None:\\n object.__setattr__(\\n self, \\\"_mat\\\", np.diag(self._disp_over_m) + self.potential\\n )\\n return self._mat\",\n \"def get_symbol_by_index(self, index):\\n return self[self._index[index]]\",\n \"def drawSparseMatrix(ax, mat, **kwargs):\\n row = kwargs.pop('rowOffset', 0)\\n col = kwargs.pop('colOffset', 0)\\n color = kwargs.pop('color', None)\\n\\n mat = pg.utils.sparseMatrix2coo(mat)\\n mat.row += row\\n mat.col += col\\n gci = ax.spy(mat, color=color, **kwargs)\\n\\n ax.autoscale(enable=True, axis='both', tight=True)\\n return gci\",\n \"def get_atom_info(self):\\n return\",\n \"def get_weight_matrix(self):\\n\\n\\t\\treturn self.weights.get_value(borrow=True)\",\n \"def symbols_details(self):\\n pass\",\n \"def getstate(self):\\n out = []\\n for row in self.a:\\n out.append([])\\n for item in row:\\n out[-1].append(itemstate(item))\\n return (\\\"matrix\\\", out, self.y, self.x, self.converter, self.onlydiag())\",\n \"def _check_scipy_index_typing(sparse_matrix):\\n\\n int_max = np.iinfo(MKL.MKL_INT_NUMPY).max\\n if (sparse_matrix.nnz > int_max) or (max(sparse_matrix.shape) > int_max):\\n msg = \\\"MKL interface is {t} and cannot hold matrix {m}\\\\n\\\".format(m=repr(sparse_matrix), t=MKL.MKL_INT_NUMPY)\\n msg += \\\"Try changing MKL to int64 with the environment variable MKL_INTERFACE_LAYER=ILP64\\\"\\n raise ValueError(msg)\\n\\n # Cast indexes to MKL_INT type\\n if sparse_matrix.indptr.dtype != MKL.MKL_INT_NUMPY:\\n sparse_matrix.indptr = sparse_matrix.indptr.astype(MKL.MKL_INT_NUMPY)\\n if sparse_matrix.indices.dtype != MKL.MKL_INT_NUMPY:\\n sparse_matrix.indices = sparse_matrix.indices.astype(MKL.MKL_INT_NUMPY)\",\n \"def _calc_matrix(self):\\n\\t\\tz = self.zoom\\n\\t\\talloc = self.allocation\\n\\t\\tif self.image:\\n\\t\\t\\tiw, ih = self.image.get_width(), self.image.get_height()\\n\\t\\telse:\\n\\t\\t\\tiw, ih = 0, 0\\n#\\t\\tif __debug__: print self._vadj.lower, self._vadj.value, self._vadj.upper\\n\\t\\t\\n\\t\\ti2w = cairo.Matrix(\\n\\t\\t\\tz,0,\\n\\t\\t\\t0,z,\\n\\t\\t\\t-self._hadj.value if alloc.width < iw*z else (alloc.width - iw*z)/2, \\n\\t\\t\\t-self._vadj.value if alloc.height < ih*z else (alloc.height - ih*z)/2,\\n\\t\\t\\t)\\n\\t\\t\\n\\t\\tself._i2w_matrix = i2w\\n\\t\\t\\n\\t\\tw2i = cairo.Matrix(*i2w) #copy\\n\\t\\tw2i.invert()\\n\\t\\tself._w2i_matrix = w2i\",\n \"def find_symBinding(self, filleridx):\\n symBinding = dict()\\n for i, filler in enumerate(filleridx):\\n binding = self.index2filler[int(filler)] + \\\"/\\\" + self.index2role[i]\\n symBinding[binding] = self.find_bindings(binding)\\n return symBinding\",\n \"def getSymmetries(self, board, pi):\\n return [(board, pi), (board[:, ::-1], pi[::-1])]\",\n \"def getsolutioninfo(self,whichsol_):\\n pobj_ = ctypes.c_double()\\n pviolcon_ = ctypes.c_double()\\n pviolvar_ = ctypes.c_double()\\n pviolbarvar_ = ctypes.c_double()\\n pviolcone_ = ctypes.c_double()\\n pviolitg_ = ctypes.c_double()\\n dobj_ = ctypes.c_double()\\n dviolcon_ = ctypes.c_double()\\n dviolvar_ = ctypes.c_double()\\n dviolbarvar_ = ctypes.c_double()\\n dviolcone_ = ctypes.c_double()\\n res = __library__.MSK_XX_getsolutioninfo(self.__nativep,whichsol_,ctypes.byref(pobj_),ctypes.byref(pviolcon_),ctypes.byref(pviolvar_),ctypes.byref(pviolbarvar_),ctypes.byref(pviolcone_),ctypes.byref(pviolitg_),ctypes.byref(dobj_),ctypes.byref(dviolcon_),ctypes.byref(dviolvar_),ctypes.byref(dviolbarvar_),ctypes.byref(dviolcone_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n pobj_ = pobj_.value\\n _pobj_return_value = pobj_\\n pviolcon_ = pviolcon_.value\\n _pviolcon_return_value = pviolcon_\\n pviolvar_ = pviolvar_.value\\n _pviolvar_return_value = pviolvar_\\n pviolbarvar_ = pviolbarvar_.value\\n _pviolbarvar_return_value = pviolbarvar_\\n pviolcone_ = pviolcone_.value\\n _pviolcone_return_value = pviolcone_\\n pviolitg_ = pviolitg_.value\\n _pviolitg_return_value = pviolitg_\\n dobj_ = dobj_.value\\n _dobj_return_value = dobj_\\n dviolcon_ = dviolcon_.value\\n _dviolcon_return_value = dviolcon_\\n dviolvar_ = dviolvar_.value\\n _dviolvar_return_value = dviolvar_\\n dviolbarvar_ = dviolbarvar_.value\\n _dviolbarvar_return_value = dviolbarvar_\\n dviolcone_ = dviolcone_.value\\n _dviolcone_return_value = dviolcone_\\n return (_pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value)\",\n \"def getDistanceMatrix(self):\\n return self.distmat.as_matrix()\",\n \"def list_syms():\\n\\tSymStringVec=[];\\n\\tSymStringVec.append(\\\"CSYM\\\");\\n\\tSymStringVec.append(\\\"DSYM\\\");\\n\\tSymStringVec.append(\\\"TET_SYM\\\");\\n\\tSymStringVec.append(\\\"OCT_SYM\\\");\\n\\tSymStringVec.append(\\\"ICOS_SYM\\\");\\n\\tSymStringVec.append(\\\"ISYM\\\");\\n\\treturn SymStringVec\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8472209","0.61438686","0.60188043","0.59274673","0.5842971","0.57795507","0.57204145","0.57204145","0.57204145","0.57204145","0.5698095","0.5413046","0.5405503","0.5376912","0.5374786","0.531883","0.5297684","0.523657","0.51200414","0.51060134","0.5081327","0.5045709","0.50061107","0.49957713","0.4995688","0.49688283","0.4937803","0.49020213","0.48640984","0.48640984","0.4850856","0.479342","0.47726145","0.4762569","0.47615206","0.47454268","0.47435454","0.4739944","0.47080076","0.46945208","0.46743587","0.4662488","0.46598193","0.46519512","0.4642063","0.4625938","0.4625561","0.4613884","0.4607499","0.46009988","0.45949262","0.45895478","0.45827988","0.454786","0.45354462","0.4532753","0.45313025","0.45261535","0.4525011","0.4513898","0.45101136","0.45043966","0.45020503","0.4494375","0.44894382","0.4485301","0.4476648","0.4468859","0.44644785","0.44532952","0.44476125","0.44411936","0.4434885","0.44337165","0.4430325","0.44274566","0.44270366","0.44248366","0.44231138","0.44189864","0.44183055","0.4406174","0.4405367","0.43843433","0.43813947","0.43761414","0.43658108","0.43611065","0.43607992","0.4337908","0.4328209","0.4327833","0.43228838","0.43212846","0.4319415","0.4317055","0.43154183","0.43137714","0.43105477","0.43078947"],"string":"[\n \"0.8472209\",\n \"0.61438686\",\n \"0.60188043\",\n \"0.59274673\",\n \"0.5842971\",\n \"0.57795507\",\n \"0.57204145\",\n \"0.57204145\",\n \"0.57204145\",\n \"0.57204145\",\n \"0.5698095\",\n \"0.5413046\",\n \"0.5405503\",\n \"0.5376912\",\n \"0.5374786\",\n \"0.531883\",\n \"0.5297684\",\n \"0.523657\",\n \"0.51200414\",\n \"0.51060134\",\n \"0.5081327\",\n \"0.5045709\",\n \"0.50061107\",\n \"0.49957713\",\n \"0.4995688\",\n \"0.49688283\",\n \"0.4937803\",\n \"0.49020213\",\n \"0.48640984\",\n \"0.48640984\",\n \"0.4850856\",\n \"0.479342\",\n \"0.47726145\",\n \"0.4762569\",\n \"0.47615206\",\n \"0.47454268\",\n \"0.47435454\",\n \"0.4739944\",\n \"0.47080076\",\n \"0.46945208\",\n \"0.46743587\",\n \"0.4662488\",\n \"0.46598193\",\n \"0.46519512\",\n \"0.4642063\",\n \"0.4625938\",\n \"0.4625561\",\n \"0.4613884\",\n \"0.4607499\",\n \"0.46009988\",\n \"0.45949262\",\n \"0.45895478\",\n \"0.45827988\",\n \"0.454786\",\n \"0.45354462\",\n \"0.4532753\",\n \"0.45313025\",\n \"0.45261535\",\n \"0.4525011\",\n \"0.4513898\",\n \"0.45101136\",\n \"0.45043966\",\n \"0.45020503\",\n \"0.4494375\",\n \"0.44894382\",\n \"0.4485301\",\n \"0.4476648\",\n \"0.4468859\",\n \"0.44644785\",\n \"0.44532952\",\n \"0.44476125\",\n \"0.44411936\",\n \"0.4434885\",\n \"0.44337165\",\n \"0.4430325\",\n \"0.44274566\",\n \"0.44270366\",\n \"0.44248366\",\n \"0.44231138\",\n \"0.44189864\",\n \"0.44183055\",\n \"0.4406174\",\n \"0.4405367\",\n \"0.43843433\",\n \"0.43813947\",\n \"0.43761414\",\n \"0.43658108\",\n \"0.43611065\",\n \"0.43607992\",\n \"0.4337908\",\n \"0.4328209\",\n \"0.4327833\",\n \"0.43228838\",\n \"0.43212846\",\n \"0.4319415\",\n \"0.4317055\",\n \"0.43154183\",\n \"0.43137714\",\n \"0.43105477\",\n \"0.43078947\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8566959"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11973,"cells":{"query":{"kind":"string","value":"Obtains the number of symmetric matrices stored. getnumsymmat(self)"},"document":{"kind":"string","value":"def getnumsymmat(self):\n num_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumsymmat(self.__nativep,ctypes.byref(num_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n num_ = num_.value\n _num_return_value = num_\n return (_num_return_value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getnumsymmat(self): # 3\n res,resargs = self.__obj.getnumsymmat()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n return _num_return_value","def getsymmatinfo(self,idx_): # 3\n res,resargs = self.__obj.getsymmatinfo(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dim_return_value,_nz_return_value,_type_return_value = resargs\n _type_return_value = symmattype(_type_return_value)\n return _dim_return_value,_nz_return_value,_type_return_value","def getsymmatinfo(self,idx_):\n dim_ = ctypes.c_int32()\n nz_ = ctypes.c_int64()\n type_ = ctypes.c_int32()\n res = __library__.MSK_XX_getsymmatinfo(self.__nativep,idx_,ctypes.byref(dim_),ctypes.byref(nz_),ctypes.byref(type_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n dim_ = dim_.value\n _dim_return_value = dim_\n nz_ = nz_.value\n _nz_return_value = nz_\n _type_return_value = symmattype(type_.value)\n return (_dim_return_value,_nz_return_value,_type_return_value)","def get_symmetry(self):\n\n # used to build the symmetry string\n symmetry = self.get_atoms()[0].get_symmetry_class()\n\n # used to count how many atoms there are of the current symmetry\n symmetric_atom_count = 1\n\n for atom in self.get_atoms()[1:]:\n\n # if this atom has a different symmetry than the one before it\n if atom.get_symmetry_class() != symmetry[-1]:\n\n # record the number of atoms with the previous symmetry\n symmetry += str(symmetric_atom_count) + atom.get_symmetry_class()\n\n # reset the atom counter\n symmetric_atom_count = 1\n\n # if this atom has the same symmetry than the one before it\n else:\n\n # record this atom in the atom count\n symmetric_atom_count += 1\n\n # record the number of atoms with the last symmetry\n symmetry += str(symmetric_atom_count)\n\n return symmetry","def get_conn_matrix_len(self):\n\n return len(self.connection_matrix) * self.brain[\"n_osc\"]","def sym_K(self):\n raise NotImplementedError","def axes_of_symmetry(self):\n if self.number_axes_of_symmetry is None: # distinguish from Falsy 0\n raise NotImplementedError(self.message_unknown)\n return self.number_axes_of_symmetry","def get_sym_matrix(self):\n temp_T = Matrix.eye(3)\n for i in range(len(self.lengths)):\n angle_mat = self.T_a.subs(self.q,self.angles[i]).evalf()\n len_mat = self.T_x.subs(self.l,self.lengths[i]).evalf()\n temp_T = temp_T * angle_mat * len_mat\n \n return temp_T","def shape(self):\n return self.symbolic.shape","def size(adj_mat):\n return adj_mat.shape[0]","def num_syls(syls):\n\treturn len([c for c in syls if c in ['0','1','2']])","def get_num_connections(self):\n\n synapses = 0\n for mat in self.weights:\n synapses += mat.size\n return synapses","def getSize(self):\n if self.sym != None:\n return self.sym.getSize()\n return self.define.getSize()","def sym(self) -> np.ndarray:\n if self._sym is None:\n self._sym = symmetrize_discrete_vector_field(self.F, mode=\"sym\")\n return self._sym","def find_symmetry(self):\n from spglib import get_spacegroup\n cell = ( self.lattice, self.fractional_coordinates, self.atomic_nos )\n self.spacegroup = get_spacegroup(cell, symmprec=1e-5)\n print(\"Symmetry space group is\", self.spacegroup)","def get_number_of_atoms_to_optimize(self):\n v = self.c.get(simulation_cell=True)\n return len(v.data.stoichiometry)","def N(self):\n return _hypre.HypreParMatrix_N(self)","def GetGlobalNumRows(self):\n return _hypre.HypreParMatrix_GetGlobalNumRows(self)","def SymmetriseMatrix(adjmatrix):\n\n if galib.metrics.Reciprocity(adjmatrix) == 1:\n # if Reciprocity(adjmatrix) == 1:\n return adjmatrix\n else:\n return 0.5 * (adjmatrix + adjmatrix.T)","def is_symmetric(mat):\n return np.allclose(mat.T, mat)","def getSize(self):\n if self.subsym == None:\n if self.size == 0:\n return 1\n else:\n return self.size\n else:\n if self.size == 0:\n return self.subsym.getSize()\n else:\n return self.size * self.subsym.getSize()","def sym_adj(adj):\n adj = sp.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense()","def GetGlobalNumCols(self):\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)","def numSpecial(self, mat: list[list[int]]) -> int:\n ans = 0\n col_cache = {}\n for row in mat:\n # print(row)\n ones = []\n for i, n in enumerate(row):\n if n == 1:\n ones.append(i)\n # print(ones)\n if len(ones) == 1:\n j = ones[0]\n cols = [row[j] for row in mat]\n s = col_cache.get(j, sum(cols))\n col_cache[j] = s\n if s == 1:\n ans += 1\n return ans","def symmetric(matrix):\n return sp.allclose(matrix, matrix.T)","def sym_adj(adj):\n adj = ss.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = ss.diags(d_inv_sqrt)\n return np.array(adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense())","def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) -> None:\n is_unity = validate_unity(symmetry[0])\n if len(symmetry) == 1 and is_unity:\n return\n build_symmetry_operations(symmetry)\n validate_matrix_symmetry(mat, symmetry)","def getlen(self):\n if self.onlydiag():\n return self.lendiag()\n else:\n return len(self)","def global_symbols_size(self):\n size = 0\n for s in self.global_symbols:\n if self.global_symbols[s].type == 'procedure': continue\n size += self.global_symbols[s].size\n return size","def expected_size(self):\n return self.nsym * self.symbol_len_per_byte","def matShape(mat):\n return (len(mat),len(mat[0]))","def GetNumRows(self):\n return _hypre.HypreParMatrix_GetNumRows(self)","def nel(self):\n return nzion(self.sym)","def GetNumCols(self):\n return _hypre.HypreParMatrix_GetNumCols(self)","def is_symmetric(self):\n _is_sym = self._is_sym\n if _is_sym is not None:\n return _is_sym\n\n n = self.degree\n if n >= 8:\n if self.is_transitive():\n _is_alt_sym = self._eval_is_alt_sym_monte_carlo()\n if _is_alt_sym:\n if any(g.is_odd for g in self.generators):\n self._is_sym, self._is_alt = True, False\n return True\n\n self._is_sym, self._is_alt = False, True\n return False\n\n return self._eval_is_alt_sym_naive(only_sym=True)\n\n self._is_sym, self._is_alt = False, False\n return False\n\n return self._eval_is_alt_sym_naive(only_sym=True)","def is_symmetric(mat, eps=None):\n if eps is None:\n eps = np.finfo(mat.dtype).eps\n\n assert mat.ndim == 2\n if mat.shape[0] != mat.shape[1]:\n return False\n\n return np.allclose(mat, mat.T, atol=eps)","def is_symmetric(self):\n return self.args[0].is_symmetric()","def getNumLayouts(self):\n return _libsbml.LayoutModelPlugin_getNumLayouts(self)","def rotational_symmetry_number(self):\n pg = self.get_full_point_group()\n pg = self.full_point_group_with_n()\n if pg in ['ATOM', 'C1', 'Ci', 'Cs', 'C_inf_v']:\n sigma = 1\n elif pg == 'D_inf_h':\n sigma = 2\n elif pg in ['T', 'Td']:\n sigma = 12\n elif pg == 'Oh':\n sigma = 24\n elif pg == 'Ih':\n sigma = 60\n elif pg in ['Cn', 'Cnv', 'Cnh']:\n sigma = self.full_pg_n()\n elif pg in ['Dn', 'Dnd', 'Dnh']:\n sigma = 2 * self.full_pg_n()\n elif pg == 'Sn':\n sigma = self.full_pg_n() / 2\n else:\n raise ValidationError(\"Can't ID full symmetry group: \" + pg)\n\n return sigma","def getNumVertexAttrib(self):\n return _osgAnimation.RigTransformHardware_getNumVertexAttrib(self)","def local_symbols_size(self):\n size = 0\n for s in self.symbols[-1]:\n if self.symbols[-1][s].type == 'procedure': continue\n if self.symbols[-1][s].isparam: continue\n #if self.symbols[-1][s].isparam: continue\n size += self.symbols[-1][s].size\n return size","def get_num_atoms(self):\n\n return len(self.atoms)","def count(self, syms = None ):\n if syms == None:\n syms = self.alpha.getSymbols()\n for sym in syms:\n idx = self.alpha.getIndex( sym )\n self.cnt[idx] += 1.0\n self.tot += 1","def get_number_symbols(self) -> int:\n if self.sons:\n return sum([son.get_number_symbols() for son in self.sons])\n return 1","def nmodes(self):\n if self.mode_selection is not None:\n return len(self.mode_selection)\n else:\n return len(self.mol.normal_modes.modes.freqs)","def symbol_width(self):\n return self._symbol.get_extents().width","def get_atom_count(self):\n return self._graph.get_vertex_count()","def matrix_add_symmetric(M, M_sym):\n M[0, 0] += M_sym[0]\n M[0, 1] += M_sym[1]\n M[1, 0] += M_sym[1]\n M[0, 2] += M_sym[2]\n M[2, 0] += M_sym[2]\n\n M[1, 1] += M_sym[3]\n M[1, 2] += M_sym[4]\n M[2, 1] += M_sym[4]\n\n M[2, 2] += M_sym[5]\n\n return M","def __len__(self):\n \n return len(self.num_atoms)","def _symmetric_matrix_square_root(mat, eps=1e-10):\n # Unlike numpy, tensorflow's return order is (s, u, v)\n s, u, v = linalg_ops.svd(mat)\n # sqrt is unstable around 0, just use 0 in such case\n si = array_ops.where(math_ops.less(s, eps), s, math_ops.sqrt(s))\n # Note that the v returned by Tensorflow is v = V\n # (when referencing the equation A = U S V^T)\n # This is unlike Numpy which returns v = V^T\n return math_ops.matmul(\n math_ops.matmul(u, array_ops.diag(si)), v, transpose_b=True)","def n_atoms(self):\n return self._n_atoms","def memlen(self):\n\n return len(self.memory)","def nnz(self):\n\t\treturn self.st.size()","def freq_and_modes(matrix, k_over_m):\r\n val, vect = np.linalg.eigh(matrix)\r\n \r\n #for different masses, use eig with no h\r\n \r\n freq = np.sqrt(val)\r\n return freq, vect","def num_sigmas(self):\n return 2*self.n + 1","def getNumAtoms(self):\n return int(self._getPointerValue('NATOM'))","def symmeterize(self):\n A = self.to_coo_matrix()\n symg = wgraph_from_adjacency((A + A.T) / 2)\n self.E = symg.E\n self.edges = symg.edges\n self.weights = symg.weights\n return self","def get_location_count(self):\n return len(self.matrix)","def matrix_shape(matrix):\n return [*get_length(matrix)]","def matrix_shape(matrix):\n return [*get_length(matrix)]","def GetNumberCols(self):\n return len(self.__colsKey)","def m_symb(self):\n return self._m_symb","def get_iter_num(self):\n\tif len(self.cost) > 0:\n first_key = list(self.cost.keys())[0]\n num = len(self.cost[first_key]) - 1\n\telse:\n\t first_key = list(self.prim_var.keys())[0]\n num = len(self.prim_var[first_key]) - 1\n\treturn num","def sparsity(self):\n nelem = self._nelem\n\n if nelem is None:\n self._logger.warning(\n \"this matrix will be considered as dense as it has not had its number of elements defined\")\n nelem = self._size\n\n return 1.0 - nelem / self._size","def _symbolic_system(self):\n system = sym.Matrix(self._symbolic_equations)\n return system.subs(self._symbolic_change_of_vars)","def SetOperatorSymmetry(self, is_sym):\n return _hypre.HypreSmoother_SetOperatorSymmetry(self, is_sym)","def get_local_nnz(self):\n return _hypre.HypreParMatrix_get_local_nnz(self)","def dim(self,mat):\n result = np.shape(mat)\n self.dimensions = result\n return self.dimensions","def symbolic_mutual_information(symX, symY):\n\n if len(symX) != len(symY):\n raise ValueError('All arrays must have same length')\n \n symX = np.array(symX)\n symY = np.array(symY)\n \n symbols = np.unique(np.concatenate((symX,symY))).tolist()\n \n jp = symbolic_joint_probabilities(symX, symY)\n pX = symbolic_probabilities(symX)\n pY = symbolic_probabilities(symY)\n \n MI = 0\n\n for yi, b in pY.items():\n for xi, a in pX.items():\n try:\n c = jp[yi,xi]\n MI += c * np.log(c /(a * b)) / np.log(len(symbols))\n except KeyError:\n continue\n except:\n print(\"Unexpected Error\")\n raise\n \n return MI","def GetInstanceCount():\n return _gmat_py.GmatBase_GetInstanceCount()","def natoms(self):\n return len(self.atoms)","def number_syllables(self):\n return len(self.array_form)","def shape(self) -> (int,int):\n return (len(self.mat), len(self.mat[0]))","def size(self):\n return len(self._adjacency_list.keys())","def numAtoms(self):\n return self.nAtoms","def number_of_basis(self):\n return self._pre_kernel.shape[0]","def getNumTables(self):\n return self.numtables","def n(self):\n return nrow(self._array)","def dctSymDim(pdct, symIndex):\n return _dctmcc.dctSymDim(pdct, symIndex)","def __len__(self):\n return self.rdmol.GetNumAtoms()","def num_carts(self):\n return self._num_carts","def size(self):\r\n return len(atoms)","def getFreq(self, sym = None):\n if self.tot > 0:\n if sym == None:\n freq = tuple([ y / self.tot for y in self.cnt ])\n return freq\n else:\n idx = self.alpha.getIndex( sym )\n return self.cnt[idx] / self.tot\n return None","def get_matrix_stride(mat):\n itemsize = mat.itemsize\n stride = mat.strides[0] // itemsize\n assert mat.strides == (stride * itemsize, itemsize)\n return stride","def test_sym_sqrtm(self): \n # create random symmetric n x n matrix\n n = 5\n A = 5.0 * 2.0*(torch.rand(n,n) - 0.5)\n A = A + A.T\n\n # reference implementation of scipy\n sqA_scipy = sla.sqrtm(A.numpy())\n isqA_scipy = sla.inv(sla.sqrtm(A.numpy()))\n # my own implementation using pure torch functions\n sqA,isqA = (x.numpy() for x in _sym_sqrtm(A))\n \n self.assertTrue(np.isclose(sqA, sqA_scipy).all())\n self.assertTrue(np.isclose(isqA, isqA_scipy).all())","def get_symbolic_model(self):\n return self.sym_func","def get_symbolic_model(self):\n return self.sym_func","def nr_codes(self):\n return self.C.shape[1]","def GmatBase_GetInstanceCount():\n return _gmat_py.GmatBase_GetInstanceCount()","def OwnsDiag(self):\n return _hypre.HypreParMatrix_OwnsDiag(self)","def size(self):\n return self.__row_count * self.__col_count","def __len__(self):\n return np.size(self.A,0)","def n_qubits(self):\n return int(np.log2(len(self.mat)))","def anz_von_sym(lst):\n anz = 0\n for i in range(len(lst) - 1):\n for j in range(i, len(lst)):\n if symetrisch(lst[i], lst[j]):\n anz += 1\n print(\"Anzahl symmetrischer Paaren:\", anz)","def get_local_true_nnz(self):\n return _hypre.HypreParMatrix_get_local_true_nnz(self)","def _N(self):\n return len(self._array)","def symmetrize(W):\n if W.shape[0] < W.shape[1]:\n raise ValueError('Input must be a rectangular matrix (more rows than columns).')\n\n Wsym = np.abs(W)/2 + W/2 # zero out negative entries\n Wsub = Wsym[:Wsym.shape[1],:] # extract topmost square\n Wsub = Wsub/2 + Wsub.T/2 # average off-diagonal pairs\n np.fill_diagonal(Wsub,0) # zero out diagonals\n Wsym[:Wsym.shape[1],:] = Wsub\n return Wsym","def get_number_of_stools(self):\n return len(self._stools)","def count_matrix_largest(self, effective=False):\n return self.count_matrix(connected_set=0, effective=effective)","def __len__(self):\n return self.dbms.getNbTables(self.db)"],"string":"[\n \"def getnumsymmat(self): # 3\\n res,resargs = self.__obj.getnumsymmat()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _num_return_value = resargs\\n return _num_return_value\",\n \"def getsymmatinfo(self,idx_): # 3\\n res,resargs = self.__obj.getsymmatinfo(idx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _dim_return_value,_nz_return_value,_type_return_value = resargs\\n _type_return_value = symmattype(_type_return_value)\\n return _dim_return_value,_nz_return_value,_type_return_value\",\n \"def getsymmatinfo(self,idx_):\\n dim_ = ctypes.c_int32()\\n nz_ = ctypes.c_int64()\\n type_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getsymmatinfo(self.__nativep,idx_,ctypes.byref(dim_),ctypes.byref(nz_),ctypes.byref(type_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n dim_ = dim_.value\\n _dim_return_value = dim_\\n nz_ = nz_.value\\n _nz_return_value = nz_\\n _type_return_value = symmattype(type_.value)\\n return (_dim_return_value,_nz_return_value,_type_return_value)\",\n \"def get_symmetry(self):\\n\\n # used to build the symmetry string\\n symmetry = self.get_atoms()[0].get_symmetry_class()\\n\\n # used to count how many atoms there are of the current symmetry\\n symmetric_atom_count = 1\\n\\n for atom in self.get_atoms()[1:]:\\n\\n # if this atom has a different symmetry than the one before it\\n if atom.get_symmetry_class() != symmetry[-1]:\\n\\n # record the number of atoms with the previous symmetry\\n symmetry += str(symmetric_atom_count) + atom.get_symmetry_class()\\n\\n # reset the atom counter\\n symmetric_atom_count = 1\\n\\n # if this atom has the same symmetry than the one before it\\n else:\\n\\n # record this atom in the atom count\\n symmetric_atom_count += 1\\n\\n # record the number of atoms with the last symmetry\\n symmetry += str(symmetric_atom_count)\\n\\n return symmetry\",\n \"def get_conn_matrix_len(self):\\n\\n return len(self.connection_matrix) * self.brain[\\\"n_osc\\\"]\",\n \"def sym_K(self):\\n raise NotImplementedError\",\n \"def axes_of_symmetry(self):\\n if self.number_axes_of_symmetry is None: # distinguish from Falsy 0\\n raise NotImplementedError(self.message_unknown)\\n return self.number_axes_of_symmetry\",\n \"def get_sym_matrix(self):\\n temp_T = Matrix.eye(3)\\n for i in range(len(self.lengths)):\\n angle_mat = self.T_a.subs(self.q,self.angles[i]).evalf()\\n len_mat = self.T_x.subs(self.l,self.lengths[i]).evalf()\\n temp_T = temp_T * angle_mat * len_mat\\n \\n return temp_T\",\n \"def shape(self):\\n return self.symbolic.shape\",\n \"def size(adj_mat):\\n return adj_mat.shape[0]\",\n \"def num_syls(syls):\\n\\treturn len([c for c in syls if c in ['0','1','2']])\",\n \"def get_num_connections(self):\\n\\n synapses = 0\\n for mat in self.weights:\\n synapses += mat.size\\n return synapses\",\n \"def getSize(self):\\n if self.sym != None:\\n return self.sym.getSize()\\n return self.define.getSize()\",\n \"def sym(self) -> np.ndarray:\\n if self._sym is None:\\n self._sym = symmetrize_discrete_vector_field(self.F, mode=\\\"sym\\\")\\n return self._sym\",\n \"def find_symmetry(self):\\n from spglib import get_spacegroup\\n cell = ( self.lattice, self.fractional_coordinates, self.atomic_nos )\\n self.spacegroup = get_spacegroup(cell, symmprec=1e-5)\\n print(\\\"Symmetry space group is\\\", self.spacegroup)\",\n \"def get_number_of_atoms_to_optimize(self):\\n v = self.c.get(simulation_cell=True)\\n return len(v.data.stoichiometry)\",\n \"def N(self):\\n return _hypre.HypreParMatrix_N(self)\",\n \"def GetGlobalNumRows(self):\\n return _hypre.HypreParMatrix_GetGlobalNumRows(self)\",\n \"def SymmetriseMatrix(adjmatrix):\\n\\n if galib.metrics.Reciprocity(adjmatrix) == 1:\\n # if Reciprocity(adjmatrix) == 1:\\n return adjmatrix\\n else:\\n return 0.5 * (adjmatrix + adjmatrix.T)\",\n \"def is_symmetric(mat):\\n return np.allclose(mat.T, mat)\",\n \"def getSize(self):\\n if self.subsym == None:\\n if self.size == 0:\\n return 1\\n else:\\n return self.size\\n else:\\n if self.size == 0:\\n return self.subsym.getSize()\\n else:\\n return self.size * self.subsym.getSize()\",\n \"def sym_adj(adj):\\n adj = sp.coo_matrix(adj)\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense()\",\n \"def GetGlobalNumCols(self):\\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)\",\n \"def numSpecial(self, mat: list[list[int]]) -> int:\\n ans = 0\\n col_cache = {}\\n for row in mat:\\n # print(row)\\n ones = []\\n for i, n in enumerate(row):\\n if n == 1:\\n ones.append(i)\\n # print(ones)\\n if len(ones) == 1:\\n j = ones[0]\\n cols = [row[j] for row in mat]\\n s = col_cache.get(j, sum(cols))\\n col_cache[j] = s\\n if s == 1:\\n ans += 1\\n return ans\",\n \"def symmetric(matrix):\\n return sp.allclose(matrix, matrix.T)\",\n \"def sym_adj(adj):\\n adj = ss.coo_matrix(adj)\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = ss.diags(d_inv_sqrt)\\n return np.array(adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense())\",\n \"def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) -> None:\\n is_unity = validate_unity(symmetry[0])\\n if len(symmetry) == 1 and is_unity:\\n return\\n build_symmetry_operations(symmetry)\\n validate_matrix_symmetry(mat, symmetry)\",\n \"def getlen(self):\\n if self.onlydiag():\\n return self.lendiag()\\n else:\\n return len(self)\",\n \"def global_symbols_size(self):\\n size = 0\\n for s in self.global_symbols:\\n if self.global_symbols[s].type == 'procedure': continue\\n size += self.global_symbols[s].size\\n return size\",\n \"def expected_size(self):\\n return self.nsym * self.symbol_len_per_byte\",\n \"def matShape(mat):\\n return (len(mat),len(mat[0]))\",\n \"def GetNumRows(self):\\n return _hypre.HypreParMatrix_GetNumRows(self)\",\n \"def nel(self):\\n return nzion(self.sym)\",\n \"def GetNumCols(self):\\n return _hypre.HypreParMatrix_GetNumCols(self)\",\n \"def is_symmetric(self):\\n _is_sym = self._is_sym\\n if _is_sym is not None:\\n return _is_sym\\n\\n n = self.degree\\n if n >= 8:\\n if self.is_transitive():\\n _is_alt_sym = self._eval_is_alt_sym_monte_carlo()\\n if _is_alt_sym:\\n if any(g.is_odd for g in self.generators):\\n self._is_sym, self._is_alt = True, False\\n return True\\n\\n self._is_sym, self._is_alt = False, True\\n return False\\n\\n return self._eval_is_alt_sym_naive(only_sym=True)\\n\\n self._is_sym, self._is_alt = False, False\\n return False\\n\\n return self._eval_is_alt_sym_naive(only_sym=True)\",\n \"def is_symmetric(mat, eps=None):\\n if eps is None:\\n eps = np.finfo(mat.dtype).eps\\n\\n assert mat.ndim == 2\\n if mat.shape[0] != mat.shape[1]:\\n return False\\n\\n return np.allclose(mat, mat.T, atol=eps)\",\n \"def is_symmetric(self):\\n return self.args[0].is_symmetric()\",\n \"def getNumLayouts(self):\\n return _libsbml.LayoutModelPlugin_getNumLayouts(self)\",\n \"def rotational_symmetry_number(self):\\n pg = self.get_full_point_group()\\n pg = self.full_point_group_with_n()\\n if pg in ['ATOM', 'C1', 'Ci', 'Cs', 'C_inf_v']:\\n sigma = 1\\n elif pg == 'D_inf_h':\\n sigma = 2\\n elif pg in ['T', 'Td']:\\n sigma = 12\\n elif pg == 'Oh':\\n sigma = 24\\n elif pg == 'Ih':\\n sigma = 60\\n elif pg in ['Cn', 'Cnv', 'Cnh']:\\n sigma = self.full_pg_n()\\n elif pg in ['Dn', 'Dnd', 'Dnh']:\\n sigma = 2 * self.full_pg_n()\\n elif pg == 'Sn':\\n sigma = self.full_pg_n() / 2\\n else:\\n raise ValidationError(\\\"Can't ID full symmetry group: \\\" + pg)\\n\\n return sigma\",\n \"def getNumVertexAttrib(self):\\n return _osgAnimation.RigTransformHardware_getNumVertexAttrib(self)\",\n \"def local_symbols_size(self):\\n size = 0\\n for s in self.symbols[-1]:\\n if self.symbols[-1][s].type == 'procedure': continue\\n if self.symbols[-1][s].isparam: continue\\n #if self.symbols[-1][s].isparam: continue\\n size += self.symbols[-1][s].size\\n return size\",\n \"def get_num_atoms(self):\\n\\n return len(self.atoms)\",\n \"def count(self, syms = None ):\\n if syms == None:\\n syms = self.alpha.getSymbols()\\n for sym in syms:\\n idx = self.alpha.getIndex( sym )\\n self.cnt[idx] += 1.0\\n self.tot += 1\",\n \"def get_number_symbols(self) -> int:\\n if self.sons:\\n return sum([son.get_number_symbols() for son in self.sons])\\n return 1\",\n \"def nmodes(self):\\n if self.mode_selection is not None:\\n return len(self.mode_selection)\\n else:\\n return len(self.mol.normal_modes.modes.freqs)\",\n \"def symbol_width(self):\\n return self._symbol.get_extents().width\",\n \"def get_atom_count(self):\\n return self._graph.get_vertex_count()\",\n \"def matrix_add_symmetric(M, M_sym):\\n M[0, 0] += M_sym[0]\\n M[0, 1] += M_sym[1]\\n M[1, 0] += M_sym[1]\\n M[0, 2] += M_sym[2]\\n M[2, 0] += M_sym[2]\\n\\n M[1, 1] += M_sym[3]\\n M[1, 2] += M_sym[4]\\n M[2, 1] += M_sym[4]\\n\\n M[2, 2] += M_sym[5]\\n\\n return M\",\n \"def __len__(self):\\n \\n return len(self.num_atoms)\",\n \"def _symmetric_matrix_square_root(mat, eps=1e-10):\\n # Unlike numpy, tensorflow's return order is (s, u, v)\\n s, u, v = linalg_ops.svd(mat)\\n # sqrt is unstable around 0, just use 0 in such case\\n si = array_ops.where(math_ops.less(s, eps), s, math_ops.sqrt(s))\\n # Note that the v returned by Tensorflow is v = V\\n # (when referencing the equation A = U S V^T)\\n # This is unlike Numpy which returns v = V^T\\n return math_ops.matmul(\\n math_ops.matmul(u, array_ops.diag(si)), v, transpose_b=True)\",\n \"def n_atoms(self):\\n return self._n_atoms\",\n \"def memlen(self):\\n\\n return len(self.memory)\",\n \"def nnz(self):\\n\\t\\treturn self.st.size()\",\n \"def freq_and_modes(matrix, k_over_m):\\r\\n val, vect = np.linalg.eigh(matrix)\\r\\n \\r\\n #for different masses, use eig with no h\\r\\n \\r\\n freq = np.sqrt(val)\\r\\n return freq, vect\",\n \"def num_sigmas(self):\\n return 2*self.n + 1\",\n \"def getNumAtoms(self):\\n return int(self._getPointerValue('NATOM'))\",\n \"def symmeterize(self):\\n A = self.to_coo_matrix()\\n symg = wgraph_from_adjacency((A + A.T) / 2)\\n self.E = symg.E\\n self.edges = symg.edges\\n self.weights = symg.weights\\n return self\",\n \"def get_location_count(self):\\n return len(self.matrix)\",\n \"def matrix_shape(matrix):\\n return [*get_length(matrix)]\",\n \"def matrix_shape(matrix):\\n return [*get_length(matrix)]\",\n \"def GetNumberCols(self):\\n return len(self.__colsKey)\",\n \"def m_symb(self):\\n return self._m_symb\",\n \"def get_iter_num(self):\\n\\tif len(self.cost) > 0:\\n first_key = list(self.cost.keys())[0]\\n num = len(self.cost[first_key]) - 1\\n\\telse:\\n\\t first_key = list(self.prim_var.keys())[0]\\n num = len(self.prim_var[first_key]) - 1\\n\\treturn num\",\n \"def sparsity(self):\\n nelem = self._nelem\\n\\n if nelem is None:\\n self._logger.warning(\\n \\\"this matrix will be considered as dense as it has not had its number of elements defined\\\")\\n nelem = self._size\\n\\n return 1.0 - nelem / self._size\",\n \"def _symbolic_system(self):\\n system = sym.Matrix(self._symbolic_equations)\\n return system.subs(self._symbolic_change_of_vars)\",\n \"def SetOperatorSymmetry(self, is_sym):\\n return _hypre.HypreSmoother_SetOperatorSymmetry(self, is_sym)\",\n \"def get_local_nnz(self):\\n return _hypre.HypreParMatrix_get_local_nnz(self)\",\n \"def dim(self,mat):\\n result = np.shape(mat)\\n self.dimensions = result\\n return self.dimensions\",\n \"def symbolic_mutual_information(symX, symY):\\n\\n if len(symX) != len(symY):\\n raise ValueError('All arrays must have same length')\\n \\n symX = np.array(symX)\\n symY = np.array(symY)\\n \\n symbols = np.unique(np.concatenate((symX,symY))).tolist()\\n \\n jp = symbolic_joint_probabilities(symX, symY)\\n pX = symbolic_probabilities(symX)\\n pY = symbolic_probabilities(symY)\\n \\n MI = 0\\n\\n for yi, b in pY.items():\\n for xi, a in pX.items():\\n try:\\n c = jp[yi,xi]\\n MI += c * np.log(c /(a * b)) / np.log(len(symbols))\\n except KeyError:\\n continue\\n except:\\n print(\\\"Unexpected Error\\\")\\n raise\\n \\n return MI\",\n \"def GetInstanceCount():\\n return _gmat_py.GmatBase_GetInstanceCount()\",\n \"def natoms(self):\\n return len(self.atoms)\",\n \"def number_syllables(self):\\n return len(self.array_form)\",\n \"def shape(self) -> (int,int):\\n return (len(self.mat), len(self.mat[0]))\",\n \"def size(self):\\n return len(self._adjacency_list.keys())\",\n \"def numAtoms(self):\\n return self.nAtoms\",\n \"def number_of_basis(self):\\n return self._pre_kernel.shape[0]\",\n \"def getNumTables(self):\\n return self.numtables\",\n \"def n(self):\\n return nrow(self._array)\",\n \"def dctSymDim(pdct, symIndex):\\n return _dctmcc.dctSymDim(pdct, symIndex)\",\n \"def __len__(self):\\n return self.rdmol.GetNumAtoms()\",\n \"def num_carts(self):\\n return self._num_carts\",\n \"def size(self):\\r\\n return len(atoms)\",\n \"def getFreq(self, sym = None):\\n if self.tot > 0:\\n if sym == None:\\n freq = tuple([ y / self.tot for y in self.cnt ])\\n return freq\\n else:\\n idx = self.alpha.getIndex( sym )\\n return self.cnt[idx] / self.tot\\n return None\",\n \"def get_matrix_stride(mat):\\n itemsize = mat.itemsize\\n stride = mat.strides[0] // itemsize\\n assert mat.strides == (stride * itemsize, itemsize)\\n return stride\",\n \"def test_sym_sqrtm(self): \\n # create random symmetric n x n matrix\\n n = 5\\n A = 5.0 * 2.0*(torch.rand(n,n) - 0.5)\\n A = A + A.T\\n\\n # reference implementation of scipy\\n sqA_scipy = sla.sqrtm(A.numpy())\\n isqA_scipy = sla.inv(sla.sqrtm(A.numpy()))\\n # my own implementation using pure torch functions\\n sqA,isqA = (x.numpy() for x in _sym_sqrtm(A))\\n \\n self.assertTrue(np.isclose(sqA, sqA_scipy).all())\\n self.assertTrue(np.isclose(isqA, isqA_scipy).all())\",\n \"def get_symbolic_model(self):\\n return self.sym_func\",\n \"def get_symbolic_model(self):\\n return self.sym_func\",\n \"def nr_codes(self):\\n return self.C.shape[1]\",\n \"def GmatBase_GetInstanceCount():\\n return _gmat_py.GmatBase_GetInstanceCount()\",\n \"def OwnsDiag(self):\\n return _hypre.HypreParMatrix_OwnsDiag(self)\",\n \"def size(self):\\n return self.__row_count * self.__col_count\",\n \"def __len__(self):\\n return np.size(self.A,0)\",\n \"def n_qubits(self):\\n return int(np.log2(len(self.mat)))\",\n \"def anz_von_sym(lst):\\n anz = 0\\n for i in range(len(lst) - 1):\\n for j in range(i, len(lst)):\\n if symetrisch(lst[i], lst[j]):\\n anz += 1\\n print(\\\"Anzahl symmetrischer Paaren:\\\", anz)\",\n \"def get_local_true_nnz(self):\\n return _hypre.HypreParMatrix_get_local_true_nnz(self)\",\n \"def _N(self):\\n return len(self._array)\",\n \"def symmetrize(W):\\n if W.shape[0] < W.shape[1]:\\n raise ValueError('Input must be a rectangular matrix (more rows than columns).')\\n\\n Wsym = np.abs(W)/2 + W/2 # zero out negative entries\\n Wsub = Wsym[:Wsym.shape[1],:] # extract topmost square\\n Wsub = Wsub/2 + Wsub.T/2 # average off-diagonal pairs\\n np.fill_diagonal(Wsub,0) # zero out diagonals\\n Wsym[:Wsym.shape[1],:] = Wsub\\n return Wsym\",\n \"def get_number_of_stools(self):\\n return len(self._stools)\",\n \"def count_matrix_largest(self, effective=False):\\n return self.count_matrix(connected_set=0, effective=effective)\",\n \"def __len__(self):\\n return self.dbms.getNbTables(self.db)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7524286","0.6255513","0.62463","0.6031631","0.5934353","0.58589876","0.5748166","0.5643437","0.5588444","0.5525005","0.551974","0.5482156","0.547971","0.54718024","0.54672223","0.5427831","0.54226774","0.5403771","0.5391538","0.5375537","0.53549564","0.53485143","0.5328631","0.53220564","0.5302894","0.52880394","0.5283709","0.52629054","0.5262857","0.5261725","0.52598685","0.5247981","0.5224038","0.5223555","0.5196189","0.51876736","0.5183067","0.5174165","0.5152882","0.511735","0.5108108","0.5062754","0.5060944","0.5057121","0.5043406","0.5028562","0.5024713","0.5018689","0.50079685","0.4999351","0.49887592","0.49881384","0.49672514","0.49645197","0.49571735","0.49437326","0.49411297","0.4933388","0.49243772","0.49243772","0.4917899","0.49139777","0.49122813","0.4894926","0.4884923","0.4877607","0.48674375","0.48671982","0.48600468","0.4855455","0.48542368","0.48453185","0.48436344","0.48423436","0.48352382","0.4833663","0.4822577","0.48170868","0.48142755","0.48099944","0.4804484","0.4802458","0.479925","0.47962502","0.47924352","0.4791545","0.4791545","0.47909084","0.4788122","0.47824502","0.4780796","0.47757587","0.47701663","0.47655162","0.47562456","0.47545633","0.4739115","0.47371006","0.4732782","0.47207308"],"string":"[\n \"0.7524286\",\n \"0.6255513\",\n \"0.62463\",\n \"0.6031631\",\n \"0.5934353\",\n \"0.58589876\",\n \"0.5748166\",\n \"0.5643437\",\n \"0.5588444\",\n \"0.5525005\",\n \"0.551974\",\n \"0.5482156\",\n \"0.547971\",\n \"0.54718024\",\n \"0.54672223\",\n \"0.5427831\",\n \"0.54226774\",\n \"0.5403771\",\n \"0.5391538\",\n \"0.5375537\",\n \"0.53549564\",\n \"0.53485143\",\n \"0.5328631\",\n \"0.53220564\",\n \"0.5302894\",\n \"0.52880394\",\n \"0.5283709\",\n \"0.52629054\",\n \"0.5262857\",\n \"0.5261725\",\n \"0.52598685\",\n \"0.5247981\",\n \"0.5224038\",\n \"0.5223555\",\n \"0.5196189\",\n \"0.51876736\",\n \"0.5183067\",\n \"0.5174165\",\n \"0.5152882\",\n \"0.511735\",\n \"0.5108108\",\n \"0.5062754\",\n \"0.5060944\",\n \"0.5057121\",\n \"0.5043406\",\n \"0.5028562\",\n \"0.5024713\",\n \"0.5018689\",\n \"0.50079685\",\n \"0.4999351\",\n \"0.49887592\",\n \"0.49881384\",\n \"0.49672514\",\n \"0.49645197\",\n \"0.49571735\",\n \"0.49437326\",\n \"0.49411297\",\n \"0.4933388\",\n \"0.49243772\",\n \"0.49243772\",\n \"0.4917899\",\n \"0.49139777\",\n \"0.49122813\",\n \"0.4894926\",\n \"0.4884923\",\n \"0.4877607\",\n \"0.48674375\",\n \"0.48671982\",\n \"0.48600468\",\n \"0.4855455\",\n \"0.48542368\",\n \"0.48453185\",\n \"0.48436344\",\n \"0.48423436\",\n \"0.48352382\",\n \"0.4833663\",\n \"0.4822577\",\n \"0.48170868\",\n \"0.48142755\",\n \"0.48099944\",\n \"0.4804484\",\n \"0.4802458\",\n \"0.479925\",\n \"0.47962502\",\n \"0.47924352\",\n \"0.4791545\",\n \"0.4791545\",\n \"0.47909084\",\n \"0.4788122\",\n \"0.47824502\",\n \"0.4780796\",\n \"0.47757587\",\n \"0.47701663\",\n \"0.47655162\",\n \"0.47562456\",\n \"0.47545633\",\n \"0.4739115\",\n \"0.47371006\",\n \"0.4732782\",\n \"0.47207308\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7143468"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11974,"cells":{"query":{"kind":"string","value":"Gets a single symmetric matrix from the matrix store. getsparsesymmat(self,idx_,subi_,subj_,valij_)"},"document":{"kind":"string","value":"def getsparsesymmat(self,idx_,subi_,subj_,valij_):\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _subi_minlength = (maxlen_)\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxlen_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (maxlen_)\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxlen_)))\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\n raise ValueError(\"Argument subj must be writable\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = (maxlen_)\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),(maxlen_)))\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\n raise ValueError(\"Argument valij must be writable\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valij_copyarray:\n valij_[:] = _valij_np_tmp"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_valij = False\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n _copyback_valij = True\n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n _copyback_valij = True\n if valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij has wrong length\")\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_valij:\n valij[:] = _tmparr_valij\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi","def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)","def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij)\n elif nz_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if nz_ is None: nz_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _idx_return_value = resargs\n return _idx_return_value","def getsymmatinfo(self,idx_):\n dim_ = ctypes.c_int32()\n nz_ = ctypes.c_int64()\n type_ = ctypes.c_int32()\n res = __library__.MSK_XX_getsymmatinfo(self.__nativep,idx_,ctypes.byref(dim_),ctypes.byref(nz_),ctypes.byref(type_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n dim_ = dim_.value\n _dim_return_value = dim_\n nz_ = nz_.value\n _nz_return_value = nz_\n _type_return_value = symmattype(type_.value)\n return (_dim_return_value,_nz_return_value,_type_return_value)","def getsymmatinfo(self,idx_): # 3\n res,resargs = self.__obj.getsymmatinfo(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dim_return_value,_nz_return_value,_type_return_value = resargs\n _type_return_value = symmattype(_type_return_value)\n return _dim_return_value,_nz_return_value,_type_return_value","def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\"<i4\")\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\"<f8\")\n size = j_obj.getSize()\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()","def sparse_matlab(i, j, v, m, n):\n return csr_matrix((v, (i, j)), shape=(m, n))","def SymmetriseMatrix(adjmatrix):\n\n if galib.metrics.Reciprocity(adjmatrix) == 1:\n # if Reciprocity(adjmatrix) == 1:\n return adjmatrix\n else:\n return 0.5 * (adjmatrix + adjmatrix.T)","def get_stain_matrix(I):","def sparse_matrix(data, stype=\"csr\", dtype=complex):\n return _SPARSE_CONSTRUCTORS[stype](data, dtype=dtype)","def jmat(ind: int):\n return _jm[ind - 1]","def to_sparse(self, method='csr_matrix'):\r\n data = self.data.values\r\n if method == 'csr_matrix':\r\n data_sp = sps.csr_matrix(data)\r\n elif method == 'bsr_matrix':\r\n data_sp = sps.bsr_matrix(data)\r\n elif method == 'coo_matrix':\r\n data_sp = sps.coo_matrix(data)\r\n elif method == 'csc_matrix':\r\n data_sp = sps.csc_matrix(data)\r\n elif method == 'dia_matrix':\r\n data_sp = sps.dia_matrix(data)\r\n elif method == 'dok_matrix':\r\n data_sp = sps.dok_matrix(data)\r\n elif method == 'lil_matrix':\r\n data_sp = sps.lil_matrix(data)\r\n else:\r\n raise ValueError('The method does not exist in scipy.sparse')\r\n return data_sp","def get_solver_mats(self, x_nd, rot_coef):\n raise NotImplementedError","def get_sparse(self, key, element):\n return self.get(self._sparse2seq(key), element)","def scipy_sparse_to_spmatrix(A):\n coo = A.tocoo()\n SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)\n return SP","def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):\n num_ = None\n if num_ is None:\n num_ = len(dims_)\n elif num_ != len(dims_):\n raise IndexError(\"Inconsistent length of array dims\")\n if num_ is None:\n num_ = len(nz_)\n elif num_ != len(nz_):\n raise IndexError(\"Inconsistent length of array nz\")\n if dims_ is None:\n raise ValueError(\"Argument dims cannot be None\")\n if dims_ is None:\n raise ValueError(\"Argument dims may not be None\")\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\n _dims_copyarray = False\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dims_ is not None:\n _dims_copyarray = True\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\n _dims_np_tmp[:] = dims_\n assert _dims_np_tmp.flags.contiguous\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dims_copyarray = False\n _dims_tmp = None\n \n if nz_ is None:\n raise ValueError(\"Argument nz cannot be None\")\n if nz_ is None:\n raise ValueError(\"Argument nz may not be None\")\n if isinstance(nz_, numpy.ndarray) and nz_.dtype is numpy.dtype(numpy.int64) and nz_.flags.contiguous:\n _nz_copyarray = False\n _nz_tmp = ctypes.cast(nz_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nz_ is not None:\n _nz_copyarray = True\n _nz_np_tmp = numpy.zeros(len(nz_),numpy.dtype(numpy.int64))\n _nz_np_tmp[:] = nz_\n assert _nz_np_tmp.flags.contiguous\n _nz_tmp = ctypes.cast(_nz_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nz_copyarray = False\n _nz_tmp = None\n \n _subi_minlength = sum((nz_))\n if sum((nz_)) > 0 and subi_ is not None and len(subi_) != sum((nz_)):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),sum((nz_))))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = sum((nz_))\n if sum((nz_)) > 0 and subj_ is not None and len(subj_) != sum((nz_)):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),sum((nz_))))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = sum((nz_))\n if sum((nz_)) > 0 and valij_ is not None and len(valij_) != sum((nz_)):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),sum((nz_))))\n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n _idx_minlength = (num_)\n if (num_) > 0 and idx_ is not None and len(idx_) != (num_):\n raise ValueError(\"Array argument idx is not long enough: Is %d, expected %d\" % (len(idx_),(num_)))\n if isinstance(idx_,numpy.ndarray) and not idx_.flags.writeable:\n raise ValueError(\"Argument idx must be writable\")\n if idx_ is None:\n raise ValueError(\"Argument idx may not be None\")\n if isinstance(idx_, numpy.ndarray) and idx_.dtype is numpy.dtype(numpy.int64) and idx_.flags.contiguous:\n _idx_copyarray = False\n _idx_tmp = ctypes.cast(idx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif idx_ is not None:\n _idx_copyarray = True\n _idx_np_tmp = numpy.zeros(len(idx_),numpy.dtype(numpy.int64))\n _idx_np_tmp[:] = idx_\n assert _idx_np_tmp.flags.contiguous\n _idx_tmp = ctypes.cast(_idx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _idx_copyarray = False\n _idx_tmp = None\n \n res = __library__.MSK_XX_appendsparsesymmatlist(self.__nativep,num_,_dims_tmp,_nz_tmp,_subi_tmp,_subj_tmp,_valij_tmp,_idx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _idx_copyarray:\n idx_[:] = _idx_np_tmp","def to_s_matrix(w,v):\n pass","def save_sparse_matrix(self,\n artifact_type: str,\n params: Dict[str, Any],\n sparse_matrix: sp.csr_matrix,\n ignore_duplicate: bool = False) -> str:\n ppr_idx = None\n if \"ppr_idx\" in params.keys() and not isinstance(params[\"ppr_idx\"], int):\n ppr_idx = np.array(params[\"ppr_idx\"])\n params[\"ppr_idx\"] = hash(frozenset(params[\"ppr_idx\"]))\n\n if ignore_duplicate:\n # check there's no entry with the exact same config already present\n ids = Storage.locked_call(\n lambda: self._find_meta_by_exact_params(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n if len(ids) > 0:\n logging.info(f\"Ignoring duplicate save in save_sparse_matrix call\")\n return self._build_artifact_path(artifact_type, ids[0].doc_id).replace(\".pt\", \".npz\")\n\n ids = Storage.locked_call(\n lambda: self._upsert_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout,\n )\n if len(ids) != 1:\n raise RuntimeError(f'The index contains duplicates (artifact_type={artifact_type}, params={params})')\n\n try:\n path = self._build_artifact_path(artifact_type, ids[0]).replace(\".pt\", \".npz\")\n sp.save_npz(path, sparse_matrix)\n logging.info(f\"Saved sparse matrix to storage\")\n if ppr_idx is not None:\n ppr_path = path.replace(\".npz\", \"idx.npy\")\n np.save(ppr_path, ppr_idx)\n logging.info(f\"Saved ppr index to storage\")\n return path\n except: # noqa: E722\n Storage.locked_call(\n lambda: self._remove_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n raise","def sparse_matrix(shape, integer=False):\n dtype = numpy.int_ if integer else numpy.float_\n return scipy.sparse.lil_matrix(shape, dtype=dtype)","def generate_direct_solver(self, grid=None):\n if grid is None:\n # LOG.debug(\"Generate Solver for internal Spare Matrix: %s\" % self.sp_matrix)\n solver = spla.factorized(self.sp_matrix)\n else:\n # LOG.debug(\"Generate Solver for given Grid %s\" % (grid,))\n sp_matrix = self.to_sparse_matrix(grid, \"csc\")\n # LOG.debug(\" with Sparse Matrix: %s\" % sp_matrix.todense())\n # print(\"Jahier\\n\", sp_matrix.todense())\n # print(\"Jahier.shape\\n\", sp_matrix.todense().shape)\n solver = spla.factorized(sp_matrix)\n return solver","def wrapDBMatrix(self,mat):\n return mat.todense()","def sparsify(W,conn):\n \n N = W.shape[0]\n W_sparse = sparse.lil_matrix((N,N)) \n for row, weights in itertools.izip(conn, W):\n W_sparse[row[0],row[1:]] = weights[1:]\n return W_sparse","def get_sym_matrix(self):\n temp_T = Matrix.eye(3)\n for i in range(len(self.lengths)):\n angle_mat = self.T_a.subs(self.q,self.angles[i]).evalf()\n len_mat = self.T_x.subs(self.l,self.lengths[i]).evalf()\n temp_T = temp_T * angle_mat * len_mat\n \n return temp_T","def dict2sparseMatrix(wDict,std=0,diag=0):\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\n nAreas = len(list(wDict.keys()))\n for i in wDict:\n data[i,i] = diag\n ne = len(wDict[i])+ diag\n for j in wDict[i]:\n if std:\n data[i,j] = 1 / float(ne)\n else:\n data[i,j] = 1\n return data","def get_cvxopt_sparse_intf():\n import cvxpy.interface.cvxopt_interface.sparse_matrix_interface as smi\n return smi.SparseMatrixInterface()","def to_sparse(self):\n if self.rep.fmt == 'sparse':\n return self\n\n return self.from_rep(self.rep.to_sdm())","def to_sparse(self):\n from divisi2.sparse import SparseMatrix\n return SparseMatrix(self, self.row_labels, self.col_labels)","def getSparse(self): # as opposed to makeSparse which keeps the same form and return nothing\n return copy.deepcopy(self.makeSparse())","def make_sparse(self, fmt='csc', make_method=None):\n if make_method:\n self.sparse = make_method(self.hamiltonian)\n else:\n self.sparse = self.hamiltonian.to_matrix(sparse=fmt)","def return_adjacencyMatrix(self):\n return self.__mat","def sym_adj(adj):\n adj = ss.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = ss.diags(d_inv_sqrt)\n return np.array(adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense())","def eval_sparse(self, array_in, array_out, sp_matrix=None):\n if sp_matrix is None:\n sp_matrix = self.to_sparse_matrix(array_in.shape, \"csc\")\n # print(\"usually:\", sp_matrix.todense())\n array_out[:] = sp_matrix.dot(array_in.reshape(-1)).reshape(array_out.shape)","def __getitem__(self, index):\n try:\n i, j = index\n except (AssertionError, TypeError):\n raise IndexError('invalid index')\n\n if not np.isscalar(i) and np.isscalar(j):\n warn('Indexing into a lil_matrix with multiple indices is slow. '\n 'Pre-converting to CSC or CSR beforehand is more efficient.',\n SparseEfficiencyWarning)\n\n if np.isscalar(i):\n if np.isscalar(j):\n return self._get1(i, j)\n if isinstance(j, slice):\n j = self._slicetoseq(j, self.shape[1])\n if issequence(j):\n return self.__class__([[self._get1(i, jj) for jj in j]])\n elif issequence(i) and issequence(j):\n return self.__class__([[self._get1(ii, jj) for (ii, jj) in zip(i, j)]])\n elif issequence(i) or isinstance(i, slice):\n if isinstance(i, slice):\n i = self._slicetoseq(i, self.shape[0])\n if np.isscalar(j):\n return self.__class__([[self._get1(ii, j)] for ii in i])\n if isinstance(j, slice):\n j = self._slicetoseq(j, self.shape[1])\n if issequence(j):\n return self.__class__([[self._get1(ii, jj) for jj in j] for ii in i])\n else:\n raise IndexError","def sym_adj(adj):\n adj = sp.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense()","def getitem(self, i, j):\n # XXX: flint matrices do not support negative indices\n # XXX: They also raise ValueError instead of IndexError\n m, n = self.shape\n if i < 0:\n i += m\n if j < 0:\n j += n\n try:\n return self.rep[i, j]\n except ValueError:\n raise IndexError(f\"Invalid indices ({i}, {j}) for Matrix of shape {self.shape}\")","def _symmetric_matrix_square_root(mat, eps=1e-10):\n # Unlike numpy, tensorflow's return order is (s, u, v)\n s, u, v = linalg_ops.svd(mat)\n # sqrt is unstable around 0, just use 0 in such case\n si = array_ops.where(math_ops.less(s, eps), s, math_ops.sqrt(s))\n # Note that the v returned by Tensorflow is v = V\n # (when referencing the equation A = U S V^T)\n # This is unlike Numpy which returns v = V^T\n return math_ops.matmul(\n math_ops.matmul(u, array_ops.diag(si)), v, transpose_b=True)","def test_import_sparse_values_mat():\n x = sps.csr_matrix(np.random.rand(3, 2))\n export_data('/tmp/test.mat', x)\n assert np.array_equal(x.toarray(), import_data('/tmp/test.mat').toarray())","def ZSparseMatrix2Scipy(matrix):\n data = np.ndarray(matrix.get_value_size(), dtype=float);\n outer_idx = np.ndarray(matrix.get_outer_size(), dtype=np.int32);\n inner_idx = np.ndarray(matrix.get_inner_size(), dtype=np.int32);\n\n matrix.get_values(data);\n matrix.get_outer_indices(outer_idx);\n matrix.get_inner_indices(inner_idx);\n\n return scipy.sparse.csc_matrix((data, inner_idx, outer_idx),\n shape = (matrix.num_rows(), matrix.num_cols()),\n dtype = float);","def _sparse_projection(self,mat,sparsity=1,random_seed=10):\n [n,_] = mat.shape\n sketch = np.zeros((self.rp_dim ,n),dtype=float)\n for i in range(n):\n nnz_loc = np.random.choice(self.rp_dim ,size=sparsity,replace=False)\n nnz_sign = np.random.choice([-1,1],size=sparsity,replace=True)\n sketch[nnz_loc,i] = nnz_sign\n self.sketch_mat = sketch\n return (1./np.sqrt(sparsity))*sketch@mat","def set_sparsity(self,use_sparse):\n \n if hasattr(self.problem,'sparse_jac'):\n self.use_sparse = use_sparse\n else:\n raise KINSOL_Exception(\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\")","def get_sol_mat(self, clust):\r\n\r\n return self.__model_data[clust]","def _identity_sparse(d, stype=\"csr\", dtype=complex):\n return sp.eye(d, dtype=dtype, format=stype)","def _build_sparse(self, name, wrt, consize, param_vals, sub_param_conns,\n full_param_conns, rels):\n\n jac = None\n\n # Additional sparsity for index connections\n for param in wrt:\n\n sub_conns = sub_param_conns.get(param)\n if not sub_conns:\n continue\n\n # If we have a simultaneous full connection, then we move on\n full_conns = full_param_conns.get(param)\n if full_conns.intersection(rels):\n continue\n\n rel_idx = set()\n for target, idx in iteritems(sub_conns):\n\n # If a target of the indexed desvar connection is\n # in the relevant path for this constraint, then\n # those indices are relevant.\n if target in rels:\n rel_idx.update(idx)\n\n nrel = len(rel_idx)\n if nrel > 0:\n\n if jac is None:\n jac = {}\n\n if param not in jac:\n # A coo matrix for the Jacobian\n # mat = {'coo':[row, col, data],\n # 'shape':[nrow, ncols]}\n coo = {}\n coo['shape'] = [consize, len(param_vals[param])]\n jac[param] = coo\n\n row = []\n col = []\n for i in range(consize):\n row.extend([i]*nrel)\n col.extend(rel_idx)\n data = np.ones((len(row), ))\n\n jac[param]['coo'] = [np.array(row), np.array(col), data]\n\n if name not in self.sub_sparsity:\n self.sub_sparsity[name] = {}\n self.sub_sparsity[name][param] = np.array(list(rel_idx))\n\n return jac","def compute_matrix(self):\n\n fac = self.a / self.dx ** 2\n\n diagonal = np.ones(self.nx) * 2 * fac\n lower = np.ones(self.nx - 1) * -fac\n upper = np.ones(self.nx - 1) * -fac\n\n matrix = sp.diags(\n diagonals=[diagonal, lower, upper],\n offsets=[0, -1, 1], shape=(self.nx, self.nx),\n format='csr')\n\n return matrix","def build_lhs_matrix(self):\n j=3\n diags1 = self.build_linear_diags()\n diags1 += self.build_dispersion_diags()\n\n # Ones down primary diagonal\n diags2 = np.zeros_like(diags1)\n diags2[j,:] = 1.\n\n cff = self.dt*(1+self.c_im)*0.5 \n diags = diags2 - cff*diags1\n \n # Build the sparse matrix\n cols = [ii for ii in range(-self._j, self._j+1)]\n M = sparse.spdiags(diags, cols, self.N, self.N)\n\n return M, diags","def symmeterize(self):\n A = self.to_coo_matrix()\n symg = wgraph_from_adjacency((A + A.T) / 2)\n self.E = symg.E\n self.edges = symg.edges\n self.weights = symg.weights\n return self","def smat(v):\n \n k = len(v)\n n = TriLengthToSquare(k)\n \n A = np.zeros([n,n])\n A[np.triu_indices(n)] = v\n A[np.triu_indices(n,1)] *= 2 / np.sqrt(2)\n return (A + A.T) / 2","def drawSparseMatrix(ax, mat, **kwargs):\n row = kwargs.pop('rowOffset', 0)\n col = kwargs.pop('colOffset', 0)\n color = kwargs.pop('color', None)\n\n mat = pg.utils.sparseMatrix2coo(mat)\n mat.row += row\n mat.col += col\n gci = ax.spy(mat, color=color, **kwargs)\n\n ax.autoscale(enable=True, axis='both', tight=True)\n return gci","def SpMV_viaMKL( A, x ):\n SpMV = mkl.mkl_cspblas_dcsrgemv\n # Dissecting the \"cspblas_dcsrgemv\" name:\n # \"c\" - for \"c-blas\" like interface (as opposed to fortran)\n # Also means expects sparse arrays to use 0-based indexing, which python does\n # \"sp\" for sparse\n # \"d\" for double-precision\n # \"csr\" for compressed row format\n # \"ge\" for \"general\", e.g., the matrix has no special structure such as symmetry\n # \"mv\" for \"matrix-vector\" multiply\n\n if not sparse.isspmatrix_csr(A):\n raise Exception(\"Matrix must be in csr format\")\n (m,n) = A.shape\n\n # The data of the matrix\n data = A.data.ctypes.data_as(POINTER(c_double))\n indptr = A.indptr.ctypes.data_as(POINTER(c_int))\n indices = A.indices.ctypes.data_as(POINTER(c_int))\n\n # Allocate output, using same conventions as input\n nVectors = 1\n if x.ndim is 1:\n y = np.empty(m,dtype=np.double,order='F')\n if x.size != n:\n raise Exception(\"x must have n entries. x.size is %d, n is %d\" % (x.size,n))\n elif x.shape[1] is 1:\n y = np.empty((m,1),dtype=np.double,order='F')\n if x.shape[0] != n:\n raise Exception(\"x must have n entries. x.size is %d, n is %d\" % (x.size,n))\n else:\n nVectors = x.shape[1]\n y = np.empty((m,nVectors),dtype=np.double,order='F')\n if x.shape[0] != n:\n raise Exception(\"x must have n entries. x.size is %d, n is %d\" % (x.size,n))\n\n # Check input\n if x.dtype.type is not np.double:\n x = x.astype(np.double,copy=True)\n # Put it in column-major order, otherwise for nVectors > 1 this FAILS completely\n if x.flags['F_CONTIGUOUS'] is not True:\n x = x.copy(order='F')\n\n if nVectors == 1:\n np_x = x.ctypes.data_as(POINTER(c_double))\n np_y = y.ctypes.data_as(POINTER(c_double))\n # now call MKL. This returns the answer in np_y, which links to y\n SpMV(byref(c_char(b\"N\")), byref(c_int(m)),data ,indptr, indices, np_x, np_y ) \n else:\n for columns in range(nVectors):\n xx = x[:,columns]\n yy = y[:,columns]\n np_x = xx.ctypes.data_as(POINTER(c_double))\n np_y = yy.ctypes.data_as(POINTER(c_double))\n SpMV(byref(c_char(b\"N\")), byref(c_int(m)),data,indptr, indices, np_x, np_y ) \n\n return y","def get_sparse_backend():\n backend = biom_config['python_code_sparse_backend']\n if backend is None:\n backend = 'CSMat'\n\n if backend not in sparse_backends:\n raise InvalidSparseBackendException(\"Unrecognized sparse backend \"\n \"'%s'. Choose from %s.\" % (backend,\n ', '.join(sparse_backends)))\n\n valid_backend = False\n if backend == 'ScipySparseMat':\n try:\n from biom.backends.scipysparse import ScipySparseMat, to_scipy, \\\n dict_to_scipy, list_dict_to_scipy, list_nparray_to_scipy, \\\n nparray_to_scipy, list_list_to_scipy\n SparseObj = ScipySparseMat\n to_sparse = to_scipy\n dict_to_sparseobj = dict_to_scipy\n list_dict_to_sparseobj = list_dict_to_scipy\n list_nparray_to_sparseobj = list_nparray_to_scipy\n nparray_to_sparseobj = nparray_to_scipy\n list_list_to_sparseobj = list_list_to_scipy\n valid_backend = True\n except ImportError:\n valid_backend = False\n stderr.write(\"Cannot load ScipySparseMat (requires that scipy is \"\n \"installed). Using CSMat sparse backend.\\n\")\n\n if backend == 'CSMat' or (not valid_backend):\n try:\n from biom.backends.csmat import CSMat, to_csmat, dict_to_csmat, \\\n list_dict_to_csmat, list_nparray_to_csmat, nparray_to_csmat, \\\n list_list_to_csmat\n SparseObj = CSMat\n to_sparse = to_csmat\n dict_to_sparseobj = dict_to_csmat\n list_dict_to_sparseobj = list_dict_to_csmat\n list_nparray_to_sparseobj = list_nparray_to_csmat\n nparray_to_sparseobj = nparray_to_csmat\n list_list_to_sparseobj = list_list_to_csmat\n valid_backend = True\n except ImportError:\n valid_backend = False\n stderr.write('Cannot load CSMat sparse backend.\\n')\n\n if not valid_backend:\n raise InvalidSparseBackendException(\"The sparse matrix backend '%s' \"\n \"could not be loaded. Please check your biom-format \"\n \"installation.\" % backend)\n\n return SparseObj, to_sparse, dict_to_sparseobj, list_dict_to_sparseobj, \\\n list_nparray_to_sparseobj, nparray_to_sparseobj, \\\n list_list_to_sparseobj","def getScalingMatrix(sx, sy, sz):\n return MatrixExtended([\n [sx, 0, 0, 0],\n [0, sy, 0, 0],\n [0, 0, sz, 0],\n [0, 0, 0, 1]])","def matrix_spy(self, mtrx):\r\n import matplotlib.pylab as pl\r\n pl.spy(mtrx,precision=0.01, markersize=1)\r\n pl.show()","def take_matrix(self):\n matrix = aux.matrix(self.take_vec(), self.order)\n\n return matrix","def term_to_matrix_sparse(spin_operator):\n X = sparse.csr_matrix(np.array([[0, 1], [1, 0]]))\n Y = sparse.csr_matrix(np.array([[0, -1j], [1j, 0]]))\n Z = sparse.csr_matrix(np.diag([1, -1]))\n I = sparse.csr_matrix(np.diag([1, 1]))\n dic_Pauli = {\"I\": I, \"X\": X, \"Y\": Y, \"Z\": Z}\n matrix_final = 0\n nbqbits = spin_operator.nbqbits\n for term in spin_operator.terms:\n result_one_term = 0\n char_term = [char for char in term.op]\n qb_term = term.qbits\n dic_op = {}\n for n in range(nbqbits):\n dic_op[n] = I\n for n in range(len(term.qbits)):\n dic_op[qb_term[n]] = dic_Pauli[char_term[n]]\n matrix = 0\n for d in dic_op:\n if type(matrix) == int:\n matrix = dic_op[d]\n else:\n matrix = scipy.sparse.kron(matrix, dic_op[d])\n result_one_term = sparse.csr_matrix(matrix * term.coeff)\n matrix_final += result_one_term\n return matrix_final","def __getitem__(self, index):\n if not (type(index) in MATRIX_VALID_INTS):\n return NotImplemented\n return self._value[index]","def to_csr(self):\n return sparse.csr_matrix((self.data, (self.col, self.row)),\n shape=(self.nrows, self.ncols))","def scaling_matrix(sx, sy, sz):\n S = np.array([[sx, 0, 0, 0],\n [0, sy, 0, 0],\n [0, 0, sz, 0],\n [0, 0, 0, 1]])\n return S","def getSparseAdjacencyMatrix( graph, attribute=None, transposed=False ):\n if (attribute is not None) and (attribute not in graph.es.attribute_names()):\n raise ValueError( \"Attribute does not exists.\" )\n \n row = []\n col = []\n data = []\n \n if attribute is None:\n if transposed:\n for edge in graph.es():\n s,t = edge.tuple\n row.append(t)\n col.append(s)\n else:\n for edge in graph.es():\n s,t = edge.tuple\n row.append(s)\n col.append(t)\n data = np.ones(len(graph.es()))\n else:\n if transposed:\n for edge in graph.es():\n s,t = edge.tuple\n row.append(t)\n col.append(s)\n else:\n for edge in graph.es():\n s,t = edge.tuple\n row.append(s)\n col.append(t)\n data = np.array(graph.es()[attribute])\n\n return sparse.coo_matrix((data, (row, col)) , shape=(len(graph.vs), len(graph.vs))).tocsr()","def minor(self, i, j):\n if not self.isSquare():\n raise ValueError(\"Minor is not defined for non-square matrix\")\n if (self._height == 1 and self._width == 1):\n raise ValueError(\"Minor is not defined for 1x1 matrix\")\n m = Matrix(self)\n m.deleteRow(i)\n m.deleteColumn(j)\n return m.determinant()","def asMatrix(self):\n output = np.zeros((self.size[0],self.size[1]))\n for pos in self.matrixDict:\n output[pos[0]][pos[1]] = self.matrixDict[pos]\n return output","def get_sudoku_matrix(self):\n return self.sudoku_matrix","def Mat_dis_s2(x, sig=0.0001, alp = 0.5):\n spe = x[:, :-2]\n spa = x[:, -2:]\n dist_spetral = Mat_dis(spe)/spe.shape[1]\n dist_spatial = Mat_dis(spa)/spa.shape[1]\n # dist_spetral = np.exp(-sig*dist_spetral)\n # dist_spatial = np.exp(-sig*dist_spatial)\n #dist_mat = alp*dist_spetral + (1-alp)*dist_spatial\n\n\n return dist_spetral, dist_spatial","def get_sparse_operator(operator, n_qubits=None, trunc=None, hbar=1.):\n from openfermion.transforms.opconversions import get_fermion_operator\n if isinstance(operator, (DiagonalCoulombHamiltonian, PolynomialTensor)):\n return jordan_wigner_sparse(get_fermion_operator(operator))\n elif isinstance(operator, FermionOperator):\n return jordan_wigner_sparse(operator, n_qubits)\n elif isinstance(operator, QubitOperator):\n return qubit_operator_sparse(operator, n_qubits)\n elif isinstance(operator, (BosonOperator, QuadOperator)):\n return boson_operator_sparse(operator, trunc, hbar)\n else:\n raise TypeError('Failed to convert a {} to a sparse matrix.'.format(\n type(operator).__name__))","def similarity_matrix(feat_mat):\n sim_mat = cosine_similarity(feat_mat)\n np.fill_diagonal(sim_mat, 0)\n return sim_mat","def write_matfile(Sint_df, outputfilepath='Sint_no_cofactor_20160831.mat'):\n # convert dataframe to matrix\n Smat = Sint_df.as_matrix()\n\n # get all indices for non-zero elements in Smat (row, col)\n Smat_nzr, Smat_nzc = np.nonzero(Smat)\n\n # get all non-zero elements from Smat\n Smat_nze = Smat[Smat_nzr, Smat_nzc]\n\n # Adjust for matlab coordinate\n Smat_nzr = Smat_nzr + 1\n Smat_nzc = Smat_nzc + 1\n\n # This final line gives the size of the S matrix in matlab\n nr, nc = Smat.shape\n\n # Create a 2D array\n sparseMat = np.vstack((Smat_nzr, Smat_nzc, Smat_nze)).T\n sparseMat = np.vstack((sparseMat, np.array([[nr, nc, 0]])))\n\n # Create a numpy object array from dataframe index\n reactionList = Sint_df.columns.ravel()\n\n # Write only one matlab .mat file\n scipy.io.savemat(outputfilepath,\n mdict={'Sint_sparse': sparseMat,\n 'reactionList': np.array(reactionList)}\n )\n\n return sparseMat, reactionList","def normalize_adj( adj : np.ndarray, \n sparse : bool = False\n ) -> Union[np.ndarray, sp.spmatrix]:\n if sparse:\n adj = sp.coo_matrix(adj) # [N,N]\n rowsum = np.array(adj.sum(1)) # [N,]\n \n d_inv_sqrt = np.power(rowsum, -0.5) # [N,], may issue runtime warnings (div by zero)\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. # []\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) if sparse else np.diag(d_inv_sqrt) #[N,N]\n \n if sparse:\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n else:\n return ((adj @ d_mat_inv_sqrt).transpose() @ d_mat_inv_sqrt) # not quite sure why this order = D^T A^T D, D^T = D, A^T = A - the transpose is unncessary?!","def coregionalization_sparse(optimize=True, plot=True):\r\n #fetch the data from the non sparse examples\r\n m = coregionalization_toy2(optimize=False, plot=False)\r\n X, Y = m.X, m.likelihood.Y\r\n\r\n #construct a model\r\n m = GPy.models.SparseGPRegression(X,Y)\r\n m.constrain_fixed('iip_\\d+_1') # don't optimize the inducing input indexes\r\n\r\n if optimize:\r\n m.optimize('bfgs', max_iters=100, messages=1)\r\n\r\n if plot:\r\n m.plot(fixed_inputs=[(1,0)])\r\n m.plot(fixed_inputs=[(1,1)], ax=pb.gca())\r\n\r\n return m","def __getitem__(self, key):\n # TODO: fix 1-by-1 case, deprecate support for vector indexing.\n # TODO: add isvector() method\n # TODO: add method that distinguishes between 1x1, 1xn, mx1, mxn\n # TODO: make sure that there's distinct ways to index a 1 by 1 matrix\n # and retrieve a value.\n rows, cols, is_view = self._cleankey(key)\n if not (is_view): #simple index case\n i, j = rows[0], cols[0]\n return self.data[i, j]\n return MPView(self, rows, cols)","def test_canonicalization_of_vectors_w_symm(free_alg):\n\n dr = free_alg\n p = dr.names\n x = IndexedBase('x')\n r = p.R\n i, j = p.i, p.j\n\n vs = Vec('vs')\n dr.set_symm(vs, Perm([1, 0]), valence=2)\n tensor = dr.sum((i, r), (j, r), x[i, j] * vs[j, i])\n res = tensor.simplify()\n assert res.n_terms == 1\n term = res.local_terms[0]\n assert term.sums == ((i, r), (j, r))\n assert term.amp == x[i, j]\n assert term.vecs == (vs[i, j],)\n\n va = Vec('va')\n dr.set_symm(va, Perm([1, 0], NEG), valence=2)\n tensor = dr.sum((i, r), (j, r), x[i, j] * va[j, i])\n res = tensor.simplify()\n assert res.n_terms == 1\n term = res.local_terms[0]\n assert term.sums == ((i, r), (j, r))\n assert term.amp == -x[i, j]\n assert term.vecs == (va[i, j],)","def find_sparse_matrix(self,\n artifact_type: str,\n match_condition: Dict[str, Any],\n find_first=False,\n return_id: bool = False,\n return_documents_only=False\n ) -> List[Union[Tuple[sp.csr_matrix, Dict[str, Any]],\n Tuple[sp.csr_matrix, Dict[str, Any], int]]]:\n\n if \"ppr_idx\" in match_condition.keys() and not isinstance(match_condition[\"ppr_idx\"], int):\n match_condition[\"ppr_idx\"] = hash(frozenset(match_condition[\"ppr_idx\"]))\n\n raw_documents = Storage.locked_call(\n lambda: self._find_meta(artifact_type, match_condition),\n self._get_lock_path(artifact_type),\n self.lock_timeout,\n )\n # to get the most recent documents first we revert the list\n raw_documents.reverse()\n if return_documents_only:\n for document in raw_documents:\n if \"ppr_idx\" in document['params'].keys() and isinstance(document['params'][\"ppr_idx\"], int):\n path = self._build_artifact_path(artifact_type, document.doc_id).replace(\".pt\", \".npz\")\n ppr_path = path.replace(\".npz\", \"idx.npy\")\n document['params'][\"ppr_idx\"] = np.load(ppr_path)\n return raw_documents\n results = []\n for document in raw_documents:\n document_id = document.doc_id\n document = dict(document)\n path = self._build_artifact_path(artifact_type, document_id).replace(\".pt\", \".npz\")\n sparse_matrix = sp.load_npz(path)\n\n if \"ppr_idx\" in document['params'].keys() and isinstance(document['params'][\"ppr_idx\"], int):\n ppr_path = path.replace(\".npz\", \"idx.npy\")\n document['params'][\"ppr_idx\"] = np.load(ppr_path)\n\n if return_id:\n results.append((sparse_matrix, document['params'], document_id))\n else:\n results.append((sparse_matrix, document['params']))\n\n if find_first:\n return results\n return results","def clone(self):\n return DoubleSpMatrix(len(self)).copy_from_sp_(self)","def sparse_matrix (base_type=float):\n return defaultdict (lambda: sparse_vector (base_type))","def test_import_sparse_type_mat():\n x = sps.csr_matrix(np.random.rand(3, 2))\n export_data('/tmp/test.mat', x)\n assert x.dtype == import_data('/tmp/test.mat').dtype","def matrix(self) -> sympy.Matrix:\n raise NotImplementedError()","def create_preference_weighted_similarity_matrix(self):\n # We only need to compare the distances to points within the epsilon shell (to determine if a point is a core point)\n # Since the subspace scaling factor kappa is >>1 (and not <1), no distances to other points will be needed for \n # the core point evaluation\n\n # get points in epsilon shell: attententio point itself is not in neigh_ind list\n _, neigh_ind = self.neigbors_clf.radius_neighbors(radius=self.epsilon)\n row, col, pwsim = [], [], []\n for i, ith_neigh_ind in enumerate(neigh_ind):\n # Calculate preference weighted similarity measure with point and neighbors in eps shell\n sq_diffs = np.square(self.data[ith_neigh_ind,:] - self.data[i,:])\n sum_weighted_sq_diffs = np.inner(self.subspace_preference_vectors[i,:], sq_diffs)\n pwsim_ith = np.sqrt(sum_weighted_sq_diffs)\n \n # Info for sparse matrix\n pwsim.extend(pwsim_ith.tolist()) # Data\n row.extend([i]*(pwsim_ith.shape[0])) # ith Row \n col.extend(ith_neigh_ind.tolist()) # column info\n\n # Construct sparse matrix with data, row, and column info\n A = csr_matrix((pwsim, (row, col)), shape=(self.nb_points, self.nb_points))\n # Create symmetric version: take the elementwise maximum of A and its transpose A.T\n transpose_is_bigger = A.T>A\n A = A - A.multiply(transpose_is_bigger) + (A.T).multiply(transpose_is_bigger)\n \n return A","def retrievematrixelement(self, coord):\n currentelement = self.matrix\n for u in coord:\n currentelement = currentelement[u]\n\n return currentelement","def symplectify(M):\n nv = M.shape[0]\n S = jmat(nv // 2)\n I = numpy.identity(nv)\n\n V = S @ (I - M) @ inv(I + M)\n # V should be almost symmetric. Replace with symmetrized version.\n W = (V + V.T) / 2\n # Now reconstruct M from W\n SW = S @ W\n MS = (I + SW) @ inv(I - SW)\n return MS","def sparse_arrays(self):\n return self._sparse_arrays","def array_to_matrix(y, ix, s, nodata=None):\n if nodata is None:\n x = np.ones(s)*np.NaN\n else:\n x = np.ones(s)*nodata\n x[ix] = y\n\n return x","def getskx(self,whichsol_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.getskx(whichsol_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_skx:\n for __tmp_var_0 in range(len(skx_)): skx[__tmp_var_0] = stakey(_tmparr_skx[__tmp_var_0])","def get_mat(self,diag_entries=False):\n if self._mat is None or (diag_entries and not self._diag_entries):\n self.build_mat(diag_entries=diag_entries)\n return self._mat","def test_csr_matvec():\n\n from scipy.sparse import random\n\n m = 1000\n n = 500\n\n rng = numpy.random.default_rng(0)\n scipy_mat = random(m, n, format=\"csr\", random_state=rng)\n csr_mat = spmv.CsrMatrix(\n scipy_mat.data, scipy_mat.indices, scipy_mat.indptr, (m, n)\n )\n\n vec = rng.normal(size=n)\n\n expected = scipy_mat @ vec\n actual = csr_mat @ vec\n\n numpy.testing.assert_allclose(actual, expected)","def to_mat(self) -> np.matrix:\n raise NotImplementedError","def matrix(self) -> sympy.Matrix:\n return self.matrix_factory(*self.params)","def matrix_intf(self):\n return intf.DEFAULT_SPARSE_INTF","def build_mat(self):\n for row, s in enumerate(self.S):\n for col, t in enumerate(self.T):\n\n if self.symmetric and row > col:\n pass\n\n else:\n self.mat[row, col] = self.kernel(s, t, self.n)\n\n if self.symmetric:\n self.mat = self.symmetrize(self.mat)\n else:\n for idx, s in enumerate(self.S):\n self.test_normalization[idx] = self.kernel(s, s, self.n)","def sparse_real_matrix(A, axis=0):\n m, n = A.shape\n\n @sparsify\n def times1d(x):\n return A @ x\n\n @sparsify\n def trans1d(x):\n return x @ A\n \n\n @sparsify\n def times(x):\n \"\"\"Forward matrix multiplication\n \"\"\"\n if x.ndim == 1:\n return A @ x\n if x.ndim == 2:\n if axis == 0:\n return A @ x\n else:\n return x @ A.T\n # general case\n return jnp.apply_along_axis(times1d, axis, x)\n\n @sparsify\n def trans(x):\n \"\"\"Adjoint matrix multiplication\n \"\"\"\n if x.ndim == 1:\n return trans1d(x)\n if x.ndim == 2:\n if axis == 0:\n return A.T @ x\n else:\n return x @ A\n # general case\n return jnp.apply_along_axis(trans1d, axis, x)\n return Operator(times=times, trans=trans, shape=(m,n))","def get_s_matrix(data, word_form, semvecs, n_events, n_vec_dims):\n\n s = np.zeros((n_events, n_vec_dims))\n ii = 0\n for index, row in data.iterrows():\n s[ii, ] = semvecs.loc[row[word_form], :]\n ii += 1\n return s","def matrix(self, backend=None):\n if backend is None: # pragma: no cover\n backend = GlobalBackend()\n\n return backend.matrix_fused(self)","def analyze_sensitivity_sparse_grid(sparse_grid,max_order=2):\n from pyapprox.multivariate_polynomials import \\\n define_poly_options_from_variable_transformation\n from pyapprox.adaptive_sparse_grid import \\\n convert_sparse_grid_to_polynomial_chaos_expansion\n pce_opts=define_poly_options_from_variable_transformation(\n sparse_grid.variable_transformation)\n pce = convert_sparse_grid_to_polynomial_chaos_expansion(\n sparse_grid,pce_opts)\n pce_main_effects,pce_total_effects=\\\n get_main_and_total_effect_indices_from_pce(\n pce.get_coefficients(),pce.get_indices())\n\n interaction_terms, pce_sobol_indices = get_sobol_indices(\n pce.get_coefficients(),pce.get_indices(),max_order=max_order)\n \n return SensivitityResult(\n {'main_effects':pce_main_effects,\n 'total_effects':pce_total_effects,\n 'sobol_indices':pce_sobol_indices,\n 'sobol_interaction_indices':interaction_terms,\n 'pce':pce})","def get_symmetry_mates(self, model=None, altloc=\"first\",\n extra_fields=[], include_bonds=False):\n # Get base structure\n structure = self.get_structure(\n model,\n altloc,\n extra_fields,\n include_bonds,\n )\n # Get lines containing transformations for crystallographic symmetry\n remark_lines = self.get_remark(290)\n if remark_lines is None:\n raise InvalidFileError(\n \"File does not contain crystallographic symmetry \"\n \"information (REMARK 350)\"\n )\n transform_lines = [\n line for line in remark_lines if line.startswith(\" SMTRY\")\n ]\n rotations, translations = _parse_transformations(\n transform_lines\n )\n return _apply_transformations(\n structure, rotations, translations\n )","def matrix_add_symmetric(M, M_sym):\n M[0, 0] += M_sym[0]\n M[0, 1] += M_sym[1]\n M[1, 0] += M_sym[1]\n M[0, 2] += M_sym[2]\n M[2, 0] += M_sym[2]\n\n M[1, 1] += M_sym[3]\n M[1, 2] += M_sym[4]\n M[2, 1] += M_sym[4]\n\n M[2, 2] += M_sym[5]\n\n return M","def csr_matrix(*args, **kws):\n mat = ss.csr_matrix(*args, **kws)\n return CudaCSRMatrix().from_host_matrix(mat)","def makesparse(matrix):\n n = matrix[0].size\n elements = []\n for i in range(n):\n for j in range(n):\n if matrix[i][j] != 0 :\n temp = MatrixElement(i, j, matrix[i][j])\n elements.append(temp)\n return SparseMatrix(n, elements)","def csr(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"csr\")","def index_condensed_matrix(n, i, j):\n if i == j:\n main_warning(\"Diagonal elements (i=j) are not stored in condensed matrices.\")\n return None\n elif i > j:\n i, j = j, i\n return int(i * (n - (i + 3) * 0.5) + j - 1)","def calc_sparse(self, calc_aw=False, vector_calc=True):\n # Local copies of instance attributes for faster access\n b = self.b\n qs = self.q_sim\n qo = self.q_obs\n maxlead = self.max_lead\n maxlag = self.max_lag\n measure = self.measure\n time = self.time\n\n # Prepare sparse matrices with only cells around diagonal defined.\n # padds 1 extra cell on each side to avoid false zeros later\n n = len(self.q_obs)\n w = self.max_lag + self.max_lead + 3\n data = np.array([np.full(n, np.inf)]).repeat(w, axis=0)\n offsets = np.arange(-self.max_lead-1, self.max_lag + 1.5)\n\n # Calculate work between obs and sim pairs, not needed anymore.\n if calc_aw:\n aw = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\n st = pd.Timestamp.now()\n # print('Start aw loops')\n # iterate through all flow observations\n for i_o in range(len(qo)):\n # Check only simulations within the allowed window\n for i_s in range(max(0, i_o - self.max_lead),\n min(len(qs), i_o + maxlag + 1)):\n if measure in ('nse', 'square'):\n aw[i_s, i_o] = (qs[i_s] - qo[i_o]) ** 2 + b ** 2 * (i_s - i_o) ** 2\n elif measure in ('mae', 'abs'):\n aw[i_s, i_o] = np.abs(qs[i_s] - qo[i_o]) + b * np.abs(i_s - i_o)\n # print('End aw loops', (pd.Timestamp.now()-st)/pd.Timedelta('1s'))\n self.aw = aw\n\n # Old debugging code: run original implementation alongside\n # Applies to all variables appended with _orig\n # aw_orig = np.full(shape=(len(self.q_obs), len(self.q_sim)), fill_value=np.nan)\n # # iterate through all flow observations\n # for i_o in range(len(self.q_obs)):\n # # Check only simulations within the allowed window\n # for i_s in range(max(0, i_o - self.max_lead),\n # min(len(self.q_sim), i_o + self.max_lag + 1)):\n # aw_orig[i_s, i_o] = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + self.b ** 2 * (i_s - i_o) ** 2\n\n # Calculate cumulative work along possible paths\n cw0 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\n cw1 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\n\n # Old debugging code: run original implementation (with dense arrays) alongside\n # Applies to all variables marked _orig\n # # Calculate cumulative work along possible paths\n # cw_orig = np.ones(shape=aw_orig.shape + (2,)) * np.nan\n # # Populate first column\n # cw_orig[:, 0, 0] = aw_orig[:, 0]\n\n if self.keep_internals:\n # self.aw_orig = aw_orig\n self.cw0 = cw0\n self.cw1 = cw1\n # self.cw_orig = cw_orig\n\n # Populate other columns\n for i_o in range(n):\n iss = max(0, i_o - maxlead) # sim index start\n ise = min(n, i_o + maxlag + 1) # sim index end\n isv = np.arange(iss, ise) # sim index as vector\n if time is not None:\n dt = time[isv] - time[i_o]\n # Heavily penalize sim-obs combinations outside allowed window\n # so that they are not selected\n dt[(dt < -maxlag) | (dt > maxlead)] = np.inf\n else:\n dt = isv - i_o\n if vector_calc:\n # Vectorized version of the calculation.\n # For understanding it is best to take a look at the else clause below\n # Calculate the work for each (sim, obs_i) pair\n if measure in ('nse', 'square'):\n e = (qs[iss:ise] - qo[i_o]) ** 2 + b ** 2 * dt ** 2\n elif measure in ('mae', 'abs'):\n e = np.abs(qs[iss:ise] - qo[i_o]) + b * np.abs(dt)\n if i_o == 0:\n # Only populate first column of cw0 and move to i_o = 1\n cw0[iss:ise, 0] = e.reshape(len(e), 1)\n continue\n # Repeat the same simulation point\n d = cw0[iss:ise, i_o - 1].toarray()\n d[d == 0] = np.nan\n cw1[iss:ise, i_o] = e.reshape(len(e), 1) + d\n\n # Find the 'cheapest' available preceding sim point\n points = np.full((len(isv), 4), np.inf)\n # Use the previous simulation point\n st = max(0, iss - 1)\n end = min(n + 1, ise - 1)\n l = end - st\n points[-l:, 0] = cw0[st:end, i_o - 1].toarray().squeeze()\n points[-l:, 1] = cw1[st:end, i_o - 1].toarray().squeeze()\n # Skip a simulation point\n st = max(0, iss - 2)\n end = min(n + 1, ise - 2)\n l = end - st\n points[-l:, 2] = cw0[st:end, i_o - 1].toarray().squeeze()\n points[-l:, 3] = cw1[st:end, i_o - 1].toarray().squeeze()\n # points[points == 0] = np.nan\n cw0[iss:ise, i_o] = (e + np.min(points, axis=1)).reshape(len(e), 1)\n else:\n # Old, non-vectorized code. Slower.\n # Left in place since it is easier to understand than vectorized code above\n def zero_to_nan(x):\n if x == 0:\n return np.nan\n else:\n return x\n\n for i_s in range(max(0, i_o - self.max_lead),\n min(n, i_o + self.max_lag + 1)):\n # print(i_o, i_s, sep='\\t')\n # e = aw[i_s, i_o]\n if time:\n dt = time[isv] - time[i_o]\n # Heavily penalize sim-obs combinations outside allowed window\n # so that they are not selected\n dt[dt > maxlead] = 999999999\n dt[dt < -maxlag] = 999999999\n else:\n dt = isv - i_o\n if measure in ('nse', 'square'):\n e = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + b ** 2 * dt ** 2\n elif measure in ('mae', 'abs'):\n e = np.abs(self.q_sim[i_s] - self.q_obs[i_o]) + np.abs(b * dt)\n # e_orig = aw_orig[i_s, i_o]\n # if e != e_orig:\n # print('ediff', e, e_orig, sep='\\t')\n\n # Repeat the same simulation point\n cw1[i_s, i_o] = e + zero_to_nan(cw0[i_s, i_o - 1])\n # cw_orig[i_s, i_o, 1] = e + cw_orig[i_s, i_o - 1, 0]\n # if cw1[i_s, i_o] != cw_orig[i_s, i_o, 1]:\n # print('cw1diff', cw1[i_s, i_o], cw_orig[i_s, i_o, 1], sep='\\t')\n\n if i_s == 0:\n continue\n # Else:\n # Find the 'cheapest' available preceding point\n # Use the previous simulation point\n points = [zero_to_nan(cw0[i_s - 1, i_o - 1]),\n zero_to_nan(cw1[i_s - 1, i_o - 1])]\n # m1 = np.nanmin(cw_orig[i_s - 1, i_o - 1, :])\n # m2 = np.inf\n if i_s > 1:\n # Skip a simulation point\n points += [cw0[i_s - 2, i_o - 1], cw1[i_s - 2, i_o - 1]]\n # m2 = np.nanmin(cw_orig[i_s - 2, i_o - 1, :])\n # m = min(m1, m2)\n try:\n cp = min([p for p in points if p > 0])\n except ValueError:\n cp = np.nan\n # if cp != m:\n # print('cpdiff', cp, m, sep='\\t')\n # print('\\torig', cw_orig[i_s - 1, i_o - 1, :], cw_orig[i_s - 2, i_o - 1, :])\n # print('\\tspar', points)\n cw0[i_s, i_o] = e + cp\n # cw_orig[i_s, i_o, 0] = e + m\n # print('')\n # Find the cheapest point in the last column, i.e. end of cheapest path, i.e. optimum score\n # nz0 = cw0[cw0[:, -1].nonzero()[0], -1].toarray().min()\n # nz1 = cw1[cw1[:, -1].nonzero()[0], -1].toarray().min()\n nz0 = cw0[-self.max_lag:, -1].toarray()\n nz1 = cw1[-self.max_lag:, -1].toarray()\n self.opt_score = min(nz0[nz0 != 0].min(), nz1[nz1 != 0].min())\n\n self.of = 1 - self.opt_score / self.fbench\n\n if self.calc_rays:\n self._do_calc_rays(cw0, cw1)\n\n return self.of","def sym(self) -> np.ndarray:\n if self._sym is None:\n self._sym = symmetrize_discrete_vector_field(self.F, mode=\"sym\")\n return self._sym","def read_sec_matrix(self, matrix_id, dataset_id, table_name):\n tbl = self.Table(matrix_id, dataset_id, table_name, config_related=True)\n return tbl.to_dataframe()","def find_symmetry(self):\n from spglib import get_spacegroup\n cell = ( self.lattice, self.fractional_coordinates, self.atomic_nos )\n self.spacegroup = get_spacegroup(cell, symmprec=1e-5)\n print(\"Symmetry space group is\", self.spacegroup)"],"string":"[\n \"def getsparsesymmat(self,idx_,subi,subj,valij): # 3\\n maxlen_ = self.getsymmatinfo((idx_))[1]\\n _copyback_subi = False\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n _copyback_subi = True\\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n _copyback_subi = True\\n if subi_ is not None and len(subi_) != (maxlen_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxlen_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n _copyback_valij = False\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n _copyback_valij = True\\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n _copyback_valij = True\\n if valij_ is not None and len(valij_) != (maxlen_):\\n raise ValueError(\\\"Array argument valij has wrong length\\\")\\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_valij:\\n valij[:] = _tmparr_valij\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n if _copyback_subi:\\n subi[:] = _tmparr_subi\",\n \"def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi_)\\n elif nz_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj_)\\n elif nz_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij_)\\n elif nz_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n idx_ = ctypes.c_int64()\\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n idx_ = idx_.value\\n _idx_return_value = idx_\\n return (_idx_return_value)\",\n \"def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi)\\n elif nz_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj)\\n elif nz_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij)\\n elif nz_ != len(valij):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if nz_ is None: nz_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valij is None: raise TypeError(\\\"Invalid type for argument valij\\\")\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n \\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n \\n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _idx_return_value = resargs\\n return _idx_return_value\",\n \"def getsymmatinfo(self,idx_):\\n dim_ = ctypes.c_int32()\\n nz_ = ctypes.c_int64()\\n type_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getsymmatinfo(self.__nativep,idx_,ctypes.byref(dim_),ctypes.byref(nz_),ctypes.byref(type_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n dim_ = dim_.value\\n _dim_return_value = dim_\\n nz_ = nz_.value\\n _nz_return_value = nz_\\n _type_return_value = symmattype(type_.value)\\n return (_dim_return_value,_nz_return_value,_type_return_value)\",\n \"def getsymmatinfo(self,idx_): # 3\\n res,resargs = self.__obj.getsymmatinfo(idx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _dim_return_value,_nz_return_value,_type_return_value = resargs\\n _type_return_value = symmattype(_type_return_value)\\n return _dim_return_value,_nz_return_value,_type_return_value\",\n \"def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\\\"<i4\\\")\\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\\\"<f8\\\")\\n size = j_obj.getSize()\\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()\",\n \"def sparse_matlab(i, j, v, m, n):\\n return csr_matrix((v, (i, j)), shape=(m, n))\",\n \"def SymmetriseMatrix(adjmatrix):\\n\\n if galib.metrics.Reciprocity(adjmatrix) == 1:\\n # if Reciprocity(adjmatrix) == 1:\\n return adjmatrix\\n else:\\n return 0.5 * (adjmatrix + adjmatrix.T)\",\n \"def get_stain_matrix(I):\",\n \"def sparse_matrix(data, stype=\\\"csr\\\", dtype=complex):\\n return _SPARSE_CONSTRUCTORS[stype](data, dtype=dtype)\",\n \"def jmat(ind: int):\\n return _jm[ind - 1]\",\n \"def to_sparse(self, method='csr_matrix'):\\r\\n data = self.data.values\\r\\n if method == 'csr_matrix':\\r\\n data_sp = sps.csr_matrix(data)\\r\\n elif method == 'bsr_matrix':\\r\\n data_sp = sps.bsr_matrix(data)\\r\\n elif method == 'coo_matrix':\\r\\n data_sp = sps.coo_matrix(data)\\r\\n elif method == 'csc_matrix':\\r\\n data_sp = sps.csc_matrix(data)\\r\\n elif method == 'dia_matrix':\\r\\n data_sp = sps.dia_matrix(data)\\r\\n elif method == 'dok_matrix':\\r\\n data_sp = sps.dok_matrix(data)\\r\\n elif method == 'lil_matrix':\\r\\n data_sp = sps.lil_matrix(data)\\r\\n else:\\r\\n raise ValueError('The method does not exist in scipy.sparse')\\r\\n return data_sp\",\n \"def get_solver_mats(self, x_nd, rot_coef):\\n raise NotImplementedError\",\n \"def get_sparse(self, key, element):\\n return self.get(self._sparse2seq(key), element)\",\n \"def scipy_sparse_to_spmatrix(A):\\n coo = A.tocoo()\\n SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)\\n return SP\",\n \"def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):\\n num_ = None\\n if num_ is None:\\n num_ = len(dims_)\\n elif num_ != len(dims_):\\n raise IndexError(\\\"Inconsistent length of array dims\\\")\\n if num_ is None:\\n num_ = len(nz_)\\n elif num_ != len(nz_):\\n raise IndexError(\\\"Inconsistent length of array nz\\\")\\n if dims_ is None:\\n raise ValueError(\\\"Argument dims cannot be None\\\")\\n if dims_ is None:\\n raise ValueError(\\\"Argument dims may not be None\\\")\\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\\n _dims_copyarray = False\\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif dims_ is not None:\\n _dims_copyarray = True\\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\\n _dims_np_tmp[:] = dims_\\n assert _dims_np_tmp.flags.contiguous\\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _dims_copyarray = False\\n _dims_tmp = None\\n \\n if nz_ is None:\\n raise ValueError(\\\"Argument nz cannot be None\\\")\\n if nz_ is None:\\n raise ValueError(\\\"Argument nz may not be None\\\")\\n if isinstance(nz_, numpy.ndarray) and nz_.dtype is numpy.dtype(numpy.int64) and nz_.flags.contiguous:\\n _nz_copyarray = False\\n _nz_tmp = ctypes.cast(nz_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif nz_ is not None:\\n _nz_copyarray = True\\n _nz_np_tmp = numpy.zeros(len(nz_),numpy.dtype(numpy.int64))\\n _nz_np_tmp[:] = nz_\\n assert _nz_np_tmp.flags.contiguous\\n _nz_tmp = ctypes.cast(_nz_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _nz_copyarray = False\\n _nz_tmp = None\\n \\n _subi_minlength = sum((nz_))\\n if sum((nz_)) > 0 and subi_ is not None and len(subi_) != sum((nz_)):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),sum((nz_))))\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = sum((nz_))\\n if sum((nz_)) > 0 and subj_ is not None and len(subj_) != sum((nz_)):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),sum((nz_))))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _valij_minlength = sum((nz_))\\n if sum((nz_)) > 0 and valij_ is not None and len(valij_) != sum((nz_)):\\n raise ValueError(\\\"Array argument valij is not long enough: Is %d, expected %d\\\" % (len(valij_),sum((nz_))))\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n _idx_minlength = (num_)\\n if (num_) > 0 and idx_ is not None and len(idx_) != (num_):\\n raise ValueError(\\\"Array argument idx is not long enough: Is %d, expected %d\\\" % (len(idx_),(num_)))\\n if isinstance(idx_,numpy.ndarray) and not idx_.flags.writeable:\\n raise ValueError(\\\"Argument idx must be writable\\\")\\n if idx_ is None:\\n raise ValueError(\\\"Argument idx may not be None\\\")\\n if isinstance(idx_, numpy.ndarray) and idx_.dtype is numpy.dtype(numpy.int64) and idx_.flags.contiguous:\\n _idx_copyarray = False\\n _idx_tmp = ctypes.cast(idx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif idx_ is not None:\\n _idx_copyarray = True\\n _idx_np_tmp = numpy.zeros(len(idx_),numpy.dtype(numpy.int64))\\n _idx_np_tmp[:] = idx_\\n assert _idx_np_tmp.flags.contiguous\\n _idx_tmp = ctypes.cast(_idx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _idx_copyarray = False\\n _idx_tmp = None\\n \\n res = __library__.MSK_XX_appendsparsesymmatlist(self.__nativep,num_,_dims_tmp,_nz_tmp,_subi_tmp,_subj_tmp,_valij_tmp,_idx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _idx_copyarray:\\n idx_[:] = _idx_np_tmp\",\n \"def to_s_matrix(w,v):\\n pass\",\n \"def save_sparse_matrix(self,\\n artifact_type: str,\\n params: Dict[str, Any],\\n sparse_matrix: sp.csr_matrix,\\n ignore_duplicate: bool = False) -> str:\\n ppr_idx = None\\n if \\\"ppr_idx\\\" in params.keys() and not isinstance(params[\\\"ppr_idx\\\"], int):\\n ppr_idx = np.array(params[\\\"ppr_idx\\\"])\\n params[\\\"ppr_idx\\\"] = hash(frozenset(params[\\\"ppr_idx\\\"]))\\n\\n if ignore_duplicate:\\n # check there's no entry with the exact same config already present\\n ids = Storage.locked_call(\\n lambda: self._find_meta_by_exact_params(artifact_type, params),\\n self._get_lock_path(artifact_type),\\n self.lock_timeout\\n )\\n if len(ids) > 0:\\n logging.info(f\\\"Ignoring duplicate save in save_sparse_matrix call\\\")\\n return self._build_artifact_path(artifact_type, ids[0].doc_id).replace(\\\".pt\\\", \\\".npz\\\")\\n\\n ids = Storage.locked_call(\\n lambda: self._upsert_meta(artifact_type, params),\\n self._get_lock_path(artifact_type),\\n self.lock_timeout,\\n )\\n if len(ids) != 1:\\n raise RuntimeError(f'The index contains duplicates (artifact_type={artifact_type}, params={params})')\\n\\n try:\\n path = self._build_artifact_path(artifact_type, ids[0]).replace(\\\".pt\\\", \\\".npz\\\")\\n sp.save_npz(path, sparse_matrix)\\n logging.info(f\\\"Saved sparse matrix to storage\\\")\\n if ppr_idx is not None:\\n ppr_path = path.replace(\\\".npz\\\", \\\"idx.npy\\\")\\n np.save(ppr_path, ppr_idx)\\n logging.info(f\\\"Saved ppr index to storage\\\")\\n return path\\n except: # noqa: E722\\n Storage.locked_call(\\n lambda: self._remove_meta(artifact_type, params),\\n self._get_lock_path(artifact_type),\\n self.lock_timeout\\n )\\n raise\",\n \"def sparse_matrix(shape, integer=False):\\n dtype = numpy.int_ if integer else numpy.float_\\n return scipy.sparse.lil_matrix(shape, dtype=dtype)\",\n \"def generate_direct_solver(self, grid=None):\\n if grid is None:\\n # LOG.debug(\\\"Generate Solver for internal Spare Matrix: %s\\\" % self.sp_matrix)\\n solver = spla.factorized(self.sp_matrix)\\n else:\\n # LOG.debug(\\\"Generate Solver for given Grid %s\\\" % (grid,))\\n sp_matrix = self.to_sparse_matrix(grid, \\\"csc\\\")\\n # LOG.debug(\\\" with Sparse Matrix: %s\\\" % sp_matrix.todense())\\n # print(\\\"Jahier\\\\n\\\", sp_matrix.todense())\\n # print(\\\"Jahier.shape\\\\n\\\", sp_matrix.todense().shape)\\n solver = spla.factorized(sp_matrix)\\n return solver\",\n \"def wrapDBMatrix(self,mat):\\n return mat.todense()\",\n \"def sparsify(W,conn):\\n \\n N = W.shape[0]\\n W_sparse = sparse.lil_matrix((N,N)) \\n for row, weights in itertools.izip(conn, W):\\n W_sparse[row[0],row[1:]] = weights[1:]\\n return W_sparse\",\n \"def get_sym_matrix(self):\\n temp_T = Matrix.eye(3)\\n for i in range(len(self.lengths)):\\n angle_mat = self.T_a.subs(self.q,self.angles[i]).evalf()\\n len_mat = self.T_x.subs(self.l,self.lengths[i]).evalf()\\n temp_T = temp_T * angle_mat * len_mat\\n \\n return temp_T\",\n \"def dict2sparseMatrix(wDict,std=0,diag=0):\\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\\n nAreas = len(list(wDict.keys()))\\n for i in wDict:\\n data[i,i] = diag\\n ne = len(wDict[i])+ diag\\n for j in wDict[i]:\\n if std:\\n data[i,j] = 1 / float(ne)\\n else:\\n data[i,j] = 1\\n return data\",\n \"def get_cvxopt_sparse_intf():\\n import cvxpy.interface.cvxopt_interface.sparse_matrix_interface as smi\\n return smi.SparseMatrixInterface()\",\n \"def to_sparse(self):\\n if self.rep.fmt == 'sparse':\\n return self\\n\\n return self.from_rep(self.rep.to_sdm())\",\n \"def to_sparse(self):\\n from divisi2.sparse import SparseMatrix\\n return SparseMatrix(self, self.row_labels, self.col_labels)\",\n \"def getSparse(self): # as opposed to makeSparse which keeps the same form and return nothing\\n return copy.deepcopy(self.makeSparse())\",\n \"def make_sparse(self, fmt='csc', make_method=None):\\n if make_method:\\n self.sparse = make_method(self.hamiltonian)\\n else:\\n self.sparse = self.hamiltonian.to_matrix(sparse=fmt)\",\n \"def return_adjacencyMatrix(self):\\n return self.__mat\",\n \"def sym_adj(adj):\\n adj = ss.coo_matrix(adj)\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = ss.diags(d_inv_sqrt)\\n return np.array(adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense())\",\n \"def eval_sparse(self, array_in, array_out, sp_matrix=None):\\n if sp_matrix is None:\\n sp_matrix = self.to_sparse_matrix(array_in.shape, \\\"csc\\\")\\n # print(\\\"usually:\\\", sp_matrix.todense())\\n array_out[:] = sp_matrix.dot(array_in.reshape(-1)).reshape(array_out.shape)\",\n \"def __getitem__(self, index):\\n try:\\n i, j = index\\n except (AssertionError, TypeError):\\n raise IndexError('invalid index')\\n\\n if not np.isscalar(i) and np.isscalar(j):\\n warn('Indexing into a lil_matrix with multiple indices is slow. '\\n 'Pre-converting to CSC or CSR beforehand is more efficient.',\\n SparseEfficiencyWarning)\\n\\n if np.isscalar(i):\\n if np.isscalar(j):\\n return self._get1(i, j)\\n if isinstance(j, slice):\\n j = self._slicetoseq(j, self.shape[1])\\n if issequence(j):\\n return self.__class__([[self._get1(i, jj) for jj in j]])\\n elif issequence(i) and issequence(j):\\n return self.__class__([[self._get1(ii, jj) for (ii, jj) in zip(i, j)]])\\n elif issequence(i) or isinstance(i, slice):\\n if isinstance(i, slice):\\n i = self._slicetoseq(i, self.shape[0])\\n if np.isscalar(j):\\n return self.__class__([[self._get1(ii, j)] for ii in i])\\n if isinstance(j, slice):\\n j = self._slicetoseq(j, self.shape[1])\\n if issequence(j):\\n return self.__class__([[self._get1(ii, jj) for jj in j] for ii in i])\\n else:\\n raise IndexError\",\n \"def sym_adj(adj):\\n adj = sp.coo_matrix(adj)\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense()\",\n \"def getitem(self, i, j):\\n # XXX: flint matrices do not support negative indices\\n # XXX: They also raise ValueError instead of IndexError\\n m, n = self.shape\\n if i < 0:\\n i += m\\n if j < 0:\\n j += n\\n try:\\n return self.rep[i, j]\\n except ValueError:\\n raise IndexError(f\\\"Invalid indices ({i}, {j}) for Matrix of shape {self.shape}\\\")\",\n \"def _symmetric_matrix_square_root(mat, eps=1e-10):\\n # Unlike numpy, tensorflow's return order is (s, u, v)\\n s, u, v = linalg_ops.svd(mat)\\n # sqrt is unstable around 0, just use 0 in such case\\n si = array_ops.where(math_ops.less(s, eps), s, math_ops.sqrt(s))\\n # Note that the v returned by Tensorflow is v = V\\n # (when referencing the equation A = U S V^T)\\n # This is unlike Numpy which returns v = V^T\\n return math_ops.matmul(\\n math_ops.matmul(u, array_ops.diag(si)), v, transpose_b=True)\",\n \"def test_import_sparse_values_mat():\\n x = sps.csr_matrix(np.random.rand(3, 2))\\n export_data('/tmp/test.mat', x)\\n assert np.array_equal(x.toarray(), import_data('/tmp/test.mat').toarray())\",\n \"def ZSparseMatrix2Scipy(matrix):\\n data = np.ndarray(matrix.get_value_size(), dtype=float);\\n outer_idx = np.ndarray(matrix.get_outer_size(), dtype=np.int32);\\n inner_idx = np.ndarray(matrix.get_inner_size(), dtype=np.int32);\\n\\n matrix.get_values(data);\\n matrix.get_outer_indices(outer_idx);\\n matrix.get_inner_indices(inner_idx);\\n\\n return scipy.sparse.csc_matrix((data, inner_idx, outer_idx),\\n shape = (matrix.num_rows(), matrix.num_cols()),\\n dtype = float);\",\n \"def _sparse_projection(self,mat,sparsity=1,random_seed=10):\\n [n,_] = mat.shape\\n sketch = np.zeros((self.rp_dim ,n),dtype=float)\\n for i in range(n):\\n nnz_loc = np.random.choice(self.rp_dim ,size=sparsity,replace=False)\\n nnz_sign = np.random.choice([-1,1],size=sparsity,replace=True)\\n sketch[nnz_loc,i] = nnz_sign\\n self.sketch_mat = sketch\\n return (1./np.sqrt(sparsity))*sketch@mat\",\n \"def set_sparsity(self,use_sparse):\\n \\n if hasattr(self.problem,'sparse_jac'):\\n self.use_sparse = use_sparse\\n else:\\n raise KINSOL_Exception(\\\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\\\")\",\n \"def get_sol_mat(self, clust):\\r\\n\\r\\n return self.__model_data[clust]\",\n \"def _identity_sparse(d, stype=\\\"csr\\\", dtype=complex):\\n return sp.eye(d, dtype=dtype, format=stype)\",\n \"def _build_sparse(self, name, wrt, consize, param_vals, sub_param_conns,\\n full_param_conns, rels):\\n\\n jac = None\\n\\n # Additional sparsity for index connections\\n for param in wrt:\\n\\n sub_conns = sub_param_conns.get(param)\\n if not sub_conns:\\n continue\\n\\n # If we have a simultaneous full connection, then we move on\\n full_conns = full_param_conns.get(param)\\n if full_conns.intersection(rels):\\n continue\\n\\n rel_idx = set()\\n for target, idx in iteritems(sub_conns):\\n\\n # If a target of the indexed desvar connection is\\n # in the relevant path for this constraint, then\\n # those indices are relevant.\\n if target in rels:\\n rel_idx.update(idx)\\n\\n nrel = len(rel_idx)\\n if nrel > 0:\\n\\n if jac is None:\\n jac = {}\\n\\n if param not in jac:\\n # A coo matrix for the Jacobian\\n # mat = {'coo':[row, col, data],\\n # 'shape':[nrow, ncols]}\\n coo = {}\\n coo['shape'] = [consize, len(param_vals[param])]\\n jac[param] = coo\\n\\n row = []\\n col = []\\n for i in range(consize):\\n row.extend([i]*nrel)\\n col.extend(rel_idx)\\n data = np.ones((len(row), ))\\n\\n jac[param]['coo'] = [np.array(row), np.array(col), data]\\n\\n if name not in self.sub_sparsity:\\n self.sub_sparsity[name] = {}\\n self.sub_sparsity[name][param] = np.array(list(rel_idx))\\n\\n return jac\",\n \"def compute_matrix(self):\\n\\n fac = self.a / self.dx ** 2\\n\\n diagonal = np.ones(self.nx) * 2 * fac\\n lower = np.ones(self.nx - 1) * -fac\\n upper = np.ones(self.nx - 1) * -fac\\n\\n matrix = sp.diags(\\n diagonals=[diagonal, lower, upper],\\n offsets=[0, -1, 1], shape=(self.nx, self.nx),\\n format='csr')\\n\\n return matrix\",\n \"def build_lhs_matrix(self):\\n j=3\\n diags1 = self.build_linear_diags()\\n diags1 += self.build_dispersion_diags()\\n\\n # Ones down primary diagonal\\n diags2 = np.zeros_like(diags1)\\n diags2[j,:] = 1.\\n\\n cff = self.dt*(1+self.c_im)*0.5 \\n diags = diags2 - cff*diags1\\n \\n # Build the sparse matrix\\n cols = [ii for ii in range(-self._j, self._j+1)]\\n M = sparse.spdiags(diags, cols, self.N, self.N)\\n\\n return M, diags\",\n \"def symmeterize(self):\\n A = self.to_coo_matrix()\\n symg = wgraph_from_adjacency((A + A.T) / 2)\\n self.E = symg.E\\n self.edges = symg.edges\\n self.weights = symg.weights\\n return self\",\n \"def smat(v):\\n \\n k = len(v)\\n n = TriLengthToSquare(k)\\n \\n A = np.zeros([n,n])\\n A[np.triu_indices(n)] = v\\n A[np.triu_indices(n,1)] *= 2 / np.sqrt(2)\\n return (A + A.T) / 2\",\n \"def drawSparseMatrix(ax, mat, **kwargs):\\n row = kwargs.pop('rowOffset', 0)\\n col = kwargs.pop('colOffset', 0)\\n color = kwargs.pop('color', None)\\n\\n mat = pg.utils.sparseMatrix2coo(mat)\\n mat.row += row\\n mat.col += col\\n gci = ax.spy(mat, color=color, **kwargs)\\n\\n ax.autoscale(enable=True, axis='both', tight=True)\\n return gci\",\n \"def SpMV_viaMKL( A, x ):\\n SpMV = mkl.mkl_cspblas_dcsrgemv\\n # Dissecting the \\\"cspblas_dcsrgemv\\\" name:\\n # \\\"c\\\" - for \\\"c-blas\\\" like interface (as opposed to fortran)\\n # Also means expects sparse arrays to use 0-based indexing, which python does\\n # \\\"sp\\\" for sparse\\n # \\\"d\\\" for double-precision\\n # \\\"csr\\\" for compressed row format\\n # \\\"ge\\\" for \\\"general\\\", e.g., the matrix has no special structure such as symmetry\\n # \\\"mv\\\" for \\\"matrix-vector\\\" multiply\\n\\n if not sparse.isspmatrix_csr(A):\\n raise Exception(\\\"Matrix must be in csr format\\\")\\n (m,n) = A.shape\\n\\n # The data of the matrix\\n data = A.data.ctypes.data_as(POINTER(c_double))\\n indptr = A.indptr.ctypes.data_as(POINTER(c_int))\\n indices = A.indices.ctypes.data_as(POINTER(c_int))\\n\\n # Allocate output, using same conventions as input\\n nVectors = 1\\n if x.ndim is 1:\\n y = np.empty(m,dtype=np.double,order='F')\\n if x.size != n:\\n raise Exception(\\\"x must have n entries. x.size is %d, n is %d\\\" % (x.size,n))\\n elif x.shape[1] is 1:\\n y = np.empty((m,1),dtype=np.double,order='F')\\n if x.shape[0] != n:\\n raise Exception(\\\"x must have n entries. x.size is %d, n is %d\\\" % (x.size,n))\\n else:\\n nVectors = x.shape[1]\\n y = np.empty((m,nVectors),dtype=np.double,order='F')\\n if x.shape[0] != n:\\n raise Exception(\\\"x must have n entries. x.size is %d, n is %d\\\" % (x.size,n))\\n\\n # Check input\\n if x.dtype.type is not np.double:\\n x = x.astype(np.double,copy=True)\\n # Put it in column-major order, otherwise for nVectors > 1 this FAILS completely\\n if x.flags['F_CONTIGUOUS'] is not True:\\n x = x.copy(order='F')\\n\\n if nVectors == 1:\\n np_x = x.ctypes.data_as(POINTER(c_double))\\n np_y = y.ctypes.data_as(POINTER(c_double))\\n # now call MKL. This returns the answer in np_y, which links to y\\n SpMV(byref(c_char(b\\\"N\\\")), byref(c_int(m)),data ,indptr, indices, np_x, np_y ) \\n else:\\n for columns in range(nVectors):\\n xx = x[:,columns]\\n yy = y[:,columns]\\n np_x = xx.ctypes.data_as(POINTER(c_double))\\n np_y = yy.ctypes.data_as(POINTER(c_double))\\n SpMV(byref(c_char(b\\\"N\\\")), byref(c_int(m)),data,indptr, indices, np_x, np_y ) \\n\\n return y\",\n \"def get_sparse_backend():\\n backend = biom_config['python_code_sparse_backend']\\n if backend is None:\\n backend = 'CSMat'\\n\\n if backend not in sparse_backends:\\n raise InvalidSparseBackendException(\\\"Unrecognized sparse backend \\\"\\n \\\"'%s'. Choose from %s.\\\" % (backend,\\n ', '.join(sparse_backends)))\\n\\n valid_backend = False\\n if backend == 'ScipySparseMat':\\n try:\\n from biom.backends.scipysparse import ScipySparseMat, to_scipy, \\\\\\n dict_to_scipy, list_dict_to_scipy, list_nparray_to_scipy, \\\\\\n nparray_to_scipy, list_list_to_scipy\\n SparseObj = ScipySparseMat\\n to_sparse = to_scipy\\n dict_to_sparseobj = dict_to_scipy\\n list_dict_to_sparseobj = list_dict_to_scipy\\n list_nparray_to_sparseobj = list_nparray_to_scipy\\n nparray_to_sparseobj = nparray_to_scipy\\n list_list_to_sparseobj = list_list_to_scipy\\n valid_backend = True\\n except ImportError:\\n valid_backend = False\\n stderr.write(\\\"Cannot load ScipySparseMat (requires that scipy is \\\"\\n \\\"installed). Using CSMat sparse backend.\\\\n\\\")\\n\\n if backend == 'CSMat' or (not valid_backend):\\n try:\\n from biom.backends.csmat import CSMat, to_csmat, dict_to_csmat, \\\\\\n list_dict_to_csmat, list_nparray_to_csmat, nparray_to_csmat, \\\\\\n list_list_to_csmat\\n SparseObj = CSMat\\n to_sparse = to_csmat\\n dict_to_sparseobj = dict_to_csmat\\n list_dict_to_sparseobj = list_dict_to_csmat\\n list_nparray_to_sparseobj = list_nparray_to_csmat\\n nparray_to_sparseobj = nparray_to_csmat\\n list_list_to_sparseobj = list_list_to_csmat\\n valid_backend = True\\n except ImportError:\\n valid_backend = False\\n stderr.write('Cannot load CSMat sparse backend.\\\\n')\\n\\n if not valid_backend:\\n raise InvalidSparseBackendException(\\\"The sparse matrix backend '%s' \\\"\\n \\\"could not be loaded. Please check your biom-format \\\"\\n \\\"installation.\\\" % backend)\\n\\n return SparseObj, to_sparse, dict_to_sparseobj, list_dict_to_sparseobj, \\\\\\n list_nparray_to_sparseobj, nparray_to_sparseobj, \\\\\\n list_list_to_sparseobj\",\n \"def getScalingMatrix(sx, sy, sz):\\n return MatrixExtended([\\n [sx, 0, 0, 0],\\n [0, sy, 0, 0],\\n [0, 0, sz, 0],\\n [0, 0, 0, 1]])\",\n \"def matrix_spy(self, mtrx):\\r\\n import matplotlib.pylab as pl\\r\\n pl.spy(mtrx,precision=0.01, markersize=1)\\r\\n pl.show()\",\n \"def take_matrix(self):\\n matrix = aux.matrix(self.take_vec(), self.order)\\n\\n return matrix\",\n \"def term_to_matrix_sparse(spin_operator):\\n X = sparse.csr_matrix(np.array([[0, 1], [1, 0]]))\\n Y = sparse.csr_matrix(np.array([[0, -1j], [1j, 0]]))\\n Z = sparse.csr_matrix(np.diag([1, -1]))\\n I = sparse.csr_matrix(np.diag([1, 1]))\\n dic_Pauli = {\\\"I\\\": I, \\\"X\\\": X, \\\"Y\\\": Y, \\\"Z\\\": Z}\\n matrix_final = 0\\n nbqbits = spin_operator.nbqbits\\n for term in spin_operator.terms:\\n result_one_term = 0\\n char_term = [char for char in term.op]\\n qb_term = term.qbits\\n dic_op = {}\\n for n in range(nbqbits):\\n dic_op[n] = I\\n for n in range(len(term.qbits)):\\n dic_op[qb_term[n]] = dic_Pauli[char_term[n]]\\n matrix = 0\\n for d in dic_op:\\n if type(matrix) == int:\\n matrix = dic_op[d]\\n else:\\n matrix = scipy.sparse.kron(matrix, dic_op[d])\\n result_one_term = sparse.csr_matrix(matrix * term.coeff)\\n matrix_final += result_one_term\\n return matrix_final\",\n \"def __getitem__(self, index):\\n if not (type(index) in MATRIX_VALID_INTS):\\n return NotImplemented\\n return self._value[index]\",\n \"def to_csr(self):\\n return sparse.csr_matrix((self.data, (self.col, self.row)),\\n shape=(self.nrows, self.ncols))\",\n \"def scaling_matrix(sx, sy, sz):\\n S = np.array([[sx, 0, 0, 0],\\n [0, sy, 0, 0],\\n [0, 0, sz, 0],\\n [0, 0, 0, 1]])\\n return S\",\n \"def getSparseAdjacencyMatrix( graph, attribute=None, transposed=False ):\\n if (attribute is not None) and (attribute not in graph.es.attribute_names()):\\n raise ValueError( \\\"Attribute does not exists.\\\" )\\n \\n row = []\\n col = []\\n data = []\\n \\n if attribute is None:\\n if transposed:\\n for edge in graph.es():\\n s,t = edge.tuple\\n row.append(t)\\n col.append(s)\\n else:\\n for edge in graph.es():\\n s,t = edge.tuple\\n row.append(s)\\n col.append(t)\\n data = np.ones(len(graph.es()))\\n else:\\n if transposed:\\n for edge in graph.es():\\n s,t = edge.tuple\\n row.append(t)\\n col.append(s)\\n else:\\n for edge in graph.es():\\n s,t = edge.tuple\\n row.append(s)\\n col.append(t)\\n data = np.array(graph.es()[attribute])\\n\\n return sparse.coo_matrix((data, (row, col)) , shape=(len(graph.vs), len(graph.vs))).tocsr()\",\n \"def minor(self, i, j):\\n if not self.isSquare():\\n raise ValueError(\\\"Minor is not defined for non-square matrix\\\")\\n if (self._height == 1 and self._width == 1):\\n raise ValueError(\\\"Minor is not defined for 1x1 matrix\\\")\\n m = Matrix(self)\\n m.deleteRow(i)\\n m.deleteColumn(j)\\n return m.determinant()\",\n \"def asMatrix(self):\\n output = np.zeros((self.size[0],self.size[1]))\\n for pos in self.matrixDict:\\n output[pos[0]][pos[1]] = self.matrixDict[pos]\\n return output\",\n \"def get_sudoku_matrix(self):\\n return self.sudoku_matrix\",\n \"def Mat_dis_s2(x, sig=0.0001, alp = 0.5):\\n spe = x[:, :-2]\\n spa = x[:, -2:]\\n dist_spetral = Mat_dis(spe)/spe.shape[1]\\n dist_spatial = Mat_dis(spa)/spa.shape[1]\\n # dist_spetral = np.exp(-sig*dist_spetral)\\n # dist_spatial = np.exp(-sig*dist_spatial)\\n #dist_mat = alp*dist_spetral + (1-alp)*dist_spatial\\n\\n\\n return dist_spetral, dist_spatial\",\n \"def get_sparse_operator(operator, n_qubits=None, trunc=None, hbar=1.):\\n from openfermion.transforms.opconversions import get_fermion_operator\\n if isinstance(operator, (DiagonalCoulombHamiltonian, PolynomialTensor)):\\n return jordan_wigner_sparse(get_fermion_operator(operator))\\n elif isinstance(operator, FermionOperator):\\n return jordan_wigner_sparse(operator, n_qubits)\\n elif isinstance(operator, QubitOperator):\\n return qubit_operator_sparse(operator, n_qubits)\\n elif isinstance(operator, (BosonOperator, QuadOperator)):\\n return boson_operator_sparse(operator, trunc, hbar)\\n else:\\n raise TypeError('Failed to convert a {} to a sparse matrix.'.format(\\n type(operator).__name__))\",\n \"def similarity_matrix(feat_mat):\\n sim_mat = cosine_similarity(feat_mat)\\n np.fill_diagonal(sim_mat, 0)\\n return sim_mat\",\n \"def write_matfile(Sint_df, outputfilepath='Sint_no_cofactor_20160831.mat'):\\n # convert dataframe to matrix\\n Smat = Sint_df.as_matrix()\\n\\n # get all indices for non-zero elements in Smat (row, col)\\n Smat_nzr, Smat_nzc = np.nonzero(Smat)\\n\\n # get all non-zero elements from Smat\\n Smat_nze = Smat[Smat_nzr, Smat_nzc]\\n\\n # Adjust for matlab coordinate\\n Smat_nzr = Smat_nzr + 1\\n Smat_nzc = Smat_nzc + 1\\n\\n # This final line gives the size of the S matrix in matlab\\n nr, nc = Smat.shape\\n\\n # Create a 2D array\\n sparseMat = np.vstack((Smat_nzr, Smat_nzc, Smat_nze)).T\\n sparseMat = np.vstack((sparseMat, np.array([[nr, nc, 0]])))\\n\\n # Create a numpy object array from dataframe index\\n reactionList = Sint_df.columns.ravel()\\n\\n # Write only one matlab .mat file\\n scipy.io.savemat(outputfilepath,\\n mdict={'Sint_sparse': sparseMat,\\n 'reactionList': np.array(reactionList)}\\n )\\n\\n return sparseMat, reactionList\",\n \"def normalize_adj( adj : np.ndarray, \\n sparse : bool = False\\n ) -> Union[np.ndarray, sp.spmatrix]:\\n if sparse:\\n adj = sp.coo_matrix(adj) # [N,N]\\n rowsum = np.array(adj.sum(1)) # [N,]\\n \\n d_inv_sqrt = np.power(rowsum, -0.5) # [N,], may issue runtime warnings (div by zero)\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. # []\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) if sparse else np.diag(d_inv_sqrt) #[N,N]\\n \\n if sparse:\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\\n else:\\n return ((adj @ d_mat_inv_sqrt).transpose() @ d_mat_inv_sqrt) # not quite sure why this order = D^T A^T D, D^T = D, A^T = A - the transpose is unncessary?!\",\n \"def coregionalization_sparse(optimize=True, plot=True):\\r\\n #fetch the data from the non sparse examples\\r\\n m = coregionalization_toy2(optimize=False, plot=False)\\r\\n X, Y = m.X, m.likelihood.Y\\r\\n\\r\\n #construct a model\\r\\n m = GPy.models.SparseGPRegression(X,Y)\\r\\n m.constrain_fixed('iip_\\\\d+_1') # don't optimize the inducing input indexes\\r\\n\\r\\n if optimize:\\r\\n m.optimize('bfgs', max_iters=100, messages=1)\\r\\n\\r\\n if plot:\\r\\n m.plot(fixed_inputs=[(1,0)])\\r\\n m.plot(fixed_inputs=[(1,1)], ax=pb.gca())\\r\\n\\r\\n return m\",\n \"def __getitem__(self, key):\\n # TODO: fix 1-by-1 case, deprecate support for vector indexing.\\n # TODO: add isvector() method\\n # TODO: add method that distinguishes between 1x1, 1xn, mx1, mxn\\n # TODO: make sure that there's distinct ways to index a 1 by 1 matrix\\n # and retrieve a value.\\n rows, cols, is_view = self._cleankey(key)\\n if not (is_view): #simple index case\\n i, j = rows[0], cols[0]\\n return self.data[i, j]\\n return MPView(self, rows, cols)\",\n \"def test_canonicalization_of_vectors_w_symm(free_alg):\\n\\n dr = free_alg\\n p = dr.names\\n x = IndexedBase('x')\\n r = p.R\\n i, j = p.i, p.j\\n\\n vs = Vec('vs')\\n dr.set_symm(vs, Perm([1, 0]), valence=2)\\n tensor = dr.sum((i, r), (j, r), x[i, j] * vs[j, i])\\n res = tensor.simplify()\\n assert res.n_terms == 1\\n term = res.local_terms[0]\\n assert term.sums == ((i, r), (j, r))\\n assert term.amp == x[i, j]\\n assert term.vecs == (vs[i, j],)\\n\\n va = Vec('va')\\n dr.set_symm(va, Perm([1, 0], NEG), valence=2)\\n tensor = dr.sum((i, r), (j, r), x[i, j] * va[j, i])\\n res = tensor.simplify()\\n assert res.n_terms == 1\\n term = res.local_terms[0]\\n assert term.sums == ((i, r), (j, r))\\n assert term.amp == -x[i, j]\\n assert term.vecs == (va[i, j],)\",\n \"def find_sparse_matrix(self,\\n artifact_type: str,\\n match_condition: Dict[str, Any],\\n find_first=False,\\n return_id: bool = False,\\n return_documents_only=False\\n ) -> List[Union[Tuple[sp.csr_matrix, Dict[str, Any]],\\n Tuple[sp.csr_matrix, Dict[str, Any], int]]]:\\n\\n if \\\"ppr_idx\\\" in match_condition.keys() and not isinstance(match_condition[\\\"ppr_idx\\\"], int):\\n match_condition[\\\"ppr_idx\\\"] = hash(frozenset(match_condition[\\\"ppr_idx\\\"]))\\n\\n raw_documents = Storage.locked_call(\\n lambda: self._find_meta(artifact_type, match_condition),\\n self._get_lock_path(artifact_type),\\n self.lock_timeout,\\n )\\n # to get the most recent documents first we revert the list\\n raw_documents.reverse()\\n if return_documents_only:\\n for document in raw_documents:\\n if \\\"ppr_idx\\\" in document['params'].keys() and isinstance(document['params'][\\\"ppr_idx\\\"], int):\\n path = self._build_artifact_path(artifact_type, document.doc_id).replace(\\\".pt\\\", \\\".npz\\\")\\n ppr_path = path.replace(\\\".npz\\\", \\\"idx.npy\\\")\\n document['params'][\\\"ppr_idx\\\"] = np.load(ppr_path)\\n return raw_documents\\n results = []\\n for document in raw_documents:\\n document_id = document.doc_id\\n document = dict(document)\\n path = self._build_artifact_path(artifact_type, document_id).replace(\\\".pt\\\", \\\".npz\\\")\\n sparse_matrix = sp.load_npz(path)\\n\\n if \\\"ppr_idx\\\" in document['params'].keys() and isinstance(document['params'][\\\"ppr_idx\\\"], int):\\n ppr_path = path.replace(\\\".npz\\\", \\\"idx.npy\\\")\\n document['params'][\\\"ppr_idx\\\"] = np.load(ppr_path)\\n\\n if return_id:\\n results.append((sparse_matrix, document['params'], document_id))\\n else:\\n results.append((sparse_matrix, document['params']))\\n\\n if find_first:\\n return results\\n return results\",\n \"def clone(self):\\n return DoubleSpMatrix(len(self)).copy_from_sp_(self)\",\n \"def sparse_matrix (base_type=float):\\n return defaultdict (lambda: sparse_vector (base_type))\",\n \"def test_import_sparse_type_mat():\\n x = sps.csr_matrix(np.random.rand(3, 2))\\n export_data('/tmp/test.mat', x)\\n assert x.dtype == import_data('/tmp/test.mat').dtype\",\n \"def matrix(self) -> sympy.Matrix:\\n raise NotImplementedError()\",\n \"def create_preference_weighted_similarity_matrix(self):\\n # We only need to compare the distances to points within the epsilon shell (to determine if a point is a core point)\\n # Since the subspace scaling factor kappa is >>1 (and not <1), no distances to other points will be needed for \\n # the core point evaluation\\n\\n # get points in epsilon shell: attententio point itself is not in neigh_ind list\\n _, neigh_ind = self.neigbors_clf.radius_neighbors(radius=self.epsilon)\\n row, col, pwsim = [], [], []\\n for i, ith_neigh_ind in enumerate(neigh_ind):\\n # Calculate preference weighted similarity measure with point and neighbors in eps shell\\n sq_diffs = np.square(self.data[ith_neigh_ind,:] - self.data[i,:])\\n sum_weighted_sq_diffs = np.inner(self.subspace_preference_vectors[i,:], sq_diffs)\\n pwsim_ith = np.sqrt(sum_weighted_sq_diffs)\\n \\n # Info for sparse matrix\\n pwsim.extend(pwsim_ith.tolist()) # Data\\n row.extend([i]*(pwsim_ith.shape[0])) # ith Row \\n col.extend(ith_neigh_ind.tolist()) # column info\\n\\n # Construct sparse matrix with data, row, and column info\\n A = csr_matrix((pwsim, (row, col)), shape=(self.nb_points, self.nb_points))\\n # Create symmetric version: take the elementwise maximum of A and its transpose A.T\\n transpose_is_bigger = A.T>A\\n A = A - A.multiply(transpose_is_bigger) + (A.T).multiply(transpose_is_bigger)\\n \\n return A\",\n \"def retrievematrixelement(self, coord):\\n currentelement = self.matrix\\n for u in coord:\\n currentelement = currentelement[u]\\n\\n return currentelement\",\n \"def symplectify(M):\\n nv = M.shape[0]\\n S = jmat(nv // 2)\\n I = numpy.identity(nv)\\n\\n V = S @ (I - M) @ inv(I + M)\\n # V should be almost symmetric. Replace with symmetrized version.\\n W = (V + V.T) / 2\\n # Now reconstruct M from W\\n SW = S @ W\\n MS = (I + SW) @ inv(I - SW)\\n return MS\",\n \"def sparse_arrays(self):\\n return self._sparse_arrays\",\n \"def array_to_matrix(y, ix, s, nodata=None):\\n if nodata is None:\\n x = np.ones(s)*np.NaN\\n else:\\n x = np.ones(s)*nodata\\n x[ix] = y\\n\\n return x\",\n \"def getskx(self,whichsol_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skx = False\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n _copyback_skx = True\\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n _copyback_skx = True\\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.getskx(whichsol_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_skx:\\n for __tmp_var_0 in range(len(skx_)): skx[__tmp_var_0] = stakey(_tmparr_skx[__tmp_var_0])\",\n \"def get_mat(self,diag_entries=False):\\n if self._mat is None or (diag_entries and not self._diag_entries):\\n self.build_mat(diag_entries=diag_entries)\\n return self._mat\",\n \"def test_csr_matvec():\\n\\n from scipy.sparse import random\\n\\n m = 1000\\n n = 500\\n\\n rng = numpy.random.default_rng(0)\\n scipy_mat = random(m, n, format=\\\"csr\\\", random_state=rng)\\n csr_mat = spmv.CsrMatrix(\\n scipy_mat.data, scipy_mat.indices, scipy_mat.indptr, (m, n)\\n )\\n\\n vec = rng.normal(size=n)\\n\\n expected = scipy_mat @ vec\\n actual = csr_mat @ vec\\n\\n numpy.testing.assert_allclose(actual, expected)\",\n \"def to_mat(self) -> np.matrix:\\n raise NotImplementedError\",\n \"def matrix(self) -> sympy.Matrix:\\n return self.matrix_factory(*self.params)\",\n \"def matrix_intf(self):\\n return intf.DEFAULT_SPARSE_INTF\",\n \"def build_mat(self):\\n for row, s in enumerate(self.S):\\n for col, t in enumerate(self.T):\\n\\n if self.symmetric and row > col:\\n pass\\n\\n else:\\n self.mat[row, col] = self.kernel(s, t, self.n)\\n\\n if self.symmetric:\\n self.mat = self.symmetrize(self.mat)\\n else:\\n for idx, s in enumerate(self.S):\\n self.test_normalization[idx] = self.kernel(s, s, self.n)\",\n \"def sparse_real_matrix(A, axis=0):\\n m, n = A.shape\\n\\n @sparsify\\n def times1d(x):\\n return A @ x\\n\\n @sparsify\\n def trans1d(x):\\n return x @ A\\n \\n\\n @sparsify\\n def times(x):\\n \\\"\\\"\\\"Forward matrix multiplication\\n \\\"\\\"\\\"\\n if x.ndim == 1:\\n return A @ x\\n if x.ndim == 2:\\n if axis == 0:\\n return A @ x\\n else:\\n return x @ A.T\\n # general case\\n return jnp.apply_along_axis(times1d, axis, x)\\n\\n @sparsify\\n def trans(x):\\n \\\"\\\"\\\"Adjoint matrix multiplication\\n \\\"\\\"\\\"\\n if x.ndim == 1:\\n return trans1d(x)\\n if x.ndim == 2:\\n if axis == 0:\\n return A.T @ x\\n else:\\n return x @ A\\n # general case\\n return jnp.apply_along_axis(trans1d, axis, x)\\n return Operator(times=times, trans=trans, shape=(m,n))\",\n \"def get_s_matrix(data, word_form, semvecs, n_events, n_vec_dims):\\n\\n s = np.zeros((n_events, n_vec_dims))\\n ii = 0\\n for index, row in data.iterrows():\\n s[ii, ] = semvecs.loc[row[word_form], :]\\n ii += 1\\n return s\",\n \"def matrix(self, backend=None):\\n if backend is None: # pragma: no cover\\n backend = GlobalBackend()\\n\\n return backend.matrix_fused(self)\",\n \"def analyze_sensitivity_sparse_grid(sparse_grid,max_order=2):\\n from pyapprox.multivariate_polynomials import \\\\\\n define_poly_options_from_variable_transformation\\n from pyapprox.adaptive_sparse_grid import \\\\\\n convert_sparse_grid_to_polynomial_chaos_expansion\\n pce_opts=define_poly_options_from_variable_transformation(\\n sparse_grid.variable_transformation)\\n pce = convert_sparse_grid_to_polynomial_chaos_expansion(\\n sparse_grid,pce_opts)\\n pce_main_effects,pce_total_effects=\\\\\\n get_main_and_total_effect_indices_from_pce(\\n pce.get_coefficients(),pce.get_indices())\\n\\n interaction_terms, pce_sobol_indices = get_sobol_indices(\\n pce.get_coefficients(),pce.get_indices(),max_order=max_order)\\n \\n return SensivitityResult(\\n {'main_effects':pce_main_effects,\\n 'total_effects':pce_total_effects,\\n 'sobol_indices':pce_sobol_indices,\\n 'sobol_interaction_indices':interaction_terms,\\n 'pce':pce})\",\n \"def get_symmetry_mates(self, model=None, altloc=\\\"first\\\",\\n extra_fields=[], include_bonds=False):\\n # Get base structure\\n structure = self.get_structure(\\n model,\\n altloc,\\n extra_fields,\\n include_bonds,\\n )\\n # Get lines containing transformations for crystallographic symmetry\\n remark_lines = self.get_remark(290)\\n if remark_lines is None:\\n raise InvalidFileError(\\n \\\"File does not contain crystallographic symmetry \\\"\\n \\\"information (REMARK 350)\\\"\\n )\\n transform_lines = [\\n line for line in remark_lines if line.startswith(\\\" SMTRY\\\")\\n ]\\n rotations, translations = _parse_transformations(\\n transform_lines\\n )\\n return _apply_transformations(\\n structure, rotations, translations\\n )\",\n \"def matrix_add_symmetric(M, M_sym):\\n M[0, 0] += M_sym[0]\\n M[0, 1] += M_sym[1]\\n M[1, 0] += M_sym[1]\\n M[0, 2] += M_sym[2]\\n M[2, 0] += M_sym[2]\\n\\n M[1, 1] += M_sym[3]\\n M[1, 2] += M_sym[4]\\n M[2, 1] += M_sym[4]\\n\\n M[2, 2] += M_sym[5]\\n\\n return M\",\n \"def csr_matrix(*args, **kws):\\n mat = ss.csr_matrix(*args, **kws)\\n return CudaCSRMatrix().from_host_matrix(mat)\",\n \"def makesparse(matrix):\\n n = matrix[0].size\\n elements = []\\n for i in range(n):\\n for j in range(n):\\n if matrix[i][j] != 0 :\\n temp = MatrixElement(i, j, matrix[i][j])\\n elements.append(temp)\\n return SparseMatrix(n, elements)\",\n \"def csr(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"csr\\\")\",\n \"def index_condensed_matrix(n, i, j):\\n if i == j:\\n main_warning(\\\"Diagonal elements (i=j) are not stored in condensed matrices.\\\")\\n return None\\n elif i > j:\\n i, j = j, i\\n return int(i * (n - (i + 3) * 0.5) + j - 1)\",\n \"def calc_sparse(self, calc_aw=False, vector_calc=True):\\n # Local copies of instance attributes for faster access\\n b = self.b\\n qs = self.q_sim\\n qo = self.q_obs\\n maxlead = self.max_lead\\n maxlag = self.max_lag\\n measure = self.measure\\n time = self.time\\n\\n # Prepare sparse matrices with only cells around diagonal defined.\\n # padds 1 extra cell on each side to avoid false zeros later\\n n = len(self.q_obs)\\n w = self.max_lag + self.max_lead + 3\\n data = np.array([np.full(n, np.inf)]).repeat(w, axis=0)\\n offsets = np.arange(-self.max_lead-1, self.max_lag + 1.5)\\n\\n # Calculate work between obs and sim pairs, not needed anymore.\\n if calc_aw:\\n aw = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\\n st = pd.Timestamp.now()\\n # print('Start aw loops')\\n # iterate through all flow observations\\n for i_o in range(len(qo)):\\n # Check only simulations within the allowed window\\n for i_s in range(max(0, i_o - self.max_lead),\\n min(len(qs), i_o + maxlag + 1)):\\n if measure in ('nse', 'square'):\\n aw[i_s, i_o] = (qs[i_s] - qo[i_o]) ** 2 + b ** 2 * (i_s - i_o) ** 2\\n elif measure in ('mae', 'abs'):\\n aw[i_s, i_o] = np.abs(qs[i_s] - qo[i_o]) + b * np.abs(i_s - i_o)\\n # print('End aw loops', (pd.Timestamp.now()-st)/pd.Timedelta('1s'))\\n self.aw = aw\\n\\n # Old debugging code: run original implementation alongside\\n # Applies to all variables appended with _orig\\n # aw_orig = np.full(shape=(len(self.q_obs), len(self.q_sim)), fill_value=np.nan)\\n # # iterate through all flow observations\\n # for i_o in range(len(self.q_obs)):\\n # # Check only simulations within the allowed window\\n # for i_s in range(max(0, i_o - self.max_lead),\\n # min(len(self.q_sim), i_o + self.max_lag + 1)):\\n # aw_orig[i_s, i_o] = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + self.b ** 2 * (i_s - i_o) ** 2\\n\\n # Calculate cumulative work along possible paths\\n cw0 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\\n cw1 = dia_matrix((data, offsets), shape=(n, n), dtype=np.float64).tocsr()\\n\\n # Old debugging code: run original implementation (with dense arrays) alongside\\n # Applies to all variables marked _orig\\n # # Calculate cumulative work along possible paths\\n # cw_orig = np.ones(shape=aw_orig.shape + (2,)) * np.nan\\n # # Populate first column\\n # cw_orig[:, 0, 0] = aw_orig[:, 0]\\n\\n if self.keep_internals:\\n # self.aw_orig = aw_orig\\n self.cw0 = cw0\\n self.cw1 = cw1\\n # self.cw_orig = cw_orig\\n\\n # Populate other columns\\n for i_o in range(n):\\n iss = max(0, i_o - maxlead) # sim index start\\n ise = min(n, i_o + maxlag + 1) # sim index end\\n isv = np.arange(iss, ise) # sim index as vector\\n if time is not None:\\n dt = time[isv] - time[i_o]\\n # Heavily penalize sim-obs combinations outside allowed window\\n # so that they are not selected\\n dt[(dt < -maxlag) | (dt > maxlead)] = np.inf\\n else:\\n dt = isv - i_o\\n if vector_calc:\\n # Vectorized version of the calculation.\\n # For understanding it is best to take a look at the else clause below\\n # Calculate the work for each (sim, obs_i) pair\\n if measure in ('nse', 'square'):\\n e = (qs[iss:ise] - qo[i_o]) ** 2 + b ** 2 * dt ** 2\\n elif measure in ('mae', 'abs'):\\n e = np.abs(qs[iss:ise] - qo[i_o]) + b * np.abs(dt)\\n if i_o == 0:\\n # Only populate first column of cw0 and move to i_o = 1\\n cw0[iss:ise, 0] = e.reshape(len(e), 1)\\n continue\\n # Repeat the same simulation point\\n d = cw0[iss:ise, i_o - 1].toarray()\\n d[d == 0] = np.nan\\n cw1[iss:ise, i_o] = e.reshape(len(e), 1) + d\\n\\n # Find the 'cheapest' available preceding sim point\\n points = np.full((len(isv), 4), np.inf)\\n # Use the previous simulation point\\n st = max(0, iss - 1)\\n end = min(n + 1, ise - 1)\\n l = end - st\\n points[-l:, 0] = cw0[st:end, i_o - 1].toarray().squeeze()\\n points[-l:, 1] = cw1[st:end, i_o - 1].toarray().squeeze()\\n # Skip a simulation point\\n st = max(0, iss - 2)\\n end = min(n + 1, ise - 2)\\n l = end - st\\n points[-l:, 2] = cw0[st:end, i_o - 1].toarray().squeeze()\\n points[-l:, 3] = cw1[st:end, i_o - 1].toarray().squeeze()\\n # points[points == 0] = np.nan\\n cw0[iss:ise, i_o] = (e + np.min(points, axis=1)).reshape(len(e), 1)\\n else:\\n # Old, non-vectorized code. Slower.\\n # Left in place since it is easier to understand than vectorized code above\\n def zero_to_nan(x):\\n if x == 0:\\n return np.nan\\n else:\\n return x\\n\\n for i_s in range(max(0, i_o - self.max_lead),\\n min(n, i_o + self.max_lag + 1)):\\n # print(i_o, i_s, sep='\\\\t')\\n # e = aw[i_s, i_o]\\n if time:\\n dt = time[isv] - time[i_o]\\n # Heavily penalize sim-obs combinations outside allowed window\\n # so that they are not selected\\n dt[dt > maxlead] = 999999999\\n dt[dt < -maxlag] = 999999999\\n else:\\n dt = isv - i_o\\n if measure in ('nse', 'square'):\\n e = (self.q_sim[i_s] - self.q_obs[i_o]) ** 2 + b ** 2 * dt ** 2\\n elif measure in ('mae', 'abs'):\\n e = np.abs(self.q_sim[i_s] - self.q_obs[i_o]) + np.abs(b * dt)\\n # e_orig = aw_orig[i_s, i_o]\\n # if e != e_orig:\\n # print('ediff', e, e_orig, sep='\\\\t')\\n\\n # Repeat the same simulation point\\n cw1[i_s, i_o] = e + zero_to_nan(cw0[i_s, i_o - 1])\\n # cw_orig[i_s, i_o, 1] = e + cw_orig[i_s, i_o - 1, 0]\\n # if cw1[i_s, i_o] != cw_orig[i_s, i_o, 1]:\\n # print('cw1diff', cw1[i_s, i_o], cw_orig[i_s, i_o, 1], sep='\\\\t')\\n\\n if i_s == 0:\\n continue\\n # Else:\\n # Find the 'cheapest' available preceding point\\n # Use the previous simulation point\\n points = [zero_to_nan(cw0[i_s - 1, i_o - 1]),\\n zero_to_nan(cw1[i_s - 1, i_o - 1])]\\n # m1 = np.nanmin(cw_orig[i_s - 1, i_o - 1, :])\\n # m2 = np.inf\\n if i_s > 1:\\n # Skip a simulation point\\n points += [cw0[i_s - 2, i_o - 1], cw1[i_s - 2, i_o - 1]]\\n # m2 = np.nanmin(cw_orig[i_s - 2, i_o - 1, :])\\n # m = min(m1, m2)\\n try:\\n cp = min([p for p in points if p > 0])\\n except ValueError:\\n cp = np.nan\\n # if cp != m:\\n # print('cpdiff', cp, m, sep='\\\\t')\\n # print('\\\\torig', cw_orig[i_s - 1, i_o - 1, :], cw_orig[i_s - 2, i_o - 1, :])\\n # print('\\\\tspar', points)\\n cw0[i_s, i_o] = e + cp\\n # cw_orig[i_s, i_o, 0] = e + m\\n # print('')\\n # Find the cheapest point in the last column, i.e. end of cheapest path, i.e. optimum score\\n # nz0 = cw0[cw0[:, -1].nonzero()[0], -1].toarray().min()\\n # nz1 = cw1[cw1[:, -1].nonzero()[0], -1].toarray().min()\\n nz0 = cw0[-self.max_lag:, -1].toarray()\\n nz1 = cw1[-self.max_lag:, -1].toarray()\\n self.opt_score = min(nz0[nz0 != 0].min(), nz1[nz1 != 0].min())\\n\\n self.of = 1 - self.opt_score / self.fbench\\n\\n if self.calc_rays:\\n self._do_calc_rays(cw0, cw1)\\n\\n return self.of\",\n \"def sym(self) -> np.ndarray:\\n if self._sym is None:\\n self._sym = symmetrize_discrete_vector_field(self.F, mode=\\\"sym\\\")\\n return self._sym\",\n \"def read_sec_matrix(self, matrix_id, dataset_id, table_name):\\n tbl = self.Table(matrix_id, dataset_id, table_name, config_related=True)\\n return tbl.to_dataframe()\",\n \"def find_symmetry(self):\\n from spglib import get_spacegroup\\n cell = ( self.lattice, self.fractional_coordinates, self.atomic_nos )\\n self.spacegroup = get_spacegroup(cell, symmprec=1e-5)\\n print(\\\"Symmetry space group is\\\", self.spacegroup)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75738734","0.6238482","0.6137533","0.56444985","0.5625718","0.558371","0.54747504","0.52915025","0.52799106","0.52322876","0.5228932","0.51636285","0.5140221","0.5121791","0.50425875","0.49852064","0.4978507","0.49779502","0.49698326","0.4951648","0.49289328","0.49200913","0.48945662","0.48862562","0.4880411","0.48706338","0.48516077","0.48509476","0.48347786","0.48201743","0.48180822","0.47815293","0.47790477","0.47737208","0.47691327","0.47591245","0.47522023","0.4749003","0.47358546","0.47184655","0.47107488","0.470272","0.4699506","0.46990004","0.46941116","0.4684229","0.46830764","0.46744162","0.466165","0.46593234","0.46438846","0.46330696","0.46067834","0.45995328","0.4599286","0.45908988","0.4589856","0.4578715","0.45702899","0.4560105","0.45586127","0.4557975","0.45455372","0.4528898","0.4522391","0.45125932","0.45065168","0.45049387","0.45010415","0.44965726","0.44937697","0.44871756","0.44812596","0.447308","0.44722846","0.44689608","0.44678956","0.44678608","0.4457832","0.44566453","0.44504878","0.4426898","0.44179153","0.44102788","0.4409692","0.44075155","0.4407493","0.4400059","0.43964604","0.43888614","0.43873653","0.4386426","0.4385107","0.43748462","0.43727022","0.43715116","0.43636554","0.43607658","0.4359298","0.43579772"],"string":"[\n \"0.75738734\",\n \"0.6238482\",\n \"0.6137533\",\n \"0.56444985\",\n \"0.5625718\",\n \"0.558371\",\n \"0.54747504\",\n \"0.52915025\",\n \"0.52799106\",\n \"0.52322876\",\n \"0.5228932\",\n \"0.51636285\",\n \"0.5140221\",\n \"0.5121791\",\n \"0.50425875\",\n \"0.49852064\",\n \"0.4978507\",\n \"0.49779502\",\n \"0.49698326\",\n \"0.4951648\",\n \"0.49289328\",\n \"0.49200913\",\n \"0.48945662\",\n \"0.48862562\",\n \"0.4880411\",\n \"0.48706338\",\n \"0.48516077\",\n \"0.48509476\",\n \"0.48347786\",\n \"0.48201743\",\n \"0.48180822\",\n \"0.47815293\",\n \"0.47790477\",\n \"0.47737208\",\n \"0.47691327\",\n \"0.47591245\",\n \"0.47522023\",\n \"0.4749003\",\n \"0.47358546\",\n \"0.47184655\",\n \"0.47107488\",\n \"0.470272\",\n \"0.4699506\",\n \"0.46990004\",\n \"0.46941116\",\n \"0.4684229\",\n \"0.46830764\",\n \"0.46744162\",\n \"0.466165\",\n \"0.46593234\",\n \"0.46438846\",\n \"0.46330696\",\n \"0.46067834\",\n \"0.45995328\",\n \"0.4599286\",\n \"0.45908988\",\n \"0.4589856\",\n \"0.4578715\",\n \"0.45702899\",\n \"0.4560105\",\n \"0.45586127\",\n \"0.4557975\",\n \"0.45455372\",\n \"0.4528898\",\n \"0.4522391\",\n \"0.45125932\",\n \"0.45065168\",\n \"0.45049387\",\n \"0.45010415\",\n \"0.44965726\",\n \"0.44937697\",\n \"0.44871756\",\n \"0.44812596\",\n \"0.447308\",\n \"0.44722846\",\n \"0.44689608\",\n \"0.44678956\",\n \"0.44678608\",\n \"0.4457832\",\n \"0.44566453\",\n \"0.44504878\",\n \"0.4426898\",\n \"0.44179153\",\n \"0.44102788\",\n \"0.4409692\",\n \"0.44075155\",\n \"0.4407493\",\n \"0.4400059\",\n \"0.43964604\",\n \"0.43888614\",\n \"0.43873653\",\n \"0.4386426\",\n \"0.4385107\",\n \"0.43748462\",\n \"0.43727022\",\n \"0.43715116\",\n \"0.43636554\",\n \"0.43607658\",\n \"0.4359298\",\n \"0.43579772\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77381927"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11975,"cells":{"query":{"kind":"string","value":"Sets a double parameter. putdouparam(self,param_,parvalue_)"},"document":{"kind":"string","value":"def putdouparam(self,param_,parvalue_):\n res = __library__.MSK_XX_putdouparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putdouparam(self,param_,parvalue_): # 3\n if not isinstance(param_,dparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putdouparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setDouble(self, address: ghidra.program.model.address.Address, value: float) -> None:\n ...","def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError","def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value","def _set_par(vid, par, value):\n traci.vehicle.setParameter(vid, \"carFollowModel.%s\" % par, str(value))","def set_parameter_value(self, parameter, value):\n pass","def setParameter(self, name, value):","def update_parameter(cur, par, new_value):\n cur.execute(\"UPDATE parameters SET value=%f WHERE par='%s';\" % \n (new_value, par))","def update_parameter(self, param, val, force=False):\n self._update_dict[param] = val\n if force:\n self._cur_val[param] = None","def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setDoubleValue(self, *args):\n return _libsbml.ConversionOption_setDoubleValue(self, *args)","def set_parameter(self, params, name, val):\n raise NotImplementedError()","def plot_parameter(self, parm):\n # If user wants to plot density, make sure it exists\n if parm == 'density' and 'density' not in self.ds.data_vars:\n self.insert_density()\n \n if parm == 'theta' and 'theta' not in self.ds.data_vars:\n self.insert_potential_density()\n \n if parm == 'N' and 'N' not in self.ds.data_vars:\n self.insert_buoyancy_frequency()\n \n # Use xarray to plot this parameter\n self.ds[parm].plot(y=self.ztsp[0])\n if plt.ylim()[0] <= 0:\n plt.gca().invert_yaxis()\n plt.tight_layout()\n \n plt.show()","def SetParameterValue(self, paramName, value):\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n if paramName == ISENTROPIC_PAR:\n if (self.ideal != None):\n self.ideal.SetParameterValue(paramName, value)","def set_param(self, param, value):\n self._set_param_client(param, value)","def SetParameterValue(self, paramName, value):\n if paramName == NUMBTABLE_PAR:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n self.LookupTable.SetParameterValue(paramName, value)\n\n if paramName == IGNORECURVE_PAR: \n #...ignore the lookuptable and remove any specifications\n if value == 'None': value = None\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\n if value:\n port = self.GetPort(HEAD_PORT)\n port.SetValue(None, FIXED_V) \n else:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)","def setDoubleValue(self, *args):\n return _libsbml.ConversionProperties_setDoubleValue(self, *args)","def set_parameter(self, param, value, stripe = 0, location = 3):\n if param in self.fpga.cabac_top[0].params:\n self.fpga.set_cabac_value(param, value, stripe)\n time.sleep(0.1)\n self.fpga.send_cabac_config(stripe)\n\n elif param in [\"V_SL\", \"V_SH\", \"V_RGL\", \"V_RGH\", \"V_PL\", \"V_PH\"]:\n self.fpga.set_clock_voltages({param: value})\n\n elif param == \"I_OS\":\n self.fpga.set_current_source(value, stripe)\n\n else:\n print(\"Warning: unidentified parameter for the REB: %s\" % param)","def set_param(param, num, set_val):\n param[0][num] = set_val","def set_parameter(self, sensor_name, parameter_name, parameter_value):\n if parameter_name == 'perspective_angle':\n parameter_value = parameter_value / (180 * 2) * math.pi\n if parameter_name in self.params_f:\n error = vrep.simxSetObjectFloatParameter(\n self.client_id,\n self.handles[sensor_name + self.postfix],\n self.params_f[parameter_name],\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxSetFloatSignal(\n self.client_id,\n 'change_params',\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxClearFloatSignal(\n self.client_id,\n 'change_params',\n ONE_SHOT_MODE\n )\n return error\n elif parameter_name in self.params_i:\n error = vrep.simxSetObjectFloatParameter(\n self.client_id,\n self.handles[sensor_name + self.postfix],\n self.params_i[parameter_name],\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxSetFloatSignal(\n self.client_id,\n 'change_params',\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxClearFloatSignal(\n self.client_id,\n 'change_params',\n ONE_SHOT_MODE\n )\n return error\n else:\n return 'Parameter not found'","def set_trial_param(\n self,\n trial_id: int,\n param_name: str,\n param_value_internal: float,\n distribution: \"distributions.BaseDistribution\",\n ) -> bool:\n raise NotImplementedError","def setParamSingleStep(self, instr: str, param: str, value: Any):\n box = self._itemsdict[instr][param]['double_box']\n if box is not None:\n box.setSingleStep(value)","def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val","def putnadouparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnadouparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def update_parameter(self, name, freq, value):\n if name not in self._parameters.keys():\n self.add_parameter(name, [freq], [value])\n else:\n param = self.get_parameter(name)\n param.update_value(freq, value)","def setd(self, node, new_double):\n\n self.daq.syncSetDouble(f'/{self.device_id}/{node}', new_double)","def set_pi(self, val):\n self.__pi = val","def set_parameter(self, params, name, val):\n if name == \"model\":\n params.model = val\n return params\n available_models = [\n entry_point.name\n for entry_point in pkg_resources.iter_entry_points(\n \"dxtbx.scaling_model_ext\"\n )\n ]\n phil_branches = [\n params.weighting.error_model,\n params.cut_data,\n params.scaling_options,\n params.reflection_selection,\n params.reflection_selection.random,\n params.reflection_selection.random.multi_dataset,\n ]\n if params.model:\n phil_branches.append(params.__getattribute__(str(params.model)))\n elif (\".\" in name) and (name.split(\".\")[0] in available_models):\n # if the user hasn't specified the model, but have done\n # e.g physical.parameter = *, then set model=physical\n params.model = name.split(\".\")[0]\n phil_branches.append(params.__getattribute__(str(params.model)))\n if \".\" in name: # handle e.g physical.absorption_correction\n name = name.split(\".\")[-1]\n for branch in phil_branches:\n try:\n branch.__setattr__(name, val)\n return params\n except AttributeError:\n pass\n # if get here, haven't found what we're trying to set\n raise ValueError(\"Unable to set chosen attribute \" + str(name) + \"=\" + str(val))","def set_param(self, param_value):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n\n setting[1] = param_value\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def setSynapseDynamics(self, param, value):\n raise NotImplementedError","def set_parameter(self, output):\n self.model.set_parameter(output);","def setParameter(self,arg,value):\n self._params[arg] = value\n return self._params","def adapt(self, domain_distr_param: str, domain_distr_param_value: Union[float, int]):\n if domain_distr_param not in self.get_field_names():\n raise KeyError(f'The domain parameter {self.name} does not have a domain distribution parameter '\n f'called {domain_distr_param}!')\n setattr(self, domain_distr_param, domain_distr_param_value)","def _set_params(self,x):\r\n self.k._set_params(x)","def set_parametervalue(self, Value, nodename=None, node=None, root=None, ):\n\n if nodename != None:\n node = self.find_node(nodename, root)\n\n node_event = self._refind_nodes(self.fn_to_reg([\"Manual\"]), root=node)[0]\n\n if node_event != False:\n self.set_nodevalue(node_event, Value)\n return True\n\n return False","def gui_set_param(self,param_name,val):\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\n self._handle_gui_set(param_name,force=True)","def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_param(params, pname, value=None, bounds=None):\n if value is not None:\n for p in params.flattened():\n if p.name == pname:\n p.value = value\n break\n\n if bounds is not None:\n for p in params.flattened():\n if p.name == pname:\n p.bounds = bounds\n p.vary = True\n break","def _p_set_par_ ( o , index , value ) :\n return o.setPar ( index , value )","def posterior_sample_parameter(self, parameter):\n pass","def __setitem__(self, name: str, value):\n super(Parameter, self).__setitem__(name, value)","def set_parameter(doc, o_path, param, value, mn_consts):\n if isinstance(value, str):\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\n elif isinstance(value, int):\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\n elif isinstance(value, list):\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)","def set_param(self, param_key, value):\n return self._params.set_param_value(param_key, value)","def put_param(self, attr_name, val):\n self._params[attr_name] = val","def getdouparam(self,param_):\n parvalue_ = ctypes.c_double()\n res = __library__.MSK_XX_getdouparam(self.__nativep,param_,ctypes.byref(parvalue_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n parvalue_ = parvalue_.value\n _parvalue_return_value = parvalue_\n return (_parvalue_return_value)","def set_parameters(self, params):\n self.kp = params.pgain","def log_param(self, name: str, value):\n self.params[name] = value\n\n self._sync_log_event()","def set_reg_param(self,reg_param):\n \n type_name = type(reg_param).__name__\n if re.search('float',type_name) != None:\n if reg_param < 0:\n raise KINSOL_Exception(\"Value sent to set_reg_param must be equal to or larger than zero.\")\n else:\n self.reg_param = reg_param\n else:\n raise KINSOL_Exception(\"The variable sent to 'set_reg_param' must be a float.\")","def set_params(self, params):","def set_value(self, device_name, val):\n epics.caput(device_name, val)\n\n\t\t#mu = mu\n\t\t#sig = math.sqrt(abs(mu))\n\t\t#y = (float(x)-mu)/(sig)","def parameter(self, parameter):\n\n self._parameter = parameter","def setvalue(self,num,name,val):\n self.M.reconfigure(num,{name:float(val)})","def set_param_motor():\n servo.setSpeed(0, 0) # max = 255\n servo.setAccel(0, 0)\n servo.setSpeed(1, 150) # max = 255\n servo.setAccel(1, 150)","def set_param(self):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n setting[1] = str(self.param_value)\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def putparam(self,parname_,parvalue_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def test_ParameterVariable_init_basic_type_value(self):\n\n par = provide_parameter(\"double\", \"test\", value=518)\n\n self.assertEqual(par.name, \"test\")\n self.assertEqual(par.type, \"double\")\n self.assertEqual(par.value, 518)","def _set_parameter(self):\n # Get parameter keys\n self.input_parameter = self.parameter_combobox.currentText()\n self.result_parameter = self.result_parameters[self.input_parameter]\n # Adjust axes labels\n self.ax.set_xlabel('{} steunpunt'.format(self.input_parameter))\n self.ax.set_ylabel('{} uitvoerlocatie'.format(self.input_parameter))\n # Set data\n self._set_data()","def add_parameter(self,\n name, # The name of the parameter\n scaling=None, # The type of scaling to be used for the parameter\n type=\"int\", # The type of the parameter, such as float\n min=0, # The minimum value of the parameter\n max=100, # The maximum value of the parameter\n significance=1, # The smallest significant step size\n value=None, # The value or value parameters\n distribution=None): # The distribution of the parameter\n config = {\"scaling\" : scaling, \n \"type\": type,\n \"min\": min, \n \"max\": max, \n \"significance\": significance,\n \"value\": value,\n \"distribution\": distribution}\n self.param_names.append(name)\n self.param_settings.append(config)","def _paramUpdate(self):\n\n # Update the database attributes accordingly.\n dt.utilities.DB_attrs_save(self.Database, self.newParam)","def _set_field(self, instrument_name, parameter_name, field, value, force_update):\n if self.verbose >= 2:\n print('_set_field: %s %s: %s' % (instrument_name, parameter_name, str(value)))\n tree_widget = self._itemsdict[instrument_name][parameter_name]['widget']\n double_box = self._itemsdict[instrument_name][parameter_name]['double_box']\n\n field_index = self._fields.index(field)\n\n double_value = False\n if field_index == 0 and double_box is not None:\n double_value = True\n if not double_value:\n tree_widget.setText(field_index + 1, str(value))\n else:\n # update a float value\n try:\n update_value = np.abs(tree_widget.value() - value) > 1e-9\n except Exception as ex:\n logging.debug(ex)\n update_value = True\n if update_value or force_update:\n if not double_box.hasFocus(): # do not update when editing\n logging.debug('update %s to %s' % (parameter_name, value))\n try:\n oldstate = double_box.blockSignals(True)\n double_box.setValue(value)\n double_box.blockSignals(oldstate)\n except Exception as ex:\n logging.debug(ex)","def update_param(self, update_param):\n\n self._update_param = update_param","def set_parameter_and_step(self, pname, value, nstep=2, warning_action=\"default\"):\n setattr(self.p, pname, value)\n with warnings.catch_warnings():\n warnings.simplefilter(warning_action)\n for _ in range(nstep):\n self.step()","def _update_parameter(self, dWxh, dbh, dWhy, dby):\n # Add code to update all the weights and biases here","def change_general_param(self, param, val):\n assert param in self.params, '%s is not recognized as a valid parameter' % param\n self.params[param].change_value(val)","def _set_param(self, name, value):\n self._frozenjson._data[name] = value","def set_option(self, name, value):\n self._params[name] = value","def set_option(self, name, value):\n self._params[name] = value","def write_parameter(self, path, value, attr=None):\n if path.startswith('sample'):\n entry = self.entry.nxroot['entry']\n else:\n entry = self.entry\n if value is not None:\n if attr and path in entry:\n entry[path].attrs[attr] = value\n elif path in entry:\n if isinstance(entry[path], NXgroup):\n del entry[path]\n entry[path] = value\n else:\n entry[path].replace(value)\n elif attr is None:\n entry[path] = value","def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\n self._parameters.append(Parameter(parameter_name, parameter_value))","def _mn_set_par_ ( self , i , val , fix = False ) :\n if not i in self : raise IndexError\n #\n if hasattr ( val , 'value' ) : val = val.value()\n #\n ierr = _mn_exec_ ( self , \"SET PAR\" , i + 1 , val )\n #\n if fix : self.FixParameter ( i ) \n #\n return ierr","def set_parameter_value(self,name,val,parameterized_object=None):\n source = parameterized_object or self.get_source_po(name)\n object.__setattr__(source,name,val)\n\n # update the tkvar\n if name in self._tkvars:\n self._tkvars[name]._original_set(self._object2string(name,val))","def _update_param_from_tkvar(self,param_name,force=False):\n self.debug(\"TkPO._update_param_from_tkvar(%s)\"%param_name)\n\n param_obj = self.get_parameter_object(param_name)\n\n if not lookup_by_class(self.param_immediately_apply_change,\n type(param_obj)) and not force:\n return\n else:\n super(TkParameterized,self)._update_param_from_tkvar(param_name)","def set_params(self, *arg):\n pass","def set_parameters(self, par):\n try:\n for l in self.cell.layers:\n r_curve = cmf.VanGenuchtenMualem(\n Ksat=10**par.pKsat, phi=par.porosity, alpha=par.alpha, n=par.n\n )\n r_curve.w0 = r_curve.fit_w0()\n l.soil = r_curve\n self.cell.saturated_depth = 0.5\n self.gw.potential = self.cell.z - 0.5\n except RuntimeError as e:\n sys.stderr.write(\"Set parameters failed with:\\n\" + str(par) + \"\\n\" + str(e))\n raise","def setValue(self, *args):\n return _libsbml.Parameter_setValue(self, *args)","def set(self,name,val):\n matches = self.grep_param_names(name)\n if len(matches):\n x = self._get_params()\n x[matches] = val\n self._set_params(x)\n else:\n raise AttributeError, \"no parameter matches %s\"%name","def set_setpoint(self, value):\n value = value * self.conf['PSICONV']\n log.debug(\"Set pressure regulator %d to %f\", self.id_, value)\n self.synth.cbox.set_dac(self.id_, value)","def __setup_parameters__(self):\r\n self.M=self.N+1\r\n self.u=1+self.pu\r\n self.d=1-self.pd\r\n self.qu=(math.exp((self.r-self.div)*self.dt)-self.d)/(self.u-self.d)\r\n self.qd=1-self.qu","def _write_double_params_region(self, spec, sim_period):\n\n spec.switch_write_focus(self.REGIONS.DOUBLE_PARAMS.value)\n\n # moc dec 1\n dt = 1.0 / self._fs\n\n spec.write_value(\n math.exp(-(dt / self.MOC_TAU_0)), data_type=DataType.FLOAT_64)\n # moc dec 2 & 3 are currently redundant as moc_factor_2 & 3 are\n # hard coded = 0 in c code - i.e. current version only uses moc\n # dec 1 and moc_factor_1\n\n # moc dec 2\n spec.write_value(\n math.exp(-(dt / self.MOC_TAU_1)), data_type=DataType.FLOAT_64)\n\n # moc dec 3\n spec.write_value(\n math.exp(-(dt / self.MOC_TAU_2)), data_type=DataType.FLOAT_64)\n\n # moc_factor_1 (scaled by 1/sim time step * ratio of drnl dt and\n # sim time step)\n spec.write_value(\n self.RATE_TO_ATTENTUATION_FACTOR * self.MOC_TAU_WEIGHT * dt,\n data_type=DataType.FLOAT_64)\n\n # ctbm\n ctbm = 1e-9 * math.pow(10.0, 32.0 / 20.0)\n spec.write_value(ctbm, data_type=DataType.FLOAT_64)\n\n # recip_ctbm\n spec.write_value(1.0 / ctbm, data_type=DataType.FLOAT_64)\n\n # disp_thresh\n spec.write_value(ctbm / 30e4, data_type=DataType.FLOAT_64)","def put_par(self, parname, value, sep=\".\"):\n pv = self.get_pvname(parname, sep=sep)\n return Pv.put(pv, value)","def pvd(self, pvd):\n\n self.logger.debug(\"In 'pvd' setter.\")\n\n self._pvd = pvd","def SetDatum(self, *args):\n return _XCAFDoc.XCAFDoc_DimTolTool_SetDatum(self, *args)","def set_param(self, param_name, param, set_val = None, type_of_param = None):\n if (set_val is None):\n set_val = True\n param_index = self.index(param_name);\n \n if (type_of_param is None):\n type_of_param = self.type_param[param_index]\n if (param is None):\n param = copy.deepcopy(self._param_val[param_index])\n elif (type(param) is type_of_param):\n param = [param]\n elif (type(param) is not list):\n error_str = (\n \"\"\" \n Trying to set parameter %s to non-integer value (or string if instance name, float if eps+rayeps value), and not a list.\n Type of param: %s\n \"\"\" % (param_name, str(type(param)))\n )\n raise TypeError(error_str)\n\n if (set_val):\n self._param_val[param_index] = copy.deepcopy(param)\n return param","def fill_admin_fieldparam(params, p, value=False):\n params[p.id] = {'param_id': p.id, 'placeholder': p.placeholder,\n 'inputtype': p.paramtype.typename, 'title': p.title}\n params[p.id]['model'] = value if value else ''","def _set_value(o, d):\n if isinstance(o, Param) and not o._mutable:\n return # ignore requests to set immutable params\n else:\n try:\n o.value = d\n except AttributeError:\n o = d # this would be an indexed parameter","def test_ParameterVariable_init_basic_type_value_comment(self):\n\n par = provide_parameter(\"double\", \"test\", value=518,\n comment=\"test comment /\")\n\n self.assertEqual(par.name, \"test\")\n self.assertEqual(par.type, \"double\")\n self.assertEqual(par.value, 518)\n self.assertEqual(par.comment, \"test comment /\")","def putnastrparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnastrparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_thetas(self, value, pos=None):\n\n # TODO: - check for the dimensionality of the arguments\n\n if isinstance(value, (float, int)) and pos is not None and self.thetas is not None:\n self.thetas[pos] = Parameter(value, scale_object=self._scale_params,\n description=\"spectral components \"+str(pos+1)+\" of the temperature profile\",\n return_dimensional=False, input_dimensional=False)\n elif hasattr(value, \"__iter__\"):\n self._create_thetas(value)\n else:\n warnings.warn('A scalar value was provided, but without the `pos` argument indicating in which ' +\n 'component of the spectral decomposition to put it: Spectral decomposition unchanged !' +\n 'Please specify it or give a vector as `value`.')","def params(self,new):\n self._params = new\n self._config_set()\n self._make_model()","def set(self, param, value):\r\n # continuous testing of inputs\r\n if self.testing_unit.testing_level > 1 and not self.testing_unit.c_test_set_inp(param, value):\r\n raise ValueError(\"set won't run, input's aren't valid.\")\r\n\r\n # continuous testing of functional inputs\r\n if self.testing_unit.testing_level > 0:\r\n if param in [\"weighting_bias\"]:\r\n if not [self.testing_unit.c_test_weighting_bias][[\"weighting_bias\"].index(param)](value):\r\n raise ValueError(\"Bad \" + param + \" input. See log or raise testing verbosity.\")\r\n\r\n self.__locals[param] = value # Security Risk\r\n return 1 # Success\r","def write_double(self, process_handle: int, address: int, value):\n self.__bufferSize = 8\n is_write = self.__write_bytes(process_handle, address, value)\n return True if is_write else False","def set_parameters(self,params):\n K3Supervisor.set_parameters(self,params)\n self.blending.set_parameters(self.parameters)","def saveParam(self):\n qApp.emit(QtCore.SIGNAL(\"saveMe\"), self._param)","def saveParam(self):\n qApp.emit(QtCore.SIGNAL(\"saveMe\"), self._param)","def getdouparam(self,param_): # 3\n if not isinstance(param_,dparam): raise TypeError(\"Argument param has wrong type\")\n res,resargs = self.__obj.getdouparam(param_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _parvalue_return_value = resargs\n return _parvalue_return_value","def _set_device_parameter(self, target, *parameters, value):\n if self.is_connected:\n key = target.name + \".\" + parameters[0]\n if not key in self._device_parameters:\n for parameter in parameters:\n if not hasattr(target, parameter):\n raise Exception(f\"The instrument {self.name} does not have parameters {parameter}.\")\n target.set(parameter, value)\n self._device_parameters[key] = value\n elif self._device_parameters[key] != value:\n for parameter in parameters:\n target.set(parameter, value)\n self._device_parameters[key] = value\n else:\n raise Exception(f\"There is no connection to the instrument {self.name}.\")","def __adjust_param(self, option):\n # Get the name of the parameter.\n name = self.__option_params[option]\n\n # Ask the user for a new value.\n value = float(input(\"Enter value for {}: \".format(name)))\n self._params.update(name, value)\n\n # Update the description with the new value.\n desc = self.__make_description(name)\n self.update_description(option, desc)\n\n # Stay on the same menu.\n return self.get_name()","def set_param(self, weights, mu, sigma):\n #XXX: when fromvalues is called, parameters are called twice...\n k, d, mode = check_gmm_param(weights, mu, sigma)\n if not k == self.k:\n raise GmParamError(\"Number of given components is %d, expected %d\" \n % (k, self.k))\n if not d == self.d:\n raise GmParamError(\"Dimension of the given model is %d, \"\\\n \"expected %d\" % (d, self.d))\n if not mode == self.mode and not d == 1:\n raise GmParamError(\"Given covariance mode is %s, expected %s\"\n % (mode, self.mode))\n self.w = weights\n self.mu = mu\n self.va = sigma\n\n self.__is_valid = True","def set_parameter_value(self, parameter_name, new_value):\n self.description[\"config\"][\"values\"][parameter_name][\"value\"] = new_value\n ## Update MongoDB\n #self.mongo_client.cps2_project.objects.update_one(\n #{\"_id\": self.mongo_id},\n #{\"$set\": {\"config.values.\" + parameter_name + \".value\": new_value,\n #\"last_modified.value\": str(datetime.utcnow())}\n #}\n #)\n print(\"Switched the parameter \" + parameter_name + \" to \" + new_value + \" and updated MongoDB.\")","def updateParms(self):\n self.p1text.setText(\"{0:g}\".format(self.parmVal[0]))\n if len(self.parmVal) > 1:\n self.p2text.setText(\"{0:g}\".format(self.parmVal[1]))\n if len(self.parmVal) > 2:\n self.p3text.setText(\"{0:g}\".format(self.parmVal[2]))\n if len(self.parmVal) > 3:\n self.p4text.setText(\"{0:g}\".format(self.parmVal[3]))"],"string":"[\n \"def putdouparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,dparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putdouparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setDouble(self, address: ghidra.program.model.address.Address, value: float) -> None:\\n ...\",\n \"def set_param(self, name, value, *, distrib=None, ref=None):\\n raise NotImplementedError\",\n \"def setParam(self,param,value):\\n if param in self.params.keys():\\n self.params[param] = value\",\n \"def _set_par(vid, par, value):\\n traci.vehicle.setParameter(vid, \\\"carFollowModel.%s\\\" % par, str(value))\",\n \"def set_parameter_value(self, parameter, value):\\n pass\",\n \"def setParameter(self, name, value):\",\n \"def update_parameter(cur, par, new_value):\\n cur.execute(\\\"UPDATE parameters SET value=%f WHERE par='%s';\\\" % \\n (new_value, par))\",\n \"def update_parameter(self, param, val, force=False):\\n self._update_dict[param] = val\\n if force:\\n self._cur_val[param] = None\",\n \"def putparam(self,parname_,parvalue_): # 3\\n res = self.__obj.putparam(parname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setDoubleValue(self, *args):\\n return _libsbml.ConversionOption_setDoubleValue(self, *args)\",\n \"def set_parameter(self, params, name, val):\\n raise NotImplementedError()\",\n \"def plot_parameter(self, parm):\\n # If user wants to plot density, make sure it exists\\n if parm == 'density' and 'density' not in self.ds.data_vars:\\n self.insert_density()\\n \\n if parm == 'theta' and 'theta' not in self.ds.data_vars:\\n self.insert_potential_density()\\n \\n if parm == 'N' and 'N' not in self.ds.data_vars:\\n self.insert_buoyancy_frequency()\\n \\n # Use xarray to plot this parameter\\n self.ds[parm].plot(y=self.ztsp[0])\\n if plt.ylim()[0] <= 0:\\n plt.gca().invert_yaxis()\\n plt.tight_layout()\\n \\n plt.show()\",\n \"def SetParameterValue(self, paramName, value):\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n if paramName == ISENTROPIC_PAR:\\n if (self.ideal != None):\\n self.ideal.SetParameterValue(paramName, value)\",\n \"def set_param(self, param, value):\\n self._set_param_client(param, value)\",\n \"def SetParameterValue(self, paramName, value):\\n if paramName == NUMBTABLE_PAR:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n self.LookupTable.SetParameterValue(paramName, value)\\n\\n if paramName == IGNORECURVE_PAR: \\n #...ignore the lookuptable and remove any specifications\\n if value == 'None': value = None\\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\\n if value:\\n port = self.GetPort(HEAD_PORT)\\n port.SetValue(None, FIXED_V) \\n else:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\",\n \"def setDoubleValue(self, *args):\\n return _libsbml.ConversionProperties_setDoubleValue(self, *args)\",\n \"def set_parameter(self, param, value, stripe = 0, location = 3):\\n if param in self.fpga.cabac_top[0].params:\\n self.fpga.set_cabac_value(param, value, stripe)\\n time.sleep(0.1)\\n self.fpga.send_cabac_config(stripe)\\n\\n elif param in [\\\"V_SL\\\", \\\"V_SH\\\", \\\"V_RGL\\\", \\\"V_RGH\\\", \\\"V_PL\\\", \\\"V_PH\\\"]:\\n self.fpga.set_clock_voltages({param: value})\\n\\n elif param == \\\"I_OS\\\":\\n self.fpga.set_current_source(value, stripe)\\n\\n else:\\n print(\\\"Warning: unidentified parameter for the REB: %s\\\" % param)\",\n \"def set_param(param, num, set_val):\\n param[0][num] = set_val\",\n \"def set_parameter(self, sensor_name, parameter_name, parameter_value):\\n if parameter_name == 'perspective_angle':\\n parameter_value = parameter_value / (180 * 2) * math.pi\\n if parameter_name in self.params_f:\\n error = vrep.simxSetObjectFloatParameter(\\n self.client_id,\\n self.handles[sensor_name + self.postfix],\\n self.params_f[parameter_name],\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxSetFloatSignal(\\n self.client_id,\\n 'change_params',\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxClearFloatSignal(\\n self.client_id,\\n 'change_params',\\n ONE_SHOT_MODE\\n )\\n return error\\n elif parameter_name in self.params_i:\\n error = vrep.simxSetObjectFloatParameter(\\n self.client_id,\\n self.handles[sensor_name + self.postfix],\\n self.params_i[parameter_name],\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxSetFloatSignal(\\n self.client_id,\\n 'change_params',\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxClearFloatSignal(\\n self.client_id,\\n 'change_params',\\n ONE_SHOT_MODE\\n )\\n return error\\n else:\\n return 'Parameter not found'\",\n \"def set_trial_param(\\n self,\\n trial_id: int,\\n param_name: str,\\n param_value_internal: float,\\n distribution: \\\"distributions.BaseDistribution\\\",\\n ) -> bool:\\n raise NotImplementedError\",\n \"def setParamSingleStep(self, instr: str, param: str, value: Any):\\n box = self._itemsdict[instr][param]['double_box']\\n if box is not None:\\n box.setSingleStep(value)\",\n \"def set_param(self, label, val):\\n assert type(label) is str, 'Parameter name \\\"%s\\\" is not string' % label\\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\\n self.params[label] = val\",\n \"def putnadouparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnadouparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def update_parameter(self, name, freq, value):\\n if name not in self._parameters.keys():\\n self.add_parameter(name, [freq], [value])\\n else:\\n param = self.get_parameter(name)\\n param.update_value(freq, value)\",\n \"def setd(self, node, new_double):\\n\\n self.daq.syncSetDouble(f'/{self.device_id}/{node}', new_double)\",\n \"def set_pi(self, val):\\n self.__pi = val\",\n \"def set_parameter(self, params, name, val):\\n if name == \\\"model\\\":\\n params.model = val\\n return params\\n available_models = [\\n entry_point.name\\n for entry_point in pkg_resources.iter_entry_points(\\n \\\"dxtbx.scaling_model_ext\\\"\\n )\\n ]\\n phil_branches = [\\n params.weighting.error_model,\\n params.cut_data,\\n params.scaling_options,\\n params.reflection_selection,\\n params.reflection_selection.random,\\n params.reflection_selection.random.multi_dataset,\\n ]\\n if params.model:\\n phil_branches.append(params.__getattribute__(str(params.model)))\\n elif (\\\".\\\" in name) and (name.split(\\\".\\\")[0] in available_models):\\n # if the user hasn't specified the model, but have done\\n # e.g physical.parameter = *, then set model=physical\\n params.model = name.split(\\\".\\\")[0]\\n phil_branches.append(params.__getattribute__(str(params.model)))\\n if \\\".\\\" in name: # handle e.g physical.absorption_correction\\n name = name.split(\\\".\\\")[-1]\\n for branch in phil_branches:\\n try:\\n branch.__setattr__(name, val)\\n return params\\n except AttributeError:\\n pass\\n # if get here, haven't found what we're trying to set\\n raise ValueError(\\\"Unable to set chosen attribute \\\" + str(name) + \\\"=\\\" + str(val))\",\n \"def set_param(self, param_value):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n\\n setting[1] = param_value\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def setSynapseDynamics(self, param, value):\\n raise NotImplementedError\",\n \"def set_parameter(self, output):\\n self.model.set_parameter(output);\",\n \"def setParameter(self,arg,value):\\n self._params[arg] = value\\n return self._params\",\n \"def adapt(self, domain_distr_param: str, domain_distr_param_value: Union[float, int]):\\n if domain_distr_param not in self.get_field_names():\\n raise KeyError(f'The domain parameter {self.name} does not have a domain distribution parameter '\\n f'called {domain_distr_param}!')\\n setattr(self, domain_distr_param, domain_distr_param_value)\",\n \"def _set_params(self,x):\\r\\n self.k._set_params(x)\",\n \"def set_parametervalue(self, Value, nodename=None, node=None, root=None, ):\\n\\n if nodename != None:\\n node = self.find_node(nodename, root)\\n\\n node_event = self._refind_nodes(self.fn_to_reg([\\\"Manual\\\"]), root=node)[0]\\n\\n if node_event != False:\\n self.set_nodevalue(node_event, Value)\\n return True\\n\\n return False\",\n \"def gui_set_param(self,param_name,val):\\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\\n self._handle_gui_set(param_name,force=True)\",\n \"def putnaintparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnaintparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_param(params, pname, value=None, bounds=None):\\n if value is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.value = value\\n break\\n\\n if bounds is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.bounds = bounds\\n p.vary = True\\n break\",\n \"def _p_set_par_ ( o , index , value ) :\\n return o.setPar ( index , value )\",\n \"def posterior_sample_parameter(self, parameter):\\n pass\",\n \"def __setitem__(self, name: str, value):\\n super(Parameter, self).__setitem__(name, value)\",\n \"def set_parameter(doc, o_path, param, value, mn_consts):\\n if isinstance(value, str):\\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\\n elif isinstance(value, int):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\\n elif isinstance(value, list):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)\",\n \"def set_param(self, param_key, value):\\n return self._params.set_param_value(param_key, value)\",\n \"def put_param(self, attr_name, val):\\n self._params[attr_name] = val\",\n \"def getdouparam(self,param_):\\n parvalue_ = ctypes.c_double()\\n res = __library__.MSK_XX_getdouparam(self.__nativep,param_,ctypes.byref(parvalue_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n parvalue_ = parvalue_.value\\n _parvalue_return_value = parvalue_\\n return (_parvalue_return_value)\",\n \"def set_parameters(self, params):\\n self.kp = params.pgain\",\n \"def log_param(self, name: str, value):\\n self.params[name] = value\\n\\n self._sync_log_event()\",\n \"def set_reg_param(self,reg_param):\\n \\n type_name = type(reg_param).__name__\\n if re.search('float',type_name) != None:\\n if reg_param < 0:\\n raise KINSOL_Exception(\\\"Value sent to set_reg_param must be equal to or larger than zero.\\\")\\n else:\\n self.reg_param = reg_param\\n else:\\n raise KINSOL_Exception(\\\"The variable sent to 'set_reg_param' must be a float.\\\")\",\n \"def set_params(self, params):\",\n \"def set_value(self, device_name, val):\\n epics.caput(device_name, val)\\n\\n\\t\\t#mu = mu\\n\\t\\t#sig = math.sqrt(abs(mu))\\n\\t\\t#y = (float(x)-mu)/(sig)\",\n \"def parameter(self, parameter):\\n\\n self._parameter = parameter\",\n \"def setvalue(self,num,name,val):\\n self.M.reconfigure(num,{name:float(val)})\",\n \"def set_param_motor():\\n servo.setSpeed(0, 0) # max = 255\\n servo.setAccel(0, 0)\\n servo.setSpeed(1, 150) # max = 255\\n servo.setAccel(1, 150)\",\n \"def set_param(self):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n setting[1] = str(self.param_value)\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def putparam(self,parname_,parvalue_):\\n if isinstance(parname_,unicode):\\n parname_ = parname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def test_ParameterVariable_init_basic_type_value(self):\\n\\n par = provide_parameter(\\\"double\\\", \\\"test\\\", value=518)\\n\\n self.assertEqual(par.name, \\\"test\\\")\\n self.assertEqual(par.type, \\\"double\\\")\\n self.assertEqual(par.value, 518)\",\n \"def _set_parameter(self):\\n # Get parameter keys\\n self.input_parameter = self.parameter_combobox.currentText()\\n self.result_parameter = self.result_parameters[self.input_parameter]\\n # Adjust axes labels\\n self.ax.set_xlabel('{} steunpunt'.format(self.input_parameter))\\n self.ax.set_ylabel('{} uitvoerlocatie'.format(self.input_parameter))\\n # Set data\\n self._set_data()\",\n \"def add_parameter(self,\\n name, # The name of the parameter\\n scaling=None, # The type of scaling to be used for the parameter\\n type=\\\"int\\\", # The type of the parameter, such as float\\n min=0, # The minimum value of the parameter\\n max=100, # The maximum value of the parameter\\n significance=1, # The smallest significant step size\\n value=None, # The value or value parameters\\n distribution=None): # The distribution of the parameter\\n config = {\\\"scaling\\\" : scaling, \\n \\\"type\\\": type,\\n \\\"min\\\": min, \\n \\\"max\\\": max, \\n \\\"significance\\\": significance,\\n \\\"value\\\": value,\\n \\\"distribution\\\": distribution}\\n self.param_names.append(name)\\n self.param_settings.append(config)\",\n \"def _paramUpdate(self):\\n\\n # Update the database attributes accordingly.\\n dt.utilities.DB_attrs_save(self.Database, self.newParam)\",\n \"def _set_field(self, instrument_name, parameter_name, field, value, force_update):\\n if self.verbose >= 2:\\n print('_set_field: %s %s: %s' % (instrument_name, parameter_name, str(value)))\\n tree_widget = self._itemsdict[instrument_name][parameter_name]['widget']\\n double_box = self._itemsdict[instrument_name][parameter_name]['double_box']\\n\\n field_index = self._fields.index(field)\\n\\n double_value = False\\n if field_index == 0 and double_box is not None:\\n double_value = True\\n if not double_value:\\n tree_widget.setText(field_index + 1, str(value))\\n else:\\n # update a float value\\n try:\\n update_value = np.abs(tree_widget.value() - value) > 1e-9\\n except Exception as ex:\\n logging.debug(ex)\\n update_value = True\\n if update_value or force_update:\\n if not double_box.hasFocus(): # do not update when editing\\n logging.debug('update %s to %s' % (parameter_name, value))\\n try:\\n oldstate = double_box.blockSignals(True)\\n double_box.setValue(value)\\n double_box.blockSignals(oldstate)\\n except Exception as ex:\\n logging.debug(ex)\",\n \"def update_param(self, update_param):\\n\\n self._update_param = update_param\",\n \"def set_parameter_and_step(self, pname, value, nstep=2, warning_action=\\\"default\\\"):\\n setattr(self.p, pname, value)\\n with warnings.catch_warnings():\\n warnings.simplefilter(warning_action)\\n for _ in range(nstep):\\n self.step()\",\n \"def _update_parameter(self, dWxh, dbh, dWhy, dby):\\n # Add code to update all the weights and biases here\",\n \"def change_general_param(self, param, val):\\n assert param in self.params, '%s is not recognized as a valid parameter' % param\\n self.params[param].change_value(val)\",\n \"def _set_param(self, name, value):\\n self._frozenjson._data[name] = value\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def write_parameter(self, path, value, attr=None):\\n if path.startswith('sample'):\\n entry = self.entry.nxroot['entry']\\n else:\\n entry = self.entry\\n if value is not None:\\n if attr and path in entry:\\n entry[path].attrs[attr] = value\\n elif path in entry:\\n if isinstance(entry[path], NXgroup):\\n del entry[path]\\n entry[path] = value\\n else:\\n entry[path].replace(value)\\n elif attr is None:\\n entry[path] = value\",\n \"def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\\n self._parameters.append(Parameter(parameter_name, parameter_value))\",\n \"def _mn_set_par_ ( self , i , val , fix = False ) :\\n if not i in self : raise IndexError\\n #\\n if hasattr ( val , 'value' ) : val = val.value()\\n #\\n ierr = _mn_exec_ ( self , \\\"SET PAR\\\" , i + 1 , val )\\n #\\n if fix : self.FixParameter ( i ) \\n #\\n return ierr\",\n \"def set_parameter_value(self,name,val,parameterized_object=None):\\n source = parameterized_object or self.get_source_po(name)\\n object.__setattr__(source,name,val)\\n\\n # update the tkvar\\n if name in self._tkvars:\\n self._tkvars[name]._original_set(self._object2string(name,val))\",\n \"def _update_param_from_tkvar(self,param_name,force=False):\\n self.debug(\\\"TkPO._update_param_from_tkvar(%s)\\\"%param_name)\\n\\n param_obj = self.get_parameter_object(param_name)\\n\\n if not lookup_by_class(self.param_immediately_apply_change,\\n type(param_obj)) and not force:\\n return\\n else:\\n super(TkParameterized,self)._update_param_from_tkvar(param_name)\",\n \"def set_params(self, *arg):\\n pass\",\n \"def set_parameters(self, par):\\n try:\\n for l in self.cell.layers:\\n r_curve = cmf.VanGenuchtenMualem(\\n Ksat=10**par.pKsat, phi=par.porosity, alpha=par.alpha, n=par.n\\n )\\n r_curve.w0 = r_curve.fit_w0()\\n l.soil = r_curve\\n self.cell.saturated_depth = 0.5\\n self.gw.potential = self.cell.z - 0.5\\n except RuntimeError as e:\\n sys.stderr.write(\\\"Set parameters failed with:\\\\n\\\" + str(par) + \\\"\\\\n\\\" + str(e))\\n raise\",\n \"def setValue(self, *args):\\n return _libsbml.Parameter_setValue(self, *args)\",\n \"def set(self,name,val):\\n matches = self.grep_param_names(name)\\n if len(matches):\\n x = self._get_params()\\n x[matches] = val\\n self._set_params(x)\\n else:\\n raise AttributeError, \\\"no parameter matches %s\\\"%name\",\n \"def set_setpoint(self, value):\\n value = value * self.conf['PSICONV']\\n log.debug(\\\"Set pressure regulator %d to %f\\\", self.id_, value)\\n self.synth.cbox.set_dac(self.id_, value)\",\n \"def __setup_parameters__(self):\\r\\n self.M=self.N+1\\r\\n self.u=1+self.pu\\r\\n self.d=1-self.pd\\r\\n self.qu=(math.exp((self.r-self.div)*self.dt)-self.d)/(self.u-self.d)\\r\\n self.qd=1-self.qu\",\n \"def _write_double_params_region(self, spec, sim_period):\\n\\n spec.switch_write_focus(self.REGIONS.DOUBLE_PARAMS.value)\\n\\n # moc dec 1\\n dt = 1.0 / self._fs\\n\\n spec.write_value(\\n math.exp(-(dt / self.MOC_TAU_0)), data_type=DataType.FLOAT_64)\\n # moc dec 2 & 3 are currently redundant as moc_factor_2 & 3 are\\n # hard coded = 0 in c code - i.e. current version only uses moc\\n # dec 1 and moc_factor_1\\n\\n # moc dec 2\\n spec.write_value(\\n math.exp(-(dt / self.MOC_TAU_1)), data_type=DataType.FLOAT_64)\\n\\n # moc dec 3\\n spec.write_value(\\n math.exp(-(dt / self.MOC_TAU_2)), data_type=DataType.FLOAT_64)\\n\\n # moc_factor_1 (scaled by 1/sim time step * ratio of drnl dt and\\n # sim time step)\\n spec.write_value(\\n self.RATE_TO_ATTENTUATION_FACTOR * self.MOC_TAU_WEIGHT * dt,\\n data_type=DataType.FLOAT_64)\\n\\n # ctbm\\n ctbm = 1e-9 * math.pow(10.0, 32.0 / 20.0)\\n spec.write_value(ctbm, data_type=DataType.FLOAT_64)\\n\\n # recip_ctbm\\n spec.write_value(1.0 / ctbm, data_type=DataType.FLOAT_64)\\n\\n # disp_thresh\\n spec.write_value(ctbm / 30e4, data_type=DataType.FLOAT_64)\",\n \"def put_par(self, parname, value, sep=\\\".\\\"):\\n pv = self.get_pvname(parname, sep=sep)\\n return Pv.put(pv, value)\",\n \"def pvd(self, pvd):\\n\\n self.logger.debug(\\\"In 'pvd' setter.\\\")\\n\\n self._pvd = pvd\",\n \"def SetDatum(self, *args):\\n return _XCAFDoc.XCAFDoc_DimTolTool_SetDatum(self, *args)\",\n \"def set_param(self, param_name, param, set_val = None, type_of_param = None):\\n if (set_val is None):\\n set_val = True\\n param_index = self.index(param_name);\\n \\n if (type_of_param is None):\\n type_of_param = self.type_param[param_index]\\n if (param is None):\\n param = copy.deepcopy(self._param_val[param_index])\\n elif (type(param) is type_of_param):\\n param = [param]\\n elif (type(param) is not list):\\n error_str = (\\n \\\"\\\"\\\" \\n Trying to set parameter %s to non-integer value (or string if instance name, float if eps+rayeps value), and not a list.\\n Type of param: %s\\n \\\"\\\"\\\" % (param_name, str(type(param)))\\n )\\n raise TypeError(error_str)\\n\\n if (set_val):\\n self._param_val[param_index] = copy.deepcopy(param)\\n return param\",\n \"def fill_admin_fieldparam(params, p, value=False):\\n params[p.id] = {'param_id': p.id, 'placeholder': p.placeholder,\\n 'inputtype': p.paramtype.typename, 'title': p.title}\\n params[p.id]['model'] = value if value else ''\",\n \"def _set_value(o, d):\\n if isinstance(o, Param) and not o._mutable:\\n return # ignore requests to set immutable params\\n else:\\n try:\\n o.value = d\\n except AttributeError:\\n o = d # this would be an indexed parameter\",\n \"def test_ParameterVariable_init_basic_type_value_comment(self):\\n\\n par = provide_parameter(\\\"double\\\", \\\"test\\\", value=518,\\n comment=\\\"test comment /\\\")\\n\\n self.assertEqual(par.name, \\\"test\\\")\\n self.assertEqual(par.type, \\\"double\\\")\\n self.assertEqual(par.value, 518)\\n self.assertEqual(par.comment, \\\"test comment /\\\")\",\n \"def putnastrparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnastrparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_thetas(self, value, pos=None):\\n\\n # TODO: - check for the dimensionality of the arguments\\n\\n if isinstance(value, (float, int)) and pos is not None and self.thetas is not None:\\n self.thetas[pos] = Parameter(value, scale_object=self._scale_params,\\n description=\\\"spectral components \\\"+str(pos+1)+\\\" of the temperature profile\\\",\\n return_dimensional=False, input_dimensional=False)\\n elif hasattr(value, \\\"__iter__\\\"):\\n self._create_thetas(value)\\n else:\\n warnings.warn('A scalar value was provided, but without the `pos` argument indicating in which ' +\\n 'component of the spectral decomposition to put it: Spectral decomposition unchanged !' +\\n 'Please specify it or give a vector as `value`.')\",\n \"def params(self,new):\\n self._params = new\\n self._config_set()\\n self._make_model()\",\n \"def set(self, param, value):\\r\\n # continuous testing of inputs\\r\\n if self.testing_unit.testing_level > 1 and not self.testing_unit.c_test_set_inp(param, value):\\r\\n raise ValueError(\\\"set won't run, input's aren't valid.\\\")\\r\\n\\r\\n # continuous testing of functional inputs\\r\\n if self.testing_unit.testing_level > 0:\\r\\n if param in [\\\"weighting_bias\\\"]:\\r\\n if not [self.testing_unit.c_test_weighting_bias][[\\\"weighting_bias\\\"].index(param)](value):\\r\\n raise ValueError(\\\"Bad \\\" + param + \\\" input. See log or raise testing verbosity.\\\")\\r\\n\\r\\n self.__locals[param] = value # Security Risk\\r\\n return 1 # Success\\r\",\n \"def write_double(self, process_handle: int, address: int, value):\\n self.__bufferSize = 8\\n is_write = self.__write_bytes(process_handle, address, value)\\n return True if is_write else False\",\n \"def set_parameters(self,params):\\n K3Supervisor.set_parameters(self,params)\\n self.blending.set_parameters(self.parameters)\",\n \"def saveParam(self):\\n qApp.emit(QtCore.SIGNAL(\\\"saveMe\\\"), self._param)\",\n \"def saveParam(self):\\n qApp.emit(QtCore.SIGNAL(\\\"saveMe\\\"), self._param)\",\n \"def getdouparam(self,param_): # 3\\n if not isinstance(param_,dparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res,resargs = self.__obj.getdouparam(param_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _parvalue_return_value = resargs\\n return _parvalue_return_value\",\n \"def _set_device_parameter(self, target, *parameters, value):\\n if self.is_connected:\\n key = target.name + \\\".\\\" + parameters[0]\\n if not key in self._device_parameters:\\n for parameter in parameters:\\n if not hasattr(target, parameter):\\n raise Exception(f\\\"The instrument {self.name} does not have parameters {parameter}.\\\")\\n target.set(parameter, value)\\n self._device_parameters[key] = value\\n elif self._device_parameters[key] != value:\\n for parameter in parameters:\\n target.set(parameter, value)\\n self._device_parameters[key] = value\\n else:\\n raise Exception(f\\\"There is no connection to the instrument {self.name}.\\\")\",\n \"def __adjust_param(self, option):\\n # Get the name of the parameter.\\n name = self.__option_params[option]\\n\\n # Ask the user for a new value.\\n value = float(input(\\\"Enter value for {}: \\\".format(name)))\\n self._params.update(name, value)\\n\\n # Update the description with the new value.\\n desc = self.__make_description(name)\\n self.update_description(option, desc)\\n\\n # Stay on the same menu.\\n return self.get_name()\",\n \"def set_param(self, weights, mu, sigma):\\n #XXX: when fromvalues is called, parameters are called twice...\\n k, d, mode = check_gmm_param(weights, mu, sigma)\\n if not k == self.k:\\n raise GmParamError(\\\"Number of given components is %d, expected %d\\\" \\n % (k, self.k))\\n if not d == self.d:\\n raise GmParamError(\\\"Dimension of the given model is %d, \\\"\\\\\\n \\\"expected %d\\\" % (d, self.d))\\n if not mode == self.mode and not d == 1:\\n raise GmParamError(\\\"Given covariance mode is %s, expected %s\\\"\\n % (mode, self.mode))\\n self.w = weights\\n self.mu = mu\\n self.va = sigma\\n\\n self.__is_valid = True\",\n \"def set_parameter_value(self, parameter_name, new_value):\\n self.description[\\\"config\\\"][\\\"values\\\"][parameter_name][\\\"value\\\"] = new_value\\n ## Update MongoDB\\n #self.mongo_client.cps2_project.objects.update_one(\\n #{\\\"_id\\\": self.mongo_id},\\n #{\\\"$set\\\": {\\\"config.values.\\\" + parameter_name + \\\".value\\\": new_value,\\n #\\\"last_modified.value\\\": str(datetime.utcnow())}\\n #}\\n #)\\n print(\\\"Switched the parameter \\\" + parameter_name + \\\" to \\\" + new_value + \\\" and updated MongoDB.\\\")\",\n \"def updateParms(self):\\n self.p1text.setText(\\\"{0:g}\\\".format(self.parmVal[0]))\\n if len(self.parmVal) > 1:\\n self.p2text.setText(\\\"{0:g}\\\".format(self.parmVal[1]))\\n if len(self.parmVal) > 2:\\n self.p3text.setText(\\\"{0:g}\\\".format(self.parmVal[2]))\\n if len(self.parmVal) > 3:\\n self.p4text.setText(\\\"{0:g}\\\".format(self.parmVal[3]))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68033856","0.66483873","0.6322731","0.62533104","0.62401855","0.6139179","0.61077297","0.6105442","0.60806364","0.6070325","0.6022202","0.60140693","0.600733","0.5987992","0.5986172","0.5898448","0.5880513","0.5780829","0.57804143","0.5722894","0.5679257","0.56750154","0.56186754","0.5608217","0.56066984","0.5603155","0.5595689","0.55932283","0.55595803","0.555427","0.5542238","0.5536386","0.5518559","0.5516334","0.5492513","0.54616606","0.54562217","0.5429461","0.542737","0.54171056","0.5407589","0.5397817","0.5389136","0.53805315","0.5370109","0.5343491","0.533889","0.5330386","0.5329961","0.53269047","0.53202754","0.53193545","0.5312282","0.53018963","0.5291076","0.5288395","0.52852637","0.5264493","0.52587306","0.52572525","0.5236838","0.52280426","0.5204385","0.5196911","0.518807","0.5187562","0.5187562","0.51764053","0.5161399","0.5161152","0.5159844","0.5149034","0.51429737","0.513986","0.5134421","0.51327497","0.5113652","0.5100496","0.5095884","0.5095256","0.5094436","0.5091713","0.5085745","0.50846905","0.50761145","0.50673","0.50638735","0.505501","0.50304854","0.5028706","0.5028023","0.50270724","0.5020616","0.5020616","0.5020099","0.5015868","0.50064313","0.49936944","0.49920303","0.49842092"],"string":"[\n \"0.68033856\",\n \"0.66483873\",\n \"0.6322731\",\n \"0.62533104\",\n \"0.62401855\",\n \"0.6139179\",\n \"0.61077297\",\n \"0.6105442\",\n \"0.60806364\",\n \"0.6070325\",\n \"0.6022202\",\n \"0.60140693\",\n \"0.600733\",\n \"0.5987992\",\n \"0.5986172\",\n \"0.5898448\",\n \"0.5880513\",\n \"0.5780829\",\n \"0.57804143\",\n \"0.5722894\",\n \"0.5679257\",\n \"0.56750154\",\n \"0.56186754\",\n \"0.5608217\",\n \"0.56066984\",\n \"0.5603155\",\n \"0.5595689\",\n \"0.55932283\",\n \"0.55595803\",\n \"0.555427\",\n \"0.5542238\",\n \"0.5536386\",\n \"0.5518559\",\n \"0.5516334\",\n \"0.5492513\",\n \"0.54616606\",\n \"0.54562217\",\n \"0.5429461\",\n \"0.542737\",\n \"0.54171056\",\n \"0.5407589\",\n \"0.5397817\",\n \"0.5389136\",\n \"0.53805315\",\n \"0.5370109\",\n \"0.5343491\",\n \"0.533889\",\n \"0.5330386\",\n \"0.5329961\",\n \"0.53269047\",\n \"0.53202754\",\n \"0.53193545\",\n \"0.5312282\",\n \"0.53018963\",\n \"0.5291076\",\n \"0.5288395\",\n \"0.52852637\",\n \"0.5264493\",\n \"0.52587306\",\n \"0.52572525\",\n \"0.5236838\",\n \"0.52280426\",\n \"0.5204385\",\n \"0.5196911\",\n \"0.518807\",\n \"0.5187562\",\n \"0.5187562\",\n \"0.51764053\",\n \"0.5161399\",\n \"0.5161152\",\n \"0.5159844\",\n \"0.5149034\",\n \"0.51429737\",\n \"0.513986\",\n \"0.5134421\",\n \"0.51327497\",\n \"0.5113652\",\n \"0.5100496\",\n \"0.5095884\",\n \"0.5095256\",\n \"0.5094436\",\n \"0.5091713\",\n \"0.5085745\",\n \"0.50846905\",\n \"0.50761145\",\n \"0.50673\",\n \"0.50638735\",\n \"0.505501\",\n \"0.50304854\",\n \"0.5028706\",\n \"0.5028023\",\n \"0.50270724\",\n \"0.5020616\",\n \"0.5020616\",\n \"0.5020099\",\n \"0.5015868\",\n \"0.50064313\",\n \"0.49936944\",\n \"0.49920303\",\n \"0.49842092\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5916938"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":11976,"cells":{"query":{"kind":"string","value":"Sets an integer parameter. putintparam(self,param_,parvalue_)"},"document":{"kind":"string","value":"def putintparam(self,param_,parvalue_):\n res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putintparam(self,param_,parvalue_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putintparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getintparam(self,param_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res,resargs = self.__obj.getintparam(param_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _parvalue_return_value = resargs\n return _parvalue_return_value","def set_param(self, param, value):\n self._set_param_client(param, value)","def getintparam(self,param_):\n parvalue_ = ctypes.c_int32()\n res = __library__.MSK_XX_getintparam(self.__nativep,param_,ctypes.byref(parvalue_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n parvalue_ = parvalue_.value\n _parvalue_return_value = parvalue_\n return (_parvalue_return_value)","def setInteger(self, value):","def setInteger(self, value):","def setInt(self, addr: ghidra.program.model.address.Address, value: int) -> None:\n ...","def set_param(param, num, set_val):\n param[0][num] = set_val","def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value","def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","async def set_param(self, param: str, value: int) -> ArchonCommand:\n cmd = await self.send_command(f\"FASTLOADPARAM {param} {value}\")\n if not cmd.succeeded():\n raise ArchonError(\n f\"Failed setting parameter {param!r} ({cmd.status.name}).\"\n )\n return cmd","def setInt(self, address: ghidra.program.model.address.Address, value: int) -> None:\n ...","def setInteger(self, value):\n assert self._is_int is True\n self._value = value","def setInteger(self, value: int):\n self.value = value","def _mn_set_par_ ( self , i , val , fix = False ) :\n if not i in self : raise IndexError\n #\n if hasattr ( val , 'value' ) : val = val.value()\n #\n ierr = _mn_exec_ ( self , \"SET PAR\" , i + 1 , val )\n #\n if fix : self.FixParameter ( i ) \n #\n return ierr","def setInt(self, addr: ghidra.program.model.address.Address, value: int, bigEndian: bool) -> None:\n ...","def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val","def set_parameter(self, params, name, val):\n raise NotImplementedError()","def putparam(self,parname_,parvalue_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_param(self, param_key, value):\n return self._params.set_param_value(param_key, value)","def setParameter(self, name, value):","def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getIntParam(self, paramkey, default=None):\n value = self.request.getParameter(paramkey)\n if value is None: return default\n try: return int(value)\n except: return default","def set_parameter_value(self, parameter, value):\n pass","def set_parameter(doc, o_path, param, value, mn_consts):\n if isinstance(value, str):\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\n elif isinstance(value, int):\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\n elif isinstance(value, list):\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)","def putnaintparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setIntValue(self, *args):\n return _libsbml.ConversionProperties_setIntValue(self, *args)","def integer(self, integer):\n\n self._integer = integer","def setInt(self, key, value):\n self.__config.setValue(key, QtCore.QVariant(value))\n self.__saved = False","def setParameter(self,arg,value):\n self._params[arg] = value\n return self._params","def SetParameterValue(self, paramName, value):\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n if paramName == ISENTROPIC_PAR:\n if (self.ideal != None):\n self.ideal.SetParameterValue(paramName, value)","def setIntValue(self, *args):\n return _libsbml.ConversionOption_setIntValue(self, *args)","def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError","def set(self, param, value):\r\n # continuous testing of inputs\r\n if self.testing_unit.testing_level > 1 and not self.testing_unit.c_test_set_inp(param, value):\r\n raise ValueError(\"set won't run, input's aren't valid.\")\r\n\r\n # continuous testing of functional inputs\r\n if self.testing_unit.testing_level > 0:\r\n if param in [\"weighting_bias\"]:\r\n if not [self.testing_unit.c_test_weighting_bias][[\"weighting_bias\"].index(param)](value):\r\n raise ValueError(\"Bad \" + param + \" input. See log or raise testing verbosity.\")\r\n\r\n self.__locals[param] = value # Security Risk\r\n return 1 # Success\r","def put_param(self, attr_name, val):\n self._params[attr_name] = val","def set_param(param, value, verbose=False):\n set_param_raw(param, yaml.load(value), verbose=verbose)","def set_param(command):\n namespace = app.main(command)\n assert namespace.command == 'sp' or namespace.command == \"setparam\"\n assert namespace.name == \"test\"\n assert namespace.value == \"test\"","def seti(self, node, new_int):\n\n self.daq.syncSetInt(f'/{self.device_id}/{node}', new_int)","def change_general_param(self, param, val):\n assert param in self.params, '%s is not recognized as a valid parameter' % param\n self.params[param].change_value(val)","def set_param(self, name, value):\n name = str(name)\n value = str(value)\n cxnlib.CXNNetSetParam(self.handle,\n ctypes.c_char_p(name.encode('utf-8')),\n ctypes.c_char_p(value.encode('utf-8')))","def writeInt(self, value: int):\n self._pack('!i', value)","def _p_set_par_ ( o , index , value ) :\n return o.setPar ( index , value )","def set_params(self, params):","def _set_int(self, commands, name):\n if name in commands:\n try:\n value = int(commands[name])\n setattr(self, name, value)\n except ValueError:\n pass","def setIntegerOption(self, option, value):\n result = self.__lib.voikkoSetIntegerOption(self.__handle, option, value)\n if result == 0:\n raise VoikkoException(\"Could not set integer option %s to value %s\" % (option, value))","def value(self, p_int): # real signature unknown; restored from __doc__\n pass","def add_id(self, param_id):\n self.param_id = param_id","def setChanInt(\n self,\n u,\n chan,\n intval,\n ):\n\n # print \"setting chan %d to %d\" % (chan,intval)\n # sys.stdout.flush()\n\n self.DMX[u].set_chan_int(chan, intval)","def __type_correct_int(self):\n\n strTestName = 'Type (int) is given (correct)'\n RxCSObject = _RxCSobject()\n\n RxCSObject.paramAddOpt('parameter1', 'type \\'int\\' parameter')\n RxCSObject.paramType('parameter1', (int))\n RxCSObject.parameter1 = int(1)\n\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)","def getIntParam(self, params, name):\n try:\n return int(params.get(name))\n except:\n return None","def set_param(self, param_name, param, set_val = None, type_of_param = None):\n if (set_val is None):\n set_val = True\n param_index = self.index(param_name);\n \n if (type_of_param is None):\n type_of_param = self.type_param[param_index]\n if (param is None):\n param = copy.deepcopy(self._param_val[param_index])\n elif (type(param) is type_of_param):\n param = [param]\n elif (type(param) is not list):\n error_str = (\n \"\"\" \n Trying to set parameter %s to non-integer value (or string if instance name, float if eps+rayeps value), and not a list.\n Type of param: %s\n \"\"\" % (param_name, str(type(param)))\n )\n raise TypeError(error_str)\n\n if (set_val):\n self._param_val[param_index] = copy.deepcopy(param)\n return param","def SetSpacing(self, p_int, ):\n ...","def set_pi(self, val):\n self.__pi = val","def param(self, param):\n if param is not None and len(param) < 1:\n raise ValueError(\"Invalid value for `param`, length must be greater than or equal to `1`\") # noqa: E501\n\n self._param = param","def test_creation_int():\n value = 1\n\n num_a = param.Integer(value=value)\n assert num_a.value == value","def _set_par(vid, par, value):\n traci.vehicle.setParameter(vid, \"carFollowModel.%s\" % par, str(value))","def ipi(self, ipi):\n\n self._ipi = ipi","def isintparname(self,parname_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n param_ = ctypes.c_int32()\n res = __library__.MSK_XX_isintparname(self.__nativep,parname_,ctypes.byref(param_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = iparam(param_.value)\n return (_param_return_value)","def set_params(self, *arg):\n pass","def gui_set_param(self,param_name,val):\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\n self._handle_gui_set(param_name,force=True)","def _get_int_param(request, param):\n try:\n int_param = utils.validate_integer(request.GET[param], param,\n min_value=0)\n except exception.InvalidInput as e:\n raise webob.exc.HTTPBadRequest(explanation=e.format_message())\n return int_param","def putstrparam(self,param_,parvalue_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putstrparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def update_parameter(self, param, val, force=False):\n self._update_dict[param] = val\n if force:\n self._cur_val[param] = None","def SetParameterValue(self, paramName, value):\n if paramName == NUMBTABLE_PAR:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n self.LookupTable.SetParameterValue(paramName, value)\n\n if paramName == IGNORECURVE_PAR: \n #...ignore the lookuptable and remove any specifications\n if value == 'None': value = None\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\n if value:\n port = self.GetPort(HEAD_PORT)\n port.SetValue(None, FIXED_V) \n else:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)","def setTemplateParameter(self,name,value):\n self.tplparam[name] = value","def SetParameterValue(self, paramName, value):\n super(LookupTable, self).SetParameterValue(paramName, value)\n if paramName == NUMBTABLE_PAR:\n self.SetTableCount()\n elif paramName == NUMBSERIES_PAR:\n self.SetSeriesCount()\n elif paramName == TABLETAGTYPE_PAR:\n self.SetTableTagType()\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\n idx = long(paramName[len(SERIESTYPE_PAR):])\n self.SetSeriesTypes(idx)","def update_parameter(cur, par, new_value):\n cur.execute(\"UPDATE parameters SET value=%f WHERE par='%s';\" % \n (new_value, par))","def int_parameter(level, maxval):\n return int(level * maxval / 10)","def set(self, key, param: Param):\n if not isinstance(param, Param):\n raise ValueError(\"Only accepts a Param instance.\")\n self._params[key] = param","def Set(self,value):\n self.Bus.Write_uInt8(self.Address,0x50+self.Pin,value)","def set_param_raw(param, value, verbose=False):\n if type(value) == dict:\n # #1098 changing dictionary behavior to be an update, rather\n # than replace behavior.\n for k, v in value.items():\n # dictionary keys must be non-unicode strings\n if isinstance(k, str):\n set_param_raw(ns_join(param, k), v, verbose=verbose)\n else:\n raise RosParamException(\"YAML dictionaries must have string keys. Invalid dictionary is:\\n%s\"%value)\n else:\n try:\n expected_type = long\n except NameError :\n expected_type = int\n \n if type(value) == expected_type:\n if value > sys.maxsize:\n raise RosParamException(\"Overflow: Parameter Server integers must be 32-bit signed integers:\\n\\t-%s <= value <= %s\"%(maxint - 1, maxint))\n \n try:\n get_param_server().setParam(param, value)\n except socket.error:\n raise RosParamIOException(\"Unable to communicate with master!\")\n if verbose:\n print(\"set parameter [%s] to [%s]\"%(param, value))","def set_param(self, param_value):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n\n setting[1] = param_value\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def def_pid(self,pid):\n self.pid=int(pid)","def set_int_reset():\n int_reset = request.params.get(\"int_reset\", 0) == \"true\"\n pid = request.params.get(\"pid\", 1, type=int)\n retval = RP_LIB.rp_PIDSetIntReset(pid, int_reset)\n if retval != 0:\n LOG.error(\"Failed to set PID integrator reset. Error code: %s\", ERROR_CODES[retval])","def c_test_set_inp(self, param, value):\r\n ret = 1\r\n if \"__hash__\" not in dir(param): # param must be hashable\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" is not hashable. It will be unable to be set in a dict.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is hashable.\")\r\n if param in [\"population_size\", \"time_constraint\", \"generations\", \"point_count\",\r\n \"PSO_VELOCITY_WEIGHT\", \"PSO_INDIVIDUAL_WEIGHT\", \"PSO_GROUP_WEIGHT\"]:\r\n if not ((isinstance(value, int) or\r\n isinstance(value, float) or\r\n isinstance(value, long))):\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be of a number. It is \" + str(value))\r\n ret = 0\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is correctly set to a number.\")\r\n if value < 0:\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be greater than zero.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is greater than zero.\")\r\n if param in [\"population_size\", \"generations\", \"point_count\"]:\r\n if not isinstance(value, int):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be an integer. It is \" + str(value))\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is an integer.\")\r\n if param in [\"fitness_function\", \"weighting_bias\"]:\r\n if not callable(value):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be a callable function.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is a callable function.\")\r\n if param == \"end_condition\":\r\n if value not in [\"time_constraint\", \"generations\"]:\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be 'time_constraint' or 'generations'\")\r\n else:\r\n if self.verbosity > 1:\r\n print(\"ERROR: \" + param + \" is a correct string.\")\r\n if param == \"seed\":\r\n if not (value is None or isinstance(value, int)):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" is incorrectly set.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is correctly set.\")\r\n return ret","def InsertPoint(self, p_int, ):\n ...","def isintparname(self,parname_): # 3\n res,resargs = self.__obj.isintparname(parname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = resargs\n _param_return_value = iparam(_param_return_value)\n return _param_return_value","def set_param(self, name, val):\n # name will be 'colorR', 'colorG', 'colorB'\n rgb255 = int(val * 255)\n if name == 'colorR':\n self.color.r = rgb255\n elif name == 'colorG':\n self.color.g = rgb255\n elif name == 'colorB':\n self.color.b = rgb255","def write_int32(self, val, timeout = 0):\n self.write(struct.pack(\"!i\", val), timeout)","def set_value(self,parameter_number,value):\n code = int(\"01100000\",2) | parameter_number\n command = pack('<BH',code,int(rint(value)))\n reply = self.query(command = command,ser = self.ser, count=1)\n if len(reply) != 1:\n warn(\"expecting 1, got %d bytes\" % len(reply)); return\n reply_code, = unpack('B',reply)\n if reply_code != code: warn(\"expecting 0x%X, got 0x%X\" % (code,reply_code))","def set_parameters(self, params):\n self.kp = params.pgain","def CONST_INT(self, t):\n t.value = int(t.value)\n return t","def set_parameter(self, param, value, stripe = 0, location = 3):\n if param in self.fpga.cabac_top[0].params:\n self.fpga.set_cabac_value(param, value, stripe)\n time.sleep(0.1)\n self.fpga.send_cabac_config(stripe)\n\n elif param in [\"V_SL\", \"V_SH\", \"V_RGL\", \"V_RGH\", \"V_PL\", \"V_PH\"]:\n self.fpga.set_clock_voltages({param: value})\n\n elif param == \"I_OS\":\n self.fpga.set_current_source(value, stripe)\n\n else:\n print(\"Warning: unidentified parameter for the REB: %s\" % param)","def set_params(self, *argv, **kwargs):\n pass","def set_params(self, *argv, **kwargs):\n pass","def set_params(self, *argv, **kwargs):\n pass","def set_params(self, *argv, **kwargs):\n pass","def set_params(self, *argv, **kwargs):\n pass","def _setIntFeature(self, valueToSet):\n\n errorCode = VimbaDLL.featureIntSet(self._handle,\n self._name,\n valueToSet)\n if errorCode != 0:\n raise VimbaException(errorCode)","def __setJobParam( self, name, value ):\n if not self.jobID:\n return S_ERROR( 'JobID not defined' )\n\n self.log.verbose( 'setJobParameter(%s, %s, %s)' % ( self.jobID, name, value ) )\n return RPCClient( 'WorkloadManagement/JobStateUpdate', timeout = 120 ).setJobParameter( int( self.jobID ), str( name ), str( value ) )","def setContentInt(_session, _segment, _el, _data):\n _session.set_content_int(_el, _data)\n _session.gen3_f_a_f(_segment, keynodes.ui.format_int, _el, sc.SC_A_CONST|sc.SC_POS)","def setSpan(self, p_int, p_int_1, p_int_2, p_int_3): # real signature unknown; restored from __doc__\r\n pass","def getintparam(name, default=None, stash=None, params=None):\n v = getparamlist(name, stash=stash, params=params)\n if len(v) > 0: return int(v[0])\n return default","def putstrparam(self,param_,parvalue_):\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def update_param(self, update_param):\n\n self._update_param = update_param","def setParams(self, paramSet):\r\n pass","def put_var_param(self, var_type, num_vars):\n if var_type.upper() not in EX_VAR_TYPES:\n raise ExodusIIWriterError(\n \"var_type {0} not recognized\".format(var_type))\n ierr = exolib.py_expvp(self.exoid, var_type.lower(), num_vars)\n if ierr:\n raise ExodusIIWriterError(\"Error putting var params\")","def param(self, param_nb: int) -> int:\n mode = get_digit_right_to_left(self.modes, param_nb - 1)\n param_index = self.pointer + param_nb\n if mode == 1:\n # immediate mode\n return param_index\n if mode == 2:\n # relative mode\n return self.relative_base + self.program[param_index]\n else:\n # position mode\n return self.program[param_index]","def set(self,name,val):\n matches = self.grep_param_names(name)\n if len(matches):\n x = self._get_params()\n x[matches] = val\n self._set_params(x)\n else:\n raise AttributeError, \"no parameter matches %s\"%name","def set_param(params, pname, value=None, bounds=None):\n if value is not None:\n for p in params.flattened():\n if p.name == pname:\n p.value = value\n break\n\n if bounds is not None:\n for p in params.flattened():\n if p.name == pname:\n p.bounds = bounds\n p.vary = True\n break"],"string":"[\n \"def putintparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,iparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putintparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getintparam(self,param_): # 3\\n if not isinstance(param_,iparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res,resargs = self.__obj.getintparam(param_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _parvalue_return_value = resargs\\n return _parvalue_return_value\",\n \"def set_param(self, param, value):\\n self._set_param_client(param, value)\",\n \"def getintparam(self,param_):\\n parvalue_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getintparam(self.__nativep,param_,ctypes.byref(parvalue_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n parvalue_ = parvalue_.value\\n _parvalue_return_value = parvalue_\\n return (_parvalue_return_value)\",\n \"def setInteger(self, value):\",\n \"def setInteger(self, value):\",\n \"def setInt(self, addr: ghidra.program.model.address.Address, value: int) -> None:\\n ...\",\n \"def set_param(param, num, set_val):\\n param[0][num] = set_val\",\n \"def setParam(self,param,value):\\n if param in self.params.keys():\\n self.params[param] = value\",\n \"def putparam(self,parname_,parvalue_): # 3\\n res = self.__obj.putparam(parname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"async def set_param(self, param: str, value: int) -> ArchonCommand:\\n cmd = await self.send_command(f\\\"FASTLOADPARAM {param} {value}\\\")\\n if not cmd.succeeded():\\n raise ArchonError(\\n f\\\"Failed setting parameter {param!r} ({cmd.status.name}).\\\"\\n )\\n return cmd\",\n \"def setInt(self, address: ghidra.program.model.address.Address, value: int) -> None:\\n ...\",\n \"def setInteger(self, value):\\n assert self._is_int is True\\n self._value = value\",\n \"def setInteger(self, value: int):\\n self.value = value\",\n \"def _mn_set_par_ ( self , i , val , fix = False ) :\\n if not i in self : raise IndexError\\n #\\n if hasattr ( val , 'value' ) : val = val.value()\\n #\\n ierr = _mn_exec_ ( self , \\\"SET PAR\\\" , i + 1 , val )\\n #\\n if fix : self.FixParameter ( i ) \\n #\\n return ierr\",\n \"def setInt(self, addr: ghidra.program.model.address.Address, value: int, bigEndian: bool) -> None:\\n ...\",\n \"def set_param(self, label, val):\\n assert type(label) is str, 'Parameter name \\\"%s\\\" is not string' % label\\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\\n self.params[label] = val\",\n \"def set_parameter(self, params, name, val):\\n raise NotImplementedError()\",\n \"def putparam(self,parname_,parvalue_):\\n if isinstance(parname_,unicode):\\n parname_ = parname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_param(self, param_key, value):\\n return self._params.set_param_value(param_key, value)\",\n \"def setParameter(self, name, value):\",\n \"def putnaintparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnaintparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getIntParam(self, paramkey, default=None):\\n value = self.request.getParameter(paramkey)\\n if value is None: return default\\n try: return int(value)\\n except: return default\",\n \"def set_parameter_value(self, parameter, value):\\n pass\",\n \"def set_parameter(doc, o_path, param, value, mn_consts):\\n if isinstance(value, str):\\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\\n elif isinstance(value, int):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\\n elif isinstance(value, list):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)\",\n \"def putnaintparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setIntValue(self, *args):\\n return _libsbml.ConversionProperties_setIntValue(self, *args)\",\n \"def integer(self, integer):\\n\\n self._integer = integer\",\n \"def setInt(self, key, value):\\n self.__config.setValue(key, QtCore.QVariant(value))\\n self.__saved = False\",\n \"def setParameter(self,arg,value):\\n self._params[arg] = value\\n return self._params\",\n \"def SetParameterValue(self, paramName, value):\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n if paramName == ISENTROPIC_PAR:\\n if (self.ideal != None):\\n self.ideal.SetParameterValue(paramName, value)\",\n \"def setIntValue(self, *args):\\n return _libsbml.ConversionOption_setIntValue(self, *args)\",\n \"def set_param(self, name, value, *, distrib=None, ref=None):\\n raise NotImplementedError\",\n \"def set(self, param, value):\\r\\n # continuous testing of inputs\\r\\n if self.testing_unit.testing_level > 1 and not self.testing_unit.c_test_set_inp(param, value):\\r\\n raise ValueError(\\\"set won't run, input's aren't valid.\\\")\\r\\n\\r\\n # continuous testing of functional inputs\\r\\n if self.testing_unit.testing_level > 0:\\r\\n if param in [\\\"weighting_bias\\\"]:\\r\\n if not [self.testing_unit.c_test_weighting_bias][[\\\"weighting_bias\\\"].index(param)](value):\\r\\n raise ValueError(\\\"Bad \\\" + param + \\\" input. See log or raise testing verbosity.\\\")\\r\\n\\r\\n self.__locals[param] = value # Security Risk\\r\\n return 1 # Success\\r\",\n \"def put_param(self, attr_name, val):\\n self._params[attr_name] = val\",\n \"def set_param(param, value, verbose=False):\\n set_param_raw(param, yaml.load(value), verbose=verbose)\",\n \"def set_param(command):\\n namespace = app.main(command)\\n assert namespace.command == 'sp' or namespace.command == \\\"setparam\\\"\\n assert namespace.name == \\\"test\\\"\\n assert namespace.value == \\\"test\\\"\",\n \"def seti(self, node, new_int):\\n\\n self.daq.syncSetInt(f'/{self.device_id}/{node}', new_int)\",\n \"def change_general_param(self, param, val):\\n assert param in self.params, '%s is not recognized as a valid parameter' % param\\n self.params[param].change_value(val)\",\n \"def set_param(self, name, value):\\n name = str(name)\\n value = str(value)\\n cxnlib.CXNNetSetParam(self.handle,\\n ctypes.c_char_p(name.encode('utf-8')),\\n ctypes.c_char_p(value.encode('utf-8')))\",\n \"def writeInt(self, value: int):\\n self._pack('!i', value)\",\n \"def _p_set_par_ ( o , index , value ) :\\n return o.setPar ( index , value )\",\n \"def set_params(self, params):\",\n \"def _set_int(self, commands, name):\\n if name in commands:\\n try:\\n value = int(commands[name])\\n setattr(self, name, value)\\n except ValueError:\\n pass\",\n \"def setIntegerOption(self, option, value):\\n result = self.__lib.voikkoSetIntegerOption(self.__handle, option, value)\\n if result == 0:\\n raise VoikkoException(\\\"Could not set integer option %s to value %s\\\" % (option, value))\",\n \"def value(self, p_int): # real signature unknown; restored from __doc__\\n pass\",\n \"def add_id(self, param_id):\\n self.param_id = param_id\",\n \"def setChanInt(\\n self,\\n u,\\n chan,\\n intval,\\n ):\\n\\n # print \\\"setting chan %d to %d\\\" % (chan,intval)\\n # sys.stdout.flush()\\n\\n self.DMX[u].set_chan_int(chan, intval)\",\n \"def __type_correct_int(self):\\n\\n strTestName = 'Type (int) is given (correct)'\\n RxCSObject = _RxCSobject()\\n\\n RxCSObject.paramAddOpt('parameter1', 'type \\\\'int\\\\' parameter')\\n RxCSObject.paramType('parameter1', (int))\\n RxCSObject.parameter1 = int(1)\\n\\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)\",\n \"def getIntParam(self, params, name):\\n try:\\n return int(params.get(name))\\n except:\\n return None\",\n \"def set_param(self, param_name, param, set_val = None, type_of_param = None):\\n if (set_val is None):\\n set_val = True\\n param_index = self.index(param_name);\\n \\n if (type_of_param is None):\\n type_of_param = self.type_param[param_index]\\n if (param is None):\\n param = copy.deepcopy(self._param_val[param_index])\\n elif (type(param) is type_of_param):\\n param = [param]\\n elif (type(param) is not list):\\n error_str = (\\n \\\"\\\"\\\" \\n Trying to set parameter %s to non-integer value (or string if instance name, float if eps+rayeps value), and not a list.\\n Type of param: %s\\n \\\"\\\"\\\" % (param_name, str(type(param)))\\n )\\n raise TypeError(error_str)\\n\\n if (set_val):\\n self._param_val[param_index] = copy.deepcopy(param)\\n return param\",\n \"def SetSpacing(self, p_int, ):\\n ...\",\n \"def set_pi(self, val):\\n self.__pi = val\",\n \"def param(self, param):\\n if param is not None and len(param) < 1:\\n raise ValueError(\\\"Invalid value for `param`, length must be greater than or equal to `1`\\\") # noqa: E501\\n\\n self._param = param\",\n \"def test_creation_int():\\n value = 1\\n\\n num_a = param.Integer(value=value)\\n assert num_a.value == value\",\n \"def _set_par(vid, par, value):\\n traci.vehicle.setParameter(vid, \\\"carFollowModel.%s\\\" % par, str(value))\",\n \"def ipi(self, ipi):\\n\\n self._ipi = ipi\",\n \"def isintparname(self,parname_):\\n if isinstance(parname_,unicode):\\n parname_ = parname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n param_ = ctypes.c_int32()\\n res = __library__.MSK_XX_isintparname(self.__nativep,parname_,ctypes.byref(param_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _param_return_value = iparam(param_.value)\\n return (_param_return_value)\",\n \"def set_params(self, *arg):\\n pass\",\n \"def gui_set_param(self,param_name,val):\\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\\n self._handle_gui_set(param_name,force=True)\",\n \"def _get_int_param(request, param):\\n try:\\n int_param = utils.validate_integer(request.GET[param], param,\\n min_value=0)\\n except exception.InvalidInput as e:\\n raise webob.exc.HTTPBadRequest(explanation=e.format_message())\\n return int_param\",\n \"def putstrparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,sparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putstrparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def update_parameter(self, param, val, force=False):\\n self._update_dict[param] = val\\n if force:\\n self._cur_val[param] = None\",\n \"def SetParameterValue(self, paramName, value):\\n if paramName == NUMBTABLE_PAR:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n self.LookupTable.SetParameterValue(paramName, value)\\n\\n if paramName == IGNORECURVE_PAR: \\n #...ignore the lookuptable and remove any specifications\\n if value == 'None': value = None\\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\\n if value:\\n port = self.GetPort(HEAD_PORT)\\n port.SetValue(None, FIXED_V) \\n else:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\",\n \"def setTemplateParameter(self,name,value):\\n self.tplparam[name] = value\",\n \"def SetParameterValue(self, paramName, value):\\n super(LookupTable, self).SetParameterValue(paramName, value)\\n if paramName == NUMBTABLE_PAR:\\n self.SetTableCount()\\n elif paramName == NUMBSERIES_PAR:\\n self.SetSeriesCount()\\n elif paramName == TABLETAGTYPE_PAR:\\n self.SetTableTagType()\\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\\n idx = long(paramName[len(SERIESTYPE_PAR):])\\n self.SetSeriesTypes(idx)\",\n \"def update_parameter(cur, par, new_value):\\n cur.execute(\\\"UPDATE parameters SET value=%f WHERE par='%s';\\\" % \\n (new_value, par))\",\n \"def int_parameter(level, maxval):\\n return int(level * maxval / 10)\",\n \"def set(self, key, param: Param):\\n if not isinstance(param, Param):\\n raise ValueError(\\\"Only accepts a Param instance.\\\")\\n self._params[key] = param\",\n \"def Set(self,value):\\n self.Bus.Write_uInt8(self.Address,0x50+self.Pin,value)\",\n \"def set_param_raw(param, value, verbose=False):\\n if type(value) == dict:\\n # #1098 changing dictionary behavior to be an update, rather\\n # than replace behavior.\\n for k, v in value.items():\\n # dictionary keys must be non-unicode strings\\n if isinstance(k, str):\\n set_param_raw(ns_join(param, k), v, verbose=verbose)\\n else:\\n raise RosParamException(\\\"YAML dictionaries must have string keys. Invalid dictionary is:\\\\n%s\\\"%value)\\n else:\\n try:\\n expected_type = long\\n except NameError :\\n expected_type = int\\n \\n if type(value) == expected_type:\\n if value > sys.maxsize:\\n raise RosParamException(\\\"Overflow: Parameter Server integers must be 32-bit signed integers:\\\\n\\\\t-%s <= value <= %s\\\"%(maxint - 1, maxint))\\n \\n try:\\n get_param_server().setParam(param, value)\\n except socket.error:\\n raise RosParamIOException(\\\"Unable to communicate with master!\\\")\\n if verbose:\\n print(\\\"set parameter [%s] to [%s]\\\"%(param, value))\",\n \"def set_param(self, param_value):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n\\n setting[1] = param_value\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def def_pid(self,pid):\\n self.pid=int(pid)\",\n \"def set_int_reset():\\n int_reset = request.params.get(\\\"int_reset\\\", 0) == \\\"true\\\"\\n pid = request.params.get(\\\"pid\\\", 1, type=int)\\n retval = RP_LIB.rp_PIDSetIntReset(pid, int_reset)\\n if retval != 0:\\n LOG.error(\\\"Failed to set PID integrator reset. Error code: %s\\\", ERROR_CODES[retval])\",\n \"def c_test_set_inp(self, param, value):\\r\\n ret = 1\\r\\n if \\\"__hash__\\\" not in dir(param): # param must be hashable\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is not hashable. It will be unable to be set in a dict.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is hashable.\\\")\\r\\n if param in [\\\"population_size\\\", \\\"time_constraint\\\", \\\"generations\\\", \\\"point_count\\\",\\r\\n \\\"PSO_VELOCITY_WEIGHT\\\", \\\"PSO_INDIVIDUAL_WEIGHT\\\", \\\"PSO_GROUP_WEIGHT\\\"]:\\r\\n if not ((isinstance(value, int) or\\r\\n isinstance(value, float) or\\r\\n isinstance(value, long))):\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be of a number. It is \\\" + str(value))\\r\\n ret = 0\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is correctly set to a number.\\\")\\r\\n if value < 0:\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be greater than zero.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is greater than zero.\\\")\\r\\n if param in [\\\"population_size\\\", \\\"generations\\\", \\\"point_count\\\"]:\\r\\n if not isinstance(value, int):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be an integer. It is \\\" + str(value))\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is an integer.\\\")\\r\\n if param in [\\\"fitness_function\\\", \\\"weighting_bias\\\"]:\\r\\n if not callable(value):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be a callable function.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is a callable function.\\\")\\r\\n if param == \\\"end_condition\\\":\\r\\n if value not in [\\\"time_constraint\\\", \\\"generations\\\"]:\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be 'time_constraint' or 'generations'\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is a correct string.\\\")\\r\\n if param == \\\"seed\\\":\\r\\n if not (value is None or isinstance(value, int)):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is incorrectly set.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is correctly set.\\\")\\r\\n return ret\",\n \"def InsertPoint(self, p_int, ):\\n ...\",\n \"def isintparname(self,parname_): # 3\\n res,resargs = self.__obj.isintparname(parname_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _param_return_value = resargs\\n _param_return_value = iparam(_param_return_value)\\n return _param_return_value\",\n \"def set_param(self, name, val):\\n # name will be 'colorR', 'colorG', 'colorB'\\n rgb255 = int(val * 255)\\n if name == 'colorR':\\n self.color.r = rgb255\\n elif name == 'colorG':\\n self.color.g = rgb255\\n elif name == 'colorB':\\n self.color.b = rgb255\",\n \"def write_int32(self, val, timeout = 0):\\n self.write(struct.pack(\\\"!i\\\", val), timeout)\",\n \"def set_value(self,parameter_number,value):\\n code = int(\\\"01100000\\\",2) | parameter_number\\n command = pack('<BH',code,int(rint(value)))\\n reply = self.query(command = command,ser = self.ser, count=1)\\n if len(reply) != 1:\\n warn(\\\"expecting 1, got %d bytes\\\" % len(reply)); return\\n reply_code, = unpack('B',reply)\\n if reply_code != code: warn(\\\"expecting 0x%X, got 0x%X\\\" % (code,reply_code))\",\n \"def set_parameters(self, params):\\n self.kp = params.pgain\",\n \"def CONST_INT(self, t):\\n t.value = int(t.value)\\n return t\",\n \"def set_parameter(self, param, value, stripe = 0, location = 3):\\n if param in self.fpga.cabac_top[0].params:\\n self.fpga.set_cabac_value(param, value, stripe)\\n time.sleep(0.1)\\n self.fpga.send_cabac_config(stripe)\\n\\n elif param in [\\\"V_SL\\\", \\\"V_SH\\\", \\\"V_RGL\\\", \\\"V_RGH\\\", \\\"V_PL\\\", \\\"V_PH\\\"]:\\n self.fpga.set_clock_voltages({param: value})\\n\\n elif param == \\\"I_OS\\\":\\n self.fpga.set_current_source(value, stripe)\\n\\n else:\\n print(\\\"Warning: unidentified parameter for the REB: %s\\\" % param)\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def _setIntFeature(self, valueToSet):\\n\\n errorCode = VimbaDLL.featureIntSet(self._handle,\\n self._name,\\n valueToSet)\\n if errorCode != 0:\\n raise VimbaException(errorCode)\",\n \"def __setJobParam( self, name, value ):\\n if not self.jobID:\\n return S_ERROR( 'JobID not defined' )\\n\\n self.log.verbose( 'setJobParameter(%s, %s, %s)' % ( self.jobID, name, value ) )\\n return RPCClient( 'WorkloadManagement/JobStateUpdate', timeout = 120 ).setJobParameter( int( self.jobID ), str( name ), str( value ) )\",\n \"def setContentInt(_session, _segment, _el, _data):\\n _session.set_content_int(_el, _data)\\n _session.gen3_f_a_f(_segment, keynodes.ui.format_int, _el, sc.SC_A_CONST|sc.SC_POS)\",\n \"def setSpan(self, p_int, p_int_1, p_int_2, p_int_3): # real signature unknown; restored from __doc__\\r\\n pass\",\n \"def getintparam(name, default=None, stash=None, params=None):\\n v = getparamlist(name, stash=stash, params=params)\\n if len(v) > 0: return int(v[0])\\n return default\",\n \"def putstrparam(self,param_,parvalue_):\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def update_param(self, update_param):\\n\\n self._update_param = update_param\",\n \"def setParams(self, paramSet):\\r\\n pass\",\n \"def put_var_param(self, var_type, num_vars):\\n if var_type.upper() not in EX_VAR_TYPES:\\n raise ExodusIIWriterError(\\n \\\"var_type {0} not recognized\\\".format(var_type))\\n ierr = exolib.py_expvp(self.exoid, var_type.lower(), num_vars)\\n if ierr:\\n raise ExodusIIWriterError(\\\"Error putting var params\\\")\",\n \"def param(self, param_nb: int) -> int:\\n mode = get_digit_right_to_left(self.modes, param_nb - 1)\\n param_index = self.pointer + param_nb\\n if mode == 1:\\n # immediate mode\\n return param_index\\n if mode == 2:\\n # relative mode\\n return self.relative_base + self.program[param_index]\\n else:\\n # position mode\\n return self.program[param_index]\",\n \"def set(self,name,val):\\n matches = self.grep_param_names(name)\\n if len(matches):\\n x = self._get_params()\\n x[matches] = val\\n self._set_params(x)\\n else:\\n raise AttributeError, \\\"no parameter matches %s\\\"%name\",\n \"def set_param(params, pname, value=None, bounds=None):\\n if value is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.value = value\\n break\\n\\n if bounds is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.bounds = bounds\\n p.vary = True\\n break\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8837676","0.71326786","0.6860121","0.68544596","0.6800701","0.6800701","0.6740805","0.6733184","0.6618874","0.658969","0.6509817","0.6441833","0.6363545","0.6344362","0.633649","0.6328604","0.63135433","0.62845325","0.6263861","0.62351775","0.6199873","0.60748166","0.60120517","0.59740245","0.59688926","0.5924039","0.59031224","0.58957654","0.5817367","0.5803042","0.5795092","0.57872546","0.57698804","0.5754965","0.5743083","0.5729954","0.5713813","0.56916136","0.5680228","0.56362176","0.56072724","0.55872834","0.5579613","0.55645","0.5560534","0.55573255","0.55217403","0.55211633","0.55151075","0.55150884","0.5511944","0.55041367","0.54980505","0.54907465","0.5481916","0.54675424","0.5457352","0.545646","0.54405457","0.5439031","0.54188204","0.5414874","0.5404988","0.5401782","0.5383275","0.53389597","0.5329879","0.53294015","0.5329353","0.5320158","0.5296212","0.5274778","0.5248647","0.5244681","0.52329266","0.5221932","0.5203294","0.5201902","0.519794","0.5194548","0.5192895","0.5182292","0.5180439","0.51778156","0.51778156","0.51778156","0.51778156","0.51778156","0.5159927","0.5149056","0.5148729","0.5147374","0.51428205","0.5142498","0.514231","0.5140818","0.5129188","0.5121792","0.5121566","0.51109946"],"string":"[\n \"0.8837676\",\n \"0.71326786\",\n \"0.6860121\",\n \"0.68544596\",\n \"0.6800701\",\n \"0.6800701\",\n \"0.6740805\",\n \"0.6733184\",\n \"0.6618874\",\n \"0.658969\",\n \"0.6509817\",\n \"0.6441833\",\n \"0.6363545\",\n \"0.6344362\",\n \"0.633649\",\n \"0.6328604\",\n \"0.63135433\",\n \"0.62845325\",\n \"0.6263861\",\n \"0.62351775\",\n \"0.6199873\",\n \"0.60748166\",\n \"0.60120517\",\n \"0.59740245\",\n \"0.59688926\",\n \"0.5924039\",\n \"0.59031224\",\n \"0.58957654\",\n \"0.5817367\",\n \"0.5803042\",\n \"0.5795092\",\n \"0.57872546\",\n \"0.57698804\",\n \"0.5754965\",\n \"0.5743083\",\n \"0.5729954\",\n \"0.5713813\",\n \"0.56916136\",\n \"0.5680228\",\n \"0.56362176\",\n \"0.56072724\",\n \"0.55872834\",\n \"0.5579613\",\n \"0.55645\",\n \"0.5560534\",\n \"0.55573255\",\n \"0.55217403\",\n \"0.55211633\",\n \"0.55151075\",\n \"0.55150884\",\n \"0.5511944\",\n \"0.55041367\",\n \"0.54980505\",\n \"0.54907465\",\n \"0.5481916\",\n \"0.54675424\",\n \"0.5457352\",\n \"0.545646\",\n \"0.54405457\",\n \"0.5439031\",\n \"0.54188204\",\n \"0.5414874\",\n \"0.5404988\",\n \"0.5401782\",\n \"0.5383275\",\n \"0.53389597\",\n \"0.5329879\",\n \"0.53294015\",\n \"0.5329353\",\n \"0.5320158\",\n \"0.5296212\",\n \"0.5274778\",\n \"0.5248647\",\n \"0.5244681\",\n \"0.52329266\",\n \"0.5221932\",\n \"0.5203294\",\n \"0.5201902\",\n \"0.519794\",\n \"0.5194548\",\n \"0.5192895\",\n \"0.5182292\",\n \"0.5180439\",\n \"0.51778156\",\n \"0.51778156\",\n \"0.51778156\",\n \"0.51778156\",\n \"0.51778156\",\n \"0.5159927\",\n \"0.5149056\",\n \"0.5148729\",\n \"0.5147374\",\n \"0.51428205\",\n \"0.5142498\",\n \"0.514231\",\n \"0.5140818\",\n \"0.5129188\",\n \"0.5121792\",\n \"0.5121566\",\n \"0.51109946\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.85035557"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11977,"cells":{"query":{"kind":"string","value":"Sets the number of preallocated constraints in the optimization task. putmaxnumcon(self,maxnumcon_)"},"document":{"kind":"string","value":"def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumvar(self,maxnumvar_):\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)","def getmaxnumcon(self): # 3\n res,resargs = self.__obj.getmaxnumcon()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcon_return_value = resargs\n return _maxnumcon_return_value","def putmaxnumbarvar(self,maxnumbarvar_):\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_):\n res = __library__.MSK_XX_resizetask(self.__nativep,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setNumWorkers(self, num):\r\n self.numWorkers = num","def putmaxnumanz(self,maxnumanz_):\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors","def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_max_nb_robots(nb): #py:set_max_nb_robots\n RUR._set_max_nb_robots_(nb)","def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_workers(self, nworkers):\n\n self.max_workers = nworkers","def getmaxnumcon(self):\n maxnumcon_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcon(self.__nativep,ctypes.byref(maxnumcon_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcon_ = maxnumcon_.value\n _maxnumcon_return_value = maxnumcon_\n return (_maxnumcon_return_value)","def _set_constraint(self):\n pass","def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_): # 3\n res = self.__obj.resizetask(maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def non_heap_max(self, non_heap_max):\n\n self._non_heap_max = non_heap_max","def set_max_eval_concurrency(self, max_eval_concurrency):\n self.max_eval_concurrency = max_eval_concurrency","def max_pool_size(self, max_pool_size: ConfigNodePropertyInteger):\n\n self._max_pool_size = max_pool_size","def _process_threadpool_limits_initializier():\n import numpy # required for loky's autodetection\n from threadpoolctl import threadpool_limits\n\n threadpool_limits(limits=1)","def max_num_links(self, max_num_links):\n self._max_num_links = max_num_links","def max_in_gbps(self, max_in_gbps):\n self._max_in_gbps = max_in_gbps","def set_numpins(self, n):\n self.numpins = n","def set_constraint_scaling_factor(self, con):\n condata = self.get_representative_data_object(con)\n vardata = self.con2var[condata]\n scaling_factor = self.scaling_factor\n\n var_factor = scaling_factor[vardata]\n if self.dim == 0:\n scaling_factor[con] = var_factor\n else:\n for c in con.values():\n scaling_factor[c] = var_factor","def SetConstraint(self, model) :\n if 'pp' in self.__type : self.SetPPConstraint( model )\n elif self.__type == 'prBin' and self.bound!=0 : self.SetPRBinConstraint( model )\n elif self.__type == 'prCat' and self.bound != 0 : self.SetPRCatConstraint(model)\n elif self.__type == 'prBinCat' and self.bound != 0 : self.SetPRBinCatConstraint(model)\n elif self.bound == 0 : return\n else : raise RuntimeError( 'SetConstraint : Unknown type for Constraint : ', self.__type )","def setOutMax(self, out_max):\n\t\tself.out_max = out_max","def set_num_jobs(self, num):\n self.num_jobs = num","def set_number_of_neurons_per_core(neuronType, maxPermitted):\n if not inspect.isclass(neuronType):\n neuronType = globals()[neuronType]\n if neuronType is None:\n raise Exception(\"Unknown Vertex Type {}\".format(neuronType))\n \n if hasattr(neuronType, \"custom_max_atoms_per_core\"):\n neuronType.custom_max_atoms_per_core = maxPermitted\n else:\n raise Exception(\"{} is not a Vertex type\".format(neuronType))","def maxCODQty(self, maxCODQty):\n\n self._maxCODQty = maxCODQty","def _fcn_minmax_brain(self):\n self.atlas._update_cbar_minmax()\n self.cbqt.cbobjs._objs['brain']['clim'] = self.atlas._clim\n kwargs = self.cbqt.cbobjs._objs['brain'].to_kwargs(True)\n self.atlas.update_from_dict(kwargs)\n self.atlas._update_cbar()","def set_num_parallel_workers(num):\n if num <= 0 or num > INT32_MAX:\n raise ValueError(\"Number of parallel workers given is not within the required range.\")\n _config.set_num_parallel_workers(num)","def maximize(self, budget, optimizer):\n\n\t\tpass","def maxsize(self, maxsize):\n self.shape = (int(maxsize), ) + self.shape[1:]\n self.clear()","def set_max_noutput_items(self, m):\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)","def maximize(self):\n raise NotImplementedError","def set_max_noutput_items(self, m):\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)","def set_nb_clusters(self):\n \n print(\"Finding the optimal number of clusters...\")\n \n sample = ro.r.matrix(self.df[self.df[\"filename\"].between(1, 4)][\"active_power\"].to_numpy())\n \n r=ro.r(\"\"\"\n check = function(matrix) {\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\n\n n_clust = n_clust$data\n\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\n return(max_cluster)\n }\n \"\"\")\n\n result = r(sample)\n self.conf[\"nb_clust\"] = int(result[0])\n \n print(f\"Optimal number of clusters is {self.conf['nb_clust']}\\n\")","def SetPRBinConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for val in tot :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def SetPRCatConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for line in tot :\n for val in line :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def SetPRBinCatConstraint( self, model ) :\n tot = np.dot( self.wish.T, self.dispo )\n for val in tot :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def __init__(self, qubit_count: int, max_depth: int, **kwargs) -> None:\n self._constraints = constraints.QuantumCircuitConstraints(\n qubit_count=qubit_count, max_depth=max_depth, **kwargs\n )","def getmaxnumcone(self): # 3\n res,resargs = self.__obj.getmaxnumcone()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcone_return_value = resargs\n return _maxnumcone_return_value","def max_cpus_per_machine(self, max_cpus_per_machine):\n\n self._max_cpus_per_machine = max_cpus_per_machine","def set_max_evaluations(self,ev):\n self.max_evaluations = ev","def set_max_vehicle_capacity(self, new_max_vehicle):\n if(new_max_vehicle == None):\n self._logger.write(\"Error! new_max_vehicle cannot be a NoneType\")\n elif(type(new_max_vehicle) != int):\n self._logger.write(\"Error! new_max_vehicle must be of type int\")\n else:\n try:\n self._max_vehicle_capacity = new_max_vehicle\n except Exception as e:\n self._logger.write(\"Error! Could not set the new max_vehicle:\\n %s\" % e)","def setmaxsize(self, maxsize):\n self.maxsize = maxsize","def getmaxnumcone(self):\n maxnumcone_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcone(self.__nativep,ctypes.byref(maxnumcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcone_ = maxnumcone_.value\n _maxnumcone_return_value = maxnumcone_\n return (_maxnumcone_return_value)","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.doaesprit_sptr_set_max_noutput_items(self, m)","def limit_num_clients(self, limit_num_clients):\n\n self._limit_num_clients = limit_num_clients","def constraints_max_offer_per_cust(n_row, n_col):\n constraints = np.identity(n_row * n_col)\n return constraints","def max_concurrent_requests(self, max_concurrent_requests):\n\n self._max_concurrent_requests = max_concurrent_requests","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)","def set_cpu_limit(self, nVmCpuLimit):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuLimit', self.handle, nVmCpuLimit)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)","def set_max(self, max):\n self.set_val((self.val[0], max))","def setNumThreads(self, num):\r\n self.threads = num","def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)","def limit(self, custom_limit):\n # NOTE(gibi): this operation needs escalated privileges (e.g. admin)\n # as the owner of the app cannot set its own app's limits. But\n # authorization is out of scope.\n self._limit = custom_limit","def setImax(self, I_max):\n\t\tself.I_max = I_max","def set_max_participants(self, max_part):\n self.max_participants = max_part","def set_nms(self, nms_thresh=0.45, nms_topk=400, post_nms=100):\r\n self._clear_cached_op()\r\n self.nms_thresh = nms_thresh\r\n self.nms_topk = nms_topk\r\n self.post_nms = post_nms","def set_constraint(self, g, g_min, g_max):\n self.g += g\n self.g_min += g_min\n self.g_max += g_max","def setNumThreads(self, num):\n # implement ThreadPool interface\n assert not self.prepared, \"You can't change number of threads for working server\"\n self.threads = num","def setNumThreads(self, num):\r\n # implement ThreadPool interface\r\n assert not self.prepared, \"You can't change number of threads for working server\"\r\n self.threads = num","def set_maximum_iterations(self, n):\n self.maximum_iterations = n\n return","def constraint(self):\n with self.mutating:\n self.queue = heapq.nsmallest(self.max_size, self.queue)\n heapq.heapify(self.queue)","def update_maxp(self) -> None:\n maxp = self.otf[\"maxp\"]\n ttdata = self.ufo.lib.get(TRUETYPE_INSTRUCTIONS_KEY, None)\n if ttdata:\n for name in (\n \"maxStorage\",\n \"maxFunctionDefs\",\n \"maxInstructionDefs\",\n \"maxStackElements\",\n # \"maxSizeOfInstructions\", # Is recalculated below\n \"maxZones\",\n \"maxTwilightPoints\",\n ):\n value = ttdata.get(name, None)\n if value is not None:\n setattr(maxp, name, value)\n\n # Recalculate maxp.maxSizeOfInstructions\n sizes = [\n len(ttglyph.program.getBytecode())\n for ttglyph in self.otf[\"glyf\"].glyphs.values()\n if hasattr(ttglyph, \"program\")\n ]\n maxp.maxSizeOfInstructions = max(sizes, default=0)","def activate_maximal_subnet(self) -> None:\n self._m_handler.activate_maximum_subnet()","def ugc_limit(self, ugc_limit):\n\n self._ugc_limit = ugc_limit","def set_max(self, val):\n self._max = val","def number_of_constraints(self):\n return len(self.constraints)","def getNumConstraints(self):\n return _libsbml.Model_getNumConstraints(self)","def num_conll(self):\n pass","def set_max_noutput_items(self, m):\n return _spacegrant_swig.general_burster_2_sptr_set_max_noutput_items(self, m)","def MAXMEM(self):","def set_lim(self, new_lim: int):\n self.__max_loop = new_lim\n self.__check_interpreter()","def set_max_noutput_items(self, m):\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)","def maximum_over_capacity(self, maximum_over_capacity):\n self._maximum_over_capacity = maximum_over_capacity","def set_max_noutput_items(self, m):\n return _spacegrant_swig.ax25_pdu_packer_sptr_set_max_noutput_items(self, m)","def max_pods_constraint(self) -> Optional[pulumi.Input['MaxPodsConstraintArgs']]:\n return pulumi.get(self, \"max_pods_constraint\")","def set_tree_limit(self, n):\n if self.handle is None:\n raise AttributeError('Model not loaded yet')\n _check_call(_LIB.TreeliteSetTreeLimit(self.handle, ctypes.c_size_t(n)))","def set_threadpool_size(nthreads):\n os.environ[\"OMP_THREAD_LIMIT\"] = \"0\" if nthreads is None else str(nthreads)","def set_max_reps(self, max_reps):\n self.max_reps = int(max_reps)","def abs_max_cool_setpoint_limit(self) -> int:\n return self.cluster.get(\"abs_max_cool_setpoint_limit\", 3200)","async def max(self, ctx, limit: int):\n self.data_check(ctx)\n server = ctx.message.server\n\n self.riceCog2[server.id][\"max\"] = limit\n dataIO.save_json(self.warning_settings,\n self.riceCog2)\n await self.bot.say(\"Warn limit is now: \\n{}\".format(limit))","def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_framer_sptr_set_max_noutput_items(self, m)","def setup_solver(self):\n option = Options()\n if logger.getEffectiveLevel() == logging.DEBUG:\n # option.printLevel = PrintLevel.HIGH\n option.printLevel = PrintLevel.NONE\n else:\n option.printLevel = PrintLevel.NONE\n self.solver_minimizing = SQProblem(self.nV, self.nC)\n self.solver_minimizing.setOptions(option)\n self.solver_maximizing = SQProblem(self.nV, self.nC)\n self.solver_maximizing.setOptions(option)\n\n self.solver_minimizing_recent_index = -2\n self.solver_maximizing_recent_index = -2","def set_max_edges(self, edges):\n self.max_edges = edges","def concurrent_tasks_limit(self):\n return self._concurrent_tasks_limit","def SetLimit(self, *args):\n return _BRepAlgo.BRepAlgo_NormalProjection_SetLimit(self, *args)","def setErrorMax(self, error_max):\n\t\tself.error_max = error_max","def getmaxnumvar(self): # 3\n res,resargs = self.__obj.getmaxnumvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumvar_return_value = resargs\n return _maxnumvar_return_value","def org_apache_felix_http_jetty_threadpool_max(self, org_apache_felix_http_jetty_threadpool_max: ConfigNodePropertyInteger):\n\n self._org_apache_felix_http_jetty_threadpool_max = org_apache_felix_http_jetty_threadpool_max","def setMaxValue(self, max_value):\r\n\t\tself.MaxValue = max_value"],"string":"[\n \"def putmaxnumcon(self,maxnumcon_): # 3\\n res = self.__obj.putmaxnumcon(maxnumcon_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcone(self,maxnumcone_):\\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcone(self,maxnumcone_): # 3\\n res = self.__obj.putmaxnumcone(maxnumcone_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumvar(self,maxnumvar_): # 3\\n res = self.__obj.putmaxnumvar(maxnumvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumvar(self,maxnumvar_):\\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_max_nb_instructions(nb): #py:set_max_nb_instructions\\n RUR._set_max_nb_instructions_(nb)\",\n \"def getmaxnumcon(self): # 3\\n res,resargs = self.__obj.getmaxnumcon()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumcon_return_value = resargs\\n return _maxnumcon_return_value\",\n \"def putmaxnumbarvar(self,maxnumbarvar_):\\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumbarvar(self,maxnumbarvar_): # 3\\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_):\\n res = __library__.MSK_XX_resizetask(self.__nativep,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumqnz(self,maxnumqnz_): # 3\\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setNumWorkers(self, num):\\r\\n self.numWorkers = num\",\n \"def putmaxnumanz(self,maxnumanz_):\\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def max_num_neighbors(self, max_num_neighbors):\\n self._max_num_neighbors = max_num_neighbors\",\n \"def putmaxnumanz(self,maxnumanz_): # 3\\n res = self.__obj.putmaxnumanz(maxnumanz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_max_nb_robots(nb): #py:set_max_nb_robots\\n RUR._set_max_nb_robots_(nb)\",\n \"def putmaxnumqnz(self,maxnumqnz_):\\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_workers(self, nworkers):\\n\\n self.max_workers = nworkers\",\n \"def getmaxnumcon(self):\\n maxnumcon_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumcon(self.__nativep,ctypes.byref(maxnumcon_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumcon_ = maxnumcon_.value\\n _maxnumcon_return_value = maxnumcon_\\n return (_maxnumcon_return_value)\",\n \"def _set_constraint(self):\\n pass\",\n \"def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_): # 3\\n res = self.__obj.resizetask(maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def non_heap_max(self, non_heap_max):\\n\\n self._non_heap_max = non_heap_max\",\n \"def set_max_eval_concurrency(self, max_eval_concurrency):\\n self.max_eval_concurrency = max_eval_concurrency\",\n \"def max_pool_size(self, max_pool_size: ConfigNodePropertyInteger):\\n\\n self._max_pool_size = max_pool_size\",\n \"def _process_threadpool_limits_initializier():\\n import numpy # required for loky's autodetection\\n from threadpoolctl import threadpool_limits\\n\\n threadpool_limits(limits=1)\",\n \"def max_num_links(self, max_num_links):\\n self._max_num_links = max_num_links\",\n \"def max_in_gbps(self, max_in_gbps):\\n self._max_in_gbps = max_in_gbps\",\n \"def set_numpins(self, n):\\n self.numpins = n\",\n \"def set_constraint_scaling_factor(self, con):\\n condata = self.get_representative_data_object(con)\\n vardata = self.con2var[condata]\\n scaling_factor = self.scaling_factor\\n\\n var_factor = scaling_factor[vardata]\\n if self.dim == 0:\\n scaling_factor[con] = var_factor\\n else:\\n for c in con.values():\\n scaling_factor[c] = var_factor\",\n \"def SetConstraint(self, model) :\\n if 'pp' in self.__type : self.SetPPConstraint( model )\\n elif self.__type == 'prBin' and self.bound!=0 : self.SetPRBinConstraint( model )\\n elif self.__type == 'prCat' and self.bound != 0 : self.SetPRCatConstraint(model)\\n elif self.__type == 'prBinCat' and self.bound != 0 : self.SetPRBinCatConstraint(model)\\n elif self.bound == 0 : return\\n else : raise RuntimeError( 'SetConstraint : Unknown type for Constraint : ', self.__type )\",\n \"def setOutMax(self, out_max):\\n\\t\\tself.out_max = out_max\",\n \"def set_num_jobs(self, num):\\n self.num_jobs = num\",\n \"def set_number_of_neurons_per_core(neuronType, maxPermitted):\\n if not inspect.isclass(neuronType):\\n neuronType = globals()[neuronType]\\n if neuronType is None:\\n raise Exception(\\\"Unknown Vertex Type {}\\\".format(neuronType))\\n \\n if hasattr(neuronType, \\\"custom_max_atoms_per_core\\\"):\\n neuronType.custom_max_atoms_per_core = maxPermitted\\n else:\\n raise Exception(\\\"{} is not a Vertex type\\\".format(neuronType))\",\n \"def maxCODQty(self, maxCODQty):\\n\\n self._maxCODQty = maxCODQty\",\n \"def _fcn_minmax_brain(self):\\n self.atlas._update_cbar_minmax()\\n self.cbqt.cbobjs._objs['brain']['clim'] = self.atlas._clim\\n kwargs = self.cbqt.cbobjs._objs['brain'].to_kwargs(True)\\n self.atlas.update_from_dict(kwargs)\\n self.atlas._update_cbar()\",\n \"def set_num_parallel_workers(num):\\n if num <= 0 or num > INT32_MAX:\\n raise ValueError(\\\"Number of parallel workers given is not within the required range.\\\")\\n _config.set_num_parallel_workers(num)\",\n \"def maximize(self, budget, optimizer):\\n\\n\\t\\tpass\",\n \"def maxsize(self, maxsize):\\n self.shape = (int(maxsize), ) + self.shape[1:]\\n self.clear()\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)\",\n \"def maximize(self):\\n raise NotImplementedError\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)\",\n \"def set_nb_clusters(self):\\n \\n print(\\\"Finding the optimal number of clusters...\\\")\\n \\n sample = ro.r.matrix(self.df[self.df[\\\"filename\\\"].between(1, 4)][\\\"active_power\\\"].to_numpy())\\n \\n r=ro.r(\\\"\\\"\\\"\\n check = function(matrix) {\\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\\n\\n n_clust = n_clust$data\\n\\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\\n return(max_cluster)\\n }\\n \\\"\\\"\\\")\\n\\n result = r(sample)\\n self.conf[\\\"nb_clust\\\"] = int(result[0])\\n \\n print(f\\\"Optimal number of clusters is {self.conf['nb_clust']}\\\\n\\\")\",\n \"def SetPRBinConstraint(self, model ) :\\n tot = np.multiply(self.wish, self.dispo)\\n for val in tot :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def SetPRCatConstraint(self, model ) :\\n tot = np.multiply(self.wish, self.dispo)\\n for line in tot :\\n for val in line :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def SetPRBinCatConstraint( self, model ) :\\n tot = np.dot( self.wish.T, self.dispo )\\n for val in tot :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def __init__(self, qubit_count: int, max_depth: int, **kwargs) -> None:\\n self._constraints = constraints.QuantumCircuitConstraints(\\n qubit_count=qubit_count, max_depth=max_depth, **kwargs\\n )\",\n \"def getmaxnumcone(self): # 3\\n res,resargs = self.__obj.getmaxnumcone()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumcone_return_value = resargs\\n return _maxnumcone_return_value\",\n \"def max_cpus_per_machine(self, max_cpus_per_machine):\\n\\n self._max_cpus_per_machine = max_cpus_per_machine\",\n \"def set_max_evaluations(self,ev):\\n self.max_evaluations = ev\",\n \"def set_max_vehicle_capacity(self, new_max_vehicle):\\n if(new_max_vehicle == None):\\n self._logger.write(\\\"Error! new_max_vehicle cannot be a NoneType\\\")\\n elif(type(new_max_vehicle) != int):\\n self._logger.write(\\\"Error! new_max_vehicle must be of type int\\\")\\n else:\\n try:\\n self._max_vehicle_capacity = new_max_vehicle\\n except Exception as e:\\n self._logger.write(\\\"Error! Could not set the new max_vehicle:\\\\n %s\\\" % e)\",\n \"def setmaxsize(self, maxsize):\\n self.maxsize = maxsize\",\n \"def getmaxnumcone(self):\\n maxnumcone_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumcone(self.__nativep,ctypes.byref(maxnumcone_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumcone_ = maxnumcone_.value\\n _maxnumcone_return_value = maxnumcone_\\n return (_maxnumcone_return_value)\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.doaesprit_sptr_set_max_noutput_items(self, m)\",\n \"def limit_num_clients(self, limit_num_clients):\\n\\n self._limit_num_clients = limit_num_clients\",\n \"def constraints_max_offer_per_cust(n_row, n_col):\\n constraints = np.identity(n_row * n_col)\\n return constraints\",\n \"def max_concurrent_requests(self, max_concurrent_requests):\\n\\n self._max_concurrent_requests = max_concurrent_requests\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)\",\n \"def set_cpu_limit(self, nVmCpuLimit):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetCpuLimit', self.handle, nVmCpuLimit)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)\",\n \"def set_max(self, max):\\n self.set_val((self.val[0], max))\",\n \"def setNumThreads(self, num):\\r\\n self.threads = num\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)\",\n \"def limit(self, custom_limit):\\n # NOTE(gibi): this operation needs escalated privileges (e.g. admin)\\n # as the owner of the app cannot set its own app's limits. But\\n # authorization is out of scope.\\n self._limit = custom_limit\",\n \"def setImax(self, I_max):\\n\\t\\tself.I_max = I_max\",\n \"def set_max_participants(self, max_part):\\n self.max_participants = max_part\",\n \"def set_nms(self, nms_thresh=0.45, nms_topk=400, post_nms=100):\\r\\n self._clear_cached_op()\\r\\n self.nms_thresh = nms_thresh\\r\\n self.nms_topk = nms_topk\\r\\n self.post_nms = post_nms\",\n \"def set_constraint(self, g, g_min, g_max):\\n self.g += g\\n self.g_min += g_min\\n self.g_max += g_max\",\n \"def setNumThreads(self, num):\\n # implement ThreadPool interface\\n assert not self.prepared, \\\"You can't change number of threads for working server\\\"\\n self.threads = num\",\n \"def setNumThreads(self, num):\\r\\n # implement ThreadPool interface\\r\\n assert not self.prepared, \\\"You can't change number of threads for working server\\\"\\r\\n self.threads = num\",\n \"def set_maximum_iterations(self, n):\\n self.maximum_iterations = n\\n return\",\n \"def constraint(self):\\n with self.mutating:\\n self.queue = heapq.nsmallest(self.max_size, self.queue)\\n heapq.heapify(self.queue)\",\n \"def update_maxp(self) -> None:\\n maxp = self.otf[\\\"maxp\\\"]\\n ttdata = self.ufo.lib.get(TRUETYPE_INSTRUCTIONS_KEY, None)\\n if ttdata:\\n for name in (\\n \\\"maxStorage\\\",\\n \\\"maxFunctionDefs\\\",\\n \\\"maxInstructionDefs\\\",\\n \\\"maxStackElements\\\",\\n # \\\"maxSizeOfInstructions\\\", # Is recalculated below\\n \\\"maxZones\\\",\\n \\\"maxTwilightPoints\\\",\\n ):\\n value = ttdata.get(name, None)\\n if value is not None:\\n setattr(maxp, name, value)\\n\\n # Recalculate maxp.maxSizeOfInstructions\\n sizes = [\\n len(ttglyph.program.getBytecode())\\n for ttglyph in self.otf[\\\"glyf\\\"].glyphs.values()\\n if hasattr(ttglyph, \\\"program\\\")\\n ]\\n maxp.maxSizeOfInstructions = max(sizes, default=0)\",\n \"def activate_maximal_subnet(self) -> None:\\n self._m_handler.activate_maximum_subnet()\",\n \"def ugc_limit(self, ugc_limit):\\n\\n self._ugc_limit = ugc_limit\",\n \"def set_max(self, val):\\n self._max = val\",\n \"def number_of_constraints(self):\\n return len(self.constraints)\",\n \"def getNumConstraints(self):\\n return _libsbml.Model_getNumConstraints(self)\",\n \"def num_conll(self):\\n pass\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.general_burster_2_sptr_set_max_noutput_items(self, m)\",\n \"def MAXMEM(self):\",\n \"def set_lim(self, new_lim: int):\\n self.__max_loop = new_lim\\n self.__check_interpreter()\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)\",\n \"def maximum_over_capacity(self, maximum_over_capacity):\\n self._maximum_over_capacity = maximum_over_capacity\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.ax25_pdu_packer_sptr_set_max_noutput_items(self, m)\",\n \"def max_pods_constraint(self) -> Optional[pulumi.Input['MaxPodsConstraintArgs']]:\\n return pulumi.get(self, \\\"max_pods_constraint\\\")\",\n \"def set_tree_limit(self, n):\\n if self.handle is None:\\n raise AttributeError('Model not loaded yet')\\n _check_call(_LIB.TreeliteSetTreeLimit(self.handle, ctypes.c_size_t(n)))\",\n \"def set_threadpool_size(nthreads):\\n os.environ[\\\"OMP_THREAD_LIMIT\\\"] = \\\"0\\\" if nthreads is None else str(nthreads)\",\n \"def set_max_reps(self, max_reps):\\n self.max_reps = int(max_reps)\",\n \"def abs_max_cool_setpoint_limit(self) -> int:\\n return self.cluster.get(\\\"abs_max_cool_setpoint_limit\\\", 3200)\",\n \"async def max(self, ctx, limit: int):\\n self.data_check(ctx)\\n server = ctx.message.server\\n\\n self.riceCog2[server.id][\\\"max\\\"] = limit\\n dataIO.save_json(self.warning_settings,\\n self.riceCog2)\\n await self.bot.say(\\\"Warn limit is now: \\\\n{}\\\".format(limit))\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.hdlc_framer_sptr_set_max_noutput_items(self, m)\",\n \"def setup_solver(self):\\n option = Options()\\n if logger.getEffectiveLevel() == logging.DEBUG:\\n # option.printLevel = PrintLevel.HIGH\\n option.printLevel = PrintLevel.NONE\\n else:\\n option.printLevel = PrintLevel.NONE\\n self.solver_minimizing = SQProblem(self.nV, self.nC)\\n self.solver_minimizing.setOptions(option)\\n self.solver_maximizing = SQProblem(self.nV, self.nC)\\n self.solver_maximizing.setOptions(option)\\n\\n self.solver_minimizing_recent_index = -2\\n self.solver_maximizing_recent_index = -2\",\n \"def set_max_edges(self, edges):\\n self.max_edges = edges\",\n \"def concurrent_tasks_limit(self):\\n return self._concurrent_tasks_limit\",\n \"def SetLimit(self, *args):\\n return _BRepAlgo.BRepAlgo_NormalProjection_SetLimit(self, *args)\",\n \"def setErrorMax(self, error_max):\\n\\t\\tself.error_max = error_max\",\n \"def getmaxnumvar(self): # 3\\n res,resargs = self.__obj.getmaxnumvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumvar_return_value = resargs\\n return _maxnumvar_return_value\",\n \"def org_apache_felix_http_jetty_threadpool_max(self, org_apache_felix_http_jetty_threadpool_max: ConfigNodePropertyInteger):\\n\\n self._org_apache_felix_http_jetty_threadpool_max = org_apache_felix_http_jetty_threadpool_max\",\n \"def setMaxValue(self, max_value):\\r\\n\\t\\tself.MaxValue = max_value\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7620724","0.6740477","0.6646557","0.60926807","0.5997314","0.59877884","0.59074914","0.589972","0.5885956","0.57590777","0.5751454","0.5703682","0.5699466","0.56882566","0.5686642","0.568195","0.56570315","0.5625261","0.5607635","0.5532643","0.5513424","0.5498084","0.53858757","0.5384151","0.5372652","0.53134924","0.5308602","0.5308152","0.52772665","0.5263952","0.5257781","0.5250993","0.5230242","0.52034","0.5201272","0.5179916","0.5166602","0.5157064","0.51514965","0.5140343","0.513502","0.5123789","0.5121359","0.5114887","0.5106135","0.50916976","0.5078518","0.5061504","0.50507957","0.50465405","0.5039562","0.50242484","0.500826","0.49977052","0.49919888","0.49836245","0.49814284","0.49730176","0.49718297","0.49560824","0.49331477","0.49254698","0.4922099","0.49191192","0.49107406","0.49047956","0.49041173","0.48990938","0.48973066","0.48940778","0.48931342","0.48881975","0.48879611","0.4886479","0.48824683","0.48797125","0.4877963","0.48756155","0.48675662","0.4856189","0.48543543","0.48534483","0.4852851","0.48505107","0.48469695","0.48421144","0.48404068","0.4835288","0.4824848","0.48189175","0.4814844","0.48139292","0.48137912","0.48039854","0.4801255","0.47937623","0.47933134","0.47899064","0.47746634","0.47744927"],"string":"[\n \"0.7620724\",\n \"0.6740477\",\n \"0.6646557\",\n \"0.60926807\",\n \"0.5997314\",\n \"0.59877884\",\n \"0.59074914\",\n \"0.589972\",\n \"0.5885956\",\n \"0.57590777\",\n \"0.5751454\",\n \"0.5703682\",\n \"0.5699466\",\n \"0.56882566\",\n \"0.5686642\",\n \"0.568195\",\n \"0.56570315\",\n \"0.5625261\",\n \"0.5607635\",\n \"0.5532643\",\n \"0.5513424\",\n \"0.5498084\",\n \"0.53858757\",\n \"0.5384151\",\n \"0.5372652\",\n \"0.53134924\",\n \"0.5308602\",\n \"0.5308152\",\n \"0.52772665\",\n \"0.5263952\",\n \"0.5257781\",\n \"0.5250993\",\n \"0.5230242\",\n \"0.52034\",\n \"0.5201272\",\n \"0.5179916\",\n \"0.5166602\",\n \"0.5157064\",\n \"0.51514965\",\n \"0.5140343\",\n \"0.513502\",\n \"0.5123789\",\n \"0.5121359\",\n \"0.5114887\",\n \"0.5106135\",\n \"0.50916976\",\n \"0.5078518\",\n \"0.5061504\",\n \"0.50507957\",\n \"0.50465405\",\n \"0.5039562\",\n \"0.50242484\",\n \"0.500826\",\n \"0.49977052\",\n \"0.49919888\",\n \"0.49836245\",\n \"0.49814284\",\n \"0.49730176\",\n \"0.49718297\",\n \"0.49560824\",\n \"0.49331477\",\n \"0.49254698\",\n \"0.4922099\",\n \"0.49191192\",\n \"0.49107406\",\n \"0.49047956\",\n \"0.49041173\",\n \"0.48990938\",\n \"0.48973066\",\n \"0.48940778\",\n \"0.48931342\",\n \"0.48881975\",\n \"0.48879611\",\n \"0.4886479\",\n \"0.48824683\",\n \"0.48797125\",\n \"0.4877963\",\n \"0.48756155\",\n \"0.48675662\",\n \"0.4856189\",\n \"0.48543543\",\n \"0.48534483\",\n \"0.4852851\",\n \"0.48505107\",\n \"0.48469695\",\n \"0.48421144\",\n \"0.48404068\",\n \"0.4835288\",\n \"0.4824848\",\n \"0.48189175\",\n \"0.4814844\",\n \"0.48139292\",\n \"0.48137912\",\n \"0.48039854\",\n \"0.4801255\",\n \"0.47937623\",\n \"0.47933134\",\n \"0.47899064\",\n \"0.47746634\",\n \"0.47744927\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73856825"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11978,"cells":{"query":{"kind":"string","value":"Sets the number of preallocated conic constraints in the optimization task. putmaxnumcone(self,maxnumcone_)"},"document":{"kind":"string","value":"def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getmaxnumcone(self): # 3\n res,resargs = self.__obj.getmaxnumcone()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcone_return_value = resargs\n return _maxnumcone_return_value","def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getmaxnumcone(self):\n maxnumcone_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcone(self.__nativep,ctypes.byref(maxnumcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcone_ = maxnumcone_.value\n _maxnumcone_return_value = maxnumcone_\n return (_maxnumcone_return_value)","def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_): # 3\n res = self.__obj.resizetask(maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_max_nb_robots(nb): #py:set_max_nb_robots\n RUR._set_max_nb_robots_(nb)","def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_):\n res = __library__.MSK_XX_resizetask(self.__nativep,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_max_eval_concurrency(self, max_eval_concurrency):\n self.max_eval_concurrency = max_eval_concurrency","def set_workers(self, nworkers):\n\n self.max_workers = nworkers","def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors","def getmaxnumcon(self): # 3\n res,resargs = self.__obj.getmaxnumcon()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcon_return_value = resargs\n return _maxnumcon_return_value","def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)","def getnumcone(self): # 3\n res,resargs = self.__obj.getnumcone()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numcone_return_value = resargs\n return _numcone_return_value","def set_number_of_neurons_per_core(neuronType, maxPermitted):\n if not inspect.isclass(neuronType):\n neuronType = globals()[neuronType]\n if neuronType is None:\n raise Exception(\"Unknown Vertex Type {}\".format(neuronType))\n \n if hasattr(neuronType, \"custom_max_atoms_per_core\"):\n neuronType.custom_max_atoms_per_core = maxPermitted\n else:\n raise Exception(\"{} is not a Vertex type\".format(neuronType))","def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def max_concurrent_requests(self, max_concurrent_requests):\n\n self._max_concurrent_requests = max_concurrent_requests","def limit_num_clients(self, limit_num_clients):\n\n self._limit_num_clients = limit_num_clients","def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setNumWorkers(self, num):\r\n self.numWorkers = num","def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumanz(self,maxnumanz_):\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def maxCODQty(self, maxCODQty):\n\n self._maxCODQty = maxCODQty","def abs_max_cool_setpoint_limit(self) -> int:\n return self.cluster.get(\"abs_max_cool_setpoint_limit\", 3200)","def getnumcone(self):\n numcone_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumcone(self.__nativep,ctypes.byref(numcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numcone_ = numcone_.value\n _numcone_return_value = numcone_\n return (_numcone_return_value)","def getmaxnumcon(self):\n maxnumcon_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcon(self.__nativep,ctypes.byref(maxnumcon_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcon_ = maxnumcon_.value\n _maxnumcon_return_value = maxnumcon_\n return (_maxnumcon_return_value)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)","def __init__(self, qubit_count: int, max_depth: int, **kwargs) -> None:\n self._constraints = constraints.QuantumCircuitConstraints(\n qubit_count=qubit_count, max_depth=max_depth, **kwargs\n )","def num_conll(self):\n pass","def constraints_max_offer_per_cust(n_row, n_col):\n constraints = np.identity(n_row * n_col)\n return constraints","def set_nb_clusters(self):\n \n print(\"Finding the optimal number of clusters...\")\n \n sample = ro.r.matrix(self.df[self.df[\"filename\"].between(1, 4)][\"active_power\"].to_numpy())\n \n r=ro.r(\"\"\"\n check = function(matrix) {\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\n\n n_clust = n_clust$data\n\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\n return(max_cluster)\n }\n \"\"\")\n\n result = r(sample)\n self.conf[\"nb_clust\"] = int(result[0])\n \n print(f\"Optimal number of clusters is {self.conf['nb_clust']}\\n\")","def max_cardinality():\r\n #create a list containing the number of each vertex involvement.\r\n array = []\r\n for i in adj:\r\n array += [i[0],i[1]]\r\n\r\n #compute the degree by counting the involment\r\n degree = Counter(array).most_common()\r\n\r\n #retrieve the degree only\r\n degree_ = [ i[1] for i in degree]\r\n\r\n degree_ = np.array(degree_)\r\n \r\n max_m = None\r\n \r\n #check if m is valid\r\n for i in range(degree[0][1]+2)[2:]:\r\n \r\n #valid if there are at least m vertex with degree equals to at least m-1 \r\n if i < len(np.where(degree_>=i-1)[0]):\r\n max_m = i\r\n else:\r\n break\r\n max_m += 1\r\n print(f'maximum possible clique cardinality :{max_m}')\r\n return max_m","def setNThreads(self,n):\n assert(n>0)\n self._c_param.n_threads = n","def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)","def putmaxnumvar(self,maxnumvar_):\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def number_of_constraints(self):\n return len(self.constraints)","def max_num_links(self, max_num_links):\n self._max_num_links = max_num_links","def _fcn_minmax_brain(self):\n self.atlas._update_cbar_minmax()\n self.cbqt.cbobjs._objs['brain']['clim'] = self.atlas._clim\n kwargs = self.cbqt.cbobjs._objs['brain'].to_kwargs(True)\n self.atlas.update_from_dict(kwargs)\n self.atlas._update_cbar()","def no_of_constraints(self):\n return np.sum(self._no_of_constraints_by_object())","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.doaesprit_sptr_set_max_noutput_items(self, m)","def _set_constraint(self):\n pass","def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def num_cones(self):\n return self._shape_count(_sff.cone)","def max_pool_size(self, max_pool_size: ConfigNodePropertyInteger):\n\n self._max_pool_size = max_pool_size","def concurrent_tasks_limit(self):\n return self._concurrent_tasks_limit","def set_max_clients(self, clients: int = 50_000) -> None:\n self.set_db_conf(\"maxclients\", str(clients))","def max_cpus_per_machine(self, max_cpus_per_machine):\n\n self._max_cpus_per_machine = max_cpus_per_machine","def maxsize(self, maxsize):\n self.shape = (int(maxsize), ) + self.shape[1:]\n self.clear()","def get_max_num_onsets():\r\n \"\"\" based on the numbers above, should equal to 932945... \"\"\"\r\n c1 = len(gen_onset_c1())\r\n c2 = len(gen_onset_c2())\r\n c3 = len(gen_onset_c3_c4())\r\n c4 = len(gen_onset_c3_c4())\r\n temp = c1\r\n temp = temp + ( c1 * c2 )\r\n temp = temp + ( c1 * c3 )\r\n temp = temp + ( c1 * c2 * c3 )\r\n temp = temp + ( c1 * c3 * c4 )\r\n temp = temp + ( c1 * c2 * c3 * c4 )\r\n return temp","def SetPRCatConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for line in tot :\n for val in line :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def set_tree_limit(self, n):\n if self.handle is None:\n raise AttributeError('Model not loaded yet')\n _check_call(_LIB.TreeliteSetTreeLimit(self.handle, ctypes.c_size_t(n)))","def optimize_on_n_cops(self):\n self.optimize_on_target_profiles()\n max_revenue = -INF\n while self.get_revenue_per_hour() >= max_revenue:\n max_revenue = self.get_revenue_per_hour()\n prev_target_profiles = self.target_profiles\n self.cops += 1\n self.optimize_on_target_profiles()\n self.cops -= 1\n self.target_profiles = prev_target_profiles\n self.target_profile = reduce(lambda x, y: x + y, self.target_profiles)","def set_max_vehicle_capacity(self, new_max_vehicle):\n if(new_max_vehicle == None):\n self._logger.write(\"Error! new_max_vehicle cannot be a NoneType\")\n elif(type(new_max_vehicle) != int):\n self._logger.write(\"Error! new_max_vehicle must be of type int\")\n else:\n try:\n self._max_vehicle_capacity = new_max_vehicle\n except Exception as e:\n self._logger.write(\"Error! Could not set the new max_vehicle:\\n %s\" % e)","def getNumConstraints(self):\n return _libsbml.Model_getNumConstraints(self)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)","def control_edge_count_max(self) -> int:\n return int(self.graph_tuple_stats.control_edge_count_max or 0)","def set_num_parallel_workers(num):\n if num <= 0 or num > INT32_MAX:\n raise ValueError(\"Number of parallel workers given is not within the required range.\")\n _config.set_num_parallel_workers(num)","def setMaxCandidates(self, candidates):\n return self._set(maxCandidates=candidates)","def setMaxCandidates(self, candidates):\n return self._set(maxCandidates=candidates)","def putcone(self,k_,ct_,conepar_,submem_):\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem_)\n elif nummem_ != len(submem_):\n raise IndexError(\"Inconsistent length of array submem\")\n if submem_ is None:\n raise ValueError(\"Argument submem cannot be None\")\n if submem_ is None:\n raise ValueError(\"Argument submem may not be None\")\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\n _submem_copyarray = False\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif submem_ is not None:\n _submem_copyarray = True\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\n _submem_np_tmp[:] = submem_\n assert _submem_np_tmp.flags.contiguous\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _submem_copyarray = False\n _submem_tmp = None\n \n res = __library__.MSK_XX_putcone(self.__nativep,k_,ct_,conepar_,nummem_,_submem_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumbarvar(self,maxnumbarvar_):\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def org_apache_felix_http_jetty_threadpool_max(self, org_apache_felix_http_jetty_threadpool_max: ConfigNodePropertyInteger):\n\n self._org_apache_felix_http_jetty_threadpool_max = org_apache_felix_http_jetty_threadpool_max","def set_caero_grid(self, ncaeros_points: int, model: BDF) -> None:\n gui = self.gui\n points = vtk.vtkPoints()\n points.SetNumberOfPoints(ncaeros_points)\n\n max_cpoints = []\n min_cpoints = []\n\n zfighting_offset = 0.0001\n caero_grid = gui.alt_grids['caero']\n j = 0\n for unused_eid, element in sorted(model.caeros.items()):\n if isinstance(element, (CAERO1, CAERO3, CAERO4, CAERO5, CAERO7)):\n # wing panel\n cpoints = element.get_points()\n cpoints[0][2] += zfighting_offset\n cpoints[1][2] += zfighting_offset\n max_cpoints.append(np.array(cpoints).max(axis=0))\n min_cpoints.append(np.array(cpoints).min(axis=0))\n\n elem = vtkQuad()\n point_ids = elem.GetPointIds()\n point_ids.SetId(0, j)\n point_ids.SetId(1, j + 1)\n point_ids.SetId(2, j + 2)\n point_ids.SetId(3, j + 3)\n points.InsertPoint(j, *cpoints[0])\n points.InsertPoint(j + 1, *cpoints[1])\n points.InsertPoint(j + 2, *cpoints[2])\n points.InsertPoint(j + 3, *cpoints[3])\n caero_grid.InsertNextCell(elem.GetCellType(), point_ids)\n j += 4\n elif isinstance(element, (CAERO2, BODY7)):\n # slender body\n\n #if 0: # pragma: no cover\n # 1D version\n #cpoints = element.get_points()\n #cpoints[:, 2] += zfighting_offset\n #max_cpoints.append(np.array(cpoints).max(axis=0))\n #min_cpoints.append(np.array(cpoints).min(axis=0))\n\n #elem = vtk.vtkLine()\n #point_ids = elem.GetPointIds()\n #point_ids.SetId(0, j)\n #point_ids.SetId(1, j + 1)\n #points.InsertPoint(j, *cpoints[0])\n #points.InsertPoint(j + 1, *cpoints[1])\n #j += 2\n #caero_grid.InsertNextCell(elem.GetCellType(), point_ids)\n\n #else:\n # 3D version\n xyz, elems = element.get_points_elements_3d()\n assert xyz is not None, element\n xyz[:, 2] += zfighting_offset\n for elemi in elems:\n elem = vtkQuad()\n point_ids = elem.GetPointIds()\n point_ids.SetId(0, j)\n point_ids.SetId(1, j + 1)\n point_ids.SetId(2, j + 2)\n point_ids.SetId(3, j + 3)\n n1, n2, n3, n4 = elemi\n\n points.InsertPoint(j, *xyz[n1])\n points.InsertPoint(j + 1, *xyz[n2])\n points.InsertPoint(j + 2, *xyz[n3])\n points.InsertPoint(j + 3, *xyz[n4])\n\n #cpoints = element.get_points()\n #cpoints[0][2] += zfighting_offset\n #cpoints[1][2] += zfighting_offset\n #max_cpoints.append(np.array(cpoints).max(axis=0))\n #min_cpoints.append(np.array(cpoints).min(axis=0))\n\n caero_grid.InsertNextCell(elem.GetCellType(), point_ids)\n j += 4\n else:\n gui.log_info(\"skipping %s\" % element.type)\n\n if ncaeros_points and len(max_cpoints):\n gui.log_info('CAERO.max = %s' % np.vstack(max_cpoints).max(axis=0))\n gui.log_info('CAERO.min = %s' % np.vstack(min_cpoints).min(axis=0))\n caero_grid.SetPoints(points)\n #gui.alt_grids['caero']\n #edge_mapper.SetResolveCoincidentTopologyToPolygonOffset()","def set_numpins(self, n):\n self.numpins = n","def set_threadpool_size(nthreads):\n os.environ[\"OMP_THREAD_LIMIT\"] = \"0\" if nthreads is None else str(nthreads)","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)","def abs_min_cool_setpoint_limit(self) -> int:\n return self.cluster.get(\"abs_min_cool_setpoint_limit\", 1600)","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)","def setOutMax(self, out_max):\n\t\tself.out_max = out_max","def set_max_noutput_items(self, *args, **kwargs):\n return _TestA_swig.cleanslate_sptr_set_max_noutput_items(self, *args, **kwargs)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.ax25_pdu_packer_sptr_set_max_noutput_items(self, m)","def set_max_participants(self, max_part):\n self.max_participants = max_part","def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_framer_sptr_set_max_noutput_items(self, m)","def set_maximum_iterations(self, n):\n self.maximum_iterations = n\n return","def maxclients(self) -> Optional[int]:\n return pulumi.get(self, \"maxclients\")","def get_max_cleverbot_requests(self):\n return int(self.bot_data_file[\"maxCleverbotRequests\"])","def SetPRBinCatConstraint( self, model ) :\n tot = np.dot( self.wish.T, self.dispo )\n for val in tot :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def _process_threadpool_limits_initializier():\n import numpy # required for loky's autodetection\n from threadpoolctl import threadpool_limits\n\n threadpool_limits(limits=1)","def set_max_edges(self, edges):\n self.max_edges = edges","def set_max_noutput_items(self, m):\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)","def setmaxsize(self, maxsize):\n self.maxsize = maxsize","def limit_num_clients(self):\n return self._limit_num_clients","def max_concurrency(self) -> Optional[int]:\n result = get_feature(self.vm, \"qubes-vm-update-max-concurrency\", None)\n if result is None:\n return result\n return int(result)","def putcone(self,k_,ct_,conepar_,submem): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem)\n elif nummem_ != len(submem):\n raise IndexError(\"Inconsistent length of array submem\")\n if nummem_ is None: nummem_ = 0\n if submem is None: raise TypeError(\"Invalid type for argument submem\")\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n \n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n \n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)","def max_mireds(self):\n return 333","def getnumconemem(self,k_): # 3\n res,resargs = self.__obj.getnumconemem(k_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nummem_return_value = resargs\n return _nummem_return_value","def set_max_noutput_items(self, m):\n return _spacegrant_swig.general_burster_2_sptr_set_max_noutput_items(self, m)","def SetConstraint(self, model) :\n if 'pp' in self.__type : self.SetPPConstraint( model )\n elif self.__type == 'prBin' and self.bound!=0 : self.SetPRBinConstraint( model )\n elif self.__type == 'prCat' and self.bound != 0 : self.SetPRCatConstraint(model)\n elif self.__type == 'prBinCat' and self.bound != 0 : self.SetPRBinCatConstraint(model)\n elif self.bound == 0 : return\n else : raise RuntimeError( 'SetConstraint : Unknown type for Constraint : ', self.__type )","async def max(self, ctx, limit: int):\n self.data_check(ctx)\n server = ctx.message.server\n\n self.riceCog2[server.id][\"max\"] = limit\n dataIO.save_json(self.warning_settings,\n self.riceCog2)\n await self.bot.say(\"Warn limit is now: \\n{}\".format(limit))","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)","def SetAntLimit(cls, value=0):\n cls.antLimit = value","def set_max_evaluations(self,ev):\n self.max_evaluations = ev","def set_num_jobs(self, num):\n self.num_jobs = num","def setNumThreads(self, num):\r\n self.threads = num","def set_max_output_buffer(self, *args):\n return _TestA_swig.cleanslate_sptr_set_max_output_buffer(self, *args)","def setMaxNbEvts(self, nbevts):\n self._checkArgs( { 'nbevts' : types.IntType } )\n self.maxNumberOfEvents = nbevts","def set_caero_subpanel_grid(self, ncaero_sub_points: int, model: BDF) -> None:\n nodes, elements = get_caero_subpanel_grid(model)\n if elements.shape[0] == 0:\n return\n grid = self.gui.alt_grids['caero_subpanels']\n quad_etype = 9\n create_vtk_cells_of_constant_element_type(grid, elements, quad_etype)\n\n vtk_points = numpy_to_vtk_points(nodes, points=None, dtype='<f', deep=1)\n grid.SetPoints(vtk_points)\n return","def set_max_noutput_items(self, m):\n return _spacegrant_swig.message_debug_sptr_set_max_noutput_items(self, m)"],"string":"[\n \"def putmaxnumcone(self,maxnumcone_): # 3\\n res = self.__obj.putmaxnumcone(maxnumcone_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcon(self,maxnumcon_): # 3\\n res = self.__obj.putmaxnumcon(maxnumcon_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getmaxnumcone(self): # 3\\n res,resargs = self.__obj.getmaxnumcone()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumcone_return_value = resargs\\n return _maxnumcone_return_value\",\n \"def putmaxnumcon(self,maxnumcon_):\\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getmaxnumcone(self):\\n maxnumcone_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumcone(self.__nativep,ctypes.byref(maxnumcone_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumcone_ = maxnumcone_.value\\n _maxnumcone_return_value = maxnumcone_\\n return (_maxnumcone_return_value)\",\n \"def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_): # 3\\n res = self.__obj.resizetask(maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_max_nb_robots(nb): #py:set_max_nb_robots\\n RUR._set_max_nb_robots_(nb)\",\n \"def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_):\\n res = __library__.MSK_XX_resizetask(self.__nativep,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_max_eval_concurrency(self, max_eval_concurrency):\\n self.max_eval_concurrency = max_eval_concurrency\",\n \"def set_workers(self, nworkers):\\n\\n self.max_workers = nworkers\",\n \"def max_num_neighbors(self, max_num_neighbors):\\n self._max_num_neighbors = max_num_neighbors\",\n \"def getmaxnumcon(self): # 3\\n res,resargs = self.__obj.getmaxnumcon()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumcon_return_value = resargs\\n return _maxnumcon_return_value\",\n \"def set_max_nb_instructions(nb): #py:set_max_nb_instructions\\n RUR._set_max_nb_instructions_(nb)\",\n \"def getnumcone(self): # 3\\n res,resargs = self.__obj.getnumcone()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numcone_return_value = resargs\\n return _numcone_return_value\",\n \"def set_number_of_neurons_per_core(neuronType, maxPermitted):\\n if not inspect.isclass(neuronType):\\n neuronType = globals()[neuronType]\\n if neuronType is None:\\n raise Exception(\\\"Unknown Vertex Type {}\\\".format(neuronType))\\n \\n if hasattr(neuronType, \\\"custom_max_atoms_per_core\\\"):\\n neuronType.custom_max_atoms_per_core = maxPermitted\\n else:\\n raise Exception(\\\"{} is not a Vertex type\\\".format(neuronType))\",\n \"def putmaxnumanz(self,maxnumanz_): # 3\\n res = self.__obj.putmaxnumanz(maxnumanz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def max_concurrent_requests(self, max_concurrent_requests):\\n\\n self._max_concurrent_requests = max_concurrent_requests\",\n \"def limit_num_clients(self, limit_num_clients):\\n\\n self._limit_num_clients = limit_num_clients\",\n \"def putmaxnumqnz(self,maxnumqnz_): # 3\\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setNumWorkers(self, num):\\r\\n self.numWorkers = num\",\n \"def putmaxnumvar(self,maxnumvar_): # 3\\n res = self.__obj.putmaxnumvar(maxnumvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumanz(self,maxnumanz_):\\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def maxCODQty(self, maxCODQty):\\n\\n self._maxCODQty = maxCODQty\",\n \"def abs_max_cool_setpoint_limit(self) -> int:\\n return self.cluster.get(\\\"abs_max_cool_setpoint_limit\\\", 3200)\",\n \"def getnumcone(self):\\n numcone_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getnumcone(self.__nativep,ctypes.byref(numcone_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numcone_ = numcone_.value\\n _numcone_return_value = numcone_\\n return (_numcone_return_value)\",\n \"def getmaxnumcon(self):\\n maxnumcon_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumcon(self.__nativep,ctypes.byref(maxnumcon_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumcon_ = maxnumcon_.value\\n _maxnumcon_return_value = maxnumcon_\\n return (_maxnumcon_return_value)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)\",\n \"def __init__(self, qubit_count: int, max_depth: int, **kwargs) -> None:\\n self._constraints = constraints.QuantumCircuitConstraints(\\n qubit_count=qubit_count, max_depth=max_depth, **kwargs\\n )\",\n \"def num_conll(self):\\n pass\",\n \"def constraints_max_offer_per_cust(n_row, n_col):\\n constraints = np.identity(n_row * n_col)\\n return constraints\",\n \"def set_nb_clusters(self):\\n \\n print(\\\"Finding the optimal number of clusters...\\\")\\n \\n sample = ro.r.matrix(self.df[self.df[\\\"filename\\\"].between(1, 4)][\\\"active_power\\\"].to_numpy())\\n \\n r=ro.r(\\\"\\\"\\\"\\n check = function(matrix) {\\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\\n\\n n_clust = n_clust$data\\n\\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\\n return(max_cluster)\\n }\\n \\\"\\\"\\\")\\n\\n result = r(sample)\\n self.conf[\\\"nb_clust\\\"] = int(result[0])\\n \\n print(f\\\"Optimal number of clusters is {self.conf['nb_clust']}\\\\n\\\")\",\n \"def max_cardinality():\\r\\n #create a list containing the number of each vertex involvement.\\r\\n array = []\\r\\n for i in adj:\\r\\n array += [i[0],i[1]]\\r\\n\\r\\n #compute the degree by counting the involment\\r\\n degree = Counter(array).most_common()\\r\\n\\r\\n #retrieve the degree only\\r\\n degree_ = [ i[1] for i in degree]\\r\\n\\r\\n degree_ = np.array(degree_)\\r\\n \\r\\n max_m = None\\r\\n \\r\\n #check if m is valid\\r\\n for i in range(degree[0][1]+2)[2:]:\\r\\n \\r\\n #valid if there are at least m vertex with degree equals to at least m-1 \\r\\n if i < len(np.where(degree_>=i-1)[0]):\\r\\n max_m = i\\r\\n else:\\r\\n break\\r\\n max_m += 1\\r\\n print(f'maximum possible clique cardinality :{max_m}')\\r\\n return max_m\",\n \"def setNThreads(self,n):\\n assert(n>0)\\n self._c_param.n_threads = n\",\n \"def putmaxnumqnz(self,maxnumqnz_):\\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)\",\n \"def putmaxnumvar(self,maxnumvar_):\\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def number_of_constraints(self):\\n return len(self.constraints)\",\n \"def max_num_links(self, max_num_links):\\n self._max_num_links = max_num_links\",\n \"def _fcn_minmax_brain(self):\\n self.atlas._update_cbar_minmax()\\n self.cbqt.cbobjs._objs['brain']['clim'] = self.atlas._clim\\n kwargs = self.cbqt.cbobjs._objs['brain'].to_kwargs(True)\\n self.atlas.update_from_dict(kwargs)\\n self.atlas._update_cbar()\",\n \"def no_of_constraints(self):\\n return np.sum(self._no_of_constraints_by_object())\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.doaesprit_sptr_set_max_noutput_items(self, m)\",\n \"def _set_constraint(self):\\n pass\",\n \"def putmaxnumbarvar(self,maxnumbarvar_): # 3\\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def num_cones(self):\\n return self._shape_count(_sff.cone)\",\n \"def max_pool_size(self, max_pool_size: ConfigNodePropertyInteger):\\n\\n self._max_pool_size = max_pool_size\",\n \"def concurrent_tasks_limit(self):\\n return self._concurrent_tasks_limit\",\n \"def set_max_clients(self, clients: int = 50_000) -> None:\\n self.set_db_conf(\\\"maxclients\\\", str(clients))\",\n \"def max_cpus_per_machine(self, max_cpus_per_machine):\\n\\n self._max_cpus_per_machine = max_cpus_per_machine\",\n \"def maxsize(self, maxsize):\\n self.shape = (int(maxsize), ) + self.shape[1:]\\n self.clear()\",\n \"def get_max_num_onsets():\\r\\n \\\"\\\"\\\" based on the numbers above, should equal to 932945... \\\"\\\"\\\"\\r\\n c1 = len(gen_onset_c1())\\r\\n c2 = len(gen_onset_c2())\\r\\n c3 = len(gen_onset_c3_c4())\\r\\n c4 = len(gen_onset_c3_c4())\\r\\n temp = c1\\r\\n temp = temp + ( c1 * c2 )\\r\\n temp = temp + ( c1 * c3 )\\r\\n temp = temp + ( c1 * c2 * c3 )\\r\\n temp = temp + ( c1 * c3 * c4 )\\r\\n temp = temp + ( c1 * c2 * c3 * c4 )\\r\\n return temp\",\n \"def SetPRCatConstraint(self, model ) :\\n tot = np.multiply(self.wish, self.dispo)\\n for line in tot :\\n for val in line :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def set_tree_limit(self, n):\\n if self.handle is None:\\n raise AttributeError('Model not loaded yet')\\n _check_call(_LIB.TreeliteSetTreeLimit(self.handle, ctypes.c_size_t(n)))\",\n \"def optimize_on_n_cops(self):\\n self.optimize_on_target_profiles()\\n max_revenue = -INF\\n while self.get_revenue_per_hour() >= max_revenue:\\n max_revenue = self.get_revenue_per_hour()\\n prev_target_profiles = self.target_profiles\\n self.cops += 1\\n self.optimize_on_target_profiles()\\n self.cops -= 1\\n self.target_profiles = prev_target_profiles\\n self.target_profile = reduce(lambda x, y: x + y, self.target_profiles)\",\n \"def set_max_vehicle_capacity(self, new_max_vehicle):\\n if(new_max_vehicle == None):\\n self._logger.write(\\\"Error! new_max_vehicle cannot be a NoneType\\\")\\n elif(type(new_max_vehicle) != int):\\n self._logger.write(\\\"Error! new_max_vehicle must be of type int\\\")\\n else:\\n try:\\n self._max_vehicle_capacity = new_max_vehicle\\n except Exception as e:\\n self._logger.write(\\\"Error! Could not set the new max_vehicle:\\\\n %s\\\" % e)\",\n \"def getNumConstraints(self):\\n return _libsbml.Model_getNumConstraints(self)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)\",\n \"def control_edge_count_max(self) -> int:\\n return int(self.graph_tuple_stats.control_edge_count_max or 0)\",\n \"def set_num_parallel_workers(num):\\n if num <= 0 or num > INT32_MAX:\\n raise ValueError(\\\"Number of parallel workers given is not within the required range.\\\")\\n _config.set_num_parallel_workers(num)\",\n \"def setMaxCandidates(self, candidates):\\n return self._set(maxCandidates=candidates)\",\n \"def setMaxCandidates(self, candidates):\\n return self._set(maxCandidates=candidates)\",\n \"def putcone(self,k_,ct_,conepar_,submem_):\\n nummem_ = None\\n if nummem_ is None:\\n nummem_ = len(submem_)\\n elif nummem_ != len(submem_):\\n raise IndexError(\\\"Inconsistent length of array submem\\\")\\n if submem_ is None:\\n raise ValueError(\\\"Argument submem cannot be None\\\")\\n if submem_ is None:\\n raise ValueError(\\\"Argument submem may not be None\\\")\\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\\n _submem_copyarray = False\\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif submem_ is not None:\\n _submem_copyarray = True\\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\\n _submem_np_tmp[:] = submem_\\n assert _submem_np_tmp.flags.contiguous\\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _submem_copyarray = False\\n _submem_tmp = None\\n \\n res = __library__.MSK_XX_putcone(self.__nativep,k_,ct_,conepar_,nummem_,_submem_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumbarvar(self,maxnumbarvar_):\\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def org_apache_felix_http_jetty_threadpool_max(self, org_apache_felix_http_jetty_threadpool_max: ConfigNodePropertyInteger):\\n\\n self._org_apache_felix_http_jetty_threadpool_max = org_apache_felix_http_jetty_threadpool_max\",\n \"def set_caero_grid(self, ncaeros_points: int, model: BDF) -> None:\\n gui = self.gui\\n points = vtk.vtkPoints()\\n points.SetNumberOfPoints(ncaeros_points)\\n\\n max_cpoints = []\\n min_cpoints = []\\n\\n zfighting_offset = 0.0001\\n caero_grid = gui.alt_grids['caero']\\n j = 0\\n for unused_eid, element in sorted(model.caeros.items()):\\n if isinstance(element, (CAERO1, CAERO3, CAERO4, CAERO5, CAERO7)):\\n # wing panel\\n cpoints = element.get_points()\\n cpoints[0][2] += zfighting_offset\\n cpoints[1][2] += zfighting_offset\\n max_cpoints.append(np.array(cpoints).max(axis=0))\\n min_cpoints.append(np.array(cpoints).min(axis=0))\\n\\n elem = vtkQuad()\\n point_ids = elem.GetPointIds()\\n point_ids.SetId(0, j)\\n point_ids.SetId(1, j + 1)\\n point_ids.SetId(2, j + 2)\\n point_ids.SetId(3, j + 3)\\n points.InsertPoint(j, *cpoints[0])\\n points.InsertPoint(j + 1, *cpoints[1])\\n points.InsertPoint(j + 2, *cpoints[2])\\n points.InsertPoint(j + 3, *cpoints[3])\\n caero_grid.InsertNextCell(elem.GetCellType(), point_ids)\\n j += 4\\n elif isinstance(element, (CAERO2, BODY7)):\\n # slender body\\n\\n #if 0: # pragma: no cover\\n # 1D version\\n #cpoints = element.get_points()\\n #cpoints[:, 2] += zfighting_offset\\n #max_cpoints.append(np.array(cpoints).max(axis=0))\\n #min_cpoints.append(np.array(cpoints).min(axis=0))\\n\\n #elem = vtk.vtkLine()\\n #point_ids = elem.GetPointIds()\\n #point_ids.SetId(0, j)\\n #point_ids.SetId(1, j + 1)\\n #points.InsertPoint(j, *cpoints[0])\\n #points.InsertPoint(j + 1, *cpoints[1])\\n #j += 2\\n #caero_grid.InsertNextCell(elem.GetCellType(), point_ids)\\n\\n #else:\\n # 3D version\\n xyz, elems = element.get_points_elements_3d()\\n assert xyz is not None, element\\n xyz[:, 2] += zfighting_offset\\n for elemi in elems:\\n elem = vtkQuad()\\n point_ids = elem.GetPointIds()\\n point_ids.SetId(0, j)\\n point_ids.SetId(1, j + 1)\\n point_ids.SetId(2, j + 2)\\n point_ids.SetId(3, j + 3)\\n n1, n2, n3, n4 = elemi\\n\\n points.InsertPoint(j, *xyz[n1])\\n points.InsertPoint(j + 1, *xyz[n2])\\n points.InsertPoint(j + 2, *xyz[n3])\\n points.InsertPoint(j + 3, *xyz[n4])\\n\\n #cpoints = element.get_points()\\n #cpoints[0][2] += zfighting_offset\\n #cpoints[1][2] += zfighting_offset\\n #max_cpoints.append(np.array(cpoints).max(axis=0))\\n #min_cpoints.append(np.array(cpoints).min(axis=0))\\n\\n caero_grid.InsertNextCell(elem.GetCellType(), point_ids)\\n j += 4\\n else:\\n gui.log_info(\\\"skipping %s\\\" % element.type)\\n\\n if ncaeros_points and len(max_cpoints):\\n gui.log_info('CAERO.max = %s' % np.vstack(max_cpoints).max(axis=0))\\n gui.log_info('CAERO.min = %s' % np.vstack(min_cpoints).min(axis=0))\\n caero_grid.SetPoints(points)\\n #gui.alt_grids['caero']\\n #edge_mapper.SetResolveCoincidentTopologyToPolygonOffset()\",\n \"def set_numpins(self, n):\\n self.numpins = n\",\n \"def set_threadpool_size(nthreads):\\n os.environ[\\\"OMP_THREAD_LIMIT\\\"] = \\\"0\\\" if nthreads is None else str(nthreads)\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)\",\n \"def abs_min_cool_setpoint_limit(self) -> int:\\n return self.cluster.get(\\\"abs_min_cool_setpoint_limit\\\", 1600)\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)\",\n \"def setOutMax(self, out_max):\\n\\t\\tself.out_max = out_max\",\n \"def set_max_noutput_items(self, *args, **kwargs):\\n return _TestA_swig.cleanslate_sptr_set_max_noutput_items(self, *args, **kwargs)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.ax25_pdu_packer_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_participants(self, max_part):\\n self.max_participants = max_part\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.hdlc_framer_sptr_set_max_noutput_items(self, m)\",\n \"def set_maximum_iterations(self, n):\\n self.maximum_iterations = n\\n return\",\n \"def maxclients(self) -> Optional[int]:\\n return pulumi.get(self, \\\"maxclients\\\")\",\n \"def get_max_cleverbot_requests(self):\\n return int(self.bot_data_file[\\\"maxCleverbotRequests\\\"])\",\n \"def SetPRBinCatConstraint( self, model ) :\\n tot = np.dot( self.wish.T, self.dispo )\\n for val in tot :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def _process_threadpool_limits_initializier():\\n import numpy # required for loky's autodetection\\n from threadpoolctl import threadpool_limits\\n\\n threadpool_limits(limits=1)\",\n \"def set_max_edges(self, edges):\\n self.max_edges = edges\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)\",\n \"def setmaxsize(self, maxsize):\\n self.maxsize = maxsize\",\n \"def limit_num_clients(self):\\n return self._limit_num_clients\",\n \"def max_concurrency(self) -> Optional[int]:\\n result = get_feature(self.vm, \\\"qubes-vm-update-max-concurrency\\\", None)\\n if result is None:\\n return result\\n return int(result)\",\n \"def putcone(self,k_,ct_,conepar_,submem): # 3\\n if not isinstance(ct_,conetype): raise TypeError(\\\"Argument ct has wrong type\\\")\\n nummem_ = None\\n if nummem_ is None:\\n nummem_ = len(submem)\\n elif nummem_ != len(submem):\\n raise IndexError(\\\"Inconsistent length of array submem\\\")\\n if nummem_ is None: nummem_ = 0\\n if submem is None: raise TypeError(\\\"Invalid type for argument submem\\\")\\n if submem is None:\\n submem_ = None\\n else:\\n try:\\n submem_ = memoryview(submem)\\n except TypeError:\\n try:\\n _tmparr_submem = array.array(\\\"i\\\",submem)\\n except TypeError:\\n raise TypeError(\\\"Argument submem has wrong type\\\")\\n else:\\n submem_ = memoryview(_tmparr_submem)\\n \\n else:\\n if submem_.format != \\\"i\\\":\\n submem_ = memoryview(array.array(\\\"i\\\",submem))\\n \\n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)\",\n \"def max_mireds(self):\\n return 333\",\n \"def getnumconemem(self,k_): # 3\\n res,resargs = self.__obj.getnumconemem(k_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nummem_return_value = resargs\\n return _nummem_return_value\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.general_burster_2_sptr_set_max_noutput_items(self, m)\",\n \"def SetConstraint(self, model) :\\n if 'pp' in self.__type : self.SetPPConstraint( model )\\n elif self.__type == 'prBin' and self.bound!=0 : self.SetPRBinConstraint( model )\\n elif self.__type == 'prCat' and self.bound != 0 : self.SetPRCatConstraint(model)\\n elif self.__type == 'prBinCat' and self.bound != 0 : self.SetPRBinCatConstraint(model)\\n elif self.bound == 0 : return\\n else : raise RuntimeError( 'SetConstraint : Unknown type for Constraint : ', self.__type )\",\n \"async def max(self, ctx, limit: int):\\n self.data_check(ctx)\\n server = ctx.message.server\\n\\n self.riceCog2[server.id][\\\"max\\\"] = limit\\n dataIO.save_json(self.warning_settings,\\n self.riceCog2)\\n await self.bot.say(\\\"Warn limit is now: \\\\n{}\\\".format(limit))\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)\",\n \"def SetAntLimit(cls, value=0):\\n cls.antLimit = value\",\n \"def set_max_evaluations(self,ev):\\n self.max_evaluations = ev\",\n \"def set_num_jobs(self, num):\\n self.num_jobs = num\",\n \"def setNumThreads(self, num):\\r\\n self.threads = num\",\n \"def set_max_output_buffer(self, *args):\\n return _TestA_swig.cleanslate_sptr_set_max_output_buffer(self, *args)\",\n \"def setMaxNbEvts(self, nbevts):\\n self._checkArgs( { 'nbevts' : types.IntType } )\\n self.maxNumberOfEvents = nbevts\",\n \"def set_caero_subpanel_grid(self, ncaero_sub_points: int, model: BDF) -> None:\\n nodes, elements = get_caero_subpanel_grid(model)\\n if elements.shape[0] == 0:\\n return\\n grid = self.gui.alt_grids['caero_subpanels']\\n quad_etype = 9\\n create_vtk_cells_of_constant_element_type(grid, elements, quad_etype)\\n\\n vtk_points = numpy_to_vtk_points(nodes, points=None, dtype='<f', deep=1)\\n grid.SetPoints(vtk_points)\\n return\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.message_debug_sptr_set_max_noutput_items(self, m)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7859737","0.687435","0.6576984","0.6516009","0.6250021","0.5742838","0.57224095","0.5664653","0.5662323","0.5644606","0.5565795","0.55509084","0.55272174","0.54718906","0.5445443","0.54382503","0.5431226","0.5375135","0.53488725","0.5343298","0.5334908","0.52925533","0.5292211","0.52714384","0.52018744","0.5191569","0.5190401","0.51797","0.5178937","0.5178857","0.5174907","0.5165666","0.5136168","0.51303875","0.5118595","0.5118281","0.51129234","0.5107911","0.5100436","0.50835127","0.5082754","0.508021","0.5077508","0.5073955","0.5069917","0.5051949","0.5046316","0.50402695","0.5022177","0.50123495","0.50122595","0.5002925","0.49989134","0.49903095","0.49886477","0.49772987","0.49760342","0.49560782","0.49538124","0.49538124","0.49473768","0.49439144","0.49352965","0.49342558","0.49321255","0.49243596","0.49220395","0.49162757","0.49134496","0.48990765","0.48880753","0.4884171","0.48647663","0.48547673","0.48503682","0.48396048","0.4830109","0.48290262","0.48285672","0.48240098","0.48214537","0.4811071","0.47918606","0.47907054","0.4790143","0.47772637","0.47677264","0.47630927","0.47616947","0.47503626","0.4749343","0.47476035","0.47429636","0.47412416","0.47331268","0.47311687","0.47303846","0.47291943","0.47198284","0.47192734"],"string":"[\n \"0.7859737\",\n \"0.687435\",\n \"0.6576984\",\n \"0.6516009\",\n \"0.6250021\",\n \"0.5742838\",\n \"0.57224095\",\n \"0.5664653\",\n \"0.5662323\",\n \"0.5644606\",\n \"0.5565795\",\n \"0.55509084\",\n \"0.55272174\",\n \"0.54718906\",\n \"0.5445443\",\n \"0.54382503\",\n \"0.5431226\",\n \"0.5375135\",\n \"0.53488725\",\n \"0.5343298\",\n \"0.5334908\",\n \"0.52925533\",\n \"0.5292211\",\n \"0.52714384\",\n \"0.52018744\",\n \"0.5191569\",\n \"0.5190401\",\n \"0.51797\",\n \"0.5178937\",\n \"0.5178857\",\n \"0.5174907\",\n \"0.5165666\",\n \"0.5136168\",\n \"0.51303875\",\n \"0.5118595\",\n \"0.5118281\",\n \"0.51129234\",\n \"0.5107911\",\n \"0.5100436\",\n \"0.50835127\",\n \"0.5082754\",\n \"0.508021\",\n \"0.5077508\",\n \"0.5073955\",\n \"0.5069917\",\n \"0.5051949\",\n \"0.5046316\",\n \"0.50402695\",\n \"0.5022177\",\n \"0.50123495\",\n \"0.50122595\",\n \"0.5002925\",\n \"0.49989134\",\n \"0.49903095\",\n \"0.49886477\",\n \"0.49772987\",\n \"0.49760342\",\n \"0.49560782\",\n \"0.49538124\",\n \"0.49538124\",\n \"0.49473768\",\n \"0.49439144\",\n \"0.49352965\",\n \"0.49342558\",\n \"0.49321255\",\n \"0.49243596\",\n \"0.49220395\",\n \"0.49162757\",\n \"0.49134496\",\n \"0.48990765\",\n \"0.48880753\",\n \"0.4884171\",\n \"0.48647663\",\n \"0.48547673\",\n \"0.48503682\",\n \"0.48396048\",\n \"0.4830109\",\n \"0.48290262\",\n \"0.48285672\",\n \"0.48240098\",\n \"0.48214537\",\n \"0.4811071\",\n \"0.47918606\",\n \"0.47907054\",\n \"0.4790143\",\n \"0.47772637\",\n \"0.47677264\",\n \"0.47630927\",\n \"0.47616947\",\n \"0.47503626\",\n \"0.4749343\",\n \"0.47476035\",\n \"0.47429636\",\n \"0.47412416\",\n \"0.47331268\",\n \"0.47311687\",\n \"0.47303846\",\n \"0.47291943\",\n \"0.47198284\",\n \"0.47192734\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76632214"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11979,"cells":{"query":{"kind":"string","value":"Obtains the number of preallocated cones in the optimization task. getmaxnumcone(self)"},"document":{"kind":"string","value":"def getmaxnumcone(self):\n maxnumcone_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcone(self.__nativep,ctypes.byref(maxnumcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcone_ = maxnumcone_.value\n _maxnumcone_return_value = maxnumcone_\n return (_maxnumcone_return_value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getmaxnumcone(self): # 3\n res,resargs = self.__obj.getmaxnumcone()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcone_return_value = resargs\n return _maxnumcone_return_value","def getnumcone(self): # 3\n res,resargs = self.__obj.getnumcone()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numcone_return_value = resargs\n return _numcone_return_value","def getmaxnumcon(self): # 3\n res,resargs = self.__obj.getmaxnumcon()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcon_return_value = resargs\n return _maxnumcon_return_value","def getnumcone(self):\n numcone_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumcone(self.__nativep,ctypes.byref(numcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numcone_ = numcone_.value\n _numcone_return_value = numcone_\n return (_numcone_return_value)","def num_cones(self):\n return self._shape_count(_sff.cone)","def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def num_conll(self):\n pass","def getmaxnumcon(self):\n maxnumcon_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcon(self.__nativep,ctypes.byref(maxnumcon_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcon_ = maxnumcon_.value\n _maxnumcon_return_value = maxnumcon_\n return (_maxnumcon_return_value)","def control_edge_count_max(self) -> int:\n return int(self.graph_tuple_stats.control_edge_count_max or 0)","def number_of_electrodes(self):\n return self._pre_kernel.shape[1]","def max_cardinality():\r\n #create a list containing the number of each vertex involvement.\r\n array = []\r\n for i in adj:\r\n array += [i[0],i[1]]\r\n\r\n #compute the degree by counting the involment\r\n degree = Counter(array).most_common()\r\n\r\n #retrieve the degree only\r\n degree_ = [ i[1] for i in degree]\r\n\r\n degree_ = np.array(degree_)\r\n \r\n max_m = None\r\n \r\n #check if m is valid\r\n for i in range(degree[0][1]+2)[2:]:\r\n \r\n #valid if there are at least m vertex with degree equals to at least m-1 \r\n if i < len(np.where(degree_>=i-1)[0]):\r\n max_m = i\r\n else:\r\n break\r\n max_m += 1\r\n print(f'maximum possible clique cardinality :{max_m}')\r\n return max_m","def maximum_number_of_workers(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"maximum_number_of_workers\")","def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def maximum_number_of_workers(self) -> pulumi.Output[Optional[int]]:\n return pulumi.get(self, \"maximum_number_of_workers\")","def concurrent_tasks_limit(self):\n return self._concurrent_tasks_limit","def largest_cc_size(ugraph):\n\tconnected = cc_visited(ugraph)\n\tmaxnum = 0\n\tfor content in connected:\n\t\tmaxnum = max(maxnum,len(content))\n\treturn maxnum","def n_cs(self):\n return self._configurations[0].n_cs","def num_cochains(self) -> int:\n if self.__num_cochains__ is not None:\n return self.__num_cochains__\n return self.ptr.numel() + 1","def getnumconemem(self,k_): # 3\n res,resargs = self.__obj.getnumconemem(k_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nummem_return_value = resargs\n return _nummem_return_value","def n_cs(self):\n return np.size(self._cs, 0)","def get_max_cleverbot_requests(self):\n return int(self.bot_data_file[\"maxCleverbotRequests\"])","def cmax(self):\n return self['cmax']","def cmax(self):\n return self[\"cmax\"]","def max_node_count(self) -> int:\n return pulumi.get(self, \"max_node_count\")","def getnumcon(self): # 3\n res,resargs = self.__obj.getnumcon()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numcon_return_value = resargs\n return _numcon_return_value","def call_edge_count_max(self) -> int:\n return int(self.graph_tuple_stats.call_edge_count_max or 0)","def max_concurrency(self) -> Optional[int]:\n result = get_feature(self.vm, \"qubes-vm-update-max-concurrency\", None)\n if result is None:\n return result\n return int(result)","def nClumps(self):\n \n return len(self)","def maxclients(self) -> Optional[int]:\n return pulumi.get(self, \"maxclients\")","def maxContigLength(self):\n\t\tstats = self.scores()\n\t\treturn stats['largestContig']","def get_max_num_onsets():\r\n \"\"\" based on the numbers above, should equal to 932945... \"\"\"\r\n c1 = len(gen_onset_c1())\r\n c2 = len(gen_onset_c2())\r\n c3 = len(gen_onset_c3_c4())\r\n c4 = len(gen_onset_c3_c4())\r\n temp = c1\r\n temp = temp + ( c1 * c2 )\r\n temp = temp + ( c1 * c3 )\r\n temp = temp + ( c1 * c2 * c3 )\r\n temp = temp + ( c1 * c3 * c4 )\r\n temp = temp + ( c1 * c2 * c3 * c4 )\r\n return temp","def N(self):\n return len(self.cavity_grid.cavities) + 1","def max_node_count(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"max_node_count\")","def max_num_neighbors(self):\n return self._max_num_neighbors","def node_count_max(self) -> int:\n return int(self.graph_tuple_stats.node_count_max or 0)","def concurrent_connections(self):\n return self.connector.limit","def maximum_elastic_worker_count(self) -> pulumi.Output[Optional[int]]:\n return pulumi.get(self, \"maximum_elastic_worker_count\")","def max_counts(self):\n\n return np.nanmax(self.pre_proc_data)","def nC(self):\n return int(self._n.prod())","def nThreads(self):\n return self._c_param.n_threads","def get_best_clique(self):\n\t\treturn [i+1 for i in range(self._size) if self._globalMinimumState[i] == 1]","def edge_count_max(self) -> int:\n return int(self.graph_tuple_stats.edge_count_max or 0)","def limit_num_clients(self):\n return self._limit_num_clients","def maxTasksAchievable(self):\n maxTasks = 0\n for w in self._workers:\n maxTasks = maxTasks + w.multitask\n return maxTasks","def carn_count(self):\n return len(self.carnivores)","def n_cf(self):\n return self._configurations[0].n_cf","def get_ncores(self):\n return self._ncores","def largest_cc_size(ugraph):\n if not ugraph:\n return 0\n return max(len(cc) for cc in cc_visited(ugraph))","def max_nodes(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"max_nodes\")","def get_num_parallel_workers():\n return _config.get_num_parallel_workers()","def max_principal_components(self):\n return self._max_principal_components","def nworkers(self):\n return len(self._workers)","def max_count(self):\n return self.config.get('max_count', 500)","def potential_max(self):\n\n return self._args.max","def contraction_max_algos():\n return cutensor.contractionMaxAlgos()","def largest_cc_size(ugraph):\n ccomp = cc_visited(ugraph)\n if len(ccomp) == 0:\n return 0\n \n return max([len(s) for s in ccomp])","def number_of_basis(self):\n return self._pre_kernel.shape[0]","def max_mireds(self):\n return 333","def get_max_num_cr(gdq_cube, jump_flag): # pragma: no cover\n cr_flagged = np.empty(gdq_cube.shape, dtype=np.uint8)\n cr_flagged[:] = np.where(np.bitwise_and(gdq_cube, jump_flag), 1, 0)\n max_num_cr = cr_flagged.sum(axis=0, dtype=np.int32).max()\n del cr_flagged\n\n return max_num_cr","def get_Ec_max(self):\n return self.Ec_max","def nCy(self):\n if self.dim < 2:\n return None\n return int(self._n[1])","def nC(self):\n return int(self.vnC.prod())","def max_em_iterations(self):\n return self._max_em_iterations","def numprocesses(self):\n info = self.info()\n return info['max_processes']","def abs_max_cool_setpoint_limit(self) -> int:\n return self.cluster.get(\"abs_max_cool_setpoint_limit\", 3200)","def getNchan(self):\n return self.shape(squeeze=False)[2]","def max_pool_size(self) -> ConfigNodePropertyInteger:\n return self._max_pool_size","def get_n_workers(self):\n return self.df.worker.nunique()","def max_known_number(self):\n return len(self.number_list)-1","def cargo_max(self) -> Union[float, int]:\n return self.proto.cargo_space_max","def max(self) -> int:\n return self._status['party_size'][1]","def total_max_node_count(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"total_max_node_count\")","def max_node_size(self):\n return self.max_node_capacity","def num_clbits(self):\n return 0","def max_pending(self):\n return self._max_pending","def numCoordsets(self):\n\n return self._n_csets","def n_cf(self):\n return np.size(self._ref_ii, 0)","def max_capacity(self) -> int:\n return self._max_capacity","def max_noutput_items(self) -> \"int\":\n return _beamforming_swig.doaesprit_sptr_max_noutput_items(self)","def num_carns(self):\n return self._num_carns","def get_number_of_atoms_to_optimize(self):\n v = self.c.get(simulation_cell=True)\n return len(v.data.stoichiometry)","def get_number_of_cows(self):\n bulls = self.get_number_of_bulls()\n list_of_cows = set(self.puzzle) & set(self.guess)\n cows = (len(list_of_cows) - bulls)\n return cows","def max_edges(self):\n return self._max_edges","def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def largest_cc_size(ugraph):\r\n\ttotal_list = cc_visited(ugraph)\r\n\tmax_length_list = []\r\n\tfor each_list in total_list:\r\n\t\tif len(max_length_list) < len(each_list):\r\n\t\t\tmax_length_list = each_list\r\n\treturn len(max_length_list)","def data_edge_count_max(self) -> int:\n return int(self.graph_tuple_stats.data_edge_count_max or 0)","def num_workers(self):\n return self._num_workers","def getnumcon(self):\n numcon_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumcon(self.__nativep,ctypes.byref(numcon_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numcon_ = numcon_.value\n _numcon_return_value = numcon_\n return (_numcon_return_value)","def get_conn_matrix_len(self):\n\n return len(self.connection_matrix) * self.brain[\"n_osc\"]","def _num_edges(self):\n return int(self._edge_map[-1])","def cost(self):\n c = self.n\n for i in range(self.n):\n queen = self.queens[i]\n safe = True\n for j in range(self.n):\n if i == j:\n continue\n other_queen = self.queens[j]\n if (queen[0] == other_queen[0]):\n safe = False\n elif (queen[1] == other_queen[1]):\n safe = False\n elif abs(queen[0]-other_queen[0]) == abs(queen[1]-other_queen[1]):\n safe = False\n if safe:\n c -= 1\n return c","def getNumCompartments(self):\n return _libsbml.Model_getNumCompartments(self)","def nt(self):\n if self.nClumps() > 0:\n \n return len(self[0])\n \n else:\n \n return 0","def get_prefetch_size():\n return _config.get_op_connector_size()","def maxdim(self):\n return self._maxdim","def pool_size():\r\n if DESIRED_THREADS > 1:\r\n return min(DESIRED_THREADS, multiprocessing.cpu_count())\r\n else:\r\n raise Exception(\"ARG ERROR: DESIRED_THREADS is not valid\")","def max_individuals(self) -> int:\n return self.group_size.upper * self.groups_allowed","def get_iter_num(self):\n\tif len(self.cost) > 0:\n first_key = list(self.cost.keys())[0]\n num = len(self.cost[first_key]) - 1\n\telse:\n\t first_key = list(self.prim_var.keys())[0]\n num = len(self.prim_var[first_key]) - 1\n\treturn num","def nCz(self):\n if self.dim < 3:\n return None\n return int(self._n[2])","def getMaxMancount(self):\n return self.__size * 20"],"string":"[\n \"def getmaxnumcone(self): # 3\\n res,resargs = self.__obj.getmaxnumcone()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumcone_return_value = resargs\\n return _maxnumcone_return_value\",\n \"def getnumcone(self): # 3\\n res,resargs = self.__obj.getnumcone()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numcone_return_value = resargs\\n return _numcone_return_value\",\n \"def getmaxnumcon(self): # 3\\n res,resargs = self.__obj.getmaxnumcon()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumcon_return_value = resargs\\n return _maxnumcon_return_value\",\n \"def getnumcone(self):\\n numcone_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getnumcone(self.__nativep,ctypes.byref(numcone_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numcone_ = numcone_.value\\n _numcone_return_value = numcone_\\n return (_numcone_return_value)\",\n \"def num_cones(self):\\n return self._shape_count(_sff.cone)\",\n \"def putmaxnumcone(self,maxnumcone_): # 3\\n res = self.__obj.putmaxnumcone(maxnumcone_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def num_conll(self):\\n pass\",\n \"def getmaxnumcon(self):\\n maxnumcon_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumcon(self.__nativep,ctypes.byref(maxnumcon_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumcon_ = maxnumcon_.value\\n _maxnumcon_return_value = maxnumcon_\\n return (_maxnumcon_return_value)\",\n \"def control_edge_count_max(self) -> int:\\n return int(self.graph_tuple_stats.control_edge_count_max or 0)\",\n \"def number_of_electrodes(self):\\n return self._pre_kernel.shape[1]\",\n \"def max_cardinality():\\r\\n #create a list containing the number of each vertex involvement.\\r\\n array = []\\r\\n for i in adj:\\r\\n array += [i[0],i[1]]\\r\\n\\r\\n #compute the degree by counting the involment\\r\\n degree = Counter(array).most_common()\\r\\n\\r\\n #retrieve the degree only\\r\\n degree_ = [ i[1] for i in degree]\\r\\n\\r\\n degree_ = np.array(degree_)\\r\\n \\r\\n max_m = None\\r\\n \\r\\n #check if m is valid\\r\\n for i in range(degree[0][1]+2)[2:]:\\r\\n \\r\\n #valid if there are at least m vertex with degree equals to at least m-1 \\r\\n if i < len(np.where(degree_>=i-1)[0]):\\r\\n max_m = i\\r\\n else:\\r\\n break\\r\\n max_m += 1\\r\\n print(f'maximum possible clique cardinality :{max_m}')\\r\\n return max_m\",\n \"def maximum_number_of_workers(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"maximum_number_of_workers\\\")\",\n \"def putmaxnumcone(self,maxnumcone_):\\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def maximum_number_of_workers(self) -> pulumi.Output[Optional[int]]:\\n return pulumi.get(self, \\\"maximum_number_of_workers\\\")\",\n \"def concurrent_tasks_limit(self):\\n return self._concurrent_tasks_limit\",\n \"def largest_cc_size(ugraph):\\n\\tconnected = cc_visited(ugraph)\\n\\tmaxnum = 0\\n\\tfor content in connected:\\n\\t\\tmaxnum = max(maxnum,len(content))\\n\\treturn maxnum\",\n \"def n_cs(self):\\n return self._configurations[0].n_cs\",\n \"def num_cochains(self) -> int:\\n if self.__num_cochains__ is not None:\\n return self.__num_cochains__\\n return self.ptr.numel() + 1\",\n \"def getnumconemem(self,k_): # 3\\n res,resargs = self.__obj.getnumconemem(k_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nummem_return_value = resargs\\n return _nummem_return_value\",\n \"def n_cs(self):\\n return np.size(self._cs, 0)\",\n \"def get_max_cleverbot_requests(self):\\n return int(self.bot_data_file[\\\"maxCleverbotRequests\\\"])\",\n \"def cmax(self):\\n return self['cmax']\",\n \"def cmax(self):\\n return self[\\\"cmax\\\"]\",\n \"def max_node_count(self) -> int:\\n return pulumi.get(self, \\\"max_node_count\\\")\",\n \"def getnumcon(self): # 3\\n res,resargs = self.__obj.getnumcon()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numcon_return_value = resargs\\n return _numcon_return_value\",\n \"def call_edge_count_max(self) -> int:\\n return int(self.graph_tuple_stats.call_edge_count_max or 0)\",\n \"def max_concurrency(self) -> Optional[int]:\\n result = get_feature(self.vm, \\\"qubes-vm-update-max-concurrency\\\", None)\\n if result is None:\\n return result\\n return int(result)\",\n \"def nClumps(self):\\n \\n return len(self)\",\n \"def maxclients(self) -> Optional[int]:\\n return pulumi.get(self, \\\"maxclients\\\")\",\n \"def maxContigLength(self):\\n\\t\\tstats = self.scores()\\n\\t\\treturn stats['largestContig']\",\n \"def get_max_num_onsets():\\r\\n \\\"\\\"\\\" based on the numbers above, should equal to 932945... \\\"\\\"\\\"\\r\\n c1 = len(gen_onset_c1())\\r\\n c2 = len(gen_onset_c2())\\r\\n c3 = len(gen_onset_c3_c4())\\r\\n c4 = len(gen_onset_c3_c4())\\r\\n temp = c1\\r\\n temp = temp + ( c1 * c2 )\\r\\n temp = temp + ( c1 * c3 )\\r\\n temp = temp + ( c1 * c2 * c3 )\\r\\n temp = temp + ( c1 * c3 * c4 )\\r\\n temp = temp + ( c1 * c2 * c3 * c4 )\\r\\n return temp\",\n \"def N(self):\\n return len(self.cavity_grid.cavities) + 1\",\n \"def max_node_count(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"max_node_count\\\")\",\n \"def max_num_neighbors(self):\\n return self._max_num_neighbors\",\n \"def node_count_max(self) -> int:\\n return int(self.graph_tuple_stats.node_count_max or 0)\",\n \"def concurrent_connections(self):\\n return self.connector.limit\",\n \"def maximum_elastic_worker_count(self) -> pulumi.Output[Optional[int]]:\\n return pulumi.get(self, \\\"maximum_elastic_worker_count\\\")\",\n \"def max_counts(self):\\n\\n return np.nanmax(self.pre_proc_data)\",\n \"def nC(self):\\n return int(self._n.prod())\",\n \"def nThreads(self):\\n return self._c_param.n_threads\",\n \"def get_best_clique(self):\\n\\t\\treturn [i+1 for i in range(self._size) if self._globalMinimumState[i] == 1]\",\n \"def edge_count_max(self) -> int:\\n return int(self.graph_tuple_stats.edge_count_max or 0)\",\n \"def limit_num_clients(self):\\n return self._limit_num_clients\",\n \"def maxTasksAchievable(self):\\n maxTasks = 0\\n for w in self._workers:\\n maxTasks = maxTasks + w.multitask\\n return maxTasks\",\n \"def carn_count(self):\\n return len(self.carnivores)\",\n \"def n_cf(self):\\n return self._configurations[0].n_cf\",\n \"def get_ncores(self):\\n return self._ncores\",\n \"def largest_cc_size(ugraph):\\n if not ugraph:\\n return 0\\n return max(len(cc) for cc in cc_visited(ugraph))\",\n \"def max_nodes(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"max_nodes\\\")\",\n \"def get_num_parallel_workers():\\n return _config.get_num_parallel_workers()\",\n \"def max_principal_components(self):\\n return self._max_principal_components\",\n \"def nworkers(self):\\n return len(self._workers)\",\n \"def max_count(self):\\n return self.config.get('max_count', 500)\",\n \"def potential_max(self):\\n\\n return self._args.max\",\n \"def contraction_max_algos():\\n return cutensor.contractionMaxAlgos()\",\n \"def largest_cc_size(ugraph):\\n ccomp = cc_visited(ugraph)\\n if len(ccomp) == 0:\\n return 0\\n \\n return max([len(s) for s in ccomp])\",\n \"def number_of_basis(self):\\n return self._pre_kernel.shape[0]\",\n \"def max_mireds(self):\\n return 333\",\n \"def get_max_num_cr(gdq_cube, jump_flag): # pragma: no cover\\n cr_flagged = np.empty(gdq_cube.shape, dtype=np.uint8)\\n cr_flagged[:] = np.where(np.bitwise_and(gdq_cube, jump_flag), 1, 0)\\n max_num_cr = cr_flagged.sum(axis=0, dtype=np.int32).max()\\n del cr_flagged\\n\\n return max_num_cr\",\n \"def get_Ec_max(self):\\n return self.Ec_max\",\n \"def nCy(self):\\n if self.dim < 2:\\n return None\\n return int(self._n[1])\",\n \"def nC(self):\\n return int(self.vnC.prod())\",\n \"def max_em_iterations(self):\\n return self._max_em_iterations\",\n \"def numprocesses(self):\\n info = self.info()\\n return info['max_processes']\",\n \"def abs_max_cool_setpoint_limit(self) -> int:\\n return self.cluster.get(\\\"abs_max_cool_setpoint_limit\\\", 3200)\",\n \"def getNchan(self):\\n return self.shape(squeeze=False)[2]\",\n \"def max_pool_size(self) -> ConfigNodePropertyInteger:\\n return self._max_pool_size\",\n \"def get_n_workers(self):\\n return self.df.worker.nunique()\",\n \"def max_known_number(self):\\n return len(self.number_list)-1\",\n \"def cargo_max(self) -> Union[float, int]:\\n return self.proto.cargo_space_max\",\n \"def max(self) -> int:\\n return self._status['party_size'][1]\",\n \"def total_max_node_count(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"total_max_node_count\\\")\",\n \"def max_node_size(self):\\n return self.max_node_capacity\",\n \"def num_clbits(self):\\n return 0\",\n \"def max_pending(self):\\n return self._max_pending\",\n \"def numCoordsets(self):\\n\\n return self._n_csets\",\n \"def n_cf(self):\\n return np.size(self._ref_ii, 0)\",\n \"def max_capacity(self) -> int:\\n return self._max_capacity\",\n \"def max_noutput_items(self) -> \\\"int\\\":\\n return _beamforming_swig.doaesprit_sptr_max_noutput_items(self)\",\n \"def num_carns(self):\\n return self._num_carns\",\n \"def get_number_of_atoms_to_optimize(self):\\n v = self.c.get(simulation_cell=True)\\n return len(v.data.stoichiometry)\",\n \"def get_number_of_cows(self):\\n bulls = self.get_number_of_bulls()\\n list_of_cows = set(self.puzzle) & set(self.guess)\\n cows = (len(list_of_cows) - bulls)\\n return cows\",\n \"def max_edges(self):\\n return self._max_edges\",\n \"def putmaxnumcon(self,maxnumcon_): # 3\\n res = self.__obj.putmaxnumcon(maxnumcon_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def largest_cc_size(ugraph):\\r\\n\\ttotal_list = cc_visited(ugraph)\\r\\n\\tmax_length_list = []\\r\\n\\tfor each_list in total_list:\\r\\n\\t\\tif len(max_length_list) < len(each_list):\\r\\n\\t\\t\\tmax_length_list = each_list\\r\\n\\treturn len(max_length_list)\",\n \"def data_edge_count_max(self) -> int:\\n return int(self.graph_tuple_stats.data_edge_count_max or 0)\",\n \"def num_workers(self):\\n return self._num_workers\",\n \"def getnumcon(self):\\n numcon_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getnumcon(self.__nativep,ctypes.byref(numcon_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numcon_ = numcon_.value\\n _numcon_return_value = numcon_\\n return (_numcon_return_value)\",\n \"def get_conn_matrix_len(self):\\n\\n return len(self.connection_matrix) * self.brain[\\\"n_osc\\\"]\",\n \"def _num_edges(self):\\n return int(self._edge_map[-1])\",\n \"def cost(self):\\n c = self.n\\n for i in range(self.n):\\n queen = self.queens[i]\\n safe = True\\n for j in range(self.n):\\n if i == j:\\n continue\\n other_queen = self.queens[j]\\n if (queen[0] == other_queen[0]):\\n safe = False\\n elif (queen[1] == other_queen[1]):\\n safe = False\\n elif abs(queen[0]-other_queen[0]) == abs(queen[1]-other_queen[1]):\\n safe = False\\n if safe:\\n c -= 1\\n return c\",\n \"def getNumCompartments(self):\\n return _libsbml.Model_getNumCompartments(self)\",\n \"def nt(self):\\n if self.nClumps() > 0:\\n \\n return len(self[0])\\n \\n else:\\n \\n return 0\",\n \"def get_prefetch_size():\\n return _config.get_op_connector_size()\",\n \"def maxdim(self):\\n return self._maxdim\",\n \"def pool_size():\\r\\n if DESIRED_THREADS > 1:\\r\\n return min(DESIRED_THREADS, multiprocessing.cpu_count())\\r\\n else:\\r\\n raise Exception(\\\"ARG ERROR: DESIRED_THREADS is not valid\\\")\",\n \"def max_individuals(self) -> int:\\n return self.group_size.upper * self.groups_allowed\",\n \"def get_iter_num(self):\\n\\tif len(self.cost) > 0:\\n first_key = list(self.cost.keys())[0]\\n num = len(self.cost[first_key]) - 1\\n\\telse:\\n\\t first_key = list(self.prim_var.keys())[0]\\n num = len(self.prim_var[first_key]) - 1\\n\\treturn num\",\n \"def nCz(self):\\n if self.dim < 3:\\n return None\\n return int(self._n[2])\",\n \"def getMaxMancount(self):\\n return self.__size * 20\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82559127","0.74485886","0.7001308","0.6936185","0.6920529","0.67586905","0.6735455","0.64610845","0.64500237","0.64104486","0.6385375","0.6377788","0.63512313","0.6300954","0.6270852","0.6235373","0.6222689","0.6206008","0.62018204","0.61789495","0.617264","0.6168127","0.60945046","0.60941845","0.6088023","0.60830295","0.6073222","0.6049861","0.60468364","0.60220975","0.59942776","0.59639","0.59638125","0.5943593","0.59389776","0.5928236","0.59194297","0.59157395","0.5914128","0.59086704","0.590455","0.58989996","0.58800304","0.5877433","0.5873091","0.5871005","0.5841538","0.5839016","0.58381146","0.5823864","0.5813536","0.58044046","0.5797009","0.5794882","0.5793663","0.5788771","0.5787608","0.5782371","0.5779766","0.57682025","0.57667065","0.5764236","0.57572734","0.57519585","0.5751599","0.57275784","0.5715123","0.5701813","0.5699134","0.569443","0.56849957","0.5680451","0.5668203","0.5661682","0.56546044","0.56412023","0.5633775","0.5627453","0.56248736","0.56229943","0.5622431","0.5617591","0.5613703","0.5612721","0.5607747","0.5605808","0.5596475","0.55932486","0.559153","0.55881494","0.55841166","0.55809224","0.55798244","0.55755067","0.5571661","0.556927","0.5565121","0.5560941","0.55553025","0.5545938"],"string":"[\n \"0.82559127\",\n \"0.74485886\",\n \"0.7001308\",\n \"0.6936185\",\n \"0.6920529\",\n \"0.67586905\",\n \"0.6735455\",\n \"0.64610845\",\n \"0.64500237\",\n \"0.64104486\",\n \"0.6385375\",\n \"0.6377788\",\n \"0.63512313\",\n \"0.6300954\",\n \"0.6270852\",\n \"0.6235373\",\n \"0.6222689\",\n \"0.6206008\",\n \"0.62018204\",\n \"0.61789495\",\n \"0.617264\",\n \"0.6168127\",\n \"0.60945046\",\n \"0.60941845\",\n \"0.6088023\",\n \"0.60830295\",\n \"0.6073222\",\n \"0.6049861\",\n \"0.60468364\",\n \"0.60220975\",\n \"0.59942776\",\n \"0.59639\",\n \"0.59638125\",\n \"0.5943593\",\n \"0.59389776\",\n \"0.5928236\",\n \"0.59194297\",\n \"0.59157395\",\n \"0.5914128\",\n \"0.59086704\",\n \"0.590455\",\n \"0.58989996\",\n \"0.58800304\",\n \"0.5877433\",\n \"0.5873091\",\n \"0.5871005\",\n \"0.5841538\",\n \"0.5839016\",\n \"0.58381146\",\n \"0.5823864\",\n \"0.5813536\",\n \"0.58044046\",\n \"0.5797009\",\n \"0.5794882\",\n \"0.5793663\",\n \"0.5788771\",\n \"0.5787608\",\n \"0.5782371\",\n \"0.5779766\",\n \"0.57682025\",\n \"0.57667065\",\n \"0.5764236\",\n \"0.57572734\",\n \"0.57519585\",\n \"0.5751599\",\n \"0.57275784\",\n \"0.5715123\",\n \"0.5701813\",\n \"0.5699134\",\n \"0.569443\",\n \"0.56849957\",\n \"0.5680451\",\n \"0.5668203\",\n \"0.5661682\",\n \"0.56546044\",\n \"0.56412023\",\n \"0.5633775\",\n \"0.5627453\",\n \"0.56248736\",\n \"0.56229943\",\n \"0.5622431\",\n \"0.5617591\",\n \"0.5613703\",\n \"0.5612721\",\n \"0.5607747\",\n \"0.5605808\",\n \"0.5596475\",\n \"0.55932486\",\n \"0.559153\",\n \"0.55881494\",\n \"0.55841166\",\n \"0.55809224\",\n \"0.55798244\",\n \"0.55755067\",\n \"0.5571661\",\n \"0.556927\",\n \"0.5565121\",\n \"0.5560941\",\n \"0.55553025\",\n \"0.5545938\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7739373"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11980,"cells":{"query":{"kind":"string","value":"Sets the number of preallocated variables in the optimization task. putmaxnumvar(self,maxnumvar_)"},"document":{"kind":"string","value":"def putmaxnumvar(self,maxnumvar_):\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumbarvar(self,maxnumbarvar_):\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getmaxnumvar(self): # 3\n res,resargs = self.__obj.getmaxnumvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumvar_return_value = resargs\n return _maxnumvar_return_value","def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getmaxnumbarvar(self): # 3\n res,resargs = self.__obj.getmaxnumbarvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumbarvar_return_value = resargs\n return _maxnumbarvar_return_value","def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getmaxnumvar(self):\n maxnumvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumvar(self.__nativep,ctypes.byref(maxnumvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumvar_ = maxnumvar_.value\n _maxnumvar_return_value = maxnumvar_\n return (_maxnumvar_return_value)","def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors","def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumanz(self,maxnumanz_):\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)","def getmaxnumbarvar(self):\n maxnumbarvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumbarvar(self.__nativep,ctypes.byref(maxnumbarvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumbarvar_ = maxnumbarvar_.value\n _maxnumbarvar_return_value = maxnumbarvar_\n return (_maxnumbarvar_return_value)","def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_max_nb_robots(nb): #py:set_max_nb_robots\n RUR._set_max_nb_robots_(nb)","def update_maxp(self) -> None:\n maxp = self.otf[\"maxp\"]\n ttdata = self.ufo.lib.get(TRUETYPE_INSTRUCTIONS_KEY, None)\n if ttdata:\n for name in (\n \"maxStorage\",\n \"maxFunctionDefs\",\n \"maxInstructionDefs\",\n \"maxStackElements\",\n # \"maxSizeOfInstructions\", # Is recalculated below\n \"maxZones\",\n \"maxTwilightPoints\",\n ):\n value = ttdata.get(name, None)\n if value is not None:\n setattr(maxp, name, value)\n\n # Recalculate maxp.maxSizeOfInstructions\n sizes = [\n len(ttglyph.program.getBytecode())\n for ttglyph in self.otf[\"glyf\"].glyphs.values()\n if hasattr(ttglyph, \"program\")\n ]\n maxp.maxSizeOfInstructions = max(sizes, default=0)","def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def non_heap_max(self, non_heap_max):\n\n self._non_heap_max = non_heap_max","def setOutMax(self, out_max):\n\t\tself.out_max = out_max","def set_workers(self, nworkers):\n\n self.max_workers = nworkers","def set_max(self, val):\n self._max = val","def set_max(self, max):\n self.set_val((self.val[0], max))","def set_max_noutput_items(self, m):\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)","def appendvars(self,num_):\n res = __library__.MSK_XX_appendvars(self.__nativep,num_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_maxVal(self, val):\n self.maxVal = val","def set_max_noutput_items(self, m):\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)","def set_max_reps(self, max_reps):\n self.max_reps = int(max_reps)","def set_numpins(self, n):\n self.numpins = n","def setMaxValue(self, max_value):\r\n\t\tself.MaxValue = max_value","def setMaxValue(self, max_value):\r\n\t\tself.MaxValue = max_value","def setNumWorkers(self, num):\r\n self.numWorkers = num","def set_maximum_iterations(self, n):\n self.maximum_iterations = n\n return","def getnumvar(self): # 3\n res,resargs = self.__obj.getnumvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numvar_return_value = resargs\n return _numvar_return_value","def variables_num(self):\n return 1","def nvar(self):\n return len(self.__vars)","def set_max_evaluations(self,ev):\n self.max_evaluations = ev","def max_pool_size(self, max_pool_size: ConfigNodePropertyInteger):\n\n self._max_pool_size = max_pool_size","def setImax(self, I_max):\n\t\tself.I_max = I_max","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)","def set_num_jobs(self, num):\n self.num_jobs = num","def set_max_participants(self, max_part):\n self.max_participants = max_part","def max_memory_mb(self, max_memory_mb):\n\n self._max_memory_mb = max_memory_mb","def appendvars(self,num_): # 3\n res = self.__obj.appendvars(num_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def max_in_gbps(self, max_in_gbps):\n self._max_in_gbps = max_in_gbps","def max_nodes(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"max_nodes\")","def setMaxSharedMemory():\n # First verify that kernel.shmmax is not set and is below the requested value.\n logging.debug(\"loading %s\", basedefs.FILE_SYSCTL)\n txtHandler = utils.TextConfigFileHandler(basedefs.FILE_SYSCTL)\n txtHandler.open()\n\n # Compare to basedefs.CONST_SHMMAX\n currentShmmax = txtHandler.getParam(\"kernel.shmmax\")\n if currentShmmax and (int(currentShmmax) >= basedefs.CONST_SHMMAX):\n logging.debug(\"current shared memory max in kernel is %s, there is no need to update the kernel parameters\", currentShmmax)\n return\n\n # If we got here, it means we need to update kernel.shmmax in sysctl.conf\n logging.debug(\"setting SHARED MEMORY MAX to: %s\", basedefs.CONST_SHMMAX)\n txtHandler.editParam(\"kernel.shmmax\", basedefs.CONST_SHMMAX)\n txtHandler.close()\n\n # Execute sysctl -a\n cmd = [\n basedefs.EXEC_SYSCTL, \"-e\", \"-p\",\n ]\n utils.execCmd(cmdList=cmd, failOnError=True, msg=output_messages.ERR_EXP_FAILED_KERNEL_PARAMS)","def setmaxsize(self, maxsize):\n self.maxsize = maxsize","def max_num_links(self, max_num_links):\n self._max_num_links = max_num_links","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.doaesprit_sptr_set_max_noutput_items(self, m)","def maxsize(self, maxsize):\n self.shape = (int(maxsize), ) + self.shape[1:]\n self.clear()","def max_cpus_per_machine(self, max_cpus_per_machine):\n\n self._max_cpus_per_machine = max_cpus_per_machine","def set_max_noutput_items(self, m):\n return _spacegrant_swig.general_burster_2_sptr_set_max_noutput_items(self, m)","def getnumbarvar(self): # 3\n res,resargs = self.__obj.getnumbarvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numbarvar_return_value = resargs\n return _numbarvar_return_value","def set_node_variable_number(self, number):\n if number == 0: # pragma: no cover\n return\n\n self._f.dimensions[\"num_nod_var\"] = number\n\n self._f.create_variable(\n \"name_nod_var\", (\"num_nod_var\", \"len_name\"),\n dtype=\"|S1\", **self._comp_opts)\n\n for _i in range(number):\n name = \"vals_nod_var%i\" % (_i + 1)\n self._f.create_variable(\n name, (\"time_step\", \"num_nodes\"),\n dtype=self.__f_dtype, **self._comp_opts)","def media_videos_count_max(self, media_videos_count_max):\n\n self._media_videos_count_max = media_videos_count_max","def set_is_max(self, is_max):\n self.__is_max = is_max","def max(self, max):\n\n self._max = max","def max(self, max):\n\n self._max = max","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.NRZI_sptr_set_max_output_buffer(self, *args)","def node_count_max(self) -> int:\n return int(self.graph_tuple_stats.node_count_max or 0)","def set_number_of_neurons_per_core(neuronType, maxPermitted):\n if not inspect.isclass(neuronType):\n neuronType = globals()[neuronType]\n if neuronType is None:\n raise Exception(\"Unknown Vertex Type {}\".format(neuronType))\n \n if hasattr(neuronType, \"custom_max_atoms_per_core\"):\n neuronType.custom_max_atoms_per_core = maxPermitted\n else:\n raise Exception(\"{} is not a Vertex type\".format(neuronType))","def MAXMEM(self):","def getnumintvar(self): # 3\n res,resargs = self.__obj.getnumintvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numintvar_return_value = resargs\n return _numintvar_return_value","def set_max_noutput_items(self, m):\n return _spacegrant_swig.udp_debug_sptr_set_max_noutput_items(self, m)","def set_max_eval_concurrency(self, max_eval_concurrency):\n self.max_eval_concurrency = max_eval_concurrency","def __init__(self, maxNumbers: int):\n self.nums = set(range(maxNumbers))","def set_max_noutput_items(self, m):\n return _add_vector_swig.add_vector_2_cpp_sptr_set_max_noutput_items(self, m)","def max_memory_gib(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"max_memory_gib\")","def max_memory_gib(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"max_memory_gib\")","def _set_maximum(self):\n self._level_gen.maximum_length = self._maximum_length_spinbox.value()\n self._refresh_view()","def set_max_vehicle_capacity(self, new_max_vehicle):\n if(new_max_vehicle == None):\n self._logger.write(\"Error! new_max_vehicle cannot be a NoneType\")\n elif(type(new_max_vehicle) != int):\n self._logger.write(\"Error! new_max_vehicle must be of type int\")\n else:\n try:\n self._max_vehicle_capacity = new_max_vehicle\n except Exception as e:\n self._logger.write(\"Error! Could not set the new max_vehicle:\\n %s\" % e)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.message_debug_sptr_set_max_noutput_items(self, m)","def vm_num_gt(self, vm_num_gt):\n\n self._vm_num_gt = vm_num_gt","def set_max_position(self, max_us):\n raise NotImplementedError()","def set_threadpool_size(nthreads):\n os.environ[\"OMP_THREAD_LIMIT\"] = \"0\" if nthreads is None else str(nthreads)","def maxCODQty(self, maxCODQty):\n\n self._maxCODQty = maxCODQty","def set_global_variable_number(self, number):\n if not number: # pragma: no cover\n return\n\n self._f.dimensions[\"num_glo_var\"] = number\n\n self._f.create_variable(\n \"name_glo_var\", (\"num_glo_var\", \"len_name\"),\n dtype=\"|S1\", **self._comp_opts)\n self._f.create_variable(\n \"vals_glo_var\", (\"time_step\", \"num_glo_var\"),\n dtype=self.__f_dtype, **self._comp_opts)","def vm_template_num_gt(self, vm_template_num_gt):\n\n self._vm_template_num_gt = vm_template_num_gt","def max_node_count(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"max_node_count\")","def set_max_noutput_items(self, m):\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)","def heap_max(self, heap_max):\n\n self._heap_max = heap_max","def max_node_count(self) -> int:\n return pulumi.get(self, \"max_node_count\")","def set_max_noutput_items(self, m):\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)","def quick_run_limit(self):\n try:\n return int(environment.get(\"Quick\"))\n except KeyError:\n return maxsize","def set_max_noutput_items(self, m):\n return _spacegrant_swig.binary_sink_sptr_set_max_noutput_items(self, m)","def __init__(__self__, *,\n max_pods_per_node: Optional[pulumi.Input[str]] = None):\n if max_pods_per_node is not None:\n pulumi.set(__self__, \"max_pods_per_node\", max_pods_per_node)","def set_max_noutput_items(self, *args, **kwargs):\n return _uhd_swig.usrp_source_sptr_set_max_noutput_items(self, *args, **kwargs)","def maximum_number(self, maximum_number):\n\n self._maximum_number = maximum_number","def _set_max_steps(self, mx):\n self._max = max(0, mx)\n\n if self._max:\n self._step_width = Helper.len(str(self._max))\n else:\n self._step_width = 4","def __init__(self, max: int) -> None:\n self.progress_bar = None\n self.progress = 0\n self._max = max","def num_vars(self):\n return self.nvars","def max_value(self, max_value):\n\n self._max_value = max_value","def max_value(self, max_value):\n\n self._max_value = max_value","def max_value(self, max_value):\n\n self._max_value = max_value","def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_framer_sptr_set_max_noutput_items(self, m)"],"string":"[\n \"def putmaxnumvar(self,maxnumvar_): # 3\\n res = self.__obj.putmaxnumvar(maxnumvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumbarvar(self,maxnumbarvar_): # 3\\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumbarvar(self,maxnumbarvar_):\\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getmaxnumvar(self): # 3\\n res,resargs = self.__obj.getmaxnumvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumvar_return_value = resargs\\n return _maxnumvar_return_value\",\n \"def putmaxnumqnz(self,maxnumqnz_): # 3\\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcon(self,maxnumcon_): # 3\\n res = self.__obj.putmaxnumcon(maxnumcon_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getmaxnumbarvar(self): # 3\\n res,resargs = self.__obj.getmaxnumbarvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumbarvar_return_value = resargs\\n return _maxnumbarvar_return_value\",\n \"def putmaxnumqnz(self,maxnumqnz_):\\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getmaxnumvar(self):\\n maxnumvar_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumvar(self.__nativep,ctypes.byref(maxnumvar_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumvar_ = maxnumvar_.value\\n _maxnumvar_return_value = maxnumvar_\\n return (_maxnumvar_return_value)\",\n \"def putmaxnumanz(self,maxnumanz_): # 3\\n res = self.__obj.putmaxnumanz(maxnumanz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def max_num_neighbors(self, max_num_neighbors):\\n self._max_num_neighbors = max_num_neighbors\",\n \"def putmaxnumcon(self,maxnumcon_):\\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumanz(self,maxnumanz_):\\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_max_nb_instructions(nb): #py:set_max_nb_instructions\\n RUR._set_max_nb_instructions_(nb)\",\n \"def getmaxnumbarvar(self):\\n maxnumbarvar_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumbarvar(self.__nativep,ctypes.byref(maxnumbarvar_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumbarvar_ = maxnumbarvar_.value\\n _maxnumbarvar_return_value = maxnumbarvar_\\n return (_maxnumbarvar_return_value)\",\n \"def putmaxnumcone(self,maxnumcone_): # 3\\n res = self.__obj.putmaxnumcone(maxnumcone_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_max_nb_robots(nb): #py:set_max_nb_robots\\n RUR._set_max_nb_robots_(nb)\",\n \"def update_maxp(self) -> None:\\n maxp = self.otf[\\\"maxp\\\"]\\n ttdata = self.ufo.lib.get(TRUETYPE_INSTRUCTIONS_KEY, None)\\n if ttdata:\\n for name in (\\n \\\"maxStorage\\\",\\n \\\"maxFunctionDefs\\\",\\n \\\"maxInstructionDefs\\\",\\n \\\"maxStackElements\\\",\\n # \\\"maxSizeOfInstructions\\\", # Is recalculated below\\n \\\"maxZones\\\",\\n \\\"maxTwilightPoints\\\",\\n ):\\n value = ttdata.get(name, None)\\n if value is not None:\\n setattr(maxp, name, value)\\n\\n # Recalculate maxp.maxSizeOfInstructions\\n sizes = [\\n len(ttglyph.program.getBytecode())\\n for ttglyph in self.otf[\\\"glyf\\\"].glyphs.values()\\n if hasattr(ttglyph, \\\"program\\\")\\n ]\\n maxp.maxSizeOfInstructions = max(sizes, default=0)\",\n \"def putmaxnumcone(self,maxnumcone_):\\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def non_heap_max(self, non_heap_max):\\n\\n self._non_heap_max = non_heap_max\",\n \"def setOutMax(self, out_max):\\n\\t\\tself.out_max = out_max\",\n \"def set_workers(self, nworkers):\\n\\n self.max_workers = nworkers\",\n \"def set_max(self, val):\\n self._max = val\",\n \"def set_max(self, max):\\n self.set_val((self.val[0], max))\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)\",\n \"def appendvars(self,num_):\\n res = __library__.MSK_XX_appendvars(self.__nativep,num_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_maxVal(self, val):\\n self.maxVal = val\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_reps(self, max_reps):\\n self.max_reps = int(max_reps)\",\n \"def set_numpins(self, n):\\n self.numpins = n\",\n \"def setMaxValue(self, max_value):\\r\\n\\t\\tself.MaxValue = max_value\",\n \"def setMaxValue(self, max_value):\\r\\n\\t\\tself.MaxValue = max_value\",\n \"def setNumWorkers(self, num):\\r\\n self.numWorkers = num\",\n \"def set_maximum_iterations(self, n):\\n self.maximum_iterations = n\\n return\",\n \"def getnumvar(self): # 3\\n res,resargs = self.__obj.getnumvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numvar_return_value = resargs\\n return _numvar_return_value\",\n \"def variables_num(self):\\n return 1\",\n \"def nvar(self):\\n return len(self.__vars)\",\n \"def set_max_evaluations(self,ev):\\n self.max_evaluations = ev\",\n \"def max_pool_size(self, max_pool_size: ConfigNodePropertyInteger):\\n\\n self._max_pool_size = max_pool_size\",\n \"def setImax(self, I_max):\\n\\t\\tself.I_max = I_max\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)\",\n \"def set_num_jobs(self, num):\\n self.num_jobs = num\",\n \"def set_max_participants(self, max_part):\\n self.max_participants = max_part\",\n \"def max_memory_mb(self, max_memory_mb):\\n\\n self._max_memory_mb = max_memory_mb\",\n \"def appendvars(self,num_): # 3\\n res = self.__obj.appendvars(num_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def max_in_gbps(self, max_in_gbps):\\n self._max_in_gbps = max_in_gbps\",\n \"def max_nodes(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"max_nodes\\\")\",\n \"def setMaxSharedMemory():\\n # First verify that kernel.shmmax is not set and is below the requested value.\\n logging.debug(\\\"loading %s\\\", basedefs.FILE_SYSCTL)\\n txtHandler = utils.TextConfigFileHandler(basedefs.FILE_SYSCTL)\\n txtHandler.open()\\n\\n # Compare to basedefs.CONST_SHMMAX\\n currentShmmax = txtHandler.getParam(\\\"kernel.shmmax\\\")\\n if currentShmmax and (int(currentShmmax) >= basedefs.CONST_SHMMAX):\\n logging.debug(\\\"current shared memory max in kernel is %s, there is no need to update the kernel parameters\\\", currentShmmax)\\n return\\n\\n # If we got here, it means we need to update kernel.shmmax in sysctl.conf\\n logging.debug(\\\"setting SHARED MEMORY MAX to: %s\\\", basedefs.CONST_SHMMAX)\\n txtHandler.editParam(\\\"kernel.shmmax\\\", basedefs.CONST_SHMMAX)\\n txtHandler.close()\\n\\n # Execute sysctl -a\\n cmd = [\\n basedefs.EXEC_SYSCTL, \\\"-e\\\", \\\"-p\\\",\\n ]\\n utils.execCmd(cmdList=cmd, failOnError=True, msg=output_messages.ERR_EXP_FAILED_KERNEL_PARAMS)\",\n \"def setmaxsize(self, maxsize):\\n self.maxsize = maxsize\",\n \"def max_num_links(self, max_num_links):\\n self._max_num_links = max_num_links\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.doaesprit_sptr_set_max_noutput_items(self, m)\",\n \"def maxsize(self, maxsize):\\n self.shape = (int(maxsize), ) + self.shape[1:]\\n self.clear()\",\n \"def max_cpus_per_machine(self, max_cpus_per_machine):\\n\\n self._max_cpus_per_machine = max_cpus_per_machine\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.general_burster_2_sptr_set_max_noutput_items(self, m)\",\n \"def getnumbarvar(self): # 3\\n res,resargs = self.__obj.getnumbarvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numbarvar_return_value = resargs\\n return _numbarvar_return_value\",\n \"def set_node_variable_number(self, number):\\n if number == 0: # pragma: no cover\\n return\\n\\n self._f.dimensions[\\\"num_nod_var\\\"] = number\\n\\n self._f.create_variable(\\n \\\"name_nod_var\\\", (\\\"num_nod_var\\\", \\\"len_name\\\"),\\n dtype=\\\"|S1\\\", **self._comp_opts)\\n\\n for _i in range(number):\\n name = \\\"vals_nod_var%i\\\" % (_i + 1)\\n self._f.create_variable(\\n name, (\\\"time_step\\\", \\\"num_nodes\\\"),\\n dtype=self.__f_dtype, **self._comp_opts)\",\n \"def media_videos_count_max(self, media_videos_count_max):\\n\\n self._media_videos_count_max = media_videos_count_max\",\n \"def set_is_max(self, is_max):\\n self.__is_max = is_max\",\n \"def max(self, max):\\n\\n self._max = max\",\n \"def max(self, max):\\n\\n self._max = max\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.NRZI_sptr_set_max_output_buffer(self, *args)\",\n \"def node_count_max(self) -> int:\\n return int(self.graph_tuple_stats.node_count_max or 0)\",\n \"def set_number_of_neurons_per_core(neuronType, maxPermitted):\\n if not inspect.isclass(neuronType):\\n neuronType = globals()[neuronType]\\n if neuronType is None:\\n raise Exception(\\\"Unknown Vertex Type {}\\\".format(neuronType))\\n \\n if hasattr(neuronType, \\\"custom_max_atoms_per_core\\\"):\\n neuronType.custom_max_atoms_per_core = maxPermitted\\n else:\\n raise Exception(\\\"{} is not a Vertex type\\\".format(neuronType))\",\n \"def MAXMEM(self):\",\n \"def getnumintvar(self): # 3\\n res,resargs = self.__obj.getnumintvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numintvar_return_value = resargs\\n return _numintvar_return_value\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.udp_debug_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_eval_concurrency(self, max_eval_concurrency):\\n self.max_eval_concurrency = max_eval_concurrency\",\n \"def __init__(self, maxNumbers: int):\\n self.nums = set(range(maxNumbers))\",\n \"def set_max_noutput_items(self, m):\\n return _add_vector_swig.add_vector_2_cpp_sptr_set_max_noutput_items(self, m)\",\n \"def max_memory_gib(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"max_memory_gib\\\")\",\n \"def max_memory_gib(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"max_memory_gib\\\")\",\n \"def _set_maximum(self):\\n self._level_gen.maximum_length = self._maximum_length_spinbox.value()\\n self._refresh_view()\",\n \"def set_max_vehicle_capacity(self, new_max_vehicle):\\n if(new_max_vehicle == None):\\n self._logger.write(\\\"Error! new_max_vehicle cannot be a NoneType\\\")\\n elif(type(new_max_vehicle) != int):\\n self._logger.write(\\\"Error! new_max_vehicle must be of type int\\\")\\n else:\\n try:\\n self._max_vehicle_capacity = new_max_vehicle\\n except Exception as e:\\n self._logger.write(\\\"Error! Could not set the new max_vehicle:\\\\n %s\\\" % e)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.message_debug_sptr_set_max_noutput_items(self, m)\",\n \"def vm_num_gt(self, vm_num_gt):\\n\\n self._vm_num_gt = vm_num_gt\",\n \"def set_max_position(self, max_us):\\n raise NotImplementedError()\",\n \"def set_threadpool_size(nthreads):\\n os.environ[\\\"OMP_THREAD_LIMIT\\\"] = \\\"0\\\" if nthreads is None else str(nthreads)\",\n \"def maxCODQty(self, maxCODQty):\\n\\n self._maxCODQty = maxCODQty\",\n \"def set_global_variable_number(self, number):\\n if not number: # pragma: no cover\\n return\\n\\n self._f.dimensions[\\\"num_glo_var\\\"] = number\\n\\n self._f.create_variable(\\n \\\"name_glo_var\\\", (\\\"num_glo_var\\\", \\\"len_name\\\"),\\n dtype=\\\"|S1\\\", **self._comp_opts)\\n self._f.create_variable(\\n \\\"vals_glo_var\\\", (\\\"time_step\\\", \\\"num_glo_var\\\"),\\n dtype=self.__f_dtype, **self._comp_opts)\",\n \"def vm_template_num_gt(self, vm_template_num_gt):\\n\\n self._vm_template_num_gt = vm_template_num_gt\",\n \"def max_node_count(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"max_node_count\\\")\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)\",\n \"def heap_max(self, heap_max):\\n\\n self._heap_max = heap_max\",\n \"def max_node_count(self) -> int:\\n return pulumi.get(self, \\\"max_node_count\\\")\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)\",\n \"def quick_run_limit(self):\\n try:\\n return int(environment.get(\\\"Quick\\\"))\\n except KeyError:\\n return maxsize\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.binary_sink_sptr_set_max_noutput_items(self, m)\",\n \"def __init__(__self__, *,\\n max_pods_per_node: Optional[pulumi.Input[str]] = None):\\n if max_pods_per_node is not None:\\n pulumi.set(__self__, \\\"max_pods_per_node\\\", max_pods_per_node)\",\n \"def set_max_noutput_items(self, *args, **kwargs):\\n return _uhd_swig.usrp_source_sptr_set_max_noutput_items(self, *args, **kwargs)\",\n \"def maximum_number(self, maximum_number):\\n\\n self._maximum_number = maximum_number\",\n \"def _set_max_steps(self, mx):\\n self._max = max(0, mx)\\n\\n if self._max:\\n self._step_width = Helper.len(str(self._max))\\n else:\\n self._step_width = 4\",\n \"def __init__(self, max: int) -> None:\\n self.progress_bar = None\\n self.progress = 0\\n self._max = max\",\n \"def num_vars(self):\\n return self.nvars\",\n \"def max_value(self, max_value):\\n\\n self._max_value = max_value\",\n \"def max_value(self, max_value):\\n\\n self._max_value = max_value\",\n \"def max_value(self, max_value):\\n\\n self._max_value = max_value\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.hdlc_framer_sptr_set_max_noutput_items(self, m)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80868816","0.7560464","0.73389435","0.65560055","0.65400666","0.6336814","0.63105714","0.62413776","0.61817116","0.6158278","0.60976154","0.60662615","0.6011411","0.5994649","0.5986585","0.5917469","0.58999217","0.58730257","0.58647376","0.58646524","0.57081693","0.5631341","0.5595974","0.5582642","0.55472887","0.5540637","0.5538168","0.5523251","0.54823864","0.5444105","0.54430217","0.5439219","0.5439219","0.54341125","0.54309684","0.5426088","0.5424894","0.54220766","0.541216","0.54107547","0.54088694","0.5405046","0.5400751","0.53966707","0.5392973","0.5388355","0.53845644","0.5382049","0.5380903","0.5376587","0.53750616","0.53671616","0.53243035","0.53195214","0.5317568","0.5300736","0.5299682","0.52761835","0.52755606","0.5260576","0.5260576","0.52546936","0.5230673","0.5230515","0.52061546","0.5205698","0.52049977","0.5202855","0.5196993","0.5196302","0.51847845","0.51836896","0.51836896","0.51835704","0.518318","0.5178285","0.5174785","0.51747185","0.5171008","0.5159889","0.51530504","0.514929","0.51466393","0.5145893","0.5142869","0.5142323","0.513649","0.5130495","0.51261324","0.5123785","0.5123457","0.5122519","0.51103514","0.51099104","0.5083195","0.5081507","0.50811845","0.50811845","0.50811845","0.5080712"],"string":"[\n \"0.80868816\",\n \"0.7560464\",\n \"0.73389435\",\n \"0.65560055\",\n \"0.65400666\",\n \"0.6336814\",\n \"0.63105714\",\n \"0.62413776\",\n \"0.61817116\",\n \"0.6158278\",\n \"0.60976154\",\n \"0.60662615\",\n \"0.6011411\",\n \"0.5994649\",\n \"0.5986585\",\n \"0.5917469\",\n \"0.58999217\",\n \"0.58730257\",\n \"0.58647376\",\n \"0.58646524\",\n \"0.57081693\",\n \"0.5631341\",\n \"0.5595974\",\n \"0.5582642\",\n \"0.55472887\",\n \"0.5540637\",\n \"0.5538168\",\n \"0.5523251\",\n \"0.54823864\",\n \"0.5444105\",\n \"0.54430217\",\n \"0.5439219\",\n \"0.5439219\",\n \"0.54341125\",\n \"0.54309684\",\n \"0.5426088\",\n \"0.5424894\",\n \"0.54220766\",\n \"0.541216\",\n \"0.54107547\",\n \"0.54088694\",\n \"0.5405046\",\n \"0.5400751\",\n \"0.53966707\",\n \"0.5392973\",\n \"0.5388355\",\n \"0.53845644\",\n \"0.5382049\",\n \"0.5380903\",\n \"0.5376587\",\n \"0.53750616\",\n \"0.53671616\",\n \"0.53243035\",\n \"0.53195214\",\n \"0.5317568\",\n \"0.5300736\",\n \"0.5299682\",\n \"0.52761835\",\n \"0.52755606\",\n \"0.5260576\",\n \"0.5260576\",\n \"0.52546936\",\n \"0.5230673\",\n \"0.5230515\",\n \"0.52061546\",\n \"0.5205698\",\n \"0.52049977\",\n \"0.5202855\",\n \"0.5196993\",\n \"0.5196302\",\n \"0.51847845\",\n \"0.51836896\",\n \"0.51836896\",\n \"0.51835704\",\n \"0.518318\",\n \"0.5178285\",\n \"0.5174785\",\n \"0.51747185\",\n \"0.5171008\",\n \"0.5159889\",\n \"0.51530504\",\n \"0.514929\",\n \"0.51466393\",\n \"0.5145893\",\n \"0.5142869\",\n \"0.5142323\",\n \"0.513649\",\n \"0.5130495\",\n \"0.51261324\",\n \"0.5123785\",\n \"0.5123457\",\n \"0.5122519\",\n \"0.51103514\",\n \"0.51099104\",\n \"0.5083195\",\n \"0.5081507\",\n \"0.50811845\",\n \"0.50811845\",\n \"0.50811845\",\n \"0.5080712\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76906484"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11981,"cells":{"query":{"kind":"string","value":"Sets the number of preallocated symmetric matrix variables. putmaxnumbarvar(self,maxnumbarvar_)"},"document":{"kind":"string","value":"def putmaxnumbarvar(self,maxnumbarvar_):\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumvar(self,maxnumvar_):\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getmaxnumbarvar(self): # 3\n res,resargs = self.__obj.getmaxnumbarvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumbarvar_return_value = resargs\n return _maxnumbarvar_return_value","def getmaxnumbarvar(self):\n maxnumbarvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumbarvar(self.__nativep,ctypes.byref(maxnumbarvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumbarvar_ = maxnumbarvar_.value\n _maxnumbarvar_return_value = maxnumbarvar_\n return (_maxnumbarvar_return_value)","def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors","def set_max_noutput_items(self, m):\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)","def getnumbarvar(self): # 3\n res,resargs = self.__obj.getnumbarvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numbarvar_return_value = resargs\n return _numbarvar_return_value","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)","def getnumbarvar(self):\n numbarvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumbarvar(self.__nativep,ctypes.byref(numbarvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numbarvar_ = numbarvar_.value\n _numbarvar_return_value = numbarvar_\n return (_numbarvar_return_value)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.general_burster_2_sptr_set_max_noutput_items(self, m)","def getmaxnumvar(self): # 3\n res,resargs = self.__obj.getmaxnumvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumvar_return_value = resargs\n return _maxnumvar_return_value","def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)","def set_max_nb_robots(nb): #py:set_max_nb_robots\n RUR._set_max_nb_robots_(nb)","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)","def maxsize(self, maxsize):\n self.shape = (int(maxsize), ) + self.shape[1:]\n self.clear()","def getmaxnumvar(self):\n maxnumvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumvar(self.__nativep,ctypes.byref(maxnumvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumvar_ = maxnumvar_.value\n _maxnumvar_return_value = maxnumvar_\n return (_maxnumvar_return_value)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)","def setImax(self, I_max):\n\t\tself.I_max = I_max","def update_maxp(self) -> None:\n maxp = self.otf[\"maxp\"]\n ttdata = self.ufo.lib.get(TRUETYPE_INSTRUCTIONS_KEY, None)\n if ttdata:\n for name in (\n \"maxStorage\",\n \"maxFunctionDefs\",\n \"maxInstructionDefs\",\n \"maxStackElements\",\n # \"maxSizeOfInstructions\", # Is recalculated below\n \"maxZones\",\n \"maxTwilightPoints\",\n ):\n value = ttdata.get(name, None)\n if value is not None:\n setattr(maxp, name, value)\n\n # Recalculate maxp.maxSizeOfInstructions\n sizes = [\n len(ttglyph.program.getBytecode())\n for ttglyph in self.otf[\"glyf\"].glyphs.values()\n if hasattr(ttglyph, \"program\")\n ]\n maxp.maxSizeOfInstructions = max(sizes, default=0)","def non_heap_max(self, non_heap_max):\n\n self._non_heap_max = non_heap_max","def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.binary_sink_sptr_set_max_noutput_items(self, m)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_framer_sptr_set_max_noutput_items(self, m)","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.NRZI_sptr_set_max_output_buffer(self, *args)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.message_debug_sptr_set_max_noutput_items(self, m)","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.doaesprit_sptr_set_max_noutput_items(self, m)","def putmaxnumanz(self,maxnumanz_):\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.ax25_pdu_packer_sptr_set_max_noutput_items(self, m)","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)","def setMaxSharedMemory():\n # First verify that kernel.shmmax is not set and is below the requested value.\n logging.debug(\"loading %s\", basedefs.FILE_SYSCTL)\n txtHandler = utils.TextConfigFileHandler(basedefs.FILE_SYSCTL)\n txtHandler.open()\n\n # Compare to basedefs.CONST_SHMMAX\n currentShmmax = txtHandler.getParam(\"kernel.shmmax\")\n if currentShmmax and (int(currentShmmax) >= basedefs.CONST_SHMMAX):\n logging.debug(\"current shared memory max in kernel is %s, there is no need to update the kernel parameters\", currentShmmax)\n return\n\n # If we got here, it means we need to update kernel.shmmax in sysctl.conf\n logging.debug(\"setting SHARED MEMORY MAX to: %s\", basedefs.CONST_SHMMAX)\n txtHandler.editParam(\"kernel.shmmax\", basedefs.CONST_SHMMAX)\n txtHandler.close()\n\n # Execute sysctl -a\n cmd = [\n basedefs.EXEC_SYSCTL, \"-e\", \"-p\",\n ]\n utils.execCmd(cmdList=cmd, failOnError=True, msg=output_messages.ERR_EXP_FAILED_KERNEL_PARAMS)","def modify_max_sides(self,max_sides):\n if max_sides<self.max_sides:\n if not np.all( self.cells['nodes'][:,max_sides:] == self.UNDEFINED ):\n raise GridException(\"Some cells cannot fit in requested max_sides\")\n\n old_max_sides=self.max_sides\n self.max_sides=max_sides\n\n # update dtypes for cells\n # for now, assume that only nodes and edges are affected.\n old_dtype=np.dtype( self.cell_dtype ).descr\n\n new_cell_dtype=[]\n for typeinfo in old_dtype:\n name=typeinfo[0]\n vtype=typeinfo[1]\n if len(typeinfo)>2:\n shape=typeinfo[2]\n else:\n shape=None\n\n if name in ['edges','nodes']:\n new_cell_dtype.append( (name,vtype,self.max_sides) )\n else:\n new_cell_dtype.append( typeinfo ) # just copy\n\n self.cell_dtype=new_cell_dtype\n new_cells=np.zeros(self.Ncells(),new_cell_dtype)\n\n for typeinfo in old_dtype:\n name=typeinfo[0]\n if name in ['edges','nodes']:\n if old_max_sides > self.max_sides:\n new_cells[name][:,:] = self.cells[name][:,:self.max_sides]\n else:\n new_cells[name][:,:old_max_sides] = self.cells[name][:,:]\n new_cells[name][:,old_max_sides:]=self.UNDEFINED\n else:\n new_cells[name]=self.cells[name]\n\n self.cells=new_cells","def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def is_set_max_noutput_items(self):\n return _spacegrant_swig.NRZI_sptr_is_set_max_noutput_items(self)","def setOutMax(self, out_max):\n\t\tself.out_max = out_max","def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)","def variables_num(self):\n return 1","def set_max_noutput_items(self, m):\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)","def set_smart_guard_max_snapshots_count(self, nMaxSnapshotsCount):\n\t\tcall_sdk_function('PrlVmCfg_SetSmartGuardMaxSnapshotsCount', self.handle, nMaxSnapshotsCount)","def updatemaxbombs(self):\n tiles: int = int(self.widthbox.get()) * int(self.heightbox.get())\n self.bombsbox.configure(to=tiles/2)","def nvar(self):\n return self.h.shape[0]","def set_max_noutput_items(self, m):\n return _spacegrant_swig.udp_debug_sptr_set_max_noutput_items(self, m)","def is_set_max_noutput_items(self):\n return _spacegrant_swig.DeNRZI_sptr_is_set_max_noutput_items(self)","def nvar(self):\n return len(self.__vars)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.ax25_pdu_unpacker_sptr_set_max_noutput_items(self, m)","def set_max_noutput_items(self, m):\n return _add_vector_swig.add_vector_2_cpp_sptr_set_max_noutput_items(self, m)","def max_memory_mb(self, max_memory_mb):\n\n self._max_memory_mb = max_memory_mb","def is_set_max_noutput_items(self):\n return _spacegrant_swig.general_burster_2_sptr_is_set_max_noutput_items(self)","def max_in_gbps(self, max_in_gbps):\n self._max_in_gbps = max_in_gbps","def maxCODQty(self, maxCODQty):\n\n self._maxCODQty = maxCODQty","def _set_max_steps(self, mx):\n self._max = max(0, mx)\n\n if self._max:\n self._step_width = Helper.len(str(self._max))\n else:\n self._step_width = 4","def setmaxsize(self, maxsize):\n self.maxsize = maxsize","def set_max_output_buffer(self, *args) -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_output_buffer(self, *args)","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.general_burster_2_sptr_set_max_output_buffer(self, *args)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.ax25_udp_pdu_gen_sptr_set_max_noutput_items(self, m)","def set_is_max(self, is_max):\n self.__is_max = is_max","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.invert_bit_sptr_set_max_output_buffer(self, *args)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.ax25_udp_pdu_receiver_sptr_set_max_noutput_items(self, m)","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.randomsampler_sptr_set_max_noutput_items(self, m)","def is_set_max_noutput_items(self):\n return _spacegrant_swig.message_debug_sptr_is_set_max_noutput_items(self)","def graph_data_size_max(self) -> int:\n return int(self.graph_tuple_stats.graph_data_size_max or 0)","def max_noutput_items(self):\n return _spacegrant_swig.NRZI_sptr_max_noutput_items(self)","def dual_size(k_max: int):\n n = 2 * k_max + 1\n return n","def media_videos_count_max(self, media_videos_count_max):\n\n self._media_videos_count_max = media_videos_count_max","def max_num_links(self, max_num_links):\n self._max_num_links = max_num_links","def is_set_max_noutput_items(self):\n return _spacegrant_swig.invert_bit_sptr_is_set_max_noutput_items(self)","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.message_debug_sptr_set_max_output_buffer(self, *args)","def set_max(self, max):\n self.set_val((self.val[0], max))","def max_memory_gib(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"max_memory_gib\")","def max_memory_gib(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"max_memory_gib\")","def set_max_output_buffer(self, *args) -> \"void\":\n return _beamforming_swig.beamformer_sptr_set_max_output_buffer(self, *args)","def is_set_max_noutput_items(self):\n return _spacegrant_swig.ax25_pdu_packer_sptr_is_set_max_noutput_items(self)","def control_edge_count_max(self) -> int:\n return int(self.graph_tuple_stats.control_edge_count_max or 0)","def __init__(self, max: int) -> None:\n self.progress_bar = None\n self.progress = 0\n self._max = max","def max_noutput_items(self):\n return _spacegrant_swig.DeNRZI_sptr_max_noutput_items(self)","def is_set_max_noutput_items(self) -> \"bool\":\n return _beamforming_swig.phasedarray_sptr_is_set_max_noutput_items(self)","def is_set_max_noutput_items(self):\n return _spacegrant_swig.binary_sink_sptr_is_set_max_noutput_items(self)","def update_maximum_size(self):\n if self.initialized:\n max_size = self._compute_maximum_size()\n self.set_max_size(max_size)","def max_mb_ram_per_machine(self, max_mb_ram_per_machine):\n\n self._max_mb_ram_per_machine = max_mb_ram_per_machine","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.ax25_pdu_packer_sptr_set_max_output_buffer(self, *args)","def set_max_edges(self, edges):\n self.max_edges = edges","def set_workers(self, nworkers):\n\n self.max_workers = nworkers","def set_numpins(self, n):\n self.numpins = n","def set_max_vehicle_capacity(self, new_max_vehicle):\n if(new_max_vehicle == None):\n self._logger.write(\"Error! new_max_vehicle cannot be a NoneType\")\n elif(type(new_max_vehicle) != int):\n self._logger.write(\"Error! new_max_vehicle must be of type int\")\n else:\n try:\n self._max_vehicle_capacity = new_max_vehicle\n except Exception as e:\n self._logger.write(\"Error! Could not set the new max_vehicle:\\n %s\" % e)","def _set_maximum(self):\n self._level_gen.maximum_length = self._maximum_length_spinbox.value()\n self._refresh_view()","def is_set_max_noutput_items(self):\n return _spacegrant_swig.udp_debug_sptr_is_set_max_noutput_items(self)","def max_counts(self):\n\n return np.nanmax(self.pre_proc_data)","def set_max_noutput_items(self, *args, **kwargs):\n return _TestA_swig.my_qpsk_demod_cb_sptr_set_max_noutput_items(self, *args, **kwargs)","def max_noutput_items(self):\n return _spacegrant_swig.general_burster_2_sptr_max_noutput_items(self)","def set_number_of_neurons_per_core(neuronType, maxPermitted):\n if not inspect.isclass(neuronType):\n neuronType = globals()[neuronType]\n if neuronType is None:\n raise Exception(\"Unknown Vertex Type {}\".format(neuronType))\n \n if hasattr(neuronType, \"custom_max_atoms_per_core\"):\n neuronType.custom_max_atoms_per_core = maxPermitted\n else:\n raise Exception(\"{} is not a Vertex type\".format(neuronType))","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.binary_sink_sptr_set_max_output_buffer(self, *args)","def is_set_max_noutput_items(self):\n return _spacegrant_swig.hdlc_framer_sptr_is_set_max_noutput_items(self)","def node_count_max(self) -> int:\n return int(self.graph_tuple_stats.node_count_max or 0)","def is_set_max_noutput_items(self):\n return _spacegrant_swig.G3RUH_descramble_sptr_is_set_max_noutput_items(self)"],"string":"[\n \"def putmaxnumbarvar(self,maxnumbarvar_): # 3\\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumvar(self,maxnumvar_): # 3\\n res = self.__obj.putmaxnumvar(maxnumvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumvar(self,maxnumvar_):\\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getmaxnumbarvar(self): # 3\\n res,resargs = self.__obj.getmaxnumbarvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumbarvar_return_value = resargs\\n return _maxnumbarvar_return_value\",\n \"def getmaxnumbarvar(self):\\n maxnumbarvar_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumbarvar(self.__nativep,ctypes.byref(maxnumbarvar_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumbarvar_ = maxnumbarvar_.value\\n _maxnumbarvar_return_value = maxnumbarvar_\\n return (_maxnumbarvar_return_value)\",\n \"def putmaxnumqnz(self,maxnumqnz_): # 3\\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcon(self,maxnumcon_): # 3\\n res = self.__obj.putmaxnumcon(maxnumcon_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcon(self,maxnumcon_):\\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumqnz(self,maxnumqnz_):\\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def max_num_neighbors(self, max_num_neighbors):\\n self._max_num_neighbors = max_num_neighbors\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)\",\n \"def getnumbarvar(self): # 3\\n res,resargs = self.__obj.getnumbarvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numbarvar_return_value = resargs\\n return _numbarvar_return_value\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)\",\n \"def getnumbarvar(self):\\n numbarvar_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getnumbarvar(self.__nativep,ctypes.byref(numbarvar_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numbarvar_ = numbarvar_.value\\n _numbarvar_return_value = numbarvar_\\n return (_numbarvar_return_value)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.general_burster_2_sptr_set_max_noutput_items(self, m)\",\n \"def getmaxnumvar(self): # 3\\n res,resargs = self.__obj.getmaxnumvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumvar_return_value = resargs\\n return _maxnumvar_return_value\",\n \"def set_max_nb_instructions(nb): #py:set_max_nb_instructions\\n RUR._set_max_nb_instructions_(nb)\",\n \"def set_max_nb_robots(nb): #py:set_max_nb_robots\\n RUR._set_max_nb_robots_(nb)\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)\",\n \"def maxsize(self, maxsize):\\n self.shape = (int(maxsize), ) + self.shape[1:]\\n self.clear()\",\n \"def getmaxnumvar(self):\\n maxnumvar_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumvar(self.__nativep,ctypes.byref(maxnumvar_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumvar_ = maxnumvar_.value\\n _maxnumvar_return_value = maxnumvar_\\n return (_maxnumvar_return_value)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)\",\n \"def setImax(self, I_max):\\n\\t\\tself.I_max = I_max\",\n \"def update_maxp(self) -> None:\\n maxp = self.otf[\\\"maxp\\\"]\\n ttdata = self.ufo.lib.get(TRUETYPE_INSTRUCTIONS_KEY, None)\\n if ttdata:\\n for name in (\\n \\\"maxStorage\\\",\\n \\\"maxFunctionDefs\\\",\\n \\\"maxInstructionDefs\\\",\\n \\\"maxStackElements\\\",\\n # \\\"maxSizeOfInstructions\\\", # Is recalculated below\\n \\\"maxZones\\\",\\n \\\"maxTwilightPoints\\\",\\n ):\\n value = ttdata.get(name, None)\\n if value is not None:\\n setattr(maxp, name, value)\\n\\n # Recalculate maxp.maxSizeOfInstructions\\n sizes = [\\n len(ttglyph.program.getBytecode())\\n for ttglyph in self.otf[\\\"glyf\\\"].glyphs.values()\\n if hasattr(ttglyph, \\\"program\\\")\\n ]\\n maxp.maxSizeOfInstructions = max(sizes, default=0)\",\n \"def non_heap_max(self, non_heap_max):\\n\\n self._non_heap_max = non_heap_max\",\n \"def putmaxnumcone(self,maxnumcone_):\\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.binary_sink_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.hdlc_framer_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.NRZI_sptr_set_max_output_buffer(self, *args)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.message_debug_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.doaesprit_sptr_set_max_noutput_items(self, m)\",\n \"def putmaxnumanz(self,maxnumanz_):\\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcone(self,maxnumcone_): # 3\\n res = self.__obj.putmaxnumcone(maxnumcone_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.ax25_pdu_packer_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)\",\n \"def setMaxSharedMemory():\\n # First verify that kernel.shmmax is not set and is below the requested value.\\n logging.debug(\\\"loading %s\\\", basedefs.FILE_SYSCTL)\\n txtHandler = utils.TextConfigFileHandler(basedefs.FILE_SYSCTL)\\n txtHandler.open()\\n\\n # Compare to basedefs.CONST_SHMMAX\\n currentShmmax = txtHandler.getParam(\\\"kernel.shmmax\\\")\\n if currentShmmax and (int(currentShmmax) >= basedefs.CONST_SHMMAX):\\n logging.debug(\\\"current shared memory max in kernel is %s, there is no need to update the kernel parameters\\\", currentShmmax)\\n return\\n\\n # If we got here, it means we need to update kernel.shmmax in sysctl.conf\\n logging.debug(\\\"setting SHARED MEMORY MAX to: %s\\\", basedefs.CONST_SHMMAX)\\n txtHandler.editParam(\\\"kernel.shmmax\\\", basedefs.CONST_SHMMAX)\\n txtHandler.close()\\n\\n # Execute sysctl -a\\n cmd = [\\n basedefs.EXEC_SYSCTL, \\\"-e\\\", \\\"-p\\\",\\n ]\\n utils.execCmd(cmdList=cmd, failOnError=True, msg=output_messages.ERR_EXP_FAILED_KERNEL_PARAMS)\",\n \"def modify_max_sides(self,max_sides):\\n if max_sides<self.max_sides:\\n if not np.all( self.cells['nodes'][:,max_sides:] == self.UNDEFINED ):\\n raise GridException(\\\"Some cells cannot fit in requested max_sides\\\")\\n\\n old_max_sides=self.max_sides\\n self.max_sides=max_sides\\n\\n # update dtypes for cells\\n # for now, assume that only nodes and edges are affected.\\n old_dtype=np.dtype( self.cell_dtype ).descr\\n\\n new_cell_dtype=[]\\n for typeinfo in old_dtype:\\n name=typeinfo[0]\\n vtype=typeinfo[1]\\n if len(typeinfo)>2:\\n shape=typeinfo[2]\\n else:\\n shape=None\\n\\n if name in ['edges','nodes']:\\n new_cell_dtype.append( (name,vtype,self.max_sides) )\\n else:\\n new_cell_dtype.append( typeinfo ) # just copy\\n\\n self.cell_dtype=new_cell_dtype\\n new_cells=np.zeros(self.Ncells(),new_cell_dtype)\\n\\n for typeinfo in old_dtype:\\n name=typeinfo[0]\\n if name in ['edges','nodes']:\\n if old_max_sides > self.max_sides:\\n new_cells[name][:,:] = self.cells[name][:,:self.max_sides]\\n else:\\n new_cells[name][:,:old_max_sides] = self.cells[name][:,:]\\n new_cells[name][:,old_max_sides:]=self.UNDEFINED\\n else:\\n new_cells[name]=self.cells[name]\\n\\n self.cells=new_cells\",\n \"def putmaxnumanz(self,maxnumanz_): # 3\\n res = self.__obj.putmaxnumanz(maxnumanz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.NRZI_sptr_is_set_max_noutput_items(self)\",\n \"def setOutMax(self, out_max):\\n\\t\\tself.out_max = out_max\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)\",\n \"def variables_num(self):\\n return 1\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)\",\n \"def set_smart_guard_max_snapshots_count(self, nMaxSnapshotsCount):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetSmartGuardMaxSnapshotsCount', self.handle, nMaxSnapshotsCount)\",\n \"def updatemaxbombs(self):\\n tiles: int = int(self.widthbox.get()) * int(self.heightbox.get())\\n self.bombsbox.configure(to=tiles/2)\",\n \"def nvar(self):\\n return self.h.shape[0]\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.udp_debug_sptr_set_max_noutput_items(self, m)\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.DeNRZI_sptr_is_set_max_noutput_items(self)\",\n \"def nvar(self):\\n return len(self.__vars)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.ax25_pdu_unpacker_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_noutput_items(self, m):\\n return _add_vector_swig.add_vector_2_cpp_sptr_set_max_noutput_items(self, m)\",\n \"def max_memory_mb(self, max_memory_mb):\\n\\n self._max_memory_mb = max_memory_mb\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.general_burster_2_sptr_is_set_max_noutput_items(self)\",\n \"def max_in_gbps(self, max_in_gbps):\\n self._max_in_gbps = max_in_gbps\",\n \"def maxCODQty(self, maxCODQty):\\n\\n self._maxCODQty = maxCODQty\",\n \"def _set_max_steps(self, mx):\\n self._max = max(0, mx)\\n\\n if self._max:\\n self._step_width = Helper.len(str(self._max))\\n else:\\n self._step_width = 4\",\n \"def setmaxsize(self, maxsize):\\n self.maxsize = maxsize\",\n \"def set_max_output_buffer(self, *args) -> \\\"void\\\":\\n return _beamforming_swig.phasedarray_sptr_set_max_output_buffer(self, *args)\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.general_burster_2_sptr_set_max_output_buffer(self, *args)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.ax25_udp_pdu_gen_sptr_set_max_noutput_items(self, m)\",\n \"def set_is_max(self, is_max):\\n self.__is_max = is_max\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.invert_bit_sptr_set_max_output_buffer(self, *args)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.ax25_udp_pdu_receiver_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.randomsampler_sptr_set_max_noutput_items(self, m)\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.message_debug_sptr_is_set_max_noutput_items(self)\",\n \"def graph_data_size_max(self) -> int:\\n return int(self.graph_tuple_stats.graph_data_size_max or 0)\",\n \"def max_noutput_items(self):\\n return _spacegrant_swig.NRZI_sptr_max_noutput_items(self)\",\n \"def dual_size(k_max: int):\\n n = 2 * k_max + 1\\n return n\",\n \"def media_videos_count_max(self, media_videos_count_max):\\n\\n self._media_videos_count_max = media_videos_count_max\",\n \"def max_num_links(self, max_num_links):\\n self._max_num_links = max_num_links\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.invert_bit_sptr_is_set_max_noutput_items(self)\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.message_debug_sptr_set_max_output_buffer(self, *args)\",\n \"def set_max(self, max):\\n self.set_val((self.val[0], max))\",\n \"def max_memory_gib(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"max_memory_gib\\\")\",\n \"def max_memory_gib(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"max_memory_gib\\\")\",\n \"def set_max_output_buffer(self, *args) -> \\\"void\\\":\\n return _beamforming_swig.beamformer_sptr_set_max_output_buffer(self, *args)\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.ax25_pdu_packer_sptr_is_set_max_noutput_items(self)\",\n \"def control_edge_count_max(self) -> int:\\n return int(self.graph_tuple_stats.control_edge_count_max or 0)\",\n \"def __init__(self, max: int) -> None:\\n self.progress_bar = None\\n self.progress = 0\\n self._max = max\",\n \"def max_noutput_items(self):\\n return _spacegrant_swig.DeNRZI_sptr_max_noutput_items(self)\",\n \"def is_set_max_noutput_items(self) -> \\\"bool\\\":\\n return _beamforming_swig.phasedarray_sptr_is_set_max_noutput_items(self)\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.binary_sink_sptr_is_set_max_noutput_items(self)\",\n \"def update_maximum_size(self):\\n if self.initialized:\\n max_size = self._compute_maximum_size()\\n self.set_max_size(max_size)\",\n \"def max_mb_ram_per_machine(self, max_mb_ram_per_machine):\\n\\n self._max_mb_ram_per_machine = max_mb_ram_per_machine\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.ax25_pdu_packer_sptr_set_max_output_buffer(self, *args)\",\n \"def set_max_edges(self, edges):\\n self.max_edges = edges\",\n \"def set_workers(self, nworkers):\\n\\n self.max_workers = nworkers\",\n \"def set_numpins(self, n):\\n self.numpins = n\",\n \"def set_max_vehicle_capacity(self, new_max_vehicle):\\n if(new_max_vehicle == None):\\n self._logger.write(\\\"Error! new_max_vehicle cannot be a NoneType\\\")\\n elif(type(new_max_vehicle) != int):\\n self._logger.write(\\\"Error! new_max_vehicle must be of type int\\\")\\n else:\\n try:\\n self._max_vehicle_capacity = new_max_vehicle\\n except Exception as e:\\n self._logger.write(\\\"Error! Could not set the new max_vehicle:\\\\n %s\\\" % e)\",\n \"def _set_maximum(self):\\n self._level_gen.maximum_length = self._maximum_length_spinbox.value()\\n self._refresh_view()\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.udp_debug_sptr_is_set_max_noutput_items(self)\",\n \"def max_counts(self):\\n\\n return np.nanmax(self.pre_proc_data)\",\n \"def set_max_noutput_items(self, *args, **kwargs):\\n return _TestA_swig.my_qpsk_demod_cb_sptr_set_max_noutput_items(self, *args, **kwargs)\",\n \"def max_noutput_items(self):\\n return _spacegrant_swig.general_burster_2_sptr_max_noutput_items(self)\",\n \"def set_number_of_neurons_per_core(neuronType, maxPermitted):\\n if not inspect.isclass(neuronType):\\n neuronType = globals()[neuronType]\\n if neuronType is None:\\n raise Exception(\\\"Unknown Vertex Type {}\\\".format(neuronType))\\n \\n if hasattr(neuronType, \\\"custom_max_atoms_per_core\\\"):\\n neuronType.custom_max_atoms_per_core = maxPermitted\\n else:\\n raise Exception(\\\"{} is not a Vertex type\\\".format(neuronType))\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.binary_sink_sptr_set_max_output_buffer(self, *args)\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.hdlc_framer_sptr_is_set_max_noutput_items(self)\",\n \"def node_count_max(self) -> int:\\n return int(self.graph_tuple_stats.node_count_max or 0)\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.G3RUH_descramble_sptr_is_set_max_noutput_items(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7951648","0.6959753","0.6795598","0.65727705","0.6438065","0.6048308","0.5996996","0.5852882","0.5841968","0.5822205","0.5682764","0.5667983","0.5649398","0.5640302","0.5615086","0.56006753","0.55580956","0.5552524","0.5495012","0.54564613","0.5411751","0.53988004","0.53824806","0.53719604","0.5364293","0.53622717","0.53509885","0.53323233","0.532731","0.53160566","0.5315593","0.530056","0.52983403","0.5284302","0.52710533","0.526945","0.5256761","0.5242954","0.5233877","0.5224201","0.5205752","0.5203181","0.51974237","0.51805997","0.5175506","0.5162663","0.5155083","0.51361585","0.51285213","0.51254535","0.5124695","0.51218116","0.51071936","0.5097575","0.5085185","0.50829744","0.50460505","0.50430536","0.50313425","0.5018059","0.50095004","0.5004127","0.49937323","0.4978424","0.49659875","0.49650198","0.49636397","0.49379933","0.49319986","0.4927569","0.49266702","0.4923005","0.4915244","0.4901173","0.48984587","0.48984587","0.48955107","0.4879566","0.48717907","0.48678628","0.48678127","0.4866428","0.4863074","0.48629606","0.48597464","0.48592317","0.48505068","0.4848617","0.48446888","0.48356503","0.48351523","0.48300034","0.4826983","0.4826547","0.48263934","0.4825228","0.48169369","0.48144016","0.48128703","0.48125896"],"string":"[\n \"0.7951648\",\n \"0.6959753\",\n \"0.6795598\",\n \"0.65727705\",\n \"0.6438065\",\n \"0.6048308\",\n \"0.5996996\",\n \"0.5852882\",\n \"0.5841968\",\n \"0.5822205\",\n \"0.5682764\",\n \"0.5667983\",\n \"0.5649398\",\n \"0.5640302\",\n \"0.5615086\",\n \"0.56006753\",\n \"0.55580956\",\n \"0.5552524\",\n \"0.5495012\",\n \"0.54564613\",\n \"0.5411751\",\n \"0.53988004\",\n \"0.53824806\",\n \"0.53719604\",\n \"0.5364293\",\n \"0.53622717\",\n \"0.53509885\",\n \"0.53323233\",\n \"0.532731\",\n \"0.53160566\",\n \"0.5315593\",\n \"0.530056\",\n \"0.52983403\",\n \"0.5284302\",\n \"0.52710533\",\n \"0.526945\",\n \"0.5256761\",\n \"0.5242954\",\n \"0.5233877\",\n \"0.5224201\",\n \"0.5205752\",\n \"0.5203181\",\n \"0.51974237\",\n \"0.51805997\",\n \"0.5175506\",\n \"0.5162663\",\n \"0.5155083\",\n \"0.51361585\",\n \"0.51285213\",\n \"0.51254535\",\n \"0.5124695\",\n \"0.51218116\",\n \"0.51071936\",\n \"0.5097575\",\n \"0.5085185\",\n \"0.50829744\",\n \"0.50460505\",\n \"0.50430536\",\n \"0.50313425\",\n \"0.5018059\",\n \"0.50095004\",\n \"0.5004127\",\n \"0.49937323\",\n \"0.4978424\",\n \"0.49659875\",\n \"0.49650198\",\n \"0.49636397\",\n \"0.49379933\",\n \"0.49319986\",\n \"0.4927569\",\n \"0.49266702\",\n \"0.4923005\",\n \"0.4915244\",\n \"0.4901173\",\n \"0.48984587\",\n \"0.48984587\",\n \"0.48955107\",\n \"0.4879566\",\n \"0.48717907\",\n \"0.48678628\",\n \"0.48678127\",\n \"0.4866428\",\n \"0.4863074\",\n \"0.48629606\",\n \"0.48597464\",\n \"0.48592317\",\n \"0.48505068\",\n \"0.4848617\",\n \"0.48446888\",\n \"0.48356503\",\n \"0.48351523\",\n \"0.48300034\",\n \"0.4826983\",\n \"0.4826547\",\n \"0.48263934\",\n \"0.4825228\",\n \"0.48169369\",\n \"0.48144016\",\n \"0.48128703\",\n \"0.48125896\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78251684"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11982,"cells":{"query":{"kind":"string","value":"Sets the number of preallocated nonzero entries in the linear coefficient matrix. putmaxnumanz(self,maxnumanz_)"},"document":{"kind":"string","value":"def putmaxnumanz(self,maxnumanz_):\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getmaxnumanz(self):\n maxnumanz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getmaxnumanz64(self.__nativep,ctypes.byref(maxnumanz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumanz_ = maxnumanz_.value\n _maxnumanz_return_value = maxnumanz_\n return (_maxnumanz_return_value)","def getmaxnumanz(self): # 3\n res,resargs = self.__obj.getmaxnumanz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumanz_return_value = resargs\n return _maxnumanz_return_value","def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumvar(self,maxnumvar_):\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors","def putmaxnumbarvar(self,maxnumbarvar_):\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getmaxnumqnz(self):\n maxnumqnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getmaxnumqnz64(self.__nativep,ctypes.byref(maxnumqnz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumqnz_ = maxnumqnz_.value\n _maxnumqnz_return_value = maxnumqnz_\n return (_maxnumqnz_return_value)","def getnumbaranz(self):\n nz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumbaranz(self.__nativep,ctypes.byref(nz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nz_ = nz_.value\n _nz_return_value = nz_\n return (_nz_return_value)","def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setOutMax(self, out_max):\n\t\tself.out_max = out_max","def Max_ner(self, lst, max_ner):\r\n for i in range(len(lst)):\r\n if len(lst[i]) >= max_ner:\r\n lst[i] = lst[i][:max_ner]\r\n else:\r\n length = len(lst[i])\r\n for _ in range(max_ner - length):\r\n lst[i].append(0)\r\n return lst","def getnumanz(self):\n numanz_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumanz(self.__nativep,ctypes.byref(numanz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numanz_ = numanz_.value\n _numanz_return_value = numanz_\n return (_numanz_return_value)","def N_z(self) -> int:\n return self.params.N_z","def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)","def getmaxnumqnz(self): # 3\n res,resargs = self.__obj.getmaxnumqnz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumqnz_return_value = resargs\n return _maxnumqnz_return_value","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)","def set_max(self, val):\n self._max = val","def non_heap_max(self, non_heap_max):\n\n self._non_heap_max = non_heap_max","def NNZ(self):\n return _hypre.HypreParMatrix_NNZ(self)","def set_max(self, max):\n self.set_val((self.val[0], max))","def setImax(self, I_max):\n\t\tself.I_max = I_max","def nnz(self):","def set_max_position(self, max_us):\n raise NotImplementedError()","def set_max_nb_robots(nb): #py:set_max_nb_robots\n RUR._set_max_nb_robots_(nb)","def mapped_max(self, mapped_max):\n\n self._mapped_max = mapped_max","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.NRZI_sptr_set_max_output_buffer(self, *args)","def maxsize(self, maxsize):\n self.shape = (int(maxsize), ) + self.shape[1:]\n self.clear()","def set_max_noutput_items(self, m):\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)","def expanding_max_nb(a, minp=1):\n out = np.empty_like(a, dtype=np.float_)\n for col in range(a.shape[1]):\n out[:, col] = expanding_max_1d_nb(a[:, col], minp=minp)\n return out","def nNz(self):\n if self.dim < 3:\n return None\n return self.nCz + 1","def reset_zoc(self):\n if not self.parent_block_count:\n raise ValueError(\"cannot reset zoc until parent_block_count is set\")\n zoc_len = np.prod(self.parent_block_count)\n self.zoc = np.zeros(zoc_len, dtype=np.int32)","def set_z(self, value: int) -> None:\n assert -self.__max_value <= value and value <= self.__max_value\n\n should_sync = self.__z != value\n self.__z = value\n if should_sync:\n self.__sync_z()","def set_maxVal(self, val):\n self.maxVal = val","def max(self, max):\n\n self._max = max","def max(self, max):\n\n self._max = max","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.invert_bit_sptr_set_max_output_buffer(self, *args)","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)","def nnz(self):\n return len(self.value)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)","def __init__(self, MAX_VALUE = 6):\n self.DIE_MAX = MAX_VALUE\n self.NUMBER = 0","def count_max(alon):\n return count_max_acc(alon, alon[0], 0, 0)","def getnumanz(self): # 3\n res,resargs = self.__obj.getnumanz()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numanz_return_value = resargs\n return _numanz_return_value","def set_max_output_buffer(self, *args) -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_output_buffer(self, *args)","def getmaxnumcone(self):\n maxnumcone_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcone(self.__nativep,ctypes.byref(maxnumcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcone_ = maxnumcone_.value\n _maxnumcone_return_value = maxnumcone_\n return (_maxnumcone_return_value)","def _set_maximum(self):\n self._level_gen.maximum_length = self._maximum_length_spinbox.value()\n self._refresh_view()","def getZMax(self):\n return self.zmax","def getnumbaranz(self): # 3\n res,resargs = self.__obj.getnumbaranz()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nz_return_value = resargs\n return _nz_return_value","def relu(self, z):\n return np.maximum(0, z)","def max_known_number(self):\n return len(self.number_list)-1","def NNZ(self):\n return len(self.__IndList)","def circulation_default_extension_max_count(loan):\n return float(\"inf\")","def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)","def direct_max(self, direct_max):\n\n self._direct_max = direct_max","def zero_pad_back(self, zb):\n self.zb = zb\n self.nfft = self.N+self.zb+self.zf\n if self.analysis_window is not None:\n self.analysis_window = np.concatenate((self.analysis_window, np.zeros(zb)))\n if self.synthesis_window is not None:\n self.synthesis_window = np.concatenate((self.synthesis_window, np.zeros(zb)))","def set_max_noutput_items(self, m):\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)","def getnumbarcnz(self):\n nz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumbarcnz(self.__nativep,ctypes.byref(nz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nz_ = nz_.value\n _nz_return_value = nz_\n return (_nz_return_value)","def test_maxIndex(self):\t\t\n self.assertEqual(attempt.maxIndexZ, 113)\n self.assertEqual(attempt.maxIndexW, 134)","def max_positions(self):\n return int(1e5)","def maxCODQty(self, maxCODQty):\n\n self._maxCODQty = maxCODQty","def update_maxp(self) -> None:\n maxp = self.otf[\"maxp\"]\n ttdata = self.ufo.lib.get(TRUETYPE_INSTRUCTIONS_KEY, None)\n if ttdata:\n for name in (\n \"maxStorage\",\n \"maxFunctionDefs\",\n \"maxInstructionDefs\",\n \"maxStackElements\",\n # \"maxSizeOfInstructions\", # Is recalculated below\n \"maxZones\",\n \"maxTwilightPoints\",\n ):\n value = ttdata.get(name, None)\n if value is not None:\n setattr(maxp, name, value)\n\n # Recalculate maxp.maxSizeOfInstructions\n sizes = [\n len(ttglyph.program.getBytecode())\n for ttglyph in self.otf[\"glyf\"].glyphs.values()\n if hasattr(ttglyph, \"program\")\n ]\n maxp.maxSizeOfInstructions = max(sizes, default=0)","def calibrate(self,isMax,findZ):\r\n N = self.number_of_nodes()\r\n if isMax==1:\r\n for i in self.nodes():\r\n self.node[i]['fac'].val=scipy.log(self.node[i]['fac'].val)\r\n \r\n for i,j in self.edges():\r\n self.edge[i][j]['msg'] = factor([],[],[])\r\n self.edge[i][j]['msg_passed'] = False# message passed from i to j or not\r\n \r\n I,J = get_next_cliques(self)\r\n while I >= 0: \r\n self.pass_message(I,J,isMax,findZ)\r\n self.edge[I][J]['msg_passed'] = True\r\n I,J = get_next_cliques(self)\r\n \r\n for i in self.nodes():\r\n if isMax==0:\r\n self.node[i]['fac'] *= reduce(mul,(self.edge[j][i]['msg'] for j in self.predecessors(i)))\r\n else:\r\n self.node[i]['fac'] += reduce(add,(self.edge[j][i]['msg'] for j in self.predecessors(i)))\r\n self.nop += N*scipy.prod(self.node[i]['fac'].card) # check this\r","def relu(Z):\n\n A = np.maximum(0, Z)\n\n assert (A.shape == Z.shape)\n\n return A","def getmaxnumvar(self):\n maxnumvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumvar(self.__nativep,ctypes.byref(maxnumvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumvar_ = maxnumvar_.value\n _maxnumvar_return_value = maxnumvar_\n return (_maxnumvar_return_value)","def nnz(self):\n return len(self.data)","def set_max_output_buffer(self, *args) -> \"void\":\n return _beamforming_swig.beamformer_sptr_set_max_output_buffer(self, *args)","def max_unavailable(self, max_unavailable):\n\n self._max_unavailable = max_unavailable","def z_max(self):\n return self.get_max_value(self.Z_INDEX)","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)","def setMaxValue(self, max_value):\r\n\t\tself.MaxValue = max_value\r\n\t\tself.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts)","def setMaxValue(self, max_value):\r\n\t\tself.MaxValue = max_value\r\n\t\tself.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts)","def required_nb_data_func(list_nb_coeff):\n return max(list_nb_coeff)","def required_nb_data_func(list_nb_coeff):\n return max(list_nb_coeff)","def required_nb_data_func(list_nb_coeff):\n return max(list_nb_coeff)","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_output_buffer(self, *args)","def SetMaxIter(self, max_iter):\n return _hypre.HypreBoomerAMG_SetMaxIter(self, max_iter)","def max_reduce_nb(col, a, *args):\n return np.nanmax(a)","def OpenZmax(self, *args):\n return _Bnd.Bnd_Box_OpenZmax(self, *args)","def nnz(self):\n t = self.get_MSC()\n return len(np.unique(t['masks']))","def getMaxMancount(self):\n return self.__size * 20","def SetMaxIter(self, max_iter):\n return _hypre.HypreAME_SetMaxIter(self, max_iter)","def setMaxValue(self, max_value):\r\n\t\tself.MaxValue = max_value","def setMaxValue(self, max_value):\r\n\t\tself.MaxValue = max_value","def getnumbarcnz(self): # 3\n res,resargs = self.__obj.getnumbarcnz()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nz_return_value = resargs\n return _nz_return_value","def max_positions(self):\n return int(100000.0)","def set_nb_clusters(self):\n \n print(\"Finding the optimal number of clusters...\")\n \n sample = ro.r.matrix(self.df[self.df[\"filename\"].between(1, 4)][\"active_power\"].to_numpy())\n \n r=ro.r(\"\"\"\n check = function(matrix) {\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\n\n n_clust = n_clust$data\n\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\n return(max_cluster)\n }\n \"\"\")\n\n result = r(sample)\n self.conf[\"nb_clust\"] = int(result[0])\n \n print(f\"Optimal number of clusters is {self.conf['nb_clust']}\\n\")","def buildZPriorTerm(self):\r\n\r\n # shape is (batch size,)\r\n self.z_prior = - T.mean(T.sum(T.exp(self.log_pzgxw)*(self.log_pzgxw + T.log(self.hyper['num_clust'])), axis=3), axis=[1,2])\r\n\r\n self.z_prior_modif = - T.maximum(self.hyper['treshold_z_prior'], - self.z_prior)","def max_temp(self):\n return 99","def set_max_noutput_items(self, m):\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)","def max_num_links(self, max_num_links):\n self._max_num_links = max_num_links","def updateVmaxCritDen(self, newVmax, newCritDen):\r\n Link.updateVmaxCritDen(self, newVmax, newCritDen)\r\n # now update cells, keep length and number of cells the same\r\n for cell in self.cells:\r\n cell.capacity = self.params['qcap'] / 3600.0 # already updated for link\r\n cell.delta = self.params['bws'] / self.params['ffs'] # already upated in link \r\n return None","def set_is_max(self, is_max):\n self.__is_max = is_max","def getmaxnumbarvar(self):\n maxnumbarvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumbarvar(self.__nativep,ctypes.byref(maxnumbarvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumbarvar_ = maxnumbarvar_.value\n _maxnumbarvar_return_value = maxnumbarvar_\n return (_maxnumbarvar_return_value)","def set_limit_max():\n limit_max = request.params.get(\"limit_max\", 0, type=float)\n output = request.params.get(\"output\", 1, type=int)\n retval = RP_LIB.rp_LimitMax(output, ctypes.c_float(limit_max))\n if retval != 0:\n LOG.error(\"Failed to set maximum output voltage. Error code: %s\", ERROR_CODES[retval])"],"string":"[\n \"def putmaxnumanz(self,maxnumanz_): # 3\\n res = self.__obj.putmaxnumanz(maxnumanz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumqnz(self,maxnumqnz_):\\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumqnz(self,maxnumqnz_): # 3\\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcone(self,maxnumcone_):\\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getmaxnumanz(self):\\n maxnumanz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getmaxnumanz64(self.__nativep,ctypes.byref(maxnumanz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumanz_ = maxnumanz_.value\\n _maxnumanz_return_value = maxnumanz_\\n return (_maxnumanz_return_value)\",\n \"def getmaxnumanz(self): # 3\\n res,resargs = self.__obj.getmaxnumanz64()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumanz_return_value = resargs\\n return _maxnumanz_return_value\",\n \"def putmaxnumcone(self,maxnumcone_): # 3\\n res = self.__obj.putmaxnumcone(maxnumcone_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumvar(self,maxnumvar_):\\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcon(self,maxnumcon_):\\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def max_num_neighbors(self, max_num_neighbors):\\n self._max_num_neighbors = max_num_neighbors\",\n \"def putmaxnumbarvar(self,maxnumbarvar_):\\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumvar(self,maxnumvar_): # 3\\n res = self.__obj.putmaxnumvar(maxnumvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcon(self,maxnumcon_): # 3\\n res = self.__obj.putmaxnumcon(maxnumcon_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getmaxnumqnz(self):\\n maxnumqnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getmaxnumqnz64(self.__nativep,ctypes.byref(maxnumqnz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumqnz_ = maxnumqnz_.value\\n _maxnumqnz_return_value = maxnumqnz_\\n return (_maxnumqnz_return_value)\",\n \"def getnumbaranz(self):\\n nz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getnumbaranz(self.__nativep,ctypes.byref(nz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nz_ = nz_.value\\n _nz_return_value = nz_\\n return (_nz_return_value)\",\n \"def putmaxnumbarvar(self,maxnumbarvar_): # 3\\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setOutMax(self, out_max):\\n\\t\\tself.out_max = out_max\",\n \"def Max_ner(self, lst, max_ner):\\r\\n for i in range(len(lst)):\\r\\n if len(lst[i]) >= max_ner:\\r\\n lst[i] = lst[i][:max_ner]\\r\\n else:\\r\\n length = len(lst[i])\\r\\n for _ in range(max_ner - length):\\r\\n lst[i].append(0)\\r\\n return lst\",\n \"def getnumanz(self):\\n numanz_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getnumanz(self.__nativep,ctypes.byref(numanz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numanz_ = numanz_.value\\n _numanz_return_value = numanz_\\n return (_numanz_return_value)\",\n \"def N_z(self) -> int:\\n return self.params.N_z\",\n \"def set_max_nb_instructions(nb): #py:set_max_nb_instructions\\n RUR._set_max_nb_instructions_(nb)\",\n \"def getmaxnumqnz(self): # 3\\n res,resargs = self.__obj.getmaxnumqnz64()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumqnz_return_value = resargs\\n return _maxnumqnz_return_value\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)\",\n \"def set_max(self, val):\\n self._max = val\",\n \"def non_heap_max(self, non_heap_max):\\n\\n self._non_heap_max = non_heap_max\",\n \"def NNZ(self):\\n return _hypre.HypreParMatrix_NNZ(self)\",\n \"def set_max(self, max):\\n self.set_val((self.val[0], max))\",\n \"def setImax(self, I_max):\\n\\t\\tself.I_max = I_max\",\n \"def nnz(self):\",\n \"def set_max_position(self, max_us):\\n raise NotImplementedError()\",\n \"def set_max_nb_robots(nb): #py:set_max_nb_robots\\n RUR._set_max_nb_robots_(nb)\",\n \"def mapped_max(self, mapped_max):\\n\\n self._mapped_max = mapped_max\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.NRZI_sptr_set_max_output_buffer(self, *args)\",\n \"def maxsize(self, maxsize):\\n self.shape = (int(maxsize), ) + self.shape[1:]\\n self.clear()\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)\",\n \"def expanding_max_nb(a, minp=1):\\n out = np.empty_like(a, dtype=np.float_)\\n for col in range(a.shape[1]):\\n out[:, col] = expanding_max_1d_nb(a[:, col], minp=minp)\\n return out\",\n \"def nNz(self):\\n if self.dim < 3:\\n return None\\n return self.nCz + 1\",\n \"def reset_zoc(self):\\n if not self.parent_block_count:\\n raise ValueError(\\\"cannot reset zoc until parent_block_count is set\\\")\\n zoc_len = np.prod(self.parent_block_count)\\n self.zoc = np.zeros(zoc_len, dtype=np.int32)\",\n \"def set_z(self, value: int) -> None:\\n assert -self.__max_value <= value and value <= self.__max_value\\n\\n should_sync = self.__z != value\\n self.__z = value\\n if should_sync:\\n self.__sync_z()\",\n \"def set_maxVal(self, val):\\n self.maxVal = val\",\n \"def max(self, max):\\n\\n self._max = max\",\n \"def max(self, max):\\n\\n self._max = max\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.invert_bit_sptr_set_max_output_buffer(self, *args)\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)\",\n \"def nnz(self):\\n return len(self.value)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)\",\n \"def __init__(self, MAX_VALUE = 6):\\n self.DIE_MAX = MAX_VALUE\\n self.NUMBER = 0\",\n \"def count_max(alon):\\n return count_max_acc(alon, alon[0], 0, 0)\",\n \"def getnumanz(self): # 3\\n res,resargs = self.__obj.getnumanz()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numanz_return_value = resargs\\n return _numanz_return_value\",\n \"def set_max_output_buffer(self, *args) -> \\\"void\\\":\\n return _beamforming_swig.phasedarray_sptr_set_max_output_buffer(self, *args)\",\n \"def getmaxnumcone(self):\\n maxnumcone_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumcone(self.__nativep,ctypes.byref(maxnumcone_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumcone_ = maxnumcone_.value\\n _maxnumcone_return_value = maxnumcone_\\n return (_maxnumcone_return_value)\",\n \"def _set_maximum(self):\\n self._level_gen.maximum_length = self._maximum_length_spinbox.value()\\n self._refresh_view()\",\n \"def getZMax(self):\\n return self.zmax\",\n \"def getnumbaranz(self): # 3\\n res,resargs = self.__obj.getnumbaranz()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nz_return_value = resargs\\n return _nz_return_value\",\n \"def relu(self, z):\\n return np.maximum(0, z)\",\n \"def max_known_number(self):\\n return len(self.number_list)-1\",\n \"def NNZ(self):\\n return len(self.__IndList)\",\n \"def circulation_default_extension_max_count(loan):\\n return float(\\\"inf\\\")\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)\",\n \"def direct_max(self, direct_max):\\n\\n self._direct_max = direct_max\",\n \"def zero_pad_back(self, zb):\\n self.zb = zb\\n self.nfft = self.N+self.zb+self.zf\\n if self.analysis_window is not None:\\n self.analysis_window = np.concatenate((self.analysis_window, np.zeros(zb)))\\n if self.synthesis_window is not None:\\n self.synthesis_window = np.concatenate((self.synthesis_window, np.zeros(zb)))\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)\",\n \"def getnumbarcnz(self):\\n nz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getnumbarcnz(self.__nativep,ctypes.byref(nz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nz_ = nz_.value\\n _nz_return_value = nz_\\n return (_nz_return_value)\",\n \"def test_maxIndex(self):\\t\\t\\n self.assertEqual(attempt.maxIndexZ, 113)\\n self.assertEqual(attempt.maxIndexW, 134)\",\n \"def max_positions(self):\\n return int(1e5)\",\n \"def maxCODQty(self, maxCODQty):\\n\\n self._maxCODQty = maxCODQty\",\n \"def update_maxp(self) -> None:\\n maxp = self.otf[\\\"maxp\\\"]\\n ttdata = self.ufo.lib.get(TRUETYPE_INSTRUCTIONS_KEY, None)\\n if ttdata:\\n for name in (\\n \\\"maxStorage\\\",\\n \\\"maxFunctionDefs\\\",\\n \\\"maxInstructionDefs\\\",\\n \\\"maxStackElements\\\",\\n # \\\"maxSizeOfInstructions\\\", # Is recalculated below\\n \\\"maxZones\\\",\\n \\\"maxTwilightPoints\\\",\\n ):\\n value = ttdata.get(name, None)\\n if value is not None:\\n setattr(maxp, name, value)\\n\\n # Recalculate maxp.maxSizeOfInstructions\\n sizes = [\\n len(ttglyph.program.getBytecode())\\n for ttglyph in self.otf[\\\"glyf\\\"].glyphs.values()\\n if hasattr(ttglyph, \\\"program\\\")\\n ]\\n maxp.maxSizeOfInstructions = max(sizes, default=0)\",\n \"def calibrate(self,isMax,findZ):\\r\\n N = self.number_of_nodes()\\r\\n if isMax==1:\\r\\n for i in self.nodes():\\r\\n self.node[i]['fac'].val=scipy.log(self.node[i]['fac'].val)\\r\\n \\r\\n for i,j in self.edges():\\r\\n self.edge[i][j]['msg'] = factor([],[],[])\\r\\n self.edge[i][j]['msg_passed'] = False# message passed from i to j or not\\r\\n \\r\\n I,J = get_next_cliques(self)\\r\\n while I >= 0: \\r\\n self.pass_message(I,J,isMax,findZ)\\r\\n self.edge[I][J]['msg_passed'] = True\\r\\n I,J = get_next_cliques(self)\\r\\n \\r\\n for i in self.nodes():\\r\\n if isMax==0:\\r\\n self.node[i]['fac'] *= reduce(mul,(self.edge[j][i]['msg'] for j in self.predecessors(i)))\\r\\n else:\\r\\n self.node[i]['fac'] += reduce(add,(self.edge[j][i]['msg'] for j in self.predecessors(i)))\\r\\n self.nop += N*scipy.prod(self.node[i]['fac'].card) # check this\\r\",\n \"def relu(Z):\\n\\n A = np.maximum(0, Z)\\n\\n assert (A.shape == Z.shape)\\n\\n return A\",\n \"def getmaxnumvar(self):\\n maxnumvar_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumvar(self.__nativep,ctypes.byref(maxnumvar_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumvar_ = maxnumvar_.value\\n _maxnumvar_return_value = maxnumvar_\\n return (_maxnumvar_return_value)\",\n \"def nnz(self):\\n return len(self.data)\",\n \"def set_max_output_buffer(self, *args) -> \\\"void\\\":\\n return _beamforming_swig.beamformer_sptr_set_max_output_buffer(self, *args)\",\n \"def max_unavailable(self, max_unavailable):\\n\\n self._max_unavailable = max_unavailable\",\n \"def z_max(self):\\n return self.get_max_value(self.Z_INDEX)\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)\",\n \"def setMaxValue(self, max_value):\\r\\n\\t\\tself.MaxValue = max_value\\r\\n\\t\\tself.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts)\",\n \"def setMaxValue(self, max_value):\\r\\n\\t\\tself.MaxValue = max_value\\r\\n\\t\\tself.OutputValueIncrement = (self.MaxValue - self.MinValue)/(self.RampDuration/self.Ts)\",\n \"def required_nb_data_func(list_nb_coeff):\\n return max(list_nb_coeff)\",\n \"def required_nb_data_func(list_nb_coeff):\\n return max(list_nb_coeff)\",\n \"def required_nb_data_func(list_nb_coeff):\\n return max(list_nb_coeff)\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_output_buffer(self, *args)\",\n \"def SetMaxIter(self, max_iter):\\n return _hypre.HypreBoomerAMG_SetMaxIter(self, max_iter)\",\n \"def max_reduce_nb(col, a, *args):\\n return np.nanmax(a)\",\n \"def OpenZmax(self, *args):\\n return _Bnd.Bnd_Box_OpenZmax(self, *args)\",\n \"def nnz(self):\\n t = self.get_MSC()\\n return len(np.unique(t['masks']))\",\n \"def getMaxMancount(self):\\n return self.__size * 20\",\n \"def SetMaxIter(self, max_iter):\\n return _hypre.HypreAME_SetMaxIter(self, max_iter)\",\n \"def setMaxValue(self, max_value):\\r\\n\\t\\tself.MaxValue = max_value\",\n \"def setMaxValue(self, max_value):\\r\\n\\t\\tself.MaxValue = max_value\",\n \"def getnumbarcnz(self): # 3\\n res,resargs = self.__obj.getnumbarcnz()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nz_return_value = resargs\\n return _nz_return_value\",\n \"def max_positions(self):\\n return int(100000.0)\",\n \"def set_nb_clusters(self):\\n \\n print(\\\"Finding the optimal number of clusters...\\\")\\n \\n sample = ro.r.matrix(self.df[self.df[\\\"filename\\\"].between(1, 4)][\\\"active_power\\\"].to_numpy())\\n \\n r=ro.r(\\\"\\\"\\\"\\n check = function(matrix) {\\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\\n\\n n_clust = n_clust$data\\n\\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\\n return(max_cluster)\\n }\\n \\\"\\\"\\\")\\n\\n result = r(sample)\\n self.conf[\\\"nb_clust\\\"] = int(result[0])\\n \\n print(f\\\"Optimal number of clusters is {self.conf['nb_clust']}\\\\n\\\")\",\n \"def buildZPriorTerm(self):\\r\\n\\r\\n # shape is (batch size,)\\r\\n self.z_prior = - T.mean(T.sum(T.exp(self.log_pzgxw)*(self.log_pzgxw + T.log(self.hyper['num_clust'])), axis=3), axis=[1,2])\\r\\n\\r\\n self.z_prior_modif = - T.maximum(self.hyper['treshold_z_prior'], - self.z_prior)\",\n \"def max_temp(self):\\n return 99\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)\",\n \"def max_num_links(self, max_num_links):\\n self._max_num_links = max_num_links\",\n \"def updateVmaxCritDen(self, newVmax, newCritDen):\\r\\n Link.updateVmaxCritDen(self, newVmax, newCritDen)\\r\\n # now update cells, keep length and number of cells the same\\r\\n for cell in self.cells:\\r\\n cell.capacity = self.params['qcap'] / 3600.0 # already updated for link\\r\\n cell.delta = self.params['bws'] / self.params['ffs'] # already upated in link \\r\\n return None\",\n \"def set_is_max(self, is_max):\\n self.__is_max = is_max\",\n \"def getmaxnumbarvar(self):\\n maxnumbarvar_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumbarvar(self.__nativep,ctypes.byref(maxnumbarvar_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumbarvar_ = maxnumbarvar_.value\\n _maxnumbarvar_return_value = maxnumbarvar_\\n return (_maxnumbarvar_return_value)\",\n \"def set_limit_max():\\n limit_max = request.params.get(\\\"limit_max\\\", 0, type=float)\\n output = request.params.get(\\\"output\\\", 1, type=int)\\n retval = RP_LIB.rp_LimitMax(output, ctypes.c_float(limit_max))\\n if retval != 0:\\n LOG.error(\\\"Failed to set maximum output voltage. Error code: %s\\\", ERROR_CODES[retval])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7441124","0.68018794","0.66798395","0.6399272","0.6247705","0.61643946","0.6043132","0.59559965","0.57927316","0.57495356","0.57457256","0.56957024","0.5552648","0.5549431","0.5518782","0.55130196","0.55069184","0.54949814","0.53994","0.5381273","0.5353293","0.53387547","0.53298783","0.5319342","0.5309368","0.5306598","0.53033555","0.52742773","0.5223341","0.52227163","0.52206284","0.52065253","0.51965165","0.51915056","0.5185604","0.51559895","0.5123545","0.5103997","0.51002187","0.5099344","0.50979924","0.50979924","0.50695187","0.50644463","0.5063412","0.50625694","0.50607306","0.50559765","0.5053391","0.505069","0.5043703","0.5031201","0.5029729","0.5010567","0.50085133","0.5005837","0.500436","0.49964026","0.49962556","0.49916738","0.4986934","0.49693567","0.4950412","0.4940799","0.4937367","0.49353704","0.49317524","0.49229085","0.49069118","0.49048993","0.48880523","0.48845103","0.4879531","0.48771375","0.48756692","0.48733744","0.48733744","0.48706645","0.48706645","0.48706645","0.48647204","0.4849177","0.48437452","0.48424283","0.48421937","0.48419318","0.48344323","0.48330638","0.48330638","0.48253483","0.48242545","0.48196068","0.48189864","0.48179924","0.4801652","0.47948918","0.47879738","0.47876206","0.47852293","0.47829902"],"string":"[\n \"0.7441124\",\n \"0.68018794\",\n \"0.66798395\",\n \"0.6399272\",\n \"0.6247705\",\n \"0.61643946\",\n \"0.6043132\",\n \"0.59559965\",\n \"0.57927316\",\n \"0.57495356\",\n \"0.57457256\",\n \"0.56957024\",\n \"0.5552648\",\n \"0.5549431\",\n \"0.5518782\",\n \"0.55130196\",\n \"0.55069184\",\n \"0.54949814\",\n \"0.53994\",\n \"0.5381273\",\n \"0.5353293\",\n \"0.53387547\",\n \"0.53298783\",\n \"0.5319342\",\n \"0.5309368\",\n \"0.5306598\",\n \"0.53033555\",\n \"0.52742773\",\n \"0.5223341\",\n \"0.52227163\",\n \"0.52206284\",\n \"0.52065253\",\n \"0.51965165\",\n \"0.51915056\",\n \"0.5185604\",\n \"0.51559895\",\n \"0.5123545\",\n \"0.5103997\",\n \"0.51002187\",\n \"0.5099344\",\n \"0.50979924\",\n \"0.50979924\",\n \"0.50695187\",\n \"0.50644463\",\n \"0.5063412\",\n \"0.50625694\",\n \"0.50607306\",\n \"0.50559765\",\n \"0.5053391\",\n \"0.505069\",\n \"0.5043703\",\n \"0.5031201\",\n \"0.5029729\",\n \"0.5010567\",\n \"0.50085133\",\n \"0.5005837\",\n \"0.500436\",\n \"0.49964026\",\n \"0.49962556\",\n \"0.49916738\",\n \"0.4986934\",\n \"0.49693567\",\n \"0.4950412\",\n \"0.4940799\",\n \"0.4937367\",\n \"0.49353704\",\n \"0.49317524\",\n \"0.49229085\",\n \"0.49069118\",\n \"0.49048993\",\n \"0.48880523\",\n \"0.48845103\",\n \"0.4879531\",\n \"0.48771375\",\n \"0.48756692\",\n \"0.48733744\",\n \"0.48733744\",\n \"0.48706645\",\n \"0.48706645\",\n \"0.48706645\",\n \"0.48647204\",\n \"0.4849177\",\n \"0.48437452\",\n \"0.48424283\",\n \"0.48421937\",\n \"0.48419318\",\n \"0.48344323\",\n \"0.48330638\",\n \"0.48330638\",\n \"0.48253483\",\n \"0.48242545\",\n \"0.48196068\",\n \"0.48189864\",\n \"0.48179924\",\n \"0.4801652\",\n \"0.47948918\",\n \"0.47879738\",\n \"0.47876206\",\n \"0.47852293\",\n \"0.47829902\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7562487"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11983,"cells":{"query":{"kind":"string","value":"Sets the number of preallocated nonzero entries in quadratic terms. putmaxnumqnz(self,maxnumqnz_)"},"document":{"kind":"string","value":"def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getmaxnumqnz(self): # 3\n res,resargs = self.__obj.getmaxnumqnz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumqnz_return_value = resargs\n return _maxnumqnz_return_value","def getmaxnumqnz(self):\n maxnumqnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getmaxnumqnz64(self.__nativep,ctypes.byref(maxnumqnz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumqnz_ = maxnumqnz_.value\n _maxnumqnz_return_value = maxnumqnz_\n return (_maxnumqnz_return_value)","def getnumqobjnz(self): # 3\n res,resargs = self.__obj.getnumqobjnz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqonz_return_value = resargs\n return _numqonz_return_value","def getnumqobjnz(self):\n numqonz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumqobjnz64(self.__nativep,ctypes.byref(numqonz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqonz_ = numqonz_.value\n _numqonz_return_value = numqonz_\n return (_numqonz_return_value)","def putmaxnumbarvar(self,maxnumbarvar_):\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumanz(self,maxnumanz_):\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumvar(self,maxnumvar_):\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def num_qubits(self, num_qubits: int) -> None:\n if self.num_qubits != num_qubits:\n # invalidate the circuit\n self._invalidate()\n self.qregs = []\n if num_qubits is not None and num_qubits > 0:\n self.qregs = [QuantumRegister(num_qubits, name=\"q\")]","def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def N_z(self) -> int:\n return self.params.N_z","def num_qubits(self) -> int:\n raise NotImplementedError()","def nnz(self):","def num_qubits(self) -> int:\n return super().num_qubits","def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def NNZ(self):\n return _hypre.HypreParMatrix_NNZ(self)","def nnz(self):\n return len(self.value)","def n_qubits(self):\n return int(np.log2(len(self.mat)))","def getnumbarcnz(self):\n nz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumbarcnz(self.__nativep,ctypes.byref(nz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nz_ = nz_.value\n _nz_return_value = nz_\n return (_nz_return_value)","def qsize(self) -> int:\n pass","def getnumbarcnz(self): # 3\n res,resargs = self.__obj.getnumbarcnz()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nz_return_value = resargs\n return _nz_return_value","def nnz(self):\n return len(self.data)","def getnumqconknz(self,k_): # 3\n res,resargs = self.__obj.getnumqconknz64(k_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqcnz_return_value = resargs\n return _numqcnz_return_value","def num_qubits(self) -> int:\n return self._circuit.num_qubits","def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def k_space_grad_fq_allocation(n, qmax_bin, mem):\n return int(math.floor(\n float(.8 * mem - 16 * qmax_bin * n - 12 * n) / (\n 16 * (2 * qmax_bin + 1))))","def NNZ(self):\n return len(self.__IndList)","def nnz(self):\n\t\treturn self.st.size()","def non_heap_max(self, non_heap_max):\n\n self._non_heap_max = non_heap_max","def set_max_noutput_items(self, m):\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)","def maxCODQty(self, maxCODQty):\n\n self._maxCODQty = maxCODQty","def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors","def __init__(self, max_n):\n self._max_n = max_n\n self.__pq = [0] * (max_n + 1)\n self.__qp = [-1] * (max_n + 1)\n self.__keys = [None] * (max_n + 1)\n self.__n = 0","def nNz(self):\n if self.dim < 3:\n return None\n return self.nCz + 1","def __init__(self, qubit_count: int, max_depth: int, **kwargs) -> None:\n self._constraints = constraints.QuantumCircuitConstraints(\n qubit_count=qubit_count, max_depth=max_depth, **kwargs\n )","def set_max_noutput_items(self, m):\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)","def qf_init(self, N):\n for x in range(N):\n self.id.append(x)\n\n for x in range(N):\n self.sz.append(1)","def getnumbaranz(self):\n nz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumbaranz(self.__nativep,ctypes.byref(nz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nz_ = nz_.value\n _nz_return_value = nz_\n return (_nz_return_value)","def max_positions(self):\n return int(1e5)","def qmax(self):\n return self.qy_len if self.min_qside == 'x' else self.qx_len","def nnz(self):\n return self.to_ddm().nnz()","def nnz(self):\n t = self.get_MSC()\n return len(np.unique(t['masks']))","def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)","def qf_init(self, N):\n for x in range(N):\n self.id.append(x)\n self.sz.append(1)","def zero_qnumbers(self):\n self.qd.fill(0)\n for i in range(len(self.qD)):\n self.qD[i].fill(0)\n # enable chaining\n return self","def dual_size(k_max: int):\n n = 2 * k_max + 1\n return n","def nnz(self):\n return self.rep.nnz()","def getnumqconknz(self,k_):\n numqcnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumqconknz64(self.__nativep,k_,ctypes.byref(numqcnz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqcnz_ = numqcnz_.value\n _numqcnz_return_value = numqcnz_\n return (_numqcnz_return_value)","def max_positions(self):\n return int(100000.0)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)","def _resize(self, maxval):\n assert maxval >= self._N\n temp = [None for i in range(maxval)] # (Item[]) new [maxval] # Item[]\n q_len = len(self._q)\n for i in range(self._N):\n temp[i] = self._q[(self._first + i) % q_len]\n self._q = temp\n self._first = 0\n self._last = self._N","def max_positions(self):\n return 1e6 # an arbitrary large number","def max_positions(self):\n return 1e6 # an arbitrary large number","def get_Q_size(self):\n return len(self.qTable)","def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)","def reset_q_table(self):\n for i in range(len(self.qtable)):\n for j in range(len(self.qtable[0])):\n self.qtable[i][j] = 0\n self.epsilon = 1 # Reset espilon as well.","def is_set_max_noutput_items(self):\n return _spacegrant_swig.NRZI_sptr_is_set_max_noutput_items(self)","def is_set_max_noutput_items(self):\n return _spacegrant_swig.DeNRZI_sptr_is_set_max_noutput_items(self)","def set_max_output_buffer(self, *args):\n return _spacegrant_swig.NRZI_sptr_set_max_output_buffer(self, *args)","def set_z(self, value: int) -> None:\n assert -self.__max_value <= value and value <= self.__max_value\n\n should_sync = self.__z != value\n self.__z = value\n if should_sync:\n self.__sync_z()","def max_positions(self):\n return int(1e5) # an arbitrary large number","def max_positions(self):\n return int(1e5) # an arbitrary large number","def magma_izamax(n, dx, incx, queue):\n\n return _libmagma.magma_izamax(n, int(dx), incx, queue)","def nz(self, n: int) -> float:\n result = self._read_inline(f\"nz({n})\")\n return result","def num_state_qubits(self, num_state_qubits: int) -> None:\n if num_state_qubits != self._num_state_qubits:\n self._invalidate()\n self._num_state_qubits = num_state_qubits\n self._reset_registers(num_state_qubits)","def maxsize(self, maxsize):\n self.shape = (int(maxsize), ) + self.shape[1:]\n self.clear()","def nCz(self):\n if self.dim < 3:\n return None\n return int(self._n[2])","def getmaxnumbarvar(self):\n maxnumbarvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumbarvar(self.__nativep,ctypes.byref(maxnumbarvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumbarvar_ = maxnumbarvar_.value\n _maxnumbarvar_return_value = maxnumbarvar_\n return (_maxnumbarvar_return_value)","def getmaxnumbarvar(self): # 3\n res,resargs = self.__obj.getmaxnumbarvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumbarvar_return_value = resargs\n return _maxnumbarvar_return_value","def max_noutput_items(self):\n return _spacegrant_swig.DeNRZI_sptr_max_noutput_items(self)","def stats(self):\n nqbits = self.operator.num_qubits","def zernike_num_coeff(n):\n \n\tif not (n>=0):\n\t\tprint('Input parameter must be >= 0')\n\t\traise AssertionError() \n \n\treturn sum(xrange(n+1)) + n+1","def reset_zoc(self):\n if not self.parent_block_count:\n raise ValueError(\"cannot reset zoc until parent_block_count is set\")\n zoc_len = np.prod(self.parent_block_count)\n self.zoc = np.zeros(zoc_len, dtype=np.int32)","def getmaxnumanz(self): # 3\n res,resargs = self.__obj.getmaxnumanz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumanz_return_value = resargs\n return _maxnumanz_return_value","def set_numpins(self, n):\n self.numpins = n","def required_nb_data_func(list_nb_coeff):\n return max(list_nb_coeff)","def required_nb_data_func(list_nb_coeff):\n return max(list_nb_coeff)","def required_nb_data_func(list_nb_coeff):\n return max(list_nb_coeff)","def max_noutput_items(self):\n return _spacegrant_swig.NRZI_sptr_max_noutput_items(self)","def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)","def n(self):\n if not self.table:\n return 0\n return max(self.omega) + 1","def nz(self):\n return self._dim[2]","def find_n_qubits(gates):\n return max((get_maximum_index(g.targets) for g in gates), default=-1) + 1","def zmnmx ( self , xmin , xmax ) :\n return self.vmnmx ( self.zvar , zmin , zmax )","def is_set_max_noutput_items(self):\n return _spacegrant_swig.invert_bit_sptr_is_set_max_noutput_items(self)","def qsize(self):\n return self.q_size.current_value","def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_):\n res = __library__.MSK_XX_resizetask(self.__nativep,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def max_in_gbps(self, max_in_gbps):\n self._max_in_gbps = max_in_gbps","def set_max_noutput_items(self, *args, **kwargs):\n return _TestA_swig.my_qpsk_demod_cb_sptr_set_max_noutput_items(self, *args, **kwargs)","def getmaxnumvar(self):\n maxnumvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumvar(self.__nativep,ctypes.byref(maxnumvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumvar_ = maxnumvar_.value\n _maxnumvar_return_value = maxnumvar_\n return (_maxnumvar_return_value)","def _set_z_size(self):\n self._level_gen.size = (self._level_gen.size[X],\n self._level_gen.size[Y],\n self._level_size_z_spinbox.value())\n self._refresh_view()","def setZeroModeParameters(self, zmp):\n\t\tif not len(zmp) == 2*self.nZero:\n\t\t\traise IndexError(\"Number of zero mode parameters does not match\")\n\t\tself.hasZMP = True\n\t\tself.zeroModeParameters = zmp","def set_max_noutput_items(self, m):\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)","def getmaxnumanz(self):\n maxnumanz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getmaxnumanz64(self.__nativep,ctypes.byref(maxnumanz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumanz_ = maxnumanz_.value\n _maxnumanz_return_value = maxnumanz_\n return (_maxnumanz_return_value)","def set_max_output_buffer(self, *args) -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_output_buffer(self, *args)"],"string":"[\n \"def putmaxnumqnz(self,maxnumqnz_): # 3\\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getmaxnumqnz(self): # 3\\n res,resargs = self.__obj.getmaxnumqnz64()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumqnz_return_value = resargs\\n return _maxnumqnz_return_value\",\n \"def getmaxnumqnz(self):\\n maxnumqnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getmaxnumqnz64(self.__nativep,ctypes.byref(maxnumqnz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumqnz_ = maxnumqnz_.value\\n _maxnumqnz_return_value = maxnumqnz_\\n return (_maxnumqnz_return_value)\",\n \"def getnumqobjnz(self): # 3\\n res,resargs = self.__obj.getnumqobjnz64()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numqonz_return_value = resargs\\n return _numqonz_return_value\",\n \"def getnumqobjnz(self):\\n numqonz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getnumqobjnz64(self.__nativep,ctypes.byref(numqonz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numqonz_ = numqonz_.value\\n _numqonz_return_value = numqonz_\\n return (_numqonz_return_value)\",\n \"def putmaxnumbarvar(self,maxnumbarvar_):\\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumanz(self,maxnumanz_):\\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumvar(self,maxnumvar_):\\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumbarvar(self,maxnumbarvar_): # 3\\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def num_qubits(self, num_qubits: int) -> None:\\n if self.num_qubits != num_qubits:\\n # invalidate the circuit\\n self._invalidate()\\n self.qregs = []\\n if num_qubits is not None and num_qubits > 0:\\n self.qregs = [QuantumRegister(num_qubits, name=\\\"q\\\")]\",\n \"def putmaxnumvar(self,maxnumvar_): # 3\\n res = self.__obj.putmaxnumvar(maxnumvar_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumanz(self,maxnumanz_): # 3\\n res = self.__obj.putmaxnumanz(maxnumanz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def N_z(self) -> int:\\n return self.params.N_z\",\n \"def num_qubits(self) -> int:\\n raise NotImplementedError()\",\n \"def nnz(self):\",\n \"def num_qubits(self) -> int:\\n return super().num_qubits\",\n \"def putmaxnumcone(self,maxnumcone_):\\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcon(self,maxnumcon_):\\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def NNZ(self):\\n return _hypre.HypreParMatrix_NNZ(self)\",\n \"def nnz(self):\\n return len(self.value)\",\n \"def n_qubits(self):\\n return int(np.log2(len(self.mat)))\",\n \"def getnumbarcnz(self):\\n nz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getnumbarcnz(self.__nativep,ctypes.byref(nz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nz_ = nz_.value\\n _nz_return_value = nz_\\n return (_nz_return_value)\",\n \"def qsize(self) -> int:\\n pass\",\n \"def getnumbarcnz(self): # 3\\n res,resargs = self.__obj.getnumbarcnz()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nz_return_value = resargs\\n return _nz_return_value\",\n \"def nnz(self):\\n return len(self.data)\",\n \"def getnumqconknz(self,k_): # 3\\n res,resargs = self.__obj.getnumqconknz64(k_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numqcnz_return_value = resargs\\n return _numqcnz_return_value\",\n \"def num_qubits(self) -> int:\\n return self._circuit.num_qubits\",\n \"def putmaxnumcone(self,maxnumcone_): # 3\\n res = self.__obj.putmaxnumcone(maxnumcone_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putmaxnumcon(self,maxnumcon_): # 3\\n res = self.__obj.putmaxnumcon(maxnumcon_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def k_space_grad_fq_allocation(n, qmax_bin, mem):\\n return int(math.floor(\\n float(.8 * mem - 16 * qmax_bin * n - 12 * n) / (\\n 16 * (2 * qmax_bin + 1))))\",\n \"def NNZ(self):\\n return len(self.__IndList)\",\n \"def nnz(self):\\n\\t\\treturn self.st.size()\",\n \"def non_heap_max(self, non_heap_max):\\n\\n self._non_heap_max = non_heap_max\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)\",\n \"def maxCODQty(self, maxCODQty):\\n\\n self._maxCODQty = maxCODQty\",\n \"def max_num_neighbors(self, max_num_neighbors):\\n self._max_num_neighbors = max_num_neighbors\",\n \"def __init__(self, max_n):\\n self._max_n = max_n\\n self.__pq = [0] * (max_n + 1)\\n self.__qp = [-1] * (max_n + 1)\\n self.__keys = [None] * (max_n + 1)\\n self.__n = 0\",\n \"def nNz(self):\\n if self.dim < 3:\\n return None\\n return self.nCz + 1\",\n \"def __init__(self, qubit_count: int, max_depth: int, **kwargs) -> None:\\n self._constraints = constraints.QuantumCircuitConstraints(\\n qubit_count=qubit_count, max_depth=max_depth, **kwargs\\n )\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)\",\n \"def qf_init(self, N):\\n for x in range(N):\\n self.id.append(x)\\n\\n for x in range(N):\\n self.sz.append(1)\",\n \"def getnumbaranz(self):\\n nz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getnumbaranz(self.__nativep,ctypes.byref(nz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nz_ = nz_.value\\n _nz_return_value = nz_\\n return (_nz_return_value)\",\n \"def max_positions(self):\\n return int(1e5)\",\n \"def qmax(self):\\n return self.qy_len if self.min_qside == 'x' else self.qx_len\",\n \"def nnz(self):\\n return self.to_ddm().nnz()\",\n \"def nnz(self):\\n t = self.get_MSC()\\n return len(np.unique(t['masks']))\",\n \"def set_max_noutput_items(self, m: \\\"int\\\") -> \\\"void\\\":\\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)\",\n \"def qf_init(self, N):\\n for x in range(N):\\n self.id.append(x)\\n self.sz.append(1)\",\n \"def zero_qnumbers(self):\\n self.qd.fill(0)\\n for i in range(len(self.qD)):\\n self.qD[i].fill(0)\\n # enable chaining\\n return self\",\n \"def dual_size(k_max: int):\\n n = 2 * k_max + 1\\n return n\",\n \"def nnz(self):\\n return self.rep.nnz()\",\n \"def getnumqconknz(self,k_):\\n numqcnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getnumqconknz64(self.__nativep,k_,ctypes.byref(numqcnz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numqcnz_ = numqcnz_.value\\n _numqcnz_return_value = numqcnz_\\n return (_numqcnz_return_value)\",\n \"def max_positions(self):\\n return int(100000.0)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)\",\n \"def _resize(self, maxval):\\n assert maxval >= self._N\\n temp = [None for i in range(maxval)] # (Item[]) new [maxval] # Item[]\\n q_len = len(self._q)\\n for i in range(self._N):\\n temp[i] = self._q[(self._first + i) % q_len]\\n self._q = temp\\n self._first = 0\\n self._last = self._N\",\n \"def max_positions(self):\\n return 1e6 # an arbitrary large number\",\n \"def max_positions(self):\\n return 1e6 # an arbitrary large number\",\n \"def get_Q_size(self):\\n return len(self.qTable)\",\n \"def set_max_nb_instructions(nb): #py:set_max_nb_instructions\\n RUR._set_max_nb_instructions_(nb)\",\n \"def reset_q_table(self):\\n for i in range(len(self.qtable)):\\n for j in range(len(self.qtable[0])):\\n self.qtable[i][j] = 0\\n self.epsilon = 1 # Reset espilon as well.\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.NRZI_sptr_is_set_max_noutput_items(self)\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.DeNRZI_sptr_is_set_max_noutput_items(self)\",\n \"def set_max_output_buffer(self, *args):\\n return _spacegrant_swig.NRZI_sptr_set_max_output_buffer(self, *args)\",\n \"def set_z(self, value: int) -> None:\\n assert -self.__max_value <= value and value <= self.__max_value\\n\\n should_sync = self.__z != value\\n self.__z = value\\n if should_sync:\\n self.__sync_z()\",\n \"def max_positions(self):\\n return int(1e5) # an arbitrary large number\",\n \"def max_positions(self):\\n return int(1e5) # an arbitrary large number\",\n \"def magma_izamax(n, dx, incx, queue):\\n\\n return _libmagma.magma_izamax(n, int(dx), incx, queue)\",\n \"def nz(self, n: int) -> float:\\n result = self._read_inline(f\\\"nz({n})\\\")\\n return result\",\n \"def num_state_qubits(self, num_state_qubits: int) -> None:\\n if num_state_qubits != self._num_state_qubits:\\n self._invalidate()\\n self._num_state_qubits = num_state_qubits\\n self._reset_registers(num_state_qubits)\",\n \"def maxsize(self, maxsize):\\n self.shape = (int(maxsize), ) + self.shape[1:]\\n self.clear()\",\n \"def nCz(self):\\n if self.dim < 3:\\n return None\\n return int(self._n[2])\",\n \"def getmaxnumbarvar(self):\\n maxnumbarvar_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumbarvar(self.__nativep,ctypes.byref(maxnumbarvar_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumbarvar_ = maxnumbarvar_.value\\n _maxnumbarvar_return_value = maxnumbarvar_\\n return (_maxnumbarvar_return_value)\",\n \"def getmaxnumbarvar(self): # 3\\n res,resargs = self.__obj.getmaxnumbarvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumbarvar_return_value = resargs\\n return _maxnumbarvar_return_value\",\n \"def max_noutput_items(self):\\n return _spacegrant_swig.DeNRZI_sptr_max_noutput_items(self)\",\n \"def stats(self):\\n nqbits = self.operator.num_qubits\",\n \"def zernike_num_coeff(n):\\n \\n\\tif not (n>=0):\\n\\t\\tprint('Input parameter must be >= 0')\\n\\t\\traise AssertionError() \\n \\n\\treturn sum(xrange(n+1)) + n+1\",\n \"def reset_zoc(self):\\n if not self.parent_block_count:\\n raise ValueError(\\\"cannot reset zoc until parent_block_count is set\\\")\\n zoc_len = np.prod(self.parent_block_count)\\n self.zoc = np.zeros(zoc_len, dtype=np.int32)\",\n \"def getmaxnumanz(self): # 3\\n res,resargs = self.__obj.getmaxnumanz64()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumanz_return_value = resargs\\n return _maxnumanz_return_value\",\n \"def set_numpins(self, n):\\n self.numpins = n\",\n \"def required_nb_data_func(list_nb_coeff):\\n return max(list_nb_coeff)\",\n \"def required_nb_data_func(list_nb_coeff):\\n return max(list_nb_coeff)\",\n \"def required_nb_data_func(list_nb_coeff):\\n return max(list_nb_coeff)\",\n \"def max_noutput_items(self):\\n return _spacegrant_swig.NRZI_sptr_max_noutput_items(self)\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.hdlc_deframer_sptr_set_max_noutput_items(self, m)\",\n \"def n(self):\\n if not self.table:\\n return 0\\n return max(self.omega) + 1\",\n \"def nz(self):\\n return self._dim[2]\",\n \"def find_n_qubits(gates):\\n return max((get_maximum_index(g.targets) for g in gates), default=-1) + 1\",\n \"def zmnmx ( self , xmin , xmax ) :\\n return self.vmnmx ( self.zvar , zmin , zmax )\",\n \"def is_set_max_noutput_items(self):\\n return _spacegrant_swig.invert_bit_sptr_is_set_max_noutput_items(self)\",\n \"def qsize(self):\\n return self.q_size.current_value\",\n \"def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_):\\n res = __library__.MSK_XX_resizetask(self.__nativep,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def max_in_gbps(self, max_in_gbps):\\n self._max_in_gbps = max_in_gbps\",\n \"def set_max_noutput_items(self, *args, **kwargs):\\n return _TestA_swig.my_qpsk_demod_cb_sptr_set_max_noutput_items(self, *args, **kwargs)\",\n \"def getmaxnumvar(self):\\n maxnumvar_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getmaxnumvar(self.__nativep,ctypes.byref(maxnumvar_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumvar_ = maxnumvar_.value\\n _maxnumvar_return_value = maxnumvar_\\n return (_maxnumvar_return_value)\",\n \"def _set_z_size(self):\\n self._level_gen.size = (self._level_gen.size[X],\\n self._level_gen.size[Y],\\n self._level_size_z_spinbox.value())\\n self._refresh_view()\",\n \"def setZeroModeParameters(self, zmp):\\n\\t\\tif not len(zmp) == 2*self.nZero:\\n\\t\\t\\traise IndexError(\\\"Number of zero mode parameters does not match\\\")\\n\\t\\tself.hasZMP = True\\n\\t\\tself.zeroModeParameters = zmp\",\n \"def set_max_noutput_items(self, m):\\n return _spacegrant_swig.G3RUH_descramble_sptr_set_max_noutput_items(self, m)\",\n \"def getmaxnumanz(self):\\n maxnumanz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getmaxnumanz64(self.__nativep,ctypes.byref(maxnumanz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n maxnumanz_ = maxnumanz_.value\\n _maxnumanz_return_value = maxnumanz_\\n return (_maxnumanz_return_value)\",\n \"def set_max_output_buffer(self, *args) -> \\\"void\\\":\\n return _beamforming_swig.phasedarray_sptr_set_max_output_buffer(self, *args)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8385307","0.7157034","0.6808096","0.6252767","0.6003535","0.6001192","0.5939193","0.59272075","0.5830991","0.5783531","0.5719293","0.5708783","0.56221986","0.5621964","0.5618641","0.5608385","0.55962783","0.55303234","0.54971045","0.5450261","0.54395735","0.54374975","0.5437029","0.5387193","0.5310357","0.5301221","0.5273116","0.52507025","0.525045","0.5206621","0.5169394","0.5168609","0.51159286","0.5106247","0.5102228","0.5071191","0.5067379","0.504871","0.5027783","0.5015318","0.49979922","0.4996305","0.4984797","0.4963193","0.49592876","0.49480623","0.49258488","0.49224743","0.48936006","0.4888219","0.48813868","0.48610508","0.4849019","0.48452777","0.48441854","0.4826206","0.4817957","0.4817957","0.4801367","0.47918123","0.477047","0.476384","0.47623152","0.47481716","0.47459602","0.47454146","0.47454146","0.47363466","0.4725965","0.47184944","0.4711041","0.47102317","0.4706416","0.47044164","0.4702326","0.46893492","0.46885812","0.4687225","0.4664801","0.4649918","0.46386048","0.46386048","0.46386048","0.46373942","0.46351463","0.46312428","0.46230763","0.46104518","0.46080947","0.4606308","0.46015355","0.4591329","0.4576806","0.45677558","0.45451802","0.45417896","0.4535825","0.4531259","0.45266193","0.4526306"],"string":"[\n \"0.8385307\",\n \"0.7157034\",\n \"0.6808096\",\n \"0.6252767\",\n \"0.6003535\",\n \"0.6001192\",\n \"0.5939193\",\n \"0.59272075\",\n \"0.5830991\",\n \"0.5783531\",\n \"0.5719293\",\n \"0.5708783\",\n \"0.56221986\",\n \"0.5621964\",\n \"0.5618641\",\n \"0.5608385\",\n \"0.55962783\",\n \"0.55303234\",\n \"0.54971045\",\n \"0.5450261\",\n \"0.54395735\",\n \"0.54374975\",\n \"0.5437029\",\n \"0.5387193\",\n \"0.5310357\",\n \"0.5301221\",\n \"0.5273116\",\n \"0.52507025\",\n \"0.525045\",\n \"0.5206621\",\n \"0.5169394\",\n \"0.5168609\",\n \"0.51159286\",\n \"0.5106247\",\n \"0.5102228\",\n \"0.5071191\",\n \"0.5067379\",\n \"0.504871\",\n \"0.5027783\",\n \"0.5015318\",\n \"0.49979922\",\n \"0.4996305\",\n \"0.4984797\",\n \"0.4963193\",\n \"0.49592876\",\n \"0.49480623\",\n \"0.49258488\",\n \"0.49224743\",\n \"0.48936006\",\n \"0.4888219\",\n \"0.48813868\",\n \"0.48610508\",\n \"0.4849019\",\n \"0.48452777\",\n \"0.48441854\",\n \"0.4826206\",\n \"0.4817957\",\n \"0.4817957\",\n \"0.4801367\",\n \"0.47918123\",\n \"0.477047\",\n \"0.476384\",\n \"0.47623152\",\n \"0.47481716\",\n \"0.47459602\",\n \"0.47454146\",\n \"0.47454146\",\n \"0.47363466\",\n \"0.4725965\",\n \"0.47184944\",\n \"0.4711041\",\n \"0.47102317\",\n \"0.4706416\",\n \"0.47044164\",\n \"0.4702326\",\n \"0.46893492\",\n \"0.46885812\",\n \"0.4687225\",\n \"0.4664801\",\n \"0.4649918\",\n \"0.46386048\",\n \"0.46386048\",\n \"0.46386048\",\n \"0.46373942\",\n \"0.46351463\",\n \"0.46312428\",\n \"0.46230763\",\n \"0.46104518\",\n \"0.46080947\",\n \"0.4606308\",\n \"0.46015355\",\n \"0.4591329\",\n \"0.4576806\",\n \"0.45677558\",\n \"0.45451802\",\n \"0.45417896\",\n \"0.4535825\",\n \"0.4531259\",\n \"0.45266193\",\n \"0.4526306\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81725997"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11984,"cells":{"query":{"kind":"string","value":"Obtains the number of preallocated nonzeros for all quadratic terms in objective and constraints. getmaxnumqnz(self)"},"document":{"kind":"string","value":"def getmaxnumqnz(self):\n maxnumqnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getmaxnumqnz64(self.__nativep,ctypes.byref(maxnumqnz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumqnz_ = maxnumqnz_.value\n _maxnumqnz_return_value = maxnumqnz_\n return (_maxnumqnz_return_value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getmaxnumqnz(self): # 3\n res,resargs = self.__obj.getmaxnumqnz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumqnz_return_value = resargs\n return _maxnumqnz_return_value","def getnumqobjnz(self): # 3\n res,resargs = self.__obj.getnumqobjnz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqonz_return_value = resargs\n return _numqonz_return_value","def nnz(self):\n return len(self.value)","def nnz(self):\n return len(self.data)","def n_qubits(self):\n return int(np.log2(len(self.mat)))","def num_qubits(self) -> int:\n raise NotImplementedError()","def nnz(self):\n return self.rep.nnz()","def nNz(self):\n if self.dim < 3:\n return None\n return self.nCz + 1","def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def nnz(self):\n\t\treturn self.st.size()","def getnumqobjnz(self):\n numqonz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumqobjnz64(self.__nativep,ctypes.byref(numqonz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqonz_ = numqonz_.value\n _numqonz_return_value = numqonz_\n return (_numqonz_return_value)","def getnumbarcnz(self): # 3\n res,resargs = self.__obj.getnumbarcnz()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nz_return_value = resargs\n return _nz_return_value","def nnz(self):\n t = self.get_MSC()\n return len(np.unique(t['masks']))","def num_qubits(self) -> int:\n return self._circuit.num_qubits","def NNZ(self):\n return len(self.__IndList)","def nnz(self):\n return self.to_ddm().nnz()","def nnz(self):","def N_z(self) -> int:\n return self.params.N_z","def num_qubits(self) -> int:\n return super().num_qubits","def NNZ(self):\n return _hypre.HypreParMatrix_NNZ(self)","def getnumqconknz(self,k_): # 3\n res,resargs = self.__obj.getnumqconknz64(k_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqcnz_return_value = resargs\n return _numqcnz_return_value","def find_n_qubits(gates):\n return max((get_maximum_index(g.targets) for g in gates), default=-1) + 1","def no_of_dofs_unconstrained(self):\n return self._no_of_dofs_unconstrained","def get_local_nnz(self):\n return _hypre.HypreParMatrix_get_local_nnz(self)","def get_Q_size(self):\n return len(self.qTable)","def qsize(self) -> int:\n pass","def num_cells(self):\n if hasattr(self, '__num_cells__'):\n return self.__num_cells__\n if self.x is not None:\n return self.x.size(self.__cat_dim__('x', self.x))\n if self.boundary_index is not None:\n return int(self.boundary_index[1,:].max()) + 1\n assert self.upper_index is None and self.lower_index is None\n return None","def n(self):\n if not self.table:\n return 0\n return max(self.omega) + 1","def stats(self):\n nqbits = self.operator.num_qubits","def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getnumbarcnz(self):\n nz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumbarcnz(self.__nativep,ctypes.byref(nz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nz_ = nz_.value\n _nz_return_value = nz_\n return (_nz_return_value)","def get_max_num_cr(gdq_cube, jump_flag): # pragma: no cover\n cr_flagged = np.empty(gdq_cube.shape, dtype=np.uint8)\n cr_flagged[:] = np.where(np.bitwise_and(gdq_cube, jump_flag), 1, 0)\n max_num_cr = cr_flagged.sum(axis=0, dtype=np.int32).max()\n del cr_flagged\n\n return max_num_cr","def _N(self):\n return len(self._array)","def nCz(self):\n if self.dim < 3:\n return None\n return int(self._n[2])","def NumCoefficients(self):\n return nchoosek(self.degree + self.dimension, self.degree, exact=True)","def nz(self):\n return self._dim[2]","def number_of_basis(self):\n return self._pre_kernel.shape[0]","def size(self):\r\n # Anthony stage 2\r\n return number_size(self.n) - 1","def nquads(self):\n return len(self.tree)","def max_num_neighbors(self):\n return self._max_num_neighbors","def get_local_true_nnz(self):\n return _hypre.HypreParMatrix_get_local_true_nnz(self)","def ncells(self):\n return self.izone.size","def no_of_constraints(self):\n return np.sum(self._no_of_constraints_by_object())","def _get_nr_of_bits(self):\n return sum(self._size_var)","def num_cells_global(self):\n return self.get_dim_attribute('num_cells')","def nClumps(self):\n \n return len(self)","def nt(self):\n if self.nClumps() > 0:\n \n return len(self[0])\n \n else:\n \n return 0","def n_equations(self):\n try: \n return self._n_equations\n except AttributeError:\n self._n_equations = len(self.equations())\n return self._n_equations","def n_cf(self):\n return np.size(self._ref_ii, 0)","def qsize(self) -> int:\n return len(self._queue)","def N(self):\n return len(self.cavity_grid.cavities) + 1","def qsize(self):\r\n return len(self._queue)","def getNZ(self):\n return self._get_nz( )","def qmax(self):\n return self.qy_len if self.min_qside == 'x' else self.qx_len","def nN(self):\n return int(self.vnN.prod())","def n_thres(self):\n return np.size(self.thres)","def N ( self ) :\n return self.__N","def numel(self):\n return self.t.size","def num_cells(self):\n cbi = self.cbi\n if cbi is None:\n return None\n return cbi[-1] # pylint: disable=E1136","def num_cells(self):\n cbi = self.cbi\n if cbi is None:\n return None\n return cbi[-1] # pylint: disable=E1136","def num_cells(self):\n cbi = self.cbi\n if cbi is None:\n return None\n return cbi[-1] # pylint: disable=E1136","def num_cells(self):\n cbi = self.cbi\n if cbi is None:\n return None\n return cbi[-1] # pylint: disable=E1136","def GetGlobalNumRows(self):\n return _hypre.HypreParMatrix_GetGlobalNumRows(self)","def qsize(self):\n return self.q_size.current_value","def get_nrof_aux(self):\n aux = 0\n for l in self.aux_array:\n if l:\n aux += 1\n return aux","def get_number_of_atoms_to_optimize(self):\n v = self.c.get(simulation_cell=True)\n return len(v.data.stoichiometry)","def __len__(self):\n return np.size(self.A,0)","def getnumbaranz(self):\n nz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumbaranz(self.__nativep,ctypes.byref(nz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nz_ = nz_.value\n _nz_return_value = nz_\n return (_nz_return_value)","def nbytes(self):\n return self.nnz * self.dtype.itemsize","def qsize(self) -> int:\n return self._queue.qsize()","def n_cs(self):\n return np.size(self._cs, 0)","def N(self):\n return _hypre.HypreParMatrix_N(self)","def dimension(self):\n return len(self.qubit_values)","def max_counts(self):\n\n return np.nanmax(self.pre_proc_data)","def max_size(self):\n sizes = np.array([m.sum() for m in self.masks])\n return sizes.max()","def get_nonzeros(self):\n return self.tape.get_nonzeros(self.machine.eval_symbol,\n self.machine.eval_state(self.state))","def nN(self):\n return int((self._n+1).prod())","def get_iter_num(self):\n\tif len(self.cost) > 0:\n first_key = list(self.cost.keys())[0]\n num = len(self.cost[first_key]) - 1\n\telse:\n\t first_key = list(self.prim_var.keys())[0]\n num = len(self.prim_var[first_key]) - 1\n\treturn num","def num_probit_terms_approx(self):\n return self._num_probit_terms_approx","def num_cells_up(self):\n if hasattr(self, '__num_cells_up__'):\n return self.__num_cells_up__\n elif self.shared_coboundaries is not None:\n assert self.upper_index is not None\n return int(self.shared_coboundaries.max()) + 1\n assert self.upper_index is None\n return 0","def number_of_solutions(self):\n return len(self._solutions)","def npks(self):\n try:\n return self.xp.size\n except Exception:\n return 0","def getN(self)->int:\n return self.n","def nevals(self):\n return self._n","def _get_free_capacity(self):\n\n capacity = np.ones(len(self.grid.T)) * len(self.grid)\n capacity -= np.count_nonzero(self.grid, axis=0)\n return capacity","def getmaxnumbarvar(self): # 3\n res,resargs = self.__obj.getmaxnumbarvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumbarvar_return_value = resargs\n return _maxnumbarvar_return_value","def nNy(self):\n if self.dim < 2:\n return None\n return self.nCy + 1","def getNumQuads(self):\n return len(self._quadrilaterals)","def getmaxnumvar(self): # 3\n res,resargs = self.__obj.getmaxnumvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumvar_return_value = resargs\n return _maxnumvar_return_value","def count_matrix_largest(self, effective=False):\n return self.count_matrix(connected_set=0, effective=effective)","def size(self):\n return self.N","def numel(self) -> int:\n return sum(p.numel() for p in self.parameters)","def getQueueCount(self, force=False):\n if self.queuecount == -1 or force:\n self.queuecount = self.db.queuecount()\n\n return self.queuecount","def num_empty(self):\n count = 0\n for i in self.__buckets:\n if i.size() == 0:\n count += 1\n return count","def required_nb_data_func(list_nb_coeff):\n return max(list_nb_coeff)","def required_nb_data_func(list_nb_coeff):\n return max(list_nb_coeff)","def required_nb_data_func(list_nb_coeff):\n return max(list_nb_coeff)","def size(self):\n return self._N","def GetNumberOfVariables(self):\n\n # nvar = 0\n # for i in self.variables_order:\n # # DO NOT COUNT VARIABLES THAT GET CONDENSED OUT\n # if i!=0:\n # if mesh.element_type == \"tri\":\n # nvar += (i+1)*(i+2) // 2\n # elif mesh.element_type == \"tet\":\n # nvar += (i+1)*(i+2)*(i+3) // 6\n # elif mesh.element_type == \"quad\":\n # nvar += (i+1)**2\n # elif mesh.element_type == \"hex\":\n # nvar += (i+1)**3\n\n # nvar = sum(self.variables_order)\n if self.nvar == None:\n self.nvar = self.ndim\n return self.nvar","def vnN(self):\n return np.array(\n [x for x in [self.nNx, self.nNy, self.nNz] if x is not None],\n dtype=int\n )"],"string":"[\n \"def getmaxnumqnz(self): # 3\\n res,resargs = self.__obj.getmaxnumqnz64()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumqnz_return_value = resargs\\n return _maxnumqnz_return_value\",\n \"def getnumqobjnz(self): # 3\\n res,resargs = self.__obj.getnumqobjnz64()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numqonz_return_value = resargs\\n return _numqonz_return_value\",\n \"def nnz(self):\\n return len(self.value)\",\n \"def nnz(self):\\n return len(self.data)\",\n \"def n_qubits(self):\\n return int(np.log2(len(self.mat)))\",\n \"def num_qubits(self) -> int:\\n raise NotImplementedError()\",\n \"def nnz(self):\\n return self.rep.nnz()\",\n \"def nNz(self):\\n if self.dim < 3:\\n return None\\n return self.nCz + 1\",\n \"def putmaxnumqnz(self,maxnumqnz_): # 3\\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def nnz(self):\\n\\t\\treturn self.st.size()\",\n \"def getnumqobjnz(self):\\n numqonz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getnumqobjnz64(self.__nativep,ctypes.byref(numqonz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numqonz_ = numqonz_.value\\n _numqonz_return_value = numqonz_\\n return (_numqonz_return_value)\",\n \"def getnumbarcnz(self): # 3\\n res,resargs = self.__obj.getnumbarcnz()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nz_return_value = resargs\\n return _nz_return_value\",\n \"def nnz(self):\\n t = self.get_MSC()\\n return len(np.unique(t['masks']))\",\n \"def num_qubits(self) -> int:\\n return self._circuit.num_qubits\",\n \"def NNZ(self):\\n return len(self.__IndList)\",\n \"def nnz(self):\\n return self.to_ddm().nnz()\",\n \"def nnz(self):\",\n \"def N_z(self) -> int:\\n return self.params.N_z\",\n \"def num_qubits(self) -> int:\\n return super().num_qubits\",\n \"def NNZ(self):\\n return _hypre.HypreParMatrix_NNZ(self)\",\n \"def getnumqconknz(self,k_): # 3\\n res,resargs = self.__obj.getnumqconknz64(k_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numqcnz_return_value = resargs\\n return _numqcnz_return_value\",\n \"def find_n_qubits(gates):\\n return max((get_maximum_index(g.targets) for g in gates), default=-1) + 1\",\n \"def no_of_dofs_unconstrained(self):\\n return self._no_of_dofs_unconstrained\",\n \"def get_local_nnz(self):\\n return _hypre.HypreParMatrix_get_local_nnz(self)\",\n \"def get_Q_size(self):\\n return len(self.qTable)\",\n \"def qsize(self) -> int:\\n pass\",\n \"def num_cells(self):\\n if hasattr(self, '__num_cells__'):\\n return self.__num_cells__\\n if self.x is not None:\\n return self.x.size(self.__cat_dim__('x', self.x))\\n if self.boundary_index is not None:\\n return int(self.boundary_index[1,:].max()) + 1\\n assert self.upper_index is None and self.lower_index is None\\n return None\",\n \"def n(self):\\n if not self.table:\\n return 0\\n return max(self.omega) + 1\",\n \"def stats(self):\\n nqbits = self.operator.num_qubits\",\n \"def putmaxnumqnz(self,maxnumqnz_):\\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getnumbarcnz(self):\\n nz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getnumbarcnz(self.__nativep,ctypes.byref(nz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nz_ = nz_.value\\n _nz_return_value = nz_\\n return (_nz_return_value)\",\n \"def get_max_num_cr(gdq_cube, jump_flag): # pragma: no cover\\n cr_flagged = np.empty(gdq_cube.shape, dtype=np.uint8)\\n cr_flagged[:] = np.where(np.bitwise_and(gdq_cube, jump_flag), 1, 0)\\n max_num_cr = cr_flagged.sum(axis=0, dtype=np.int32).max()\\n del cr_flagged\\n\\n return max_num_cr\",\n \"def _N(self):\\n return len(self._array)\",\n \"def nCz(self):\\n if self.dim < 3:\\n return None\\n return int(self._n[2])\",\n \"def NumCoefficients(self):\\n return nchoosek(self.degree + self.dimension, self.degree, exact=True)\",\n \"def nz(self):\\n return self._dim[2]\",\n \"def number_of_basis(self):\\n return self._pre_kernel.shape[0]\",\n \"def size(self):\\r\\n # Anthony stage 2\\r\\n return number_size(self.n) - 1\",\n \"def nquads(self):\\n return len(self.tree)\",\n \"def max_num_neighbors(self):\\n return self._max_num_neighbors\",\n \"def get_local_true_nnz(self):\\n return _hypre.HypreParMatrix_get_local_true_nnz(self)\",\n \"def ncells(self):\\n return self.izone.size\",\n \"def no_of_constraints(self):\\n return np.sum(self._no_of_constraints_by_object())\",\n \"def _get_nr_of_bits(self):\\n return sum(self._size_var)\",\n \"def num_cells_global(self):\\n return self.get_dim_attribute('num_cells')\",\n \"def nClumps(self):\\n \\n return len(self)\",\n \"def nt(self):\\n if self.nClumps() > 0:\\n \\n return len(self[0])\\n \\n else:\\n \\n return 0\",\n \"def n_equations(self):\\n try: \\n return self._n_equations\\n except AttributeError:\\n self._n_equations = len(self.equations())\\n return self._n_equations\",\n \"def n_cf(self):\\n return np.size(self._ref_ii, 0)\",\n \"def qsize(self) -> int:\\n return len(self._queue)\",\n \"def N(self):\\n return len(self.cavity_grid.cavities) + 1\",\n \"def qsize(self):\\r\\n return len(self._queue)\",\n \"def getNZ(self):\\n return self._get_nz( )\",\n \"def qmax(self):\\n return self.qy_len if self.min_qside == 'x' else self.qx_len\",\n \"def nN(self):\\n return int(self.vnN.prod())\",\n \"def n_thres(self):\\n return np.size(self.thres)\",\n \"def N ( self ) :\\n return self.__N\",\n \"def numel(self):\\n return self.t.size\",\n \"def num_cells(self):\\n cbi = self.cbi\\n if cbi is None:\\n return None\\n return cbi[-1] # pylint: disable=E1136\",\n \"def num_cells(self):\\n cbi = self.cbi\\n if cbi is None:\\n return None\\n return cbi[-1] # pylint: disable=E1136\",\n \"def num_cells(self):\\n cbi = self.cbi\\n if cbi is None:\\n return None\\n return cbi[-1] # pylint: disable=E1136\",\n \"def num_cells(self):\\n cbi = self.cbi\\n if cbi is None:\\n return None\\n return cbi[-1] # pylint: disable=E1136\",\n \"def GetGlobalNumRows(self):\\n return _hypre.HypreParMatrix_GetGlobalNumRows(self)\",\n \"def qsize(self):\\n return self.q_size.current_value\",\n \"def get_nrof_aux(self):\\n aux = 0\\n for l in self.aux_array:\\n if l:\\n aux += 1\\n return aux\",\n \"def get_number_of_atoms_to_optimize(self):\\n v = self.c.get(simulation_cell=True)\\n return len(v.data.stoichiometry)\",\n \"def __len__(self):\\n return np.size(self.A,0)\",\n \"def getnumbaranz(self):\\n nz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getnumbaranz(self.__nativep,ctypes.byref(nz_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nz_ = nz_.value\\n _nz_return_value = nz_\\n return (_nz_return_value)\",\n \"def nbytes(self):\\n return self.nnz * self.dtype.itemsize\",\n \"def qsize(self) -> int:\\n return self._queue.qsize()\",\n \"def n_cs(self):\\n return np.size(self._cs, 0)\",\n \"def N(self):\\n return _hypre.HypreParMatrix_N(self)\",\n \"def dimension(self):\\n return len(self.qubit_values)\",\n \"def max_counts(self):\\n\\n return np.nanmax(self.pre_proc_data)\",\n \"def max_size(self):\\n sizes = np.array([m.sum() for m in self.masks])\\n return sizes.max()\",\n \"def get_nonzeros(self):\\n return self.tape.get_nonzeros(self.machine.eval_symbol,\\n self.machine.eval_state(self.state))\",\n \"def nN(self):\\n return int((self._n+1).prod())\",\n \"def get_iter_num(self):\\n\\tif len(self.cost) > 0:\\n first_key = list(self.cost.keys())[0]\\n num = len(self.cost[first_key]) - 1\\n\\telse:\\n\\t first_key = list(self.prim_var.keys())[0]\\n num = len(self.prim_var[first_key]) - 1\\n\\treturn num\",\n \"def num_probit_terms_approx(self):\\n return self._num_probit_terms_approx\",\n \"def num_cells_up(self):\\n if hasattr(self, '__num_cells_up__'):\\n return self.__num_cells_up__\\n elif self.shared_coboundaries is not None:\\n assert self.upper_index is not None\\n return int(self.shared_coboundaries.max()) + 1\\n assert self.upper_index is None\\n return 0\",\n \"def number_of_solutions(self):\\n return len(self._solutions)\",\n \"def npks(self):\\n try:\\n return self.xp.size\\n except Exception:\\n return 0\",\n \"def getN(self)->int:\\n return self.n\",\n \"def nevals(self):\\n return self._n\",\n \"def _get_free_capacity(self):\\n\\n capacity = np.ones(len(self.grid.T)) * len(self.grid)\\n capacity -= np.count_nonzero(self.grid, axis=0)\\n return capacity\",\n \"def getmaxnumbarvar(self): # 3\\n res,resargs = self.__obj.getmaxnumbarvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumbarvar_return_value = resargs\\n return _maxnumbarvar_return_value\",\n \"def nNy(self):\\n if self.dim < 2:\\n return None\\n return self.nCy + 1\",\n \"def getNumQuads(self):\\n return len(self._quadrilaterals)\",\n \"def getmaxnumvar(self): # 3\\n res,resargs = self.__obj.getmaxnumvar()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _maxnumvar_return_value = resargs\\n return _maxnumvar_return_value\",\n \"def count_matrix_largest(self, effective=False):\\n return self.count_matrix(connected_set=0, effective=effective)\",\n \"def size(self):\\n return self.N\",\n \"def numel(self) -> int:\\n return sum(p.numel() for p in self.parameters)\",\n \"def getQueueCount(self, force=False):\\n if self.queuecount == -1 or force:\\n self.queuecount = self.db.queuecount()\\n\\n return self.queuecount\",\n \"def num_empty(self):\\n count = 0\\n for i in self.__buckets:\\n if i.size() == 0:\\n count += 1\\n return count\",\n \"def required_nb_data_func(list_nb_coeff):\\n return max(list_nb_coeff)\",\n \"def required_nb_data_func(list_nb_coeff):\\n return max(list_nb_coeff)\",\n \"def required_nb_data_func(list_nb_coeff):\\n return max(list_nb_coeff)\",\n \"def size(self):\\n return self._N\",\n \"def GetNumberOfVariables(self):\\n\\n # nvar = 0\\n # for i in self.variables_order:\\n # # DO NOT COUNT VARIABLES THAT GET CONDENSED OUT\\n # if i!=0:\\n # if mesh.element_type == \\\"tri\\\":\\n # nvar += (i+1)*(i+2) // 2\\n # elif mesh.element_type == \\\"tet\\\":\\n # nvar += (i+1)*(i+2)*(i+3) // 6\\n # elif mesh.element_type == \\\"quad\\\":\\n # nvar += (i+1)**2\\n # elif mesh.element_type == \\\"hex\\\":\\n # nvar += (i+1)**3\\n\\n # nvar = sum(self.variables_order)\\n if self.nvar == None:\\n self.nvar = self.ndim\\n return self.nvar\",\n \"def vnN(self):\\n return np.array(\\n [x for x in [self.nNx, self.nNy, self.nNz] if x is not None],\\n dtype=int\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78014475","0.72534436","0.6734257","0.66473365","0.66222763","0.65740526","0.65578455","0.65316844","0.6484274","0.64780957","0.64710224","0.64367837","0.6432523","0.6382804","0.6381963","0.6368233","0.63451123","0.63023317","0.63014734","0.62077934","0.61610377","0.6101039","0.60561013","0.60139054","0.59857017","0.59702104","0.59666467","0.5951486","0.59464043","0.59365326","0.59346825","0.5924565","0.587606","0.58527166","0.5826901","0.5817777","0.57709783","0.5719226","0.5715263","0.5638934","0.5637383","0.5625427","0.5620117","0.55857676","0.55776834","0.55756354","0.556788","0.5543765","0.5542039","0.553762","0.55324686","0.5521529","0.55213296","0.5516528","0.55106866","0.55046964","0.5500961","0.5497029","0.5495938","0.5495938","0.5495938","0.5495938","0.5486272","0.54850847","0.5458402","0.54537755","0.54502004","0.5448201","0.544722","0.5446078","0.5437675","0.5436792","0.5430829","0.5428776","0.5427982","0.5426893","0.54267627","0.54115254","0.54111904","0.5407666","0.54035443","0.5401461","0.5398786","0.53939044","0.5392185","0.538843","0.5387383","0.53696334","0.5351353","0.5349396","0.53474444","0.53455734","0.53441834","0.5341716","0.53405136","0.53405136","0.53405136","0.53404415","0.5326301","0.5320998"],"string":"[\n \"0.78014475\",\n \"0.72534436\",\n \"0.6734257\",\n \"0.66473365\",\n \"0.66222763\",\n \"0.65740526\",\n \"0.65578455\",\n \"0.65316844\",\n \"0.6484274\",\n \"0.64780957\",\n \"0.64710224\",\n \"0.64367837\",\n \"0.6432523\",\n \"0.6382804\",\n \"0.6381963\",\n \"0.6368233\",\n \"0.63451123\",\n \"0.63023317\",\n \"0.63014734\",\n \"0.62077934\",\n \"0.61610377\",\n \"0.6101039\",\n \"0.60561013\",\n \"0.60139054\",\n \"0.59857017\",\n \"0.59702104\",\n \"0.59666467\",\n \"0.5951486\",\n \"0.59464043\",\n \"0.59365326\",\n \"0.59346825\",\n \"0.5924565\",\n \"0.587606\",\n \"0.58527166\",\n \"0.5826901\",\n \"0.5817777\",\n \"0.57709783\",\n \"0.5719226\",\n \"0.5715263\",\n \"0.5638934\",\n \"0.5637383\",\n \"0.5625427\",\n \"0.5620117\",\n \"0.55857676\",\n \"0.55776834\",\n \"0.55756354\",\n \"0.556788\",\n \"0.5543765\",\n \"0.5542039\",\n \"0.553762\",\n \"0.55324686\",\n \"0.5521529\",\n \"0.55213296\",\n \"0.5516528\",\n \"0.55106866\",\n \"0.55046964\",\n \"0.5500961\",\n \"0.5497029\",\n \"0.5495938\",\n \"0.5495938\",\n \"0.5495938\",\n \"0.5495938\",\n \"0.5486272\",\n \"0.54850847\",\n \"0.5458402\",\n \"0.54537755\",\n \"0.54502004\",\n \"0.5448201\",\n \"0.544722\",\n \"0.5446078\",\n \"0.5437675\",\n \"0.5436792\",\n \"0.5430829\",\n \"0.5428776\",\n \"0.5427982\",\n \"0.5426893\",\n \"0.54267627\",\n \"0.54115254\",\n \"0.54111904\",\n \"0.5407666\",\n \"0.54035443\",\n \"0.5401461\",\n \"0.5398786\",\n \"0.53939044\",\n \"0.5392185\",\n \"0.538843\",\n \"0.5387383\",\n \"0.53696334\",\n \"0.5351353\",\n \"0.5349396\",\n \"0.53474444\",\n \"0.53455734\",\n \"0.53441834\",\n \"0.5341716\",\n \"0.53405136\",\n \"0.53405136\",\n \"0.53405136\",\n \"0.53404415\",\n \"0.5326301\",\n \"0.5320998\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69867957"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":11985,"cells":{"query":{"kind":"string","value":"Sets a double parameter. putnadouparam(self,paramname_,parvalue_)"},"document":{"kind":"string","value":"def putnadouparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError","def setParameter(self, name, value):","def putnadouparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnadouparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setDouble(self, address: ghidra.program.model.address.Address, value: float) -> None:\n ...","def set_parameter(self, params, name, val):\n raise NotImplementedError()","def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _set_par(vid, par, value):\n traci.vehicle.setParameter(vid, \"carFollowModel.%s\" % par, str(value))","def SetParameterValue(self, paramName, value):\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n if paramName == ISENTROPIC_PAR:\n if (self.ideal != None):\n self.ideal.SetParameterValue(paramName, value)","def putdouparam(self,param_,parvalue_): # 3\n if not isinstance(param_,dparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putdouparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setDoubleValue(self, *args):\n return _libsbml.ConversionOption_setDoubleValue(self, *args)","def set_parameter_value(self, parameter, value):\n pass","def putnastrparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnastrparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value","def SetParameterValue(self, paramName, value):\n if paramName == NUMBTABLE_PAR:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n self.LookupTable.SetParameterValue(paramName, value)\n\n if paramName == IGNORECURVE_PAR: \n #...ignore the lookuptable and remove any specifications\n if value == 'None': value = None\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\n if value:\n port = self.GetPort(HEAD_PORT)\n port.SetValue(None, FIXED_V) \n else:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)","def setDoubleValue(self, *args):\n return _libsbml.ConversionProperties_setDoubleValue(self, *args)","def __setitem__(self, name: str, value):\n super(Parameter, self).__setitem__(name, value)","def setParamSingleStep(self, instr: str, param: str, value: Any):\n box = self._itemsdict[instr][param]['double_box']\n if box is not None:\n box.setSingleStep(value)","def plot_parameter(self, parm):\n # If user wants to plot density, make sure it exists\n if parm == 'density' and 'density' not in self.ds.data_vars:\n self.insert_density()\n \n if parm == 'theta' and 'theta' not in self.ds.data_vars:\n self.insert_potential_density()\n \n if parm == 'N' and 'N' not in self.ds.data_vars:\n self.insert_buoyancy_frequency()\n \n # Use xarray to plot this parameter\n self.ds[parm].plot(y=self.ztsp[0])\n if plt.ylim()[0] <= 0:\n plt.gca().invert_yaxis()\n plt.tight_layout()\n \n plt.show()","def set_parameter(self, sensor_name, parameter_name, parameter_value):\n if parameter_name == 'perspective_angle':\n parameter_value = parameter_value / (180 * 2) * math.pi\n if parameter_name in self.params_f:\n error = vrep.simxSetObjectFloatParameter(\n self.client_id,\n self.handles[sensor_name + self.postfix],\n self.params_f[parameter_name],\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxSetFloatSignal(\n self.client_id,\n 'change_params',\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxClearFloatSignal(\n self.client_id,\n 'change_params',\n ONE_SHOT_MODE\n )\n return error\n elif parameter_name in self.params_i:\n error = vrep.simxSetObjectFloatParameter(\n self.client_id,\n self.handles[sensor_name + self.postfix],\n self.params_i[parameter_name],\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxSetFloatSignal(\n self.client_id,\n 'change_params',\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxClearFloatSignal(\n self.client_id,\n 'change_params',\n ONE_SHOT_MODE\n )\n return error\n else:\n return 'Parameter not found'","def set_parametervalue(self, Value, nodename=None, node=None, root=None, ):\n\n if nodename != None:\n node = self.find_node(nodename, root)\n\n node_event = self._refind_nodes(self.fn_to_reg([\"Manual\"]), root=node)[0]\n\n if node_event != False:\n self.set_nodevalue(node_event, Value)\n return True\n\n return False","def setvalue(self,num,name,val):\n self.M.reconfigure(num,{name:float(val)})","def set_param(self, param, value):\n self._set_param_client(param, value)","def gui_set_param(self,param_name,val):\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\n self._handle_gui_set(param_name,force=True)","def update_parameter(cur, par, new_value):\n cur.execute(\"UPDATE parameters SET value=%f WHERE par='%s';\" % \n (new_value, par))","def update_parameter(self, name, freq, value):\n if name not in self._parameters.keys():\n self.add_parameter(name, [freq], [value])\n else:\n param = self.get_parameter(name)\n param.update_value(freq, value)","def put_param(self, attr_name, val):\n self._params[attr_name] = val","def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val","def set_option(self, name, value):\n self._params[name] = value","def set_option(self, name, value):\n self._params[name] = value","def _set_param(self, name, value):\n self._frozenjson._data[name] = value","def set_parameter(doc, o_path, param, value, mn_consts):\n if isinstance(value, str):\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\n elif isinstance(value, int):\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\n elif isinstance(value, list):\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)","def putparam(self,parname_,parvalue_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putnaintparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_parameter_and_step(self, pname, value, nstep=2, warning_action=\"default\"):\n setattr(self.p, pname, value)\n with warnings.catch_warnings():\n warnings.simplefilter(warning_action)\n for _ in range(nstep):\n self.step()","def set_trial_param(\n self,\n trial_id: int,\n param_name: str,\n param_value_internal: float,\n distribution: \"distributions.BaseDistribution\",\n ) -> bool:\n raise NotImplementedError","def set_param(param, num, set_val):\n param[0][num] = set_val","def set_param(params, pname, value=None, bounds=None):\n if value is not None:\n for p in params.flattened():\n if p.name == pname:\n p.value = value\n break\n\n if bounds is not None:\n for p in params.flattened():\n if p.name == pname:\n p.bounds = bounds\n p.vary = True\n break","def setParameter(self,arg,value):\n self._params[arg] = value\n return self._params","def update_parameter(self, param, val, force=False):\n self._update_dict[param] = val\n if force:\n self._cur_val[param] = None","def set_parameter(self, output):\n self.model.set_parameter(output);","def set_param(self, param_value):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n\n setting[1] = param_value\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def log_param(self, name: str, value):\n self.params[name] = value\n\n self._sync_log_event()","def set(self,name,val):\n matches = self.grep_param_names(name)\n if len(matches):\n x = self._get_params()\n x[matches] = val\n self._set_params(x)\n else:\n raise AttributeError, \"no parameter matches %s\"%name","def set_parameter_value(self,name,val,parameterized_object=None):\n source = parameterized_object or self.get_source_po(name)\n object.__setattr__(source,name,val)\n\n # update the tkvar\n if name in self._tkvars:\n self._tkvars[name]._original_set(self._object2string(name,val))","def _set_params(self,x):\r\n self.k._set_params(x)","def set_pi(self, val):\n self.__pi = val","def set_param(self, name, value):\n name = str(name)\n value = str(value)\n cxnlib.CXNNetSetParam(self.handle,\n ctypes.c_char_p(name.encode('utf-8')),\n ctypes.c_char_p(value.encode('utf-8')))","def _set_field(self, instrument_name, parameter_name, field, value, force_update):\n if self.verbose >= 2:\n print('_set_field: %s %s: %s' % (instrument_name, parameter_name, str(value)))\n tree_widget = self._itemsdict[instrument_name][parameter_name]['widget']\n double_box = self._itemsdict[instrument_name][parameter_name]['double_box']\n\n field_index = self._fields.index(field)\n\n double_value = False\n if field_index == 0 and double_box is not None:\n double_value = True\n if not double_value:\n tree_widget.setText(field_index + 1, str(value))\n else:\n # update a float value\n try:\n update_value = np.abs(tree_widget.value() - value) > 1e-9\n except Exception as ex:\n logging.debug(ex)\n update_value = True\n if update_value or force_update:\n if not double_box.hasFocus(): # do not update when editing\n logging.debug('update %s to %s' % (parameter_name, value))\n try:\n oldstate = double_box.blockSignals(True)\n double_box.setValue(value)\n double_box.blockSignals(oldstate)\n except Exception as ex:\n logging.debug(ex)","def setTemplateParameter(self,name,value):\n self.tplparam[name] = value","def set_parameter(self, params, name, val):\n if name == \"model\":\n params.model = val\n return params\n available_models = [\n entry_point.name\n for entry_point in pkg_resources.iter_entry_points(\n \"dxtbx.scaling_model_ext\"\n )\n ]\n phil_branches = [\n params.weighting.error_model,\n params.cut_data,\n params.scaling_options,\n params.reflection_selection,\n params.reflection_selection.random,\n params.reflection_selection.random.multi_dataset,\n ]\n if params.model:\n phil_branches.append(params.__getattribute__(str(params.model)))\n elif (\".\" in name) and (name.split(\".\")[0] in available_models):\n # if the user hasn't specified the model, but have done\n # e.g physical.parameter = *, then set model=physical\n params.model = name.split(\".\")[0]\n phil_branches.append(params.__getattribute__(str(params.model)))\n if \".\" in name: # handle e.g physical.absorption_correction\n name = name.split(\".\")[-1]\n for branch in phil_branches:\n try:\n branch.__setattr__(name, val)\n return params\n except AttributeError:\n pass\n # if get here, haven't found what we're trying to set\n raise ValueError(\"Unable to set chosen attribute \" + str(name) + \"=\" + str(val))","def setSynapseDynamics(self, param, value):\n raise NotImplementedError","def write_parameter(self, path, value, attr=None):\n if path.startswith('sample'):\n entry = self.entry.nxroot['entry']\n else:\n entry = self.entry\n if value is not None:\n if attr and path in entry:\n entry[path].attrs[attr] = value\n elif path in entry:\n if isinstance(entry[path], NXgroup):\n del entry[path]\n entry[path] = value\n else:\n entry[path].replace(value)\n elif attr is None:\n entry[path] = value","def put_par(self, parname, value, sep=\".\"):\n pv = self.get_pvname(parname, sep=sep)\n return Pv.put(pv, value)","def _p_set_par_ ( o , index , value ) :\n return o.setPar ( index , value )","def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\n self._parameters.append(Parameter(parameter_name, parameter_value))","def set_value(self, device_name, val):\n epics.caput(device_name, val)\n\n\t\t#mu = mu\n\t\t#sig = math.sqrt(abs(mu))\n\t\t#y = (float(x)-mu)/(sig)","def setValue(self, *args):\n return _libsbml.Parameter_setValue(self, *args)","def tset(self, parametername, value_array):\n raise NotImplementedError","def putdouparam(self,param_,parvalue_):\n res = __library__.MSK_XX_putdouparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_parameter(self, param, value, stripe = 0, location = 3):\n if param in self.fpga.cabac_top[0].params:\n self.fpga.set_cabac_value(param, value, stripe)\n time.sleep(0.1)\n self.fpga.send_cabac_config(stripe)\n\n elif param in [\"V_SL\", \"V_SH\", \"V_RGL\", \"V_RGH\", \"V_PL\", \"V_PH\"]:\n self.fpga.set_clock_voltages({param: value})\n\n elif param == \"I_OS\":\n self.fpga.set_current_source(value, stripe)\n\n else:\n print(\"Warning: unidentified parameter for the REB: %s\" % param)","def set_params(self, *arg):\n pass","def setd(self, node, new_double):\n\n self.daq.syncSetDouble(f'/{self.device_id}/{node}', new_double)","def set_param(self):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n setting[1] = str(self.param_value)\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def set_params(self, params):","def par_attr_changed(self, widget, adj, par, par_attr):\n try:\n val = float(widget.get_text())\n except ValueError:\n pass\n else:\n if par_attr == 'min':\n adj.set_lower(val)\n elif par_attr == 'max':\n adj.set_upper(val)\n elif par_attr == 'val':\n adj.set_value(val)\n incr = (adj.get_upper() - adj.get_lower()) / 100.0\n adj.set_step_increment(incr)\n adj.set_page_increment(incr)\n setattr(par, par_attr, val)","def adapt(self, domain_distr_param: str, domain_distr_param_value: Union[float, int]):\n if domain_distr_param not in self.get_field_names():\n raise KeyError(f'The domain parameter {self.name} does not have a domain distribution parameter '\n f'called {domain_distr_param}!')\n setattr(self, domain_distr_param, domain_distr_param_value)","def _set_parameter(self):\n # Get parameter keys\n self.input_parameter = self.parameter_combobox.currentText()\n self.result_parameter = self.result_parameters[self.input_parameter]\n # Adjust axes labels\n self.ax.set_xlabel('{} steunpunt'.format(self.input_parameter))\n self.ax.set_ylabel('{} uitvoerlocatie'.format(self.input_parameter))\n # Set data\n self._set_data()","def putnastrparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_parameters(self, params):\n self.kp = params.pgain","def add_parameter(self,\n name, # The name of the parameter\n scaling=None, # The type of scaling to be used for the parameter\n type=\"int\", # The type of the parameter, such as float\n min=0, # The minimum value of the parameter\n max=100, # The maximum value of the parameter\n significance=1, # The smallest significant step size\n value=None, # The value or value parameters\n distribution=None): # The distribution of the parameter\n config = {\"scaling\" : scaling, \n \"type\": type,\n \"min\": min, \n \"max\": max, \n \"significance\": significance,\n \"value\": value,\n \"distribution\": distribution}\n self.param_names.append(name)\n self.param_settings.append(config)","def param_name(self, value):\n self._param_name = value","def set_parameter_value(self, parameter_name, new_value):\n self.description[\"config\"][\"values\"][parameter_name][\"value\"] = new_value\n ## Update MongoDB\n #self.mongo_client.cps2_project.objects.update_one(\n #{\"_id\": self.mongo_id},\n #{\"$set\": {\"config.values.\" + parameter_name + \".value\": new_value,\n #\"last_modified.value\": str(datetime.utcnow())}\n #}\n #)\n print(\"Switched the parameter \" + parameter_name + \" to \" + new_value + \" and updated MongoDB.\")","def AddParameter(self, param_name, param_value):\n if(param_name == \"NPOP1_1\"):\n if(self.npop1_1 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop1_1 = param_value\n elif(param_name == \"NPOP1_2\"):\n if(self.npop1_2 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop1_2 = param_value\n elif(param_name == \"NPOP2_1\"):\n if(self.npop2_1 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop2_1 = param_value\n elif(param_name == \"NPOP2_2\"):\n if(self.npop2_2 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop2_2 = param_value\n elif(param_name == \"T_DIV\"):\n if(self.t_div != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.t_div = param_value\n elif(param_name == \"NPOP_A\"):\n if(self.npop_a != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop_a = param_value\n elif(param_name == \"MU\"):\n if(self.mu != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.mu = param_value\n elif(param_name == \"T_I\"):\n if(self.t_i != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.t_i = param_value\n elif(param_name == \"P_I\"):\n if(self.p_i != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.p_i = param_value\n else:\n raise Exception(\"Invalid parameter name: \" + param_name + \"!\")","def set_reg_param(self,reg_param):\n \n type_name = type(reg_param).__name__\n if re.search('float',type_name) != None:\n if reg_param < 0:\n raise KINSOL_Exception(\"Value sent to set_reg_param must be equal to or larger than zero.\")\n else:\n self.reg_param = reg_param\n else:\n raise KINSOL_Exception(\"The variable sent to 'set_reg_param' must be a float.\")","def SetParameterValue(self, paramName, value):\n super(LookupTable, self).SetParameterValue(paramName, value)\n if paramName == NUMBTABLE_PAR:\n self.SetTableCount()\n elif paramName == NUMBSERIES_PAR:\n self.SetSeriesCount()\n elif paramName == TABLETAGTYPE_PAR:\n self.SetTableTagType()\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\n idx = long(paramName[len(SERIESTYPE_PAR):])\n self.SetSeriesTypes(idx)","def SetExportParam(self, name, value):\n parent, subname = self.FindExport(name)\n subname = Handle._FixExportName(parent.obj, subname)\n if not hasattr(parent.obj, subname):\n raise KeyError(name)\n if not parent.obj.dirty:\n parent.obj.StartTransaction()\n parent.obj.dirty = True\n setattr(parent.obj, subname, value)\n return parent.obj","def _onSetParameterA(self, value):\n self._parameters['a'] = value\n self._logger.info(\"Parameter 'a' of function '{}' changed to {}\".format(self._function, value))\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())","def set_param(self, param_key, value):\n return self._params.set_param_value(param_key, value)","def _mn_set_par_ ( self , i , val , fix = False ) :\n if not i in self : raise IndexError\n #\n if hasattr ( val , 'value' ) : val = val.value()\n #\n ierr = _mn_exec_ ( self , \"SET PAR\" , i + 1 , val )\n #\n if fix : self.FixParameter ( i ) \n #\n return ierr","def __setitem__(self, name, val):\r\n matches = self.grep_param_names(name)\r\n if len(matches):\r\n val = np.array(val)\r\n assert (val.size == 1) or val.size == len(matches), \"Shape mismatch: {}:({},)\".format(val.size, len(matches))\r\n x = self._get_params()\r\n x[matches] = val\r\n self._set_params(x)\r\n else:\r\n raise AttributeError, \"no parameter matches %s\" % name","def test_ParameterVariable_init_basic_type_value(self):\n\n par = provide_parameter(\"double\", \"test\", value=518)\n\n self.assertEqual(par.name, \"test\")\n self.assertEqual(par.type, \"double\")\n self.assertEqual(par.value, 518)","def setParameterNode(self, parameterNode):\r\n # framework\r\n profbox()\r\n self.parameterNode = parameterNode","def _put_ssm_param(self, parameter, parameter_name):\n self.ssm_client.put_parameter(\n Name=parameter_name,\n Type=\"String\",\n Value=json.dumps(parameter),\n Overwrite=True,\n Tier=\"Intelligent-Tiering\",\n )","def set_value(self,x):\n self._value = float(x)","def fill_admin_fieldparam(params, p, value=False):\n params[p.id] = {'param_id': p.id, 'placeholder': p.placeholder,\n 'inputtype': p.paramtype.typename, 'title': p.title}\n params[p.id]['model'] = value if value else ''","def setUniformValue(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\n pass","def _set_valued_param(self, name, comp, spec, mode):\n vp = ValuedParam(name)\n signal = vp.read(comp, spec, mode)\n self._signals.update(signal)\n self._crossrefs[name] = vp\n setattr(self, name, vp)","def set_params(self, **kwargs):\n ...","def __setitem__(self, name: str, value):\n super(StdPrm, self).__setitem__(name, value)\n if not self.in_init:\n if name == \"value\":\n super(StdPrm, self).__setitem__(\"changed\", True)","def set_param_motor():\n servo.setSpeed(0, 0) # max = 255\n servo.setAccel(0, 0)\n servo.setSpeed(1, 150) # max = 255\n servo.setAccel(1, 150)","def setPercent(*args):","def setPercent(*args):","def _setParameterValue(self, name, value, typeString = ''):\n warnings.warn(\"This function is deprecated; parameters have been replaced with uniform inputs in 1.38.\", DeprecationWarning, stacklevel = 2)","def set(self, name, value=None):\n if isinstance(name, dict):\n for key, value in name.items():\n try:\n param, value = self.coerceParameter(key, value)\n self.params[param] = value\n except KeyError:\n pass\n elif isinstance(name, str):\n try:\n param, value = self.coerceParameter(name, value)\n self.params[param] = value\n except KeyError:\n pass","def posterior_sample_parameter(self, parameter):\n pass","def set_param(command):\n namespace = app.main(command)\n assert namespace.command == 'sp' or namespace.command == \"setparam\"\n assert namespace.name == \"test\"\n assert namespace.value == \"test\"","def setParameterNode(self, parameterNode):\n #framework\n profbox()\n self.parameterNode = parameterNode","def setFloat(self, key, value, prec):\n #value = (\"%(format)s\" % {'format': \"%%.%df\" % prec}) % value\n self.__config.setValue(key, QtCore.QVariant(value))\n self.__saved = False","def set_value(self,x):\n self._value = x"],"string":"[\n \"def set_param(self, name, value, *, distrib=None, ref=None):\\n raise NotImplementedError\",\n \"def setParameter(self, name, value):\",\n \"def putnadouparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnadouparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setDouble(self, address: ghidra.program.model.address.Address, value: float) -> None:\\n ...\",\n \"def set_parameter(self, params, name, val):\\n raise NotImplementedError()\",\n \"def putparam(self,parname_,parvalue_): # 3\\n res = self.__obj.putparam(parname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putnaintparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnaintparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _set_par(vid, par, value):\\n traci.vehicle.setParameter(vid, \\\"carFollowModel.%s\\\" % par, str(value))\",\n \"def SetParameterValue(self, paramName, value):\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n if paramName == ISENTROPIC_PAR:\\n if (self.ideal != None):\\n self.ideal.SetParameterValue(paramName, value)\",\n \"def putdouparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,dparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putdouparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setDoubleValue(self, *args):\\n return _libsbml.ConversionOption_setDoubleValue(self, *args)\",\n \"def set_parameter_value(self, parameter, value):\\n pass\",\n \"def putnastrparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnastrparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setParam(self,param,value):\\n if param in self.params.keys():\\n self.params[param] = value\",\n \"def SetParameterValue(self, paramName, value):\\n if paramName == NUMBTABLE_PAR:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n self.LookupTable.SetParameterValue(paramName, value)\\n\\n if paramName == IGNORECURVE_PAR: \\n #...ignore the lookuptable and remove any specifications\\n if value == 'None': value = None\\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\\n if value:\\n port = self.GetPort(HEAD_PORT)\\n port.SetValue(None, FIXED_V) \\n else:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\",\n \"def setDoubleValue(self, *args):\\n return _libsbml.ConversionProperties_setDoubleValue(self, *args)\",\n \"def __setitem__(self, name: str, value):\\n super(Parameter, self).__setitem__(name, value)\",\n \"def setParamSingleStep(self, instr: str, param: str, value: Any):\\n box = self._itemsdict[instr][param]['double_box']\\n if box is not None:\\n box.setSingleStep(value)\",\n \"def plot_parameter(self, parm):\\n # If user wants to plot density, make sure it exists\\n if parm == 'density' and 'density' not in self.ds.data_vars:\\n self.insert_density()\\n \\n if parm == 'theta' and 'theta' not in self.ds.data_vars:\\n self.insert_potential_density()\\n \\n if parm == 'N' and 'N' not in self.ds.data_vars:\\n self.insert_buoyancy_frequency()\\n \\n # Use xarray to plot this parameter\\n self.ds[parm].plot(y=self.ztsp[0])\\n if plt.ylim()[0] <= 0:\\n plt.gca().invert_yaxis()\\n plt.tight_layout()\\n \\n plt.show()\",\n \"def set_parameter(self, sensor_name, parameter_name, parameter_value):\\n if parameter_name == 'perspective_angle':\\n parameter_value = parameter_value / (180 * 2) * math.pi\\n if parameter_name in self.params_f:\\n error = vrep.simxSetObjectFloatParameter(\\n self.client_id,\\n self.handles[sensor_name + self.postfix],\\n self.params_f[parameter_name],\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxSetFloatSignal(\\n self.client_id,\\n 'change_params',\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxClearFloatSignal(\\n self.client_id,\\n 'change_params',\\n ONE_SHOT_MODE\\n )\\n return error\\n elif parameter_name in self.params_i:\\n error = vrep.simxSetObjectFloatParameter(\\n self.client_id,\\n self.handles[sensor_name + self.postfix],\\n self.params_i[parameter_name],\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxSetFloatSignal(\\n self.client_id,\\n 'change_params',\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxClearFloatSignal(\\n self.client_id,\\n 'change_params',\\n ONE_SHOT_MODE\\n )\\n return error\\n else:\\n return 'Parameter not found'\",\n \"def set_parametervalue(self, Value, nodename=None, node=None, root=None, ):\\n\\n if nodename != None:\\n node = self.find_node(nodename, root)\\n\\n node_event = self._refind_nodes(self.fn_to_reg([\\\"Manual\\\"]), root=node)[0]\\n\\n if node_event != False:\\n self.set_nodevalue(node_event, Value)\\n return True\\n\\n return False\",\n \"def setvalue(self,num,name,val):\\n self.M.reconfigure(num,{name:float(val)})\",\n \"def set_param(self, param, value):\\n self._set_param_client(param, value)\",\n \"def gui_set_param(self,param_name,val):\\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\\n self._handle_gui_set(param_name,force=True)\",\n \"def update_parameter(cur, par, new_value):\\n cur.execute(\\\"UPDATE parameters SET value=%f WHERE par='%s';\\\" % \\n (new_value, par))\",\n \"def update_parameter(self, name, freq, value):\\n if name not in self._parameters.keys():\\n self.add_parameter(name, [freq], [value])\\n else:\\n param = self.get_parameter(name)\\n param.update_value(freq, value)\",\n \"def put_param(self, attr_name, val):\\n self._params[attr_name] = val\",\n \"def set_param(self, label, val):\\n assert type(label) is str, 'Parameter name \\\"%s\\\" is not string' % label\\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\\n self.params[label] = val\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def _set_param(self, name, value):\\n self._frozenjson._data[name] = value\",\n \"def set_parameter(doc, o_path, param, value, mn_consts):\\n if isinstance(value, str):\\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\\n elif isinstance(value, int):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\\n elif isinstance(value, list):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)\",\n \"def putparam(self,parname_,parvalue_):\\n if isinstance(parname_,unicode):\\n parname_ = parname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putnaintparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_parameter_and_step(self, pname, value, nstep=2, warning_action=\\\"default\\\"):\\n setattr(self.p, pname, value)\\n with warnings.catch_warnings():\\n warnings.simplefilter(warning_action)\\n for _ in range(nstep):\\n self.step()\",\n \"def set_trial_param(\\n self,\\n trial_id: int,\\n param_name: str,\\n param_value_internal: float,\\n distribution: \\\"distributions.BaseDistribution\\\",\\n ) -> bool:\\n raise NotImplementedError\",\n \"def set_param(param, num, set_val):\\n param[0][num] = set_val\",\n \"def set_param(params, pname, value=None, bounds=None):\\n if value is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.value = value\\n break\\n\\n if bounds is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.bounds = bounds\\n p.vary = True\\n break\",\n \"def setParameter(self,arg,value):\\n self._params[arg] = value\\n return self._params\",\n \"def update_parameter(self, param, val, force=False):\\n self._update_dict[param] = val\\n if force:\\n self._cur_val[param] = None\",\n \"def set_parameter(self, output):\\n self.model.set_parameter(output);\",\n \"def set_param(self, param_value):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n\\n setting[1] = param_value\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def log_param(self, name: str, value):\\n self.params[name] = value\\n\\n self._sync_log_event()\",\n \"def set(self,name,val):\\n matches = self.grep_param_names(name)\\n if len(matches):\\n x = self._get_params()\\n x[matches] = val\\n self._set_params(x)\\n else:\\n raise AttributeError, \\\"no parameter matches %s\\\"%name\",\n \"def set_parameter_value(self,name,val,parameterized_object=None):\\n source = parameterized_object or self.get_source_po(name)\\n object.__setattr__(source,name,val)\\n\\n # update the tkvar\\n if name in self._tkvars:\\n self._tkvars[name]._original_set(self._object2string(name,val))\",\n \"def _set_params(self,x):\\r\\n self.k._set_params(x)\",\n \"def set_pi(self, val):\\n self.__pi = val\",\n \"def set_param(self, name, value):\\n name = str(name)\\n value = str(value)\\n cxnlib.CXNNetSetParam(self.handle,\\n ctypes.c_char_p(name.encode('utf-8')),\\n ctypes.c_char_p(value.encode('utf-8')))\",\n \"def _set_field(self, instrument_name, parameter_name, field, value, force_update):\\n if self.verbose >= 2:\\n print('_set_field: %s %s: %s' % (instrument_name, parameter_name, str(value)))\\n tree_widget = self._itemsdict[instrument_name][parameter_name]['widget']\\n double_box = self._itemsdict[instrument_name][parameter_name]['double_box']\\n\\n field_index = self._fields.index(field)\\n\\n double_value = False\\n if field_index == 0 and double_box is not None:\\n double_value = True\\n if not double_value:\\n tree_widget.setText(field_index + 1, str(value))\\n else:\\n # update a float value\\n try:\\n update_value = np.abs(tree_widget.value() - value) > 1e-9\\n except Exception as ex:\\n logging.debug(ex)\\n update_value = True\\n if update_value or force_update:\\n if not double_box.hasFocus(): # do not update when editing\\n logging.debug('update %s to %s' % (parameter_name, value))\\n try:\\n oldstate = double_box.blockSignals(True)\\n double_box.setValue(value)\\n double_box.blockSignals(oldstate)\\n except Exception as ex:\\n logging.debug(ex)\",\n \"def setTemplateParameter(self,name,value):\\n self.tplparam[name] = value\",\n \"def set_parameter(self, params, name, val):\\n if name == \\\"model\\\":\\n params.model = val\\n return params\\n available_models = [\\n entry_point.name\\n for entry_point in pkg_resources.iter_entry_points(\\n \\\"dxtbx.scaling_model_ext\\\"\\n )\\n ]\\n phil_branches = [\\n params.weighting.error_model,\\n params.cut_data,\\n params.scaling_options,\\n params.reflection_selection,\\n params.reflection_selection.random,\\n params.reflection_selection.random.multi_dataset,\\n ]\\n if params.model:\\n phil_branches.append(params.__getattribute__(str(params.model)))\\n elif (\\\".\\\" in name) and (name.split(\\\".\\\")[0] in available_models):\\n # if the user hasn't specified the model, but have done\\n # e.g physical.parameter = *, then set model=physical\\n params.model = name.split(\\\".\\\")[0]\\n phil_branches.append(params.__getattribute__(str(params.model)))\\n if \\\".\\\" in name: # handle e.g physical.absorption_correction\\n name = name.split(\\\".\\\")[-1]\\n for branch in phil_branches:\\n try:\\n branch.__setattr__(name, val)\\n return params\\n except AttributeError:\\n pass\\n # if get here, haven't found what we're trying to set\\n raise ValueError(\\\"Unable to set chosen attribute \\\" + str(name) + \\\"=\\\" + str(val))\",\n \"def setSynapseDynamics(self, param, value):\\n raise NotImplementedError\",\n \"def write_parameter(self, path, value, attr=None):\\n if path.startswith('sample'):\\n entry = self.entry.nxroot['entry']\\n else:\\n entry = self.entry\\n if value is not None:\\n if attr and path in entry:\\n entry[path].attrs[attr] = value\\n elif path in entry:\\n if isinstance(entry[path], NXgroup):\\n del entry[path]\\n entry[path] = value\\n else:\\n entry[path].replace(value)\\n elif attr is None:\\n entry[path] = value\",\n \"def put_par(self, parname, value, sep=\\\".\\\"):\\n pv = self.get_pvname(parname, sep=sep)\\n return Pv.put(pv, value)\",\n \"def _p_set_par_ ( o , index , value ) :\\n return o.setPar ( index , value )\",\n \"def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\\n self._parameters.append(Parameter(parameter_name, parameter_value))\",\n \"def set_value(self, device_name, val):\\n epics.caput(device_name, val)\\n\\n\\t\\t#mu = mu\\n\\t\\t#sig = math.sqrt(abs(mu))\\n\\t\\t#y = (float(x)-mu)/(sig)\",\n \"def setValue(self, *args):\\n return _libsbml.Parameter_setValue(self, *args)\",\n \"def tset(self, parametername, value_array):\\n raise NotImplementedError\",\n \"def putdouparam(self,param_,parvalue_):\\n res = __library__.MSK_XX_putdouparam(self.__nativep,param_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_parameter(self, param, value, stripe = 0, location = 3):\\n if param in self.fpga.cabac_top[0].params:\\n self.fpga.set_cabac_value(param, value, stripe)\\n time.sleep(0.1)\\n self.fpga.send_cabac_config(stripe)\\n\\n elif param in [\\\"V_SL\\\", \\\"V_SH\\\", \\\"V_RGL\\\", \\\"V_RGH\\\", \\\"V_PL\\\", \\\"V_PH\\\"]:\\n self.fpga.set_clock_voltages({param: value})\\n\\n elif param == \\\"I_OS\\\":\\n self.fpga.set_current_source(value, stripe)\\n\\n else:\\n print(\\\"Warning: unidentified parameter for the REB: %s\\\" % param)\",\n \"def set_params(self, *arg):\\n pass\",\n \"def setd(self, node, new_double):\\n\\n self.daq.syncSetDouble(f'/{self.device_id}/{node}', new_double)\",\n \"def set_param(self):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n setting[1] = str(self.param_value)\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def set_params(self, params):\",\n \"def par_attr_changed(self, widget, adj, par, par_attr):\\n try:\\n val = float(widget.get_text())\\n except ValueError:\\n pass\\n else:\\n if par_attr == 'min':\\n adj.set_lower(val)\\n elif par_attr == 'max':\\n adj.set_upper(val)\\n elif par_attr == 'val':\\n adj.set_value(val)\\n incr = (adj.get_upper() - adj.get_lower()) / 100.0\\n adj.set_step_increment(incr)\\n adj.set_page_increment(incr)\\n setattr(par, par_attr, val)\",\n \"def adapt(self, domain_distr_param: str, domain_distr_param_value: Union[float, int]):\\n if domain_distr_param not in self.get_field_names():\\n raise KeyError(f'The domain parameter {self.name} does not have a domain distribution parameter '\\n f'called {domain_distr_param}!')\\n setattr(self, domain_distr_param, domain_distr_param_value)\",\n \"def _set_parameter(self):\\n # Get parameter keys\\n self.input_parameter = self.parameter_combobox.currentText()\\n self.result_parameter = self.result_parameters[self.input_parameter]\\n # Adjust axes labels\\n self.ax.set_xlabel('{} steunpunt'.format(self.input_parameter))\\n self.ax.set_ylabel('{} uitvoerlocatie'.format(self.input_parameter))\\n # Set data\\n self._set_data()\",\n \"def putnastrparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_parameters(self, params):\\n self.kp = params.pgain\",\n \"def add_parameter(self,\\n name, # The name of the parameter\\n scaling=None, # The type of scaling to be used for the parameter\\n type=\\\"int\\\", # The type of the parameter, such as float\\n min=0, # The minimum value of the parameter\\n max=100, # The maximum value of the parameter\\n significance=1, # The smallest significant step size\\n value=None, # The value or value parameters\\n distribution=None): # The distribution of the parameter\\n config = {\\\"scaling\\\" : scaling, \\n \\\"type\\\": type,\\n \\\"min\\\": min, \\n \\\"max\\\": max, \\n \\\"significance\\\": significance,\\n \\\"value\\\": value,\\n \\\"distribution\\\": distribution}\\n self.param_names.append(name)\\n self.param_settings.append(config)\",\n \"def param_name(self, value):\\n self._param_name = value\",\n \"def set_parameter_value(self, parameter_name, new_value):\\n self.description[\\\"config\\\"][\\\"values\\\"][parameter_name][\\\"value\\\"] = new_value\\n ## Update MongoDB\\n #self.mongo_client.cps2_project.objects.update_one(\\n #{\\\"_id\\\": self.mongo_id},\\n #{\\\"$set\\\": {\\\"config.values.\\\" + parameter_name + \\\".value\\\": new_value,\\n #\\\"last_modified.value\\\": str(datetime.utcnow())}\\n #}\\n #)\\n print(\\\"Switched the parameter \\\" + parameter_name + \\\" to \\\" + new_value + \\\" and updated MongoDB.\\\")\",\n \"def AddParameter(self, param_name, param_value):\\n if(param_name == \\\"NPOP1_1\\\"):\\n if(self.npop1_1 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop1_1 = param_value\\n elif(param_name == \\\"NPOP1_2\\\"):\\n if(self.npop1_2 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop1_2 = param_value\\n elif(param_name == \\\"NPOP2_1\\\"):\\n if(self.npop2_1 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop2_1 = param_value\\n elif(param_name == \\\"NPOP2_2\\\"):\\n if(self.npop2_2 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop2_2 = param_value\\n elif(param_name == \\\"T_DIV\\\"):\\n if(self.t_div != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.t_div = param_value\\n elif(param_name == \\\"NPOP_A\\\"):\\n if(self.npop_a != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop_a = param_value\\n elif(param_name == \\\"MU\\\"):\\n if(self.mu != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.mu = param_value\\n elif(param_name == \\\"T_I\\\"):\\n if(self.t_i != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.t_i = param_value\\n elif(param_name == \\\"P_I\\\"):\\n if(self.p_i != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.p_i = param_value\\n else:\\n raise Exception(\\\"Invalid parameter name: \\\" + param_name + \\\"!\\\")\",\n \"def set_reg_param(self,reg_param):\\n \\n type_name = type(reg_param).__name__\\n if re.search('float',type_name) != None:\\n if reg_param < 0:\\n raise KINSOL_Exception(\\\"Value sent to set_reg_param must be equal to or larger than zero.\\\")\\n else:\\n self.reg_param = reg_param\\n else:\\n raise KINSOL_Exception(\\\"The variable sent to 'set_reg_param' must be a float.\\\")\",\n \"def SetParameterValue(self, paramName, value):\\n super(LookupTable, self).SetParameterValue(paramName, value)\\n if paramName == NUMBTABLE_PAR:\\n self.SetTableCount()\\n elif paramName == NUMBSERIES_PAR:\\n self.SetSeriesCount()\\n elif paramName == TABLETAGTYPE_PAR:\\n self.SetTableTagType()\\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\\n idx = long(paramName[len(SERIESTYPE_PAR):])\\n self.SetSeriesTypes(idx)\",\n \"def SetExportParam(self, name, value):\\n parent, subname = self.FindExport(name)\\n subname = Handle._FixExportName(parent.obj, subname)\\n if not hasattr(parent.obj, subname):\\n raise KeyError(name)\\n if not parent.obj.dirty:\\n parent.obj.StartTransaction()\\n parent.obj.dirty = True\\n setattr(parent.obj, subname, value)\\n return parent.obj\",\n \"def _onSetParameterA(self, value):\\n self._parameters['a'] = value\\n self._logger.info(\\\"Parameter 'a' of function '{}' changed to {}\\\".format(self._function, value))\\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())\",\n \"def set_param(self, param_key, value):\\n return self._params.set_param_value(param_key, value)\",\n \"def _mn_set_par_ ( self , i , val , fix = False ) :\\n if not i in self : raise IndexError\\n #\\n if hasattr ( val , 'value' ) : val = val.value()\\n #\\n ierr = _mn_exec_ ( self , \\\"SET PAR\\\" , i + 1 , val )\\n #\\n if fix : self.FixParameter ( i ) \\n #\\n return ierr\",\n \"def __setitem__(self, name, val):\\r\\n matches = self.grep_param_names(name)\\r\\n if len(matches):\\r\\n val = np.array(val)\\r\\n assert (val.size == 1) or val.size == len(matches), \\\"Shape mismatch: {}:({},)\\\".format(val.size, len(matches))\\r\\n x = self._get_params()\\r\\n x[matches] = val\\r\\n self._set_params(x)\\r\\n else:\\r\\n raise AttributeError, \\\"no parameter matches %s\\\" % name\",\n \"def test_ParameterVariable_init_basic_type_value(self):\\n\\n par = provide_parameter(\\\"double\\\", \\\"test\\\", value=518)\\n\\n self.assertEqual(par.name, \\\"test\\\")\\n self.assertEqual(par.type, \\\"double\\\")\\n self.assertEqual(par.value, 518)\",\n \"def setParameterNode(self, parameterNode):\\r\\n # framework\\r\\n profbox()\\r\\n self.parameterNode = parameterNode\",\n \"def _put_ssm_param(self, parameter, parameter_name):\\n self.ssm_client.put_parameter(\\n Name=parameter_name,\\n Type=\\\"String\\\",\\n Value=json.dumps(parameter),\\n Overwrite=True,\\n Tier=\\\"Intelligent-Tiering\\\",\\n )\",\n \"def set_value(self,x):\\n self._value = float(x)\",\n \"def fill_admin_fieldparam(params, p, value=False):\\n params[p.id] = {'param_id': p.id, 'placeholder': p.placeholder,\\n 'inputtype': p.paramtype.typename, 'title': p.title}\\n params[p.id]['model'] = value if value else ''\",\n \"def setUniformValue(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\n pass\",\n \"def _set_valued_param(self, name, comp, spec, mode):\\n vp = ValuedParam(name)\\n signal = vp.read(comp, spec, mode)\\n self._signals.update(signal)\\n self._crossrefs[name] = vp\\n setattr(self, name, vp)\",\n \"def set_params(self, **kwargs):\\n ...\",\n \"def __setitem__(self, name: str, value):\\n super(StdPrm, self).__setitem__(name, value)\\n if not self.in_init:\\n if name == \\\"value\\\":\\n super(StdPrm, self).__setitem__(\\\"changed\\\", True)\",\n \"def set_param_motor():\\n servo.setSpeed(0, 0) # max = 255\\n servo.setAccel(0, 0)\\n servo.setSpeed(1, 150) # max = 255\\n servo.setAccel(1, 150)\",\n \"def setPercent(*args):\",\n \"def setPercent(*args):\",\n \"def _setParameterValue(self, name, value, typeString = ''):\\n warnings.warn(\\\"This function is deprecated; parameters have been replaced with uniform inputs in 1.38.\\\", DeprecationWarning, stacklevel = 2)\",\n \"def set(self, name, value=None):\\n if isinstance(name, dict):\\n for key, value in name.items():\\n try:\\n param, value = self.coerceParameter(key, value)\\n self.params[param] = value\\n except KeyError:\\n pass\\n elif isinstance(name, str):\\n try:\\n param, value = self.coerceParameter(name, value)\\n self.params[param] = value\\n except KeyError:\\n pass\",\n \"def posterior_sample_parameter(self, parameter):\\n pass\",\n \"def set_param(command):\\n namespace = app.main(command)\\n assert namespace.command == 'sp' or namespace.command == \\\"setparam\\\"\\n assert namespace.name == \\\"test\\\"\\n assert namespace.value == \\\"test\\\"\",\n \"def setParameterNode(self, parameterNode):\\n #framework\\n profbox()\\n self.parameterNode = parameterNode\",\n \"def setFloat(self, key, value, prec):\\n #value = (\\\"%(format)s\\\" % {'format': \\\"%%.%df\\\" % prec}) % value\\n self.__config.setValue(key, QtCore.QVariant(value))\\n self.__saved = False\",\n \"def set_value(self,x):\\n self._value = x\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67067724","0.66955644","0.66359127","0.65071666","0.63836837","0.63250875","0.6187464","0.61801934","0.61721647","0.6131051","0.6114965","0.6106153","0.6099241","0.60783494","0.6043893","0.6035908","0.5939651","0.5904762","0.5851977","0.584044","0.583007","0.5813857","0.57992333","0.5794463","0.577886","0.5774324","0.5768298","0.57400894","0.573047","0.573047","0.57078135","0.57011676","0.569519","0.5694269","0.56833494","0.5677953","0.5668385","0.5656973","0.56523603","0.5641078","0.5638017","0.56376517","0.5632366","0.56217754","0.5614146","0.5602192","0.5559011","0.5548712","0.55460864","0.5518852","0.5513493","0.5502223","0.5499499","0.54633194","0.5441666","0.5436952","0.5434082","0.5431676","0.5428877","0.5410317","0.539412","0.53895706","0.5387469","0.5377654","0.5369597","0.5365481","0.5350357","0.53466064","0.5334254","0.53283805","0.53263485","0.53156716","0.5310937","0.5308071","0.5288505","0.5283874","0.5244767","0.5240304","0.5230911","0.5217844","0.5215493","0.5208328","0.52050495","0.5195591","0.5194718","0.51943713","0.5171599","0.5164471","0.5158112","0.51537234","0.5133794","0.5131634","0.5131634","0.51195186","0.5104329","0.51012135","0.51006013","0.509987","0.50971806","0.50941044"],"string":"[\n \"0.67067724\",\n \"0.66955644\",\n \"0.66359127\",\n \"0.65071666\",\n \"0.63836837\",\n \"0.63250875\",\n \"0.6187464\",\n \"0.61801934\",\n \"0.61721647\",\n \"0.6131051\",\n \"0.6114965\",\n \"0.6106153\",\n \"0.6099241\",\n \"0.60783494\",\n \"0.6043893\",\n \"0.6035908\",\n \"0.5939651\",\n \"0.5904762\",\n \"0.5851977\",\n \"0.584044\",\n \"0.583007\",\n \"0.5813857\",\n \"0.57992333\",\n \"0.5794463\",\n \"0.577886\",\n \"0.5774324\",\n \"0.5768298\",\n \"0.57400894\",\n \"0.573047\",\n \"0.573047\",\n \"0.57078135\",\n \"0.57011676\",\n \"0.569519\",\n \"0.5694269\",\n \"0.56833494\",\n \"0.5677953\",\n \"0.5668385\",\n \"0.5656973\",\n \"0.56523603\",\n \"0.5641078\",\n \"0.5638017\",\n \"0.56376517\",\n \"0.5632366\",\n \"0.56217754\",\n \"0.5614146\",\n \"0.5602192\",\n \"0.5559011\",\n \"0.5548712\",\n \"0.55460864\",\n \"0.5518852\",\n \"0.5513493\",\n \"0.5502223\",\n \"0.5499499\",\n \"0.54633194\",\n \"0.5441666\",\n \"0.5436952\",\n \"0.5434082\",\n \"0.5431676\",\n \"0.5428877\",\n \"0.5410317\",\n \"0.539412\",\n \"0.53895706\",\n \"0.5387469\",\n \"0.5377654\",\n \"0.5369597\",\n \"0.5365481\",\n \"0.5350357\",\n \"0.53466064\",\n \"0.5334254\",\n \"0.53283805\",\n \"0.53263485\",\n \"0.53156716\",\n \"0.5310937\",\n \"0.5308071\",\n \"0.5288505\",\n \"0.5283874\",\n \"0.5244767\",\n \"0.5240304\",\n \"0.5230911\",\n \"0.5217844\",\n \"0.5215493\",\n \"0.5208328\",\n \"0.52050495\",\n \"0.5195591\",\n \"0.5194718\",\n \"0.51943713\",\n \"0.5171599\",\n \"0.5164471\",\n \"0.5158112\",\n \"0.51537234\",\n \"0.5133794\",\n \"0.5131634\",\n \"0.5131634\",\n \"0.51195186\",\n \"0.5104329\",\n \"0.51012135\",\n \"0.51006013\",\n \"0.509987\",\n \"0.50971806\",\n \"0.50941044\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59774894"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":11986,"cells":{"query":{"kind":"string","value":"Sets an integer parameter. putnaintparam(self,paramname_,parvalue_)"},"document":{"kind":"string","value":"def putnaintparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putintparam(self,param_,parvalue_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putintparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putintparam(self,param_,parvalue_):\n res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_parameter(self, params, name, val):\n raise NotImplementedError()","def setParameter(self, name, value):","def putparam(self,parname_,parvalue_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value","def set_param(self, param, value):\n self._set_param_client(param, value)","def set_param(param, num, set_val):\n param[0][num] = set_val","def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val","def put_param(self, attr_name, val):\n self._params[attr_name] = val","def set_param(self, param_key, value):\n return self._params.set_param_value(param_key, value)","def set_param(self, name, value):\n name = str(name)\n value = str(value)\n cxnlib.CXNNetSetParam(self.handle,\n ctypes.c_char_p(name.encode('utf-8')),\n ctypes.c_char_p(value.encode('utf-8')))","def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError","async def set_param(self, param: str, value: int) -> ArchonCommand:\n cmd = await self.send_command(f\"FASTLOADPARAM {param} {value}\")\n if not cmd.succeeded():\n raise ArchonError(\n f\"Failed setting parameter {param!r} ({cmd.status.name}).\"\n )\n return cmd","def setInteger(self, value):","def setInteger(self, value):","def set_parameter_value(self, parameter, value):\n pass","def _mn_set_par_ ( self , i , val , fix = False ) :\n if not i in self : raise IndexError\n #\n if hasattr ( val , 'value' ) : val = val.value()\n #\n ierr = _mn_exec_ ( self , \"SET PAR\" , i + 1 , val )\n #\n if fix : self.FixParameter ( i ) \n #\n return ierr","def setTemplateParameter(self,name,value):\n self.tplparam[name] = value","def set_parameter(doc, o_path, param, value, mn_consts):\n if isinstance(value, str):\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\n elif isinstance(value, int):\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\n elif isinstance(value, list):\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)","def putnastrparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnastrparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set(self,name,val):\n matches = self.grep_param_names(name)\n if len(matches):\n x = self._get_params()\n x[matches] = val\n self._set_params(x)\n else:\n raise AttributeError, \"no parameter matches %s\"%name","def SetParameterValue(self, paramName, value):\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n if paramName == ISENTROPIC_PAR:\n if (self.ideal != None):\n self.ideal.SetParameterValue(paramName, value)","def setParameter(self,arg,value):\n self._params[arg] = value\n return self._params","def putnadouparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnadouparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getintparam(self,param_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res,resargs = self.__obj.getintparam(param_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _parvalue_return_value = resargs\n return _parvalue_return_value","def setInt(self, addr: ghidra.program.model.address.Address, value: int) -> None:\n ...","def param_name(self, value):\n self._param_name = value","def SetParameterValue(self, paramName, value):\n if paramName == NUMBTABLE_PAR:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n self.LookupTable.SetParameterValue(paramName, value)\n\n if paramName == IGNORECURVE_PAR: \n #...ignore the lookuptable and remove any specifications\n if value == 'None': value = None\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\n if value:\n port = self.GetPort(HEAD_PORT)\n port.SetValue(None, FIXED_V) \n else:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)","def SetParameterValue(self, paramName, value):\n super(LookupTable, self).SetParameterValue(paramName, value)\n if paramName == NUMBTABLE_PAR:\n self.SetTableCount()\n elif paramName == NUMBSERIES_PAR:\n self.SetSeriesCount()\n elif paramName == TABLETAGTYPE_PAR:\n self.SetTableTagType()\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\n idx = long(paramName[len(SERIESTYPE_PAR):])\n self.SetSeriesTypes(idx)","def gui_set_param(self,param_name,val):\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\n self._handle_gui_set(param_name,force=True)","def set_param(self, param_name, param, set_val = None, type_of_param = None):\n if (set_val is None):\n set_val = True\n param_index = self.index(param_name);\n \n if (type_of_param is None):\n type_of_param = self.type_param[param_index]\n if (param is None):\n param = copy.deepcopy(self._param_val[param_index])\n elif (type(param) is type_of_param):\n param = [param]\n elif (type(param) is not list):\n error_str = (\n \"\"\" \n Trying to set parameter %s to non-integer value (or string if instance name, float if eps+rayeps value), and not a list.\n Type of param: %s\n \"\"\" % (param_name, str(type(param)))\n )\n raise TypeError(error_str)\n\n if (set_val):\n self._param_val[param_index] = copy.deepcopy(param)\n return param","def set_param(params, pname, value=None, bounds=None):\n if value is not None:\n for p in params.flattened():\n if p.name == pname:\n p.value = value\n break\n\n if bounds is not None:\n for p in params.flattened():\n if p.name == pname:\n p.bounds = bounds\n p.vary = True\n break","def putnastrparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_params(self, params):","def setInteger(self, value: int):\n self.value = value","def getintparam(self,param_):\n parvalue_ = ctypes.c_int32()\n res = __library__.MSK_XX_getintparam(self.__nativep,param_,ctypes.byref(parvalue_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n parvalue_ = parvalue_.value\n _parvalue_return_value = parvalue_\n return (_parvalue_return_value)","def putnadouparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_option(self, name, value):\n self._params[name] = value","def set_option(self, name, value):\n self._params[name] = value","def putstrparam(self,param_,parvalue_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putstrparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set(self, param, value):\r\n # continuous testing of inputs\r\n if self.testing_unit.testing_level > 1 and not self.testing_unit.c_test_set_inp(param, value):\r\n raise ValueError(\"set won't run, input's aren't valid.\")\r\n\r\n # continuous testing of functional inputs\r\n if self.testing_unit.testing_level > 0:\r\n if param in [\"weighting_bias\"]:\r\n if not [self.testing_unit.c_test_weighting_bias][[\"weighting_bias\"].index(param)](value):\r\n raise ValueError(\"Bad \" + param + \" input. See log or raise testing verbosity.\")\r\n\r\n self.__locals[param] = value # Security Risk\r\n return 1 # Success\r","def set_param(command):\n namespace = app.main(command)\n assert namespace.command == 'sp' or namespace.command == \"setparam\"\n assert namespace.name == \"test\"\n assert namespace.value == \"test\"","def set_param(param, value, verbose=False):\n set_param_raw(param, yaml.load(value), verbose=verbose)","def set_param(self, param_value):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n\n setting[1] = param_value\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def set_params(self, *arg):\n pass","def change_general_param(self, param, val):\n assert param in self.params, '%s is not recognized as a valid parameter' % param\n self.params[param].change_value(val)","def setInt(self, address: ghidra.program.model.address.Address, value: int) -> None:\n ...","def _set_par(vid, par, value):\n traci.vehicle.setParameter(vid, \"carFollowModel.%s\" % par, str(value))","def _set_param(self, name, value):\n self._frozenjson._data[name] = value","def _p_set_par_ ( o , index , value ) :\n return o.setPar ( index , value )","def log_param(self, name: str, value):\n self.params[name] = value\n\n self._sync_log_event()","def _put_ssm_param(self, parameter, parameter_name):\n self.ssm_client.put_parameter(\n Name=parameter_name,\n Type=\"String\",\n Value=json.dumps(parameter),\n Overwrite=True,\n Tier=\"Intelligent-Tiering\",\n )","def __setitem__(self, name: str, value):\n super(Parameter, self).__setitem__(name, value)","def setInteger(self, value):\n assert self._is_int is True\n self._value = value","def set_param(self, name, val):\n # name will be 'colorR', 'colorG', 'colorB'\n rgb255 = int(val * 255)\n if name == 'colorR':\n self.color.r = rgb255\n elif name == 'colorG':\n self.color.g = rgb255\n elif name == 'colorB':\n self.color.b = rgb255","def set_trial_param(\n self,\n trial_id: int,\n param_name: str,\n param_value_internal: float,\n distribution: \"distributions.BaseDistribution\",\n ) -> bool:\n raise NotImplementedError","def set_parameters(self, params):\n self.kp = params.pgain","def set(self, key, param: Param):\n if not isinstance(param, Param):\n raise ValueError(\"Only accepts a Param instance.\")\n self._params[key] = param","def set_parameter_value(self,name,val,parameterized_object=None):\n source = parameterized_object or self.get_source_po(name)\n object.__setattr__(source,name,val)\n\n # update the tkvar\n if name in self._tkvars:\n self._tkvars[name]._original_set(self._object2string(name,val))","def set_param(\n self, param_name, *args,\n param_type=None, memo=None, **kwargs):\n # Cast param_name to str once, for convenience:\n # (This is needed because Parameter members are Enum objects,\n # which can't be used in place of string-valued indexes)\n param_name = str(param_name)\n param = self.build_param(\n param_name, *args, param_type=param_type, memo=memo, **kwargs)\n setattr(self, param_name, param)","def set_pi(self, val):\n self.__pi = val","def putstrparam(self,param_,parvalue_):\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def isintparname(self,parname_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n param_ = ctypes.c_int32()\n res = __library__.MSK_XX_isintparname(self.__nativep,parname_,ctypes.byref(param_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = iparam(param_.value)\n return (_param_return_value)","def setInt(self, key, value):\n self.__config.setValue(key, QtCore.QVariant(value))\n self.__saved = False","def set_params(self, **kwargs):\n ...","def setvalue(self,num,name,val):\n self.M.reconfigure(num,{name:float(val)})","def isintparname(self,parname_): # 3\n res,resargs = self.__obj.isintparname(parname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = resargs\n _param_return_value = iparam(_param_return_value)\n return _param_return_value","def update_parameter(self, param, val, force=False):\n self._update_dict[param] = val\n if force:\n self._cur_val[param] = None","def update_parameter(cur, par, new_value):\n cur.execute(\"UPDATE parameters SET value=%f WHERE par='%s';\" % \n (new_value, par))","def setInt(self, addr: ghidra.program.model.address.Address, value: int, bigEndian: bool) -> None:\n ...","def set_param(self):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n setting[1] = str(self.param_value)\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def set(self, name, value=None):\n if isinstance(name, dict):\n for key, value in name.items():\n try:\n param, value = self.coerceParameter(key, value)\n self.params[param] = value\n except KeyError:\n pass\n elif isinstance(name, str):\n try:\n param, value = self.coerceParameter(name, value)\n self.params[param] = value\n except KeyError:\n pass","def param(self, param):\n if param is not None and len(param) < 1:\n raise ValueError(\"Invalid value for `param`, length must be greater than or equal to `1`\") # noqa: E501\n\n self._param = param","def TeamCityParamSetter(keyName, value):\n print(\"##teamcity[setParameter name='{}' value='{}']\".format(keyName, value))","def add_id(self, param_id):\n self.param_id = param_id","def SetSpacing(self, p_int, ):\n ...","def add_param(self, paraminfo):\n self.params[paraminfo.name] = paraminfo","def set_parameter_and_step(self, pname, value, nstep=2, warning_action=\"default\"):\n setattr(self.p, pname, value)\n with warnings.catch_warnings():\n warnings.simplefilter(warning_action)\n for _ in range(nstep):\n self.step()","def getIntParam(self, params, name):\n try:\n return int(params.get(name))\n except:\n return None","def set_num(self, num):\n self.cmd_num = num","def __setJobParam( self, name, value ):\n if not self.jobID:\n return S_ERROR( 'JobID not defined' )\n\n self.log.verbose( 'setJobParameter(%s, %s, %s)' % ( self.jobID, name, value ) )\n return RPCClient( 'WorkloadManagement/JobStateUpdate', timeout = 120 ).setJobParameter( int( self.jobID ), str( name ), str( value ) )","def _set_valued_param(self, name, comp, spec, mode):\n vp = ValuedParam(name)\n signal = vp.read(comp, spec, mode)\n self._signals.update(signal)\n self._crossrefs[name] = vp\n setattr(self, name, vp)","def setParameterNode(self, parameterNode):\r\n # framework\r\n profbox()\r\n self.parameterNode = parameterNode","def _set_params(self,x):\r\n self.k._set_params(x)","def SetPriorityValue(self, *args, **kwargs):\n pass","def set_pno(self, pno):\n self.__pno = pno","def set_component_parameter(self, key: str, value: Any):\n if not key:\n raise ValueError(\"Key is required\")\n\n if value is None:\n raise ValueError(\"Value is required\")\n\n self._node[\"app_data\"][\"component_parameters\"][key] = value","def getIntParam(self, paramkey, default=None):\n value = self.request.getParameter(paramkey)\n if value is None: return default\n try: return int(value)\n except: return default","def setPidx(self, pidx):\n self.keeper.setGbl(b\"pidx\", b\"%x\" % pidx)","def set_parameter(self, sensor_name, parameter_name, parameter_value):\n if parameter_name == 'perspective_angle':\n parameter_value = parameter_value / (180 * 2) * math.pi\n if parameter_name in self.params_f:\n error = vrep.simxSetObjectFloatParameter(\n self.client_id,\n self.handles[sensor_name + self.postfix],\n self.params_f[parameter_name],\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxSetFloatSignal(\n self.client_id,\n 'change_params',\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxClearFloatSignal(\n self.client_id,\n 'change_params',\n ONE_SHOT_MODE\n )\n return error\n elif parameter_name in self.params_i:\n error = vrep.simxSetObjectFloatParameter(\n self.client_id,\n self.handles[sensor_name + self.postfix],\n self.params_i[parameter_name],\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxSetFloatSignal(\n self.client_id,\n 'change_params',\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxClearFloatSignal(\n self.client_id,\n 'change_params',\n ONE_SHOT_MODE\n )\n return error\n else:\n return 'Parameter not found'","def set_parameter(name, parameter, value, path=None):\n if not exists(name, path=path):\n return None\n\n cmd = \"lxc-cgroup\"\n if path:\n cmd += f\" -P {shlex.quote(path)}\"\n cmd += f\" -n {name} {parameter} {value}\"\n ret = __salt__[\"cmd.run_all\"](cmd, python_shell=False)\n if ret[\"retcode\"] != 0:\n return False\n else:\n return True","def _set_compiler_param(compiler_handle, params, value=None):\n if isinstance(params, collections.Mapping):\n params = params.items()\n elif isinstance(params, STRING_TYPES) and value is not None:\n params = [(params, value)]\n for key, val in params:\n _check_call(_LIB.TreeliteCompilerSetParam(compiler_handle, c_str(key),\n c_str(str(val))))","def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\n self._parameters.append(Parameter(parameter_name, parameter_value))","def AddParameter(self, param_name, param_value):\n if(param_name == \"NPOP1_1\"):\n if(self.npop1_1 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop1_1 = param_value\n elif(param_name == \"NPOP1_2\"):\n if(self.npop1_2 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop1_2 = param_value\n elif(param_name == \"NPOP2_1\"):\n if(self.npop2_1 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop2_1 = param_value\n elif(param_name == \"NPOP2_2\"):\n if(self.npop2_2 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop2_2 = param_value\n elif(param_name == \"T_DIV\"):\n if(self.t_div != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.t_div = param_value\n elif(param_name == \"NPOP_A\"):\n if(self.npop_a != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop_a = param_value\n elif(param_name == \"MU\"):\n if(self.mu != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.mu = param_value\n elif(param_name == \"T_I\"):\n if(self.t_i != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.t_i = param_value\n elif(param_name == \"P_I\"):\n if(self.p_i != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.p_i = param_value\n else:\n raise Exception(\"Invalid parameter name: \" + param_name + \"!\")","def set_params(self, *argv, **kwargs):\n pass","def set_params(self, *argv, **kwargs):\n pass","def set_params(self, *argv, **kwargs):\n pass"],"string":"[\n \"def putintparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,iparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putintparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putintparam(self,param_,parvalue_):\\n res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putnaintparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnaintparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putparam(self,parname_,parvalue_): # 3\\n res = self.__obj.putparam(parname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_parameter(self, params, name, val):\\n raise NotImplementedError()\",\n \"def setParameter(self, name, value):\",\n \"def putparam(self,parname_,parvalue_):\\n if isinstance(parname_,unicode):\\n parname_ = parname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setParam(self,param,value):\\n if param in self.params.keys():\\n self.params[param] = value\",\n \"def set_param(self, param, value):\\n self._set_param_client(param, value)\",\n \"def set_param(param, num, set_val):\\n param[0][num] = set_val\",\n \"def set_param(self, label, val):\\n assert type(label) is str, 'Parameter name \\\"%s\\\" is not string' % label\\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\\n self.params[label] = val\",\n \"def put_param(self, attr_name, val):\\n self._params[attr_name] = val\",\n \"def set_param(self, param_key, value):\\n return self._params.set_param_value(param_key, value)\",\n \"def set_param(self, name, value):\\n name = str(name)\\n value = str(value)\\n cxnlib.CXNNetSetParam(self.handle,\\n ctypes.c_char_p(name.encode('utf-8')),\\n ctypes.c_char_p(value.encode('utf-8')))\",\n \"def set_param(self, name, value, *, distrib=None, ref=None):\\n raise NotImplementedError\",\n \"async def set_param(self, param: str, value: int) -> ArchonCommand:\\n cmd = await self.send_command(f\\\"FASTLOADPARAM {param} {value}\\\")\\n if not cmd.succeeded():\\n raise ArchonError(\\n f\\\"Failed setting parameter {param!r} ({cmd.status.name}).\\\"\\n )\\n return cmd\",\n \"def setInteger(self, value):\",\n \"def setInteger(self, value):\",\n \"def set_parameter_value(self, parameter, value):\\n pass\",\n \"def _mn_set_par_ ( self , i , val , fix = False ) :\\n if not i in self : raise IndexError\\n #\\n if hasattr ( val , 'value' ) : val = val.value()\\n #\\n ierr = _mn_exec_ ( self , \\\"SET PAR\\\" , i + 1 , val )\\n #\\n if fix : self.FixParameter ( i ) \\n #\\n return ierr\",\n \"def setTemplateParameter(self,name,value):\\n self.tplparam[name] = value\",\n \"def set_parameter(doc, o_path, param, value, mn_consts):\\n if isinstance(value, str):\\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\\n elif isinstance(value, int):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\\n elif isinstance(value, list):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)\",\n \"def putnastrparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnastrparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set(self,name,val):\\n matches = self.grep_param_names(name)\\n if len(matches):\\n x = self._get_params()\\n x[matches] = val\\n self._set_params(x)\\n else:\\n raise AttributeError, \\\"no parameter matches %s\\\"%name\",\n \"def SetParameterValue(self, paramName, value):\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n if paramName == ISENTROPIC_PAR:\\n if (self.ideal != None):\\n self.ideal.SetParameterValue(paramName, value)\",\n \"def setParameter(self,arg,value):\\n self._params[arg] = value\\n return self._params\",\n \"def putnadouparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnadouparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getintparam(self,param_): # 3\\n if not isinstance(param_,iparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res,resargs = self.__obj.getintparam(param_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _parvalue_return_value = resargs\\n return _parvalue_return_value\",\n \"def setInt(self, addr: ghidra.program.model.address.Address, value: int) -> None:\\n ...\",\n \"def param_name(self, value):\\n self._param_name = value\",\n \"def SetParameterValue(self, paramName, value):\\n if paramName == NUMBTABLE_PAR:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n self.LookupTable.SetParameterValue(paramName, value)\\n\\n if paramName == IGNORECURVE_PAR: \\n #...ignore the lookuptable and remove any specifications\\n if value == 'None': value = None\\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\\n if value:\\n port = self.GetPort(HEAD_PORT)\\n port.SetValue(None, FIXED_V) \\n else:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\",\n \"def SetParameterValue(self, paramName, value):\\n super(LookupTable, self).SetParameterValue(paramName, value)\\n if paramName == NUMBTABLE_PAR:\\n self.SetTableCount()\\n elif paramName == NUMBSERIES_PAR:\\n self.SetSeriesCount()\\n elif paramName == TABLETAGTYPE_PAR:\\n self.SetTableTagType()\\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\\n idx = long(paramName[len(SERIESTYPE_PAR):])\\n self.SetSeriesTypes(idx)\",\n \"def gui_set_param(self,param_name,val):\\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\\n self._handle_gui_set(param_name,force=True)\",\n \"def set_param(self, param_name, param, set_val = None, type_of_param = None):\\n if (set_val is None):\\n set_val = True\\n param_index = self.index(param_name);\\n \\n if (type_of_param is None):\\n type_of_param = self.type_param[param_index]\\n if (param is None):\\n param = copy.deepcopy(self._param_val[param_index])\\n elif (type(param) is type_of_param):\\n param = [param]\\n elif (type(param) is not list):\\n error_str = (\\n \\\"\\\"\\\" \\n Trying to set parameter %s to non-integer value (or string if instance name, float if eps+rayeps value), and not a list.\\n Type of param: %s\\n \\\"\\\"\\\" % (param_name, str(type(param)))\\n )\\n raise TypeError(error_str)\\n\\n if (set_val):\\n self._param_val[param_index] = copy.deepcopy(param)\\n return param\",\n \"def set_param(params, pname, value=None, bounds=None):\\n if value is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.value = value\\n break\\n\\n if bounds is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.bounds = bounds\\n p.vary = True\\n break\",\n \"def putnastrparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_params(self, params):\",\n \"def setInteger(self, value: int):\\n self.value = value\",\n \"def getintparam(self,param_):\\n parvalue_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getintparam(self.__nativep,param_,ctypes.byref(parvalue_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n parvalue_ = parvalue_.value\\n _parvalue_return_value = parvalue_\\n return (_parvalue_return_value)\",\n \"def putnadouparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def putstrparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,sparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putstrparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set(self, param, value):\\r\\n # continuous testing of inputs\\r\\n if self.testing_unit.testing_level > 1 and not self.testing_unit.c_test_set_inp(param, value):\\r\\n raise ValueError(\\\"set won't run, input's aren't valid.\\\")\\r\\n\\r\\n # continuous testing of functional inputs\\r\\n if self.testing_unit.testing_level > 0:\\r\\n if param in [\\\"weighting_bias\\\"]:\\r\\n if not [self.testing_unit.c_test_weighting_bias][[\\\"weighting_bias\\\"].index(param)](value):\\r\\n raise ValueError(\\\"Bad \\\" + param + \\\" input. See log or raise testing verbosity.\\\")\\r\\n\\r\\n self.__locals[param] = value # Security Risk\\r\\n return 1 # Success\\r\",\n \"def set_param(command):\\n namespace = app.main(command)\\n assert namespace.command == 'sp' or namespace.command == \\\"setparam\\\"\\n assert namespace.name == \\\"test\\\"\\n assert namespace.value == \\\"test\\\"\",\n \"def set_param(param, value, verbose=False):\\n set_param_raw(param, yaml.load(value), verbose=verbose)\",\n \"def set_param(self, param_value):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n\\n setting[1] = param_value\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def set_params(self, *arg):\\n pass\",\n \"def change_general_param(self, param, val):\\n assert param in self.params, '%s is not recognized as a valid parameter' % param\\n self.params[param].change_value(val)\",\n \"def setInt(self, address: ghidra.program.model.address.Address, value: int) -> None:\\n ...\",\n \"def _set_par(vid, par, value):\\n traci.vehicle.setParameter(vid, \\\"carFollowModel.%s\\\" % par, str(value))\",\n \"def _set_param(self, name, value):\\n self._frozenjson._data[name] = value\",\n \"def _p_set_par_ ( o , index , value ) :\\n return o.setPar ( index , value )\",\n \"def log_param(self, name: str, value):\\n self.params[name] = value\\n\\n self._sync_log_event()\",\n \"def _put_ssm_param(self, parameter, parameter_name):\\n self.ssm_client.put_parameter(\\n Name=parameter_name,\\n Type=\\\"String\\\",\\n Value=json.dumps(parameter),\\n Overwrite=True,\\n Tier=\\\"Intelligent-Tiering\\\",\\n )\",\n \"def __setitem__(self, name: str, value):\\n super(Parameter, self).__setitem__(name, value)\",\n \"def setInteger(self, value):\\n assert self._is_int is True\\n self._value = value\",\n \"def set_param(self, name, val):\\n # name will be 'colorR', 'colorG', 'colorB'\\n rgb255 = int(val * 255)\\n if name == 'colorR':\\n self.color.r = rgb255\\n elif name == 'colorG':\\n self.color.g = rgb255\\n elif name == 'colorB':\\n self.color.b = rgb255\",\n \"def set_trial_param(\\n self,\\n trial_id: int,\\n param_name: str,\\n param_value_internal: float,\\n distribution: \\\"distributions.BaseDistribution\\\",\\n ) -> bool:\\n raise NotImplementedError\",\n \"def set_parameters(self, params):\\n self.kp = params.pgain\",\n \"def set(self, key, param: Param):\\n if not isinstance(param, Param):\\n raise ValueError(\\\"Only accepts a Param instance.\\\")\\n self._params[key] = param\",\n \"def set_parameter_value(self,name,val,parameterized_object=None):\\n source = parameterized_object or self.get_source_po(name)\\n object.__setattr__(source,name,val)\\n\\n # update the tkvar\\n if name in self._tkvars:\\n self._tkvars[name]._original_set(self._object2string(name,val))\",\n \"def set_param(\\n self, param_name, *args,\\n param_type=None, memo=None, **kwargs):\\n # Cast param_name to str once, for convenience:\\n # (This is needed because Parameter members are Enum objects,\\n # which can't be used in place of string-valued indexes)\\n param_name = str(param_name)\\n param = self.build_param(\\n param_name, *args, param_type=param_type, memo=memo, **kwargs)\\n setattr(self, param_name, param)\",\n \"def set_pi(self, val):\\n self.__pi = val\",\n \"def putstrparam(self,param_,parvalue_):\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def isintparname(self,parname_):\\n if isinstance(parname_,unicode):\\n parname_ = parname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n param_ = ctypes.c_int32()\\n res = __library__.MSK_XX_isintparname(self.__nativep,parname_,ctypes.byref(param_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _param_return_value = iparam(param_.value)\\n return (_param_return_value)\",\n \"def setInt(self, key, value):\\n self.__config.setValue(key, QtCore.QVariant(value))\\n self.__saved = False\",\n \"def set_params(self, **kwargs):\\n ...\",\n \"def setvalue(self,num,name,val):\\n self.M.reconfigure(num,{name:float(val)})\",\n \"def isintparname(self,parname_): # 3\\n res,resargs = self.__obj.isintparname(parname_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _param_return_value = resargs\\n _param_return_value = iparam(_param_return_value)\\n return _param_return_value\",\n \"def update_parameter(self, param, val, force=False):\\n self._update_dict[param] = val\\n if force:\\n self._cur_val[param] = None\",\n \"def update_parameter(cur, par, new_value):\\n cur.execute(\\\"UPDATE parameters SET value=%f WHERE par='%s';\\\" % \\n (new_value, par))\",\n \"def setInt(self, addr: ghidra.program.model.address.Address, value: int, bigEndian: bool) -> None:\\n ...\",\n \"def set_param(self):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n setting[1] = str(self.param_value)\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def set(self, name, value=None):\\n if isinstance(name, dict):\\n for key, value in name.items():\\n try:\\n param, value = self.coerceParameter(key, value)\\n self.params[param] = value\\n except KeyError:\\n pass\\n elif isinstance(name, str):\\n try:\\n param, value = self.coerceParameter(name, value)\\n self.params[param] = value\\n except KeyError:\\n pass\",\n \"def param(self, param):\\n if param is not None and len(param) < 1:\\n raise ValueError(\\\"Invalid value for `param`, length must be greater than or equal to `1`\\\") # noqa: E501\\n\\n self._param = param\",\n \"def TeamCityParamSetter(keyName, value):\\n print(\\\"##teamcity[setParameter name='{}' value='{}']\\\".format(keyName, value))\",\n \"def add_id(self, param_id):\\n self.param_id = param_id\",\n \"def SetSpacing(self, p_int, ):\\n ...\",\n \"def add_param(self, paraminfo):\\n self.params[paraminfo.name] = paraminfo\",\n \"def set_parameter_and_step(self, pname, value, nstep=2, warning_action=\\\"default\\\"):\\n setattr(self.p, pname, value)\\n with warnings.catch_warnings():\\n warnings.simplefilter(warning_action)\\n for _ in range(nstep):\\n self.step()\",\n \"def getIntParam(self, params, name):\\n try:\\n return int(params.get(name))\\n except:\\n return None\",\n \"def set_num(self, num):\\n self.cmd_num = num\",\n \"def __setJobParam( self, name, value ):\\n if not self.jobID:\\n return S_ERROR( 'JobID not defined' )\\n\\n self.log.verbose( 'setJobParameter(%s, %s, %s)' % ( self.jobID, name, value ) )\\n return RPCClient( 'WorkloadManagement/JobStateUpdate', timeout = 120 ).setJobParameter( int( self.jobID ), str( name ), str( value ) )\",\n \"def _set_valued_param(self, name, comp, spec, mode):\\n vp = ValuedParam(name)\\n signal = vp.read(comp, spec, mode)\\n self._signals.update(signal)\\n self._crossrefs[name] = vp\\n setattr(self, name, vp)\",\n \"def setParameterNode(self, parameterNode):\\r\\n # framework\\r\\n profbox()\\r\\n self.parameterNode = parameterNode\",\n \"def _set_params(self,x):\\r\\n self.k._set_params(x)\",\n \"def SetPriorityValue(self, *args, **kwargs):\\n pass\",\n \"def set_pno(self, pno):\\n self.__pno = pno\",\n \"def set_component_parameter(self, key: str, value: Any):\\n if not key:\\n raise ValueError(\\\"Key is required\\\")\\n\\n if value is None:\\n raise ValueError(\\\"Value is required\\\")\\n\\n self._node[\\\"app_data\\\"][\\\"component_parameters\\\"][key] = value\",\n \"def getIntParam(self, paramkey, default=None):\\n value = self.request.getParameter(paramkey)\\n if value is None: return default\\n try: return int(value)\\n except: return default\",\n \"def setPidx(self, pidx):\\n self.keeper.setGbl(b\\\"pidx\\\", b\\\"%x\\\" % pidx)\",\n \"def set_parameter(self, sensor_name, parameter_name, parameter_value):\\n if parameter_name == 'perspective_angle':\\n parameter_value = parameter_value / (180 * 2) * math.pi\\n if parameter_name in self.params_f:\\n error = vrep.simxSetObjectFloatParameter(\\n self.client_id,\\n self.handles[sensor_name + self.postfix],\\n self.params_f[parameter_name],\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxSetFloatSignal(\\n self.client_id,\\n 'change_params',\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxClearFloatSignal(\\n self.client_id,\\n 'change_params',\\n ONE_SHOT_MODE\\n )\\n return error\\n elif parameter_name in self.params_i:\\n error = vrep.simxSetObjectFloatParameter(\\n self.client_id,\\n self.handles[sensor_name + self.postfix],\\n self.params_i[parameter_name],\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxSetFloatSignal(\\n self.client_id,\\n 'change_params',\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxClearFloatSignal(\\n self.client_id,\\n 'change_params',\\n ONE_SHOT_MODE\\n )\\n return error\\n else:\\n return 'Parameter not found'\",\n \"def set_parameter(name, parameter, value, path=None):\\n if not exists(name, path=path):\\n return None\\n\\n cmd = \\\"lxc-cgroup\\\"\\n if path:\\n cmd += f\\\" -P {shlex.quote(path)}\\\"\\n cmd += f\\\" -n {name} {parameter} {value}\\\"\\n ret = __salt__[\\\"cmd.run_all\\\"](cmd, python_shell=False)\\n if ret[\\\"retcode\\\"] != 0:\\n return False\\n else:\\n return True\",\n \"def _set_compiler_param(compiler_handle, params, value=None):\\n if isinstance(params, collections.Mapping):\\n params = params.items()\\n elif isinstance(params, STRING_TYPES) and value is not None:\\n params = [(params, value)]\\n for key, val in params:\\n _check_call(_LIB.TreeliteCompilerSetParam(compiler_handle, c_str(key),\\n c_str(str(val))))\",\n \"def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\\n self._parameters.append(Parameter(parameter_name, parameter_value))\",\n \"def AddParameter(self, param_name, param_value):\\n if(param_name == \\\"NPOP1_1\\\"):\\n if(self.npop1_1 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop1_1 = param_value\\n elif(param_name == \\\"NPOP1_2\\\"):\\n if(self.npop1_2 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop1_2 = param_value\\n elif(param_name == \\\"NPOP2_1\\\"):\\n if(self.npop2_1 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop2_1 = param_value\\n elif(param_name == \\\"NPOP2_2\\\"):\\n if(self.npop2_2 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop2_2 = param_value\\n elif(param_name == \\\"T_DIV\\\"):\\n if(self.t_div != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.t_div = param_value\\n elif(param_name == \\\"NPOP_A\\\"):\\n if(self.npop_a != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop_a = param_value\\n elif(param_name == \\\"MU\\\"):\\n if(self.mu != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.mu = param_value\\n elif(param_name == \\\"T_I\\\"):\\n if(self.t_i != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.t_i = param_value\\n elif(param_name == \\\"P_I\\\"):\\n if(self.p_i != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.p_i = param_value\\n else:\\n raise Exception(\\\"Invalid parameter name: \\\" + param_name + \\\"!\\\")\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def set_params(self, *argv, **kwargs):\\n pass\",\n \"def set_params(self, *argv, **kwargs):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8237327","0.8059725","0.76456845","0.73804134","0.7117238","0.710104","0.706935","0.70490587","0.69747484","0.6902738","0.6690662","0.6675924","0.6668182","0.65841776","0.6558105","0.65107626","0.6459708","0.6459708","0.6409349","0.6405313","0.63323426","0.63227516","0.6321399","0.6317174","0.6302483","0.62422615","0.6185782","0.61510617","0.6079875","0.6077471","0.6018201","0.60034","0.5972777","0.5944909","0.59254336","0.5923806","0.5916214","0.5911703","0.59082055","0.5907995","0.59023184","0.59023184","0.5900998","0.588827","0.5868998","0.5851168","0.58326644","0.5825124","0.58226055","0.58194244","0.58085465","0.5795988","0.5787944","0.5760301","0.5756735","0.57563156","0.571759","0.5692497","0.5670374","0.5664712","0.5652905","0.56401575","0.5635916","0.5622083","0.5615873","0.5604544","0.55976015","0.55859715","0.55663335","0.55611694","0.5553506","0.55477494","0.55327976","0.552413","0.55177844","0.5516859","0.5488857","0.5487131","0.5474508","0.54715854","0.54706293","0.545369","0.5452098","0.5447068","0.5444032","0.54396516","0.5437827","0.54358363","0.54334337","0.5432908","0.542321","0.5416736","0.5389193","0.5388347","0.5373991","0.53686595","0.5359812","0.5352713","0.5352713","0.5352713"],"string":"[\n \"0.8237327\",\n \"0.8059725\",\n \"0.76456845\",\n \"0.73804134\",\n \"0.7117238\",\n \"0.710104\",\n \"0.706935\",\n \"0.70490587\",\n \"0.69747484\",\n \"0.6902738\",\n \"0.6690662\",\n \"0.6675924\",\n \"0.6668182\",\n \"0.65841776\",\n \"0.6558105\",\n \"0.65107626\",\n \"0.6459708\",\n \"0.6459708\",\n \"0.6409349\",\n \"0.6405313\",\n \"0.63323426\",\n \"0.63227516\",\n \"0.6321399\",\n \"0.6317174\",\n \"0.6302483\",\n \"0.62422615\",\n \"0.6185782\",\n \"0.61510617\",\n \"0.6079875\",\n \"0.6077471\",\n \"0.6018201\",\n \"0.60034\",\n \"0.5972777\",\n \"0.5944909\",\n \"0.59254336\",\n \"0.5923806\",\n \"0.5916214\",\n \"0.5911703\",\n \"0.59082055\",\n \"0.5907995\",\n \"0.59023184\",\n \"0.59023184\",\n \"0.5900998\",\n \"0.588827\",\n \"0.5868998\",\n \"0.5851168\",\n \"0.58326644\",\n \"0.5825124\",\n \"0.58226055\",\n \"0.58194244\",\n \"0.58085465\",\n \"0.5795988\",\n \"0.5787944\",\n \"0.5760301\",\n \"0.5756735\",\n \"0.57563156\",\n \"0.571759\",\n \"0.5692497\",\n \"0.5670374\",\n \"0.5664712\",\n \"0.5652905\",\n \"0.56401575\",\n \"0.5635916\",\n \"0.5622083\",\n \"0.5615873\",\n \"0.5604544\",\n \"0.55976015\",\n \"0.55859715\",\n \"0.55663335\",\n \"0.55611694\",\n \"0.5553506\",\n \"0.55477494\",\n \"0.55327976\",\n \"0.552413\",\n \"0.55177844\",\n \"0.5516859\",\n \"0.5488857\",\n \"0.5487131\",\n \"0.5474508\",\n \"0.54715854\",\n \"0.54706293\",\n \"0.545369\",\n \"0.5452098\",\n \"0.5447068\",\n \"0.5444032\",\n \"0.54396516\",\n \"0.5437827\",\n \"0.54358363\",\n \"0.54334337\",\n \"0.5432908\",\n \"0.542321\",\n \"0.5416736\",\n \"0.5389193\",\n \"0.5388347\",\n \"0.5373991\",\n \"0.53686595\",\n \"0.5359812\",\n \"0.5352713\",\n \"0.5352713\",\n \"0.5352713\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7418072"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":11987,"cells":{"query":{"kind":"string","value":"Sets a string parameter. putnastrparam(self,paramname_,parvalue_)"},"document":{"kind":"string","value":"def putnastrparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putnastrparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnastrparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putstrparam(self,param_,parvalue_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putstrparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putstrparam(self,param_,parvalue_):\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putnaintparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_param(self, name, value):\n name = str(name)\n value = str(value)\n cxnlib.CXNNetSetParam(self.handle,\n ctypes.c_char_p(name.encode('utf-8')),\n ctypes.c_char_p(value.encode('utf-8')))","def putparam(self,parname_,parvalue_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def isstrparname(self,parname_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n param_ = ctypes.c_int32()\n res = __library__.MSK_XX_isstrparname(self.__nativep,parname_,ctypes.byref(param_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = sparam(param_.value)\n return (_param_return_value)","def setString(self, name: unicode, value: unicode) -> None:\n ...","def isstrparname(self,parname_): # 3\n res,resargs = self.__obj.isstrparname(parname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = resargs\n _param_return_value = sparam(_param_return_value)\n return _param_return_value","def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setParameter(self, name, value):","def _set_string_value_pair(self, parameter, value=None):\n if type(parameter) is str:\n if value==None:\n raise Exception(\"Error: No value given in set() function for population parameter. Exiting.\")\n self.parameters[parameter] = value\n return\n if type(parameter) is not dict:\n raise Exception(\"Error: invalid parameter type for set() function for population parameter. Exiting.\")\n # Add a dictionary-structured set of new parameters to the current set:\n self.parameters.update(parameter)","def set_parameter(self, params, name, val):\n raise NotImplementedError()","def getstrparam(self,param_):\n maxlen_ = (1 + self.getstrparamlen((param_)))\n len_ = ctypes.c_int32()\n parvalue_ = (ctypes.c_char * (maxlen_))()\n res = __library__.MSK_XX_getstrparam(self.__nativep,param_,maxlen_,ctypes.byref(len_),parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n len_ = len_.value\n _len_return_value = len_\n _parvalue_retval = parvalue_.value.decode(\"utf-8\",errors=\"replace\")\n return (_len_return_value,_parvalue_retval)","def _put_ssm_param(self, parameter, parameter_name):\n self.ssm_client.put_parameter(\n Name=parameter_name,\n Type=\"String\",\n Value=json.dumps(parameter),\n Overwrite=True,\n Tier=\"Intelligent-Tiering\",\n )","def param_name(self, value):\n self._param_name = value","def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value","def putnadouparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putnadouparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnadouparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getstrparam(self,param_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n maxlen_ = (1 + self.getstrparamlen((param_)))\n arr_parvalue = array.array(\"b\",[0]*((maxlen_)))\n memview_arr_parvalue = memoryview(arr_parvalue)\n res,resargs = self.__obj.getstrparam(param_,maxlen_,memview_arr_parvalue)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _len_return_value,retarg_parvalue = resargs\n retarg_parvalue = arr_parvalue.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return _len_return_value,retarg_parvalue","def 置项目文本(self, n, string): # real signature unknown; restored from __doc__\n self.SetString(n, string)","def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError","def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val","def put_param(self, attr_name, val):\n self._params[attr_name] = val","def set_parameter(doc, o_path, param, value, mn_consts):\n if isinstance(value, str):\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\n elif isinstance(value, int):\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\n elif isinstance(value, list):\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)","def getStrParam(self, paramkey, default=None):\n value = self.request.getParameter(paramkey)\n if value is None: return default\n return value","def set_param(self, param, value):\n self._set_param_client(param, value)","def set_param(command):\n namespace = app.main(command)\n assert namespace.command == 'sp' or namespace.command == \"setparam\"\n assert namespace.name == \"test\"\n assert namespace.value == \"test\"","def string_check(param, name):\n\tif not isinstance(param, strcomp):\n\t\traise TypeError(\"Keyword arg '%s' must be of type string. Got: %s\" % (\n\t\t\tname, type(param)))\n\telse:\n\t\tpass","def setTemplateParameter(self,name,value):\n self.tplparam[name] = value","def set(self,name,val):\n matches = self.grep_param_names(name)\n if len(matches):\n x = self._get_params()\n x[matches] = val\n self._set_params(x)\n else:\n raise AttributeError, \"no parameter matches %s\"%name","def test_str(self):\n \n from pystarlab.starlab import Option\n opt = Option(parameter=\"n\",\n long_name=\"specify number of particles\",\n is_required=True,\n default_value=None)\n self.assertEquals(str(opt), \" -n: specify number of particles [default: None] [required]\")","def __setattr__(self, name, value):\n if hasattr(self, name):\n super(JobSubmission, self).__setattr__(name, value)\n\n else:\n self.params[str(name)] = str(value) #TODO: resolve parameter cases","def SetParameterValue(self, paramName, value):\n if paramName == NUMBTABLE_PAR:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n self.LookupTable.SetParameterValue(paramName, value)\n\n if paramName == IGNORECURVE_PAR: \n #...ignore the lookuptable and remove any specifications\n if value == 'None': value = None\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\n if value:\n port = self.GetPort(HEAD_PORT)\n port.SetValue(None, FIXED_V) \n else:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)","def SetParameterValue(self, paramName, value):\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n if paramName == ISENTROPIC_PAR:\n if (self.ideal != None):\n self.ideal.SetParameterValue(paramName, value)","def set_parameter_value(self, parameter, value):\n pass","def set_param(self):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n setting[1] = str(self.param_value)\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def setFieldStringValue (self, fieldname, fieldstrvalue):\n if fieldname in self._table:\n pyval = self._table[fieldname].val_txt2py ( fieldstrvalue )\n self.setFieldValue (fieldname, pyval )","def set_param(self, param_value):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n\n setting[1] = param_value\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def gui_set_param(self,param_name,val):\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\n self._handle_gui_set(param_name,force=True)","def string_value(self, string_value):\n\n self._string_value = string_value","def set_text( self, a_string ):\n self.a_string_var.set( a_string )","def set_text( self, a_string ):\n self.a_string_var.set( a_string )","def set(self, name, value=None):\n if isinstance(name, dict):\n for key, value in name.items():\n try:\n param, value = self.coerceParameter(key, value)\n self.params[param] = value\n except KeyError:\n pass\n elif isinstance(name, str):\n try:\n param, value = self.coerceParameter(name, value)\n self.params[param] = value\n except KeyError:\n pass","def set_string(prop, value: typing.Union[str, None]) -> str:\n if (value is None) or (value == \"\"):\n raise ValueError(\"{} cannot be Null\".format(str(prop)))\n if not(isinstance(value, str)):\n raise TypeError(\"{} can only be a string\".format(str(prop)))\n return value.strip()","def log_param(self, name: str, value):\n self.params[name] = value\n\n self._sync_log_event()","def _build_set_command(self, cmd, param, val):\n try:\n str_val = self._param_dict.format(param, val)\n set_cmd = '%s=%s' % (param, str_val)\n set_cmd = set_cmd + SBE37_NEWLINE\n \n except KeyError:\n raise InstrumentParameterException('Unknown driver parameter %s' % param)\n \n return set_cmd","def __setitem__(self, name: str, value):\n super(Parameter, self).__setitem__(name, value)","def addstr(self,name,string):\n\t\tself.windows[name].addstr(string)","def set_param(self, key='', value=None, dtype='str',\n wtype=None, name=None,\n options=None, option_index=0,\n description=None, hidden=False):\n if name is None:\n name = key\n if (value is None\n and options is not None\n and option_index is not None):\n try:\n value = options[option_index]\n except IndexError:\n pass\n # if not specified\n if wtype is None:\n if options is not None:\n wtype = 'combo_box'\n elif dtype == 'bool':\n wtype = 'check_box'\n else:\n wtype = 'text_box'\n if wtype == 'check_box':\n dtype = 'bool'\n if wtype == 'group':\n hidden = True\n pdict = {'value': value,\n 'dtype': dtype,\n 'wtype': wtype,\n 'name': name,\n 'options': options,\n 'option_index': option_index,\n 'description': description,\n 'hidden': hidden}\n OrderedDict.__setitem__(self, key, pdict)","def setName(self,value):\n assert value == None or type(value) == str, repr(value)+' is not a valid name'\n self._name = value","def SetParameterValue(self, paramName, value):\n super(LookupTable, self).SetParameterValue(paramName, value)\n if paramName == NUMBTABLE_PAR:\n self.SetTableCount()\n elif paramName == NUMBSERIES_PAR:\n self.SetSeriesCount()\n elif paramName == TABLETAGTYPE_PAR:\n self.SetTableTagType()\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\n idx = long(paramName[len(SERIESTYPE_PAR):])\n self.SetSeriesTypes(idx)","def _set_par(vid, par, value):\n traci.vehicle.setParameter(vid, \"carFollowModel.%s\" % par, str(value))","def setName(self, *args):\n return _libsbml.Parameter_setName(self, *args)","def set_parameter_value(self,name,val,parameterized_object=None):\n source = parameterized_object or self.get_source_po(name)\n object.__setattr__(source,name,val)\n\n # update the tkvar\n if name in self._tkvars:\n self._tkvars[name]._original_set(self._object2string(name,val))","def check_param(param):\n return PREPEND_STR+param if keyword.iskeyword(param) else param","def set_option(self, name, value):\n self._params[name] = value","def set_option(self, name, value):\n self._params[name] = value","def set_param(self, param_key, value):\n return self._params.set_param_value(param_key, value)","def _mn_set_par_ ( self , i , val , fix = False ) :\n if not i in self : raise IndexError\n #\n if hasattr ( val , 'value' ) : val = val.value()\n #\n ierr = _mn_exec_ ( self , \"SET PAR\" , i + 1 , val )\n #\n if fix : self.FixParameter ( i ) \n #\n return ierr","def varStringMod(self, arg):\n\t\targ[0] = \"'\" + arg[0] + \"'\"\n\t\treturn arg","def format_parameter(param, required):\n\n param_string = check_param(flatten_param(param))\n if not required:\n param_string += '=None'\n return param_string","async def set_param(self, param: str, value: int) -> ArchonCommand:\n cmd = await self.send_command(f\"FASTLOADPARAM {param} {value}\")\n if not cmd.succeeded():\n raise ArchonError(\n f\"Failed setting parameter {param!r} ({cmd.status.name}).\"\n )\n return cmd","def register_str(self, name, short=None, default=None, group=None, help=None):\n self._register(name, self._parse_string, short=short, default=default,\n group=group, help=help)","def _set_compiler_param(compiler_handle, params, value=None):\n if isinstance(params, collections.Mapping):\n params = params.items()\n elif isinstance(params, STRING_TYPES) and value is not None:\n params = [(params, value)]\n for key, val in params:\n _check_call(_LIB.TreeliteCompilerSetParam(compiler_handle, c_str(key),\n c_str(str(val))))","def setPrm(self, key, val):\n if hasattr(key, \"encode\"):\n key = key.encode(\"utf-8\") # convert str to bytes\n if hasattr(val, \"encode\"):\n val = val.encode(\"utf-8\") # convert str to bytes\n return self.setVal(self.gbls, key, val)","def set_param(params, pname, value=None, bounds=None):\n if value is not None:\n for p in params.flattened():\n if p.name == pname:\n p.value = value\n break\n\n if bounds is not None:\n for p in params.flattened():\n if p.name == pname:\n p.bounds = bounds\n p.vary = True\n break","def change_general_param(self, param, val):\n assert param in self.params, '%s is not recognized as a valid parameter' % param\n self.params[param].change_value(val)","def __init__(self, *args):\n _snap.TStrStrVPr_swiginit(self, _snap.new_TStrStrVPr(*args))","def value(self, p_str, p_str_1=None): # real signature unknown; restored from __doc__ with multiple overloads\n return \"\"","def pcp_set_backend_parameter(self, parameter_name, value):\n\n\t\tif self.PCPConnectionStatus() != ConnStateType.OK:\n\t\t\tself.pcp_internal_error('invalid PCP connection')\n\t\t\treturn None\n\t\t\n\t\tif self.Pfdebug:\n\t\t\tself.Pfdebug.write(f'DEBUG: seting: \"{parameter_name} = {value}\"\\n')\n\n\t\tself._PCPWrite('A'.encode(), 1)\n\t\twsize = self.int_to_bytes(len(parameter_name) + 1 + len(value) + 1 + 4)\n\t\tself._PCPWrite(wsize, 4)\n\t\tself._PCPWrite(parameter_name.encode(), len(parameter_name))\n\t\tself._PCPWrite(NULL, 1)\n\t\tself._PCPWrite(value.encode(), len(value))\n\t\tself._PCPWrite(NULL, 1)\n\t\tif self.PCPFlush() < 0:\n\t\t\treturn None\n\t\tif self.Pfdebug:\n\t\t\tself.Pfdebug.write(f'DEBUG: send: tos=\"A\", length={self.bytes_to_int(wsize)}\\n')\n\n\t\treturn self._process_pcp_response('A')","def __setitem__(self, key: str, value: typing.Any):\n self._params[key].value = value","def AddParameter(self, param_name, param_value):\n if(param_name == \"NPOP1_1\"):\n if(self.npop1_1 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop1_1 = param_value\n elif(param_name == \"NPOP1_2\"):\n if(self.npop1_2 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop1_2 = param_value\n elif(param_name == \"NPOP2_1\"):\n if(self.npop2_1 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop2_1 = param_value\n elif(param_name == \"NPOP2_2\"):\n if(self.npop2_2 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop2_2 = param_value\n elif(param_name == \"T_DIV\"):\n if(self.t_div != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.t_div = param_value\n elif(param_name == \"NPOP_A\"):\n if(self.npop_a != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop_a = param_value\n elif(param_name == \"MU\"):\n if(self.mu != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.mu = param_value\n elif(param_name == \"T_I\"):\n if(self.t_i != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.t_i = param_value\n elif(param_name == \"P_I\"):\n if(self.p_i != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.p_i = param_value\n else:\n raise Exception(\"Invalid parameter name: \" + param_name + \"!\")","def set_trial_param(\n self,\n trial_id: int,\n param_name: str,\n param_value_internal: float,\n distribution: \"distributions.BaseDistribution\",\n ) -> bool:\n raise NotImplementedError","def putintparam(self,param_,parvalue_):\n res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setParameter(self,arg,value):\n self._params[arg] = value\n return self._params","def __allowed_values_correct_string(self):\n strTestName = 'Values of a string (correct)'\n RxCSObject = _RxCSobject()\n\n RxCSObject.paramAddMan('parameter1', 'string')\n RxCSObject.paramType('parameter1', str)\n RxCSObject.paramAllowed('parameter1', ['Allowed string #1', 'Allowed string #2'])\n\n RxCSObject.parameter1 = 'Allowed string #2'\n\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)","def set_name(self, name=\"\"):\n if isinstance(name, str):\n self.__name = name\n return 0\n print(\"type of nom is not STR\")\n return 1","def putintparam(self,param_,parvalue_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putintparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def send_string(self, tosend, ypos=None, xpos=None):\n if xpos and ypos:\n self._check_limits(ypos, xpos)\n self.move_to(ypos, xpos)\n # escape double quotes in the data to send\n tosend = tosend.decode(\"utf-8\").replace('\"', '\"')\n self.exec_command('String(\"{0}\")'.format(tosend).encode(\"utf-8\"))","def testParamsAreStrings(self):\n self.chart.display.extra_params['test'] = 32\n self.assertEqual(self.Param('test'), '32')","def setstring(self):\n self._str = 's '+' '.join([self.src, self.start, self.size,\n self.strand, self.srcSize, self.text])+'\\n'","def set_params(self, params):","def setParamSingleStep(self, instr: str, param: str, value: Any):\n box = self._itemsdict[instr][param]['double_box']\n if box is not None:\n box.setSingleStep(value)","def set_component_parameter(self, key: str, value: Any):\n if not key:\n raise ValueError(\"Key is required\")\n\n if value is None:\n raise ValueError(\"Value is required\")\n\n self._node[\"app_data\"][\"component_parameters\"][key] = value","def write_parameter(self, path, value, attr=None):\n if path.startswith('sample'):\n entry = self.entry.nxroot['entry']\n else:\n entry = self.entry\n if value is not None:\n if attr and path in entry:\n entry[path].attrs[attr] = value\n elif path in entry:\n if isinstance(entry[path], NXgroup):\n del entry[path]\n entry[path] = value\n else:\n entry[path].replace(value)\n elif attr is None:\n entry[path] = value","def add_string(self, name, **kwargs):\n self.add(Flags.StringFlag(name, **kwargs))","def spar(self, spar):\n\n self._spar = spar","def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\n self._parameters.append(Parameter(parameter_name, parameter_value))","def param_str(self, pnames=None):\n l = self.get_params(pnames)\n s = \"\"\n for p in l:\n s += \"%s : %s\\n\" % (p.public_name, p.tostr(self))\n return s","def _setParameterValue(self, name, value, typeString = ''):\n warnings.warn(\"This function is deprecated; parameters have been replaced with uniform inputs in 1.38.\", DeprecationWarning, stacklevel = 2)","def custom_string(self, custom_string):\n\n self._custom_string = custom_string","def __string(input_string, name=\"\", internal=False):\n if input_string is None:\n __ex(\"The %s is missing.\" % name, internal)\n if input_string == \"\":\n __ex(\"The %s must not be empty.\" % name, internal)","def HeaderParams(self, mstr, val):\n\n if len(val) > 0:\n if 'template' in mstr:\n self.templatename = val\n elif 'tenant' in mstr:\n self.tenantname = val\n elif 'policy' in mstr:\n self.policyname = val","def set_standard_unit(self, param_code, param_unit):\n if param_code in self.parameters:\n param_description = self.parameters[param_code][0]\n else:\n param_description = master_parameter_list[param_code][0]\n\n self.parameters[param_code] = (param_description, param_unit)\n\n if param_code in self.data:\n self.rescale_parameter(param_code)","def putPrm(self, key, val):\n if hasattr(key, \"encode\"):\n key = key.encode(\"utf-8\") # convert str to bytes\n if hasattr(val, \"encode\"):\n val = val.encode(\"utf-8\") # convert str to bytes\n return self.putVal(self.gbls, key, val)","def isp(self, isp):\n # type: (string_types) -> None\n\n if isp is not None:\n if not isinstance(isp, string_types):\n raise TypeError(\"Invalid type for `isp`, type has to be `string_types`\")\n\n self._isp = isp","def TeamCityParamSetter(keyName, value):\n print(\"##teamcity[setParameter name='{}' value='{}']\".format(keyName, value))","def setTimingProcessName(self, string: str) -> None:\n ..."],"string":"[\n \"def putnastrparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnastrparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putstrparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,sparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putstrparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putstrparam(self,param_,parvalue_):\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putnaintparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_param(self, name, value):\\n name = str(name)\\n value = str(value)\\n cxnlib.CXNNetSetParam(self.handle,\\n ctypes.c_char_p(name.encode('utf-8')),\\n ctypes.c_char_p(value.encode('utf-8')))\",\n \"def putparam(self,parname_,parvalue_):\\n if isinstance(parname_,unicode):\\n parname_ = parname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putnaintparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnaintparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def isstrparname(self,parname_):\\n if isinstance(parname_,unicode):\\n parname_ = parname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n param_ = ctypes.c_int32()\\n res = __library__.MSK_XX_isstrparname(self.__nativep,parname_,ctypes.byref(param_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _param_return_value = sparam(param_.value)\\n return (_param_return_value)\",\n \"def setString(self, name: unicode, value: unicode) -> None:\\n ...\",\n \"def isstrparname(self,parname_): # 3\\n res,resargs = self.__obj.isstrparname(parname_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _param_return_value = resargs\\n _param_return_value = sparam(_param_return_value)\\n return _param_return_value\",\n \"def putparam(self,parname_,parvalue_): # 3\\n res = self.__obj.putparam(parname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setParameter(self, name, value):\",\n \"def _set_string_value_pair(self, parameter, value=None):\\n if type(parameter) is str:\\n if value==None:\\n raise Exception(\\\"Error: No value given in set() function for population parameter. Exiting.\\\")\\n self.parameters[parameter] = value\\n return\\n if type(parameter) is not dict:\\n raise Exception(\\\"Error: invalid parameter type for set() function for population parameter. Exiting.\\\")\\n # Add a dictionary-structured set of new parameters to the current set:\\n self.parameters.update(parameter)\",\n \"def set_parameter(self, params, name, val):\\n raise NotImplementedError()\",\n \"def getstrparam(self,param_):\\n maxlen_ = (1 + self.getstrparamlen((param_)))\\n len_ = ctypes.c_int32()\\n parvalue_ = (ctypes.c_char * (maxlen_))()\\n res = __library__.MSK_XX_getstrparam(self.__nativep,param_,maxlen_,ctypes.byref(len_),parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n len_ = len_.value\\n _len_return_value = len_\\n _parvalue_retval = parvalue_.value.decode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n return (_len_return_value,_parvalue_retval)\",\n \"def _put_ssm_param(self, parameter, parameter_name):\\n self.ssm_client.put_parameter(\\n Name=parameter_name,\\n Type=\\\"String\\\",\\n Value=json.dumps(parameter),\\n Overwrite=True,\\n Tier=\\\"Intelligent-Tiering\\\",\\n )\",\n \"def param_name(self, value):\\n self._param_name = value\",\n \"def setParam(self,param,value):\\n if param in self.params.keys():\\n self.params[param] = value\",\n \"def putnadouparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putnadouparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnadouparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getstrparam(self,param_): # 3\\n if not isinstance(param_,sparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n maxlen_ = (1 + self.getstrparamlen((param_)))\\n arr_parvalue = array.array(\\\"b\\\",[0]*((maxlen_)))\\n memview_arr_parvalue = memoryview(arr_parvalue)\\n res,resargs = self.__obj.getstrparam(param_,maxlen_,memview_arr_parvalue)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _len_return_value,retarg_parvalue = resargs\\n retarg_parvalue = arr_parvalue.tobytes()[:-1].decode(\\\"utf-8\\\",errors=\\\"ignore\\\")\\n return _len_return_value,retarg_parvalue\",\n \"def 置项目文本(self, n, string): # real signature unknown; restored from __doc__\\n self.SetString(n, string)\",\n \"def set_param(self, name, value, *, distrib=None, ref=None):\\n raise NotImplementedError\",\n \"def set_param(self, label, val):\\n assert type(label) is str, 'Parameter name \\\"%s\\\" is not string' % label\\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\\n self.params[label] = val\",\n \"def put_param(self, attr_name, val):\\n self._params[attr_name] = val\",\n \"def set_parameter(doc, o_path, param, value, mn_consts):\\n if isinstance(value, str):\\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\\n elif isinstance(value, int):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\\n elif isinstance(value, list):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)\",\n \"def getStrParam(self, paramkey, default=None):\\n value = self.request.getParameter(paramkey)\\n if value is None: return default\\n return value\",\n \"def set_param(self, param, value):\\n self._set_param_client(param, value)\",\n \"def set_param(command):\\n namespace = app.main(command)\\n assert namespace.command == 'sp' or namespace.command == \\\"setparam\\\"\\n assert namespace.name == \\\"test\\\"\\n assert namespace.value == \\\"test\\\"\",\n \"def string_check(param, name):\\n\\tif not isinstance(param, strcomp):\\n\\t\\traise TypeError(\\\"Keyword arg '%s' must be of type string. Got: %s\\\" % (\\n\\t\\t\\tname, type(param)))\\n\\telse:\\n\\t\\tpass\",\n \"def setTemplateParameter(self,name,value):\\n self.tplparam[name] = value\",\n \"def set(self,name,val):\\n matches = self.grep_param_names(name)\\n if len(matches):\\n x = self._get_params()\\n x[matches] = val\\n self._set_params(x)\\n else:\\n raise AttributeError, \\\"no parameter matches %s\\\"%name\",\n \"def test_str(self):\\n \\n from pystarlab.starlab import Option\\n opt = Option(parameter=\\\"n\\\",\\n long_name=\\\"specify number of particles\\\",\\n is_required=True,\\n default_value=None)\\n self.assertEquals(str(opt), \\\" -n: specify number of particles [default: None] [required]\\\")\",\n \"def __setattr__(self, name, value):\\n if hasattr(self, name):\\n super(JobSubmission, self).__setattr__(name, value)\\n\\n else:\\n self.params[str(name)] = str(value) #TODO: resolve parameter cases\",\n \"def SetParameterValue(self, paramName, value):\\n if paramName == NUMBTABLE_PAR:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n self.LookupTable.SetParameterValue(paramName, value)\\n\\n if paramName == IGNORECURVE_PAR: \\n #...ignore the lookuptable and remove any specifications\\n if value == 'None': value = None\\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\\n if value:\\n port = self.GetPort(HEAD_PORT)\\n port.SetValue(None, FIXED_V) \\n else:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\",\n \"def SetParameterValue(self, paramName, value):\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n if paramName == ISENTROPIC_PAR:\\n if (self.ideal != None):\\n self.ideal.SetParameterValue(paramName, value)\",\n \"def set_parameter_value(self, parameter, value):\\n pass\",\n \"def set_param(self):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n setting[1] = str(self.param_value)\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def setFieldStringValue (self, fieldname, fieldstrvalue):\\n if fieldname in self._table:\\n pyval = self._table[fieldname].val_txt2py ( fieldstrvalue )\\n self.setFieldValue (fieldname, pyval )\",\n \"def set_param(self, param_value):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n\\n setting[1] = param_value\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def gui_set_param(self,param_name,val):\\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\\n self._handle_gui_set(param_name,force=True)\",\n \"def string_value(self, string_value):\\n\\n self._string_value = string_value\",\n \"def set_text( self, a_string ):\\n self.a_string_var.set( a_string )\",\n \"def set_text( self, a_string ):\\n self.a_string_var.set( a_string )\",\n \"def set(self, name, value=None):\\n if isinstance(name, dict):\\n for key, value in name.items():\\n try:\\n param, value = self.coerceParameter(key, value)\\n self.params[param] = value\\n except KeyError:\\n pass\\n elif isinstance(name, str):\\n try:\\n param, value = self.coerceParameter(name, value)\\n self.params[param] = value\\n except KeyError:\\n pass\",\n \"def set_string(prop, value: typing.Union[str, None]) -> str:\\n if (value is None) or (value == \\\"\\\"):\\n raise ValueError(\\\"{} cannot be Null\\\".format(str(prop)))\\n if not(isinstance(value, str)):\\n raise TypeError(\\\"{} can only be a string\\\".format(str(prop)))\\n return value.strip()\",\n \"def log_param(self, name: str, value):\\n self.params[name] = value\\n\\n self._sync_log_event()\",\n \"def _build_set_command(self, cmd, param, val):\\n try:\\n str_val = self._param_dict.format(param, val)\\n set_cmd = '%s=%s' % (param, str_val)\\n set_cmd = set_cmd + SBE37_NEWLINE\\n \\n except KeyError:\\n raise InstrumentParameterException('Unknown driver parameter %s' % param)\\n \\n return set_cmd\",\n \"def __setitem__(self, name: str, value):\\n super(Parameter, self).__setitem__(name, value)\",\n \"def addstr(self,name,string):\\n\\t\\tself.windows[name].addstr(string)\",\n \"def set_param(self, key='', value=None, dtype='str',\\n wtype=None, name=None,\\n options=None, option_index=0,\\n description=None, hidden=False):\\n if name is None:\\n name = key\\n if (value is None\\n and options is not None\\n and option_index is not None):\\n try:\\n value = options[option_index]\\n except IndexError:\\n pass\\n # if not specified\\n if wtype is None:\\n if options is not None:\\n wtype = 'combo_box'\\n elif dtype == 'bool':\\n wtype = 'check_box'\\n else:\\n wtype = 'text_box'\\n if wtype == 'check_box':\\n dtype = 'bool'\\n if wtype == 'group':\\n hidden = True\\n pdict = {'value': value,\\n 'dtype': dtype,\\n 'wtype': wtype,\\n 'name': name,\\n 'options': options,\\n 'option_index': option_index,\\n 'description': description,\\n 'hidden': hidden}\\n OrderedDict.__setitem__(self, key, pdict)\",\n \"def setName(self,value):\\n assert value == None or type(value) == str, repr(value)+' is not a valid name'\\n self._name = value\",\n \"def SetParameterValue(self, paramName, value):\\n super(LookupTable, self).SetParameterValue(paramName, value)\\n if paramName == NUMBTABLE_PAR:\\n self.SetTableCount()\\n elif paramName == NUMBSERIES_PAR:\\n self.SetSeriesCount()\\n elif paramName == TABLETAGTYPE_PAR:\\n self.SetTableTagType()\\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\\n idx = long(paramName[len(SERIESTYPE_PAR):])\\n self.SetSeriesTypes(idx)\",\n \"def _set_par(vid, par, value):\\n traci.vehicle.setParameter(vid, \\\"carFollowModel.%s\\\" % par, str(value))\",\n \"def setName(self, *args):\\n return _libsbml.Parameter_setName(self, *args)\",\n \"def set_parameter_value(self,name,val,parameterized_object=None):\\n source = parameterized_object or self.get_source_po(name)\\n object.__setattr__(source,name,val)\\n\\n # update the tkvar\\n if name in self._tkvars:\\n self._tkvars[name]._original_set(self._object2string(name,val))\",\n \"def check_param(param):\\n return PREPEND_STR+param if keyword.iskeyword(param) else param\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def set_param(self, param_key, value):\\n return self._params.set_param_value(param_key, value)\",\n \"def _mn_set_par_ ( self , i , val , fix = False ) :\\n if not i in self : raise IndexError\\n #\\n if hasattr ( val , 'value' ) : val = val.value()\\n #\\n ierr = _mn_exec_ ( self , \\\"SET PAR\\\" , i + 1 , val )\\n #\\n if fix : self.FixParameter ( i ) \\n #\\n return ierr\",\n \"def varStringMod(self, arg):\\n\\t\\targ[0] = \\\"'\\\" + arg[0] + \\\"'\\\"\\n\\t\\treturn arg\",\n \"def format_parameter(param, required):\\n\\n param_string = check_param(flatten_param(param))\\n if not required:\\n param_string += '=None'\\n return param_string\",\n \"async def set_param(self, param: str, value: int) -> ArchonCommand:\\n cmd = await self.send_command(f\\\"FASTLOADPARAM {param} {value}\\\")\\n if not cmd.succeeded():\\n raise ArchonError(\\n f\\\"Failed setting parameter {param!r} ({cmd.status.name}).\\\"\\n )\\n return cmd\",\n \"def register_str(self, name, short=None, default=None, group=None, help=None):\\n self._register(name, self._parse_string, short=short, default=default,\\n group=group, help=help)\",\n \"def _set_compiler_param(compiler_handle, params, value=None):\\n if isinstance(params, collections.Mapping):\\n params = params.items()\\n elif isinstance(params, STRING_TYPES) and value is not None:\\n params = [(params, value)]\\n for key, val in params:\\n _check_call(_LIB.TreeliteCompilerSetParam(compiler_handle, c_str(key),\\n c_str(str(val))))\",\n \"def setPrm(self, key, val):\\n if hasattr(key, \\\"encode\\\"):\\n key = key.encode(\\\"utf-8\\\") # convert str to bytes\\n if hasattr(val, \\\"encode\\\"):\\n val = val.encode(\\\"utf-8\\\") # convert str to bytes\\n return self.setVal(self.gbls, key, val)\",\n \"def set_param(params, pname, value=None, bounds=None):\\n if value is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.value = value\\n break\\n\\n if bounds is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.bounds = bounds\\n p.vary = True\\n break\",\n \"def change_general_param(self, param, val):\\n assert param in self.params, '%s is not recognized as a valid parameter' % param\\n self.params[param].change_value(val)\",\n \"def __init__(self, *args):\\n _snap.TStrStrVPr_swiginit(self, _snap.new_TStrStrVPr(*args))\",\n \"def value(self, p_str, p_str_1=None): # real signature unknown; restored from __doc__ with multiple overloads\\n return \\\"\\\"\",\n \"def pcp_set_backend_parameter(self, parameter_name, value):\\n\\n\\t\\tif self.PCPConnectionStatus() != ConnStateType.OK:\\n\\t\\t\\tself.pcp_internal_error('invalid PCP connection')\\n\\t\\t\\treturn None\\n\\t\\t\\n\\t\\tif self.Pfdebug:\\n\\t\\t\\tself.Pfdebug.write(f'DEBUG: seting: \\\"{parameter_name} = {value}\\\"\\\\n')\\n\\n\\t\\tself._PCPWrite('A'.encode(), 1)\\n\\t\\twsize = self.int_to_bytes(len(parameter_name) + 1 + len(value) + 1 + 4)\\n\\t\\tself._PCPWrite(wsize, 4)\\n\\t\\tself._PCPWrite(parameter_name.encode(), len(parameter_name))\\n\\t\\tself._PCPWrite(NULL, 1)\\n\\t\\tself._PCPWrite(value.encode(), len(value))\\n\\t\\tself._PCPWrite(NULL, 1)\\n\\t\\tif self.PCPFlush() < 0:\\n\\t\\t\\treturn None\\n\\t\\tif self.Pfdebug:\\n\\t\\t\\tself.Pfdebug.write(f'DEBUG: send: tos=\\\"A\\\", length={self.bytes_to_int(wsize)}\\\\n')\\n\\n\\t\\treturn self._process_pcp_response('A')\",\n \"def __setitem__(self, key: str, value: typing.Any):\\n self._params[key].value = value\",\n \"def AddParameter(self, param_name, param_value):\\n if(param_name == \\\"NPOP1_1\\\"):\\n if(self.npop1_1 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop1_1 = param_value\\n elif(param_name == \\\"NPOP1_2\\\"):\\n if(self.npop1_2 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop1_2 = param_value\\n elif(param_name == \\\"NPOP2_1\\\"):\\n if(self.npop2_1 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop2_1 = param_value\\n elif(param_name == \\\"NPOP2_2\\\"):\\n if(self.npop2_2 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop2_2 = param_value\\n elif(param_name == \\\"T_DIV\\\"):\\n if(self.t_div != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.t_div = param_value\\n elif(param_name == \\\"NPOP_A\\\"):\\n if(self.npop_a != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop_a = param_value\\n elif(param_name == \\\"MU\\\"):\\n if(self.mu != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.mu = param_value\\n elif(param_name == \\\"T_I\\\"):\\n if(self.t_i != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.t_i = param_value\\n elif(param_name == \\\"P_I\\\"):\\n if(self.p_i != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.p_i = param_value\\n else:\\n raise Exception(\\\"Invalid parameter name: \\\" + param_name + \\\"!\\\")\",\n \"def set_trial_param(\\n self,\\n trial_id: int,\\n param_name: str,\\n param_value_internal: float,\\n distribution: \\\"distributions.BaseDistribution\\\",\\n ) -> bool:\\n raise NotImplementedError\",\n \"def putintparam(self,param_,parvalue_):\\n res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setParameter(self,arg,value):\\n self._params[arg] = value\\n return self._params\",\n \"def __allowed_values_correct_string(self):\\n strTestName = 'Values of a string (correct)'\\n RxCSObject = _RxCSobject()\\n\\n RxCSObject.paramAddMan('parameter1', 'string')\\n RxCSObject.paramType('parameter1', str)\\n RxCSObject.paramAllowed('parameter1', ['Allowed string #1', 'Allowed string #2'])\\n\\n RxCSObject.parameter1 = 'Allowed string #2'\\n\\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)\",\n \"def set_name(self, name=\\\"\\\"):\\n if isinstance(name, str):\\n self.__name = name\\n return 0\\n print(\\\"type of nom is not STR\\\")\\n return 1\",\n \"def putintparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,iparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putintparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def send_string(self, tosend, ypos=None, xpos=None):\\n if xpos and ypos:\\n self._check_limits(ypos, xpos)\\n self.move_to(ypos, xpos)\\n # escape double quotes in the data to send\\n tosend = tosend.decode(\\\"utf-8\\\").replace('\\\"', '\\\"')\\n self.exec_command('String(\\\"{0}\\\")'.format(tosend).encode(\\\"utf-8\\\"))\",\n \"def testParamsAreStrings(self):\\n self.chart.display.extra_params['test'] = 32\\n self.assertEqual(self.Param('test'), '32')\",\n \"def setstring(self):\\n self._str = 's '+' '.join([self.src, self.start, self.size,\\n self.strand, self.srcSize, self.text])+'\\\\n'\",\n \"def set_params(self, params):\",\n \"def setParamSingleStep(self, instr: str, param: str, value: Any):\\n box = self._itemsdict[instr][param]['double_box']\\n if box is not None:\\n box.setSingleStep(value)\",\n \"def set_component_parameter(self, key: str, value: Any):\\n if not key:\\n raise ValueError(\\\"Key is required\\\")\\n\\n if value is None:\\n raise ValueError(\\\"Value is required\\\")\\n\\n self._node[\\\"app_data\\\"][\\\"component_parameters\\\"][key] = value\",\n \"def write_parameter(self, path, value, attr=None):\\n if path.startswith('sample'):\\n entry = self.entry.nxroot['entry']\\n else:\\n entry = self.entry\\n if value is not None:\\n if attr and path in entry:\\n entry[path].attrs[attr] = value\\n elif path in entry:\\n if isinstance(entry[path], NXgroup):\\n del entry[path]\\n entry[path] = value\\n else:\\n entry[path].replace(value)\\n elif attr is None:\\n entry[path] = value\",\n \"def add_string(self, name, **kwargs):\\n self.add(Flags.StringFlag(name, **kwargs))\",\n \"def spar(self, spar):\\n\\n self._spar = spar\",\n \"def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\\n self._parameters.append(Parameter(parameter_name, parameter_value))\",\n \"def param_str(self, pnames=None):\\n l = self.get_params(pnames)\\n s = \\\"\\\"\\n for p in l:\\n s += \\\"%s : %s\\\\n\\\" % (p.public_name, p.tostr(self))\\n return s\",\n \"def _setParameterValue(self, name, value, typeString = ''):\\n warnings.warn(\\\"This function is deprecated; parameters have been replaced with uniform inputs in 1.38.\\\", DeprecationWarning, stacklevel = 2)\",\n \"def custom_string(self, custom_string):\\n\\n self._custom_string = custom_string\",\n \"def __string(input_string, name=\\\"\\\", internal=False):\\n if input_string is None:\\n __ex(\\\"The %s is missing.\\\" % name, internal)\\n if input_string == \\\"\\\":\\n __ex(\\\"The %s must not be empty.\\\" % name, internal)\",\n \"def HeaderParams(self, mstr, val):\\n\\n if len(val) > 0:\\n if 'template' in mstr:\\n self.templatename = val\\n elif 'tenant' in mstr:\\n self.tenantname = val\\n elif 'policy' in mstr:\\n self.policyname = val\",\n \"def set_standard_unit(self, param_code, param_unit):\\n if param_code in self.parameters:\\n param_description = self.parameters[param_code][0]\\n else:\\n param_description = master_parameter_list[param_code][0]\\n\\n self.parameters[param_code] = (param_description, param_unit)\\n\\n if param_code in self.data:\\n self.rescale_parameter(param_code)\",\n \"def putPrm(self, key, val):\\n if hasattr(key, \\\"encode\\\"):\\n key = key.encode(\\\"utf-8\\\") # convert str to bytes\\n if hasattr(val, \\\"encode\\\"):\\n val = val.encode(\\\"utf-8\\\") # convert str to bytes\\n return self.putVal(self.gbls, key, val)\",\n \"def isp(self, isp):\\n # type: (string_types) -> None\\n\\n if isp is not None:\\n if not isinstance(isp, string_types):\\n raise TypeError(\\\"Invalid type for `isp`, type has to be `string_types`\\\")\\n\\n self._isp = isp\",\n \"def TeamCityParamSetter(keyName, value):\\n print(\\\"##teamcity[setParameter name='{}' value='{}']\\\".format(keyName, value))\",\n \"def setTimingProcessName(self, string: str) -> None:\\n ...\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.900503","0.8041969","0.7825734","0.6532771","0.64951867","0.64323235","0.63091147","0.62664294","0.62081355","0.6152447","0.61227375","0.5890109","0.58600485","0.58454996","0.5755116","0.5754159","0.575312","0.5688754","0.5685935","0.5649547","0.56411093","0.56101125","0.55851966","0.5574769","0.5546487","0.5542524","0.5528479","0.5432164","0.54304594","0.5417621","0.54037714","0.53794163","0.5369297","0.53512335","0.5324052","0.5321096","0.5290144","0.52847433","0.52813065","0.52560294","0.52178794","0.52066225","0.51931524","0.51931524","0.5164336","0.51625156","0.5141377","0.5131296","0.51263005","0.510605","0.5100764","0.50926566","0.509237","0.50899667","0.5072204","0.5069251","0.50590587","0.5050166","0.5050166","0.5047615","0.5042787","0.5038115","0.50235844","0.5022449","0.49973118","0.4993849","0.49795815","0.49705133","0.49692383","0.49604732","0.49388883","0.4935133","0.49338156","0.49189058","0.4912307","0.4910371","0.49094686","0.49072903","0.49052238","0.48998904","0.48916578","0.48859885","0.48809183","0.48768535","0.48724094","0.48710805","0.48705864","0.48675436","0.4867114","0.48621613","0.4855062","0.48486626","0.4844671","0.4838387","0.48378918","0.48098594","0.48046455","0.47986278","0.47985706","0.4796605"],"string":"[\n \"0.900503\",\n \"0.8041969\",\n \"0.7825734\",\n \"0.6532771\",\n \"0.64951867\",\n \"0.64323235\",\n \"0.63091147\",\n \"0.62664294\",\n \"0.62081355\",\n \"0.6152447\",\n \"0.61227375\",\n \"0.5890109\",\n \"0.58600485\",\n \"0.58454996\",\n \"0.5755116\",\n \"0.5754159\",\n \"0.575312\",\n \"0.5688754\",\n \"0.5685935\",\n \"0.5649547\",\n \"0.56411093\",\n \"0.56101125\",\n \"0.55851966\",\n \"0.5574769\",\n \"0.5546487\",\n \"0.5542524\",\n \"0.5528479\",\n \"0.5432164\",\n \"0.54304594\",\n \"0.5417621\",\n \"0.54037714\",\n \"0.53794163\",\n \"0.5369297\",\n \"0.53512335\",\n \"0.5324052\",\n \"0.5321096\",\n \"0.5290144\",\n \"0.52847433\",\n \"0.52813065\",\n \"0.52560294\",\n \"0.52178794\",\n \"0.52066225\",\n \"0.51931524\",\n \"0.51931524\",\n \"0.5164336\",\n \"0.51625156\",\n \"0.5141377\",\n \"0.5131296\",\n \"0.51263005\",\n \"0.510605\",\n \"0.5100764\",\n \"0.50926566\",\n \"0.509237\",\n \"0.50899667\",\n \"0.5072204\",\n \"0.5069251\",\n \"0.50590587\",\n \"0.5050166\",\n \"0.5050166\",\n \"0.5047615\",\n \"0.5042787\",\n \"0.5038115\",\n \"0.50235844\",\n \"0.5022449\",\n \"0.49973118\",\n \"0.4993849\",\n \"0.49795815\",\n \"0.49705133\",\n \"0.49692383\",\n \"0.49604732\",\n \"0.49388883\",\n \"0.4935133\",\n \"0.49338156\",\n \"0.49189058\",\n \"0.4912307\",\n \"0.4910371\",\n \"0.49094686\",\n \"0.49072903\",\n \"0.49052238\",\n \"0.48998904\",\n \"0.48916578\",\n \"0.48859885\",\n \"0.48809183\",\n \"0.48768535\",\n \"0.48724094\",\n \"0.48710805\",\n \"0.48705864\",\n \"0.48675436\",\n \"0.4867114\",\n \"0.48621613\",\n \"0.4855062\",\n \"0.48486626\",\n \"0.4844671\",\n \"0.4838387\",\n \"0.48378918\",\n \"0.48098594\",\n \"0.48046455\",\n \"0.47986278\",\n \"0.47985706\",\n \"0.4796605\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.87847257"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11988,"cells":{"query":{"kind":"string","value":"Assigns a new name to the objective. putobjname(self,objname_)"},"document":{"kind":"string","value":"def putobjname(self,objname_):\n if isinstance(objname_,unicode):\n objname_ = objname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putobjname(self.__nativep,objname_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putobjname(self,objname_): # 3\n res = self.__obj.putobjname(objname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_object_name(self, object_name = \"DefaultObject\"):\n self.obj_name = object_name","def set_object_name(self, agent, Name):\n\n self.send_ObjectName(agent, agent.agent_id, agent.session_id, {1:[self.LocalID, Name]})","def set_object_name(remote, object_id, new_name):\n cmd = mmapi.StoredCommands()\n cmd.AppendSceneCommand_SetObjectName(object_id, new_name)\n remote.runCommand(cmd)","def setName(self, *args):\n return _libsbml.Objective_setName(self, *args)","def setObjectName( self, name ):\n scene = self.scene()\n if ( scene ):\n name = scene.uniqueNodeName(name)\n self._objectName = name\n self._titleFont = None\n self.update()","def objective_metric_name(self, objective_metric_name):\n\n self._objective_metric_name = objective_metric_name","def setName(self, *args):\n return _libsbml.FluxObjective_setName(self, *args)","def new_name(self,new_name):\n self.name = new_name","def set_name(self,name):\r\n self._name = __name","def update_object(self, name: str) -> None:","def setname(self, name):\n self.__name = name","def update_name(self, new_name):\r\n self.__name = new_name","def update_name(self, new_name):\r\n self.__name = new_name","def name(self, name):\n self.__name = name","def set_name(self, name):\r\n self.__name = name","def set_name(self, name):\n self.name = name # overwrite the existing name with the input name","def set_name(self, name):\n self.name = name # overwrite the existing name with the input name","def set_name(self, name):\n\t\tself.name_ = name","def replace(name, newobject):","def putvarname(self,j_,name_): # 3\n res = self.__obj.putvarname(j_,name_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def name(self, name: str):\n self.inst['targetname'] = name","def updateName(self,name):\n self.name = name","def rename(self, objkey, objname, newname):\n\n refnames = eppy.modeleditor.getrefnames(self, objkey)\n for refname in refnames:\n objlists = eppy.modeleditor.getallobjlists(self, refname)\n # [('OBJKEY', refname, fieldindexlist), ...]\n for robjkey, refname, fieldindexlist in objlists:\n idfobjects = self.idfobjects[robjkey]\n for idfobject in idfobjects:\n for findex in fieldindexlist: # for each field\n if (\n idfobject[idfobject.objls[findex]].lower()\n == objname.lower()\n ):\n idfobject[idfobject.objls[findex]] = newname\n theobject = self.getobject(objkey, objname)\n fieldname = [item for item in theobject.objls if item.endswith(\"Name\")][0]\n theobject[fieldname] = newname\n return theobject","def set_name(self, newname=\"\"):\n self.name = newname","def set_name(self, name):\n self.__name = name","def set_name(self, name):\n self.__name = name","def set_name(self, PersonName):\r\n self.name = PersonName","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name) :\n\t\ttry :\n\t\t\tself._name = name\n\t\texcept Exception as e:\n\t\t\traise e","def set(self,obj,value):\r\n\t\tvalue = self.parse(value)\r\n\t\tsetattr(obj,self.name,value)","def __set_name__(self, cls, name):\n pass","def putconename(self,j_,name_): # 3\n res = self.__obj.putconename(j_,name_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def name(self, name: str):\r\n self._name = name","def name(self, name: str):\r\n self._name = name","def name(self, name: str):\r\n self._name = name","def name(self, name: str):\r\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name"],"string":"[\n \"def putobjname(self,objname_): # 3\\n res = self.__obj.putobjname(objname_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_object_name(self, object_name = \\\"DefaultObject\\\"):\\n self.obj_name = object_name\",\n \"def set_object_name(self, agent, Name):\\n\\n self.send_ObjectName(agent, agent.agent_id, agent.session_id, {1:[self.LocalID, Name]})\",\n \"def set_object_name(remote, object_id, new_name):\\n cmd = mmapi.StoredCommands()\\n cmd.AppendSceneCommand_SetObjectName(object_id, new_name)\\n remote.runCommand(cmd)\",\n \"def setName(self, *args):\\n return _libsbml.Objective_setName(self, *args)\",\n \"def setObjectName( self, name ):\\n scene = self.scene()\\n if ( scene ):\\n name = scene.uniqueNodeName(name)\\n self._objectName = name\\n self._titleFont = None\\n self.update()\",\n \"def objective_metric_name(self, objective_metric_name):\\n\\n self._objective_metric_name = objective_metric_name\",\n \"def setName(self, *args):\\n return _libsbml.FluxObjective_setName(self, *args)\",\n \"def new_name(self,new_name):\\n self.name = new_name\",\n \"def set_name(self,name):\\r\\n self._name = __name\",\n \"def update_object(self, name: str) -> None:\",\n \"def setname(self, name):\\n self.__name = name\",\n \"def update_name(self, new_name):\\r\\n self.__name = new_name\",\n \"def update_name(self, new_name):\\r\\n self.__name = new_name\",\n \"def name(self, name):\\n self.__name = name\",\n \"def set_name(self, name):\\r\\n self.__name = name\",\n \"def set_name(self, name):\\n self.name = name # overwrite the existing name with the input name\",\n \"def set_name(self, name):\\n self.name = name # overwrite the existing name with the input name\",\n \"def set_name(self, name):\\n\\t\\tself.name_ = name\",\n \"def replace(name, newobject):\",\n \"def putvarname(self,j_,name_): # 3\\n res = self.__obj.putvarname(j_,name_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def name(self, name: str):\\n self.inst['targetname'] = name\",\n \"def updateName(self,name):\\n self.name = name\",\n \"def rename(self, objkey, objname, newname):\\n\\n refnames = eppy.modeleditor.getrefnames(self, objkey)\\n for refname in refnames:\\n objlists = eppy.modeleditor.getallobjlists(self, refname)\\n # [('OBJKEY', refname, fieldindexlist), ...]\\n for robjkey, refname, fieldindexlist in objlists:\\n idfobjects = self.idfobjects[robjkey]\\n for idfobject in idfobjects:\\n for findex in fieldindexlist: # for each field\\n if (\\n idfobject[idfobject.objls[findex]].lower()\\n == objname.lower()\\n ):\\n idfobject[idfobject.objls[findex]] = newname\\n theobject = self.getobject(objkey, objname)\\n fieldname = [item for item in theobject.objls if item.endswith(\\\"Name\\\")][0]\\n theobject[fieldname] = newname\\n return theobject\",\n \"def set_name(self, newname=\\\"\\\"):\\n self.name = newname\",\n \"def set_name(self, name):\\n self.__name = name\",\n \"def set_name(self, name):\\n self.__name = name\",\n \"def set_name(self, PersonName):\\r\\n self.name = PersonName\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name) :\\n\\t\\ttry :\\n\\t\\t\\tself._name = name\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def set(self,obj,value):\\r\\n\\t\\tvalue = self.parse(value)\\r\\n\\t\\tsetattr(obj,self.name,value)\",\n \"def __set_name__(self, cls, name):\\n pass\",\n \"def putconename(self,j_,name_): # 3\\n res = self.__obj.putconename(j_,name_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def name(self, name: str):\\r\\n self._name = name\",\n \"def name(self, name: str):\\r\\n self._name = name\",\n \"def name(self, name: str):\\r\\n self._name = name\",\n \"def name(self, name: str):\\r\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.8106175\",\n \"0.71860224\",\n \"0.70278543\",\n \"0.6894341\",\n \"0.68561333\",\n \"0.66999173\",\n \"0.663241\",\n \"0.6498052\",\n \"0.6482429\",\n \"0.64018923\",\n \"0.6332358\",\n \"0.63283575\",\n \"0.6323521\",\n \"0.6323521\",\n \"0.6271868\",\n \"0.62309307\",\n \"0.6210313\",\n \"0.6210313\",\n \"0.62092906\",\n \"0.61988294\",\n \"0.6191593\",\n \"0.61654073\",\n \"0.6160827\",\n \"0.61594695\",\n \"0.61172014\",\n \"0.6107862\",\n \"0.6107862\",\n \"0.60988474\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6094943\",\n \"0.6074546\",\n \"0.60700655\",\n \"0.6054084\",\n \"0.6016904\",\n \"0.5994835\",\n \"0.5994835\",\n \"0.5994835\",\n \"0.5994835\",\n \"0.5960778\",\n \"0.5960778\",\n \"0.5960778\",\n \"0.5960778\",\n \"0.5960778\",\n \"0.5960778\",\n \"0.5960778\",\n \"0.5960778\",\n \"0.5960778\",\n \"0.5960778\",\n 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putparam(self,parname_,parvalue_)"},"document":{"kind":"string","value":"def putparam(self,parname_,parvalue_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value","def setParameter(self, name, value):","def set_parameter(self, params, name, val):\n raise NotImplementedError()","def put_param(self, attr_name, val):\n self._params[attr_name] = val","def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set(self,name,val):\n matches = self.grep_param_names(name)\n if len(matches):\n x = self._get_params()\n x[matches] = val\n self._set_params(x)\n else:\n raise AttributeError, \"no parameter matches %s\"%name","def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError","def log_param(self, name: str, value):\n self.params[name] = value\n\n self._sync_log_event()","def put_par(self, parname, value, sep=\".\"):\n pv = self.get_pvname(parname, sep=sep)\n return Pv.put(pv, value)","def __setitem__(self, name: str, value):\n super(Parameter, self).__setitem__(name, value)","def set_param(self, name, value):\n name = str(name)\n value = str(value)\n cxnlib.CXNNetSetParam(self.handle,\n ctypes.c_char_p(name.encode('utf-8')),\n ctypes.c_char_p(value.encode('utf-8')))","def putnaintparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setParameter(self,arg,value):\n self._params[arg] = value\n return self._params","def set_param(self, param, value):\n self._set_param_client(param, value)","def set_param(params, pname, value=None, bounds=None):\n if value is not None:\n for p in params.flattened():\n if p.name == pname:\n p.value = value\n break\n\n if bounds is not None:\n for p in params.flattened():\n if p.name == pname:\n p.bounds = bounds\n p.vary = True\n break","def set_parameter_value(self, parameter, value):\n pass","def SetParameterValue(self, paramName, value):\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n if paramName == ISENTROPIC_PAR:\n if (self.ideal != None):\n self.ideal.SetParameterValue(paramName, value)","def update_parameter(self, param, val, force=False):\n self._update_dict[param] = val\n if force:\n self._cur_val[param] = None","def putstrparam(self,param_,parvalue_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putstrparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putnadouparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnadouparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putintparam(self,param_,parvalue_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putintparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _set_param(self, name, value):\n self._frozenjson._data[name] = value","def set_param(self, param_key, value):\n return self._params.set_param_value(param_key, value)","def putintparam(self,param_,parvalue_):\n res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val","def param_name(self, value):\n self._param_name = value","def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\n self._parameters.append(Parameter(parameter_name, parameter_value))","def setTemplateParameter(self,name,value):\n self.tplparam[name] = value","def _set_par(vid, par, value):\n traci.vehicle.setParameter(vid, \"carFollowModel.%s\" % par, str(value))","def update_parameter(self, name, freq, value):\n if name not in self._parameters.keys():\n self.add_parameter(name, [freq], [value])\n else:\n param = self.get_parameter(name)\n param.update_value(freq, value)","def AddParameter(self, param_name, param_value):\n if(param_name == \"NPOP1_1\"):\n if(self.npop1_1 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop1_1 = param_value\n elif(param_name == \"NPOP1_2\"):\n if(self.npop1_2 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop1_2 = param_value\n elif(param_name == \"NPOP2_1\"):\n if(self.npop2_1 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop2_1 = param_value\n elif(param_name == \"NPOP2_2\"):\n if(self.npop2_2 != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop2_2 = param_value\n elif(param_name == \"T_DIV\"):\n if(self.t_div != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.t_div = param_value\n elif(param_name == \"NPOP_A\"):\n if(self.npop_a != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.npop_a = param_value\n elif(param_name == \"MU\"):\n if(self.mu != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.mu = param_value\n elif(param_name == \"T_I\"):\n if(self.t_i != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.t_i = param_value\n elif(param_name == \"P_I\"):\n if(self.p_i != \"N/A\"):\n raise Exception(\"Duplicated value specified for the parameter \" + param_name + \"!\")\n else:\n self.p_i = param_value\n else:\n raise Exception(\"Invalid parameter name: \" + param_name + \"!\")","def _p_set_par_ ( o , index , value ) :\n return o.setPar ( index , value )","def _put_ssm_param(self, parameter, parameter_name):\n self.ssm_client.put_parameter(\n Name=parameter_name,\n Type=\"String\",\n Value=json.dumps(parameter),\n Overwrite=True,\n Tier=\"Intelligent-Tiering\",\n )","def SetParameterValue(self, paramName, value):\n if paramName == NUMBTABLE_PAR:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n self.LookupTable.SetParameterValue(paramName, value)\n\n if paramName == IGNORECURVE_PAR: \n #...ignore the lookuptable and remove any specifications\n if value == 'None': value = None\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\n if value:\n port = self.GetPort(HEAD_PORT)\n port.SetValue(None, FIXED_V) \n else:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)","def update_parameter(cur, par, new_value):\n cur.execute(\"UPDATE parameters SET value=%f WHERE par='%s';\" % \n (new_value, par))","def putnastrparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnastrparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set(self, name, value=None):\n if isinstance(name, dict):\n for key, value in name.items():\n try:\n param, value = self.coerceParameter(key, value)\n self.params[param] = value\n except KeyError:\n pass\n elif isinstance(name, str):\n try:\n param, value = self.coerceParameter(name, value)\n self.params[param] = value\n except KeyError:\n pass","def set_parameter_value(self,name,val,parameterized_object=None):\n source = parameterized_object or self.get_source_po(name)\n object.__setattr__(source,name,val)\n\n # update the tkvar\n if name in self._tkvars:\n self._tkvars[name]._original_set(self._object2string(name,val))","def put(self, name, val):\n pass","def change_general_param(self, param, val):\n assert param in self.params, '%s is not recognized as a valid parameter' % param\n self.params[param].change_value(val)","def gui_set_param(self,param_name,val):\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\n self._handle_gui_set(param_name,force=True)","def set_param(param, num, set_val):\n param[0][num] = set_val","def putnadouparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putdouparam(self,param_,parvalue_): # 3\n if not isinstance(param_,dparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putdouparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_parameter(self,par_name,par_vals):\n self._check_for_parameter_syntax(par_name)\n if len(par_vals) == 0:\n raise ValueError(\"Empty value list for parameter {}\".format(par_name))\n self._params_vals[par_name] = par_vals","def set_parameter(self, sensor_name, parameter_name, parameter_value):\n if parameter_name == 'perspective_angle':\n parameter_value = parameter_value / (180 * 2) * math.pi\n if parameter_name in self.params_f:\n error = vrep.simxSetObjectFloatParameter(\n self.client_id,\n self.handles[sensor_name + self.postfix],\n self.params_f[parameter_name],\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxSetFloatSignal(\n self.client_id,\n 'change_params',\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxClearFloatSignal(\n self.client_id,\n 'change_params',\n ONE_SHOT_MODE\n )\n return error\n elif parameter_name in self.params_i:\n error = vrep.simxSetObjectFloatParameter(\n self.client_id,\n self.handles[sensor_name + self.postfix],\n self.params_i[parameter_name],\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxSetFloatSignal(\n self.client_id,\n 'change_params',\n parameter_value,\n ONE_SHOT_MODE\n )\n vrep.simxClearFloatSignal(\n self.client_id,\n 'change_params',\n ONE_SHOT_MODE\n )\n return error\n else:\n return 'Parameter not found'","def __setJobParam( self, name, value ):\n if not self.jobID:\n return S_ERROR( 'JobID not defined' )\n\n self.log.verbose( 'setJobParameter(%s, %s, %s)' % ( self.jobID, name, value ) )\n return RPCClient( 'WorkloadManagement/JobStateUpdate', timeout = 120 ).setJobParameter( int( self.jobID ), str( name ), str( value ) )","def _mn_set_par_ ( self , i , val , fix = False ) :\n if not i in self : raise IndexError\n #\n if hasattr ( val , 'value' ) : val = val.value()\n #\n ierr = _mn_exec_ ( self , \"SET PAR\" , i + 1 , val )\n #\n if fix : self.FixParameter ( i ) \n #\n return ierr","def set_parameter_value(self, parameter_name, new_value):\n self.description[\"config\"][\"values\"][parameter_name][\"value\"] = new_value\n ## Update MongoDB\n #self.mongo_client.cps2_project.objects.update_one(\n #{\"_id\": self.mongo_id},\n #{\"$set\": {\"config.values.\" + parameter_name + \".value\": new_value,\n #\"last_modified.value\": str(datetime.utcnow())}\n #}\n #)\n print(\"Switched the parameter \" + parameter_name + \" to \" + new_value + \" and updated MongoDB.\")","def set_param(self, param_value):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n\n setting[1] = param_value\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def set_option(self, name, value):\n self._params[name] = value","def set_option(self, name, value):\n self._params[name] = value","def add_param(self, paraminfo):\n self.params[paraminfo.name] = paraminfo","def set_params(self, params):","def addParameter(cTag, name, value): #@NoSelf","def _paramUpdate(self):\n\n # Update the database attributes accordingly.\n dt.utilities.DB_attrs_save(self.Database, self.newParam)","def _set_valued_param(self, name, comp, spec, mode):\n vp = ValuedParam(name)\n signal = vp.read(comp, spec, mode)\n self._signals.update(signal)\n self._crossrefs[name] = vp\n setattr(self, name, vp)","def putstrparam(self,param_,parvalue_):\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_param(self, param_name, param, set_val = None, type_of_param = None):\n if (set_val is None):\n set_val = True\n param_index = self.index(param_name);\n \n if (type_of_param is None):\n type_of_param = self.type_param[param_index]\n if (param is None):\n param = copy.deepcopy(self._param_val[param_index])\n elif (type(param) is type_of_param):\n param = [param]\n elif (type(param) is not list):\n error_str = (\n \"\"\" \n Trying to set parameter %s to non-integer value (or string if instance name, float if eps+rayeps value), and not a list.\n Type of param: %s\n \"\"\" % (param_name, str(type(param)))\n )\n raise TypeError(error_str)\n\n if (set_val):\n self._param_val[param_index] = copy.deepcopy(param)\n return param","def pcp_set_backend_parameter(self, parameter_name, value):\n\n\t\tif self.PCPConnectionStatus() != ConnStateType.OK:\n\t\t\tself.pcp_internal_error('invalid PCP connection')\n\t\t\treturn None\n\t\t\n\t\tif self.Pfdebug:\n\t\t\tself.Pfdebug.write(f'DEBUG: seting: \"{parameter_name} = {value}\"\\n')\n\n\t\tself._PCPWrite('A'.encode(), 1)\n\t\twsize = self.int_to_bytes(len(parameter_name) + 1 + len(value) + 1 + 4)\n\t\tself._PCPWrite(wsize, 4)\n\t\tself._PCPWrite(parameter_name.encode(), len(parameter_name))\n\t\tself._PCPWrite(NULL, 1)\n\t\tself._PCPWrite(value.encode(), len(value))\n\t\tself._PCPWrite(NULL, 1)\n\t\tif self.PCPFlush() < 0:\n\t\t\treturn None\n\t\tif self.Pfdebug:\n\t\t\tself.Pfdebug.write(f'DEBUG: send: tos=\"A\", length={self.bytes_to_int(wsize)}\\n')\n\n\t\treturn self._process_pcp_response('A')","def __setitem__(self, key, value):\n self.params[key].value = value","def putnastrparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __setattr__(self, name, value):\n if hasattr(self, name):\n super(JobSubmission, self).__setattr__(name, value)\n\n else:\n self.params[str(name)] = str(value) #TODO: resolve parameter cases","def set_value(self, var_name, new_value, tf_session):\n\n if(var_name in self.assign_operator):\n\n tf_session.run(\n self.assign_operator[var_name], {\n self.l_param_input[var_name]: new_value})\n else:\n print(\"Thou shall only assign learning parameters!\")","def __setitem__(self, name, val):\r\n matches = self.grep_param_names(name)\r\n if len(matches):\r\n val = np.array(val)\r\n assert (val.size == 1) or val.size == len(matches), \"Shape mismatch: {}:({},)\".format(val.size, len(matches))\r\n x = self._get_params()\r\n x[matches] = val\r\n self._set_params(x)\r\n else:\r\n raise AttributeError, \"no parameter matches %s\" % name","def add_param(self, step_id, name, value):\n if step_id not in self._params:\n self._params[step_id] = {}\n self._params[step_id][name] = value","def put_parameter(parameter_name, parameter_value, parameter_type, overwrite=False):\n ssm_client = boto3.client('ssm')\n\n try:\n result = ssm_client.put_parameter(\n Name=parameter_name,\n Value=parameter_value,\n Type=parameter_type,\n Overwrite=overwrite\n )\n logging.info(\"%s was added to parameter store\", parameter_name)\n except ClientError as e:\n logging.error(e)\n return None\n return result['Version']","def put(self, pvname, value: Union[np.ndarray, float]) -> None:\n if self.protocol == \"ca\":\n caput(pvname, value)\n\n elif self.protocol == \"pva\":\n self.context.put(pvname, value)","def __setattr__(self, name, value):\n if name in ['parameters', 'program_name']: # Allowed attributes\n self.__dict__[name] = value\n else:\n self.set_parameter(name, value) # treat as a parameter","def _update_param_from_tkvar(self,param_name):\n self.debug(\"TkPOb._update_param_from_tkvar(%s)\"%param_name)\n\n parameter,sourcePO=self.get_parameter_object(param_name,with_source=True)\n\n ### can only edit constant parameters for class objects\n if parameter.constant is True and not isinstance(sourcePO,type):\n return ### HIDDEN\n\n tkvar = self._tkvars[param_name]\n\n if self._tkvar_changed(param_name):\n # don't attempt to set if there was a string-to-object translation error\n if self.translators[param_name].last_string2object_failed:\n return ### HIDDEN\n\n # (use _original_get() because we don't want the tkvar to be reset to\n # the parameter's current value!)\n val = self._string2object(param_name,tkvar._original_get())\n\n try:\n self._set_parameter(param_name,val)\n except: # everything\n tkvar.set(tkvar._last_good_val)\n raise # whatever the parameter-setting error was\n\n self.debug(\"set %s to %s\"%(param_name,val))\n\n if hasattr(tkvar,'_on_modify'):\n tkvar._on_modify()\n\n ### call any function associated with GUI set()\n if hasattr(tkvar,'_on_set'):\n\n # CEBALERT: provide a way of allowing other gui components\n # to figure out where a callback error might have come\n # from. Callback instances (the Callback class is defined\n # in Tkinter.py) store a widget, but often it appears to\n # be the Tk instance - which is of no use in later\n # determining where an error might have originated.\n global _last_one_set\n if hasattr(self,'master'):\n _last_one_set = self.master\n\n tkvar._on_set()","def set_parameters(self, params):\n self.kp = params.pgain","def update(self, job_name, param_name, value, description=None):\n if job_name in self._jobs:\n getattr(self._jobs[job_name], param_name).update(value, description)\n else:\n self.log.error(\"Invalid job name: %s\", job_name)","def set_param(command):\n namespace = app.main(command)\n assert namespace.command == 'sp' or namespace.command == \"setparam\"\n assert namespace.name == \"test\"\n assert namespace.value == \"test\"","def add_parameter(self,\n name, # The name of the parameter\n scaling=None, # The type of scaling to be used for the parameter\n type=\"int\", # The type of the parameter, such as float\n min=0, # The minimum value of the parameter\n max=100, # The maximum value of the parameter\n significance=1, # The smallest significant step size\n value=None, # The value or value parameters\n distribution=None): # The distribution of the parameter\n config = {\"scaling\" : scaling, \n \"type\": type,\n \"min\": min, \n \"max\": max, \n \"significance\": significance,\n \"value\": value,\n \"distribution\": distribution}\n self.param_names.append(name)\n self.param_settings.append(config)","def set_parameter(self, param, value, stripe = 0, location = 3):\n if param in self.fpga.cabac_top[0].params:\n self.fpga.set_cabac_value(param, value, stripe)\n time.sleep(0.1)\n self.fpga.send_cabac_config(stripe)\n\n elif param in [\"V_SL\", \"V_SH\", \"V_RGL\", \"V_RGH\", \"V_PL\", \"V_PH\"]:\n self.fpga.set_clock_voltages({param: value})\n\n elif param == \"I_OS\":\n self.fpga.set_current_source(value, stripe)\n\n else:\n print(\"Warning: unidentified parameter for the REB: %s\" % param)","def set_parameter(self, params, name, val):\n if name == \"model\":\n params.model = val\n return params\n available_models = [\n entry_point.name\n for entry_point in pkg_resources.iter_entry_points(\n \"dxtbx.scaling_model_ext\"\n )\n ]\n phil_branches = [\n params.weighting.error_model,\n params.cut_data,\n params.scaling_options,\n params.reflection_selection,\n params.reflection_selection.random,\n params.reflection_selection.random.multi_dataset,\n ]\n if params.model:\n phil_branches.append(params.__getattribute__(str(params.model)))\n elif (\".\" in name) and (name.split(\".\")[0] in available_models):\n # if the user hasn't specified the model, but have done\n # e.g physical.parameter = *, then set model=physical\n params.model = name.split(\".\")[0]\n phil_branches.append(params.__getattribute__(str(params.model)))\n if \".\" in name: # handle e.g physical.absorption_correction\n name = name.split(\".\")[-1]\n for branch in phil_branches:\n try:\n branch.__setattr__(name, val)\n return params\n except AttributeError:\n pass\n # if get here, haven't found what we're trying to set\n raise ValueError(\"Unable to set chosen attribute \" + str(name) + \"=\" + str(val))","def set_param(self):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n setting[1] = str(self.param_value)\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def _update_param_from_tkvar(self,param_name,force=False):\n self.debug(\"TkPO._update_param_from_tkvar(%s)\"%param_name)\n\n param_obj = self.get_parameter_object(param_name)\n\n if not lookup_by_class(self.param_immediately_apply_change,\n type(param_obj)) and not force:\n return\n else:\n super(TkParameterized,self)._update_param_from_tkvar(param_name)","def set_params(self, *arg):\n pass","def set_param(param, value, verbose=False):\n set_param_raw(param, yaml.load(value), verbose=verbose)","def addParameter(self, name, value):\r\n if not name:\r\n raise InvalidRequest('Parameter name is not a valid.')\r\n\r\n if name in self._parameters:\r\n raise InvalidRequest(\"Can not use the same parameter name '{0}' \"\r\n 'in the same container twice.'.format(name))\r\n\r\n parameter = self._obj.createParameter(name, value)\r\n self._parameters[name] = parameter\r\n parameter.notifyOnDeath(self._parameterDied)","def SetExportParam(self, name, value):\n parent, subname = self.FindExport(name)\n subname = Handle._FixExportName(parent.obj, subname)\n if not hasattr(parent.obj, subname):\n raise KeyError(name)\n if not parent.obj.dirty:\n parent.obj.StartTransaction()\n parent.obj.dirty = True\n setattr(parent.obj, subname, value)\n return parent.obj","def SetParameterValue(self, paramName, value):\n super(LookupTable, self).SetParameterValue(paramName, value)\n if paramName == NUMBTABLE_PAR:\n self.SetTableCount()\n elif paramName == NUMBSERIES_PAR:\n self.SetSeriesCount()\n elif paramName == TABLETAGTYPE_PAR:\n self.SetTableTagType()\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\n idx = long(paramName[len(SERIESTYPE_PAR):])\n self.SetSeriesTypes(idx)","def getParameter(self, name):","def _set_params(self,x):\r\n self.k._set_params(x)","def _add_param(self, name):\n param = ParameterInfo()\n param._name = name\n self._parameters.append(param)\n return param","def rename_param(self, param, name):\n old_name = param.name\n new_name = self._get_unique_param_name(name, param.mode)\n \n param._name = new_name\n \n if param.mode == NodeParam.INPUT:\n self._input_params.pop(old_name)\n self._input_params[new_name] = param\n else:\n self._output_params.pop(old_name)\n self._output_params[new_name] = param\n \n return new_name","def save_parms(self, name):\n self._save_parms(name.encode())","def add_param(self, param):\n self._params.append(param)\n self.add_decompostion(param)","async def set_param(self, param: str, value: int) -> ArchonCommand:\n cmd = await self.send_command(f\"FASTLOADPARAM {param} {value}\")\n if not cmd.succeeded():\n raise ArchonError(\n f\"Failed setting parameter {param!r} ({cmd.status.name}).\"\n )\n return cmd","def __adjust_param(self, option):\n # Get the name of the parameter.\n name = self.__option_params[option]\n\n # Ask the user for a new value.\n value = float(input(\"Enter value for {}: \".format(name)))\n self._params.update(name, value)\n\n # Update the description with the new value.\n desc = self.__make_description(name)\n self.update_description(option, desc)\n\n # Stay on the same menu.\n return self.get_name()","def set(self, **parameters):\r\n for name in parameters:\r\n if name in self.prm:\r\n self.prm[name] = parameters[name]\r\n else:\r\n self._illegal_parameter(name)","def setParameterNode(self, parameterNode):\r\n # framework\r\n profbox()\r\n self.parameterNode = parameterNode","def add_param(element):\n nonlocal params\n name = element.attrib.get(\"name\", None)\n value = element.attrib.get(\"value\", \"\")\n if name:\n params[name] = value","def saveParam(self):\n qApp.emit(QtCore.SIGNAL(\"saveMe\"), self._param)","def saveParam(self):\n qApp.emit(QtCore.SIGNAL(\"saveMe\"), self._param)","def __init__(self, owner, name, value):\r\n self._registered = False\r\n\r\n ArgumentMixin.__init__(self, owner.loader)\r\n\r\n owner.registerParameter(self)\r\n self._owner = owner\r\n\r\n if isinstance(value, basestring):\r\n value = self.processArgument(value)\r\n\r\n self._name = name\r\n\r\n try:\r\n if rospy.has_param(name):\r\n log.msg('Warning: Parameter already exists.')\r\n\r\n rospy.set_param(name, value)\r\n self._registered = True\r\n except rospy.ROSException as e:\r\n raise InternalError('ROS Parameter Server reported an error: '\r\n '{0}'.format(e))","def set_parameter(self, output):\n self.model.set_parameter(output);","def __updateParameter(self, currentParam, newParam):\n for i in xrange(len(currentParam)):\n for np in newParam:\n if np['name'] == currentParam[i]['name']:\n currentParam[i] = np"],"string":"[\n \"def putparam(self,parname_,parvalue_): # 3\\n res = self.__obj.putparam(parname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setParam(self,param,value):\\n if param in self.params.keys():\\n self.params[param] = value\",\n \"def setParameter(self, name, value):\",\n \"def set_parameter(self, params, name, val):\\n raise NotImplementedError()\",\n \"def put_param(self, attr_name, val):\\n self._params[attr_name] = val\",\n \"def putnaintparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnaintparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set(self,name,val):\\n matches = self.grep_param_names(name)\\n if len(matches):\\n x = self._get_params()\\n x[matches] = val\\n self._set_params(x)\\n else:\\n raise AttributeError, \\\"no parameter matches %s\\\"%name\",\n \"def set_param(self, name, value, *, distrib=None, ref=None):\\n raise NotImplementedError\",\n \"def log_param(self, name: str, value):\\n self.params[name] = value\\n\\n self._sync_log_event()\",\n \"def put_par(self, parname, value, sep=\\\".\\\"):\\n pv = self.get_pvname(parname, sep=sep)\\n return Pv.put(pv, value)\",\n \"def __setitem__(self, name: str, value):\\n super(Parameter, self).__setitem__(name, value)\",\n \"def set_param(self, name, value):\\n name = str(name)\\n value = str(value)\\n cxnlib.CXNNetSetParam(self.handle,\\n ctypes.c_char_p(name.encode('utf-8')),\\n ctypes.c_char_p(value.encode('utf-8')))\",\n \"def putnaintparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setParameter(self,arg,value):\\n self._params[arg] = value\\n return self._params\",\n \"def set_param(self, param, value):\\n self._set_param_client(param, value)\",\n \"def set_param(params, pname, value=None, bounds=None):\\n if value is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.value = value\\n break\\n\\n if bounds is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.bounds = bounds\\n p.vary = True\\n break\",\n \"def set_parameter_value(self, parameter, value):\\n pass\",\n \"def SetParameterValue(self, paramName, value):\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n if paramName == ISENTROPIC_PAR:\\n if (self.ideal != None):\\n self.ideal.SetParameterValue(paramName, value)\",\n \"def update_parameter(self, param, val, force=False):\\n self._update_dict[param] = val\\n if force:\\n self._cur_val[param] = None\",\n \"def putstrparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,sparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putstrparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putnadouparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnadouparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putintparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,iparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putintparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _set_param(self, name, value):\\n self._frozenjson._data[name] = value\",\n \"def set_param(self, param_key, value):\\n return self._params.set_param_value(param_key, value)\",\n \"def putintparam(self,param_,parvalue_):\\n res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_param(self, label, val):\\n assert type(label) is str, 'Parameter name \\\"%s\\\" is not string' % label\\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\\n self.params[label] = val\",\n \"def param_name(self, value):\\n self._param_name = value\",\n \"def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\\n self._parameters.append(Parameter(parameter_name, parameter_value))\",\n \"def setTemplateParameter(self,name,value):\\n self.tplparam[name] = value\",\n \"def _set_par(vid, par, value):\\n traci.vehicle.setParameter(vid, \\\"carFollowModel.%s\\\" % par, str(value))\",\n \"def update_parameter(self, name, freq, value):\\n if name not in self._parameters.keys():\\n self.add_parameter(name, [freq], [value])\\n else:\\n param = self.get_parameter(name)\\n param.update_value(freq, value)\",\n \"def AddParameter(self, param_name, param_value):\\n if(param_name == \\\"NPOP1_1\\\"):\\n if(self.npop1_1 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop1_1 = param_value\\n elif(param_name == \\\"NPOP1_2\\\"):\\n if(self.npop1_2 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop1_2 = param_value\\n elif(param_name == \\\"NPOP2_1\\\"):\\n if(self.npop2_1 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop2_1 = param_value\\n elif(param_name == \\\"NPOP2_2\\\"):\\n if(self.npop2_2 != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop2_2 = param_value\\n elif(param_name == \\\"T_DIV\\\"):\\n if(self.t_div != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.t_div = param_value\\n elif(param_name == \\\"NPOP_A\\\"):\\n if(self.npop_a != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.npop_a = param_value\\n elif(param_name == \\\"MU\\\"):\\n if(self.mu != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.mu = param_value\\n elif(param_name == \\\"T_I\\\"):\\n if(self.t_i != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.t_i = param_value\\n elif(param_name == \\\"P_I\\\"):\\n if(self.p_i != \\\"N/A\\\"):\\n raise Exception(\\\"Duplicated value specified for the parameter \\\" + param_name + \\\"!\\\")\\n else:\\n self.p_i = param_value\\n else:\\n raise Exception(\\\"Invalid parameter name: \\\" + param_name + \\\"!\\\")\",\n \"def _p_set_par_ ( o , index , value ) :\\n return o.setPar ( index , value )\",\n \"def _put_ssm_param(self, parameter, parameter_name):\\n self.ssm_client.put_parameter(\\n Name=parameter_name,\\n Type=\\\"String\\\",\\n Value=json.dumps(parameter),\\n Overwrite=True,\\n Tier=\\\"Intelligent-Tiering\\\",\\n )\",\n \"def SetParameterValue(self, paramName, value):\\n if paramName == NUMBTABLE_PAR:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n self.LookupTable.SetParameterValue(paramName, value)\\n\\n if paramName == IGNORECURVE_PAR: \\n #...ignore the lookuptable and remove any specifications\\n if value == 'None': value = None\\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\\n if value:\\n port = self.GetPort(HEAD_PORT)\\n port.SetValue(None, FIXED_V) \\n else:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\",\n \"def update_parameter(cur, par, new_value):\\n cur.execute(\\\"UPDATE parameters SET value=%f WHERE par='%s';\\\" % \\n (new_value, par))\",\n \"def putnastrparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnastrparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set(self, name, value=None):\\n if isinstance(name, dict):\\n for key, value in name.items():\\n try:\\n param, value = self.coerceParameter(key, value)\\n self.params[param] = value\\n except KeyError:\\n pass\\n elif isinstance(name, str):\\n try:\\n param, value = self.coerceParameter(name, value)\\n self.params[param] = value\\n except KeyError:\\n pass\",\n \"def set_parameter_value(self,name,val,parameterized_object=None):\\n source = parameterized_object or self.get_source_po(name)\\n object.__setattr__(source,name,val)\\n\\n # update the tkvar\\n if name in self._tkvars:\\n self._tkvars[name]._original_set(self._object2string(name,val))\",\n \"def put(self, name, val):\\n pass\",\n \"def change_general_param(self, param, val):\\n assert param in self.params, '%s is not recognized as a valid parameter' % param\\n self.params[param].change_value(val)\",\n \"def gui_set_param(self,param_name,val):\\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\\n self._handle_gui_set(param_name,force=True)\",\n \"def set_param(param, num, set_val):\\n param[0][num] = set_val\",\n \"def putnadouparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putdouparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,dparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putdouparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_parameter(self,par_name,par_vals):\\n self._check_for_parameter_syntax(par_name)\\n if len(par_vals) == 0:\\n raise ValueError(\\\"Empty value list for parameter {}\\\".format(par_name))\\n self._params_vals[par_name] = par_vals\",\n \"def set_parameter(self, sensor_name, parameter_name, parameter_value):\\n if parameter_name == 'perspective_angle':\\n parameter_value = parameter_value / (180 * 2) * math.pi\\n if parameter_name in self.params_f:\\n error = vrep.simxSetObjectFloatParameter(\\n self.client_id,\\n self.handles[sensor_name + self.postfix],\\n self.params_f[parameter_name],\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxSetFloatSignal(\\n self.client_id,\\n 'change_params',\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxClearFloatSignal(\\n self.client_id,\\n 'change_params',\\n ONE_SHOT_MODE\\n )\\n return error\\n elif parameter_name in self.params_i:\\n error = vrep.simxSetObjectFloatParameter(\\n self.client_id,\\n self.handles[sensor_name + self.postfix],\\n self.params_i[parameter_name],\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxSetFloatSignal(\\n self.client_id,\\n 'change_params',\\n parameter_value,\\n ONE_SHOT_MODE\\n )\\n vrep.simxClearFloatSignal(\\n self.client_id,\\n 'change_params',\\n ONE_SHOT_MODE\\n )\\n return error\\n else:\\n return 'Parameter not found'\",\n \"def __setJobParam( self, name, value ):\\n if not self.jobID:\\n return S_ERROR( 'JobID not defined' )\\n\\n self.log.verbose( 'setJobParameter(%s, %s, %s)' % ( self.jobID, name, value ) )\\n return RPCClient( 'WorkloadManagement/JobStateUpdate', timeout = 120 ).setJobParameter( int( self.jobID ), str( name ), str( value ) )\",\n \"def _mn_set_par_ ( self , i , val , fix = False ) :\\n if not i in self : raise IndexError\\n #\\n if hasattr ( val , 'value' ) : val = val.value()\\n #\\n ierr = _mn_exec_ ( self , \\\"SET PAR\\\" , i + 1 , val )\\n #\\n if fix : self.FixParameter ( i ) \\n #\\n return ierr\",\n \"def set_parameter_value(self, parameter_name, new_value):\\n self.description[\\\"config\\\"][\\\"values\\\"][parameter_name][\\\"value\\\"] = new_value\\n ## Update MongoDB\\n #self.mongo_client.cps2_project.objects.update_one(\\n #{\\\"_id\\\": self.mongo_id},\\n #{\\\"$set\\\": {\\\"config.values.\\\" + parameter_name + \\\".value\\\": new_value,\\n #\\\"last_modified.value\\\": str(datetime.utcnow())}\\n #}\\n #)\\n print(\\\"Switched the parameter \\\" + parameter_name + \\\" to \\\" + new_value + \\\" and updated MongoDB.\\\")\",\n \"def set_param(self, param_value):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n\\n setting[1] = param_value\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def add_param(self, paraminfo):\\n self.params[paraminfo.name] = paraminfo\",\n \"def set_params(self, params):\",\n \"def addParameter(cTag, name, value): #@NoSelf\",\n \"def _paramUpdate(self):\\n\\n # Update the database attributes accordingly.\\n dt.utilities.DB_attrs_save(self.Database, self.newParam)\",\n \"def _set_valued_param(self, name, comp, spec, mode):\\n vp = ValuedParam(name)\\n signal = vp.read(comp, spec, mode)\\n self._signals.update(signal)\\n self._crossrefs[name] = vp\\n setattr(self, name, vp)\",\n \"def putstrparam(self,param_,parvalue_):\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_param(self, param_name, param, set_val = None, type_of_param = None):\\n if (set_val is None):\\n set_val = True\\n param_index = self.index(param_name);\\n \\n if (type_of_param is None):\\n type_of_param = self.type_param[param_index]\\n if (param is None):\\n param = copy.deepcopy(self._param_val[param_index])\\n elif (type(param) is type_of_param):\\n param = [param]\\n elif (type(param) is not list):\\n error_str = (\\n \\\"\\\"\\\" \\n Trying to set parameter %s to non-integer value (or string if instance name, float if eps+rayeps value), and not a list.\\n Type of param: %s\\n \\\"\\\"\\\" % (param_name, str(type(param)))\\n )\\n raise TypeError(error_str)\\n\\n if (set_val):\\n self._param_val[param_index] = copy.deepcopy(param)\\n return param\",\n \"def pcp_set_backend_parameter(self, parameter_name, value):\\n\\n\\t\\tif self.PCPConnectionStatus() != ConnStateType.OK:\\n\\t\\t\\tself.pcp_internal_error('invalid PCP connection')\\n\\t\\t\\treturn None\\n\\t\\t\\n\\t\\tif self.Pfdebug:\\n\\t\\t\\tself.Pfdebug.write(f'DEBUG: seting: \\\"{parameter_name} = {value}\\\"\\\\n')\\n\\n\\t\\tself._PCPWrite('A'.encode(), 1)\\n\\t\\twsize = self.int_to_bytes(len(parameter_name) + 1 + len(value) + 1 + 4)\\n\\t\\tself._PCPWrite(wsize, 4)\\n\\t\\tself._PCPWrite(parameter_name.encode(), len(parameter_name))\\n\\t\\tself._PCPWrite(NULL, 1)\\n\\t\\tself._PCPWrite(value.encode(), len(value))\\n\\t\\tself._PCPWrite(NULL, 1)\\n\\t\\tif self.PCPFlush() < 0:\\n\\t\\t\\treturn None\\n\\t\\tif self.Pfdebug:\\n\\t\\t\\tself.Pfdebug.write(f'DEBUG: send: tos=\\\"A\\\", length={self.bytes_to_int(wsize)}\\\\n')\\n\\n\\t\\treturn self._process_pcp_response('A')\",\n \"def __setitem__(self, key, value):\\n self.params[key].value = value\",\n \"def putnastrparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __setattr__(self, name, value):\\n if hasattr(self, name):\\n super(JobSubmission, self).__setattr__(name, value)\\n\\n else:\\n self.params[str(name)] = str(value) #TODO: resolve parameter cases\",\n \"def set_value(self, var_name, new_value, tf_session):\\n\\n if(var_name in self.assign_operator):\\n\\n tf_session.run(\\n self.assign_operator[var_name], {\\n self.l_param_input[var_name]: new_value})\\n else:\\n print(\\\"Thou shall only assign learning parameters!\\\")\",\n \"def __setitem__(self, name, val):\\r\\n matches = self.grep_param_names(name)\\r\\n if len(matches):\\r\\n val = np.array(val)\\r\\n assert (val.size == 1) or val.size == len(matches), \\\"Shape mismatch: {}:({},)\\\".format(val.size, len(matches))\\r\\n x = self._get_params()\\r\\n x[matches] = val\\r\\n self._set_params(x)\\r\\n else:\\r\\n raise AttributeError, \\\"no parameter matches %s\\\" % name\",\n \"def add_param(self, step_id, name, value):\\n if step_id not in self._params:\\n self._params[step_id] = {}\\n self._params[step_id][name] = value\",\n \"def put_parameter(parameter_name, parameter_value, parameter_type, overwrite=False):\\n ssm_client = boto3.client('ssm')\\n\\n try:\\n result = ssm_client.put_parameter(\\n Name=parameter_name,\\n Value=parameter_value,\\n Type=parameter_type,\\n Overwrite=overwrite\\n )\\n logging.info(\\\"%s was added to parameter store\\\", parameter_name)\\n except ClientError as e:\\n logging.error(e)\\n return None\\n return result['Version']\",\n \"def put(self, pvname, value: Union[np.ndarray, float]) -> None:\\n if self.protocol == \\\"ca\\\":\\n caput(pvname, value)\\n\\n elif self.protocol == \\\"pva\\\":\\n self.context.put(pvname, value)\",\n \"def __setattr__(self, name, value):\\n if name in ['parameters', 'program_name']: # Allowed attributes\\n self.__dict__[name] = value\\n else:\\n self.set_parameter(name, value) # treat as a parameter\",\n \"def _update_param_from_tkvar(self,param_name):\\n self.debug(\\\"TkPOb._update_param_from_tkvar(%s)\\\"%param_name)\\n\\n parameter,sourcePO=self.get_parameter_object(param_name,with_source=True)\\n\\n ### can only edit constant parameters for class objects\\n if parameter.constant is True and not isinstance(sourcePO,type):\\n return ### HIDDEN\\n\\n tkvar = self._tkvars[param_name]\\n\\n if self._tkvar_changed(param_name):\\n # don't attempt to set if there was a string-to-object translation error\\n if self.translators[param_name].last_string2object_failed:\\n return ### HIDDEN\\n\\n # (use _original_get() because we don't want the tkvar to be reset to\\n # the parameter's current value!)\\n val = self._string2object(param_name,tkvar._original_get())\\n\\n try:\\n self._set_parameter(param_name,val)\\n except: # everything\\n tkvar.set(tkvar._last_good_val)\\n raise # whatever the parameter-setting error was\\n\\n self.debug(\\\"set %s to %s\\\"%(param_name,val))\\n\\n if hasattr(tkvar,'_on_modify'):\\n tkvar._on_modify()\\n\\n ### call any function associated with GUI set()\\n if hasattr(tkvar,'_on_set'):\\n\\n # CEBALERT: provide a way of allowing other gui components\\n # to figure out where a callback error might have come\\n # from. Callback instances (the Callback class is defined\\n # in Tkinter.py) store a widget, but often it appears to\\n # be the Tk instance - which is of no use in later\\n # determining where an error might have originated.\\n global _last_one_set\\n if hasattr(self,'master'):\\n _last_one_set = self.master\\n\\n tkvar._on_set()\",\n \"def set_parameters(self, params):\\n self.kp = params.pgain\",\n \"def update(self, job_name, param_name, value, description=None):\\n if job_name in self._jobs:\\n getattr(self._jobs[job_name], param_name).update(value, description)\\n else:\\n self.log.error(\\\"Invalid job name: %s\\\", job_name)\",\n \"def set_param(command):\\n namespace = app.main(command)\\n assert namespace.command == 'sp' or namespace.command == \\\"setparam\\\"\\n assert namespace.name == \\\"test\\\"\\n assert namespace.value == \\\"test\\\"\",\n \"def add_parameter(self,\\n name, # The name of the parameter\\n scaling=None, # The type of scaling to be used for the parameter\\n type=\\\"int\\\", # The type of the parameter, such as float\\n min=0, # The minimum value of the parameter\\n max=100, # The maximum value of the parameter\\n significance=1, # The smallest significant step size\\n value=None, # The value or value parameters\\n distribution=None): # The distribution of the parameter\\n config = {\\\"scaling\\\" : scaling, \\n \\\"type\\\": type,\\n \\\"min\\\": min, \\n \\\"max\\\": max, \\n \\\"significance\\\": significance,\\n \\\"value\\\": value,\\n \\\"distribution\\\": distribution}\\n self.param_names.append(name)\\n self.param_settings.append(config)\",\n \"def set_parameter(self, param, value, stripe = 0, location = 3):\\n if param in self.fpga.cabac_top[0].params:\\n self.fpga.set_cabac_value(param, value, stripe)\\n time.sleep(0.1)\\n self.fpga.send_cabac_config(stripe)\\n\\n elif param in [\\\"V_SL\\\", \\\"V_SH\\\", \\\"V_RGL\\\", \\\"V_RGH\\\", \\\"V_PL\\\", \\\"V_PH\\\"]:\\n self.fpga.set_clock_voltages({param: value})\\n\\n elif param == \\\"I_OS\\\":\\n self.fpga.set_current_source(value, stripe)\\n\\n else:\\n print(\\\"Warning: unidentified parameter for the REB: %s\\\" % param)\",\n \"def set_parameter(self, params, name, val):\\n if name == \\\"model\\\":\\n params.model = val\\n return params\\n available_models = [\\n entry_point.name\\n for entry_point in pkg_resources.iter_entry_points(\\n \\\"dxtbx.scaling_model_ext\\\"\\n )\\n ]\\n phil_branches = [\\n params.weighting.error_model,\\n params.cut_data,\\n params.scaling_options,\\n params.reflection_selection,\\n params.reflection_selection.random,\\n params.reflection_selection.random.multi_dataset,\\n ]\\n if params.model:\\n phil_branches.append(params.__getattribute__(str(params.model)))\\n elif (\\\".\\\" in name) and (name.split(\\\".\\\")[0] in available_models):\\n # if the user hasn't specified the model, but have done\\n # e.g physical.parameter = *, then set model=physical\\n params.model = name.split(\\\".\\\")[0]\\n phil_branches.append(params.__getattribute__(str(params.model)))\\n if \\\".\\\" in name: # handle e.g physical.absorption_correction\\n name = name.split(\\\".\\\")[-1]\\n for branch in phil_branches:\\n try:\\n branch.__setattr__(name, val)\\n return params\\n except AttributeError:\\n pass\\n # if get here, haven't found what we're trying to set\\n raise ValueError(\\\"Unable to set chosen attribute \\\" + str(name) + \\\"=\\\" + str(val))\",\n \"def set_param(self):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n setting[1] = str(self.param_value)\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def _update_param_from_tkvar(self,param_name,force=False):\\n self.debug(\\\"TkPO._update_param_from_tkvar(%s)\\\"%param_name)\\n\\n param_obj = self.get_parameter_object(param_name)\\n\\n if not lookup_by_class(self.param_immediately_apply_change,\\n type(param_obj)) and not force:\\n return\\n else:\\n super(TkParameterized,self)._update_param_from_tkvar(param_name)\",\n \"def set_params(self, *arg):\\n pass\",\n \"def set_param(param, value, verbose=False):\\n set_param_raw(param, yaml.load(value), verbose=verbose)\",\n \"def addParameter(self, name, value):\\r\\n if not name:\\r\\n raise InvalidRequest('Parameter name is not a valid.')\\r\\n\\r\\n if name in self._parameters:\\r\\n raise InvalidRequest(\\\"Can not use the same parameter name '{0}' \\\"\\r\\n 'in the same container twice.'.format(name))\\r\\n\\r\\n parameter = self._obj.createParameter(name, value)\\r\\n self._parameters[name] = parameter\\r\\n parameter.notifyOnDeath(self._parameterDied)\",\n \"def SetExportParam(self, name, value):\\n parent, subname = self.FindExport(name)\\n subname = Handle._FixExportName(parent.obj, subname)\\n if not hasattr(parent.obj, subname):\\n raise KeyError(name)\\n if not parent.obj.dirty:\\n parent.obj.StartTransaction()\\n parent.obj.dirty = True\\n setattr(parent.obj, subname, value)\\n return parent.obj\",\n \"def SetParameterValue(self, paramName, value):\\n super(LookupTable, self).SetParameterValue(paramName, value)\\n if paramName == NUMBTABLE_PAR:\\n self.SetTableCount()\\n elif paramName == NUMBSERIES_PAR:\\n self.SetSeriesCount()\\n elif paramName == TABLETAGTYPE_PAR:\\n self.SetTableTagType()\\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\\n idx = long(paramName[len(SERIESTYPE_PAR):])\\n self.SetSeriesTypes(idx)\",\n \"def getParameter(self, name):\",\n \"def _set_params(self,x):\\r\\n self.k._set_params(x)\",\n \"def _add_param(self, name):\\n param = ParameterInfo()\\n param._name = name\\n self._parameters.append(param)\\n return param\",\n \"def rename_param(self, param, name):\\n old_name = param.name\\n new_name = self._get_unique_param_name(name, param.mode)\\n \\n param._name = new_name\\n \\n if param.mode == NodeParam.INPUT:\\n self._input_params.pop(old_name)\\n self._input_params[new_name] = param\\n else:\\n self._output_params.pop(old_name)\\n self._output_params[new_name] = param\\n \\n return new_name\",\n \"def save_parms(self, name):\\n self._save_parms(name.encode())\",\n \"def add_param(self, param):\\n self._params.append(param)\\n self.add_decompostion(param)\",\n \"async def set_param(self, param: str, value: int) -> ArchonCommand:\\n cmd = await self.send_command(f\\\"FASTLOADPARAM {param} {value}\\\")\\n if not cmd.succeeded():\\n raise ArchonError(\\n f\\\"Failed setting parameter {param!r} ({cmd.status.name}).\\\"\\n )\\n return cmd\",\n \"def __adjust_param(self, option):\\n # Get the name of the parameter.\\n name = self.__option_params[option]\\n\\n # Ask the user for a new value.\\n value = float(input(\\\"Enter value for {}: \\\".format(name)))\\n self._params.update(name, value)\\n\\n # Update the description with the new value.\\n desc = self.__make_description(name)\\n self.update_description(option, desc)\\n\\n # Stay on the same menu.\\n return self.get_name()\",\n \"def set(self, **parameters):\\r\\n for name in parameters:\\r\\n if name in self.prm:\\r\\n self.prm[name] = parameters[name]\\r\\n else:\\r\\n self._illegal_parameter(name)\",\n \"def setParameterNode(self, parameterNode):\\r\\n # framework\\r\\n profbox()\\r\\n self.parameterNode = parameterNode\",\n \"def add_param(element):\\n nonlocal params\\n name = element.attrib.get(\\\"name\\\", None)\\n value = element.attrib.get(\\\"value\\\", \\\"\\\")\\n if name:\\n params[name] = value\",\n \"def saveParam(self):\\n qApp.emit(QtCore.SIGNAL(\\\"saveMe\\\"), self._param)\",\n \"def saveParam(self):\\n qApp.emit(QtCore.SIGNAL(\\\"saveMe\\\"), self._param)\",\n \"def __init__(self, owner, name, value):\\r\\n self._registered = False\\r\\n\\r\\n ArgumentMixin.__init__(self, owner.loader)\\r\\n\\r\\n owner.registerParameter(self)\\r\\n self._owner = owner\\r\\n\\r\\n if isinstance(value, basestring):\\r\\n value = self.processArgument(value)\\r\\n\\r\\n self._name = name\\r\\n\\r\\n try:\\r\\n if rospy.has_param(name):\\r\\n log.msg('Warning: Parameter already exists.')\\r\\n\\r\\n rospy.set_param(name, value)\\r\\n self._registered = True\\r\\n except rospy.ROSException as e:\\r\\n raise InternalError('ROS Parameter Server reported an error: '\\r\\n '{0}'.format(e))\",\n \"def set_parameter(self, output):\\n self.model.set_parameter(output);\",\n \"def __updateParameter(self, currentParam, newParam):\\n for i in xrange(len(currentParam)):\\n for np in newParam:\\n if np['name'] == currentParam[i]['name']:\\n currentParam[i] = np\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.89312124","0.8022478","0.7980035","0.7786411","0.77297336","0.75346786","0.746019","0.7395771","0.7331477","0.7268502","0.7261241","0.72502863","0.72311014","0.72027457","0.7178884","0.7141566","0.70621306","0.7020081","0.7006386","0.69946426","0.69921887","0.6981263","0.69764525","0.69764125","0.6957367","0.6941811","0.6940353","0.6910571","0.6903306","0.68879795","0.6886452","0.68841106","0.68754506","0.68547535","0.6850052","0.6837839","0.6804172","0.67015","0.6699659","0.6690361","0.6677788","0.6657204","0.6650763","0.66363585","0.66245943","0.660795","0.6579179","0.65575784","0.6511224","0.65035677","0.6496009","0.64943814","0.64943814","0.64930844","0.64875495","0.64663845","0.6453976","0.64341354","0.64246863","0.64205045","0.6404279","0.636392","0.6354071","0.6352104","0.6340589","0.6323458","0.6306595","0.62960255","0.6267498","0.6262123","0.62466073","0.62374306","0.6233189","0.6232592","0.6219021","0.6218854","0.6211768","0.61797774","0.6176436","0.61535174","0.61494696","0.61313033","0.6123827","0.60807526","0.6079825","0.60796535","0.60792077","0.6076176","0.6074638","0.60711235","0.6061052","0.6058605","0.6048558","0.6035376","0.6031043","0.6024506","0.6024506","0.6020751","0.6012043","0.6010518"],"string":"[\n \"0.89312124\",\n \"0.8022478\",\n \"0.7980035\",\n \"0.7786411\",\n \"0.77297336\",\n \"0.75346786\",\n \"0.746019\",\n \"0.7395771\",\n \"0.7331477\",\n \"0.7268502\",\n \"0.7261241\",\n \"0.72502863\",\n \"0.72311014\",\n \"0.72027457\",\n \"0.7178884\",\n \"0.7141566\",\n \"0.70621306\",\n \"0.7020081\",\n \"0.7006386\",\n \"0.69946426\",\n \"0.69921887\",\n \"0.6981263\",\n \"0.69764525\",\n \"0.69764125\",\n \"0.6957367\",\n \"0.6941811\",\n \"0.6940353\",\n \"0.6910571\",\n \"0.6903306\",\n \"0.68879795\",\n \"0.6886452\",\n \"0.68841106\",\n \"0.68754506\",\n \"0.68547535\",\n \"0.6850052\",\n \"0.6837839\",\n \"0.6804172\",\n \"0.67015\",\n \"0.6699659\",\n \"0.6690361\",\n \"0.6677788\",\n \"0.6657204\",\n \"0.6650763\",\n \"0.66363585\",\n \"0.66245943\",\n \"0.660795\",\n \"0.6579179\",\n \"0.65575784\",\n \"0.6511224\",\n \"0.65035677\",\n \"0.6496009\",\n \"0.64943814\",\n \"0.64943814\",\n \"0.64930844\",\n \"0.64875495\",\n \"0.64663845\",\n \"0.6453976\",\n \"0.64341354\",\n \"0.64246863\",\n \"0.64205045\",\n \"0.6404279\",\n \"0.636392\",\n \"0.6354071\",\n \"0.6352104\",\n \"0.6340589\",\n \"0.6323458\",\n \"0.6306595\",\n \"0.62960255\",\n \"0.6267498\",\n \"0.6262123\",\n \"0.62466073\",\n \"0.62374306\",\n \"0.6233189\",\n \"0.6232592\",\n \"0.6219021\",\n \"0.6218854\",\n \"0.6211768\",\n \"0.61797774\",\n \"0.6176436\",\n \"0.61535174\",\n \"0.61494696\",\n \"0.61313033\",\n \"0.6123827\",\n \"0.60807526\",\n \"0.6079825\",\n \"0.60796535\",\n \"0.60792077\",\n \"0.6076176\",\n \"0.6074638\",\n \"0.60711235\",\n \"0.6061052\",\n \"0.6058605\",\n \"0.6048558\",\n \"0.6035376\",\n \"0.6031043\",\n \"0.6024506\",\n \"0.6024506\",\n \"0.6020751\",\n \"0.6012043\",\n \"0.6010518\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8278058"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11990,"cells":{"query":{"kind":"string","value":"Replaces all quadratic terms in constraints. putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_)"},"document":{"kind":"string","value":"def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubk is None: raise TypeError(\"Invalid type for argument qcsubk\")\n if qcsubk is None:\n qcsubk_ = None\n else:\n try:\n qcsubk_ = memoryview(qcsubk)\n except TypeError:\n try:\n _tmparr_qcsubk = array.array(\"i\",qcsubk)\n except TypeError:\n raise TypeError(\"Argument qcsubk has wrong type\")\n else:\n qcsubk_ = memoryview(_tmparr_qcsubk)\n \n else:\n if qcsubk_.format != \"i\":\n qcsubk_ = memoryview(array.array(\"i\",qcsubk))\n \n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n maxnumqcnz_ = self.getnumqconknz((k_))\n numqcnz_ = ctypes.c_int64()\n _qcsubi_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi is not long enough: Is %d, expected %d\" % (len(qcsubi_),self.getnumqconknz((k_))))\n if isinstance(qcsubi_,numpy.ndarray) and not qcsubi_.flags.writeable:\n raise ValueError(\"Argument qcsubi must be writable\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n _qcsubj_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj is not long enough: Is %d, expected %d\" % (len(qcsubj_),self.getnumqconknz((k_))))\n if isinstance(qcsubj_,numpy.ndarray) and not qcsubj_.flags.writeable:\n raise ValueError(\"Argument qcsubj must be writable\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n _qcval_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval is not long enough: Is %d, expected %d\" % (len(qcval_),self.getnumqconknz((k_))))\n if isinstance(qcval_,numpy.ndarray) and not qcval_.flags.writeable:\n raise ValueError(\"Argument qcval must be writable\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n qcsurp_ = ctypes.c_int64(_qcsubi_minlength)\n res = __library__.MSK_XX_getqconk64(self.__nativep,k_,maxnumqcnz_,ctypes.byref(qcsurp_),ctypes.byref(numqcnz_),_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqcnz_ = numqcnz_.value\n _numqcnz_return_value = numqcnz_\n if _qcsubi_copyarray:\n qcsubi_[:] = _qcsubi_np_tmp\n if _qcsubj_copyarray:\n qcsubj_[:] = _qcsubj_np_tmp\n if _qcval_copyarray:\n qcval_[:] = _qcval_np_tmp\n return (_numqcnz_return_value)","def getqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n maxnumqcnz_ = self.getnumqconknz((k_))\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n _copyback_qcsubi = False\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n _copyback_qcsubi = True\n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n _copyback_qcsubi = True\n if qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi has wrong length\")\n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n _copyback_qcsubj = False\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n _copyback_qcsubj = True\n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n _copyback_qcsubj = True\n if qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj has wrong length\")\n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n _copyback_qcval = False\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n _copyback_qcval = True\n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n _copyback_qcval = True\n if qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval has wrong length\")\n res,resargs = self.__obj.getqconk64(k_,maxnumqcnz_,len(qcsubi),qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqcnz_return_value = resargs\n if _copyback_qcval:\n qcval[:] = _tmparr_qcval\n if _copyback_qcsubj:\n qcsubj[:] = _tmparr_qcsubj\n if _copyback_qcsubi:\n qcsubi[:] = _tmparr_qcsubi\n return _numqcnz_return_value","def qmincon(self, q=None):\n\n def sumsqr(A):\n return np.sum(A**2)\n\n def cost(x, ub, lb, qm, N):\n return sumsqr(\n (2 * (N @ x + qm) - ub - lb) / (ub - lb))\n\n q = getmatrix(q, (None, self.n))\n\n qstar = np.zeros((q.shape[0], self.n))\n error = np.zeros(q.shape[0])\n success = np.zeros(q.shape[0])\n\n lb = self.qlim[0, :]\n ub = self.qlim[1, :]\n\n for k, qk in enumerate(q):\n\n J = self.jacobe(qk)\n\n N = null(J)\n\n x0 = np.zeros(N.shape[1])\n A = np.r_[N, -N]\n b = np.r_[ub - qk, qk - lb].reshape(A.shape[0],)\n\n con = LinearConstraint(A, -np.inf, b)\n\n res = minimize(\n lambda x: cost(x, ub, lb, qk, N),\n x0, constraints=con)\n\n qstar[k, :] = qk + N @ res.x\n error[k] = res.fun\n success[k] = res.success\n\n if q.shape[0] == 1:\n return qstar[0, :], success[0], error[0]\n else:\n return qstar, success, error","def add_cp_qe_RBM_terms(K, cons_pot_mesh, quad_geo_mesh):\n num_faces = cons_pot_mesh.get_faces().shape[0]\n x_c = quad_geo_mesh.get_centroid()\n w = quad_geo_mesh.get_w()\n A_m = quad_geo_mesh.get_A_m()\n S_D = quad_geo_mesh.get_surface_area()\n\n for face_num in range(num_faces):\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n def v_quad(_xi, _eta, _nodes):\n return np.identity(3)\n v_sub_mat = (1. / S_D) * gq.int_over_tri_quad(v_quad, face_nodes, face_hs)\n def omega_quad(xi, eta, nodes):\n pos = geo.quadratic_interp(xi, eta, nodes)\n X = pos - x_c\n return np.einsum(\"lrs,s->lr\", geo.LC_3, X)\n tmp_omega = gq.int_over_tri_quad(\n omega_quad,\n face_nodes,\n face_hs,\n )\n tmp_arr = []\n for m in range(3):\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\"l,ls\", w[m], tmp_omega)))\n tmp_arr = np.array(tmp_arr)\n tmp_omega_mat = np.sum(tmp_arr, axis=0)\n for src_num in range(num_faces):\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += v_sub_mat\n src_center = cons_pot_mesh.get_node(src_num)\n X_0 = src_center - x_c\n omega_mat = np.einsum(\"ijk,js,k->is\", geo.LC_3, tmp_omega_mat, X_0)\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += omega_mat","def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def custom_constr(x, qr, inverse, depth):\n qc = QuantumCircuit(qr)\n maxi, mini = max(x), min(x)\n n = x.shape[0]\n #qc_wv = Wavelets(n).construct_circuit(register=qr)\n for _ in range(depth):\n qc.h(qr)\n for i in range(n):\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini), 2*np.pi*(x[i]-mini)/(maxi-mini), qr[i])\n for i in range(n):\n qc.cx(qr[i], qr[(i + 1) % n])\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini),\n ((2*np.pi)**2*(x[i]-mini)*(x[(i+1) % n]-mini)/(maxi-mini)**2) % 2*np.pi,\n qr[(i + 1) % n])\n qc.cx(qr[i], qr[(i + 1) % n])\n #qc = qc + qc_wv\n if inverse:\n return qc.inverse()\n return qc","def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def conj(q):\n q = np.array([q[0]])\n q[0,1]=-q[0,1]\n q[0,2]=-q[0,2]\n q[0,3]=-q[0,3]\n complexconjugate = quatreal(q)\n return complexconjugate","def ikcon(self, T, q0=None):\n\n if not isinstance(T, SE3):\n T = SE3(T)\n\n trajn = len(T)\n\n try:\n if q0 is not None:\n q0 = getvector(q0, self.n, 'row')\n else:\n q0 = np.zeros((trajn, self.n))\n except ValueError:\n verifymatrix(q0, (trajn, self.n))\n\n # create output variables\n qstar = np.zeros((trajn, self.n))\n error = []\n exitflag = []\n\n omega = np.diag([1, 1, 1, 3 / self.reach])\n\n def cost(q, T, omega):\n return np.sum(\n (\n (np.linalg.pinv(T.A) @ self.fkine(q).A - np.eye(4)) @\n omega) ** 2\n )\n\n bnds = Bounds(self.qlim[0, :], self.qlim[1, :])\n\n for i in range(trajn):\n Ti = T[i]\n res = minimize(\n lambda q: cost(q, Ti, omega),\n q0[i, :], bounds=bnds, options={'gtol': 1e-6})\n qstar[i, :] = res.x\n error.append(res.fun)\n exitflag.append(res.success)\n\n if trajn > 1:\n return qstar, exitflag, error\n else:\n return qstar[0, :], exitflag[0], error[0]","def add_cp_qe_DL_terms(K, cons_pot_mesh, quad_geo_mesh):\n geo_faces = quad_geo_mesh.get_faces()\n pot_faces = cons_pot_mesh.get_faces()\n assert geo_faces.shape[0] == pot_faces.shape[0]\n num_faces = geo_faces.shape[0]\n c_0 = 1. / (4. * np.pi)\n for face_num in range(num_faces): # field points\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_n = quad_geo_mesh.get_quad_n(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n for src_num in range(num_faces): # source points\n src_center = cons_pot_mesh.get_node(src_num)\n if face_num != src_num:\n sub_mat = gq.int_over_tri_quad_n(\n make_cp_qe_quad_func(src_center),\n face_nodes,\n face_n,\n face_hs\n )\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += sub_mat\n K[(3 * src_num):(3 * src_num + 3),\n (3 * src_num):(3 * src_num + 3)] -= sub_mat\n # do nothing face_num == src_num, how it works out for constant elements\n\n for src_num in range(num_faces):\n K[(3 * src_num):(3 * src_num + 3),\n (3 * src_num):(3 * src_num + 3)] -= 4. * np.pi * np.identity(3)\n K *= c_0","def do_reduction_placzek_corrections(q,sqfg,bgd,rescale_bgd=1.0,plaz_type=None,\n gauss_damp=False,gw=20.0,qmax=None,qmin=None,\n rmin=0.0,rmax=20.0,delr=.02\n ,qminpla=10.0,qmaxpla=30.0,ndeg=2, return_correction = False,\n skip_bgd = False, return_final_sq = False, force_qmax_type='Off'):\n #first, make netsq if bgd and/or damping is present\n q = np.array(q)\n sqfg = np.array(sqfg)\n bgd = np.array(bgd)\n\n if skip_bgd:\n netsq = sqfg\n else:\n netsq = sqfg - bgd*rescale_bgd\n\n\n if gauss_damp:\n netsq = netsq*gauss(q,gw,0)\n\n\n if force_qmax_type == 'Force Data (PreCorrection)':\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n netsq -= mean_sqmax\n\n #now, apply a correction if requested\n if plaz_type != None:\n if plaz_type == 'Polynomial' or plaz_type == 'poly' or plaz_type == 'ndeg':\n sq_poly_fit = fit_ndeg_to_sq(q,netsq,ndeg=ndeg,qmin=qminpla,qmax=qmaxpla)\n this_fit = sq_poly_fit\n elif plaz_type == 'Pseudo-Voight' or plaz_type == 'pv' or plaz_type == 'hydro':\n pv_fit = fit_pv_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_fit\n elif plaz_type == 'PVoight + n0' or plaz_type == 'pvndeg0':\n pv_n0_fit = fit_pv_n0_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n0_fit\n elif plaz_type == 'PVoight + n1' or plaz_type == 'pvndeg1':\n pv_n1_fit = fit_pv_n1_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n1_fit\n elif plaz_type == 'PVoight + n2' or plaz_type == 'pvndeg2':\n pv_n2_fit = fit_pv_n2_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n2_fit\n else:\n print (\"I don't know that correction type, sorry\")\n this_fit = np.zeros(len(q))\n else:\n this_fit = np.zeros(len(q))\n\n if force_qmax_type == 'Force Data' or force_qmax_type == 'Force Both (Independent)':\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n netsq -= mean_sqmax\n if force_qmax_type == 'Force Correction' or force_qmax_type == 'Force Both (Independent)':\n qcut, sqcut = cut_data(q,this_fit,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n this_fit -= mean_sqmax\n if force_qmax_type == 'ReCorrection':\n qcut, sqcut = cut_data(q,netsq-this_fit,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n this_fit += mean_sqmax\n\n netsq = netsq - this_fit\n\n if return_correction:\n return this_fit\n\n if return_final_sq:\n return netsq\n\n #finally, generate PDF\n r,gr = make_gr_from_sq(q,netsq,qmin=qmin,qmax=qmax,rmin=rmin,rmax=rmax,delr=delr)\n\n return r,gr","def putcone(self,k_,ct_,conepar_,submem_):\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem_)\n elif nummem_ != len(submem_):\n raise IndexError(\"Inconsistent length of array submem\")\n if submem_ is None:\n raise ValueError(\"Argument submem cannot be None\")\n if submem_ is None:\n raise ValueError(\"Argument submem may not be None\")\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\n _submem_copyarray = False\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif submem_ is not None:\n _submem_copyarray = True\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\n _submem_np_tmp[:] = submem_\n assert _submem_np_tmp.flags.contiguous\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _submem_copyarray = False\n _submem_tmp = None\n \n res = __library__.MSK_XX_putcone(self.__nativep,k_,ct_,conepar_,nummem_,_submem_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putcone(self,k_,ct_,conepar_,submem): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem)\n elif nummem_ != len(submem):\n raise IndexError(\"Inconsistent length of array submem\")\n if nummem_ is None: nummem_ = 0\n if submem is None: raise TypeError(\"Invalid type for argument submem\")\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n \n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n \n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconbound(self,i_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _ect_qrs_tconst(pattern, qrs):\n beats = pattern.evidence[o.QRS]\n idx = beats.index(qrs)\n tnet = pattern.last_tnet\n hyp = pattern.hypothesis\n if idx > 0:\n prev = beats[idx - 1]\n # After the second couplet, every ectopic beat introduces a new temporal\n # network in the pattern to make it easier the minimization.\n if idx > 3:\n tnet.remove_constraint(hyp.end, prev.time)\n # We create a new temporal network for the cyclic observations\n tnet = ConstraintNetwork()\n pattern.temporal_constraints.append(tnet)\n # The duration of each couplet should not have high instantaneous\n # variations.\n refrr = beats[idx - 2].time.end - beats[idx - 3].time.start\n tnet.add_constraint(prev.time, qrs.time, Iv(refrr - C.RR_MAX_DIFF, refrr + C.RR_MAX_DIFF))\n # We guide the morphology search to be similar to the previous\n # ectopic QRS complex.\n qrs.shape = beats[idx - 2].shape\n # The reference RR varies from an upper limit to the last measurement,\n # through the contextual previous rhythm.\n refrr = C.BRADY_RR.end\n stdrr = 0.1 * refrr\n if pattern.evidence[o.Cardiac_Rhythm] and idx == 1:\n mrr, srr = pattern.evidence[o.Cardiac_Rhythm][0].meas.rr\n if mrr > 0:\n refrr, stdrr = mrr, srr\n elif idx > 1:\n refrr, stdrr = hyp.meas.rr\n # Ectopic beats must be advanced wrt the reference RR\n tnet.add_constraint(prev.time, qrs.time, Iv(C.TACHY_RR.start, max(C.TACHY_RR.start, refrr - stdrr)))\n # Beats cannot overlap\n tnet.add_constraint(prev.end, qrs.start, Iv(C.TQ_INTERVAL_MIN, np.Inf))\n BASIC_TCONST(pattern, qrs)\n tnet.add_constraint(qrs.start, qrs.end, C.QRS_DUR)\n tnet.set_before(qrs.time, hyp.end)\n # Constraints with the precedent T Wave\n _qrs_after_twave(pattern, qrs)","def vahlen_conj(q_1: Q, conj_type: str = \"-\", q_type: str = \"vc\") -> Q:\n\n vc_t, vc_x, vc_y, vc_z = q_1.t, q_1.x, q_1.y, q_1.z\n c_q = Q()\n\n if conj_type == \"-\":\n c_q.t = vc_t\n if vc_x != 0:\n c_q.x = -1 * vc_x\n if vc_y != 0:\n c_q.y = -1 * vc_y\n if vc_z != 0:\n c_q.z = -1 * vc_z\n q_type += \"*-\"\n\n if conj_type == \"'\":\n c_q.t = vc_t\n if vc_x != 0:\n c_q.x = -1 * vc_x\n if vc_y != 0:\n c_q.y = -1 * vc_y\n c_q.z = vc_z\n q_type += \"*'\"\n\n if conj_type == \"*\":\n c_q.t = vc_t\n c_q.x = vc_x\n c_q.y = vc_y\n if vc_z != 0:\n c_q.z = -1 * vc_z\n q_type += \"*\"\n\n c_q.q_type = f\"{q_1.q_type}{q_type}\"\n c_q.representation = q_1.representation\n\n return c_q","def mk_q(self, xc: list, yc: list):\n for i in range(len(xc) - 1):\n cur = cor(xc[i], xc[i + 1])\n self.xq.put((-cur.dist, cur))\n cur = cor(yc[i], yc[i + 1])\n self.yq.put((-cur.dist, cur))\n self.rnd_mk()","def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valjkl_minlength = (num_)\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl is not long enough: Is %d, expected %d\" % (len(valjkl_),(num_)))\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl cannot be None\")\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl may not be None\")\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\n _valjkl_copyarray = False\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valjkl_ is not None:\n _valjkl_copyarray = True\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\n _valjkl_np_tmp[:] = valjkl_\n assert _valjkl_np_tmp.flags.contiguous\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valjkl_copyarray = False\n _valjkl_tmp = None\n \n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_equality_constraint_multipliers(self, eq_con_multiplier_values):\n # we should check these for efficiency\n assert self.n_equality_constraints() == len(eq_con_multiplier_values)\n if (\n not hasattr(self, 'evaluate_hessian_equality_constraints')\n or self.n_equality_constraints() == 0\n ):\n return\n\n raise NotImplementedError(\n 'Derived ExternalGreyBoxModel classes need to implement'\n ' set_equality_constraint_multipliers when they'\n ' support Hessian computations.'\n )","def Q(self, k, x):\n g = np.asarray(self.g(k, x))\n Q = g @ g.T\n return Q","def update_q(self):\n beta = self.EC_beta\n self.gamma_q = (self.gamma_s - self.gamma_r) * beta + (1 - beta) * self.gamma_q\n self.Sigma_q = (self.Sigma_s - self.Sigma_r) * beta + (1 - beta) * self.Sigma_q\n try:\n assert np.all(np.logical_not(np.isnan(self.gamma_q)))\n except:\n print(\"Invalid update encountered...\")","def SetPRCatConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for line in tot :\n for val in line :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def putqobj(self,qosubi_,qosubj_,qoval_):\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi_)\n elif numqonz_ != len(qosubi_):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj_)\n elif numqonz_ != len(qosubj_):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval_)\n elif numqonz_ != len(qoval_):\n raise IndexError(\"Inconsistent length of array qoval\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi cannot be None\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi may not be None\")\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\n _qosubi_copyarray = False\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubi_ is not None:\n _qosubi_copyarray = True\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\n _qosubi_np_tmp[:] = qosubi_\n assert _qosubi_np_tmp.flags.contiguous\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubi_copyarray = False\n _qosubi_tmp = None\n \n if qosubj_ is None:\n raise ValueError(\"Argument qosubj cannot be None\")\n if qosubj_ is None:\n raise ValueError(\"Argument qosubj may not be None\")\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\n _qosubj_copyarray = False\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubj_ is not None:\n _qosubj_copyarray = True\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\n _qosubj_np_tmp[:] = qosubj_\n assert _qosubj_np_tmp.flags.contiguous\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubj_copyarray = False\n _qosubj_tmp = None\n \n if qoval_ is None:\n raise ValueError(\"Argument qoval cannot be None\")\n if qoval_ is None:\n raise ValueError(\"Argument qoval may not be None\")\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\n _qoval_copyarray = False\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qoval_ is not None:\n _qoval_copyarray = True\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\n _qoval_np_tmp[:] = qoval_\n assert _qoval_np_tmp.flags.contiguous\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qoval_copyarray = False\n _qoval_tmp = None\n \n res = __library__.MSK_XX_putqobj(self.__nativep,numqonz_,_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def conj_q(q_1: Q, q_2: Q) -> Q:\n\n _conj = deepcopy(q_1)\n\n if q_2.t:\n _conj = conj(_conj, conj_type=0)\n\n if q_2.x:\n _conj = conj(_conj, conj_type=1)\n\n if q_2.y:\n _conj = conj(_conj, conj_type=2)\n\n if q_2.z:\n _conj = flip_sign(_conj)\n\n return _conj","def _update_tarsqidoc(self, cp):\n self.tarsqidoc.remove_tlinks()\n for n1, rest in cp.graph.edges.items():\n for n2, edge in cp.graph.edges[n1].items():\n if edge.constraint is not None:\n if edge.constraint.has_simple_relation():\n self._add_constraint_to_tarsqidoc(edge)","def eqconstr(x, problem):\n x, t_final = matrify(x, problem)\n return np.concatenate([problem['dynamics'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])","def quadratic_strain(x, dof):\n base = np.zeros([6, dof])\n base[1, 0] = 1 # initial y-bending\n if dof > 2:\n base[1, 1] = x**2 # quadratic y-bending term\n base[2, dof-1] = x**2 # quadratic z-bending term\n return base","def quaternion_conjugate(self, q):\n\n \"\"\"\n in-place operation is an operation that changes directly the content of a given Tensor without making a copy.\n ALL operations on the tensor that operate in-place on it will have an _ postfix.\n \"\"\"\n q_star = q.new(4).fill_(-1)\n\n # leave the scalar unchanged and change signs of i, j, k number parts\n q_star[0] = 1.0\n\n return q * q_star","def putconbound(self,i_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconboundlist(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkc_)\n elif num_ != len(bkc_):\n raise IndexError(\"Inconsistent length of array bkc\")\n if num_ is None:\n num_ = len(blc_)\n elif num_ != len(blc_):\n raise IndexError(\"Inconsistent length of array blc\")\n if num_ is None:\n num_ = len(buc_)\n elif num_ != len(buc_):\n raise IndexError(\"Inconsistent length of array buc\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def pro_avfid_superoperator_compsubspace_phasecorrected_onlystaticqubit(U,L1,phases):\n\n Ucorrection = qtp.Qobj([[np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0, 0],\n [0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0],\n [0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0],\n [0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0, 0, 0],\n [0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0, 0],\n [0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0],\n [0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0],\n [0, 0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0],\n [0, 0, 0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0]))]],\n type='oper',\n dims=[[3, 3], [3, 3]])\n\n if U.type=='oper':\n U=Ucorrection*U\n inner = U.dag()*U_target\n part_idx = [0, 1, 3, 4] # only computational subspace\n ptrace = 0\n for i in part_idx:\n ptrace += inner[i, i]\n dim = 4 # 2 qubits comp subspace \n\n return np.real(((np.abs(ptrace))**2+dim*(1-L1))/(dim*(dim+1)))\n\n elif U.type=='super':\n U=qtp.to_super(Ucorrection)*U\n kraus_form = qtp.to_kraus(U)\n dim=4 # 2 qubits in the computational subspace\n part_idx = [0, 1, 3, 4] # only computational subspace\n psum=0\n for A_k in kraus_form:\n ptrace = 0\n inner = U_target_diffdims.dag()*A_k # otherwise dimension mismatch\n for i in part_idx:\n ptrace += inner[i, i]\n psum += (np.abs(ptrace))**2\n\n return np.real((dim*(1-L1) + psum) / (dim*(dim + 1)))","def putqobjij(self,i_,j_,qoij_): # 3\n res = self.__obj.putqobjij(i_,j_,qoij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solve_constraint_generic(problem, enqueue_condition=None) :\n q = [problem]\n extCount = 0\n while len(q)!=0:\n removed = q[0]\n q = q[1:]\n extCount+=1\n if has_empty_domains(removed) or check_all_constraints(removed)==False:\n continue\n if len(removed.unassigned_vars)==0:\n return (removed.assigned_values,extCount)\n \n var = removed.pop_next_unassigned_var()\n extensions = []\n for val in removed.get_domain(var):\n csp_new = removed.copy()\n csp_new.set_assigned_value(var,val)\n if (enqueue_condition!=None):\n propagate(enqueue_condition,csp_new,[var])\n extensions.append(csp_new)\n \n q = extensions + q\n return (None,extCount)","def convex_conj(self):\n if self.operator is None:\n tmp = IndicatorZero(space=self.domain, constant=-self.constant)\n if self.vector is None:\n return tmp\n else:\n return tmp.translated(self.vector)\n\n if self.vector is None:\n # Handle trivial case separately\n return QuadraticForm(operator=self.operator.inverse,\n constant=-self.constant)\n else:\n # Compute the needed variables\n opinv = self.operator.inverse\n vector = -opinv.adjoint(self.vector) - opinv(self.vector)\n constant = self.vector.inner(opinv(self.vector)) - self.constant\n\n # Create new quadratic form\n return QuadraticForm(operator=opinv,\n vector=vector,\n constant=constant)","def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def contractor(self, *args, **kwargs):\n vertices = copy.deepcopy(args[0])\n nrange = len(vertices[0])\n xpts = []\n ypts = []\n for i in range(nrange):\n xpts.append(vertices[0][i].value)\n ypts.append(vertices[1][i].value)\n constraint = copy.deepcopy(args[1])\n \n \n \n \n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\n \n ## the all important computation split (need to abstract this kind of thing)\n ##lhs = (np.sqrt(qxdot*qxdot + qydot*qydot)**3.) *constraint\n lhs = ( ( np.sqrt(qxdot**2 + qydot**2) )**3 )*constraint\n \n # check2 = qxdot*qyddot\n # if check2.width() < 1.e-2:\n # check2.min.value = check2.real.value\n # check2.max.value = check2.real.value\n # t1 = (lhs - check2)/qydot\n \n #\n # qyddot\n #\n check2 = qydot*qxddot\n if check2.width() < 1.e-2 and check2.contains(0.):\n check2.inf = 0.\n check2.sup = 0.\n #if qxdot.contains(0.) and abs(qxdot.min.value)>1.e-6:\n # print 'qxdot = ',qxdot\n # print 'qxdot not invertable, implement other logic please'\n if abs(float(qxdot.inf))<1.e-6:\n qxdot.inf = 1.e-10\n print 'invert qxdot'\n print 'qxdot = ', qxdot\n \n #t1 = (lhs + qydot*qxddot)/(qxdot)\n t1 = (lhs + check2)/(qxdot)\n \n t1 = t1 & qyddot # go ahead and shrink t1 to qyddot - they are logically equivalent\n total_ans = []\n useful_indices = []\n bad_indices = []\n for i in range(len(ypts)): \n min_ans = 0.\n for j in range(len(ypts)):\n if j==i:\n pass\n else:\n min_ans = (ypts[j]*float(self.localBasis[2,j])) + min_ans\n min_ans = t1 - min_ans\n if (abs(float(self.localBasis[2,i])) > 0.0):\n min_ans = min_ans/float(self.localBasis[2,i])\n useful_indices.append(i)\n else:\n bad_indices.append(i)\n total_ans.append(min_ans)\n \n new_ans = vector_AND_(ypts, total_ans)\n for i in useful_indices:\n if new_ans[i].isempty == False: # abs( new_ans[i].width() ) > 0.:\n ypts[i] = ypts[i] & new_ans[i]\n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\n else:\n print 'warning, possible constraint violation, curvature 1'\n \n ## \n ## qxdot\n ##\n check2 = qydot*qxddot\n if check2.width() < 1.e-2 and check2.contains(0.):\n check2.inf = 0.\n check2.sup = 0.\n #if qyddot.contains(0.):\n # print 'qyddot = ',qyddot\n # print 'qyddot not invertable, implement other logic please'\n \n if qyddot.contains(0.) and qyddot.width()<1.e-6:\n qxdot.inf = 0.#1.e-10\n print 'invert qyddot'\n print 'qyddot = ',qyddot\n fix = (lhs + check2)*(1./qyddot)#*(qyddot**-1.)\n fix = fix & qxdot # go ahead and shrink fix to qxdot - they are logically equivalent\n total_ans = []\n useful_indices = []\n bad_indices = []\n \n for i in range(len(xpts)): #contract on x[i]\n min_ans = 0.\n for j in range(len(xpts)): # add up all jth pieces of the dot product except i\n if j==i:\n pass\n else:\n \n min_ans = (xpts[j]*float(self.localBasis[1,j] ) ) + min_ans\n min_ans = fix - min_ans\n if (abs(float(self.localBasis[1,i]) ) >0.0 ):\n min_ans = min_ans/float(self.localBasis[1,i])\n useful_indices.append(i)\n else:\n bad_indices.append(i)\n total_ans.append(min_ans)\n \n new_ans = vector_AND_(xpts, total_ans)\n for i in useful_indices:\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\n xpts[i] = xpts[i] & new_ans[i]\n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\n else:\n print 'warning, possible constraint violation, curvature 2'\n \n \n ## switch to the other side\n \n ##\n ## contract on qydot\n ##\n check2 = qxdot*qyddot\n if check2.width() < 1.e-2 and check2.contains(0.):\n check2.inf = 0.\n check2.sup = 0.\n# if qxddot.contains(0.):\n# print 'qxddot = ',qxddot\n# print 'qxddot not invertable, implement other logic please'\n# qxddot.min.value = 0.\n if qxddot.contains(0.):\n qxddot.inf = 0.\n \n print 'invert qxddot'\n print 'qxddot = ',qxddot\n t1 = (lhs - check2)/(-qxddot)#*(-qxddot**-1)\n t1 = t1 & qydot\n total_ans = []\n useful_indices = []\n bad_indices = []\n for i in range(len(ypts)): \n min_ans = 0.\n for j in range(len(ypts)):\n if j==i:\n pass\n else:\n #print 't1 = ',t1\n #print 'ypts[{}] = {}'.format(i,ypts[i])\n #print 'localbasis[{},{}] = {}'.format(1,i,self.localBasis[1,j])\n min_ans = (ypts[j]*float(self.localBasis[1,j])) + min_ans\n min_ans = t1 - min_ans\n if (abs(float(self.localBasis[1,i])) > 0.0):\n min_ans = min_ans/float(self.localBasis[1,i])\n useful_indices.append(i)\n else:\n bad_indices.append(i)\n total_ans.append(min_ans)\n \n new_ans = vector_AND_(ypts, total_ans)\n for i in useful_indices:\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\n ypts[i] = ypts[i] & new_ans[i]\n else:\n print 'warning, possible constraint violation, curvature 3'\n \n ##contract on qxdot\n \n check2 = qxdot*qyddot\n if check2.width() < 1.e-2 and check2.contains(0.):\n check2.inf = 0.\n check2.sup = 0.\n #contract on qxddot\n# if qydot.contains(0.):\n# print 'qydot = ',qxddot\n# print 'qydot not invertable, implement other logic please'\n if qydot.contains(0.):\n qydot.inf = 0.\n print 'invert qydot'\n print 'qydot = ',qydot\n fix = (lhs - qxdot*qyddot)/(-qydot)#*(-qydot**-1)\n fix = fix & qxddot # go ahead and shrink t1 to quddot - they are logically equivalent\n total_ans = []\n useful_indices = []\n bad_indices = []\n for i in range(len(xpts)):\n min_ans = 0.\n for j in range(len(xpts)):\n if j==i:\n pass\n else:\n min_ans = (xpts[j]*float(self.localBasis[2,j] ) ) + min_ans\n min_ans = fix - min_ans\n if (abs(float(self.localBasis[2,i]) ) >0.0 ):\n min_ans = min_ans/float(self.localBasis[2,i])\n useful_indices.append(i)\n else:\n bad_indices.append(i)\n total_ans.append(min_ans)\n \n new_ans = vector_AND_(xpts, total_ans)\n for i in useful_indices:\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\n xpts[i] = xpts[i] & new_ans[i]\n else:\n print 'warning, possible constraint violation, curvature 4'\n \n for i in range(nrange):\n vertices[0][i].value = xpts[i]\n vertices[1][i].value = ypts[i]\n return vertices","def putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_):\n _subi_minlength = (num_)\n if (num_) > 0 and subi_ is not None and len(subi_) != (num_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(num_)))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valijkl_minlength = (num_)\n if (num_) > 0 and valijkl_ is not None and len(valijkl_) != (num_):\n raise ValueError(\"Array argument valijkl is not long enough: Is %d, expected %d\" % (len(valijkl_),(num_)))\n if valijkl_ is None:\n raise ValueError(\"Argument valijkl cannot be None\")\n if valijkl_ is None:\n raise ValueError(\"Argument valijkl may not be None\")\n if isinstance(valijkl_, numpy.ndarray) and valijkl_.dtype is numpy.dtype(numpy.float64) and valijkl_.flags.contiguous:\n _valijkl_copyarray = False\n _valijkl_tmp = ctypes.cast(valijkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valijkl_ is not None:\n _valijkl_copyarray = True\n _valijkl_np_tmp = numpy.zeros(len(valijkl_),numpy.dtype(numpy.float64))\n _valijkl_np_tmp[:] = valijkl_\n assert _valijkl_np_tmp.flags.contiguous\n _valijkl_tmp = ctypes.cast(_valijkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valijkl_copyarray = False\n _valijkl_tmp = None\n \n res = __library__.MSK_XX_putbarablocktriplet(self.__nativep,num_,_subi_tmp,_subj_tmp,_subk_tmp,_subl_tmp,_valijkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def ket(self: Qs) -> Qs:\n\n if self.qs_type == \"ket\":\n return self\n\n ket = conjs(deepcopy(self))\n ket.rows = self.dim\n ket.columns = 1\n\n ket.qs_type = \"ket\" if self.dim > 1 else \"scalar_q\"\n\n return ket","def putbarcj(self,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexError(\"Inconsistent length of array weights\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n \n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n \n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n \n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqobj(self,qosubi,qosubj,qoval): # 3\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi)\n elif numqonz_ != len(qosubi):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj)\n elif numqonz_ != len(qosubj):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval)\n elif numqonz_ != len(qoval):\n raise IndexError(\"Inconsistent length of array qoval\")\n if numqonz_ is None: numqonz_ = 0\n if qosubi is None: raise TypeError(\"Invalid type for argument qosubi\")\n if qosubi is None:\n qosubi_ = None\n else:\n try:\n qosubi_ = memoryview(qosubi)\n except TypeError:\n try:\n _tmparr_qosubi = array.array(\"i\",qosubi)\n except TypeError:\n raise TypeError(\"Argument qosubi has wrong type\")\n else:\n qosubi_ = memoryview(_tmparr_qosubi)\n \n else:\n if qosubi_.format != \"i\":\n qosubi_ = memoryview(array.array(\"i\",qosubi))\n \n if qosubj is None: raise TypeError(\"Invalid type for argument qosubj\")\n if qosubj is None:\n qosubj_ = None\n else:\n try:\n qosubj_ = memoryview(qosubj)\n except TypeError:\n try:\n _tmparr_qosubj = array.array(\"i\",qosubj)\n except TypeError:\n raise TypeError(\"Argument qosubj has wrong type\")\n else:\n qosubj_ = memoryview(_tmparr_qosubj)\n \n else:\n if qosubj_.format != \"i\":\n qosubj_ = memoryview(array.array(\"i\",qosubj))\n \n if qoval is None: raise TypeError(\"Invalid type for argument qoval\")\n if qoval is None:\n qoval_ = None\n else:\n try:\n qoval_ = memoryview(qoval)\n except TypeError:\n try:\n _tmparr_qoval = array.array(\"d\",qoval)\n except TypeError:\n raise TypeError(\"Argument qoval has wrong type\")\n else:\n qoval_ = memoryview(_tmparr_qoval)\n \n else:\n if qoval_.format != \"d\":\n qoval_ = memoryview(array.array(\"d\",qoval))\n \n res = self.__obj.putqobj(numqonz_,qosubi_,qosubj_,qoval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def convert_to_q(self):\n if self.measure == 'Q':\n warnings.warn('Parameters are already converted to Q!')\n else:\n kappa_sp = self.kappa_s\n kappa_yp = self.kappa_y\n self.kappa_s = self.kappa_s - self.lmbd_s * self.eta_s\n self.kappa_y = self.kappa_y - self.lmbd_y * self.eta_y\n self.scale = kappa_sp / self.kappa_s\n self.mean_v *= (kappa_yp / self.kappa_y * self.scale)\n self.lmbd = 0\n self.eta_y *= (self.scale**.5)\n self.measure = 'Q'\n self.update_ajd()","def con_ceq(x,project):\n \n cons = project.con_ceq(x)\n \n if cons: cons = array(cons)\n else: cons = zeros([0])\n \n return cons","def quadratic_dependence(dep_keys, ctfs=list(), force_zero=None, **kwargs):\n if force_zero is None and len(kwargs) == 0:\n return _dependence(\n f=np.polyval, n_params=3,\n dep_keys=dep_keys, ctfs=ctfs, force_zero=force_zero,\n name='quadratic dependence', code='p2:{}', **kwargs\n )\n else:\n return _dependence(\n f=lambda p, x: np.polyval(np.concatenate((p, np.zeros(1))), x),\n n_params=2, dep_keys=dep_keys, ctfs=ctfs, force_zero=force_zero,\n name='quadratic dependence', code='p2:{}', **kwargs\n )","def putskc(self,whichsol_,skc_):\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if skc_ is None:\n raise ValueError(\"Argument skc cannot be None\")\n if skc_ is None:\n raise ValueError(\"Argument skc may not be None\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def Q2C(self, q):\n\n #q = q.squeeze();\n C = np.empty((3,3));\n\tC[0,0] = (q[0]**2.0) + (q[1]**2.0) - (q[2]**2.0) - (q[3]**2.0);\n\tC[0,1] = 2.0 * ((q[1]*q[2]) + (q[0]*q[3]));\n\tC[0,2] = 2.0 * ((q[1]*q[3]) - (q[0]*q[2]));\n\n\tC[1,0] = 2.0 * ((q[1]*q[2]) - (q[0]*q[3]));\n\tC[1,1] = (q[0]**2.0) - (q[1]**2.0) + (q[2]**2.0) - (q[3]**2.0);\n\tC[1,2] = 2.0 * ((q[2]*q[3]) + (q[0]*q[1]));\n\n\tC[2,0] = 2.0 * ((q[1]*q[3]) + (q[0]*q[2]));\n\tC[2,1] = 2.0 * ((q[2]*q[3]) - (q[0]*q[1]));\n\tC[2,2] = (q[0]**2.0) - (q[1]**2.0) - (q[2]**2.0) + (q[3]**2.0);\n\n return C","def putclist(self,subj,val): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(val)\n elif num_ != len(val):\n raise IndexError(\"Inconsistent length of array val\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if val is None: raise TypeError(\"Invalid type for argument val\")\n if val is None:\n val_ = None\n else:\n try:\n val_ = memoryview(val)\n except TypeError:\n try:\n _tmparr_val = array.array(\"d\",val)\n except TypeError:\n raise TypeError(\"Argument val has wrong type\")\n else:\n val_ = memoryview(_tmparr_val)\n \n else:\n if val_.format != \"d\":\n val_ = memoryview(array.array(\"d\",val))\n \n res = self.__obj.putclist(num_,subj_,val_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def conj(q_1: Q, conj_type: int = 0) -> Q:\n\n end_q_type = f\"{q_1.q_type}*\"\n c_t, c_x, c_y, c_z = q_1.t, q_1.x, q_1.y, q_1.z\n cq = Q()\n\n if conj_type % 4 == 0:\n cq.t = c_t\n if c_x != 0:\n cq.x = -1 * c_x\n if c_y != 0:\n cq.y = -1 * c_y\n if c_z != 0:\n cq.z = -1 * c_z\n\n elif conj_type % 4 == 1:\n if c_t != 0:\n cq.t = -1 * c_t\n cq.x = c_x\n if c_y != 0:\n cq.y = -1 * c_y\n if c_z != 0:\n cq.z = -1 * c_z\n end_q_type += \"1\"\n\n elif conj_type % 4 == 2:\n if c_t != 0:\n cq.t = -1 * c_t\n if c_x != 0:\n cq.x = -1 * c_x\n cq.y = c_y\n if c_z != 0:\n cq.z = -1 * c_z\n end_q_type += \"2\"\n\n elif conj_type % 4 == 3:\n if c_t != 0:\n cq.t = -1 * c_t\n if c_x != 0:\n cq.x = -1 * c_x\n if c_y != 0:\n cq.y = -1 * c_y\n cq.z = c_z\n end_q_type += \"3\"\n\n cq.q_type = end_q_type\n cq.representation = q_1.representation\n\n return cq","def evaluate_C_q(self, q):\n C_q_list = []\n\n\n GlobalVariables.q_i_dim[body_id]","def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n \n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n \n if subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n \n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n \n if subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valjkl is None: raise TypeError(\"Invalid type for argument valjkl\")\n if valjkl is None:\n valjkl_ = None\n else:\n try:\n valjkl_ = memoryview(valjkl)\n except TypeError:\n try:\n _tmparr_valjkl = array.array(\"d\",valjkl)\n except TypeError:\n raise TypeError(\"Argument valjkl has wrong type\")\n else:\n valjkl_ = memoryview(_tmparr_valjkl)\n \n else:\n if valjkl_.format != \"d\":\n valjkl_ = memoryview(array.array(\"d\",valjkl))\n \n if valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl has wrong length\")\n res = self.__obj.putbarcblocktriplet(num_,subj_,subk_,subl_,valjkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqobjij(self,i_,j_,qoij_):\n res = __library__.MSK_XX_putqobjij(self.__nativep,i_,j_,qoij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def test_init_q(self):\n\n riskfree = .01\n lmbd = .01\n lmbd_s = .5\n lmbd_y = .5\n mean_v = .5\n kappa_s = 1.5\n kappa_y = .5\n eta_s = .1\n eta_y = .01\n rho = -.5\n\n param = CentTendParam(riskfree=riskfree,\n lmbd=lmbd, lmbd_s=lmbd_s, lmbd_y=lmbd_y,\n mean_v=mean_v, kappa_s=kappa_s, kappa_y=kappa_y,\n eta_s=eta_s, eta_y=eta_y, rho=rho, measure='Q')\n\n kappa_sq = kappa_s - lmbd_s * eta_s\n kappa_yq = kappa_y - lmbd_y * eta_y\n scale = kappa_s / kappa_sq\n\n self.assertEqual(param.measure, 'Q')\n self.assertEqual(param.riskfree, riskfree)\n self.assertEqual(param.lmbd, 0)\n self.assertEqual(param.lmbd_s, lmbd_s)\n self.assertEqual(param.lmbd_y, lmbd_y)\n self.assertEqual(param.mean_v, mean_v * kappa_y / kappa_yq * scale)\n self.assertEqual(param.kappa_s, kappa_sq)\n self.assertEqual(param.kappa_y, kappa_yq)\n self.assertEqual(param.eta_s, eta_s)\n self.assertEqual(param.eta_y, eta_y * scale**.5)\n self.assertEqual(param.rho, rho)\n self.assertTrue(param.is_valid())\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n param.convert_to_q()","def solve_Q(ih, jh):\n # Build A and b\n\n # Solve for c via least squares.\n\n # Form C from c\n\n # Solve for Q and return it.\n return None","def putxc(self,whichsol_,xc_):\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp","def project_curve(q):\n n,T = q.shape\n if n==2:\n dt = 0.35\n if n==3:\n dt = 0.2\n epsilon = 1e-6\n\n iter = 1\n res = ones(n)\n J = zeros((n,n))\n\n s = linspace(0,1,T)\n\n qnew = q.copy()\n qnew = qnew / sqrt(innerprod_q2(qnew,qnew))\n\n qnorm = zeros(T)\n G = zeros(n)\n C = zeros(300)\n while (norm(res) > epsilon):\n if iter > 300:\n break\n\n # Jacobian\n for i in range(0,n):\n for j in range(0,n):\n J[i,j] = 3 * trapz(qnew[i,:]*qnew[j,:],s)\n \n J += eye(n)\n\n for i in range(0,T):\n qnorm[i] = norm(qnew[:,i])\n \n # Compute the residue\n for i in range(0,n):\n G[i] = trapz(qnew[i,:]*qnorm,s)\n \n res = -G\n\n if (norm(res) < epsilon):\n break\n\n x = solve(J,res)\n C[iter] = norm(res)\n\n delG = Basis_Normal_A(qnew)\n temp = zeros((n,T))\n for i in range(0,n):\n temp += x[i]*delG[i]*dt\n \n qnew += temp\n iter += 1\n \n qnew = qnew/sqrt(innerprod_q2(qnew,qnew))\n\n return qnew","def test_solve_quadratic(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.fixed = False\n iden2.x.fixed = False\n iden3.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 0)\n self.assertAlmostEqual(iden1.x.val, 1)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 3)","def Qc(I, dT, a, b, c, d, e, f, g, h, i, k):\n x1 = I # I\n x2 = dT # dT\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\n return m * x2 + b","def solvewithbasis(self,transp_,numnz_,sub_,val_):\n _numnz_tmp = ctypes.c_int32(numnz_)\n _sub_minlength = self.getnumcon()\n if self.getnumcon() > 0 and sub_ is not None and len(sub_) != self.getnumcon():\n raise ValueError(\"Array argument sub is not long enough: Is %d, expected %d\" % (len(sub_),self.getnumcon()))\n if isinstance(sub_,numpy.ndarray) and not sub_.flags.writeable:\n raise ValueError(\"Argument sub must be writable\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _val_minlength = self.getnumcon()\n if self.getnumcon() > 0 and val_ is not None and len(val_) != self.getnumcon():\n raise ValueError(\"Array argument val is not long enough: Is %d, expected %d\" % (len(val_),self.getnumcon()))\n if isinstance(val_,numpy.ndarray) and not val_.flags.writeable:\n raise ValueError(\"Argument val must be writable\")\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\n _val_copyarray = False\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif val_ is not None:\n _val_copyarray = True\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\n _val_np_tmp[:] = val_\n assert _val_np_tmp.flags.contiguous\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _val_copyarray = False\n _val_tmp = None\n \n res = __library__.MSK_XX_solvewithbasis(self.__nativep,transp_,ctypes.byref(_numnz_tmp),_sub_tmp,_val_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = _numnz_tmp.value\n if _sub_copyarray:\n sub_[:] = _sub_np_tmp\n if _val_copyarray:\n val_[:] = _val_np_tmp\n return (_numnz_return_value)","def test_solve_quadratic_fixed(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.val = 4\n iden2.x.val = 5\n iden3.x.val = 6\n iden1.x.name = 'x1'\n iden2.x.name = 'x2'\n iden3.x.name = 'x3'\n iden2.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 10)\n self.assertAlmostEqual(iden1.x.val, 4)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 6)","def pk_constrained(self, snr=30, headroom = 0):\n # Initialize\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\n # loop over frequencies\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\n for jf, k0 in enumerate(self.controls.k0):\n # get the scaled version of the propagating directions\n k_vec = k0 * self.dir\n # Form the sensing matrix\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\n H = h_mtx.astype(complex) # cvxpy does not accept floats, apparently\n # measured data\n pm = self.pres_s[:,jf].astype(complex)\n # Performing constrained optmization cvxpy\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True) # create x variable\n # Create the problem\n epsilon = 10**(-(snr-headroom)/10)\n problem = cp.Problem(cp.Minimize(cp.norm2(x_cvx)**2),\n [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon])\n problem.solve(solver=cp.SCS, verbose=False)\n self.pk[:,jf] = x_cvx.value\n bar.update(1)\n bar.close()","def makeConjunction(solver, v, conjuncts):\n cn = solver.Constraint(0, (len(conjuncts) - 1))\n cn.SetCoefficient(v, -len(conjuncts))\n for x in conjuncts:\n cn.SetCoefficient(x, 1)","def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)","def Qc_fit(x, a, b, c, d, e, f, g, h, i, k):\n x1 = x[0] # I\n x2 = x[1] # dT\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\n return m * x2 + b","def quadratic_program_bnd(self, c, Q, lb, ub):\n if self.solver == solver_SCIPY:\n return scipy_quadratic_program_bnd(c, Q, lb, ub)\n elif self.solver == solver_GUROBI:\n return gurobi_quadratic_program_bnd(c, Q, lb, ub)\n else:\n raise ValueError('QP solver %s not available' % self.solver)","def qcontour(zmatrix, nx, ny, nlevels):\n dislin.qplcon(zmatrix, ny, ny, nlevels)","def con_cieq(x,project):\n \n cons = project.con_cieq(x)\n \n if cons: cons = array(cons)\n else: cons = zeros([0])\n \n return -cons","def refine(self, ijk):\n if self.cbc is None or not self.sub_block_count:\n raise ValueError(\n \"Cannot refine sub block model without specifying number \"\n \"of parent and sub blocks\"\n )\n try:\n inds = self.ijk_array_to_indices(ijk)\n except ValueError:\n inds = self.ijk_to_index(ijk)\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137","def update_edge_constraint(self, source, dest, constraint):\n for k, v in constraint.items():\n if k in self.G[source][dest]['constraint']:\n self.G[source][dest]['constraint'][k].update(v)\n else:\n self.G[source][dest]['constraint'].update({k: v})","def _reg_qrs_tconst(pattern, qrs):\n beats = pattern.evidence[o.QRS]\n idx = beats.index(qrs)\n tnet = pattern.last_tnet\n hyp = pattern.hypothesis\n BASIC_TCONST(pattern, qrs)\n tnet.add_constraint(qrs.start, qrs.end, C.NQRS_DUR)\n tnet.set_before(qrs.time, hyp.end)\n # Constraints with the precedent T Wave\n _qrs_after_twave(pattern, qrs)\n # The environment QRS complex determines the beginning of the bigeminy.\n if pattern.get_evidence_type(qrs)[1] is ENV:\n tnet.set_equal(hyp.start, qrs.time)\n else:\n # The first regular beat takes the reference RR from the previous rhythm\n # and the subsequent take the reference from the proper bigeminy.\n if idx == 2:\n refrr, stdrr = pattern.evidence[o.Cardiac_Rhythm][0].meas[0]\n max_var = max(2 * C.RR_MAX_DIFF, 4 * stdrr)\n tnet.add_constraint(\n beats[0].time,\n qrs.time,\n Iv(\n min(2 * refrr - max_var, refrr * C.COMPAUSE_MIN_F),\n max(2 * refrr + max_var, refrr * C.COMPAUSE_MAX_F),\n ),\n )\n else:\n ref2rr = beats[idx - 2].time.end - beats[idx - 4].time.start\n mrr, srr = hyp.meas.rr\n const = Iv(\n min(ref2rr - 2 * C.RR_MAX_DIFF, 2 * mrr - 4 * srr),\n max(ref2rr + 2 * C.RR_MAX_DIFF, 2 * mrr + 4 * srr),\n )\n tnet.add_constraint(beats[idx - 2].time, qrs.time, const)\n tnet.add_constraint(beats[idx - 2].start, qrs.start, const)\n tnet.add_constraint(beats[idx - 2].end, qrs.end, const)\n # We guide the morphology search to be similar to the previous regular\n # QRS complex.\n qrs.shape = beats[idx - 2].shape\n qrs.paced = beats[idx - 2].paced\n # Compensatory pause RR\n minrr = beats[idx - 1].time.start - beats[idx - 2].time.end\n maxrr = beats[idx - 1].time.end - beats[idx - 2].time.start\n refcompause = (\n beats[idx - 2].time.start - beats[idx - 3].time.start\n if idx > 2\n else maxrr * C.COMPAUSE_RREXT_MAX_F\n )\n mincompause = max(\n C.COMPAUSE_MIN_DUR,\n maxrr,\n min(minrr * C.COMPAUSE_RREXT_MIN_F, refcompause - C.TMARGIN, minrr + C.COMPAUSE_RREXT_MIN),\n )\n tnet.add_constraint(beats[idx - 1].time, qrs.time, Iv(mincompause, maxrr * C.COMPAUSE_RREXT_MAX_F))\n # Beats cannot overlap\n tnet.add_constraint(beats[idx - 1].end, qrs.start, Iv(C.TQ_INTERVAL_MIN, np.Inf))","def generateconenames(self,subk_,fmt_,dims_,sp_):\n num_ = None\n if num_ is None:\n num_ = len(subk_)\n elif num_ != len(subk_):\n raise IndexError(\"Inconsistent length of array subk\")\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n if isinstance(fmt_,unicode):\n fmt_ = fmt_.encode(\"utf-8\",errors=\"replace\")\n ndims_ = None\n if ndims_ is None:\n ndims_ = len(dims_)\n elif ndims_ != len(dims_):\n raise IndexError(\"Inconsistent length of array dims\")\n if dims_ is None:\n raise ValueError(\"Argument dims cannot be None\")\n if dims_ is None:\n raise ValueError(\"Argument dims may not be None\")\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\n _dims_copyarray = False\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dims_ is not None:\n _dims_copyarray = True\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\n _dims_np_tmp[:] = dims_\n assert _dims_np_tmp.flags.contiguous\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dims_copyarray = False\n _dims_tmp = None\n \n _sp_minlength = (num_)\n if (num_) > 0 and sp_ is not None and len(sp_) != (num_):\n raise ValueError(\"Array argument sp is not long enough: Is %d, expected %d\" % (len(sp_),(num_)))\n if isinstance(sp_, numpy.ndarray) and sp_.dtype is numpy.dtype(numpy.int64) and sp_.flags.contiguous:\n _sp_copyarray = False\n _sp_tmp = ctypes.cast(sp_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sp_ is not None:\n _sp_copyarray = True\n _sp_np_tmp = numpy.zeros(len(sp_),numpy.dtype(numpy.int64))\n _sp_np_tmp[:] = sp_\n assert _sp_np_tmp.flags.contiguous\n _sp_tmp = ctypes.cast(_sp_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sp_copyarray = False\n _sp_tmp = None\n \n res = __library__.MSK_XX_generateconenames(self.__nativep,num_,_subk_tmp,fmt_,ndims_,_dims_tmp,_sp_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def addConstraint(constraint, problem):\n problem += constraint","def SetPRBinCatConstraint( self, model ) :\n tot = np.dot( self.wish.T, self.dispo )\n for val in tot :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def test_convert_to_q(self):\n\n riskfree = .01\n lmbd = .01\n lmbd_s = .5\n lmbd_y = .5\n mean_v = .5\n kappa_s = 1.5\n kappa_y = .5\n eta_s = .1\n eta_y = .01\n rho = -.5\n\n theta = [riskfree, mean_v, kappa_s, kappa_y, eta_s, eta_y,\n rho, lmbd, lmbd_s, lmbd_y]\n param = CentTendParam.from_theta(theta)\n param.convert_to_q()\n\n kappa_sq = kappa_s - lmbd_s * eta_s\n kappa_yq = kappa_y - lmbd_y * eta_y\n scale = kappa_s / kappa_sq\n\n self.assertEqual(param.measure, 'Q')\n self.assertEqual(param.riskfree, riskfree)\n self.assertEqual(param.lmbd, 0)\n self.assertEqual(param.lmbd_s, lmbd_s)\n self.assertEqual(param.lmbd_y, lmbd_y)\n self.assertEqual(param.mean_v, mean_v * kappa_y / kappa_yq * scale)\n self.assertEqual(param.kappa_s, kappa_sq)\n self.assertEqual(param.kappa_y, kappa_yq)\n self.assertEqual(param.eta_s, eta_s)\n self.assertEqual(param.eta_y, eta_y * scale**.5)\n self.assertEqual(param.rho, rho)\n self.assertTrue(param.is_valid())","def mkQuadraticRelaxation(systemDct):\n # Relaxation result\n # eqn: dict\n # key: state variable\n # value: expression\n # sub: dict\n # key: new symbol\n # value: expression to rever to original\n # mat: sympy.Matrix: matrix for linearized system (N X M)\n # row: original state variables\n # column: extended set of state variables\n # value: linearized expression\n # vec: sympy.Matrix complete set of symbols in columns N X 1\n # row: extended symbol\n RelaxationResult = collections.namedtuple(\"RelaxationResult\",\n \"eqn sub mat vec\")\n # Add existing symbols\n freeSymbols = []\n [freeSymbols.extend(e.free_symbols) for e in systemDct.values()]\n [addSymbols(f.name, dct=globals()) for f in freeSymbols]\n # Add quadratic symbols\n systemStr = \" \".join([str(e) for e in systemDct.values()])\n stateSymbols = list(systemDct.keys())\n subs = {} # key: new symbol; value: quadratic expression\n for sym1 in stateSymbols:\n for sym2 in stateSymbols:\n if sym1.name <= sym2.name:\n name = \"%s%s\" % (sym1, sym2)\n quadraticEpr = sym1 * sym2\n quadraticEprStr = str(quadraticEpr)\n if quadraticEprStr in systemStr: \n addSymbols(name, dct=globals())\n subs[globals()[name]] = quadraticEpr\n subsStr = {str(k): str(v) for k, v in subs.items()}\n # Construct the vector of extended symbols\n extendedStateSyms = list(stateSymbols)\n extendedStateSyms.extend(list(subs.keys()))\n extendedStateVec = sympy.Matrix(extendedStateSyms)\n # Replace quadratic terms\n eprStrDct = {}\n linearizedDct = {}\n for sym, epr in systemDct.items():\n newEprStr = str(epr)\n for k, v in subsStr.items():\n newEprStr = newEprStr.replace(v, k)\n linearizedDct[sym] = eval(newEprStr)\n # Calculate Jacobian w.r.t. newSymbols to get matris\n stateEprVec = sympy.Matrix(list(linearizedDct.values()))\n mat = stateEprVec.jacobian(extendedStateVec)\n #\n return RelaxationResult(\n eqn=linearizedDct,\n sub=subs,\n mat=mat,\n vec=extendedStateVec,\n )\n return linearizedDct, subs","def SetConstraint(self, model) :\n if 'pp' in self.__type : self.SetPPConstraint( model )\n elif self.__type == 'prBin' and self.bound!=0 : self.SetPRBinConstraint( model )\n elif self.__type == 'prCat' and self.bound != 0 : self.SetPRCatConstraint(model)\n elif self.__type == 'prBinCat' and self.bound != 0 : self.SetPRBinCatConstraint(model)\n elif self.bound == 0 : return\n else : raise RuntimeError( 'SetConstraint : Unknown type for Constraint : ', self.__type )","def ALIGNF(km_list, ky):\n n_feat = len(km_list)\n\n #km_list_copy = []\n # center the kernel first\n #for i in range(n_feat):\n # km_list_copy.append(center(km_list[i].copy()))\n #ky_copy = center(ky.copy())\n\n\n a = np.zeros(n_feat)\n for i in range(n_feat):\n a[i] = f_dot(km_list[i], ky)\n\n M = np.zeros((n_feat, n_feat))\n for i in range(n_feat):\n for j in range(i,n_feat):\n M[i,j] = f_dot(km_list[i],km_list[j])\n M[j,i] = M[i,j]\n\n Q = 2*M\n C = -2*a\n\n Q = Q + np.diag(np.ones(n_feat)*1e-8)\n\n ################################################\n # Using mosek to solve the quadratice programming\n\n # Set upper diagonal element to zeros, mosek only accept lower triangle\n iu = np.triu_indices(n_feat,1)\n Q[iu] = 0\n\n # start solving with mosek\n inf = 0.0\n env = mosek.Env()\n env.set_Stream(mosek.streamtype.log, streamprinter)\n\n # Create a task \n task = env.Task()\n task.set_Stream(mosek.streamtype.log, streamprinter)\n\n # Set up bound for variables \n bkx = [mosek.boundkey.lo]* n_feat\n blx = [0.0] * n_feat\n #bkx = [mosek.boundkey.fr]* n_feat\n #blx = [-inf] * n_feat\n bux = [+inf] * n_feat\n\n numvar = len(bkx)\n\n task.appendvars(numvar)\n\n for j in range(numvar):\n task.putcj(j,C[j])\n task.putvarbound(j,bkx[j],blx[j],bux[j])\n\n # Set up quadratic objective \n inds = np.nonzero(Q)\n qsubi = inds[0].tolist()\n qsubj = inds[1].tolist()\n qval = Q[inds].tolist()\n\n # Input quadratic objective \n task.putqobj(qsubi,qsubj,qval)\n\n # Input objective sense (minimize/mximize) \n task.putobjsense(mosek.objsense.minimize)\n\n task.optimize()\n\n # Print a summary containing information \n # about the solution for debugging purposes \n task.solutionsummary(mosek.streamtype.msg)\n\n solsta = task.getsolsta(mosek.soltype.itr)\n if (solsta == mosek.solsta.optimal or\n solsta == mosek.solsta.near_optimal):\n # Output a solution \n xx = np.zeros(numvar, float)\n task.getxx(mosek.soltype.itr, xx)\n #xx = xx/np.linalg.norm(xx)\n return xx\n else:\n print solsta\n xx = np.zeros(numvar, float)\n task.getxx(mosek.soltype.itr, xx)\n #xx = xx/np.linalg.norm(xx)\n return xx","def compileCkt(self):\n f = np.vectorize(lambda x: isinstance(x, QubitChannel))\n table = f(self.diagram)\n col_bool = np.bitwise_or.reduce(table, axis=1)\n # filter nan in 'qubit' direction\n if not np.bitwise_and.reduce(col_bool):\n raise ValueError('Found unassigned qubit')\n # filter nan in 'time' direction\n row_bool = np.bitwise_or.reduce(table, axis=0)\n diagram = self.diagram[:, row_bool]\n table = table[:, row_bool]\n # align QubitChannel objects in the table column by column\n for time_idx in range(len(table[0, :])):\n diagram[table[:, time_idx], time_idx\n ] = QubitChannel.alignQubitChannels(\n *diagram[table[:, time_idx], time_idx]\n )\n # replace nans with null QubitChannel objects\n for qubit_idx, row in enumerate(table):\n for time_idx, flag in enumerate(row):\n if flag:\n continue\n span_idx = np.where(f(diagram[:, time_idx]))[0][0]\n wire_idx = np.where(f(diagram[qubit_idx, :]))[0][0]\n diagram[qubit_idx, time_idx] = QubitChannel.null(\n diagram[span_idx, time_idx], diagram[qubit_idx, wire_idx]\n )\n try:\n self.compiled = np.sum(diagram, axis=1)\n except SystemError:\n raise ValueError('Error during wire concatenation')","def remove_constraint(self, ckey):\n if ckey not in self.constraints:\n raise KeyError(\"Constraints not found on object key: {}\".format(ckey))\n del self.constraints[ckey]","def clear(self, clear_obj=True, clear_constraints=True):\n\n if clear_constraints:\n self.constraints = []\n self.bounds, self.constraints = self.const_creator.create_constraint('weight', weight_bound=(0,1), leverage=1)\n if clear_obj:\n self.objective = None","def add_constraint(self, constraint, problem):\n problem += constraint","def fixC(self,i,value):\n if self.coeffPattern[2] == None:\n m,n=self.m,self.n\n self.coeffPattern[2] = [None]*m\n self.coeffPattern[2][i]=value\n self._updateEstimatorSize(i)","def test_cov_q(self, ndlys=13):\n for d in self.d:\n d.flag_array[:] = False #ensure that there are no flags!\n d.select(times=np.unique(d.time_array)[:10], frequencies=d.freq_array[:16])\n for d_std in self.d_std:\n d_std.flag_array[:] = False\n d_std.select(times=np.unique(d_std.time_array)[:10], frequencies=d_std.freq_array[:16])\n self.ds = pspecdata.PSpecData(dsets=self.d, wgts=self.w, dsets_std=self.d_std)\n self.ds = pspecdata.PSpecData(dsets=self.d, wgts=self.w, dsets_std=self.d_std)\n Ntime = self.ds.Ntimes\n self.ds.set_Ndlys(ndlys)\n # Here is the analytic covariance matrix...\n chan_x, chan_y = np.meshgrid(range(self.ds.Nfreqs), range(self.ds.Nfreqs))\n cov_analytic = np.zeros((self.ds.spw_Ndlys, self.ds.spw_Ndlys), dtype=np.complex128)\n for alpha in range(self.ds.spw_Ndlys):\n for beta in range(self.ds.spw_Ndlys):\n cov_analytic[alpha, beta] = np.exp(-2j*np.pi*(alpha-beta)*(chan_x-chan_y)/self.ds.spw_Ndlys).sum()\n key1 = (0, 24, 38)\n key2 = (1, 25, 38)\n #print(cov_analytic)\n\n for input_data_weight in ['identity','iC', 'dayenu']:\n self.ds.set_weighting(input_data_weight)\n #check error raised\n if input_data_weight == 'dayenu':\n pytest.raises(ValueError,self.ds.R, key1)\n rpk = {'filter_centers':[0.],'filter_half_widths':[0.],'filter_factors':[0.]}\n self.ds.set_r_param(key1,rpk)\n self.ds.set_r_param(key2,rpk)\n for taper in taper_selection:\n qc = self.ds.cov_q_hat(key1,key2,model='dsets')\n self.assertTrue(np.allclose(np.array(list(qc.shape)),\n np.array([self.ds.Ntimes, self.ds.spw_Ndlys, self.ds.spw_Ndlys]), atol=1e-6))\n qc = self.ds.cov_q_hat(key1,key2,model='empirical')\n self.assertTrue(np.allclose(np.array(list(qc.shape)),\n np.array([self.ds.Ntimes, self.ds.spw_Ndlys, self.ds.spw_Ndlys]), atol=1e-6))\n\n \"\"\"\n Now test that analytic Error calculation gives Nchan^2\n \"\"\"\n self.ds.set_weighting('identity')\n qc = self.ds.cov_q_hat(key1, key2, model='dsets')\n self.assertTrue(np.allclose(qc,\n np.repeat(cov_analytic[np.newaxis, :, :], self.ds.Ntimes, axis=0), atol=1e-6))\n \"\"\"\n Test lists of keys\n \"\"\"\n self.ds.set_weighting('identity')\n qc=self.ds.cov_q_hat([key1], [key2], time_indices=[0], model='dsets')\n self.assertTrue(np.allclose(qc,\n np.repeat(cov_analytic[np.newaxis, :, :], self.ds.Ntimes, axis=0), atol=1e-6))\n self.assertRaises(ValueError, self.ds.cov_q_hat, key1, key2, time_indices=200)\n self.assertRaises(ValueError, self.ds.cov_q_hat, key1, key2, time_indices=\"watch out!\")","def _Q(self, x, y):\n\n # Calculate the [Q] coefficient matrix\n Q = array([[0, 0, 0, -2, 0, 0, -6*x, -2*y, 0, 0, -6*x*y, 0],\n [0, 0, 0, 0, 0, -2, 0, 0, -2*x, -6*y, 0, -6*x*y],\n [0, 0, 0, 0, -2, 0, 0, -4*x, -4*y, 0, -6*x**2, -6*y**2]])\n \n # Return the [Q] coefficient matrix\n return Q","def compare_J_terms(m, nix, srcclass=None, analytic_class=None, calced_class=None, \n only_calced_Cterms=False, fx=None):\n \n if fx is None:\n fx = fixtures.fixture_from_model(m)\n \n if srcclass is None:\n srcclass = FullSingleFieldSource\n \n if analytic_class is None:\n analytic_class = analyticsolution.NoPhaseBunchDaviesSolution\n if calced_class is None:\n calced_class = calcedsolution.NoPhaseBunchDaviesCalced\n \n asol = analytic_class(fx, srcclass)\n csol = calced_class(fx, srcclass)\n \n #Need to make analytic solution use 128 bit floats to avoid overruns\n asol.srceqns.k = np.float128(asol.srceqns.k)\n \n \n #Get background values\n bgvars = m.yresult[nix, 0:3, 0]\n a = m.ainit*np.exp(m.tresult[nix])\n #Get potentials\n potentials = m.potentials(np.array([bgvars[0]]), m.pot_params)\n \n #Set alpha and beta\n alpha = 1/(a*np.sqrt(2))\n beta = a*bgvars[2]\n \n dp1 = csol.get_dp1(csol.srceqns.fullk, alpha=alpha)\n dp1dot = csol.get_dp1dot(csol.srceqns.fullk, alpha=alpha, beta=beta)\n \n #Calculate dphi(q) and dphi(k-q)\n dp1_q = dp1[:csol.srceqns.k.shape[-1]]\n dp1dot_q = dp1dot[:csol.srceqns.k.shape[-1]] \n \n theta_terms = csol.srceqns.getthetaterms(dp1, dp1dot)\n csol.srceqns.k = np.float128(csol.srceqns.k)\n csol.srceqns.fullk = np.float128(csol.srceqns.fullk)\n \n calced_Cterms = csol.calculate_Cterms(bgvars, a, potentials) \n if only_calced_Cterms:\n analytic_Cterms = calced_Cterms\n else:\n analytic_Cterms = asol.calculate_Cterms(bgvars, a, potentials)\n \n results = {}\n \n for Jkey in csol.J_terms.iterkeys():\n afunc = asol.J_terms[Jkey]\n cfunc = csol.J_terms[Jkey]\n analytic_result = afunc(asol.srceqns.k, analytic_Cterms, alpha=alpha, beta=beta)\n calced_result = cfunc(theta_terms, dp1_q, dp1dot_q, calced_Cterms)\n diff = analytic_result - calced_result\n err = np.abs(diff)/np.abs(analytic_result)\n results[Jkey] = (diff, err, analytic_result, calced_result)\n \n return results","def ineqconstr(x, problem):\n x, t_final = matrify(x, problem)\n c = []\n\n # inter vehicles\n c += [veh_coll_avoid(x[:, :2, v1], x[:, :2, v2], problem)\n for v1 in range(problem['Nv']) for v2 in range(v1 + 1, problem['Nv'])]\n\n # obstacles\n c += [obs.avoid(x[:, :2, veh]) for obs in problem['obstacles'] for veh in range(problem['Nv'])]\n return np.concatenate(c) if c else np.array([])","def ch(self, q_control, q_target):\n if not is_qubit(q_control):\n raise AquaError('A qubit is expected for the control.')\n if not self.has_register(q_control[0]):\n raise AquaError('The control qubit is expected to be part of the circuit.')\n\n if not is_qubit(q_target):\n raise AquaError('A qubit is expected for the target.')\n if not self.has_register(q_target[0]):\n raise AquaError('The target qubit is expected to be part of the circuit.')\n\n if q_control == q_target:\n raise AquaError('The control and target need to be different qubits.')\n\n self.u3(-7 / 4 * pi, 0, 0, q_target)\n self.cx(q_control, q_target)\n self.u3(7 / 4 * pi, 0, 0, q_target)\n return self","def getdviolcon(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getdviolcon(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\n else:\n _skn_tmp = None\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def con_dcieq(x,project):\n \n dcons = project.con_dcieq(x)\n \n dim = project.n_dv\n if dcons: dcons = array(dcons)\n else: dcons = zeros([0,dim])\n \n return -dcons","def add_constraint(self, constraint):\n constraint_type = constraint[0]\n if constraint_type == 'time':\n dependent_variable = constraint[-2]\n dependee_variable = constraint[-1]\n dependent_index = self.subvariable_name.index(dependent_variable)\n dependee_index = self.subvariable_name.index(dependee_variable)\n constraint[-2] = self.value[dependent_index]\n constraint[-1] = self.value[dependee_index]\n if constraint_type in ['threshold', 'count']:\n threshold_variable = constraint[-1]\n threshold_index = self.subvariable_name.index(threshold_variable)\n constraint[-1] = self.value[threshold_index]\n if constraint_type == 'only_one':\n onlyone_variable = constraint[-1]\n onlyone_index = self.subvariable_name.index(onlyone_variable)\n constraint[-1] = self.value[onlyone_index]\n if constraint_type in self.constraint.keys():\n self.constraint[constraint_type] += [constraint[1:]]\n else:\n self.constraint[constraint_type] = [constraint[1:]]","def putbarcj(self,j_,sub_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights_)\n elif num_ != len(weights_):\n raise IndexError(\"Inconsistent length of array weights\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbarcj(self.__nativep,j_,num_,_sub_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def addConstraints(const1, const2):\n \n #get unique keys\n keys = list(set(list(const1.keys()) + list(const2.keys())))\n const_sum = {}\n \n for key in keys:\n addrhs = np.array(np.add(const1[key].rhs, const2[key].rhs))\n const_sum[key] = cenquery.Constraint(query=const1[key].query,\n rhs=addrhs, sign=const1[key].sign, name =const1[key].name )\n \n return(const_sum)","def _readeqdsk(self):\n dir_TCV = '/home/vallar/TCV/eqdsk/'\n try:\n b = ascot_Bfield.Bfield_eqdsk(dir_TCV+self.eqdsk_fname,129,129, 'TCV', COCOS=17)\n print(\"Opened \", dir_TCV+self.eqdsk_fname)\n except:\n print(\"Impossible to open \", self.eqdsk_fname)\n raise ValueError\n qprof_t = np.abs(b.eqdsk.q)\n rho_eqdsk = b.eqdsk.rhopsi\n self.param_q = interpolate.interp1d(rho_eqdsk, qprof_t)","def putbarablocktriplet(self,num_,subi,subj,subk,subl,valijkl): # 3\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subi_ is not None and len(subi_) != (num_):\n raise ValueError(\"Array argument subi has wrong length\")\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n \n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n \n if subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n \n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n \n if subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valijkl is None: raise TypeError(\"Invalid type for argument valijkl\")\n if valijkl is None:\n valijkl_ = None\n else:\n try:\n valijkl_ = memoryview(valijkl)\n except TypeError:\n try:\n _tmparr_valijkl = array.array(\"d\",valijkl)\n except TypeError:\n raise TypeError(\"Argument valijkl has wrong type\")\n else:\n valijkl_ = memoryview(_tmparr_valijkl)\n \n else:\n if valijkl_.format != \"d\":\n valijkl_ = memoryview(array.array(\"d\",valijkl))\n \n if valijkl_ is not None and len(valijkl_) != (num_):\n raise ValueError(\"Array argument valijkl has wrong length\")\n res = self.__obj.putbarablocktriplet(num_,subi_,subj_,subk_,subl_,valijkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_area_constraints(solver, objective, courses):\n areas = (\"APCOMP\",\"APMTH\",\"BE\",\"COMPSCI\",\"ENG-SCI\",\"ESE\")\n\n # For COMPSCI, avoid Friday lunch (faculty meeting) and Thursday seminar\n for c in courses:\n if not c.startswith(\"COMPSCI\"):\n continue\n for avoid in [\"F3a\",\"R5a\"]:\n if avoid in courses[c].vars_actualslots:\n objective.SetCoefficient(courses[c].vars_actualslots[avoid], PARAMS['WEIGHT_AVOID_CS_COURSES_IN_FAC_LUNCH_OR_COLLOQ']) \n \n for area in areas:\n v_day_of_week_diff = solver.IntVar(0, solver.infinity(), area + \" diff between TuTh and MWF courses\")\n objective.SetCoefficient(v_day_of_week_diff, PARAMS['WEIGHT_DIFF_NUM_COURSES_DAY_OF_WEEK']) \n \n cn_day_of_week1 = solver.Constraint(0, solver.infinity())\n cn_day_of_week2 = solver.Constraint(0, solver.infinity())\n cn_day_of_week1.SetCoefficient(v_day_of_week_diff, 1)\n cn_day_of_week2.SetCoefficient(v_day_of_week_diff, 1)\n\n v_time_of_day_diff = solver.IntVar(0, solver.infinity(), area + \" diff between times of day\")\n objective.SetCoefficient(v_time_of_day_diff, PARAMS['WEIGHT_DIFF_NUM_COURSES_TIME_OF_DAY']) \n\n cns_times_of_day = { } # each constraint will be (num in slot i - num in slot j)\n for i in range(1,6):\n cns_times_of_day[i] = { }\n for j in range(1,6):\n if i == j:\n continue\n cns_times_of_day[i][j] = solver.Constraint(0, solver.infinity())\n cns_times_of_day[i][j].SetCoefficient(v_day_of_week_diff, 1)\n\n \n for c in courses:\n if not c.startswith(area):\n continue\n\n for s in courses[c].vars_meeting_time:\n if ss.meeting_frequency(s) in (2,3):\n vmt = courses[c].vars_meeting_time[s]\n if ss.meeting_time_is_tu_th(s):\n cn_day_of_week1.SetCoefficient(vmt, 1)\n cn_day_of_week2.SetCoefficient(vmt,-1)\n else:\n cn_day_of_week1.SetCoefficient(vmt,-1)\n cn_day_of_week2.SetCoefficient(vmt, 1)\n\n if ss.meeting_time_starts_between_9_and_4(s):\n slot = ss.start_time_slot(s)\n assert slot in range(1,6), slot\n for j in range(1,6):\n if slot == j:\n continue\n cns_times_of_day[slot][j].SetCoefficient(vmt, 1)\n cns_times_of_day[j][slot].SetCoefficient(vmt,-1)","def set_c_values(self,nu=3,xd=2,a12sq=1,a13sq=1,a1=0,b1=0,p1=0,\n vx=0,vy=0,vz=0,epsilon=0.,omega=1.5*np.pi,\n a2=np.pi,b2=0*np.pi,p2=0.5*np.pi,silent=False):\n \n c_target = np.zeros(18)\n c_target[0] = nu*self.sigma0_RG \n xd_abs = xd*self.sigma2_RG\n c_target[4:10] = set_fGij(xd_abs,a12sq,a13sq,a1,b1,p1)\n c_target[10:13] = np.array([vx,vy,vz])\n c_target[13:18] = set_VGij(epsilon,omega,a2,b2,p2)\n \n if silent == False:\n print (\"Constrain peak parameters: \")\n if 'f0' in self.CONS or 'full' in self.CONS: \n print (\"f0: \",\"nu = %.1f\"%nu, \"$\\sigma_0$\")\n if 'f1' in self.CONS or 'full' in self.CONS: \n print (\"f1: \",\"f1,x = f1,y = f1,z = 0\")\n if 'f2' in self.CONS or 'full' in self.CONS: \n print (\"f2: \",r\"xd = {:.1f} $\\sigma_2$, a12sq = {:.1f}, a13sq = {:.1f},a1={:.2f}, b1={:.2f}, p1={:.2f}\".format(xd,a12sq,a13sq,a1,b1,p1))\n if 'vx' in self.CONS or 'full' in self.CONS: \n print (\"vx = {:.1f} km/s\".format(vx)) \n if 'vy' in self.CONS or 'full' in self.CONS: \n print (\"vy = {:.1f} km/s\".format(vy))\n if 'vz' in self.CONS or 'full' in self.CONS: \n print (\"vz = {:.1f} km/s\".format(vz))\n if 'TG' in self.CONS or 'full' in self.CONS: \n print (\"TG: \",\"epsilon = {:.1f} km/s/Mpc, omega = {:.2f}, a2={:.2f}, b2={:.2f}, p2={:.2f}\".format(epsilon,omega,a2,b2,p2))\n \n return c_target[self.cmask]","def __addValueConstraints(self):\n for x in range(self.width):\n for y in range(self.height):\n g = self.grid[(x, y)]\n self.solver.add(\n Or([g == Magnets.EMPTY, g == Magnets.PLUS, g == Magnets.MINUS]))\n if x > 0:\n left = self.grid[(x-1, y)]\n self.solver.add(Or([g != left, g == Magnets.EMPTY]))\n if y > 0:\n up = self.grid[(x, y-1)]\n self.solver.add(Or([g != up, g == Magnets.EMPTY]))","def putclist(self,subj_,val_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(val_)\n elif num_ != len(val_):\n raise IndexError(\"Inconsistent length of array val\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if val_ is None:\n raise ValueError(\"Argument val cannot be None\")\n if val_ is None:\n raise ValueError(\"Argument val may not be None\")\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\n _val_copyarray = False\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif val_ is not None:\n _val_copyarray = True\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\n _val_np_tmp[:] = val_\n assert _val_np_tmp.flags.contiguous\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _val_copyarray = False\n _val_tmp = None\n \n res = __library__.MSK_XX_putclist(self.__nativep,num_,_subj_tmp,_val_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"],"string":"[\n \"def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubk is None: raise TypeError(\\\"Invalid type for argument qcsubk\\\")\\n if qcsubk is None:\\n qcsubk_ = None\\n else:\\n try:\\n qcsubk_ = memoryview(qcsubk)\\n except TypeError:\\n try:\\n _tmparr_qcsubk = array.array(\\\"i\\\",qcsubk)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubk has wrong type\\\")\\n else:\\n qcsubk_ = memoryview(_tmparr_qcsubk)\\n \\n else:\\n if qcsubk_.format != \\\"i\\\":\\n qcsubk_ = memoryview(array.array(\\\"i\\\",qcsubk))\\n \\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getqconk(self,k_,qcsubi_,qcsubj_,qcval_):\\n maxnumqcnz_ = self.getnumqconknz((k_))\\n numqcnz_ = ctypes.c_int64()\\n _qcsubi_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubi is not long enough: Is %d, expected %d\\\" % (len(qcsubi_),self.getnumqconknz((k_))))\\n if isinstance(qcsubi_,numpy.ndarray) and not qcsubi_.flags.writeable:\\n raise ValueError(\\\"Argument qcsubi must be writable\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n _qcsubj_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubj is not long enough: Is %d, expected %d\\\" % (len(qcsubj_),self.getnumqconknz((k_))))\\n if isinstance(qcsubj_,numpy.ndarray) and not qcsubj_.flags.writeable:\\n raise ValueError(\\\"Argument qcsubj must be writable\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n _qcval_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcval is not long enough: Is %d, expected %d\\\" % (len(qcval_),self.getnumqconknz((k_))))\\n if isinstance(qcval_,numpy.ndarray) and not qcval_.flags.writeable:\\n raise ValueError(\\\"Argument qcval must be writable\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n qcsurp_ = ctypes.c_int64(_qcsubi_minlength)\\n res = __library__.MSK_XX_getqconk64(self.__nativep,k_,maxnumqcnz_,ctypes.byref(qcsurp_),ctypes.byref(numqcnz_),_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numqcnz_ = numqcnz_.value\\n _numqcnz_return_value = numqcnz_\\n if _qcsubi_copyarray:\\n qcsubi_[:] = _qcsubi_np_tmp\\n if _qcsubj_copyarray:\\n qcsubj_[:] = _qcsubj_np_tmp\\n if _qcval_copyarray:\\n qcval_[:] = _qcval_np_tmp\\n return (_numqcnz_return_value)\",\n \"def getqconk(self,k_,qcsubi,qcsubj,qcval): # 3\\n maxnumqcnz_ = self.getnumqconknz((k_))\\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n _copyback_qcsubi = False\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n _copyback_qcsubi = True\\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n _copyback_qcsubi = True\\n if qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubi has wrong length\\\")\\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n _copyback_qcsubj = False\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n _copyback_qcsubj = True\\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n _copyback_qcsubj = True\\n if qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubj has wrong length\\\")\\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n _copyback_qcval = False\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n _copyback_qcval = True\\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n _copyback_qcval = True\\n if qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcval has wrong length\\\")\\n res,resargs = self.__obj.getqconk64(k_,maxnumqcnz_,len(qcsubi),qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numqcnz_return_value = resargs\\n if _copyback_qcval:\\n qcval[:] = _tmparr_qcval\\n if _copyback_qcsubj:\\n qcsubj[:] = _tmparr_qcsubj\\n if _copyback_qcsubi:\\n qcsubi[:] = _tmparr_qcsubi\\n return _numqcnz_return_value\",\n \"def qmincon(self, q=None):\\n\\n def sumsqr(A):\\n return np.sum(A**2)\\n\\n def cost(x, ub, lb, qm, N):\\n return sumsqr(\\n (2 * (N @ x + qm) - ub - lb) / (ub - lb))\\n\\n q = getmatrix(q, (None, self.n))\\n\\n qstar = np.zeros((q.shape[0], self.n))\\n error = np.zeros(q.shape[0])\\n success = np.zeros(q.shape[0])\\n\\n lb = self.qlim[0, :]\\n ub = self.qlim[1, :]\\n\\n for k, qk in enumerate(q):\\n\\n J = self.jacobe(qk)\\n\\n N = null(J)\\n\\n x0 = np.zeros(N.shape[1])\\n A = np.r_[N, -N]\\n b = np.r_[ub - qk, qk - lb].reshape(A.shape[0],)\\n\\n con = LinearConstraint(A, -np.inf, b)\\n\\n res = minimize(\\n lambda x: cost(x, ub, lb, qk, N),\\n x0, constraints=con)\\n\\n qstar[k, :] = qk + N @ res.x\\n error[k] = res.fun\\n success[k] = res.success\\n\\n if q.shape[0] == 1:\\n return qstar[0, :], success[0], error[0]\\n else:\\n return qstar, success, error\",\n \"def add_cp_qe_RBM_terms(K, cons_pot_mesh, quad_geo_mesh):\\n num_faces = cons_pot_mesh.get_faces().shape[0]\\n x_c = quad_geo_mesh.get_centroid()\\n w = quad_geo_mesh.get_w()\\n A_m = quad_geo_mesh.get_A_m()\\n S_D = quad_geo_mesh.get_surface_area()\\n\\n for face_num in range(num_faces):\\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\\n face_hs = quad_geo_mesh.get_hs(face_num)\\n def v_quad(_xi, _eta, _nodes):\\n return np.identity(3)\\n v_sub_mat = (1. / S_D) * gq.int_over_tri_quad(v_quad, face_nodes, face_hs)\\n def omega_quad(xi, eta, nodes):\\n pos = geo.quadratic_interp(xi, eta, nodes)\\n X = pos - x_c\\n return np.einsum(\\\"lrs,s->lr\\\", geo.LC_3, X)\\n tmp_omega = gq.int_over_tri_quad(\\n omega_quad,\\n face_nodes,\\n face_hs,\\n )\\n tmp_arr = []\\n for m in range(3):\\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\\\"l,ls\\\", w[m], tmp_omega)))\\n tmp_arr = np.array(tmp_arr)\\n tmp_omega_mat = np.sum(tmp_arr, axis=0)\\n for src_num in range(num_faces):\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * face_num):(3 * face_num + 3)] += v_sub_mat\\n src_center = cons_pot_mesh.get_node(src_num)\\n X_0 = src_center - x_c\\n omega_mat = np.einsum(\\\"ijk,js,k->is\\\", geo.LC_3, tmp_omega_mat, X_0)\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * face_num):(3 * face_num + 3)] += omega_mat\",\n \"def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def custom_constr(x, qr, inverse, depth):\\n qc = QuantumCircuit(qr)\\n maxi, mini = max(x), min(x)\\n n = x.shape[0]\\n #qc_wv = Wavelets(n).construct_circuit(register=qr)\\n for _ in range(depth):\\n qc.h(qr)\\n for i in range(n):\\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini), 2*np.pi*(x[i]-mini)/(maxi-mini), qr[i])\\n for i in range(n):\\n qc.cx(qr[i], qr[(i + 1) % n])\\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini),\\n ((2*np.pi)**2*(x[i]-mini)*(x[(i+1) % n]-mini)/(maxi-mini)**2) % 2*np.pi,\\n qr[(i + 1) % n])\\n qc.cx(qr[i], qr[(i + 1) % n])\\n #qc = qc + qc_wv\\n if inverse:\\n return qc.inverse()\\n return qc\",\n \"def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def conj(q):\\n q = np.array([q[0]])\\n q[0,1]=-q[0,1]\\n q[0,2]=-q[0,2]\\n q[0,3]=-q[0,3]\\n complexconjugate = quatreal(q)\\n return complexconjugate\",\n \"def ikcon(self, T, q0=None):\\n\\n if not isinstance(T, SE3):\\n T = SE3(T)\\n\\n trajn = len(T)\\n\\n try:\\n if q0 is not None:\\n q0 = getvector(q0, self.n, 'row')\\n else:\\n q0 = np.zeros((trajn, self.n))\\n except ValueError:\\n verifymatrix(q0, (trajn, self.n))\\n\\n # create output variables\\n qstar = np.zeros((trajn, self.n))\\n error = []\\n exitflag = []\\n\\n omega = np.diag([1, 1, 1, 3 / self.reach])\\n\\n def cost(q, T, omega):\\n return np.sum(\\n (\\n (np.linalg.pinv(T.A) @ self.fkine(q).A - np.eye(4)) @\\n omega) ** 2\\n )\\n\\n bnds = Bounds(self.qlim[0, :], self.qlim[1, :])\\n\\n for i in range(trajn):\\n Ti = T[i]\\n res = minimize(\\n lambda q: cost(q, Ti, omega),\\n q0[i, :], bounds=bnds, options={'gtol': 1e-6})\\n qstar[i, :] = res.x\\n error.append(res.fun)\\n exitflag.append(res.success)\\n\\n if trajn > 1:\\n return qstar, exitflag, error\\n else:\\n return qstar[0, :], exitflag[0], error[0]\",\n \"def add_cp_qe_DL_terms(K, cons_pot_mesh, quad_geo_mesh):\\n geo_faces = quad_geo_mesh.get_faces()\\n pot_faces = cons_pot_mesh.get_faces()\\n assert geo_faces.shape[0] == pot_faces.shape[0]\\n num_faces = geo_faces.shape[0]\\n c_0 = 1. / (4. * np.pi)\\n for face_num in range(num_faces): # field points\\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\\n face_n = quad_geo_mesh.get_quad_n(face_num)\\n face_hs = quad_geo_mesh.get_hs(face_num)\\n for src_num in range(num_faces): # source points\\n src_center = cons_pot_mesh.get_node(src_num)\\n if face_num != src_num:\\n sub_mat = gq.int_over_tri_quad_n(\\n make_cp_qe_quad_func(src_center),\\n face_nodes,\\n face_n,\\n face_hs\\n )\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * face_num):(3 * face_num + 3)] += sub_mat\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * src_num):(3 * src_num + 3)] -= sub_mat\\n # do nothing face_num == src_num, how it works out for constant elements\\n\\n for src_num in range(num_faces):\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * src_num):(3 * src_num + 3)] -= 4. * np.pi * np.identity(3)\\n K *= c_0\",\n \"def do_reduction_placzek_corrections(q,sqfg,bgd,rescale_bgd=1.0,plaz_type=None,\\n gauss_damp=False,gw=20.0,qmax=None,qmin=None,\\n rmin=0.0,rmax=20.0,delr=.02\\n ,qminpla=10.0,qmaxpla=30.0,ndeg=2, return_correction = False,\\n skip_bgd = False, return_final_sq = False, force_qmax_type='Off'):\\n #first, make netsq if bgd and/or damping is present\\n q = np.array(q)\\n sqfg = np.array(sqfg)\\n bgd = np.array(bgd)\\n\\n if skip_bgd:\\n netsq = sqfg\\n else:\\n netsq = sqfg - bgd*rescale_bgd\\n\\n\\n if gauss_damp:\\n netsq = netsq*gauss(q,gw,0)\\n\\n\\n if force_qmax_type == 'Force Data (PreCorrection)':\\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n netsq -= mean_sqmax\\n\\n #now, apply a correction if requested\\n if plaz_type != None:\\n if plaz_type == 'Polynomial' or plaz_type == 'poly' or plaz_type == 'ndeg':\\n sq_poly_fit = fit_ndeg_to_sq(q,netsq,ndeg=ndeg,qmin=qminpla,qmax=qmaxpla)\\n this_fit = sq_poly_fit\\n elif plaz_type == 'Pseudo-Voight' or plaz_type == 'pv' or plaz_type == 'hydro':\\n pv_fit = fit_pv_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_fit\\n elif plaz_type == 'PVoight + n0' or plaz_type == 'pvndeg0':\\n pv_n0_fit = fit_pv_n0_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_n0_fit\\n elif plaz_type == 'PVoight + n1' or plaz_type == 'pvndeg1':\\n pv_n1_fit = fit_pv_n1_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_n1_fit\\n elif plaz_type == 'PVoight + n2' or plaz_type == 'pvndeg2':\\n pv_n2_fit = fit_pv_n2_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_n2_fit\\n else:\\n print (\\\"I don't know that correction type, sorry\\\")\\n this_fit = np.zeros(len(q))\\n else:\\n this_fit = np.zeros(len(q))\\n\\n if force_qmax_type == 'Force Data' or force_qmax_type == 'Force Both (Independent)':\\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n netsq -= mean_sqmax\\n if force_qmax_type == 'Force Correction' or force_qmax_type == 'Force Both (Independent)':\\n qcut, sqcut = cut_data(q,this_fit,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n this_fit -= mean_sqmax\\n if force_qmax_type == 'ReCorrection':\\n qcut, sqcut = cut_data(q,netsq-this_fit,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n this_fit += mean_sqmax\\n\\n netsq = netsq - this_fit\\n\\n if return_correction:\\n return this_fit\\n\\n if return_final_sq:\\n return netsq\\n\\n #finally, generate PDF\\n r,gr = make_gr_from_sq(q,netsq,qmin=qmin,qmax=qmax,rmin=rmin,rmax=rmax,delr=delr)\\n\\n return r,gr\",\n \"def putcone(self,k_,ct_,conepar_,submem_):\\n nummem_ = None\\n if nummem_ is None:\\n nummem_ = len(submem_)\\n elif nummem_ != len(submem_):\\n raise IndexError(\\\"Inconsistent length of array submem\\\")\\n if submem_ is None:\\n raise ValueError(\\\"Argument submem cannot be None\\\")\\n if submem_ is None:\\n raise ValueError(\\\"Argument submem may not be None\\\")\\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\\n _submem_copyarray = False\\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif submem_ is not None:\\n _submem_copyarray = True\\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\\n _submem_np_tmp[:] = submem_\\n assert _submem_np_tmp.flags.contiguous\\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _submem_copyarray = False\\n _submem_tmp = None\\n \\n res = __library__.MSK_XX_putcone(self.__nativep,k_,ct_,conepar_,nummem_,_submem_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putcone(self,k_,ct_,conepar_,submem): # 3\\n if not isinstance(ct_,conetype): raise TypeError(\\\"Argument ct has wrong type\\\")\\n nummem_ = None\\n if nummem_ is None:\\n nummem_ = len(submem)\\n elif nummem_ != len(submem):\\n raise IndexError(\\\"Inconsistent length of array submem\\\")\\n if nummem_ is None: nummem_ = 0\\n if submem is None: raise TypeError(\\\"Invalid type for argument submem\\\")\\n if submem is None:\\n submem_ = None\\n else:\\n try:\\n submem_ = memoryview(submem)\\n except TypeError:\\n try:\\n _tmparr_submem = array.array(\\\"i\\\",submem)\\n except TypeError:\\n raise TypeError(\\\"Argument submem has wrong type\\\")\\n else:\\n submem_ = memoryview(_tmparr_submem)\\n \\n else:\\n if submem_.format != \\\"i\\\":\\n submem_ = memoryview(array.array(\\\"i\\\",submem))\\n \\n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconbound(self,i_,bkc_,blc_,buc_):\\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _ect_qrs_tconst(pattern, qrs):\\n beats = pattern.evidence[o.QRS]\\n idx = beats.index(qrs)\\n tnet = pattern.last_tnet\\n hyp = pattern.hypothesis\\n if idx > 0:\\n prev = beats[idx - 1]\\n # After the second couplet, every ectopic beat introduces a new temporal\\n # network in the pattern to make it easier the minimization.\\n if idx > 3:\\n tnet.remove_constraint(hyp.end, prev.time)\\n # We create a new temporal network for the cyclic observations\\n tnet = ConstraintNetwork()\\n pattern.temporal_constraints.append(tnet)\\n # The duration of each couplet should not have high instantaneous\\n # variations.\\n refrr = beats[idx - 2].time.end - beats[idx - 3].time.start\\n tnet.add_constraint(prev.time, qrs.time, Iv(refrr - C.RR_MAX_DIFF, refrr + C.RR_MAX_DIFF))\\n # We guide the morphology search to be similar to the previous\\n # ectopic QRS complex.\\n qrs.shape = beats[idx - 2].shape\\n # The reference RR varies from an upper limit to the last measurement,\\n # through the contextual previous rhythm.\\n refrr = C.BRADY_RR.end\\n stdrr = 0.1 * refrr\\n if pattern.evidence[o.Cardiac_Rhythm] and idx == 1:\\n mrr, srr = pattern.evidence[o.Cardiac_Rhythm][0].meas.rr\\n if mrr > 0:\\n refrr, stdrr = mrr, srr\\n elif idx > 1:\\n refrr, stdrr = hyp.meas.rr\\n # Ectopic beats must be advanced wrt the reference RR\\n tnet.add_constraint(prev.time, qrs.time, Iv(C.TACHY_RR.start, max(C.TACHY_RR.start, refrr - stdrr)))\\n # Beats cannot overlap\\n tnet.add_constraint(prev.end, qrs.start, Iv(C.TQ_INTERVAL_MIN, np.Inf))\\n BASIC_TCONST(pattern, qrs)\\n tnet.add_constraint(qrs.start, qrs.end, C.QRS_DUR)\\n tnet.set_before(qrs.time, hyp.end)\\n # Constraints with the precedent T Wave\\n _qrs_after_twave(pattern, qrs)\",\n \"def vahlen_conj(q_1: Q, conj_type: str = \\\"-\\\", q_type: str = \\\"vc\\\") -> Q:\\n\\n vc_t, vc_x, vc_y, vc_z = q_1.t, q_1.x, q_1.y, q_1.z\\n c_q = Q()\\n\\n if conj_type == \\\"-\\\":\\n c_q.t = vc_t\\n if vc_x != 0:\\n c_q.x = -1 * vc_x\\n if vc_y != 0:\\n c_q.y = -1 * vc_y\\n if vc_z != 0:\\n c_q.z = -1 * vc_z\\n q_type += \\\"*-\\\"\\n\\n if conj_type == \\\"'\\\":\\n c_q.t = vc_t\\n if vc_x != 0:\\n c_q.x = -1 * vc_x\\n if vc_y != 0:\\n c_q.y = -1 * vc_y\\n c_q.z = vc_z\\n q_type += \\\"*'\\\"\\n\\n if conj_type == \\\"*\\\":\\n c_q.t = vc_t\\n c_q.x = vc_x\\n c_q.y = vc_y\\n if vc_z != 0:\\n c_q.z = -1 * vc_z\\n q_type += \\\"*\\\"\\n\\n c_q.q_type = f\\\"{q_1.q_type}{q_type}\\\"\\n c_q.representation = q_1.representation\\n\\n return c_q\",\n \"def mk_q(self, xc: list, yc: list):\\n for i in range(len(xc) - 1):\\n cur = cor(xc[i], xc[i + 1])\\n self.xq.put((-cur.dist, cur))\\n cur = cor(yc[i], yc[i + 1])\\n self.yq.put((-cur.dist, cur))\\n self.rnd_mk()\",\n \"def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\\n _subj_minlength = (num_)\\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(num_)))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _subk_minlength = (num_)\\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk is not long enough: Is %d, expected %d\\\" % (len(subk_),(num_)))\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk cannot be None\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n _subl_minlength = (num_)\\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl is not long enough: Is %d, expected %d\\\" % (len(subl_),(num_)))\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl cannot be None\\\")\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl may not be None\\\")\\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\\n _subl_copyarray = False\\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subl_ is not None:\\n _subl_copyarray = True\\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\\n _subl_np_tmp[:] = subl_\\n assert _subl_np_tmp.flags.contiguous\\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subl_copyarray = False\\n _subl_tmp = None\\n \\n _valjkl_minlength = (num_)\\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\\n raise ValueError(\\\"Array argument valjkl is not long enough: Is %d, expected %d\\\" % (len(valjkl_),(num_)))\\n if valjkl_ is None:\\n raise ValueError(\\\"Argument valjkl cannot be None\\\")\\n if valjkl_ is None:\\n raise ValueError(\\\"Argument valjkl may not be None\\\")\\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\\n _valjkl_copyarray = False\\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valjkl_ is not None:\\n _valjkl_copyarray = True\\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\\n _valjkl_np_tmp[:] = valjkl_\\n assert _valjkl_np_tmp.flags.contiguous\\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valjkl_copyarray = False\\n _valjkl_tmp = None\\n \\n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_equality_constraint_multipliers(self, eq_con_multiplier_values):\\n # we should check these for efficiency\\n assert self.n_equality_constraints() == len(eq_con_multiplier_values)\\n if (\\n not hasattr(self, 'evaluate_hessian_equality_constraints')\\n or self.n_equality_constraints() == 0\\n ):\\n return\\n\\n raise NotImplementedError(\\n 'Derived ExternalGreyBoxModel classes need to implement'\\n ' set_equality_constraint_multipliers when they'\\n ' support Hessian computations.'\\n )\",\n \"def Q(self, k, x):\\n g = np.asarray(self.g(k, x))\\n Q = g @ g.T\\n return Q\",\n \"def update_q(self):\\n beta = self.EC_beta\\n self.gamma_q = (self.gamma_s - self.gamma_r) * beta + (1 - beta) * self.gamma_q\\n self.Sigma_q = (self.Sigma_s - self.Sigma_r) * beta + (1 - beta) * self.Sigma_q\\n try:\\n assert np.all(np.logical_not(np.isnan(self.gamma_q)))\\n except:\\n print(\\\"Invalid update encountered...\\\")\",\n \"def SetPRCatConstraint(self, model ) :\\n tot = np.multiply(self.wish, self.dispo)\\n for line in tot :\\n for val in line :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def putqobj(self,qosubi_,qosubj_,qoval_):\\n numqonz_ = None\\n if numqonz_ is None:\\n numqonz_ = len(qosubi_)\\n elif numqonz_ != len(qosubi_):\\n raise IndexError(\\\"Inconsistent length of array qosubi\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qosubj_)\\n elif numqonz_ != len(qosubj_):\\n raise IndexError(\\\"Inconsistent length of array qosubj\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qoval_)\\n elif numqonz_ != len(qoval_):\\n raise IndexError(\\\"Inconsistent length of array qoval\\\")\\n if qosubi_ is None:\\n raise ValueError(\\\"Argument qosubi cannot be None\\\")\\n if qosubi_ is None:\\n raise ValueError(\\\"Argument qosubi may not be None\\\")\\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\\n _qosubi_copyarray = False\\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qosubi_ is not None:\\n _qosubi_copyarray = True\\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\\n _qosubi_np_tmp[:] = qosubi_\\n assert _qosubi_np_tmp.flags.contiguous\\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qosubi_copyarray = False\\n _qosubi_tmp = None\\n \\n if qosubj_ is None:\\n raise ValueError(\\\"Argument qosubj cannot be None\\\")\\n if qosubj_ is None:\\n raise ValueError(\\\"Argument qosubj may not be None\\\")\\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\\n _qosubj_copyarray = False\\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qosubj_ is not None:\\n _qosubj_copyarray = True\\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\\n _qosubj_np_tmp[:] = qosubj_\\n assert _qosubj_np_tmp.flags.contiguous\\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qosubj_copyarray = False\\n _qosubj_tmp = None\\n \\n if qoval_ is None:\\n raise ValueError(\\\"Argument qoval cannot be None\\\")\\n if qoval_ is None:\\n raise ValueError(\\\"Argument qoval may not be None\\\")\\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\\n _qoval_copyarray = False\\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qoval_ is not None:\\n _qoval_copyarray = True\\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\\n _qoval_np_tmp[:] = qoval_\\n assert _qoval_np_tmp.flags.contiguous\\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qoval_copyarray = False\\n _qoval_tmp = None\\n \\n res = __library__.MSK_XX_putqobj(self.__nativep,numqonz_,_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def conj_q(q_1: Q, q_2: Q) -> Q:\\n\\n _conj = deepcopy(q_1)\\n\\n if q_2.t:\\n _conj = conj(_conj, conj_type=0)\\n\\n if q_2.x:\\n _conj = conj(_conj, conj_type=1)\\n\\n if q_2.y:\\n _conj = conj(_conj, conj_type=2)\\n\\n if q_2.z:\\n _conj = flip_sign(_conj)\\n\\n return _conj\",\n \"def _update_tarsqidoc(self, cp):\\n self.tarsqidoc.remove_tlinks()\\n for n1, rest in cp.graph.edges.items():\\n for n2, edge in cp.graph.edges[n1].items():\\n if edge.constraint is not None:\\n if edge.constraint.has_simple_relation():\\n self._add_constraint_to_tarsqidoc(edge)\",\n \"def eqconstr(x, problem):\\n x, t_final = matrify(x, problem)\\n return np.concatenate([problem['dynamics'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])\",\n \"def quadratic_strain(x, dof):\\n base = np.zeros([6, dof])\\n base[1, 0] = 1 # initial y-bending\\n if dof > 2:\\n base[1, 1] = x**2 # quadratic y-bending term\\n base[2, dof-1] = x**2 # quadratic z-bending term\\n return base\",\n \"def quaternion_conjugate(self, q):\\n\\n \\\"\\\"\\\"\\n in-place operation is an operation that changes directly the content of a given Tensor without making a copy.\\n ALL operations on the tensor that operate in-place on it will have an _ postfix.\\n \\\"\\\"\\\"\\n q_star = q.new(4).fill_(-1)\\n\\n # leave the scalar unchanged and change signs of i, j, k number parts\\n q_star[0] = 1.0\\n\\n return q * q_star\",\n \"def putconbound(self,i_,bk_,bl_,bu_): # 3\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconboundlist(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkc_)\\n elif num_ != len(bkc_):\\n raise IndexError(\\\"Inconsistent length of array bkc\\\")\\n if num_ is None:\\n num_ = len(blc_)\\n elif num_ != len(blc_):\\n raise IndexError(\\\"Inconsistent length of array blc\\\")\\n if num_ is None:\\n num_ = len(buc_)\\n elif num_ != len(buc_):\\n raise IndexError(\\\"Inconsistent length of array buc\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc cannot be None\\\")\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc may not be None\\\")\\n if bkc_ is not None:\\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\\n else:\\n _bkc_tmp = None\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc cannot be None\\\")\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc may not be None\\\")\\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\\n _blc_copyarray = False\\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blc_ is not None:\\n _blc_copyarray = True\\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\\n _blc_np_tmp[:] = blc_\\n assert _blc_np_tmp.flags.contiguous\\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blc_copyarray = False\\n _blc_tmp = None\\n \\n if buc_ is None:\\n raise ValueError(\\\"Argument buc cannot be None\\\")\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc may not be None\\\")\\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\\n _buc_copyarray = False\\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif buc_ is not None:\\n _buc_copyarray = True\\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\\n _buc_np_tmp[:] = buc_\\n assert _buc_np_tmp.flags.contiguous\\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _buc_copyarray = False\\n _buc_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def pro_avfid_superoperator_compsubspace_phasecorrected_onlystaticqubit(U,L1,phases):\\n\\n Ucorrection = qtp.Qobj([[np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0, 0],\\n [0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0],\\n [0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0],\\n [0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0, 0, 0],\\n [0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0, 0],\\n [0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0],\\n [0, 0, 0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0]))]],\\n type='oper',\\n dims=[[3, 3], [3, 3]])\\n\\n if U.type=='oper':\\n U=Ucorrection*U\\n inner = U.dag()*U_target\\n part_idx = [0, 1, 3, 4] # only computational subspace\\n ptrace = 0\\n for i in part_idx:\\n ptrace += inner[i, i]\\n dim = 4 # 2 qubits comp subspace \\n\\n return np.real(((np.abs(ptrace))**2+dim*(1-L1))/(dim*(dim+1)))\\n\\n elif U.type=='super':\\n U=qtp.to_super(Ucorrection)*U\\n kraus_form = qtp.to_kraus(U)\\n dim=4 # 2 qubits in the computational subspace\\n part_idx = [0, 1, 3, 4] # only computational subspace\\n psum=0\\n for A_k in kraus_form:\\n ptrace = 0\\n inner = U_target_diffdims.dag()*A_k # otherwise dimension mismatch\\n for i in part_idx:\\n ptrace += inner[i, i]\\n psum += (np.abs(ptrace))**2\\n\\n return np.real((dim*(1-L1) + psum) / (dim*(dim + 1)))\",\n \"def putqobjij(self,i_,j_,qoij_): # 3\\n res = self.__obj.putqobjij(i_,j_,qoij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solve_constraint_generic(problem, enqueue_condition=None) :\\n q = [problem]\\n extCount = 0\\n while len(q)!=0:\\n removed = q[0]\\n q = q[1:]\\n extCount+=1\\n if has_empty_domains(removed) or check_all_constraints(removed)==False:\\n continue\\n if len(removed.unassigned_vars)==0:\\n return (removed.assigned_values,extCount)\\n \\n var = removed.pop_next_unassigned_var()\\n extensions = []\\n for val in removed.get_domain(var):\\n csp_new = removed.copy()\\n csp_new.set_assigned_value(var,val)\\n if (enqueue_condition!=None):\\n propagate(enqueue_condition,csp_new,[var])\\n extensions.append(csp_new)\\n \\n q = extensions + q\\n return (None,extCount)\",\n \"def convex_conj(self):\\n if self.operator is None:\\n tmp = IndicatorZero(space=self.domain, constant=-self.constant)\\n if self.vector is None:\\n return tmp\\n else:\\n return tmp.translated(self.vector)\\n\\n if self.vector is None:\\n # Handle trivial case separately\\n return QuadraticForm(operator=self.operator.inverse,\\n constant=-self.constant)\\n else:\\n # Compute the needed variables\\n opinv = self.operator.inverse\\n vector = -opinv.adjoint(self.vector) - opinv(self.vector)\\n constant = self.vector.inner(opinv(self.vector)) - self.constant\\n\\n # Create new quadratic form\\n return QuadraticForm(operator=opinv,\\n vector=vector,\\n constant=constant)\",\n \"def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def contractor(self, *args, **kwargs):\\n vertices = copy.deepcopy(args[0])\\n nrange = len(vertices[0])\\n xpts = []\\n ypts = []\\n for i in range(nrange):\\n xpts.append(vertices[0][i].value)\\n ypts.append(vertices[1][i].value)\\n constraint = copy.deepcopy(args[1])\\n \\n \\n \\n \\n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\\n \\n ## the all important computation split (need to abstract this kind of thing)\\n ##lhs = (np.sqrt(qxdot*qxdot + qydot*qydot)**3.) *constraint\\n lhs = ( ( np.sqrt(qxdot**2 + qydot**2) )**3 )*constraint\\n \\n # check2 = qxdot*qyddot\\n # if check2.width() < 1.e-2:\\n # check2.min.value = check2.real.value\\n # check2.max.value = check2.real.value\\n # t1 = (lhs - check2)/qydot\\n \\n #\\n # qyddot\\n #\\n check2 = qydot*qxddot\\n if check2.width() < 1.e-2 and check2.contains(0.):\\n check2.inf = 0.\\n check2.sup = 0.\\n #if qxdot.contains(0.) and abs(qxdot.min.value)>1.e-6:\\n # print 'qxdot = ',qxdot\\n # print 'qxdot not invertable, implement other logic please'\\n if abs(float(qxdot.inf))<1.e-6:\\n qxdot.inf = 1.e-10\\n print 'invert qxdot'\\n print 'qxdot = ', qxdot\\n \\n #t1 = (lhs + qydot*qxddot)/(qxdot)\\n t1 = (lhs + check2)/(qxdot)\\n \\n t1 = t1 & qyddot # go ahead and shrink t1 to qyddot - they are logically equivalent\\n total_ans = []\\n useful_indices = []\\n bad_indices = []\\n for i in range(len(ypts)): \\n min_ans = 0.\\n for j in range(len(ypts)):\\n if j==i:\\n pass\\n else:\\n min_ans = (ypts[j]*float(self.localBasis[2,j])) + min_ans\\n min_ans = t1 - min_ans\\n if (abs(float(self.localBasis[2,i])) > 0.0):\\n min_ans = min_ans/float(self.localBasis[2,i])\\n useful_indices.append(i)\\n else:\\n bad_indices.append(i)\\n total_ans.append(min_ans)\\n \\n new_ans = vector_AND_(ypts, total_ans)\\n for i in useful_indices:\\n if new_ans[i].isempty == False: # abs( new_ans[i].width() ) > 0.:\\n ypts[i] = ypts[i] & new_ans[i]\\n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\\n else:\\n print 'warning, possible constraint violation, curvature 1'\\n \\n ## \\n ## qxdot\\n ##\\n check2 = qydot*qxddot\\n if check2.width() < 1.e-2 and check2.contains(0.):\\n check2.inf = 0.\\n check2.sup = 0.\\n #if qyddot.contains(0.):\\n # print 'qyddot = ',qyddot\\n # print 'qyddot not invertable, implement other logic please'\\n \\n if qyddot.contains(0.) and qyddot.width()<1.e-6:\\n qxdot.inf = 0.#1.e-10\\n print 'invert qyddot'\\n print 'qyddot = ',qyddot\\n fix = (lhs + check2)*(1./qyddot)#*(qyddot**-1.)\\n fix = fix & qxdot # go ahead and shrink fix to qxdot - they are logically equivalent\\n total_ans = []\\n useful_indices = []\\n bad_indices = []\\n \\n for i in range(len(xpts)): #contract on x[i]\\n min_ans = 0.\\n for j in range(len(xpts)): # add up all jth pieces of the dot product except i\\n if j==i:\\n pass\\n else:\\n \\n min_ans = (xpts[j]*float(self.localBasis[1,j] ) ) + min_ans\\n min_ans = fix - min_ans\\n if (abs(float(self.localBasis[1,i]) ) >0.0 ):\\n min_ans = min_ans/float(self.localBasis[1,i])\\n useful_indices.append(i)\\n else:\\n bad_indices.append(i)\\n total_ans.append(min_ans)\\n \\n new_ans = vector_AND_(xpts, total_ans)\\n for i in useful_indices:\\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\\n xpts[i] = xpts[i] & new_ans[i]\\n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\\n else:\\n print 'warning, possible constraint violation, curvature 2'\\n \\n \\n ## switch to the other side\\n \\n ##\\n ## contract on qydot\\n ##\\n check2 = qxdot*qyddot\\n if check2.width() < 1.e-2 and check2.contains(0.):\\n check2.inf = 0.\\n check2.sup = 0.\\n# if qxddot.contains(0.):\\n# print 'qxddot = ',qxddot\\n# print 'qxddot not invertable, implement other logic please'\\n# qxddot.min.value = 0.\\n if qxddot.contains(0.):\\n qxddot.inf = 0.\\n \\n print 'invert qxddot'\\n print 'qxddot = ',qxddot\\n t1 = (lhs - check2)/(-qxddot)#*(-qxddot**-1)\\n t1 = t1 & qydot\\n total_ans = []\\n useful_indices = []\\n bad_indices = []\\n for i in range(len(ypts)): \\n min_ans = 0.\\n for j in range(len(ypts)):\\n if j==i:\\n pass\\n else:\\n #print 't1 = ',t1\\n #print 'ypts[{}] = {}'.format(i,ypts[i])\\n #print 'localbasis[{},{}] = {}'.format(1,i,self.localBasis[1,j])\\n min_ans = (ypts[j]*float(self.localBasis[1,j])) + min_ans\\n min_ans = t1 - min_ans\\n if (abs(float(self.localBasis[1,i])) > 0.0):\\n min_ans = min_ans/float(self.localBasis[1,i])\\n useful_indices.append(i)\\n else:\\n bad_indices.append(i)\\n total_ans.append(min_ans)\\n \\n new_ans = vector_AND_(ypts, total_ans)\\n for i in useful_indices:\\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\\n ypts[i] = ypts[i] & new_ans[i]\\n else:\\n print 'warning, possible constraint violation, curvature 3'\\n \\n ##contract on qxdot\\n \\n check2 = qxdot*qyddot\\n if check2.width() < 1.e-2 and check2.contains(0.):\\n check2.inf = 0.\\n check2.sup = 0.\\n #contract on qxddot\\n# if qydot.contains(0.):\\n# print 'qydot = ',qxddot\\n# print 'qydot not invertable, implement other logic please'\\n if qydot.contains(0.):\\n qydot.inf = 0.\\n print 'invert qydot'\\n print 'qydot = ',qydot\\n fix = (lhs - qxdot*qyddot)/(-qydot)#*(-qydot**-1)\\n fix = fix & qxddot # go ahead and shrink t1 to quddot - they are logically equivalent\\n total_ans = []\\n useful_indices = []\\n bad_indices = []\\n for i in range(len(xpts)):\\n min_ans = 0.\\n for j in range(len(xpts)):\\n if j==i:\\n pass\\n else:\\n min_ans = (xpts[j]*float(self.localBasis[2,j] ) ) + min_ans\\n min_ans = fix - min_ans\\n if (abs(float(self.localBasis[2,i]) ) >0.0 ):\\n min_ans = min_ans/float(self.localBasis[2,i])\\n useful_indices.append(i)\\n else:\\n bad_indices.append(i)\\n total_ans.append(min_ans)\\n \\n new_ans = vector_AND_(xpts, total_ans)\\n for i in useful_indices:\\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\\n xpts[i] = xpts[i] & new_ans[i]\\n else:\\n print 'warning, possible constraint violation, curvature 4'\\n \\n for i in range(nrange):\\n vertices[0][i].value = xpts[i]\\n vertices[1][i].value = ypts[i]\\n return vertices\",\n \"def putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_):\\n _subi_minlength = (num_)\\n if (num_) > 0 and subi_ is not None and len(subi_) != (num_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(num_)))\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = (num_)\\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(num_)))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _subk_minlength = (num_)\\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk is not long enough: Is %d, expected %d\\\" % (len(subk_),(num_)))\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk cannot be None\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n _subl_minlength = (num_)\\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl is not long enough: Is %d, expected %d\\\" % (len(subl_),(num_)))\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl cannot be None\\\")\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl may not be None\\\")\\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\\n _subl_copyarray = False\\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subl_ is not None:\\n _subl_copyarray = True\\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\\n _subl_np_tmp[:] = subl_\\n assert _subl_np_tmp.flags.contiguous\\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subl_copyarray = False\\n _subl_tmp = None\\n \\n _valijkl_minlength = (num_)\\n if (num_) > 0 and valijkl_ is not None and len(valijkl_) != (num_):\\n raise ValueError(\\\"Array argument valijkl is not long enough: Is %d, expected %d\\\" % (len(valijkl_),(num_)))\\n if valijkl_ is None:\\n raise ValueError(\\\"Argument valijkl cannot be None\\\")\\n if valijkl_ is None:\\n raise ValueError(\\\"Argument valijkl may not be None\\\")\\n if isinstance(valijkl_, numpy.ndarray) and valijkl_.dtype is numpy.dtype(numpy.float64) and valijkl_.flags.contiguous:\\n _valijkl_copyarray = False\\n _valijkl_tmp = ctypes.cast(valijkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valijkl_ is not None:\\n _valijkl_copyarray = True\\n _valijkl_np_tmp = numpy.zeros(len(valijkl_),numpy.dtype(numpy.float64))\\n _valijkl_np_tmp[:] = valijkl_\\n assert _valijkl_np_tmp.flags.contiguous\\n _valijkl_tmp = ctypes.cast(_valijkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valijkl_copyarray = False\\n _valijkl_tmp = None\\n \\n res = __library__.MSK_XX_putbarablocktriplet(self.__nativep,num_,_subi_tmp,_subj_tmp,_subk_tmp,_subl_tmp,_valijkl_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def ket(self: Qs) -> Qs:\\n\\n if self.qs_type == \\\"ket\\\":\\n return self\\n\\n ket = conjs(deepcopy(self))\\n ket.rows = self.dim\\n ket.columns = 1\\n\\n ket.qs_type = \\\"ket\\\" if self.dim > 1 else \\\"scalar_q\\\"\\n\\n return ket\",\n \"def putbarcj(self,j_,sub,weights): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights)\\n elif num_ != len(weights):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"q\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"q\\\":\\n sub_ = memoryview(array.array(\\\"q\\\",sub))\\n \\n if weights is None: raise TypeError(\\\"Invalid type for argument weights\\\")\\n if weights is None:\\n weights_ = None\\n else:\\n try:\\n weights_ = memoryview(weights)\\n except TypeError:\\n try:\\n _tmparr_weights = array.array(\\\"d\\\",weights)\\n except TypeError:\\n raise TypeError(\\\"Argument weights has wrong type\\\")\\n else:\\n weights_ = memoryview(_tmparr_weights)\\n \\n else:\\n if weights_.format != \\\"d\\\":\\n weights_ = memoryview(array.array(\\\"d\\\",weights))\\n \\n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqobj(self,qosubi,qosubj,qoval): # 3\\n numqonz_ = None\\n if numqonz_ is None:\\n numqonz_ = len(qosubi)\\n elif numqonz_ != len(qosubi):\\n raise IndexError(\\\"Inconsistent length of array qosubi\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qosubj)\\n elif numqonz_ != len(qosubj):\\n raise IndexError(\\\"Inconsistent length of array qosubj\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qoval)\\n elif numqonz_ != len(qoval):\\n raise IndexError(\\\"Inconsistent length of array qoval\\\")\\n if numqonz_ is None: numqonz_ = 0\\n if qosubi is None: raise TypeError(\\\"Invalid type for argument qosubi\\\")\\n if qosubi is None:\\n qosubi_ = None\\n else:\\n try:\\n qosubi_ = memoryview(qosubi)\\n except TypeError:\\n try:\\n _tmparr_qosubi = array.array(\\\"i\\\",qosubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubi has wrong type\\\")\\n else:\\n qosubi_ = memoryview(_tmparr_qosubi)\\n \\n else:\\n if qosubi_.format != \\\"i\\\":\\n qosubi_ = memoryview(array.array(\\\"i\\\",qosubi))\\n \\n if qosubj is None: raise TypeError(\\\"Invalid type for argument qosubj\\\")\\n if qosubj is None:\\n qosubj_ = None\\n else:\\n try:\\n qosubj_ = memoryview(qosubj)\\n except TypeError:\\n try:\\n _tmparr_qosubj = array.array(\\\"i\\\",qosubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubj has wrong type\\\")\\n else:\\n qosubj_ = memoryview(_tmparr_qosubj)\\n \\n else:\\n if qosubj_.format != \\\"i\\\":\\n qosubj_ = memoryview(array.array(\\\"i\\\",qosubj))\\n \\n if qoval is None: raise TypeError(\\\"Invalid type for argument qoval\\\")\\n if qoval is None:\\n qoval_ = None\\n else:\\n try:\\n qoval_ = memoryview(qoval)\\n except TypeError:\\n try:\\n _tmparr_qoval = array.array(\\\"d\\\",qoval)\\n except TypeError:\\n raise TypeError(\\\"Argument qoval has wrong type\\\")\\n else:\\n qoval_ = memoryview(_tmparr_qoval)\\n \\n else:\\n if qoval_.format != \\\"d\\\":\\n qoval_ = memoryview(array.array(\\\"d\\\",qoval))\\n \\n res = self.__obj.putqobj(numqonz_,qosubi_,qosubj_,qoval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def convert_to_q(self):\\n if self.measure == 'Q':\\n warnings.warn('Parameters are already converted to Q!')\\n else:\\n kappa_sp = self.kappa_s\\n kappa_yp = self.kappa_y\\n self.kappa_s = self.kappa_s - self.lmbd_s * self.eta_s\\n self.kappa_y = self.kappa_y - self.lmbd_y * self.eta_y\\n self.scale = kappa_sp / self.kappa_s\\n self.mean_v *= (kappa_yp / self.kappa_y * self.scale)\\n self.lmbd = 0\\n self.eta_y *= (self.scale**.5)\\n self.measure = 'Q'\\n self.update_ajd()\",\n \"def con_ceq(x,project):\\n \\n cons = project.con_ceq(x)\\n \\n if cons: cons = array(cons)\\n else: cons = zeros([0])\\n \\n return cons\",\n \"def quadratic_dependence(dep_keys, ctfs=list(), force_zero=None, **kwargs):\\n if force_zero is None and len(kwargs) == 0:\\n return _dependence(\\n f=np.polyval, n_params=3,\\n dep_keys=dep_keys, ctfs=ctfs, force_zero=force_zero,\\n name='quadratic dependence', code='p2:{}', **kwargs\\n )\\n else:\\n return _dependence(\\n f=lambda p, x: np.polyval(np.concatenate((p, np.zeros(1))), x),\\n n_params=2, dep_keys=dep_keys, ctfs=ctfs, force_zero=force_zero,\\n name='quadratic dependence', code='p2:{}', **kwargs\\n )\",\n \"def putskc(self,whichsol_,skc_):\\n _skc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),self.getnumcon()))\\n if skc_ is None:\\n raise ValueError(\\\"Argument skc cannot be None\\\")\\n if skc_ is None:\\n raise ValueError(\\\"Argument skc may not be None\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def Q2C(self, q):\\n\\n #q = q.squeeze();\\n C = np.empty((3,3));\\n\\tC[0,0] = (q[0]**2.0) + (q[1]**2.0) - (q[2]**2.0) - (q[3]**2.0);\\n\\tC[0,1] = 2.0 * ((q[1]*q[2]) + (q[0]*q[3]));\\n\\tC[0,2] = 2.0 * ((q[1]*q[3]) - (q[0]*q[2]));\\n\\n\\tC[1,0] = 2.0 * ((q[1]*q[2]) - (q[0]*q[3]));\\n\\tC[1,1] = (q[0]**2.0) - (q[1]**2.0) + (q[2]**2.0) - (q[3]**2.0);\\n\\tC[1,2] = 2.0 * ((q[2]*q[3]) + (q[0]*q[1]));\\n\\n\\tC[2,0] = 2.0 * ((q[1]*q[3]) + (q[0]*q[2]));\\n\\tC[2,1] = 2.0 * ((q[2]*q[3]) - (q[0]*q[1]));\\n\\tC[2,2] = (q[0]**2.0) - (q[1]**2.0) - (q[2]**2.0) + (q[3]**2.0);\\n\\n return C\",\n \"def putclist(self,subj,val): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subj)\\n elif num_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(val)\\n elif num_ != len(val):\\n raise IndexError(\\\"Inconsistent length of array val\\\")\\n if num_ is None: num_ = 0\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if val is None: raise TypeError(\\\"Invalid type for argument val\\\")\\n if val is None:\\n val_ = None\\n else:\\n try:\\n val_ = memoryview(val)\\n except TypeError:\\n try:\\n _tmparr_val = array.array(\\\"d\\\",val)\\n except TypeError:\\n raise TypeError(\\\"Argument val has wrong type\\\")\\n else:\\n val_ = memoryview(_tmparr_val)\\n \\n else:\\n if val_.format != \\\"d\\\":\\n val_ = memoryview(array.array(\\\"d\\\",val))\\n \\n res = self.__obj.putclist(num_,subj_,val_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def conj(q_1: Q, conj_type: int = 0) -> Q:\\n\\n end_q_type = f\\\"{q_1.q_type}*\\\"\\n c_t, c_x, c_y, c_z = q_1.t, q_1.x, q_1.y, q_1.z\\n cq = Q()\\n\\n if conj_type % 4 == 0:\\n cq.t = c_t\\n if c_x != 0:\\n cq.x = -1 * c_x\\n if c_y != 0:\\n cq.y = -1 * c_y\\n if c_z != 0:\\n cq.z = -1 * c_z\\n\\n elif conj_type % 4 == 1:\\n if c_t != 0:\\n cq.t = -1 * c_t\\n cq.x = c_x\\n if c_y != 0:\\n cq.y = -1 * c_y\\n if c_z != 0:\\n cq.z = -1 * c_z\\n end_q_type += \\\"1\\\"\\n\\n elif conj_type % 4 == 2:\\n if c_t != 0:\\n cq.t = -1 * c_t\\n if c_x != 0:\\n cq.x = -1 * c_x\\n cq.y = c_y\\n if c_z != 0:\\n cq.z = -1 * c_z\\n end_q_type += \\\"2\\\"\\n\\n elif conj_type % 4 == 3:\\n if c_t != 0:\\n cq.t = -1 * c_t\\n if c_x != 0:\\n cq.x = -1 * c_x\\n if c_y != 0:\\n cq.y = -1 * c_y\\n cq.z = c_z\\n end_q_type += \\\"3\\\"\\n\\n cq.q_type = end_q_type\\n cq.representation = q_1.representation\\n\\n return cq\",\n \"def evaluate_C_q(self, q):\\n C_q_list = []\\n\\n\\n GlobalVariables.q_i_dim[body_id]\",\n \"def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if subk is None: raise TypeError(\\\"Invalid type for argument subk\\\")\\n if subk is None:\\n subk_ = None\\n else:\\n try:\\n subk_ = memoryview(subk)\\n except TypeError:\\n try:\\n _tmparr_subk = array.array(\\\"i\\\",subk)\\n except TypeError:\\n raise TypeError(\\\"Argument subk has wrong type\\\")\\n else:\\n subk_ = memoryview(_tmparr_subk)\\n \\n else:\\n if subk_.format != \\\"i\\\":\\n subk_ = memoryview(array.array(\\\"i\\\",subk))\\n \\n if subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk has wrong length\\\")\\n if subl is None: raise TypeError(\\\"Invalid type for argument subl\\\")\\n if subl is None:\\n subl_ = None\\n else:\\n try:\\n subl_ = memoryview(subl)\\n except TypeError:\\n try:\\n _tmparr_subl = array.array(\\\"i\\\",subl)\\n except TypeError:\\n raise TypeError(\\\"Argument subl has wrong type\\\")\\n else:\\n subl_ = memoryview(_tmparr_subl)\\n \\n else:\\n if subl_.format != \\\"i\\\":\\n subl_ = memoryview(array.array(\\\"i\\\",subl))\\n \\n if subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl has wrong length\\\")\\n if valjkl is None: raise TypeError(\\\"Invalid type for argument valjkl\\\")\\n if valjkl is None:\\n valjkl_ = None\\n else:\\n try:\\n valjkl_ = memoryview(valjkl)\\n except TypeError:\\n try:\\n _tmparr_valjkl = array.array(\\\"d\\\",valjkl)\\n except TypeError:\\n raise TypeError(\\\"Argument valjkl has wrong type\\\")\\n else:\\n valjkl_ = memoryview(_tmparr_valjkl)\\n \\n else:\\n if valjkl_.format != \\\"d\\\":\\n valjkl_ = memoryview(array.array(\\\"d\\\",valjkl))\\n \\n if valjkl_ is not None and len(valjkl_) != (num_):\\n raise ValueError(\\\"Array argument valjkl has wrong length\\\")\\n res = self.__obj.putbarcblocktriplet(num_,subj_,subk_,subl_,valjkl_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqobjij(self,i_,j_,qoij_):\\n res = __library__.MSK_XX_putqobjij(self.__nativep,i_,j_,qoij_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def test_init_q(self):\\n\\n riskfree = .01\\n lmbd = .01\\n lmbd_s = .5\\n lmbd_y = .5\\n mean_v = .5\\n kappa_s = 1.5\\n kappa_y = .5\\n eta_s = .1\\n eta_y = .01\\n rho = -.5\\n\\n param = CentTendParam(riskfree=riskfree,\\n lmbd=lmbd, lmbd_s=lmbd_s, lmbd_y=lmbd_y,\\n mean_v=mean_v, kappa_s=kappa_s, kappa_y=kappa_y,\\n eta_s=eta_s, eta_y=eta_y, rho=rho, measure='Q')\\n\\n kappa_sq = kappa_s - lmbd_s * eta_s\\n kappa_yq = kappa_y - lmbd_y * eta_y\\n scale = kappa_s / kappa_sq\\n\\n self.assertEqual(param.measure, 'Q')\\n self.assertEqual(param.riskfree, riskfree)\\n self.assertEqual(param.lmbd, 0)\\n self.assertEqual(param.lmbd_s, lmbd_s)\\n self.assertEqual(param.lmbd_y, lmbd_y)\\n self.assertEqual(param.mean_v, mean_v * kappa_y / kappa_yq * scale)\\n self.assertEqual(param.kappa_s, kappa_sq)\\n self.assertEqual(param.kappa_y, kappa_yq)\\n self.assertEqual(param.eta_s, eta_s)\\n self.assertEqual(param.eta_y, eta_y * scale**.5)\\n self.assertEqual(param.rho, rho)\\n self.assertTrue(param.is_valid())\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n param.convert_to_q()\",\n \"def solve_Q(ih, jh):\\n # Build A and b\\n\\n # Solve for c via least squares.\\n\\n # Form C from c\\n\\n # Solve for Q and return it.\\n return None\",\n \"def putxc(self,whichsol_,xc_):\\n _xc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),self.getnumcon()))\\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\\n raise ValueError(\\\"Argument xc must be writable\\\")\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc may not be None\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _xc_copyarray:\\n xc_[:] = _xc_np_tmp\",\n \"def project_curve(q):\\n n,T = q.shape\\n if n==2:\\n dt = 0.35\\n if n==3:\\n dt = 0.2\\n epsilon = 1e-6\\n\\n iter = 1\\n res = ones(n)\\n J = zeros((n,n))\\n\\n s = linspace(0,1,T)\\n\\n qnew = q.copy()\\n qnew = qnew / sqrt(innerprod_q2(qnew,qnew))\\n\\n qnorm = zeros(T)\\n G = zeros(n)\\n C = zeros(300)\\n while (norm(res) > epsilon):\\n if iter > 300:\\n break\\n\\n # Jacobian\\n for i in range(0,n):\\n for j in range(0,n):\\n J[i,j] = 3 * trapz(qnew[i,:]*qnew[j,:],s)\\n \\n J += eye(n)\\n\\n for i in range(0,T):\\n qnorm[i] = norm(qnew[:,i])\\n \\n # Compute the residue\\n for i in range(0,n):\\n G[i] = trapz(qnew[i,:]*qnorm,s)\\n \\n res = -G\\n\\n if (norm(res) < epsilon):\\n break\\n\\n x = solve(J,res)\\n C[iter] = norm(res)\\n\\n delG = Basis_Normal_A(qnew)\\n temp = zeros((n,T))\\n for i in range(0,n):\\n temp += x[i]*delG[i]*dt\\n \\n qnew += temp\\n iter += 1\\n \\n qnew = qnew/sqrt(innerprod_q2(qnew,qnew))\\n\\n return qnew\",\n \"def test_solve_quadratic(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.fixed = False\\n iden2.x.fixed = False\\n iden3.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 0)\\n self.assertAlmostEqual(iden1.x.val, 1)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 3)\",\n \"def Qc(I, dT, a, b, c, d, e, f, g, h, i, k):\\n x1 = I # I\\n x2 = dT # dT\\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\\n return m * x2 + b\",\n \"def solvewithbasis(self,transp_,numnz_,sub_,val_):\\n _numnz_tmp = ctypes.c_int32(numnz_)\\n _sub_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and sub_ is not None and len(sub_) != self.getnumcon():\\n raise ValueError(\\\"Array argument sub is not long enough: Is %d, expected %d\\\" % (len(sub_),self.getnumcon()))\\n if isinstance(sub_,numpy.ndarray) and not sub_.flags.writeable:\\n raise ValueError(\\\"Argument sub must be writable\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _val_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and val_ is not None and len(val_) != self.getnumcon():\\n raise ValueError(\\\"Array argument val is not long enough: Is %d, expected %d\\\" % (len(val_),self.getnumcon()))\\n if isinstance(val_,numpy.ndarray) and not val_.flags.writeable:\\n raise ValueError(\\\"Argument val must be writable\\\")\\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\\n _val_copyarray = False\\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif val_ is not None:\\n _val_copyarray = True\\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\\n _val_np_tmp[:] = val_\\n assert _val_np_tmp.flags.contiguous\\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _val_copyarray = False\\n _val_tmp = None\\n \\n res = __library__.MSK_XX_solvewithbasis(self.__nativep,transp_,ctypes.byref(_numnz_tmp),_sub_tmp,_val_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = _numnz_tmp.value\\n if _sub_copyarray:\\n sub_[:] = _sub_np_tmp\\n if _val_copyarray:\\n val_[:] = _val_np_tmp\\n return (_numnz_return_value)\",\n \"def test_solve_quadratic_fixed(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.val = 4\\n iden2.x.val = 5\\n iden3.x.val = 6\\n iden1.x.name = 'x1'\\n iden2.x.name = 'x2'\\n iden3.x.name = 'x3'\\n iden2.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 10)\\n self.assertAlmostEqual(iden1.x.val, 4)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 6)\",\n \"def pk_constrained(self, snr=30, headroom = 0):\\n # Initialize\\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\\n # loop over frequencies\\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\\n for jf, k0 in enumerate(self.controls.k0):\\n # get the scaled version of the propagating directions\\n k_vec = k0 * self.dir\\n # Form the sensing matrix\\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\\n H = h_mtx.astype(complex) # cvxpy does not accept floats, apparently\\n # measured data\\n pm = self.pres_s[:,jf].astype(complex)\\n # Performing constrained optmization cvxpy\\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True) # create x variable\\n # Create the problem\\n epsilon = 10**(-(snr-headroom)/10)\\n problem = cp.Problem(cp.Minimize(cp.norm2(x_cvx)**2),\\n [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon])\\n problem.solve(solver=cp.SCS, verbose=False)\\n self.pk[:,jf] = x_cvx.value\\n bar.update(1)\\n bar.close()\",\n \"def makeConjunction(solver, v, conjuncts):\\n cn = solver.Constraint(0, (len(conjuncts) - 1))\\n cn.SetCoefficient(v, -len(conjuncts))\\n for x in conjuncts:\\n cn.SetCoefficient(x, 1)\",\n \"def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi_)\\n elif nz_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj_)\\n elif nz_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij_)\\n elif nz_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n idx_ = ctypes.c_int64()\\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n idx_ = idx_.value\\n _idx_return_value = idx_\\n return (_idx_return_value)\",\n \"def Qc_fit(x, a, b, c, d, e, f, g, h, i, k):\\n x1 = x[0] # I\\n x2 = x[1] # dT\\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\\n return m * x2 + b\",\n \"def quadratic_program_bnd(self, c, Q, lb, ub):\\n if self.solver == solver_SCIPY:\\n return scipy_quadratic_program_bnd(c, Q, lb, ub)\\n elif self.solver == solver_GUROBI:\\n return gurobi_quadratic_program_bnd(c, Q, lb, ub)\\n else:\\n raise ValueError('QP solver %s not available' % self.solver)\",\n \"def qcontour(zmatrix, nx, ny, nlevels):\\n dislin.qplcon(zmatrix, ny, ny, nlevels)\",\n \"def con_cieq(x,project):\\n \\n cons = project.con_cieq(x)\\n \\n if cons: cons = array(cons)\\n else: cons = zeros([0])\\n \\n return -cons\",\n \"def refine(self, ijk):\\n if self.cbc is None or not self.sub_block_count:\\n raise ValueError(\\n \\\"Cannot refine sub block model without specifying number \\\"\\n \\\"of parent and sub blocks\\\"\\n )\\n try:\\n inds = self.ijk_array_to_indices(ijk)\\n except ValueError:\\n inds = self.ijk_to_index(ijk)\\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137\",\n \"def update_edge_constraint(self, source, dest, constraint):\\n for k, v in constraint.items():\\n if k in self.G[source][dest]['constraint']:\\n self.G[source][dest]['constraint'][k].update(v)\\n else:\\n self.G[source][dest]['constraint'].update({k: v})\",\n \"def _reg_qrs_tconst(pattern, qrs):\\n beats = pattern.evidence[o.QRS]\\n idx = beats.index(qrs)\\n tnet = pattern.last_tnet\\n hyp = pattern.hypothesis\\n BASIC_TCONST(pattern, qrs)\\n tnet.add_constraint(qrs.start, qrs.end, C.NQRS_DUR)\\n tnet.set_before(qrs.time, hyp.end)\\n # Constraints with the precedent T Wave\\n _qrs_after_twave(pattern, qrs)\\n # The environment QRS complex determines the beginning of the bigeminy.\\n if pattern.get_evidence_type(qrs)[1] is ENV:\\n tnet.set_equal(hyp.start, qrs.time)\\n else:\\n # The first regular beat takes the reference RR from the previous rhythm\\n # and the subsequent take the reference from the proper bigeminy.\\n if idx == 2:\\n refrr, stdrr = pattern.evidence[o.Cardiac_Rhythm][0].meas[0]\\n max_var = max(2 * C.RR_MAX_DIFF, 4 * stdrr)\\n tnet.add_constraint(\\n beats[0].time,\\n qrs.time,\\n Iv(\\n min(2 * refrr - max_var, refrr * C.COMPAUSE_MIN_F),\\n max(2 * refrr + max_var, refrr * C.COMPAUSE_MAX_F),\\n ),\\n )\\n else:\\n ref2rr = beats[idx - 2].time.end - beats[idx - 4].time.start\\n mrr, srr = hyp.meas.rr\\n const = Iv(\\n min(ref2rr - 2 * C.RR_MAX_DIFF, 2 * mrr - 4 * srr),\\n max(ref2rr + 2 * C.RR_MAX_DIFF, 2 * mrr + 4 * srr),\\n )\\n tnet.add_constraint(beats[idx - 2].time, qrs.time, const)\\n tnet.add_constraint(beats[idx - 2].start, qrs.start, const)\\n tnet.add_constraint(beats[idx - 2].end, qrs.end, const)\\n # We guide the morphology search to be similar to the previous regular\\n # QRS complex.\\n qrs.shape = beats[idx - 2].shape\\n qrs.paced = beats[idx - 2].paced\\n # Compensatory pause RR\\n minrr = beats[idx - 1].time.start - beats[idx - 2].time.end\\n maxrr = beats[idx - 1].time.end - beats[idx - 2].time.start\\n refcompause = (\\n beats[idx - 2].time.start - beats[idx - 3].time.start\\n if idx > 2\\n else maxrr * C.COMPAUSE_RREXT_MAX_F\\n )\\n mincompause = max(\\n C.COMPAUSE_MIN_DUR,\\n maxrr,\\n min(minrr * C.COMPAUSE_RREXT_MIN_F, refcompause - C.TMARGIN, minrr + C.COMPAUSE_RREXT_MIN),\\n )\\n tnet.add_constraint(beats[idx - 1].time, qrs.time, Iv(mincompause, maxrr * C.COMPAUSE_RREXT_MAX_F))\\n # Beats cannot overlap\\n tnet.add_constraint(beats[idx - 1].end, qrs.start, Iv(C.TQ_INTERVAL_MIN, np.Inf))\",\n \"def generateconenames(self,subk_,fmt_,dims_,sp_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subk_)\\n elif num_ != len(subk_):\\n raise IndexError(\\\"Inconsistent length of array subk\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk cannot be None\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n if isinstance(fmt_,unicode):\\n fmt_ = fmt_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n ndims_ = None\\n if ndims_ is None:\\n ndims_ = len(dims_)\\n elif ndims_ != len(dims_):\\n raise IndexError(\\\"Inconsistent length of array dims\\\")\\n if dims_ is None:\\n raise ValueError(\\\"Argument dims cannot be None\\\")\\n if dims_ is None:\\n raise ValueError(\\\"Argument dims may not be None\\\")\\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\\n _dims_copyarray = False\\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif dims_ is not None:\\n _dims_copyarray = True\\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\\n _dims_np_tmp[:] = dims_\\n assert _dims_np_tmp.flags.contiguous\\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _dims_copyarray = False\\n _dims_tmp = None\\n \\n _sp_minlength = (num_)\\n if (num_) > 0 and sp_ is not None and len(sp_) != (num_):\\n raise ValueError(\\\"Array argument sp is not long enough: Is %d, expected %d\\\" % (len(sp_),(num_)))\\n if isinstance(sp_, numpy.ndarray) and sp_.dtype is numpy.dtype(numpy.int64) and sp_.flags.contiguous:\\n _sp_copyarray = False\\n _sp_tmp = ctypes.cast(sp_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif sp_ is not None:\\n _sp_copyarray = True\\n _sp_np_tmp = numpy.zeros(len(sp_),numpy.dtype(numpy.int64))\\n _sp_np_tmp[:] = sp_\\n assert _sp_np_tmp.flags.contiguous\\n _sp_tmp = ctypes.cast(_sp_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _sp_copyarray = False\\n _sp_tmp = None\\n \\n res = __library__.MSK_XX_generateconenames(self.__nativep,num_,_subk_tmp,fmt_,ndims_,_dims_tmp,_sp_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def addConstraint(constraint, problem):\\n problem += constraint\",\n \"def SetPRBinCatConstraint( self, model ) :\\n tot = np.dot( self.wish.T, self.dispo )\\n for val in tot :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def test_convert_to_q(self):\\n\\n riskfree = .01\\n lmbd = .01\\n lmbd_s = .5\\n lmbd_y = .5\\n mean_v = .5\\n kappa_s = 1.5\\n kappa_y = .5\\n eta_s = .1\\n eta_y = .01\\n rho = -.5\\n\\n theta = [riskfree, mean_v, kappa_s, kappa_y, eta_s, eta_y,\\n rho, lmbd, lmbd_s, lmbd_y]\\n param = CentTendParam.from_theta(theta)\\n param.convert_to_q()\\n\\n kappa_sq = kappa_s - lmbd_s * eta_s\\n kappa_yq = kappa_y - lmbd_y * eta_y\\n scale = kappa_s / kappa_sq\\n\\n self.assertEqual(param.measure, 'Q')\\n self.assertEqual(param.riskfree, riskfree)\\n self.assertEqual(param.lmbd, 0)\\n self.assertEqual(param.lmbd_s, lmbd_s)\\n self.assertEqual(param.lmbd_y, lmbd_y)\\n self.assertEqual(param.mean_v, mean_v * kappa_y / kappa_yq * scale)\\n self.assertEqual(param.kappa_s, kappa_sq)\\n self.assertEqual(param.kappa_y, kappa_yq)\\n self.assertEqual(param.eta_s, eta_s)\\n self.assertEqual(param.eta_y, eta_y * scale**.5)\\n self.assertEqual(param.rho, rho)\\n self.assertTrue(param.is_valid())\",\n \"def mkQuadraticRelaxation(systemDct):\\n # Relaxation result\\n # eqn: dict\\n # key: state variable\\n # value: expression\\n # sub: dict\\n # key: new symbol\\n # value: expression to rever to original\\n # mat: sympy.Matrix: matrix for linearized system (N X M)\\n # row: original state variables\\n # column: extended set of state variables\\n # value: linearized expression\\n # vec: sympy.Matrix complete set of symbols in columns N X 1\\n # row: extended symbol\\n RelaxationResult = collections.namedtuple(\\\"RelaxationResult\\\",\\n \\\"eqn sub mat vec\\\")\\n # Add existing symbols\\n freeSymbols = []\\n [freeSymbols.extend(e.free_symbols) for e in systemDct.values()]\\n [addSymbols(f.name, dct=globals()) for f in freeSymbols]\\n # Add quadratic symbols\\n systemStr = \\\" \\\".join([str(e) for e in systemDct.values()])\\n stateSymbols = list(systemDct.keys())\\n subs = {} # key: new symbol; value: quadratic expression\\n for sym1 in stateSymbols:\\n for sym2 in stateSymbols:\\n if sym1.name <= sym2.name:\\n name = \\\"%s%s\\\" % (sym1, sym2)\\n quadraticEpr = sym1 * sym2\\n quadraticEprStr = str(quadraticEpr)\\n if quadraticEprStr in systemStr: \\n addSymbols(name, dct=globals())\\n subs[globals()[name]] = quadraticEpr\\n subsStr = {str(k): str(v) for k, v in subs.items()}\\n # Construct the vector of extended symbols\\n extendedStateSyms = list(stateSymbols)\\n extendedStateSyms.extend(list(subs.keys()))\\n extendedStateVec = sympy.Matrix(extendedStateSyms)\\n # Replace quadratic terms\\n eprStrDct = {}\\n linearizedDct = {}\\n for sym, epr in systemDct.items():\\n newEprStr = str(epr)\\n for k, v in subsStr.items():\\n newEprStr = newEprStr.replace(v, k)\\n linearizedDct[sym] = eval(newEprStr)\\n # Calculate Jacobian w.r.t. newSymbols to get matris\\n stateEprVec = sympy.Matrix(list(linearizedDct.values()))\\n mat = stateEprVec.jacobian(extendedStateVec)\\n #\\n return RelaxationResult(\\n eqn=linearizedDct,\\n sub=subs,\\n mat=mat,\\n vec=extendedStateVec,\\n )\\n return linearizedDct, subs\",\n \"def SetConstraint(self, model) :\\n if 'pp' in self.__type : self.SetPPConstraint( model )\\n elif self.__type == 'prBin' and self.bound!=0 : self.SetPRBinConstraint( model )\\n elif self.__type == 'prCat' and self.bound != 0 : self.SetPRCatConstraint(model)\\n elif self.__type == 'prBinCat' and self.bound != 0 : self.SetPRBinCatConstraint(model)\\n elif self.bound == 0 : return\\n else : raise RuntimeError( 'SetConstraint : Unknown type for Constraint : ', self.__type )\",\n \"def ALIGNF(km_list, ky):\\n n_feat = len(km_list)\\n\\n #km_list_copy = []\\n # center the kernel first\\n #for i in range(n_feat):\\n # km_list_copy.append(center(km_list[i].copy()))\\n #ky_copy = center(ky.copy())\\n\\n\\n a = np.zeros(n_feat)\\n for i in range(n_feat):\\n a[i] = f_dot(km_list[i], ky)\\n\\n M = np.zeros((n_feat, n_feat))\\n for i in range(n_feat):\\n for j in range(i,n_feat):\\n M[i,j] = f_dot(km_list[i],km_list[j])\\n M[j,i] = M[i,j]\\n\\n Q = 2*M\\n C = -2*a\\n\\n Q = Q + np.diag(np.ones(n_feat)*1e-8)\\n\\n ################################################\\n # Using mosek to solve the quadratice programming\\n\\n # Set upper diagonal element to zeros, mosek only accept lower triangle\\n iu = np.triu_indices(n_feat,1)\\n Q[iu] = 0\\n\\n # start solving with mosek\\n inf = 0.0\\n env = mosek.Env()\\n env.set_Stream(mosek.streamtype.log, streamprinter)\\n\\n # Create a task \\n task = env.Task()\\n task.set_Stream(mosek.streamtype.log, streamprinter)\\n\\n # Set up bound for variables \\n bkx = [mosek.boundkey.lo]* n_feat\\n blx = [0.0] * n_feat\\n #bkx = [mosek.boundkey.fr]* n_feat\\n #blx = [-inf] * n_feat\\n bux = [+inf] * n_feat\\n\\n numvar = len(bkx)\\n\\n task.appendvars(numvar)\\n\\n for j in range(numvar):\\n task.putcj(j,C[j])\\n task.putvarbound(j,bkx[j],blx[j],bux[j])\\n\\n # Set up quadratic objective \\n inds = np.nonzero(Q)\\n qsubi = inds[0].tolist()\\n qsubj = inds[1].tolist()\\n qval = Q[inds].tolist()\\n\\n # Input quadratic objective \\n task.putqobj(qsubi,qsubj,qval)\\n\\n # Input objective sense (minimize/mximize) \\n task.putobjsense(mosek.objsense.minimize)\\n\\n task.optimize()\\n\\n # Print a summary containing information \\n # about the solution for debugging purposes \\n task.solutionsummary(mosek.streamtype.msg)\\n\\n solsta = task.getsolsta(mosek.soltype.itr)\\n if (solsta == mosek.solsta.optimal or\\n solsta == mosek.solsta.near_optimal):\\n # Output a solution \\n xx = np.zeros(numvar, float)\\n task.getxx(mosek.soltype.itr, xx)\\n #xx = xx/np.linalg.norm(xx)\\n return xx\\n else:\\n print solsta\\n xx = np.zeros(numvar, float)\\n task.getxx(mosek.soltype.itr, xx)\\n #xx = xx/np.linalg.norm(xx)\\n return xx\",\n \"def compileCkt(self):\\n f = np.vectorize(lambda x: isinstance(x, QubitChannel))\\n table = f(self.diagram)\\n col_bool = np.bitwise_or.reduce(table, axis=1)\\n # filter nan in 'qubit' direction\\n if not np.bitwise_and.reduce(col_bool):\\n raise ValueError('Found unassigned qubit')\\n # filter nan in 'time' direction\\n row_bool = np.bitwise_or.reduce(table, axis=0)\\n diagram = self.diagram[:, row_bool]\\n table = table[:, row_bool]\\n # align QubitChannel objects in the table column by column\\n for time_idx in range(len(table[0, :])):\\n diagram[table[:, time_idx], time_idx\\n ] = QubitChannel.alignQubitChannels(\\n *diagram[table[:, time_idx], time_idx]\\n )\\n # replace nans with null QubitChannel objects\\n for qubit_idx, row in enumerate(table):\\n for time_idx, flag in enumerate(row):\\n if flag:\\n continue\\n span_idx = np.where(f(diagram[:, time_idx]))[0][0]\\n wire_idx = np.where(f(diagram[qubit_idx, :]))[0][0]\\n diagram[qubit_idx, time_idx] = QubitChannel.null(\\n diagram[span_idx, time_idx], diagram[qubit_idx, wire_idx]\\n )\\n try:\\n self.compiled = np.sum(diagram, axis=1)\\n except SystemError:\\n raise ValueError('Error during wire concatenation')\",\n \"def remove_constraint(self, ckey):\\n if ckey not in self.constraints:\\n raise KeyError(\\\"Constraints not found on object key: {}\\\".format(ckey))\\n del self.constraints[ckey]\",\n \"def clear(self, clear_obj=True, clear_constraints=True):\\n\\n if clear_constraints:\\n self.constraints = []\\n self.bounds, self.constraints = self.const_creator.create_constraint('weight', weight_bound=(0,1), leverage=1)\\n if clear_obj:\\n self.objective = None\",\n \"def add_constraint(self, constraint, problem):\\n problem += constraint\",\n \"def fixC(self,i,value):\\n if self.coeffPattern[2] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[2] = [None]*m\\n self.coeffPattern[2][i]=value\\n self._updateEstimatorSize(i)\",\n \"def test_cov_q(self, ndlys=13):\\n for d in self.d:\\n d.flag_array[:] = False #ensure that there are no flags!\\n d.select(times=np.unique(d.time_array)[:10], frequencies=d.freq_array[:16])\\n for d_std in self.d_std:\\n d_std.flag_array[:] = False\\n d_std.select(times=np.unique(d_std.time_array)[:10], frequencies=d_std.freq_array[:16])\\n self.ds = pspecdata.PSpecData(dsets=self.d, wgts=self.w, dsets_std=self.d_std)\\n self.ds = pspecdata.PSpecData(dsets=self.d, wgts=self.w, dsets_std=self.d_std)\\n Ntime = self.ds.Ntimes\\n self.ds.set_Ndlys(ndlys)\\n # Here is the analytic covariance matrix...\\n chan_x, chan_y = np.meshgrid(range(self.ds.Nfreqs), range(self.ds.Nfreqs))\\n cov_analytic = np.zeros((self.ds.spw_Ndlys, self.ds.spw_Ndlys), dtype=np.complex128)\\n for alpha in range(self.ds.spw_Ndlys):\\n for beta in range(self.ds.spw_Ndlys):\\n cov_analytic[alpha, beta] = np.exp(-2j*np.pi*(alpha-beta)*(chan_x-chan_y)/self.ds.spw_Ndlys).sum()\\n key1 = (0, 24, 38)\\n key2 = (1, 25, 38)\\n #print(cov_analytic)\\n\\n for input_data_weight in ['identity','iC', 'dayenu']:\\n self.ds.set_weighting(input_data_weight)\\n #check error raised\\n if input_data_weight == 'dayenu':\\n pytest.raises(ValueError,self.ds.R, key1)\\n rpk = {'filter_centers':[0.],'filter_half_widths':[0.],'filter_factors':[0.]}\\n self.ds.set_r_param(key1,rpk)\\n self.ds.set_r_param(key2,rpk)\\n for taper in taper_selection:\\n qc = self.ds.cov_q_hat(key1,key2,model='dsets')\\n self.assertTrue(np.allclose(np.array(list(qc.shape)),\\n np.array([self.ds.Ntimes, self.ds.spw_Ndlys, self.ds.spw_Ndlys]), atol=1e-6))\\n qc = self.ds.cov_q_hat(key1,key2,model='empirical')\\n self.assertTrue(np.allclose(np.array(list(qc.shape)),\\n np.array([self.ds.Ntimes, self.ds.spw_Ndlys, self.ds.spw_Ndlys]), atol=1e-6))\\n\\n \\\"\\\"\\\"\\n Now test that analytic Error calculation gives Nchan^2\\n \\\"\\\"\\\"\\n self.ds.set_weighting('identity')\\n qc = self.ds.cov_q_hat(key1, key2, model='dsets')\\n self.assertTrue(np.allclose(qc,\\n np.repeat(cov_analytic[np.newaxis, :, :], self.ds.Ntimes, axis=0), atol=1e-6))\\n \\\"\\\"\\\"\\n Test lists of keys\\n \\\"\\\"\\\"\\n self.ds.set_weighting('identity')\\n qc=self.ds.cov_q_hat([key1], [key2], time_indices=[0], model='dsets')\\n self.assertTrue(np.allclose(qc,\\n np.repeat(cov_analytic[np.newaxis, :, :], self.ds.Ntimes, axis=0), atol=1e-6))\\n self.assertRaises(ValueError, self.ds.cov_q_hat, key1, key2, time_indices=200)\\n self.assertRaises(ValueError, self.ds.cov_q_hat, key1, key2, time_indices=\\\"watch out!\\\")\",\n \"def _Q(self, x, y):\\n\\n # Calculate the [Q] coefficient matrix\\n Q = array([[0, 0, 0, -2, 0, 0, -6*x, -2*y, 0, 0, -6*x*y, 0],\\n [0, 0, 0, 0, 0, -2, 0, 0, -2*x, -6*y, 0, -6*x*y],\\n [0, 0, 0, 0, -2, 0, 0, -4*x, -4*y, 0, -6*x**2, -6*y**2]])\\n \\n # Return the [Q] coefficient matrix\\n return Q\",\n \"def compare_J_terms(m, nix, srcclass=None, analytic_class=None, calced_class=None, \\n only_calced_Cterms=False, fx=None):\\n \\n if fx is None:\\n fx = fixtures.fixture_from_model(m)\\n \\n if srcclass is None:\\n srcclass = FullSingleFieldSource\\n \\n if analytic_class is None:\\n analytic_class = analyticsolution.NoPhaseBunchDaviesSolution\\n if calced_class is None:\\n calced_class = calcedsolution.NoPhaseBunchDaviesCalced\\n \\n asol = analytic_class(fx, srcclass)\\n csol = calced_class(fx, srcclass)\\n \\n #Need to make analytic solution use 128 bit floats to avoid overruns\\n asol.srceqns.k = np.float128(asol.srceqns.k)\\n \\n \\n #Get background values\\n bgvars = m.yresult[nix, 0:3, 0]\\n a = m.ainit*np.exp(m.tresult[nix])\\n #Get potentials\\n potentials = m.potentials(np.array([bgvars[0]]), m.pot_params)\\n \\n #Set alpha and beta\\n alpha = 1/(a*np.sqrt(2))\\n beta = a*bgvars[2]\\n \\n dp1 = csol.get_dp1(csol.srceqns.fullk, alpha=alpha)\\n dp1dot = csol.get_dp1dot(csol.srceqns.fullk, alpha=alpha, beta=beta)\\n \\n #Calculate dphi(q) and dphi(k-q)\\n dp1_q = dp1[:csol.srceqns.k.shape[-1]]\\n dp1dot_q = dp1dot[:csol.srceqns.k.shape[-1]] \\n \\n theta_terms = csol.srceqns.getthetaterms(dp1, dp1dot)\\n csol.srceqns.k = np.float128(csol.srceqns.k)\\n csol.srceqns.fullk = np.float128(csol.srceqns.fullk)\\n \\n calced_Cterms = csol.calculate_Cterms(bgvars, a, potentials) \\n if only_calced_Cterms:\\n analytic_Cterms = calced_Cterms\\n else:\\n analytic_Cterms = asol.calculate_Cterms(bgvars, a, potentials)\\n \\n results = {}\\n \\n for Jkey in csol.J_terms.iterkeys():\\n afunc = asol.J_terms[Jkey]\\n cfunc = csol.J_terms[Jkey]\\n analytic_result = afunc(asol.srceqns.k, analytic_Cterms, alpha=alpha, beta=beta)\\n calced_result = cfunc(theta_terms, dp1_q, dp1dot_q, calced_Cterms)\\n diff = analytic_result - calced_result\\n err = np.abs(diff)/np.abs(analytic_result)\\n results[Jkey] = (diff, err, analytic_result, calced_result)\\n \\n return results\",\n \"def ineqconstr(x, problem):\\n x, t_final = matrify(x, problem)\\n c = []\\n\\n # inter vehicles\\n c += [veh_coll_avoid(x[:, :2, v1], x[:, :2, v2], problem)\\n for v1 in range(problem['Nv']) for v2 in range(v1 + 1, problem['Nv'])]\\n\\n # obstacles\\n c += [obs.avoid(x[:, :2, veh]) for obs in problem['obstacles'] for veh in range(problem['Nv'])]\\n return np.concatenate(c) if c else np.array([])\",\n \"def ch(self, q_control, q_target):\\n if not is_qubit(q_control):\\n raise AquaError('A qubit is expected for the control.')\\n if not self.has_register(q_control[0]):\\n raise AquaError('The control qubit is expected to be part of the circuit.')\\n\\n if not is_qubit(q_target):\\n raise AquaError('A qubit is expected for the target.')\\n if not self.has_register(q_target[0]):\\n raise AquaError('The target qubit is expected to be part of the circuit.')\\n\\n if q_control == q_target:\\n raise AquaError('The control and target need to be different qubits.')\\n\\n self.u3(-7 / 4 * pi, 0, 0, q_target)\\n self.cx(q_control, q_target)\\n self.u3(7 / 4 * pi, 0, 0, q_target)\\n return self\",\n \"def getdviolcon(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getdviolcon(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\\n else:\\n _skn_tmp = None\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def con_dcieq(x,project):\\n \\n dcons = project.con_dcieq(x)\\n \\n dim = project.n_dv\\n if dcons: dcons = array(dcons)\\n else: dcons = zeros([0,dim])\\n \\n return -dcons\",\n \"def add_constraint(self, constraint):\\n constraint_type = constraint[0]\\n if constraint_type == 'time':\\n dependent_variable = constraint[-2]\\n dependee_variable = constraint[-1]\\n dependent_index = self.subvariable_name.index(dependent_variable)\\n dependee_index = self.subvariable_name.index(dependee_variable)\\n constraint[-2] = self.value[dependent_index]\\n constraint[-1] = self.value[dependee_index]\\n if constraint_type in ['threshold', 'count']:\\n threshold_variable = constraint[-1]\\n threshold_index = self.subvariable_name.index(threshold_variable)\\n constraint[-1] = self.value[threshold_index]\\n if constraint_type == 'only_one':\\n onlyone_variable = constraint[-1]\\n onlyone_index = self.subvariable_name.index(onlyone_variable)\\n constraint[-1] = self.value[onlyone_index]\\n if constraint_type in self.constraint.keys():\\n self.constraint[constraint_type] += [constraint[1:]]\\n else:\\n self.constraint[constraint_type] = [constraint[1:]]\",\n \"def putbarcj(self,j_,sub_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights_)\\n elif num_ != len(weights_):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbarcj(self.__nativep,j_,num_,_sub_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def addConstraints(const1, const2):\\n \\n #get unique keys\\n keys = list(set(list(const1.keys()) + list(const2.keys())))\\n const_sum = {}\\n \\n for key in keys:\\n addrhs = np.array(np.add(const1[key].rhs, const2[key].rhs))\\n const_sum[key] = cenquery.Constraint(query=const1[key].query,\\n rhs=addrhs, sign=const1[key].sign, name =const1[key].name )\\n \\n return(const_sum)\",\n \"def _readeqdsk(self):\\n dir_TCV = '/home/vallar/TCV/eqdsk/'\\n try:\\n b = ascot_Bfield.Bfield_eqdsk(dir_TCV+self.eqdsk_fname,129,129, 'TCV', COCOS=17)\\n print(\\\"Opened \\\", dir_TCV+self.eqdsk_fname)\\n except:\\n print(\\\"Impossible to open \\\", self.eqdsk_fname)\\n raise ValueError\\n qprof_t = np.abs(b.eqdsk.q)\\n rho_eqdsk = b.eqdsk.rhopsi\\n self.param_q = interpolate.interp1d(rho_eqdsk, qprof_t)\",\n \"def putbarablocktriplet(self,num_,subi,subj,subk,subl,valijkl): # 3\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subi_ is not None and len(subi_) != (num_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if subk is None: raise TypeError(\\\"Invalid type for argument subk\\\")\\n if subk is None:\\n subk_ = None\\n else:\\n try:\\n subk_ = memoryview(subk)\\n except TypeError:\\n try:\\n _tmparr_subk = array.array(\\\"i\\\",subk)\\n except TypeError:\\n raise TypeError(\\\"Argument subk has wrong type\\\")\\n else:\\n subk_ = memoryview(_tmparr_subk)\\n \\n else:\\n if subk_.format != \\\"i\\\":\\n subk_ = memoryview(array.array(\\\"i\\\",subk))\\n \\n if subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk has wrong length\\\")\\n if subl is None: raise TypeError(\\\"Invalid type for argument subl\\\")\\n if subl is None:\\n subl_ = None\\n else:\\n try:\\n subl_ = memoryview(subl)\\n except TypeError:\\n try:\\n _tmparr_subl = array.array(\\\"i\\\",subl)\\n except TypeError:\\n raise TypeError(\\\"Argument subl has wrong type\\\")\\n else:\\n subl_ = memoryview(_tmparr_subl)\\n \\n else:\\n if subl_.format != \\\"i\\\":\\n subl_ = memoryview(array.array(\\\"i\\\",subl))\\n \\n if subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl has wrong length\\\")\\n if valijkl is None: raise TypeError(\\\"Invalid type for argument valijkl\\\")\\n if valijkl is None:\\n valijkl_ = None\\n else:\\n try:\\n valijkl_ = memoryview(valijkl)\\n except TypeError:\\n try:\\n _tmparr_valijkl = array.array(\\\"d\\\",valijkl)\\n except TypeError:\\n raise TypeError(\\\"Argument valijkl has wrong type\\\")\\n else:\\n valijkl_ = memoryview(_tmparr_valijkl)\\n \\n else:\\n if valijkl_.format != \\\"d\\\":\\n valijkl_ = memoryview(array.array(\\\"d\\\",valijkl))\\n \\n if valijkl_ is not None and len(valijkl_) != (num_):\\n raise ValueError(\\\"Array argument valijkl has wrong length\\\")\\n res = self.__obj.putbarablocktriplet(num_,subi_,subj_,subk_,subl_,valijkl_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_area_constraints(solver, objective, courses):\\n areas = (\\\"APCOMP\\\",\\\"APMTH\\\",\\\"BE\\\",\\\"COMPSCI\\\",\\\"ENG-SCI\\\",\\\"ESE\\\")\\n\\n # For COMPSCI, avoid Friday lunch (faculty meeting) and Thursday seminar\\n for c in courses:\\n if not c.startswith(\\\"COMPSCI\\\"):\\n continue\\n for avoid in [\\\"F3a\\\",\\\"R5a\\\"]:\\n if avoid in courses[c].vars_actualslots:\\n objective.SetCoefficient(courses[c].vars_actualslots[avoid], PARAMS['WEIGHT_AVOID_CS_COURSES_IN_FAC_LUNCH_OR_COLLOQ']) \\n \\n for area in areas:\\n v_day_of_week_diff = solver.IntVar(0, solver.infinity(), area + \\\" diff between TuTh and MWF courses\\\")\\n objective.SetCoefficient(v_day_of_week_diff, PARAMS['WEIGHT_DIFF_NUM_COURSES_DAY_OF_WEEK']) \\n \\n cn_day_of_week1 = solver.Constraint(0, solver.infinity())\\n cn_day_of_week2 = solver.Constraint(0, solver.infinity())\\n cn_day_of_week1.SetCoefficient(v_day_of_week_diff, 1)\\n cn_day_of_week2.SetCoefficient(v_day_of_week_diff, 1)\\n\\n v_time_of_day_diff = solver.IntVar(0, solver.infinity(), area + \\\" diff between times of day\\\")\\n objective.SetCoefficient(v_time_of_day_diff, PARAMS['WEIGHT_DIFF_NUM_COURSES_TIME_OF_DAY']) \\n\\n cns_times_of_day = { } # each constraint will be (num in slot i - num in slot j)\\n for i in range(1,6):\\n cns_times_of_day[i] = { }\\n for j in range(1,6):\\n if i == j:\\n continue\\n cns_times_of_day[i][j] = solver.Constraint(0, solver.infinity())\\n cns_times_of_day[i][j].SetCoefficient(v_day_of_week_diff, 1)\\n\\n \\n for c in courses:\\n if not c.startswith(area):\\n continue\\n\\n for s in courses[c].vars_meeting_time:\\n if ss.meeting_frequency(s) in (2,3):\\n vmt = courses[c].vars_meeting_time[s]\\n if ss.meeting_time_is_tu_th(s):\\n cn_day_of_week1.SetCoefficient(vmt, 1)\\n cn_day_of_week2.SetCoefficient(vmt,-1)\\n else:\\n cn_day_of_week1.SetCoefficient(vmt,-1)\\n cn_day_of_week2.SetCoefficient(vmt, 1)\\n\\n if ss.meeting_time_starts_between_9_and_4(s):\\n slot = ss.start_time_slot(s)\\n assert slot in range(1,6), slot\\n for j in range(1,6):\\n if slot == j:\\n continue\\n cns_times_of_day[slot][j].SetCoefficient(vmt, 1)\\n cns_times_of_day[j][slot].SetCoefficient(vmt,-1)\",\n \"def set_c_values(self,nu=3,xd=2,a12sq=1,a13sq=1,a1=0,b1=0,p1=0,\\n vx=0,vy=0,vz=0,epsilon=0.,omega=1.5*np.pi,\\n a2=np.pi,b2=0*np.pi,p2=0.5*np.pi,silent=False):\\n \\n c_target = np.zeros(18)\\n c_target[0] = nu*self.sigma0_RG \\n xd_abs = xd*self.sigma2_RG\\n c_target[4:10] = set_fGij(xd_abs,a12sq,a13sq,a1,b1,p1)\\n c_target[10:13] = np.array([vx,vy,vz])\\n c_target[13:18] = set_VGij(epsilon,omega,a2,b2,p2)\\n \\n if silent == False:\\n print (\\\"Constrain peak parameters: \\\")\\n if 'f0' in self.CONS or 'full' in self.CONS: \\n print (\\\"f0: \\\",\\\"nu = %.1f\\\"%nu, \\\"$\\\\sigma_0$\\\")\\n if 'f1' in self.CONS or 'full' in self.CONS: \\n print (\\\"f1: \\\",\\\"f1,x = f1,y = f1,z = 0\\\")\\n if 'f2' in self.CONS or 'full' in self.CONS: \\n print (\\\"f2: \\\",r\\\"xd = {:.1f} $\\\\sigma_2$, a12sq = {:.1f}, a13sq = {:.1f},a1={:.2f}, b1={:.2f}, p1={:.2f}\\\".format(xd,a12sq,a13sq,a1,b1,p1))\\n if 'vx' in self.CONS or 'full' in self.CONS: \\n print (\\\"vx = {:.1f} km/s\\\".format(vx)) \\n if 'vy' in self.CONS or 'full' in self.CONS: \\n print (\\\"vy = {:.1f} km/s\\\".format(vy))\\n if 'vz' in self.CONS or 'full' in self.CONS: \\n print (\\\"vz = {:.1f} km/s\\\".format(vz))\\n if 'TG' in self.CONS or 'full' in self.CONS: \\n print (\\\"TG: \\\",\\\"epsilon = {:.1f} km/s/Mpc, omega = {:.2f}, a2={:.2f}, b2={:.2f}, p2={:.2f}\\\".format(epsilon,omega,a2,b2,p2))\\n \\n return c_target[self.cmask]\",\n \"def __addValueConstraints(self):\\n for x in range(self.width):\\n for y in range(self.height):\\n g = self.grid[(x, y)]\\n self.solver.add(\\n Or([g == Magnets.EMPTY, g == Magnets.PLUS, g == Magnets.MINUS]))\\n if x > 0:\\n left = self.grid[(x-1, y)]\\n self.solver.add(Or([g != left, g == Magnets.EMPTY]))\\n if y > 0:\\n up = self.grid[(x, y-1)]\\n self.solver.add(Or([g != up, g == Magnets.EMPTY]))\",\n \"def putclist(self,subj_,val_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(val_)\\n elif num_ != len(val_):\\n raise IndexError(\\\"Inconsistent length of array val\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if val_ is None:\\n raise ValueError(\\\"Argument val cannot be None\\\")\\n if val_ is None:\\n raise ValueError(\\\"Argument val may not be None\\\")\\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\\n _val_copyarray = False\\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif val_ is not None:\\n _val_copyarray = True\\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\\n _val_np_tmp[:] = val_\\n assert _val_np_tmp.flags.contiguous\\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _val_copyarray = False\\n _val_tmp = None\\n \\n res = __library__.MSK_XX_putclist(self.__nativep,num_,_subj_tmp,_val_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7356916","0.7274804","0.7158734","0.66391945","0.6536458","0.5380898","0.5333184","0.5304116","0.52284986","0.52283263","0.5153612","0.5121988","0.50708705","0.50423145","0.4997849","0.49975076","0.49906054","0.49511495","0.4933693","0.4864164","0.48633","0.48333412","0.48287615","0.48150796","0.48011255","0.47780675","0.47725552","0.47551745","0.47496006","0.4739018","0.4732969","0.47327465","0.47236556","0.47211793","0.47152272","0.47143158","0.4711768","0.47112563","0.4711228","0.46891224","0.4683448","0.46801895","0.46695694","0.4659917","0.46464443","0.46463817","0.4645612","0.4624942","0.46203396","0.46177092","0.46140358","0.46049756","0.4604805","0.46012944","0.45852602","0.45805648","0.45802924","0.45724702","0.4553915","0.45437926","0.45435447","0.45419207","0.4536004","0.45327732","0.45152313","0.4509729","0.45020702","0.4500469","0.44890785","0.4488518","0.44881272","0.4475684","0.44625312","0.44579852","0.44578114","0.44541734","0.4437631","0.4432257","0.44256517","0.44216838","0.441873","0.44174147","0.4415598","0.44137025","0.4411454","0.44082","0.43970117","0.43893558","0.43828055","0.43792358","0.4371437","0.43706807","0.4367216","0.43618914","0.4359561","0.4359035","0.43573815","0.43534958","0.43472904","0.43414637"],"string":"[\n \"0.7356916\",\n \"0.7274804\",\n \"0.7158734\",\n \"0.66391945\",\n \"0.6536458\",\n \"0.5380898\",\n \"0.5333184\",\n \"0.5304116\",\n \"0.52284986\",\n \"0.52283263\",\n \"0.5153612\",\n \"0.5121988\",\n \"0.50708705\",\n \"0.50423145\",\n \"0.4997849\",\n \"0.49975076\",\n \"0.49906054\",\n \"0.49511495\",\n \"0.4933693\",\n \"0.4864164\",\n \"0.48633\",\n \"0.48333412\",\n \"0.48287615\",\n \"0.48150796\",\n \"0.48011255\",\n \"0.47780675\",\n \"0.47725552\",\n \"0.47551745\",\n \"0.47496006\",\n \"0.4739018\",\n \"0.4732969\",\n \"0.47327465\",\n \"0.47236556\",\n \"0.47211793\",\n \"0.47152272\",\n \"0.47143158\",\n \"0.4711768\",\n \"0.47112563\",\n \"0.4711228\",\n \"0.46891224\",\n \"0.4683448\",\n \"0.46801895\",\n \"0.46695694\",\n \"0.4659917\",\n \"0.46464443\",\n \"0.46463817\",\n \"0.4645612\",\n \"0.4624942\",\n \"0.46203396\",\n \"0.46177092\",\n \"0.46140358\",\n \"0.46049756\",\n \"0.4604805\",\n \"0.46012944\",\n \"0.45852602\",\n \"0.45805648\",\n \"0.45802924\",\n \"0.45724702\",\n \"0.4553915\",\n \"0.45437926\",\n \"0.45435447\",\n \"0.45419207\",\n \"0.4536004\",\n \"0.45327732\",\n \"0.45152313\",\n \"0.4509729\",\n \"0.45020702\",\n \"0.4500469\",\n \"0.44890785\",\n \"0.4488518\",\n \"0.44881272\",\n \"0.4475684\",\n \"0.44625312\",\n \"0.44579852\",\n \"0.44578114\",\n \"0.44541734\",\n \"0.4437631\",\n \"0.4432257\",\n \"0.44256517\",\n \"0.44216838\",\n \"0.441873\",\n \"0.44174147\",\n \"0.4415598\",\n \"0.44137025\",\n \"0.4411454\",\n \"0.44082\",\n \"0.43970117\",\n \"0.43893558\",\n \"0.43828055\",\n \"0.43792358\",\n \"0.4371437\",\n \"0.43706807\",\n \"0.4367216\",\n \"0.43618914\",\n \"0.4359561\",\n \"0.4359035\",\n \"0.43573815\",\n \"0.43534958\",\n \"0.43472904\",\n \"0.43414637\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7487632"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11991,"cells":{"query":{"kind":"string","value":"Replaces all quadratic terms in a single constraint. putqconk(self,k_,qcsubi_,qcsubj_,qcval_)"},"document":{"kind":"string","value":"def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubk is None: raise TypeError(\"Invalid type for argument qcsubk\")\n if qcsubk is None:\n qcsubk_ = None\n else:\n try:\n qcsubk_ = memoryview(qcsubk)\n except TypeError:\n try:\n _tmparr_qcsubk = array.array(\"i\",qcsubk)\n except TypeError:\n raise TypeError(\"Argument qcsubk has wrong type\")\n else:\n qcsubk_ = memoryview(_tmparr_qcsubk)\n \n else:\n if qcsubk_.format != \"i\":\n qcsubk_ = memoryview(array.array(\"i\",qcsubk))\n \n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n maxnumqcnz_ = self.getnumqconknz((k_))\n numqcnz_ = ctypes.c_int64()\n _qcsubi_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi is not long enough: Is %d, expected %d\" % (len(qcsubi_),self.getnumqconknz((k_))))\n if isinstance(qcsubi_,numpy.ndarray) and not qcsubi_.flags.writeable:\n raise ValueError(\"Argument qcsubi must be writable\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n _qcsubj_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj is not long enough: Is %d, expected %d\" % (len(qcsubj_),self.getnumqconknz((k_))))\n if isinstance(qcsubj_,numpy.ndarray) and not qcsubj_.flags.writeable:\n raise ValueError(\"Argument qcsubj must be writable\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n _qcval_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval is not long enough: Is %d, expected %d\" % (len(qcval_),self.getnumqconknz((k_))))\n if isinstance(qcval_,numpy.ndarray) and not qcval_.flags.writeable:\n raise ValueError(\"Argument qcval must be writable\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n qcsurp_ = ctypes.c_int64(_qcsubi_minlength)\n res = __library__.MSK_XX_getqconk64(self.__nativep,k_,maxnumqcnz_,ctypes.byref(qcsurp_),ctypes.byref(numqcnz_),_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqcnz_ = numqcnz_.value\n _numqcnz_return_value = numqcnz_\n if _qcsubi_copyarray:\n qcsubi_[:] = _qcsubi_np_tmp\n if _qcsubj_copyarray:\n qcsubj_[:] = _qcsubj_np_tmp\n if _qcval_copyarray:\n qcval_[:] = _qcval_np_tmp\n return (_numqcnz_return_value)","def getqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n maxnumqcnz_ = self.getnumqconknz((k_))\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n _copyback_qcsubi = False\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n _copyback_qcsubi = True\n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n _copyback_qcsubi = True\n if qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi has wrong length\")\n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n _copyback_qcsubj = False\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n _copyback_qcsubj = True\n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n _copyback_qcsubj = True\n if qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj has wrong length\")\n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n _copyback_qcval = False\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n _copyback_qcval = True\n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n _copyback_qcval = True\n if qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval has wrong length\")\n res,resargs = self.__obj.getqconk64(k_,maxnumqcnz_,len(qcsubi),qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqcnz_return_value = resargs\n if _copyback_qcval:\n qcval[:] = _tmparr_qcval\n if _copyback_qcsubj:\n qcsubj[:] = _tmparr_qcsubj\n if _copyback_qcsubi:\n qcsubi[:] = _tmparr_qcsubi\n return _numqcnz_return_value","def Q(self, k, x):\n g = np.asarray(self.g(k, x))\n Q = g @ g.T\n return Q","def add_cp_qe_RBM_terms(K, cons_pot_mesh, quad_geo_mesh):\n num_faces = cons_pot_mesh.get_faces().shape[0]\n x_c = quad_geo_mesh.get_centroid()\n w = quad_geo_mesh.get_w()\n A_m = quad_geo_mesh.get_A_m()\n S_D = quad_geo_mesh.get_surface_area()\n\n for face_num in range(num_faces):\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n def v_quad(_xi, _eta, _nodes):\n return np.identity(3)\n v_sub_mat = (1. / S_D) * gq.int_over_tri_quad(v_quad, face_nodes, face_hs)\n def omega_quad(xi, eta, nodes):\n pos = geo.quadratic_interp(xi, eta, nodes)\n X = pos - x_c\n return np.einsum(\"lrs,s->lr\", geo.LC_3, X)\n tmp_omega = gq.int_over_tri_quad(\n omega_quad,\n face_nodes,\n face_hs,\n )\n tmp_arr = []\n for m in range(3):\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\"l,ls\", w[m], tmp_omega)))\n tmp_arr = np.array(tmp_arr)\n tmp_omega_mat = np.sum(tmp_arr, axis=0)\n for src_num in range(num_faces):\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += v_sub_mat\n src_center = cons_pot_mesh.get_node(src_num)\n X_0 = src_center - x_c\n omega_mat = np.einsum(\"ijk,js,k->is\", geo.LC_3, tmp_omega_mat, X_0)\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += omega_mat","def putcone(self,k_,ct_,conepar_,submem_):\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem_)\n elif nummem_ != len(submem_):\n raise IndexError(\"Inconsistent length of array submem\")\n if submem_ is None:\n raise ValueError(\"Argument submem cannot be None\")\n if submem_ is None:\n raise ValueError(\"Argument submem may not be None\")\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\n _submem_copyarray = False\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif submem_ is not None:\n _submem_copyarray = True\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\n _submem_np_tmp[:] = submem_\n assert _submem_np_tmp.flags.contiguous\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _submem_copyarray = False\n _submem_tmp = None\n \n res = __library__.MSK_XX_putcone(self.__nativep,k_,ct_,conepar_,nummem_,_submem_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valjkl_minlength = (num_)\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl is not long enough: Is %d, expected %d\" % (len(valjkl_),(num_)))\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl cannot be None\")\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl may not be None\")\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\n _valjkl_copyarray = False\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valjkl_ is not None:\n _valjkl_copyarray = True\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\n _valjkl_np_tmp[:] = valjkl_\n assert _valjkl_np_tmp.flags.contiguous\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valjkl_copyarray = False\n _valjkl_tmp = None\n \n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_cp_qe_DL_terms(K, cons_pot_mesh, quad_geo_mesh):\n geo_faces = quad_geo_mesh.get_faces()\n pot_faces = cons_pot_mesh.get_faces()\n assert geo_faces.shape[0] == pot_faces.shape[0]\n num_faces = geo_faces.shape[0]\n c_0 = 1. / (4. * np.pi)\n for face_num in range(num_faces): # field points\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_n = quad_geo_mesh.get_quad_n(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n for src_num in range(num_faces): # source points\n src_center = cons_pot_mesh.get_node(src_num)\n if face_num != src_num:\n sub_mat = gq.int_over_tri_quad_n(\n make_cp_qe_quad_func(src_center),\n face_nodes,\n face_n,\n face_hs\n )\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += sub_mat\n K[(3 * src_num):(3 * src_num + 3),\n (3 * src_num):(3 * src_num + 3)] -= sub_mat\n # do nothing face_num == src_num, how it works out for constant elements\n\n for src_num in range(num_faces):\n K[(3 * src_num):(3 * src_num + 3),\n (3 * src_num):(3 * src_num + 3)] -= 4. * np.pi * np.identity(3)\n K *= c_0","def ikcon(self, T, q0=None):\n\n if not isinstance(T, SE3):\n T = SE3(T)\n\n trajn = len(T)\n\n try:\n if q0 is not None:\n q0 = getvector(q0, self.n, 'row')\n else:\n q0 = np.zeros((trajn, self.n))\n except ValueError:\n verifymatrix(q0, (trajn, self.n))\n\n # create output variables\n qstar = np.zeros((trajn, self.n))\n error = []\n exitflag = []\n\n omega = np.diag([1, 1, 1, 3 / self.reach])\n\n def cost(q, T, omega):\n return np.sum(\n (\n (np.linalg.pinv(T.A) @ self.fkine(q).A - np.eye(4)) @\n omega) ** 2\n )\n\n bnds = Bounds(self.qlim[0, :], self.qlim[1, :])\n\n for i in range(trajn):\n Ti = T[i]\n res = minimize(\n lambda q: cost(q, Ti, omega),\n q0[i, :], bounds=bnds, options={'gtol': 1e-6})\n qstar[i, :] = res.x\n error.append(res.fun)\n exitflag.append(res.success)\n\n if trajn > 1:\n return qstar, exitflag, error\n else:\n return qstar[0, :], exitflag[0], error[0]","def putconbound(self,i_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putcone(self,k_,ct_,conepar_,submem): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem)\n elif nummem_ != len(submem):\n raise IndexError(\"Inconsistent length of array submem\")\n if nummem_ is None: nummem_ = 0\n if submem is None: raise TypeError(\"Invalid type for argument submem\")\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n \n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n \n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _append_phase(self, k, i):\n if not 0 <= i < self.num_qubits:\n raise QiskitError(\"phase qubit out of bounds.\")\n # If the kth bit is flipped, conjugate this gate\n if self.shift[i] == 1:\n k = (7 * k) % 8\n # Take all subsets \\alpha of the support of row i\n # of weight up to 3 and add k*(-2)**(|\\alpha| - 1) mod 8\n # to the corresponding term.\n support = np.arange(self.num_qubits)[np.nonzero(self.linear[i])]\n subsets_2 = itertools.combinations(support, 2)\n subsets_3 = itertools.combinations(support, 3)\n for j in support:\n value = self.poly.get_term([j])\n self.poly.set_term([j], (value + k) % 8)\n for j in subsets_2:\n value = self.poly.get_term(list(j))\n self.poly.set_term(list(j), (value + -2 * k) % 8)\n for j in subsets_3:\n value = self.poly.get_term(list(j))\n self.poly.set_term(list(j), (value + 4 * k) % 8)","def ket(self: Qs) -> Qs:\n\n if self.qs_type == \"ket\":\n return self\n\n ket = conjs(deepcopy(self))\n ket.rows = self.dim\n ket.columns = 1\n\n ket.qs_type = \"ket\" if self.dim > 1 else \"scalar_q\"\n\n return ket","def ALIGNF(km_list, ky):\n n_feat = len(km_list)\n\n #km_list_copy = []\n # center the kernel first\n #for i in range(n_feat):\n # km_list_copy.append(center(km_list[i].copy()))\n #ky_copy = center(ky.copy())\n\n\n a = np.zeros(n_feat)\n for i in range(n_feat):\n a[i] = f_dot(km_list[i], ky)\n\n M = np.zeros((n_feat, n_feat))\n for i in range(n_feat):\n for j in range(i,n_feat):\n M[i,j] = f_dot(km_list[i],km_list[j])\n M[j,i] = M[i,j]\n\n Q = 2*M\n C = -2*a\n\n Q = Q + np.diag(np.ones(n_feat)*1e-8)\n\n ################################################\n # Using mosek to solve the quadratice programming\n\n # Set upper diagonal element to zeros, mosek only accept lower triangle\n iu = np.triu_indices(n_feat,1)\n Q[iu] = 0\n\n # start solving with mosek\n inf = 0.0\n env = mosek.Env()\n env.set_Stream(mosek.streamtype.log, streamprinter)\n\n # Create a task \n task = env.Task()\n task.set_Stream(mosek.streamtype.log, streamprinter)\n\n # Set up bound for variables \n bkx = [mosek.boundkey.lo]* n_feat\n blx = [0.0] * n_feat\n #bkx = [mosek.boundkey.fr]* n_feat\n #blx = [-inf] * n_feat\n bux = [+inf] * n_feat\n\n numvar = len(bkx)\n\n task.appendvars(numvar)\n\n for j in range(numvar):\n task.putcj(j,C[j])\n task.putvarbound(j,bkx[j],blx[j],bux[j])\n\n # Set up quadratic objective \n inds = np.nonzero(Q)\n qsubi = inds[0].tolist()\n qsubj = inds[1].tolist()\n qval = Q[inds].tolist()\n\n # Input quadratic objective \n task.putqobj(qsubi,qsubj,qval)\n\n # Input objective sense (minimize/mximize) \n task.putobjsense(mosek.objsense.minimize)\n\n task.optimize()\n\n # Print a summary containing information \n # about the solution for debugging purposes \n task.solutionsummary(mosek.streamtype.msg)\n\n solsta = task.getsolsta(mosek.soltype.itr)\n if (solsta == mosek.solsta.optimal or\n solsta == mosek.solsta.near_optimal):\n # Output a solution \n xx = np.zeros(numvar, float)\n task.getxx(mosek.soltype.itr, xx)\n #xx = xx/np.linalg.norm(xx)\n return xx\n else:\n print solsta\n xx = np.zeros(numvar, float)\n task.getxx(mosek.soltype.itr, xx)\n #xx = xx/np.linalg.norm(xx)\n return xx","def putskc(self,whichsol_,skc_):\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if skc_ is None:\n raise ValueError(\"Argument skc cannot be None\")\n if skc_ is None:\n raise ValueError(\"Argument skc may not be None\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def additional_equations(self, k):\n return","def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqobjij(self,i_,j_,qoij_):\n res = __library__.MSK_XX_putqobjij(self.__nativep,i_,j_,qoij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_):\n _subi_minlength = (num_)\n if (num_) > 0 and subi_ is not None and len(subi_) != (num_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(num_)))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valijkl_minlength = (num_)\n if (num_) > 0 and valijkl_ is not None and len(valijkl_) != (num_):\n raise ValueError(\"Array argument valijkl is not long enough: Is %d, expected %d\" % (len(valijkl_),(num_)))\n if valijkl_ is None:\n raise ValueError(\"Argument valijkl cannot be None\")\n if valijkl_ is None:\n raise ValueError(\"Argument valijkl may not be None\")\n if isinstance(valijkl_, numpy.ndarray) and valijkl_.dtype is numpy.dtype(numpy.float64) and valijkl_.flags.contiguous:\n _valijkl_copyarray = False\n _valijkl_tmp = ctypes.cast(valijkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valijkl_ is not None:\n _valijkl_copyarray = True\n _valijkl_np_tmp = numpy.zeros(len(valijkl_),numpy.dtype(numpy.float64))\n _valijkl_np_tmp[:] = valijkl_\n assert _valijkl_np_tmp.flags.contiguous\n _valijkl_tmp = ctypes.cast(_valijkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valijkl_copyarray = False\n _valijkl_tmp = None\n \n res = __library__.MSK_XX_putbarablocktriplet(self.__nativep,num_,_subi_tmp,_subj_tmp,_subk_tmp,_subl_tmp,_valijkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqobjij(self,i_,j_,qoij_): # 3\n res = self.__obj.putqobjij(i_,j_,qoij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def qmincon(self, q=None):\n\n def sumsqr(A):\n return np.sum(A**2)\n\n def cost(x, ub, lb, qm, N):\n return sumsqr(\n (2 * (N @ x + qm) - ub - lb) / (ub - lb))\n\n q = getmatrix(q, (None, self.n))\n\n qstar = np.zeros((q.shape[0], self.n))\n error = np.zeros(q.shape[0])\n success = np.zeros(q.shape[0])\n\n lb = self.qlim[0, :]\n ub = self.qlim[1, :]\n\n for k, qk in enumerate(q):\n\n J = self.jacobe(qk)\n\n N = null(J)\n\n x0 = np.zeros(N.shape[1])\n A = np.r_[N, -N]\n b = np.r_[ub - qk, qk - lb].reshape(A.shape[0],)\n\n con = LinearConstraint(A, -np.inf, b)\n\n res = minimize(\n lambda x: cost(x, ub, lb, qk, N),\n x0, constraints=con)\n\n qstar[k, :] = qk + N @ res.x\n error[k] = res.fun\n success[k] = res.success\n\n if q.shape[0] == 1:\n return qstar[0, :], success[0], error[0]\n else:\n return qstar, success, error","def putconbound(self,i_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n \n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n \n if subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n \n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n \n if subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valjkl is None: raise TypeError(\"Invalid type for argument valjkl\")\n if valjkl is None:\n valjkl_ = None\n else:\n try:\n valjkl_ = memoryview(valjkl)\n except TypeError:\n try:\n _tmparr_valjkl = array.array(\"d\",valjkl)\n except TypeError:\n raise TypeError(\"Argument valjkl has wrong type\")\n else:\n valjkl_ = memoryview(_tmparr_valjkl)\n \n else:\n if valjkl_.format != \"d\":\n valjkl_ = memoryview(array.array(\"d\",valjkl))\n \n if valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl has wrong length\")\n res = self.__obj.putbarcblocktriplet(num_,subj_,subk_,subl_,valjkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def Qc_fit(x, a, b, c, d, e, f, g, h, i, k):\n x1 = x[0] # I\n x2 = x[1] # dT\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\n return m * x2 + b","def putcj(self,j_,cj_):\n res = __library__.MSK_XX_putcj(self.__nativep,j_,cj_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def quaternion_conjugate(self, q):\n\n \"\"\"\n in-place operation is an operation that changes directly the content of a given Tensor without making a copy.\n ALL operations on the tensor that operate in-place on it will have an _ postfix.\n \"\"\"\n q_star = q.new(4).fill_(-1)\n\n # leave the scalar unchanged and change signs of i, j, k number parts\n q_star[0] = 1.0\n\n return q * q_star","def conj(q):\n q = np.array([q[0]])\n q[0,1]=-q[0,1]\n q[0,2]=-q[0,2]\n q[0,3]=-q[0,3]\n complexconjugate = quatreal(q)\n return complexconjugate","def KRC(self, ik, ipd, ipl, t):\n idx = ik - 1\n\n den1 = 1 - self.delta[idx] * self.coca.PK(ik, t)\n num1 = self.delta[idx] * self.thetak[idx]\n ins = num1 / den1\n\n for l in np.arange(0, self.L):\n pl = self.coca.PL(l, t)\n ins += ((self.thetal[l] * self.gamma[l][idx]) / (1 - pl))\n\n ans = ipd * np.exp(t * ipl) * ins\n\n return ans","def custom_constr(x, qr, inverse, depth):\n qc = QuantumCircuit(qr)\n maxi, mini = max(x), min(x)\n n = x.shape[0]\n #qc_wv = Wavelets(n).construct_circuit(register=qr)\n for _ in range(depth):\n qc.h(qr)\n for i in range(n):\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini), 2*np.pi*(x[i]-mini)/(maxi-mini), qr[i])\n for i in range(n):\n qc.cx(qr[i], qr[(i + 1) % n])\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini),\n ((2*np.pi)**2*(x[i]-mini)*(x[(i+1) % n]-mini)/(maxi-mini)**2) % 2*np.pi,\n qr[(i + 1) % n])\n qc.cx(qr[i], qr[(i + 1) % n])\n #qc = qc + qc_wv\n if inverse:\n return qc.inverse()\n return qc","def putqobj(self,qosubi_,qosubj_,qoval_):\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi_)\n elif numqonz_ != len(qosubi_):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj_)\n elif numqonz_ != len(qosubj_):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval_)\n elif numqonz_ != len(qoval_):\n raise IndexError(\"Inconsistent length of array qoval\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi cannot be None\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi may not be None\")\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\n _qosubi_copyarray = False\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubi_ is not None:\n _qosubi_copyarray = True\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\n _qosubi_np_tmp[:] = qosubi_\n assert _qosubi_np_tmp.flags.contiguous\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubi_copyarray = False\n _qosubi_tmp = None\n \n if qosubj_ is None:\n raise ValueError(\"Argument qosubj cannot be None\")\n if qosubj_ is None:\n raise ValueError(\"Argument qosubj may not be None\")\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\n _qosubj_copyarray = False\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubj_ is not None:\n _qosubj_copyarray = True\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\n _qosubj_np_tmp[:] = qosubj_\n assert _qosubj_np_tmp.flags.contiguous\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubj_copyarray = False\n _qosubj_tmp = None\n \n if qoval_ is None:\n raise ValueError(\"Argument qoval cannot be None\")\n if qoval_ is None:\n raise ValueError(\"Argument qoval may not be None\")\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\n _qoval_copyarray = False\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qoval_ is not None:\n _qoval_copyarray = True\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\n _qoval_np_tmp[:] = qoval_\n assert _qoval_np_tmp.flags.contiguous\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qoval_copyarray = False\n _qoval_tmp = None\n \n res = __library__.MSK_XX_putqobj(self.__nativep,numqonz_,_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def update_q(self):\n beta = self.EC_beta\n self.gamma_q = (self.gamma_s - self.gamma_r) * beta + (1 - beta) * self.gamma_q\n self.Sigma_q = (self.Sigma_s - self.Sigma_r) * beta + (1 - beta) * self.Sigma_q\n try:\n assert np.all(np.logical_not(np.isnan(self.gamma_q)))\n except:\n print(\"Invalid update encountered...\")","def refine(self, ijk):\n if self.cbc is None or not self.sub_block_count:\n raise ValueError(\n \"Cannot refine sub block model without specifying number \"\n \"of parent and sub blocks\"\n )\n try:\n inds = self.ijk_array_to_indices(ijk)\n except ValueError:\n inds = self.ijk_to_index(ijk)\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137","def conjunction(x, vx, k):\n t = np.sort(x / np.sqrt(np.maximum(vx, 1.e-15)))\n cjt = np.sum(t[:, :k], 1)\n return cjt","def AttractionForce(q,q_k):\r\n return k_p*CalculateDistance(q,q_k)","def Qc(I, dT, a, b, c, d, e, f, g, h, i, k):\n x1 = I # I\n x2 = dT # dT\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\n return m * x2 + b","def generateconenames(self,subk_,fmt_,dims_,sp_):\n num_ = None\n if num_ is None:\n num_ = len(subk_)\n elif num_ != len(subk_):\n raise IndexError(\"Inconsistent length of array subk\")\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n if isinstance(fmt_,unicode):\n fmt_ = fmt_.encode(\"utf-8\",errors=\"replace\")\n ndims_ = None\n if ndims_ is None:\n ndims_ = len(dims_)\n elif ndims_ != len(dims_):\n raise IndexError(\"Inconsistent length of array dims\")\n if dims_ is None:\n raise ValueError(\"Argument dims cannot be None\")\n if dims_ is None:\n raise ValueError(\"Argument dims may not be None\")\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\n _dims_copyarray = False\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dims_ is not None:\n _dims_copyarray = True\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\n _dims_np_tmp[:] = dims_\n assert _dims_np_tmp.flags.contiguous\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dims_copyarray = False\n _dims_tmp = None\n \n _sp_minlength = (num_)\n if (num_) > 0 and sp_ is not None and len(sp_) != (num_):\n raise ValueError(\"Array argument sp is not long enough: Is %d, expected %d\" % (len(sp_),(num_)))\n if isinstance(sp_, numpy.ndarray) and sp_.dtype is numpy.dtype(numpy.int64) and sp_.flags.contiguous:\n _sp_copyarray = False\n _sp_tmp = ctypes.cast(sp_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sp_ is not None:\n _sp_copyarray = True\n _sp_np_tmp = numpy.zeros(len(sp_),numpy.dtype(numpy.int64))\n _sp_np_tmp[:] = sp_\n assert _sp_np_tmp.flags.contiguous\n _sp_tmp = ctypes.cast(_sp_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sp_copyarray = False\n _sp_tmp = None\n \n res = __library__.MSK_XX_generateconenames(self.__nativep,num_,_subk_tmp,fmt_,ndims_,_dims_tmp,_sp_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def additional_equations(self, k):\n ######################################################################\n # equation for specified kA_group paremeters\n if self.kA_group.is_set:\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\n self.residual[k] = self.kA_func()\n k += 1\n\n ######################################################################\n # equation for specified kA_char_group paremeters\n if self.kA_char_group.is_set:\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\n self.residual[k] = self.kA_char_func()\n k += 1","def vahlen_conj(q_1: Q, conj_type: str = \"-\", q_type: str = \"vc\") -> Q:\n\n vc_t, vc_x, vc_y, vc_z = q_1.t, q_1.x, q_1.y, q_1.z\n c_q = Q()\n\n if conj_type == \"-\":\n c_q.t = vc_t\n if vc_x != 0:\n c_q.x = -1 * vc_x\n if vc_y != 0:\n c_q.y = -1 * vc_y\n if vc_z != 0:\n c_q.z = -1 * vc_z\n q_type += \"*-\"\n\n if conj_type == \"'\":\n c_q.t = vc_t\n if vc_x != 0:\n c_q.x = -1 * vc_x\n if vc_y != 0:\n c_q.y = -1 * vc_y\n c_q.z = vc_z\n q_type += \"*'\"\n\n if conj_type == \"*\":\n c_q.t = vc_t\n c_q.x = vc_x\n c_q.y = vc_y\n if vc_z != 0:\n c_q.z = -1 * vc_z\n q_type += \"*\"\n\n c_q.q_type = f\"{q_1.q_type}{q_type}\"\n c_q.representation = q_1.representation\n\n return c_q","def remove_constraint(self, ckey):\n if ckey not in self.constraints:\n raise KeyError(\"Constraints not found on object key: {}\".format(ckey))\n del self.constraints[ckey]","def _compute_kreinParameters(self, expand=False, factor=False,\n simplify=False):\n if self._has(\"q\"):\n return\n if not self._has(\"m\"):\n self.multiplicities(expand=expand, factor=factor,\n simplify=simplify)\n if not self._has(\"k\"):\n self.kTable(expand=expand, factor=factor,\n simplify=simplify)\n q = Array3D(self._.d + 1)\n self._compute_parameters(q, self._.Q, self._.k, integral=False,\n name=DUAL_PARAMETER, sym=DUAL_SYMBOL)\n self._.q = q","def fixC(self,i,value):\n if self.coeffPattern[2] == None:\n m,n=self.m,self.n\n self.coeffPattern[2] = [None]*m\n self.coeffPattern[2][i]=value\n self._updateEstimatorSize(i)","def putbarablocktriplet(self,num_,subi,subj,subk,subl,valijkl): # 3\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subi_ is not None and len(subi_) != (num_):\n raise ValueError(\"Array argument subi has wrong length\")\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n \n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n \n if subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n \n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n \n if subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valijkl is None: raise TypeError(\"Invalid type for argument valijkl\")\n if valijkl is None:\n valijkl_ = None\n else:\n try:\n valijkl_ = memoryview(valijkl)\n except TypeError:\n try:\n _tmparr_valijkl = array.array(\"d\",valijkl)\n except TypeError:\n raise TypeError(\"Argument valijkl has wrong type\")\n else:\n valijkl_ = memoryview(_tmparr_valijkl)\n \n else:\n if valijkl_.format != \"d\":\n valijkl_ = memoryview(array.array(\"d\",valijkl))\n \n if valijkl_ is not None and len(valijkl_) != (num_):\n raise ValueError(\"Array argument valijkl has wrong length\")\n res = self.__obj.putbarablocktriplet(num_,subi_,subj_,subk_,subl_,valijkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solvewithbasis(self,transp_,numnz_,sub_,val_):\n _numnz_tmp = ctypes.c_int32(numnz_)\n _sub_minlength = self.getnumcon()\n if self.getnumcon() > 0 and sub_ is not None and len(sub_) != self.getnumcon():\n raise ValueError(\"Array argument sub is not long enough: Is %d, expected %d\" % (len(sub_),self.getnumcon()))\n if isinstance(sub_,numpy.ndarray) and not sub_.flags.writeable:\n raise ValueError(\"Argument sub must be writable\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _val_minlength = self.getnumcon()\n if self.getnumcon() > 0 and val_ is not None and len(val_) != self.getnumcon():\n raise ValueError(\"Array argument val is not long enough: Is %d, expected %d\" % (len(val_),self.getnumcon()))\n if isinstance(val_,numpy.ndarray) and not val_.flags.writeable:\n raise ValueError(\"Argument val must be writable\")\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\n _val_copyarray = False\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif val_ is not None:\n _val_copyarray = True\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\n _val_np_tmp[:] = val_\n assert _val_np_tmp.flags.contiguous\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _val_copyarray = False\n _val_tmp = None\n \n res = __library__.MSK_XX_solvewithbasis(self.__nativep,transp_,ctypes.byref(_numnz_tmp),_sub_tmp,_val_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = _numnz_tmp.value\n if _sub_copyarray:\n sub_[:] = _sub_np_tmp\n if _val_copyarray:\n val_[:] = _val_np_tmp\n return (_numnz_return_value)","def add_lp_qe_RBM_terms(K, lin_pot_mesh, quad_geo_mesh):\n pot_faces = lin_pot_mesh.get_faces()\n num_faces = pot_faces.shape[0]\n pot_nodes = lin_pot_mesh.get_nodes()\n num_nodes = pot_nodes.shape[0]\n S_D = quad_geo_mesh.get_surface_area()\n x_c = quad_geo_mesh.get_centroid()\n w = quad_geo_mesh.get_w()\n A_m = quad_geo_mesh.get_A_m()\n\n for face_num in range(num_faces):\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n for node_num in range(3): # face nodes\n face_node_global_num = pot_faces[face_num, node_num]\n v_sub_mat = (-4. * np.pi / S_D) * gq.int_over_tri_quad(\n make_lp_le_v_quad(node_num),\n face_nodes,\n face_hs,\n )\n j = 3 * face_node_global_num\n tmp_omega = gq.int_over_tri_quad(\n make_lp_le_omega_quad(node_num, x_c),\n face_nodes,\n face_hs,\n )\n tmp_arr = []\n for m in range(3):\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\"l, ls\", w[m], tmp_omega)))\n tmp_omega_mat = -4. * np.pi * np.sum(tmp_arr, axis=0)\n for src_num in range(num_nodes):\n K[(3 * src_num):(3 * src_num + 3),\n j:j+3] += -1. / (4. * np.pi) * v_sub_mat\n src_pt = lin_pot_mesh.get_node(src_num)\n X_0 = src_pt - x_c\n omega_mat = np.einsum(\"ijk,js,k\", geo.LC_3, tmp_omega_mat, X_0)\n K[(3 * src_num):(3 * src_num + 3),\n j:j+3] += 1. / (4. * np.pi) * (omega_mat)","def pk_constrained(self, snr=30, headroom = 0):\n # Initialize\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\n # loop over frequencies\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\n for jf, k0 in enumerate(self.controls.k0):\n # get the scaled version of the propagating directions\n k_vec = k0 * self.dir\n # Form the sensing matrix\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\n H = h_mtx.astype(complex) # cvxpy does not accept floats, apparently\n # measured data\n pm = self.pres_s[:,jf].astype(complex)\n # Performing constrained optmization cvxpy\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True) # create x variable\n # Create the problem\n epsilon = 10**(-(snr-headroom)/10)\n problem = cp.Problem(cp.Minimize(cp.norm2(x_cvx)**2),\n [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon])\n problem.solve(solver=cp.SCS, verbose=False)\n self.pk[:,jf] = x_cvx.value\n bar.update(1)\n bar.close()","def SetPRCatConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for line in tot :\n for val in line :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound","def putbarcj(self,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexError(\"Inconsistent length of array weights\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n \n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n \n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n \n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putclist(self,subj,val): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(val)\n elif num_ != len(val):\n raise IndexError(\"Inconsistent length of array val\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if val is None: raise TypeError(\"Invalid type for argument val\")\n if val is None:\n val_ = None\n else:\n try:\n val_ = memoryview(val)\n except TypeError:\n try:\n _tmparr_val = array.array(\"d\",val)\n except TypeError:\n raise TypeError(\"Argument val has wrong type\")\n else:\n val_ = memoryview(_tmparr_val)\n \n else:\n if val_.format != \"d\":\n val_ = memoryview(array.array(\"d\",val))\n \n res = self.__obj.putclist(num_,subj_,val_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _k_coaffine_pair(pair, bound=math.inf):\n g = pair.graph\n sigma = pair.coaffination\n kg = clique_graph(g, bound)\n coaf_k = dict([])\n for q in kg:\n coaf_k[q] = Clique([sigma[x] for x in q])\n return CoaffinePair(kg, coaf_k)","def putqobj(self,qosubi,qosubj,qoval): # 3\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi)\n elif numqonz_ != len(qosubi):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj)\n elif numqonz_ != len(qosubj):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval)\n elif numqonz_ != len(qoval):\n raise IndexError(\"Inconsistent length of array qoval\")\n if numqonz_ is None: numqonz_ = 0\n if qosubi is None: raise TypeError(\"Invalid type for argument qosubi\")\n if qosubi is None:\n qosubi_ = None\n else:\n try:\n qosubi_ = memoryview(qosubi)\n except TypeError:\n try:\n _tmparr_qosubi = array.array(\"i\",qosubi)\n except TypeError:\n raise TypeError(\"Argument qosubi has wrong type\")\n else:\n qosubi_ = memoryview(_tmparr_qosubi)\n \n else:\n if qosubi_.format != \"i\":\n qosubi_ = memoryview(array.array(\"i\",qosubi))\n \n if qosubj is None: raise TypeError(\"Invalid type for argument qosubj\")\n if qosubj is None:\n qosubj_ = None\n else:\n try:\n qosubj_ = memoryview(qosubj)\n except TypeError:\n try:\n _tmparr_qosubj = array.array(\"i\",qosubj)\n except TypeError:\n raise TypeError(\"Argument qosubj has wrong type\")\n else:\n qosubj_ = memoryview(_tmparr_qosubj)\n \n else:\n if qosubj_.format != \"i\":\n qosubj_ = memoryview(array.array(\"i\",qosubj))\n \n if qoval is None: raise TypeError(\"Invalid type for argument qoval\")\n if qoval is None:\n qoval_ = None\n else:\n try:\n qoval_ = memoryview(qoval)\n except TypeError:\n try:\n _tmparr_qoval = array.array(\"d\",qoval)\n except TypeError:\n raise TypeError(\"Argument qoval has wrong type\")\n else:\n qoval_ = memoryview(_tmparr_qoval)\n \n else:\n if qoval_.format != \"d\":\n qoval_ = memoryview(array.array(\"d\",qoval))\n \n res = self.__obj.putqobj(numqonz_,qosubi_,qosubj_,qoval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def do_reduction_placzek_corrections(q,sqfg,bgd,rescale_bgd=1.0,plaz_type=None,\n gauss_damp=False,gw=20.0,qmax=None,qmin=None,\n rmin=0.0,rmax=20.0,delr=.02\n ,qminpla=10.0,qmaxpla=30.0,ndeg=2, return_correction = False,\n skip_bgd = False, return_final_sq = False, force_qmax_type='Off'):\n #first, make netsq if bgd and/or damping is present\n q = np.array(q)\n sqfg = np.array(sqfg)\n bgd = np.array(bgd)\n\n if skip_bgd:\n netsq = sqfg\n else:\n netsq = sqfg - bgd*rescale_bgd\n\n\n if gauss_damp:\n netsq = netsq*gauss(q,gw,0)\n\n\n if force_qmax_type == 'Force Data (PreCorrection)':\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n netsq -= mean_sqmax\n\n #now, apply a correction if requested\n if plaz_type != None:\n if plaz_type == 'Polynomial' or plaz_type == 'poly' or plaz_type == 'ndeg':\n sq_poly_fit = fit_ndeg_to_sq(q,netsq,ndeg=ndeg,qmin=qminpla,qmax=qmaxpla)\n this_fit = sq_poly_fit\n elif plaz_type == 'Pseudo-Voight' or plaz_type == 'pv' or plaz_type == 'hydro':\n pv_fit = fit_pv_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_fit\n elif plaz_type == 'PVoight + n0' or plaz_type == 'pvndeg0':\n pv_n0_fit = fit_pv_n0_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n0_fit\n elif plaz_type == 'PVoight + n1' or plaz_type == 'pvndeg1':\n pv_n1_fit = fit_pv_n1_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n1_fit\n elif plaz_type == 'PVoight + n2' or plaz_type == 'pvndeg2':\n pv_n2_fit = fit_pv_n2_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n2_fit\n else:\n print (\"I don't know that correction type, sorry\")\n this_fit = np.zeros(len(q))\n else:\n this_fit = np.zeros(len(q))\n\n if force_qmax_type == 'Force Data' or force_qmax_type == 'Force Both (Independent)':\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n netsq -= mean_sqmax\n if force_qmax_type == 'Force Correction' or force_qmax_type == 'Force Both (Independent)':\n qcut, sqcut = cut_data(q,this_fit,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n this_fit -= mean_sqmax\n if force_qmax_type == 'ReCorrection':\n qcut, sqcut = cut_data(q,netsq-this_fit,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n this_fit += mean_sqmax\n\n netsq = netsq - this_fit\n\n if return_correction:\n return this_fit\n\n if return_final_sq:\n return netsq\n\n #finally, generate PDF\n r,gr = make_gr_from_sq(q,netsq,qmin=qmin,qmax=qmax,rmin=rmin,rmax=rmax,delr=delr)\n\n return r,gr","def additional_equations(self, k):\n ######################################################################\n # equation for saturated liquid at hot side outlet\n if self.subcooling.val is False:\n o1 = self.outl[0].to_flow()\n self.residual[k] = o1[2] - h_mix_pQ(o1, 0)\n k += 1","def putvarbound(self,j_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\n self.coeff[self._core.index_alpha(key[0]),\n self._core.index_beta(key[1])] = value","def syrk(self,uplo_,trans_,n_,k_,alpha_,a_,beta_,c_):\n _a_minlength = ((n_) * (k_))\n if ((n_) * (k_)) > 0 and a_ is not None and len(a_) != ((n_) * (k_)):\n raise ValueError(\"Array argument a is not long enough: Is %d, expected %d\" % (len(a_),((n_) * (k_))))\n if a_ is None:\n raise ValueError(\"Argument a cannot be None\")\n if a_ is None:\n raise ValueError(\"Argument a may not be None\")\n if isinstance(a_, numpy.ndarray) and a_.dtype is numpy.dtype(numpy.float64) and a_.flags.contiguous:\n _a_copyarray = False\n _a_tmp = ctypes.cast(a_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif a_ is not None:\n _a_copyarray = True\n _a_np_tmp = numpy.zeros(len(a_),numpy.dtype(numpy.float64))\n _a_np_tmp[:] = a_\n assert _a_np_tmp.flags.contiguous\n _a_tmp = ctypes.cast(_a_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _a_copyarray = False\n _a_tmp = None\n \n _c_minlength = ((n_) * (n_))\n if ((n_) * (n_)) > 0 and c_ is not None and len(c_) != ((n_) * (n_)):\n raise ValueError(\"Array argument c is not long enough: Is %d, expected %d\" % (len(c_),((n_) * (n_))))\n if isinstance(c_,numpy.ndarray) and not c_.flags.writeable:\n raise ValueError(\"Argument c must be writable\")\n if c_ is None:\n raise ValueError(\"Argument c may not be None\")\n if isinstance(c_, numpy.ndarray) and c_.dtype is numpy.dtype(numpy.float64) and c_.flags.contiguous:\n _c_copyarray = False\n _c_tmp = ctypes.cast(c_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif c_ is not None:\n _c_copyarray = True\n _c_np_tmp = numpy.zeros(len(c_),numpy.dtype(numpy.float64))\n _c_np_tmp[:] = c_\n assert _c_np_tmp.flags.contiguous\n _c_tmp = ctypes.cast(_c_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _c_copyarray = False\n _c_tmp = None\n \n res = __library__.MSK_XX_syrk(self.__nativep,uplo_,trans_,n_,k_,alpha_,_a_tmp,beta_,_c_tmp)\n if res != 0:\n raise Error(rescode(res),Env.getcodedesc(rescode(res))[1])\n if _c_copyarray:\n c_[:] = _c_np_tmp","def quadratic_strain(x, dof):\n base = np.zeros([6, dof])\n base[1, 0] = 1 # initial y-bending\n if dof > 2:\n base[1, 1] = x**2 # quadratic y-bending term\n base[2, dof-1] = x**2 # quadratic z-bending term\n return base","def conj_q(q_1: Q, q_2: Q) -> Q:\n\n _conj = deepcopy(q_1)\n\n if q_2.t:\n _conj = conj(_conj, conj_type=0)\n\n if q_2.x:\n _conj = conj(_conj, conj_type=1)\n\n if q_2.y:\n _conj = conj(_conj, conj_type=2)\n\n if q_2.z:\n _conj = flip_sign(_conj)\n\n return _conj","def setPressureCoeff(self, Kv):\n vv = zeros(1,'d')\n vv[0] = Kv\n if self._verbose:\n print\n print self._name+': setting pressure coefficient to '+`Kv`+' kg/Pa-s'\n self._setParameters(vv)","def putskc(self,whichsol_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None: raise TypeError(\"Invalid type for argument skc\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskc(whichsol_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)","def calc_k(self):\n\t\n\tself.k = -np.array([self.sth*self.cphi, self.sth*self.sphi, self.cth])\n\n\treturn","def _ect_qrs_tconst(pattern, qrs):\n beats = pattern.evidence[o.QRS]\n idx = beats.index(qrs)\n tnet = pattern.last_tnet\n hyp = pattern.hypothesis\n if idx > 0:\n prev = beats[idx - 1]\n # After the second couplet, every ectopic beat introduces a new temporal\n # network in the pattern to make it easier the minimization.\n if idx > 3:\n tnet.remove_constraint(hyp.end, prev.time)\n # We create a new temporal network for the cyclic observations\n tnet = ConstraintNetwork()\n pattern.temporal_constraints.append(tnet)\n # The duration of each couplet should not have high instantaneous\n # variations.\n refrr = beats[idx - 2].time.end - beats[idx - 3].time.start\n tnet.add_constraint(prev.time, qrs.time, Iv(refrr - C.RR_MAX_DIFF, refrr + C.RR_MAX_DIFF))\n # We guide the morphology search to be similar to the previous\n # ectopic QRS complex.\n qrs.shape = beats[idx - 2].shape\n # The reference RR varies from an upper limit to the last measurement,\n # through the contextual previous rhythm.\n refrr = C.BRADY_RR.end\n stdrr = 0.1 * refrr\n if pattern.evidence[o.Cardiac_Rhythm] and idx == 1:\n mrr, srr = pattern.evidence[o.Cardiac_Rhythm][0].meas.rr\n if mrr > 0:\n refrr, stdrr = mrr, srr\n elif idx > 1:\n refrr, stdrr = hyp.meas.rr\n # Ectopic beats must be advanced wrt the reference RR\n tnet.add_constraint(prev.time, qrs.time, Iv(C.TACHY_RR.start, max(C.TACHY_RR.start, refrr - stdrr)))\n # Beats cannot overlap\n tnet.add_constraint(prev.end, qrs.start, Iv(C.TQ_INTERVAL_MIN, np.Inf))\n BASIC_TCONST(pattern, qrs)\n tnet.add_constraint(qrs.start, qrs.end, C.QRS_DUR)\n tnet.set_before(qrs.time, hyp.end)\n # Constraints with the precedent T Wave\n _qrs_after_twave(pattern, qrs)","def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def jackknifed_coh_variance(tx, ty, eigvals, adaptive=True):\r\n\r\n K = tx.shape[0]\r\n\r\n # calculate leave-one-out estimates of MSC (magnitude squared coherence)\r\n jk_coh = []\r\n # coherence is symmetric (right??)\r\n sides = 'onesided'\r\n all_orders = set(range(K))\r\n\r\n import nitime.algorithms as alg\r\n\r\n # get the leave-one-out estimates\r\n for i in range(K):\r\n items = list(all_orders.difference([i]))\r\n tx_i = np.take(tx, items, axis=0)\r\n ty_i = np.take(ty, items, axis=0)\r\n eigs_i = np.take(eigvals, items)\r\n if adaptive:\r\n wx, _ = adaptive_weights(tx_i, eigs_i, sides=sides)\r\n wy, _ = adaptive_weights(ty_i, eigs_i, sides=sides)\r\n else:\r\n wx = wy = eigs_i[:, None]\r\n # The CSD\r\n sxy_i = alg.mtm_cross_spectrum(tx_i, ty_i, (wx, wy), sides=sides)\r\n # The PSDs\r\n sxx_i = alg.mtm_cross_spectrum(tx_i, tx_i, wx, sides=sides)\r\n syy_i = alg.mtm_cross_spectrum(ty_i, ty_i, wy, sides=sides)\r\n # these are the | c_i | samples\r\n msc = np.abs(sxy_i)\r\n msc /= np.sqrt(sxx_i * syy_i)\r\n jk_coh.append(msc)\r\n\r\n jk_coh = np.array(jk_coh)\r\n # now normalize the coherence estimates and take the mean\r\n normalize_coherence(jk_coh, 2 * K - 2, copy=False) # inplace\r\n jk_avg = np.mean(jk_coh, axis=0)\r\n\r\n jk_var = (jk_coh - jk_avg)\r\n np.power(jk_var, 2, jk_var)\r\n jk_var = jk_var.sum(axis=0)\r\n\r\n # Do/Don't use the alternative scaling here??\r\n f = float(K - 1) / K\r\n\r\n jk_var *= f\r\n\r\n return jk_var","def quadratic_dependence(dep_keys, ctfs=list(), force_zero=None, **kwargs):\n if force_zero is None and len(kwargs) == 0:\n return _dependence(\n f=np.polyval, n_params=3,\n dep_keys=dep_keys, ctfs=ctfs, force_zero=force_zero,\n name='quadratic dependence', code='p2:{}', **kwargs\n )\n else:\n return _dependence(\n f=lambda p, x: np.polyval(np.concatenate((p, np.zeros(1))), x),\n n_params=2, dep_keys=dep_keys, ctfs=ctfs, force_zero=force_zero,\n name='quadratic dependence', code='p2:{}', **kwargs\n )","def censor_contig(contig_end, u_contigs, o_dict):\n for c_e in [contig_end, other_end(contig_end)]:\n if c_e in u_contigs:\n u_contigs.remove(c_e)\n if c_e in o_dict:\n o_dic = o_dict[c_e]\n if o_dic != {}:\n overlapped_contig = list(o_dic.keys())[0]\n if overlapped_contig in o_dict: del o_dict[overlapped_contig][c_e]\n del o_dict[c_e]\n return","def _initialize_kindiffeq_matrices(self, kdeqs):\n\n if kdeqs:\n if len(self.q) != len(kdeqs):\n raise ValueError('There must be an equal number of kinematic '\n 'differential equations and coordinates.')\n kdeqs = Matrix(kdeqs)\n\n u = self.u\n qdot = self._qdot\n # Dictionaries setting things to zero\n u_zero = dict((i, 0) for i in u)\n uaux_zero = dict((i, 0) for i in self._uaux)\n qdot_zero = dict((i, 0) for i in qdot)\n\n f_k = msubs(kdeqs, u_zero, qdot_zero)\n k_ku = (msubs(kdeqs, qdot_zero) - f_k).jacobian(u)\n k_kqdot = (msubs(kdeqs, u_zero) - f_k).jacobian(qdot)\n\n f_k = k_kqdot.LUsolve(f_k)\n k_ku = k_kqdot.LUsolve(k_ku)\n k_kqdot = eye(len(qdot))\n\n self._qdot_u_map = solve_linear_system_LU(\n Matrix([k_kqdot.T, -(k_ku * u + f_k).T]).T, qdot)\n\n self._f_k = msubs(f_k, uaux_zero)\n self._k_ku = msubs(k_ku, uaux_zero)\n self._k_kqdot = k_kqdot\n else:\n self._qdot_u_map = None\n self._f_k = Matrix()\n self._k_ku = Matrix()\n self._k_kqdot = Matrix()","def putxc(self,whichsol_,xc_):\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp","def convert_to_q(self):\n if self.measure == 'Q':\n warnings.warn('Parameters are already converted to Q!')\n else:\n kappa_sp = self.kappa_s\n kappa_yp = self.kappa_y\n self.kappa_s = self.kappa_s - self.lmbd_s * self.eta_s\n self.kappa_y = self.kappa_y - self.lmbd_y * self.eta_y\n self.scale = kappa_sp / self.kappa_s\n self.mean_v *= (kappa_yp / self.kappa_y * self.scale)\n self.lmbd = 0\n self.eta_y *= (self.scale**.5)\n self.measure = 'Q'\n self.update_ajd()","def putconboundlist(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkc_)\n elif num_ != len(bkc_):\n raise IndexError(\"Inconsistent length of array bkc\")\n if num_ is None:\n num_ = len(blc_)\n elif num_ != len(blc_):\n raise IndexError(\"Inconsistent length of array blc\")\n if num_ is None:\n num_ = len(buc_)\n elif num_ != len(buc_):\n raise IndexError(\"Inconsistent length of array buc\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def compileCkt(self):\n f = np.vectorize(lambda x: isinstance(x, QubitChannel))\n table = f(self.diagram)\n col_bool = np.bitwise_or.reduce(table, axis=1)\n # filter nan in 'qubit' direction\n if not np.bitwise_and.reduce(col_bool):\n raise ValueError('Found unassigned qubit')\n # filter nan in 'time' direction\n row_bool = np.bitwise_or.reduce(table, axis=0)\n diagram = self.diagram[:, row_bool]\n table = table[:, row_bool]\n # align QubitChannel objects in the table column by column\n for time_idx in range(len(table[0, :])):\n diagram[table[:, time_idx], time_idx\n ] = QubitChannel.alignQubitChannels(\n *diagram[table[:, time_idx], time_idx]\n )\n # replace nans with null QubitChannel objects\n for qubit_idx, row in enumerate(table):\n for time_idx, flag in enumerate(row):\n if flag:\n continue\n span_idx = np.where(f(diagram[:, time_idx]))[0][0]\n wire_idx = np.where(f(diagram[qubit_idx, :]))[0][0]\n diagram[qubit_idx, time_idx] = QubitChannel.null(\n diagram[span_idx, time_idx], diagram[qubit_idx, wire_idx]\n )\n try:\n self.compiled = np.sum(diagram, axis=1)\n except SystemError:\n raise ValueError('Error during wire concatenation')","def putvarbound(self,j_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def adjust_cell_formula(value, k):\n if isinstance(value, basestring):\n for i in range(value.count('?(')):\n if value and '?(' in value and ')' in value:\n i = value.index('?(')\n j = value.index(')', i)\n val = value[i + 2:j]\n col, row = split_row_col(val)\n new_val = '%s%s' % (col, row+k)\n value = value.replace('?(%s)' % val, new_val)\n return value","def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def approxk( X, k ):\n U, D, Vt = svdk( X, k )\n return U.dot( diag( D ) ).dot( Vt )","def project_curve(q):\n n,T = q.shape\n if n==2:\n dt = 0.35\n if n==3:\n dt = 0.2\n epsilon = 1e-6\n\n iter = 1\n res = ones(n)\n J = zeros((n,n))\n\n s = linspace(0,1,T)\n\n qnew = q.copy()\n qnew = qnew / sqrt(innerprod_q2(qnew,qnew))\n\n qnorm = zeros(T)\n G = zeros(n)\n C = zeros(300)\n while (norm(res) > epsilon):\n if iter > 300:\n break\n\n # Jacobian\n for i in range(0,n):\n for j in range(0,n):\n J[i,j] = 3 * trapz(qnew[i,:]*qnew[j,:],s)\n \n J += eye(n)\n\n for i in range(0,T):\n qnorm[i] = norm(qnew[:,i])\n \n # Compute the residue\n for i in range(0,n):\n G[i] = trapz(qnew[i,:]*qnorm,s)\n \n res = -G\n\n if (norm(res) < epsilon):\n break\n\n x = solve(J,res)\n C[iter] = norm(res)\n\n delG = Basis_Normal_A(qnew)\n temp = zeros((n,T))\n for i in range(0,n):\n temp += x[i]*delG[i]*dt\n \n qnew += temp\n iter += 1\n \n qnew = qnew/sqrt(innerprod_q2(qnew,qnew))\n\n return qnew","def set_equality_constraint_multipliers(self, eq_con_multiplier_values):\n # we should check these for efficiency\n assert self.n_equality_constraints() == len(eq_con_multiplier_values)\n if (\n not hasattr(self, 'evaluate_hessian_equality_constraints')\n or self.n_equality_constraints() == 0\n ):\n return\n\n raise NotImplementedError(\n 'Derived ExternalGreyBoxModel classes need to implement'\n ' set_equality_constraint_multipliers when they'\n ' support Hessian computations.'\n )","def additional_equations(self, k):\n ######################################################################\n # equation for saturated gas at hot side outlet\n o1 = self.outl[0].to_flow()\n self.residual[k] = o1[2] - h_mix_pQ(o1, 1)","def Keyboard(self, key):\r\n\t\tif key == Keys.K_u:\r\n\t\t\tself.kp+=self.dp\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\tif key == Keys.K_j:\r\n\t\t\tself.kp-=self.dp\r\n\t\t\tif self.kp<0:\r\n\t\t\t\tself.kp=0.0\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\tif key == Keys.K_i:\r\n\t\t\tself.ki+=self.di\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\tif key == Keys.K_k:\r\n\t\t\tself.ki-=self.di\r\n\t\t\tif self.ki<0:\r\n\t\t\t\tself.ki=0.0\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\tif key == Keys.K_o:\r\n\t\t\tself.kd+=self.dd\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\tif key == Keys.K_l:\r\n\t\t\tself.kd-=self.dd\r\n\t\t\tif self.kd<0:\r\n\t\t\t\tself.kd=0.0\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\t\r\n\t\tif key == Keys.K_f:\r\n\t\t\tself.center+=1\r\n\t\t\tif self.center>2:\r\n\t\t\t\tself.center = 0\r\n\t\t\r\n\t\tif key == Keys.K_t:\r\n\t\t\tself.cut=1-self.cut\r\n\t\t\tself.q[0].SetCut(self.cut)\t\t\t\r\n\t\t\t\r\n\t\tif key == Keys.K_r:\r\n\t\t\tpass\t\r\n\t\t\r\n\t\tif key == Keys.K_s:\r\n\t\t\tself.q[0].saveConf()","def y2mat_Y1qk(yp, q, k, y):\n if ((y.L2 != yp.L2 or\n zero_YYY_q(yp.L1, q, y.L1) or\n not triangle_q(yp.L, q, y.L) or\n yp.M != k + y.M)):\n return 0\n t = (-1)**(yp.L-yp.M)\n rm = y2redmat_Y1q(yp, q, y)\n tj = N3j(+yp.L, +q, +y.L,\n -yp.M, +k, +y.M)\n return t * rm * tj","def test_init_q(self):\n\n riskfree = .01\n lmbd = .01\n lmbd_s = .5\n lmbd_y = .5\n mean_v = .5\n kappa_s = 1.5\n kappa_y = .5\n eta_s = .1\n eta_y = .01\n rho = -.5\n\n param = CentTendParam(riskfree=riskfree,\n lmbd=lmbd, lmbd_s=lmbd_s, lmbd_y=lmbd_y,\n mean_v=mean_v, kappa_s=kappa_s, kappa_y=kappa_y,\n eta_s=eta_s, eta_y=eta_y, rho=rho, measure='Q')\n\n kappa_sq = kappa_s - lmbd_s * eta_s\n kappa_yq = kappa_y - lmbd_y * eta_y\n scale = kappa_s / kappa_sq\n\n self.assertEqual(param.measure, 'Q')\n self.assertEqual(param.riskfree, riskfree)\n self.assertEqual(param.lmbd, 0)\n self.assertEqual(param.lmbd_s, lmbd_s)\n self.assertEqual(param.lmbd_y, lmbd_y)\n self.assertEqual(param.mean_v, mean_v * kappa_y / kappa_yq * scale)\n self.assertEqual(param.kappa_s, kappa_sq)\n self.assertEqual(param.kappa_y, kappa_yq)\n self.assertEqual(param.eta_s, eta_s)\n self.assertEqual(param.eta_y, eta_y * scale**.5)\n self.assertEqual(param.rho, rho)\n self.assertTrue(param.is_valid())\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n param.convert_to_q()","def makeConjunction(solver, v, conjuncts):\n cn = solver.Constraint(0, (len(conjuncts) - 1))\n cn.SetCoefficient(v, -len(conjuncts))\n for x in conjuncts:\n cn.SetCoefficient(x, 1)","def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def kspace_svd_solve(self, other, rcond = 1e-14, complex_precision = np.complex128, n_points = None, complx = False):\n if n_points is None:\n \n m1n = np.max(np.abs(self.coords), axis = 0)\n m2n = np.max(np.abs(other.coords), axis = 0)\n \n n_points = np.max([m1n,m2n], axis = 0) \n\n nx,ny,nz = 2*n_points + 1\n\n #print(\"kspace_svd_solve: \", nx,ny,nz)\n m1x,m1y = self.blocks.shape[1], self.blocks.shape[2]\n m2x,m2y = other.blocks.shape[1], other.blocks.shape[2]\n \n coords = np.roll(lattice_coords(n_points).reshape(nx,ny,nz, 3), -n_points, axis = (0,1,2)).reshape(nx*ny*nz, 3)\n\n #print(coords)\n\n \n m1r = self.cget(coords).reshape(nx,ny,nz,m1x,m1y)\n m2r = other.cget(coords).reshape(nx,ny,nz,m2x,m2y)\n M1 = np.fft.fftn(m1r, axes = (0,1,2))\n M2 = np.fft.fftn(m2r, axes = (0,1,2))\n M3 = np.zeros((nx,ny,nz,m1x, m2y),dtype = np.complex128)\n\n\n for c in coords:\n #M1[c[0], c[1], c[2]] = np.dot(M1[c[0], c[1], c[2]], M2[c[0], c[1], c[2]])\n\n #ci = self_k.coords[i]\n u_,s_,vh_ = np.linalg.svd(M1[c[0], c[1], c[2]])\n b = M2[c[0], c[1], c[2]]\n\n t = np.abs(s_)>(rcond*np.max(np.abs(s_))) #screening\n\n #if s_.max()/s_.min()>10e6:\n # print(\"poor condition in singular values:\", c, s_, t)\n \n \n\n\n\n pinv = np.dot(vh_[t,:].conj().T, np.dot(np.diag(s_[t]**-1), u_[:,t].conj().T))\n x = np.dot(pinv, b)\n\n if np.any(t == False):\n print(\"Warning (SVD): poorly conditioned JK matrix (singular values).\") #, s_)\n print(\"Eq. :\", np.max(np.abs(np.dot(M1[c[0], c[1], c[2]], x)-b)))\n\n\n\n #rhs = np.dot(np.dot(np.diag(s_[t]**-1), u_[:,t].conj().T), b)\n #x = np.linalg.solve(vh_[t,:], rhs)\n \n \n \n #svhx = np.linalg.solve(u_[:,t], b)\n\n #x = np.linalg.solve(np.dot(np.diag(s_[t]),vh_[t,:]), svhx )\n\n \n \"\"\"\n \n U = u_[:,t]\n S = np.diag(s_[t])\n VH = vh_[t,:]\n\n SVH = np.dot(S, VH)\n Ub = np.dot(U.conj().T, other_k.blocks[i])\n \"\"\"\n\n M3[c[0], c[1], c[2]] = x #np.linalg.solve(SVH, Ub)\n\n\n \n \n \n ret = tmat()\n if complx:\n ret.load_nparray(np.fft.ifftn(M3.reshape(nx,ny,nz,m1x,m2y), axes = (0,1,2)).reshape(coords.shape[0], m1x,m2y), coords)\n else:\n ret.load_nparray(np.fft.ifftn(M3.reshape(nx,ny,nz,m1x,m2y), axes = (0,1,2)).real.reshape(coords.shape[0], m1x,m2y), coords)\n return ret","def putcj(self,j_,cj_): # 3\n res = self.__obj.putcj(j_,cj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def Optimize_Sqq(qj,dt):\n\n # find the signal psd\n fs=1.0/dt\n M,L = 256,512\n wxx,Sxx=sa.Welch_estimator(qj,fs=fs,M=M,L=L)\n w_threshold = fs*np.pi/2\n Sxx=Sxx[wxx<w_threshold]\n wxx=wxx[wxx<w_threshold]\n\n # the psd of oscillator model is computed using Wiener-Khinchin relation\n # and called in the objective function\n print('S_qq matching objective fun: ls error of spectrum')\n spec_ls_distance = lambda params: np.linalg.norm(Sxx*(Sxx - sa.Oscillator_Spectrum(params[0],params[1]/params[0],params[1],wxx)),ord=1)\n\n # optimize via pyswarm - v1\n lb = [0.001, 0.001]\n ub = [100, 500]\n xopt, fopt = pso(spec_ls_distance, lb, ub, maxiter=10000,swarmsize=10000,minfunc=1e-10)\n k,D = xopt[0],xopt[1]\n\n b = D/k\n print('result: k='+str(k)+' b='+str(b)+' D='+str(D))\n\n return k,D","def mk_q(self, xc: list, yc: list):\n for i in range(len(xc) - 1):\n cur = cor(xc[i], xc[i + 1])\n self.xq.put((-cur.dist, cur))\n cur = cor(yc[i], yc[i + 1])\n self.yq.put((-cur.dist, cur))\n self.rnd_mk()","def _append_cx(self, i, j):\n\n if not 0 <= i < self.num_qubits or not 0 <= j < self.num_qubits:\n raise QiskitError(\"CX qubits are out of bounds.\")\n self.linear[j] = (self.linear[i] + self.linear[j]) % 2\n self.shift[j] = (self.shift[i] + self.shift[j]) % 2","def solve_Q(ih, jh):\n # Build A and b\n\n # Solve for c via least squares.\n\n # Form C from c\n\n # Solve for Q and return it.\n return None","def putbarcj(self,j_,sub_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights_)\n elif num_ != len(weights_):\n raise IndexError(\"Inconsistent length of array weights\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbarcj(self.__nativep,j_,num_,_sub_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putclist(self,subj_,val_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(val_)\n elif num_ != len(val_):\n raise IndexError(\"Inconsistent length of array val\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if val_ is None:\n raise ValueError(\"Argument val cannot be None\")\n if val_ is None:\n raise ValueError(\"Argument val may not be None\")\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\n _val_copyarray = False\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif val_ is not None:\n _val_copyarray = True\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\n _val_np_tmp[:] = val_\n assert _val_np_tmp.flags.contiguous\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _val_copyarray = False\n _val_tmp = None\n \n res = __library__.MSK_XX_putclist(self.__nativep,num_,_subj_tmp,_val_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def make_mat_cp_qe(cons_pot_mesh, quad_geo_mesh):\n pot_faces = cons_pot_mesh.get_faces()\n assert pot_faces.shape[0] == quad_geo_mesh.get_faces().shape[0]\n num_faces = pot_faces.shape[0]\n K = np.zeros((3 * num_faces, 3 * num_faces))\n add_cp_qe_DL_terms(K, cons_pot_mesh, quad_geo_mesh)\n add_cp_qe_RBM_terms(K, cons_pot_mesh, quad_geo_mesh)\n return K","def quartic_potential(x):\n k1=1\n k2=10\n return (k1*x**4)-(k2*x**2)","def Q2C(self, q):\n\n #q = q.squeeze();\n C = np.empty((3,3));\n\tC[0,0] = (q[0]**2.0) + (q[1]**2.0) - (q[2]**2.0) - (q[3]**2.0);\n\tC[0,1] = 2.0 * ((q[1]*q[2]) + (q[0]*q[3]));\n\tC[0,2] = 2.0 * ((q[1]*q[3]) - (q[0]*q[2]));\n\n\tC[1,0] = 2.0 * ((q[1]*q[2]) - (q[0]*q[3]));\n\tC[1,1] = (q[0]**2.0) - (q[1]**2.0) + (q[2]**2.0) - (q[3]**2.0);\n\tC[1,2] = 2.0 * ((q[2]*q[3]) + (q[0]*q[1]));\n\n\tC[2,0] = 2.0 * ((q[1]*q[3]) + (q[0]*q[2]));\n\tC[2,1] = 2.0 * ((q[2]*q[3]) - (q[0]*q[1]));\n\tC[2,2] = (q[0]**2.0) - (q[1]**2.0) - (q[2]**2.0) + (q[3]**2.0);\n\n return C","def putacol(self,j_,subj,valj): # 3\n nzj_ = None\n if nzj_ is None:\n nzj_ = len(subj)\n elif nzj_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nzj_ is None:\n nzj_ = len(valj)\n elif nzj_ != len(valj):\n raise IndexError(\"Inconsistent length of array valj\")\n if nzj_ is None: nzj_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valj is None: raise TypeError(\"Invalid type for argument valj\")\n if valj is None:\n valj_ = None\n else:\n try:\n valj_ = memoryview(valj)\n except TypeError:\n try:\n _tmparr_valj = array.array(\"d\",valj)\n except TypeError:\n raise TypeError(\"Argument valj has wrong type\")\n else:\n valj_ = memoryview(_tmparr_valj)\n \n else:\n if valj_.format != \"d\":\n valj_ = memoryview(array.array(\"d\",valj))\n \n res = self.__obj.putacol(j_,nzj_,subj_,valj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def zero_K(self):\n # print('zero_K axnode: ', self.cell.axnode)\n i = 0\n for node in self.cell.axnode:\n for seg in node:\n if i == 0:\n print(f\"KLT: {seg.klt.gbar:.6f} mho/cm2\")\n print(f\"KCNQ: {seg.kcnq.gbar:.6f} mho/cm2\")\n print(f\"KHT: {seg.kht.gbar:.6f} mho/cm2\")\n i = 1\n # seg.klt.gbar = 0e-3\n seg.kcnq.gbar = 0e-3\n # seg.kcnq.phi_m = seg.kcnq.phi_m - 20.\n # seg.kht.gbar = 0e-3\n # seg.kht.vshift = -20.\n pass","def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\n else:\n _skn_tmp = None\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"],"string":"[\n \"def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubk_ is None:\\n raise ValueError(\\\"Argument qcsubk cannot be None\\\")\\n if qcsubk_ is None:\\n raise ValueError(\\\"Argument qcsubk may not be None\\\")\\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\\n _qcsubk_copyarray = False\\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubk_ is not None:\\n _qcsubk_copyarray = True\\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\\n _qcsubk_np_tmp[:] = qcsubk_\\n assert _qcsubk_np_tmp.flags.contiguous\\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubk_copyarray = False\\n _qcsubk_tmp = None\\n \\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubk is None: raise TypeError(\\\"Invalid type for argument qcsubk\\\")\\n if qcsubk is None:\\n qcsubk_ = None\\n else:\\n try:\\n qcsubk_ = memoryview(qcsubk)\\n except TypeError:\\n try:\\n _tmparr_qcsubk = array.array(\\\"i\\\",qcsubk)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubk has wrong type\\\")\\n else:\\n qcsubk_ = memoryview(_tmparr_qcsubk)\\n \\n else:\\n if qcsubk_.format != \\\"i\\\":\\n qcsubk_ = memoryview(array.array(\\\"i\\\",qcsubk))\\n \\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getqconk(self,k_,qcsubi_,qcsubj_,qcval_):\\n maxnumqcnz_ = self.getnumqconknz((k_))\\n numqcnz_ = ctypes.c_int64()\\n _qcsubi_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubi is not long enough: Is %d, expected %d\\\" % (len(qcsubi_),self.getnumqconknz((k_))))\\n if isinstance(qcsubi_,numpy.ndarray) and not qcsubi_.flags.writeable:\\n raise ValueError(\\\"Argument qcsubi must be writable\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n _qcsubj_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubj is not long enough: Is %d, expected %d\\\" % (len(qcsubj_),self.getnumqconknz((k_))))\\n if isinstance(qcsubj_,numpy.ndarray) and not qcsubj_.flags.writeable:\\n raise ValueError(\\\"Argument qcsubj must be writable\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n _qcval_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcval is not long enough: Is %d, expected %d\\\" % (len(qcval_),self.getnumqconknz((k_))))\\n if isinstance(qcval_,numpy.ndarray) and not qcval_.flags.writeable:\\n raise ValueError(\\\"Argument qcval must be writable\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n qcsurp_ = ctypes.c_int64(_qcsubi_minlength)\\n res = __library__.MSK_XX_getqconk64(self.__nativep,k_,maxnumqcnz_,ctypes.byref(qcsurp_),ctypes.byref(numqcnz_),_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numqcnz_ = numqcnz_.value\\n _numqcnz_return_value = numqcnz_\\n if _qcsubi_copyarray:\\n qcsubi_[:] = _qcsubi_np_tmp\\n if _qcsubj_copyarray:\\n qcsubj_[:] = _qcsubj_np_tmp\\n if _qcval_copyarray:\\n qcval_[:] = _qcval_np_tmp\\n return (_numqcnz_return_value)\",\n \"def getqconk(self,k_,qcsubi,qcsubj,qcval): # 3\\n maxnumqcnz_ = self.getnumqconknz((k_))\\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n _copyback_qcsubi = False\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n _copyback_qcsubi = True\\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n _copyback_qcsubi = True\\n if qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubi has wrong length\\\")\\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n _copyback_qcsubj = False\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n _copyback_qcsubj = True\\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n _copyback_qcsubj = True\\n if qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubj has wrong length\\\")\\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n _copyback_qcval = False\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n _copyback_qcval = True\\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n _copyback_qcval = True\\n if qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcval has wrong length\\\")\\n res,resargs = self.__obj.getqconk64(k_,maxnumqcnz_,len(qcsubi),qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numqcnz_return_value = resargs\\n if _copyback_qcval:\\n qcval[:] = _tmparr_qcval\\n if _copyback_qcsubj:\\n qcsubj[:] = _tmparr_qcsubj\\n if _copyback_qcsubi:\\n qcsubi[:] = _tmparr_qcsubi\\n return _numqcnz_return_value\",\n \"def Q(self, k, x):\\n g = np.asarray(self.g(k, x))\\n Q = g @ g.T\\n return Q\",\n \"def add_cp_qe_RBM_terms(K, cons_pot_mesh, quad_geo_mesh):\\n num_faces = cons_pot_mesh.get_faces().shape[0]\\n x_c = quad_geo_mesh.get_centroid()\\n w = quad_geo_mesh.get_w()\\n A_m = quad_geo_mesh.get_A_m()\\n S_D = quad_geo_mesh.get_surface_area()\\n\\n for face_num in range(num_faces):\\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\\n face_hs = quad_geo_mesh.get_hs(face_num)\\n def v_quad(_xi, _eta, _nodes):\\n return np.identity(3)\\n v_sub_mat = (1. / S_D) * gq.int_over_tri_quad(v_quad, face_nodes, face_hs)\\n def omega_quad(xi, eta, nodes):\\n pos = geo.quadratic_interp(xi, eta, nodes)\\n X = pos - x_c\\n return np.einsum(\\\"lrs,s->lr\\\", geo.LC_3, X)\\n tmp_omega = gq.int_over_tri_quad(\\n omega_quad,\\n face_nodes,\\n face_hs,\\n )\\n tmp_arr = []\\n for m in range(3):\\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\\\"l,ls\\\", w[m], tmp_omega)))\\n tmp_arr = np.array(tmp_arr)\\n tmp_omega_mat = np.sum(tmp_arr, axis=0)\\n for src_num in range(num_faces):\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * face_num):(3 * face_num + 3)] += v_sub_mat\\n src_center = cons_pot_mesh.get_node(src_num)\\n X_0 = src_center - x_c\\n omega_mat = np.einsum(\\\"ijk,js,k->is\\\", geo.LC_3, tmp_omega_mat, X_0)\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * face_num):(3 * face_num + 3)] += omega_mat\",\n \"def putcone(self,k_,ct_,conepar_,submem_):\\n nummem_ = None\\n if nummem_ is None:\\n nummem_ = len(submem_)\\n elif nummem_ != len(submem_):\\n raise IndexError(\\\"Inconsistent length of array submem\\\")\\n if submem_ is None:\\n raise ValueError(\\\"Argument submem cannot be None\\\")\\n if submem_ is None:\\n raise ValueError(\\\"Argument submem may not be None\\\")\\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\\n _submem_copyarray = False\\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif submem_ is not None:\\n _submem_copyarray = True\\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\\n _submem_np_tmp[:] = submem_\\n assert _submem_np_tmp.flags.contiguous\\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _submem_copyarray = False\\n _submem_tmp = None\\n \\n res = __library__.MSK_XX_putcone(self.__nativep,k_,ct_,conepar_,nummem_,_submem_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\\n _subj_minlength = (num_)\\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(num_)))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _subk_minlength = (num_)\\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk is not long enough: Is %d, expected %d\\\" % (len(subk_),(num_)))\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk cannot be None\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n _subl_minlength = (num_)\\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl is not long enough: Is %d, expected %d\\\" % (len(subl_),(num_)))\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl cannot be None\\\")\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl may not be None\\\")\\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\\n _subl_copyarray = False\\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subl_ is not None:\\n _subl_copyarray = True\\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\\n _subl_np_tmp[:] = subl_\\n assert _subl_np_tmp.flags.contiguous\\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subl_copyarray = False\\n _subl_tmp = None\\n \\n _valjkl_minlength = (num_)\\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\\n raise ValueError(\\\"Array argument valjkl is not long enough: Is %d, expected %d\\\" % (len(valjkl_),(num_)))\\n if valjkl_ is None:\\n raise ValueError(\\\"Argument valjkl cannot be None\\\")\\n if valjkl_ is None:\\n raise ValueError(\\\"Argument valjkl may not be None\\\")\\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\\n _valjkl_copyarray = False\\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valjkl_ is not None:\\n _valjkl_copyarray = True\\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\\n _valjkl_np_tmp[:] = valjkl_\\n assert _valjkl_np_tmp.flags.contiguous\\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valjkl_copyarray = False\\n _valjkl_tmp = None\\n \\n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_cp_qe_DL_terms(K, cons_pot_mesh, quad_geo_mesh):\\n geo_faces = quad_geo_mesh.get_faces()\\n pot_faces = cons_pot_mesh.get_faces()\\n assert geo_faces.shape[0] == pot_faces.shape[0]\\n num_faces = geo_faces.shape[0]\\n c_0 = 1. / (4. * np.pi)\\n for face_num in range(num_faces): # field points\\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\\n face_n = quad_geo_mesh.get_quad_n(face_num)\\n face_hs = quad_geo_mesh.get_hs(face_num)\\n for src_num in range(num_faces): # source points\\n src_center = cons_pot_mesh.get_node(src_num)\\n if face_num != src_num:\\n sub_mat = gq.int_over_tri_quad_n(\\n make_cp_qe_quad_func(src_center),\\n face_nodes,\\n face_n,\\n face_hs\\n )\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * face_num):(3 * face_num + 3)] += sub_mat\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * src_num):(3 * src_num + 3)] -= sub_mat\\n # do nothing face_num == src_num, how it works out for constant elements\\n\\n for src_num in range(num_faces):\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * src_num):(3 * src_num + 3)] -= 4. * np.pi * np.identity(3)\\n K *= c_0\",\n \"def ikcon(self, T, q0=None):\\n\\n if not isinstance(T, SE3):\\n T = SE3(T)\\n\\n trajn = len(T)\\n\\n try:\\n if q0 is not None:\\n q0 = getvector(q0, self.n, 'row')\\n else:\\n q0 = np.zeros((trajn, self.n))\\n except ValueError:\\n verifymatrix(q0, (trajn, self.n))\\n\\n # create output variables\\n qstar = np.zeros((trajn, self.n))\\n error = []\\n exitflag = []\\n\\n omega = np.diag([1, 1, 1, 3 / self.reach])\\n\\n def cost(q, T, omega):\\n return np.sum(\\n (\\n (np.linalg.pinv(T.A) @ self.fkine(q).A - np.eye(4)) @\\n omega) ** 2\\n )\\n\\n bnds = Bounds(self.qlim[0, :], self.qlim[1, :])\\n\\n for i in range(trajn):\\n Ti = T[i]\\n res = minimize(\\n lambda q: cost(q, Ti, omega),\\n q0[i, :], bounds=bnds, options={'gtol': 1e-6})\\n qstar[i, :] = res.x\\n error.append(res.fun)\\n exitflag.append(res.success)\\n\\n if trajn > 1:\\n return qstar, exitflag, error\\n else:\\n return qstar[0, :], exitflag[0], error[0]\",\n \"def putconbound(self,i_,bkc_,blc_,buc_):\\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putcone(self,k_,ct_,conepar_,submem): # 3\\n if not isinstance(ct_,conetype): raise TypeError(\\\"Argument ct has wrong type\\\")\\n nummem_ = None\\n if nummem_ is None:\\n nummem_ = len(submem)\\n elif nummem_ != len(submem):\\n raise IndexError(\\\"Inconsistent length of array submem\\\")\\n if nummem_ is None: nummem_ = 0\\n if submem is None: raise TypeError(\\\"Invalid type for argument submem\\\")\\n if submem is None:\\n submem_ = None\\n else:\\n try:\\n submem_ = memoryview(submem)\\n except TypeError:\\n try:\\n _tmparr_submem = array.array(\\\"i\\\",submem)\\n except TypeError:\\n raise TypeError(\\\"Argument submem has wrong type\\\")\\n else:\\n submem_ = memoryview(_tmparr_submem)\\n \\n else:\\n if submem_.format != \\\"i\\\":\\n submem_ = memoryview(array.array(\\\"i\\\",submem))\\n \\n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _append_phase(self, k, i):\\n if not 0 <= i < self.num_qubits:\\n raise QiskitError(\\\"phase qubit out of bounds.\\\")\\n # If the kth bit is flipped, conjugate this gate\\n if self.shift[i] == 1:\\n k = (7 * k) % 8\\n # Take all subsets \\\\alpha of the support of row i\\n # of weight up to 3 and add k*(-2)**(|\\\\alpha| - 1) mod 8\\n # to the corresponding term.\\n support = np.arange(self.num_qubits)[np.nonzero(self.linear[i])]\\n subsets_2 = itertools.combinations(support, 2)\\n subsets_3 = itertools.combinations(support, 3)\\n for j in support:\\n value = self.poly.get_term([j])\\n self.poly.set_term([j], (value + k) % 8)\\n for j in subsets_2:\\n value = self.poly.get_term(list(j))\\n self.poly.set_term(list(j), (value + -2 * k) % 8)\\n for j in subsets_3:\\n value = self.poly.get_term(list(j))\\n self.poly.set_term(list(j), (value + 4 * k) % 8)\",\n \"def ket(self: Qs) -> Qs:\\n\\n if self.qs_type == \\\"ket\\\":\\n return self\\n\\n ket = conjs(deepcopy(self))\\n ket.rows = self.dim\\n ket.columns = 1\\n\\n ket.qs_type = \\\"ket\\\" if self.dim > 1 else \\\"scalar_q\\\"\\n\\n return ket\",\n \"def ALIGNF(km_list, ky):\\n n_feat = len(km_list)\\n\\n #km_list_copy = []\\n # center the kernel first\\n #for i in range(n_feat):\\n # km_list_copy.append(center(km_list[i].copy()))\\n #ky_copy = center(ky.copy())\\n\\n\\n a = np.zeros(n_feat)\\n for i in range(n_feat):\\n a[i] = f_dot(km_list[i], ky)\\n\\n M = np.zeros((n_feat, n_feat))\\n for i in range(n_feat):\\n for j in range(i,n_feat):\\n M[i,j] = f_dot(km_list[i],km_list[j])\\n M[j,i] = M[i,j]\\n\\n Q = 2*M\\n C = -2*a\\n\\n Q = Q + np.diag(np.ones(n_feat)*1e-8)\\n\\n ################################################\\n # Using mosek to solve the quadratice programming\\n\\n # Set upper diagonal element to zeros, mosek only accept lower triangle\\n iu = np.triu_indices(n_feat,1)\\n Q[iu] = 0\\n\\n # start solving with mosek\\n inf = 0.0\\n env = mosek.Env()\\n env.set_Stream(mosek.streamtype.log, streamprinter)\\n\\n # Create a task \\n task = env.Task()\\n task.set_Stream(mosek.streamtype.log, streamprinter)\\n\\n # Set up bound for variables \\n bkx = [mosek.boundkey.lo]* n_feat\\n blx = [0.0] * n_feat\\n #bkx = [mosek.boundkey.fr]* n_feat\\n #blx = [-inf] * n_feat\\n bux = [+inf] * n_feat\\n\\n numvar = len(bkx)\\n\\n task.appendvars(numvar)\\n\\n for j in range(numvar):\\n task.putcj(j,C[j])\\n task.putvarbound(j,bkx[j],blx[j],bux[j])\\n\\n # Set up quadratic objective \\n inds = np.nonzero(Q)\\n qsubi = inds[0].tolist()\\n qsubj = inds[1].tolist()\\n qval = Q[inds].tolist()\\n\\n # Input quadratic objective \\n task.putqobj(qsubi,qsubj,qval)\\n\\n # Input objective sense (minimize/mximize) \\n task.putobjsense(mosek.objsense.minimize)\\n\\n task.optimize()\\n\\n # Print a summary containing information \\n # about the solution for debugging purposes \\n task.solutionsummary(mosek.streamtype.msg)\\n\\n solsta = task.getsolsta(mosek.soltype.itr)\\n if (solsta == mosek.solsta.optimal or\\n solsta == mosek.solsta.near_optimal):\\n # Output a solution \\n xx = np.zeros(numvar, float)\\n task.getxx(mosek.soltype.itr, xx)\\n #xx = xx/np.linalg.norm(xx)\\n return xx\\n else:\\n print solsta\\n xx = np.zeros(numvar, float)\\n task.getxx(mosek.soltype.itr, xx)\\n #xx = xx/np.linalg.norm(xx)\\n return xx\",\n \"def putskc(self,whichsol_,skc_):\\n _skc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),self.getnumcon()))\\n if skc_ is None:\\n raise ValueError(\\\"Argument skc cannot be None\\\")\\n if skc_ is None:\\n raise ValueError(\\\"Argument skc may not be None\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def additional_equations(self, k):\\n return\",\n \"def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqobjij(self,i_,j_,qoij_):\\n res = __library__.MSK_XX_putqobjij(self.__nativep,i_,j_,qoij_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_):\\n _subi_minlength = (num_)\\n if (num_) > 0 and subi_ is not None and len(subi_) != (num_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(num_)))\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = (num_)\\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(num_)))\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _subk_minlength = (num_)\\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk is not long enough: Is %d, expected %d\\\" % (len(subk_),(num_)))\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk cannot be None\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n _subl_minlength = (num_)\\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl is not long enough: Is %d, expected %d\\\" % (len(subl_),(num_)))\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl cannot be None\\\")\\n if subl_ is None:\\n raise ValueError(\\\"Argument subl may not be None\\\")\\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\\n _subl_copyarray = False\\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subl_ is not None:\\n _subl_copyarray = True\\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\\n _subl_np_tmp[:] = subl_\\n assert _subl_np_tmp.flags.contiguous\\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subl_copyarray = False\\n _subl_tmp = None\\n \\n _valijkl_minlength = (num_)\\n if (num_) > 0 and valijkl_ is not None and len(valijkl_) != (num_):\\n raise ValueError(\\\"Array argument valijkl is not long enough: Is %d, expected %d\\\" % (len(valijkl_),(num_)))\\n if valijkl_ is None:\\n raise ValueError(\\\"Argument valijkl cannot be None\\\")\\n if valijkl_ is None:\\n raise ValueError(\\\"Argument valijkl may not be None\\\")\\n if isinstance(valijkl_, numpy.ndarray) and valijkl_.dtype is numpy.dtype(numpy.float64) and valijkl_.flags.contiguous:\\n _valijkl_copyarray = False\\n _valijkl_tmp = ctypes.cast(valijkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valijkl_ is not None:\\n _valijkl_copyarray = True\\n _valijkl_np_tmp = numpy.zeros(len(valijkl_),numpy.dtype(numpy.float64))\\n _valijkl_np_tmp[:] = valijkl_\\n assert _valijkl_np_tmp.flags.contiguous\\n _valijkl_tmp = ctypes.cast(_valijkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valijkl_copyarray = False\\n _valijkl_tmp = None\\n \\n res = __library__.MSK_XX_putbarablocktriplet(self.__nativep,num_,_subi_tmp,_subj_tmp,_subk_tmp,_subl_tmp,_valijkl_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqobjij(self,i_,j_,qoij_): # 3\\n res = self.__obj.putqobjij(i_,j_,qoij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def qmincon(self, q=None):\\n\\n def sumsqr(A):\\n return np.sum(A**2)\\n\\n def cost(x, ub, lb, qm, N):\\n return sumsqr(\\n (2 * (N @ x + qm) - ub - lb) / (ub - lb))\\n\\n q = getmatrix(q, (None, self.n))\\n\\n qstar = np.zeros((q.shape[0], self.n))\\n error = np.zeros(q.shape[0])\\n success = np.zeros(q.shape[0])\\n\\n lb = self.qlim[0, :]\\n ub = self.qlim[1, :]\\n\\n for k, qk in enumerate(q):\\n\\n J = self.jacobe(qk)\\n\\n N = null(J)\\n\\n x0 = np.zeros(N.shape[1])\\n A = np.r_[N, -N]\\n b = np.r_[ub - qk, qk - lb].reshape(A.shape[0],)\\n\\n con = LinearConstraint(A, -np.inf, b)\\n\\n res = minimize(\\n lambda x: cost(x, ub, lb, qk, N),\\n x0, constraints=con)\\n\\n qstar[k, :] = qk + N @ res.x\\n error[k] = res.fun\\n success[k] = res.success\\n\\n if q.shape[0] == 1:\\n return qstar[0, :], success[0], error[0]\\n else:\\n return qstar, success, error\",\n \"def putconbound(self,i_,bk_,bl_,bu_): # 3\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if subk is None: raise TypeError(\\\"Invalid type for argument subk\\\")\\n if subk is None:\\n subk_ = None\\n else:\\n try:\\n subk_ = memoryview(subk)\\n except TypeError:\\n try:\\n _tmparr_subk = array.array(\\\"i\\\",subk)\\n except TypeError:\\n raise TypeError(\\\"Argument subk has wrong type\\\")\\n else:\\n subk_ = memoryview(_tmparr_subk)\\n \\n else:\\n if subk_.format != \\\"i\\\":\\n subk_ = memoryview(array.array(\\\"i\\\",subk))\\n \\n if subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk has wrong length\\\")\\n if subl is None: raise TypeError(\\\"Invalid type for argument subl\\\")\\n if subl is None:\\n subl_ = None\\n else:\\n try:\\n subl_ = memoryview(subl)\\n except TypeError:\\n try:\\n _tmparr_subl = array.array(\\\"i\\\",subl)\\n except TypeError:\\n raise TypeError(\\\"Argument subl has wrong type\\\")\\n else:\\n subl_ = memoryview(_tmparr_subl)\\n \\n else:\\n if subl_.format != \\\"i\\\":\\n subl_ = memoryview(array.array(\\\"i\\\",subl))\\n \\n if subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl has wrong length\\\")\\n if valjkl is None: raise TypeError(\\\"Invalid type for argument valjkl\\\")\\n if valjkl is None:\\n valjkl_ = None\\n else:\\n try:\\n valjkl_ = memoryview(valjkl)\\n except TypeError:\\n try:\\n _tmparr_valjkl = array.array(\\\"d\\\",valjkl)\\n except TypeError:\\n raise TypeError(\\\"Argument valjkl has wrong type\\\")\\n else:\\n valjkl_ = memoryview(_tmparr_valjkl)\\n \\n else:\\n if valjkl_.format != \\\"d\\\":\\n valjkl_ = memoryview(array.array(\\\"d\\\",valjkl))\\n \\n if valjkl_ is not None and len(valjkl_) != (num_):\\n raise ValueError(\\\"Array argument valjkl has wrong length\\\")\\n res = self.__obj.putbarcblocktriplet(num_,subj_,subk_,subl_,valjkl_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def Qc_fit(x, a, b, c, d, e, f, g, h, i, k):\\n x1 = x[0] # I\\n x2 = x[1] # dT\\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\\n return m * x2 + b\",\n \"def putcj(self,j_,cj_):\\n res = __library__.MSK_XX_putcj(self.__nativep,j_,cj_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def quaternion_conjugate(self, q):\\n\\n \\\"\\\"\\\"\\n in-place operation is an operation that changes directly the content of a given Tensor without making a copy.\\n ALL operations on the tensor that operate in-place on it will have an _ postfix.\\n \\\"\\\"\\\"\\n q_star = q.new(4).fill_(-1)\\n\\n # leave the scalar unchanged and change signs of i, j, k number parts\\n q_star[0] = 1.0\\n\\n return q * q_star\",\n \"def conj(q):\\n q = np.array([q[0]])\\n q[0,1]=-q[0,1]\\n q[0,2]=-q[0,2]\\n q[0,3]=-q[0,3]\\n complexconjugate = quatreal(q)\\n return complexconjugate\",\n \"def KRC(self, ik, ipd, ipl, t):\\n idx = ik - 1\\n\\n den1 = 1 - self.delta[idx] * self.coca.PK(ik, t)\\n num1 = self.delta[idx] * self.thetak[idx]\\n ins = num1 / den1\\n\\n for l in np.arange(0, self.L):\\n pl = self.coca.PL(l, t)\\n ins += ((self.thetal[l] * self.gamma[l][idx]) / (1 - pl))\\n\\n ans = ipd * np.exp(t * ipl) * ins\\n\\n return ans\",\n \"def custom_constr(x, qr, inverse, depth):\\n qc = QuantumCircuit(qr)\\n maxi, mini = max(x), min(x)\\n n = x.shape[0]\\n #qc_wv = Wavelets(n).construct_circuit(register=qr)\\n for _ in range(depth):\\n qc.h(qr)\\n for i in range(n):\\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini), 2*np.pi*(x[i]-mini)/(maxi-mini), qr[i])\\n for i in range(n):\\n qc.cx(qr[i], qr[(i + 1) % n])\\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini),\\n ((2*np.pi)**2*(x[i]-mini)*(x[(i+1) % n]-mini)/(maxi-mini)**2) % 2*np.pi,\\n qr[(i + 1) % n])\\n qc.cx(qr[i], qr[(i + 1) % n])\\n #qc = qc + qc_wv\\n if inverse:\\n return qc.inverse()\\n return qc\",\n \"def putqobj(self,qosubi_,qosubj_,qoval_):\\n numqonz_ = None\\n if numqonz_ is None:\\n numqonz_ = len(qosubi_)\\n elif numqonz_ != len(qosubi_):\\n raise IndexError(\\\"Inconsistent length of array qosubi\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qosubj_)\\n elif numqonz_ != len(qosubj_):\\n raise IndexError(\\\"Inconsistent length of array qosubj\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qoval_)\\n elif numqonz_ != len(qoval_):\\n raise IndexError(\\\"Inconsistent length of array qoval\\\")\\n if qosubi_ is None:\\n raise ValueError(\\\"Argument qosubi cannot be None\\\")\\n if qosubi_ is None:\\n raise ValueError(\\\"Argument qosubi may not be None\\\")\\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\\n _qosubi_copyarray = False\\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qosubi_ is not None:\\n _qosubi_copyarray = True\\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\\n _qosubi_np_tmp[:] = qosubi_\\n assert _qosubi_np_tmp.flags.contiguous\\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qosubi_copyarray = False\\n _qosubi_tmp = None\\n \\n if qosubj_ is None:\\n raise ValueError(\\\"Argument qosubj cannot be None\\\")\\n if qosubj_ is None:\\n raise ValueError(\\\"Argument qosubj may not be None\\\")\\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\\n _qosubj_copyarray = False\\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qosubj_ is not None:\\n _qosubj_copyarray = True\\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\\n _qosubj_np_tmp[:] = qosubj_\\n assert _qosubj_np_tmp.flags.contiguous\\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qosubj_copyarray = False\\n _qosubj_tmp = None\\n \\n if qoval_ is None:\\n raise ValueError(\\\"Argument qoval cannot be None\\\")\\n if qoval_ is None:\\n raise ValueError(\\\"Argument qoval may not be None\\\")\\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\\n _qoval_copyarray = False\\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qoval_ is not None:\\n _qoval_copyarray = True\\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\\n _qoval_np_tmp[:] = qoval_\\n assert _qoval_np_tmp.flags.contiguous\\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qoval_copyarray = False\\n _qoval_tmp = None\\n \\n res = __library__.MSK_XX_putqobj(self.__nativep,numqonz_,_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def update_q(self):\\n beta = self.EC_beta\\n self.gamma_q = (self.gamma_s - self.gamma_r) * beta + (1 - beta) * self.gamma_q\\n self.Sigma_q = (self.Sigma_s - self.Sigma_r) * beta + (1 - beta) * self.Sigma_q\\n try:\\n assert np.all(np.logical_not(np.isnan(self.gamma_q)))\\n except:\\n print(\\\"Invalid update encountered...\\\")\",\n \"def refine(self, ijk):\\n if self.cbc is None or not self.sub_block_count:\\n raise ValueError(\\n \\\"Cannot refine sub block model without specifying number \\\"\\n \\\"of parent and sub blocks\\\"\\n )\\n try:\\n inds = self.ijk_array_to_indices(ijk)\\n except ValueError:\\n inds = self.ijk_to_index(ijk)\\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137\",\n \"def conjunction(x, vx, k):\\n t = np.sort(x / np.sqrt(np.maximum(vx, 1.e-15)))\\n cjt = np.sum(t[:, :k], 1)\\n return cjt\",\n \"def AttractionForce(q,q_k):\\r\\n return k_p*CalculateDistance(q,q_k)\",\n \"def Qc(I, dT, a, b, c, d, e, f, g, h, i, k):\\n x1 = I # I\\n x2 = dT # dT\\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\\n return m * x2 + b\",\n \"def generateconenames(self,subk_,fmt_,dims_,sp_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subk_)\\n elif num_ != len(subk_):\\n raise IndexError(\\\"Inconsistent length of array subk\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk cannot be None\\\")\\n if subk_ is None:\\n raise ValueError(\\\"Argument subk may not be None\\\")\\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\\n _subk_copyarray = False\\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subk_ is not None:\\n _subk_copyarray = True\\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\\n _subk_np_tmp[:] = subk_\\n assert _subk_np_tmp.flags.contiguous\\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subk_copyarray = False\\n _subk_tmp = None\\n \\n if isinstance(fmt_,unicode):\\n fmt_ = fmt_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n ndims_ = None\\n if ndims_ is None:\\n ndims_ = len(dims_)\\n elif ndims_ != len(dims_):\\n raise IndexError(\\\"Inconsistent length of array dims\\\")\\n if dims_ is None:\\n raise ValueError(\\\"Argument dims cannot be None\\\")\\n if dims_ is None:\\n raise ValueError(\\\"Argument dims may not be None\\\")\\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\\n _dims_copyarray = False\\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif dims_ is not None:\\n _dims_copyarray = True\\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\\n _dims_np_tmp[:] = dims_\\n assert _dims_np_tmp.flags.contiguous\\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _dims_copyarray = False\\n _dims_tmp = None\\n \\n _sp_minlength = (num_)\\n if (num_) > 0 and sp_ is not None and len(sp_) != (num_):\\n raise ValueError(\\\"Array argument sp is not long enough: Is %d, expected %d\\\" % (len(sp_),(num_)))\\n if isinstance(sp_, numpy.ndarray) and sp_.dtype is numpy.dtype(numpy.int64) and sp_.flags.contiguous:\\n _sp_copyarray = False\\n _sp_tmp = ctypes.cast(sp_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif sp_ is not None:\\n _sp_copyarray = True\\n _sp_np_tmp = numpy.zeros(len(sp_),numpy.dtype(numpy.int64))\\n _sp_np_tmp[:] = sp_\\n assert _sp_np_tmp.flags.contiguous\\n _sp_tmp = ctypes.cast(_sp_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _sp_copyarray = False\\n _sp_tmp = None\\n \\n res = __library__.MSK_XX_generateconenames(self.__nativep,num_,_subk_tmp,fmt_,ndims_,_dims_tmp,_sp_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def additional_equations(self, k):\\n ######################################################################\\n # equation for specified kA_group paremeters\\n if self.kA_group.is_set:\\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\\n self.residual[k] = self.kA_func()\\n k += 1\\n\\n ######################################################################\\n # equation for specified kA_char_group paremeters\\n if self.kA_char_group.is_set:\\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\\n self.residual[k] = self.kA_char_func()\\n k += 1\",\n \"def vahlen_conj(q_1: Q, conj_type: str = \\\"-\\\", q_type: str = \\\"vc\\\") -> Q:\\n\\n vc_t, vc_x, vc_y, vc_z = q_1.t, q_1.x, q_1.y, q_1.z\\n c_q = Q()\\n\\n if conj_type == \\\"-\\\":\\n c_q.t = vc_t\\n if vc_x != 0:\\n c_q.x = -1 * vc_x\\n if vc_y != 0:\\n c_q.y = -1 * vc_y\\n if vc_z != 0:\\n c_q.z = -1 * vc_z\\n q_type += \\\"*-\\\"\\n\\n if conj_type == \\\"'\\\":\\n c_q.t = vc_t\\n if vc_x != 0:\\n c_q.x = -1 * vc_x\\n if vc_y != 0:\\n c_q.y = -1 * vc_y\\n c_q.z = vc_z\\n q_type += \\\"*'\\\"\\n\\n if conj_type == \\\"*\\\":\\n c_q.t = vc_t\\n c_q.x = vc_x\\n c_q.y = vc_y\\n if vc_z != 0:\\n c_q.z = -1 * vc_z\\n q_type += \\\"*\\\"\\n\\n c_q.q_type = f\\\"{q_1.q_type}{q_type}\\\"\\n c_q.representation = q_1.representation\\n\\n return c_q\",\n \"def remove_constraint(self, ckey):\\n if ckey not in self.constraints:\\n raise KeyError(\\\"Constraints not found on object key: {}\\\".format(ckey))\\n del self.constraints[ckey]\",\n \"def _compute_kreinParameters(self, expand=False, factor=False,\\n simplify=False):\\n if self._has(\\\"q\\\"):\\n return\\n if not self._has(\\\"m\\\"):\\n self.multiplicities(expand=expand, factor=factor,\\n simplify=simplify)\\n if not self._has(\\\"k\\\"):\\n self.kTable(expand=expand, factor=factor,\\n simplify=simplify)\\n q = Array3D(self._.d + 1)\\n self._compute_parameters(q, self._.Q, self._.k, integral=False,\\n name=DUAL_PARAMETER, sym=DUAL_SYMBOL)\\n self._.q = q\",\n \"def fixC(self,i,value):\\n if self.coeffPattern[2] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[2] = [None]*m\\n self.coeffPattern[2][i]=value\\n self._updateEstimatorSize(i)\",\n \"def putbarablocktriplet(self,num_,subi,subj,subk,subl,valijkl): # 3\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subi_ is not None and len(subi_) != (num_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if subj_ is not None and len(subj_) != (num_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if subk is None: raise TypeError(\\\"Invalid type for argument subk\\\")\\n if subk is None:\\n subk_ = None\\n else:\\n try:\\n subk_ = memoryview(subk)\\n except TypeError:\\n try:\\n _tmparr_subk = array.array(\\\"i\\\",subk)\\n except TypeError:\\n raise TypeError(\\\"Argument subk has wrong type\\\")\\n else:\\n subk_ = memoryview(_tmparr_subk)\\n \\n else:\\n if subk_.format != \\\"i\\\":\\n subk_ = memoryview(array.array(\\\"i\\\",subk))\\n \\n if subk_ is not None and len(subk_) != (num_):\\n raise ValueError(\\\"Array argument subk has wrong length\\\")\\n if subl is None: raise TypeError(\\\"Invalid type for argument subl\\\")\\n if subl is None:\\n subl_ = None\\n else:\\n try:\\n subl_ = memoryview(subl)\\n except TypeError:\\n try:\\n _tmparr_subl = array.array(\\\"i\\\",subl)\\n except TypeError:\\n raise TypeError(\\\"Argument subl has wrong type\\\")\\n else:\\n subl_ = memoryview(_tmparr_subl)\\n \\n else:\\n if subl_.format != \\\"i\\\":\\n subl_ = memoryview(array.array(\\\"i\\\",subl))\\n \\n if subl_ is not None and len(subl_) != (num_):\\n raise ValueError(\\\"Array argument subl has wrong length\\\")\\n if valijkl is None: raise TypeError(\\\"Invalid type for argument valijkl\\\")\\n if valijkl is None:\\n valijkl_ = None\\n else:\\n try:\\n valijkl_ = memoryview(valijkl)\\n except TypeError:\\n try:\\n _tmparr_valijkl = array.array(\\\"d\\\",valijkl)\\n except TypeError:\\n raise TypeError(\\\"Argument valijkl has wrong type\\\")\\n else:\\n valijkl_ = memoryview(_tmparr_valijkl)\\n \\n else:\\n if valijkl_.format != \\\"d\\\":\\n valijkl_ = memoryview(array.array(\\\"d\\\",valijkl))\\n \\n if valijkl_ is not None and len(valijkl_) != (num_):\\n raise ValueError(\\\"Array argument valijkl has wrong length\\\")\\n res = self.__obj.putbarablocktriplet(num_,subi_,subj_,subk_,subl_,valijkl_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solvewithbasis(self,transp_,numnz_,sub_,val_):\\n _numnz_tmp = ctypes.c_int32(numnz_)\\n _sub_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and sub_ is not None and len(sub_) != self.getnumcon():\\n raise ValueError(\\\"Array argument sub is not long enough: Is %d, expected %d\\\" % (len(sub_),self.getnumcon()))\\n if isinstance(sub_,numpy.ndarray) and not sub_.flags.writeable:\\n raise ValueError(\\\"Argument sub must be writable\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _val_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and val_ is not None and len(val_) != self.getnumcon():\\n raise ValueError(\\\"Array argument val is not long enough: Is %d, expected %d\\\" % (len(val_),self.getnumcon()))\\n if isinstance(val_,numpy.ndarray) and not val_.flags.writeable:\\n raise ValueError(\\\"Argument val must be writable\\\")\\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\\n _val_copyarray = False\\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif val_ is not None:\\n _val_copyarray = True\\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\\n _val_np_tmp[:] = val_\\n assert _val_np_tmp.flags.contiguous\\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _val_copyarray = False\\n _val_tmp = None\\n \\n res = __library__.MSK_XX_solvewithbasis(self.__nativep,transp_,ctypes.byref(_numnz_tmp),_sub_tmp,_val_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = _numnz_tmp.value\\n if _sub_copyarray:\\n sub_[:] = _sub_np_tmp\\n if _val_copyarray:\\n val_[:] = _val_np_tmp\\n return (_numnz_return_value)\",\n \"def add_lp_qe_RBM_terms(K, lin_pot_mesh, quad_geo_mesh):\\n pot_faces = lin_pot_mesh.get_faces()\\n num_faces = pot_faces.shape[0]\\n pot_nodes = lin_pot_mesh.get_nodes()\\n num_nodes = pot_nodes.shape[0]\\n S_D = quad_geo_mesh.get_surface_area()\\n x_c = quad_geo_mesh.get_centroid()\\n w = quad_geo_mesh.get_w()\\n A_m = quad_geo_mesh.get_A_m()\\n\\n for face_num in range(num_faces):\\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\\n face_hs = quad_geo_mesh.get_hs(face_num)\\n for node_num in range(3): # face nodes\\n face_node_global_num = pot_faces[face_num, node_num]\\n v_sub_mat = (-4. * np.pi / S_D) * gq.int_over_tri_quad(\\n make_lp_le_v_quad(node_num),\\n face_nodes,\\n face_hs,\\n )\\n j = 3 * face_node_global_num\\n tmp_omega = gq.int_over_tri_quad(\\n make_lp_le_omega_quad(node_num, x_c),\\n face_nodes,\\n face_hs,\\n )\\n tmp_arr = []\\n for m in range(3):\\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\\\"l, ls\\\", w[m], tmp_omega)))\\n tmp_omega_mat = -4. * np.pi * np.sum(tmp_arr, axis=0)\\n for src_num in range(num_nodes):\\n K[(3 * src_num):(3 * src_num + 3),\\n j:j+3] += -1. / (4. * np.pi) * v_sub_mat\\n src_pt = lin_pot_mesh.get_node(src_num)\\n X_0 = src_pt - x_c\\n omega_mat = np.einsum(\\\"ijk,js,k\\\", geo.LC_3, tmp_omega_mat, X_0)\\n K[(3 * src_num):(3 * src_num + 3),\\n j:j+3] += 1. / (4. * np.pi) * (omega_mat)\",\n \"def pk_constrained(self, snr=30, headroom = 0):\\n # Initialize\\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\\n # loop over frequencies\\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\\n for jf, k0 in enumerate(self.controls.k0):\\n # get the scaled version of the propagating directions\\n k_vec = k0 * self.dir\\n # Form the sensing matrix\\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\\n H = h_mtx.astype(complex) # cvxpy does not accept floats, apparently\\n # measured data\\n pm = self.pres_s[:,jf].astype(complex)\\n # Performing constrained optmization cvxpy\\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True) # create x variable\\n # Create the problem\\n epsilon = 10**(-(snr-headroom)/10)\\n problem = cp.Problem(cp.Minimize(cp.norm2(x_cvx)**2),\\n [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon])\\n problem.solve(solver=cp.SCS, verbose=False)\\n self.pk[:,jf] = x_cvx.value\\n bar.update(1)\\n bar.close()\",\n \"def SetPRCatConstraint(self, model ) :\\n tot = np.multiply(self.wish, self.dispo)\\n for line in tot :\\n for val in line :\\n if not val : continue\\n if self.bound>0 : model += val <= self.valBound\\n elif self.bound<0 : model += val >= self.valBound\",\n \"def putbarcj(self,j_,sub,weights): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights)\\n elif num_ != len(weights):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"q\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"q\\\":\\n sub_ = memoryview(array.array(\\\"q\\\",sub))\\n \\n if weights is None: raise TypeError(\\\"Invalid type for argument weights\\\")\\n if weights is None:\\n weights_ = None\\n else:\\n try:\\n weights_ = memoryview(weights)\\n except TypeError:\\n try:\\n _tmparr_weights = array.array(\\\"d\\\",weights)\\n except TypeError:\\n raise TypeError(\\\"Argument weights has wrong type\\\")\\n else:\\n weights_ = memoryview(_tmparr_weights)\\n \\n else:\\n if weights_.format != \\\"d\\\":\\n weights_ = memoryview(array.array(\\\"d\\\",weights))\\n \\n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putclist(self,subj,val): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subj)\\n elif num_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(val)\\n elif num_ != len(val):\\n raise IndexError(\\\"Inconsistent length of array val\\\")\\n if num_ is None: num_ = 0\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if val is None: raise TypeError(\\\"Invalid type for argument val\\\")\\n if val is None:\\n val_ = None\\n else:\\n try:\\n val_ = memoryview(val)\\n except TypeError:\\n try:\\n _tmparr_val = array.array(\\\"d\\\",val)\\n except TypeError:\\n raise TypeError(\\\"Argument val has wrong type\\\")\\n else:\\n val_ = memoryview(_tmparr_val)\\n \\n else:\\n if val_.format != \\\"d\\\":\\n val_ = memoryview(array.array(\\\"d\\\",val))\\n \\n res = self.__obj.putclist(num_,subj_,val_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _k_coaffine_pair(pair, bound=math.inf):\\n g = pair.graph\\n sigma = pair.coaffination\\n kg = clique_graph(g, bound)\\n coaf_k = dict([])\\n for q in kg:\\n coaf_k[q] = Clique([sigma[x] for x in q])\\n return CoaffinePair(kg, coaf_k)\",\n \"def putqobj(self,qosubi,qosubj,qoval): # 3\\n numqonz_ = None\\n if numqonz_ is None:\\n numqonz_ = len(qosubi)\\n elif numqonz_ != len(qosubi):\\n raise IndexError(\\\"Inconsistent length of array qosubi\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qosubj)\\n elif numqonz_ != len(qosubj):\\n raise IndexError(\\\"Inconsistent length of array qosubj\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qoval)\\n elif numqonz_ != len(qoval):\\n raise IndexError(\\\"Inconsistent length of array qoval\\\")\\n if numqonz_ is None: numqonz_ = 0\\n if qosubi is None: raise TypeError(\\\"Invalid type for argument qosubi\\\")\\n if qosubi is None:\\n qosubi_ = None\\n else:\\n try:\\n qosubi_ = memoryview(qosubi)\\n except TypeError:\\n try:\\n _tmparr_qosubi = array.array(\\\"i\\\",qosubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubi has wrong type\\\")\\n else:\\n qosubi_ = memoryview(_tmparr_qosubi)\\n \\n else:\\n if qosubi_.format != \\\"i\\\":\\n qosubi_ = memoryview(array.array(\\\"i\\\",qosubi))\\n \\n if qosubj is None: raise TypeError(\\\"Invalid type for argument qosubj\\\")\\n if qosubj is None:\\n qosubj_ = None\\n else:\\n try:\\n qosubj_ = memoryview(qosubj)\\n except TypeError:\\n try:\\n _tmparr_qosubj = array.array(\\\"i\\\",qosubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubj has wrong type\\\")\\n else:\\n qosubj_ = memoryview(_tmparr_qosubj)\\n \\n else:\\n if qosubj_.format != \\\"i\\\":\\n qosubj_ = memoryview(array.array(\\\"i\\\",qosubj))\\n \\n if qoval is None: raise TypeError(\\\"Invalid type for argument qoval\\\")\\n if qoval is None:\\n qoval_ = None\\n else:\\n try:\\n qoval_ = memoryview(qoval)\\n except TypeError:\\n try:\\n _tmparr_qoval = array.array(\\\"d\\\",qoval)\\n except TypeError:\\n raise TypeError(\\\"Argument qoval has wrong type\\\")\\n else:\\n qoval_ = memoryview(_tmparr_qoval)\\n \\n else:\\n if qoval_.format != \\\"d\\\":\\n qoval_ = memoryview(array.array(\\\"d\\\",qoval))\\n \\n res = self.__obj.putqobj(numqonz_,qosubi_,qosubj_,qoval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def do_reduction_placzek_corrections(q,sqfg,bgd,rescale_bgd=1.0,plaz_type=None,\\n gauss_damp=False,gw=20.0,qmax=None,qmin=None,\\n rmin=0.0,rmax=20.0,delr=.02\\n ,qminpla=10.0,qmaxpla=30.0,ndeg=2, return_correction = False,\\n skip_bgd = False, return_final_sq = False, force_qmax_type='Off'):\\n #first, make netsq if bgd and/or damping is present\\n q = np.array(q)\\n sqfg = np.array(sqfg)\\n bgd = np.array(bgd)\\n\\n if skip_bgd:\\n netsq = sqfg\\n else:\\n netsq = sqfg - bgd*rescale_bgd\\n\\n\\n if gauss_damp:\\n netsq = netsq*gauss(q,gw,0)\\n\\n\\n if force_qmax_type == 'Force Data (PreCorrection)':\\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n netsq -= mean_sqmax\\n\\n #now, apply a correction if requested\\n if plaz_type != None:\\n if plaz_type == 'Polynomial' or plaz_type == 'poly' or plaz_type == 'ndeg':\\n sq_poly_fit = fit_ndeg_to_sq(q,netsq,ndeg=ndeg,qmin=qminpla,qmax=qmaxpla)\\n this_fit = sq_poly_fit\\n elif plaz_type == 'Pseudo-Voight' or plaz_type == 'pv' or plaz_type == 'hydro':\\n pv_fit = fit_pv_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_fit\\n elif plaz_type == 'PVoight + n0' or plaz_type == 'pvndeg0':\\n pv_n0_fit = fit_pv_n0_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_n0_fit\\n elif plaz_type == 'PVoight + n1' or plaz_type == 'pvndeg1':\\n pv_n1_fit = fit_pv_n1_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_n1_fit\\n elif plaz_type == 'PVoight + n2' or plaz_type == 'pvndeg2':\\n pv_n2_fit = fit_pv_n2_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_n2_fit\\n else:\\n print (\\\"I don't know that correction type, sorry\\\")\\n this_fit = np.zeros(len(q))\\n else:\\n this_fit = np.zeros(len(q))\\n\\n if force_qmax_type == 'Force Data' or force_qmax_type == 'Force Both (Independent)':\\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n netsq -= mean_sqmax\\n if force_qmax_type == 'Force Correction' or force_qmax_type == 'Force Both (Independent)':\\n qcut, sqcut = cut_data(q,this_fit,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n this_fit -= mean_sqmax\\n if force_qmax_type == 'ReCorrection':\\n qcut, sqcut = cut_data(q,netsq-this_fit,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n this_fit += mean_sqmax\\n\\n netsq = netsq - this_fit\\n\\n if return_correction:\\n return this_fit\\n\\n if return_final_sq:\\n return netsq\\n\\n #finally, generate PDF\\n r,gr = make_gr_from_sq(q,netsq,qmin=qmin,qmax=qmax,rmin=rmin,rmax=rmax,delr=delr)\\n\\n return r,gr\",\n \"def additional_equations(self, k):\\n ######################################################################\\n # equation for saturated liquid at hot side outlet\\n if self.subcooling.val is False:\\n o1 = self.outl[0].to_flow()\\n self.residual[k] = o1[2] - h_mix_pQ(o1, 0)\\n k += 1\",\n \"def putvarbound(self,j_,bkx_,blx_,bux_):\\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\\n self.coeff[self._core.index_alpha(key[0]),\\n self._core.index_beta(key[1])] = value\",\n \"def syrk(self,uplo_,trans_,n_,k_,alpha_,a_,beta_,c_):\\n _a_minlength = ((n_) * (k_))\\n if ((n_) * (k_)) > 0 and a_ is not None and len(a_) != ((n_) * (k_)):\\n raise ValueError(\\\"Array argument a is not long enough: Is %d, expected %d\\\" % (len(a_),((n_) * (k_))))\\n if a_ is None:\\n raise ValueError(\\\"Argument a cannot be None\\\")\\n if a_ is None:\\n raise ValueError(\\\"Argument a may not be None\\\")\\n if isinstance(a_, numpy.ndarray) and a_.dtype is numpy.dtype(numpy.float64) and a_.flags.contiguous:\\n _a_copyarray = False\\n _a_tmp = ctypes.cast(a_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif a_ is not None:\\n _a_copyarray = True\\n _a_np_tmp = numpy.zeros(len(a_),numpy.dtype(numpy.float64))\\n _a_np_tmp[:] = a_\\n assert _a_np_tmp.flags.contiguous\\n _a_tmp = ctypes.cast(_a_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _a_copyarray = False\\n _a_tmp = None\\n \\n _c_minlength = ((n_) * (n_))\\n if ((n_) * (n_)) > 0 and c_ is not None and len(c_) != ((n_) * (n_)):\\n raise ValueError(\\\"Array argument c is not long enough: Is %d, expected %d\\\" % (len(c_),((n_) * (n_))))\\n if isinstance(c_,numpy.ndarray) and not c_.flags.writeable:\\n raise ValueError(\\\"Argument c must be writable\\\")\\n if c_ is None:\\n raise ValueError(\\\"Argument c may not be None\\\")\\n if isinstance(c_, numpy.ndarray) and c_.dtype is numpy.dtype(numpy.float64) and c_.flags.contiguous:\\n _c_copyarray = False\\n _c_tmp = ctypes.cast(c_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif c_ is not None:\\n _c_copyarray = True\\n _c_np_tmp = numpy.zeros(len(c_),numpy.dtype(numpy.float64))\\n _c_np_tmp[:] = c_\\n assert _c_np_tmp.flags.contiguous\\n _c_tmp = ctypes.cast(_c_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _c_copyarray = False\\n _c_tmp = None\\n \\n res = __library__.MSK_XX_syrk(self.__nativep,uplo_,trans_,n_,k_,alpha_,_a_tmp,beta_,_c_tmp)\\n if res != 0:\\n raise Error(rescode(res),Env.getcodedesc(rescode(res))[1])\\n if _c_copyarray:\\n c_[:] = _c_np_tmp\",\n \"def quadratic_strain(x, dof):\\n base = np.zeros([6, dof])\\n base[1, 0] = 1 # initial y-bending\\n if dof > 2:\\n base[1, 1] = x**2 # quadratic y-bending term\\n base[2, dof-1] = x**2 # quadratic z-bending term\\n return base\",\n \"def conj_q(q_1: Q, q_2: Q) -> Q:\\n\\n _conj = deepcopy(q_1)\\n\\n if q_2.t:\\n _conj = conj(_conj, conj_type=0)\\n\\n if q_2.x:\\n _conj = conj(_conj, conj_type=1)\\n\\n if q_2.y:\\n _conj = conj(_conj, conj_type=2)\\n\\n if q_2.z:\\n _conj = flip_sign(_conj)\\n\\n return _conj\",\n \"def setPressureCoeff(self, Kv):\\n vv = zeros(1,'d')\\n vv[0] = Kv\\n if self._verbose:\\n print\\n print self._name+': setting pressure coefficient to '+`Kv`+' kg/Pa-s'\\n self._setParameters(vv)\",\n \"def putskc(self,whichsol_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None: raise TypeError(\\\"Invalid type for argument skc\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.putskc(whichsol_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi_)\\n elif nz_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj_)\\n elif nz_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij_)\\n elif nz_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n idx_ = ctypes.c_int64()\\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n idx_ = idx_.value\\n _idx_return_value = idx_\\n return (_idx_return_value)\",\n \"def calc_k(self):\\n\\t\\n\\tself.k = -np.array([self.sth*self.cphi, self.sth*self.sphi, self.cth])\\n\\n\\treturn\",\n \"def _ect_qrs_tconst(pattern, qrs):\\n beats = pattern.evidence[o.QRS]\\n idx = beats.index(qrs)\\n tnet = pattern.last_tnet\\n hyp = pattern.hypothesis\\n if idx > 0:\\n prev = beats[idx - 1]\\n # After the second couplet, every ectopic beat introduces a new temporal\\n # network in the pattern to make it easier the minimization.\\n if idx > 3:\\n tnet.remove_constraint(hyp.end, prev.time)\\n # We create a new temporal network for the cyclic observations\\n tnet = ConstraintNetwork()\\n pattern.temporal_constraints.append(tnet)\\n # The duration of each couplet should not have high instantaneous\\n # variations.\\n refrr = beats[idx - 2].time.end - beats[idx - 3].time.start\\n tnet.add_constraint(prev.time, qrs.time, Iv(refrr - C.RR_MAX_DIFF, refrr + C.RR_MAX_DIFF))\\n # We guide the morphology search to be similar to the previous\\n # ectopic QRS complex.\\n qrs.shape = beats[idx - 2].shape\\n # The reference RR varies from an upper limit to the last measurement,\\n # through the contextual previous rhythm.\\n refrr = C.BRADY_RR.end\\n stdrr = 0.1 * refrr\\n if pattern.evidence[o.Cardiac_Rhythm] and idx == 1:\\n mrr, srr = pattern.evidence[o.Cardiac_Rhythm][0].meas.rr\\n if mrr > 0:\\n refrr, stdrr = mrr, srr\\n elif idx > 1:\\n refrr, stdrr = hyp.meas.rr\\n # Ectopic beats must be advanced wrt the reference RR\\n tnet.add_constraint(prev.time, qrs.time, Iv(C.TACHY_RR.start, max(C.TACHY_RR.start, refrr - stdrr)))\\n # Beats cannot overlap\\n tnet.add_constraint(prev.end, qrs.start, Iv(C.TQ_INTERVAL_MIN, np.Inf))\\n BASIC_TCONST(pattern, qrs)\\n tnet.add_constraint(qrs.start, qrs.end, C.QRS_DUR)\\n tnet.set_before(qrs.time, hyp.end)\\n # Constraints with the precedent T Wave\\n _qrs_after_twave(pattern, qrs)\",\n \"def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def jackknifed_coh_variance(tx, ty, eigvals, adaptive=True):\\r\\n\\r\\n K = tx.shape[0]\\r\\n\\r\\n # calculate leave-one-out estimates of MSC (magnitude squared coherence)\\r\\n jk_coh = []\\r\\n # coherence is symmetric (right??)\\r\\n sides = 'onesided'\\r\\n all_orders = set(range(K))\\r\\n\\r\\n import nitime.algorithms as alg\\r\\n\\r\\n # get the leave-one-out estimates\\r\\n for i in range(K):\\r\\n items = list(all_orders.difference([i]))\\r\\n tx_i = np.take(tx, items, axis=0)\\r\\n ty_i = np.take(ty, items, axis=0)\\r\\n eigs_i = np.take(eigvals, items)\\r\\n if adaptive:\\r\\n wx, _ = adaptive_weights(tx_i, eigs_i, sides=sides)\\r\\n wy, _ = adaptive_weights(ty_i, eigs_i, sides=sides)\\r\\n else:\\r\\n wx = wy = eigs_i[:, None]\\r\\n # The CSD\\r\\n sxy_i = alg.mtm_cross_spectrum(tx_i, ty_i, (wx, wy), sides=sides)\\r\\n # The PSDs\\r\\n sxx_i = alg.mtm_cross_spectrum(tx_i, tx_i, wx, sides=sides)\\r\\n syy_i = alg.mtm_cross_spectrum(ty_i, ty_i, wy, sides=sides)\\r\\n # these are the | c_i | samples\\r\\n msc = np.abs(sxy_i)\\r\\n msc /= np.sqrt(sxx_i * syy_i)\\r\\n jk_coh.append(msc)\\r\\n\\r\\n jk_coh = np.array(jk_coh)\\r\\n # now normalize the coherence estimates and take the mean\\r\\n normalize_coherence(jk_coh, 2 * K - 2, copy=False) # inplace\\r\\n jk_avg = np.mean(jk_coh, axis=0)\\r\\n\\r\\n jk_var = (jk_coh - jk_avg)\\r\\n np.power(jk_var, 2, jk_var)\\r\\n jk_var = jk_var.sum(axis=0)\\r\\n\\r\\n # Do/Don't use the alternative scaling here??\\r\\n f = float(K - 1) / K\\r\\n\\r\\n jk_var *= f\\r\\n\\r\\n return jk_var\",\n \"def quadratic_dependence(dep_keys, ctfs=list(), force_zero=None, **kwargs):\\n if force_zero is None and len(kwargs) == 0:\\n return _dependence(\\n f=np.polyval, n_params=3,\\n dep_keys=dep_keys, ctfs=ctfs, force_zero=force_zero,\\n name='quadratic dependence', code='p2:{}', **kwargs\\n )\\n else:\\n return _dependence(\\n f=lambda p, x: np.polyval(np.concatenate((p, np.zeros(1))), x),\\n n_params=2, dep_keys=dep_keys, ctfs=ctfs, force_zero=force_zero,\\n name='quadratic dependence', code='p2:{}', **kwargs\\n )\",\n \"def censor_contig(contig_end, u_contigs, o_dict):\\n for c_e in [contig_end, other_end(contig_end)]:\\n if c_e in u_contigs:\\n u_contigs.remove(c_e)\\n if c_e in o_dict:\\n o_dic = o_dict[c_e]\\n if o_dic != {}:\\n overlapped_contig = list(o_dic.keys())[0]\\n if overlapped_contig in o_dict: del o_dict[overlapped_contig][c_e]\\n del o_dict[c_e]\\n return\",\n \"def _initialize_kindiffeq_matrices(self, kdeqs):\\n\\n if kdeqs:\\n if len(self.q) != len(kdeqs):\\n raise ValueError('There must be an equal number of kinematic '\\n 'differential equations and coordinates.')\\n kdeqs = Matrix(kdeqs)\\n\\n u = self.u\\n qdot = self._qdot\\n # Dictionaries setting things to zero\\n u_zero = dict((i, 0) for i in u)\\n uaux_zero = dict((i, 0) for i in self._uaux)\\n qdot_zero = dict((i, 0) for i in qdot)\\n\\n f_k = msubs(kdeqs, u_zero, qdot_zero)\\n k_ku = (msubs(kdeqs, qdot_zero) - f_k).jacobian(u)\\n k_kqdot = (msubs(kdeqs, u_zero) - f_k).jacobian(qdot)\\n\\n f_k = k_kqdot.LUsolve(f_k)\\n k_ku = k_kqdot.LUsolve(k_ku)\\n k_kqdot = eye(len(qdot))\\n\\n self._qdot_u_map = solve_linear_system_LU(\\n Matrix([k_kqdot.T, -(k_ku * u + f_k).T]).T, qdot)\\n\\n self._f_k = msubs(f_k, uaux_zero)\\n self._k_ku = msubs(k_ku, uaux_zero)\\n self._k_kqdot = k_kqdot\\n else:\\n self._qdot_u_map = None\\n self._f_k = Matrix()\\n self._k_ku = Matrix()\\n self._k_kqdot = Matrix()\",\n \"def putxc(self,whichsol_,xc_):\\n _xc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),self.getnumcon()))\\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\\n raise ValueError(\\\"Argument xc must be writable\\\")\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc may not be None\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _xc_copyarray:\\n xc_[:] = _xc_np_tmp\",\n \"def convert_to_q(self):\\n if self.measure == 'Q':\\n warnings.warn('Parameters are already converted to Q!')\\n else:\\n kappa_sp = self.kappa_s\\n kappa_yp = self.kappa_y\\n self.kappa_s = self.kappa_s - self.lmbd_s * self.eta_s\\n self.kappa_y = self.kappa_y - self.lmbd_y * self.eta_y\\n self.scale = kappa_sp / self.kappa_s\\n self.mean_v *= (kappa_yp / self.kappa_y * self.scale)\\n self.lmbd = 0\\n self.eta_y *= (self.scale**.5)\\n self.measure = 'Q'\\n self.update_ajd()\",\n \"def putconboundlist(self,sub_,bkc_,blc_,buc_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(bkc_)\\n elif num_ != len(bkc_):\\n raise IndexError(\\\"Inconsistent length of array bkc\\\")\\n if num_ is None:\\n num_ = len(blc_)\\n elif num_ != len(blc_):\\n raise IndexError(\\\"Inconsistent length of array blc\\\")\\n if num_ is None:\\n num_ = len(buc_)\\n elif num_ != len(buc_):\\n raise IndexError(\\\"Inconsistent length of array buc\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc cannot be None\\\")\\n if bkc_ is None:\\n raise ValueError(\\\"Argument bkc may not be None\\\")\\n if bkc_ is not None:\\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\\n else:\\n _bkc_tmp = None\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc cannot be None\\\")\\n if blc_ is None:\\n raise ValueError(\\\"Argument blc may not be None\\\")\\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\\n _blc_copyarray = False\\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif blc_ is not None:\\n _blc_copyarray = True\\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\\n _blc_np_tmp[:] = blc_\\n assert _blc_np_tmp.flags.contiguous\\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _blc_copyarray = False\\n _blc_tmp = None\\n \\n if buc_ is None:\\n raise ValueError(\\\"Argument buc cannot be None\\\")\\n if buc_ is None:\\n raise ValueError(\\\"Argument buc may not be None\\\")\\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\\n _buc_copyarray = False\\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif buc_ is not None:\\n _buc_copyarray = True\\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\\n _buc_np_tmp[:] = buc_\\n assert _buc_np_tmp.flags.contiguous\\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _buc_copyarray = False\\n _buc_tmp = None\\n \\n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def compileCkt(self):\\n f = np.vectorize(lambda x: isinstance(x, QubitChannel))\\n table = f(self.diagram)\\n col_bool = np.bitwise_or.reduce(table, axis=1)\\n # filter nan in 'qubit' direction\\n if not np.bitwise_and.reduce(col_bool):\\n raise ValueError('Found unassigned qubit')\\n # filter nan in 'time' direction\\n row_bool = np.bitwise_or.reduce(table, axis=0)\\n diagram = self.diagram[:, row_bool]\\n table = table[:, row_bool]\\n # align QubitChannel objects in the table column by column\\n for time_idx in range(len(table[0, :])):\\n diagram[table[:, time_idx], time_idx\\n ] = QubitChannel.alignQubitChannels(\\n *diagram[table[:, time_idx], time_idx]\\n )\\n # replace nans with null QubitChannel objects\\n for qubit_idx, row in enumerate(table):\\n for time_idx, flag in enumerate(row):\\n if flag:\\n continue\\n span_idx = np.where(f(diagram[:, time_idx]))[0][0]\\n wire_idx = np.where(f(diagram[qubit_idx, :]))[0][0]\\n diagram[qubit_idx, time_idx] = QubitChannel.null(\\n diagram[span_idx, time_idx], diagram[qubit_idx, wire_idx]\\n )\\n try:\\n self.compiled = np.sum(diagram, axis=1)\\n except SystemError:\\n raise ValueError('Error during wire concatenation')\",\n \"def putvarbound(self,j_,bk_,bl_,bu_): # 3\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def adjust_cell_formula(value, k):\\n if isinstance(value, basestring):\\n for i in range(value.count('?(')):\\n if value and '?(' in value and ')' in value:\\n i = value.index('?(')\\n j = value.index(')', i)\\n val = value[i + 2:j]\\n col, row = split_row_col(val)\\n new_val = '%s%s' % (col, row+k)\\n value = value.replace('?(%s)' % val, new_val)\\n return value\",\n \"def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def approxk( X, k ):\\n U, D, Vt = svdk( X, k )\\n return U.dot( diag( D ) ).dot( Vt )\",\n \"def project_curve(q):\\n n,T = q.shape\\n if n==2:\\n dt = 0.35\\n if n==3:\\n dt = 0.2\\n epsilon = 1e-6\\n\\n iter = 1\\n res = ones(n)\\n J = zeros((n,n))\\n\\n s = linspace(0,1,T)\\n\\n qnew = q.copy()\\n qnew = qnew / sqrt(innerprod_q2(qnew,qnew))\\n\\n qnorm = zeros(T)\\n G = zeros(n)\\n C = zeros(300)\\n while (norm(res) > epsilon):\\n if iter > 300:\\n break\\n\\n # Jacobian\\n for i in range(0,n):\\n for j in range(0,n):\\n J[i,j] = 3 * trapz(qnew[i,:]*qnew[j,:],s)\\n \\n J += eye(n)\\n\\n for i in range(0,T):\\n qnorm[i] = norm(qnew[:,i])\\n \\n # Compute the residue\\n for i in range(0,n):\\n G[i] = trapz(qnew[i,:]*qnorm,s)\\n \\n res = -G\\n\\n if (norm(res) < epsilon):\\n break\\n\\n x = solve(J,res)\\n C[iter] = norm(res)\\n\\n delG = Basis_Normal_A(qnew)\\n temp = zeros((n,T))\\n for i in range(0,n):\\n temp += x[i]*delG[i]*dt\\n \\n qnew += temp\\n iter += 1\\n \\n qnew = qnew/sqrt(innerprod_q2(qnew,qnew))\\n\\n return qnew\",\n \"def set_equality_constraint_multipliers(self, eq_con_multiplier_values):\\n # we should check these for efficiency\\n assert self.n_equality_constraints() == len(eq_con_multiplier_values)\\n if (\\n not hasattr(self, 'evaluate_hessian_equality_constraints')\\n or self.n_equality_constraints() == 0\\n ):\\n return\\n\\n raise NotImplementedError(\\n 'Derived ExternalGreyBoxModel classes need to implement'\\n ' set_equality_constraint_multipliers when they'\\n ' support Hessian computations.'\\n )\",\n \"def additional_equations(self, k):\\n ######################################################################\\n # equation for saturated gas at hot side outlet\\n o1 = self.outl[0].to_flow()\\n self.residual[k] = o1[2] - h_mix_pQ(o1, 1)\",\n \"def Keyboard(self, key):\\r\\n\\t\\tif key == Keys.K_u:\\r\\n\\t\\t\\tself.kp+=self.dp\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\tif key == Keys.K_j:\\r\\n\\t\\t\\tself.kp-=self.dp\\r\\n\\t\\t\\tif self.kp<0:\\r\\n\\t\\t\\t\\tself.kp=0.0\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\tif key == Keys.K_i:\\r\\n\\t\\t\\tself.ki+=self.di\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\tif key == Keys.K_k:\\r\\n\\t\\t\\tself.ki-=self.di\\r\\n\\t\\t\\tif self.ki<0:\\r\\n\\t\\t\\t\\tself.ki=0.0\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\tif key == Keys.K_o:\\r\\n\\t\\t\\tself.kd+=self.dd\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\tif key == Keys.K_l:\\r\\n\\t\\t\\tself.kd-=self.dd\\r\\n\\t\\t\\tif self.kd<0:\\r\\n\\t\\t\\t\\tself.kd=0.0\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\t\\r\\n\\t\\tif key == Keys.K_f:\\r\\n\\t\\t\\tself.center+=1\\r\\n\\t\\t\\tif self.center>2:\\r\\n\\t\\t\\t\\tself.center = 0\\r\\n\\t\\t\\r\\n\\t\\tif key == Keys.K_t:\\r\\n\\t\\t\\tself.cut=1-self.cut\\r\\n\\t\\t\\tself.q[0].SetCut(self.cut)\\t\\t\\t\\r\\n\\t\\t\\t\\r\\n\\t\\tif key == Keys.K_r:\\r\\n\\t\\t\\tpass\\t\\r\\n\\t\\t\\r\\n\\t\\tif key == Keys.K_s:\\r\\n\\t\\t\\tself.q[0].saveConf()\",\n \"def y2mat_Y1qk(yp, q, k, y):\\n if ((y.L2 != yp.L2 or\\n zero_YYY_q(yp.L1, q, y.L1) or\\n not triangle_q(yp.L, q, y.L) or\\n yp.M != k + y.M)):\\n return 0\\n t = (-1)**(yp.L-yp.M)\\n rm = y2redmat_Y1q(yp, q, y)\\n tj = N3j(+yp.L, +q, +y.L,\\n -yp.M, +k, +y.M)\\n return t * rm * tj\",\n \"def test_init_q(self):\\n\\n riskfree = .01\\n lmbd = .01\\n lmbd_s = .5\\n lmbd_y = .5\\n mean_v = .5\\n kappa_s = 1.5\\n kappa_y = .5\\n eta_s = .1\\n eta_y = .01\\n rho = -.5\\n\\n param = CentTendParam(riskfree=riskfree,\\n lmbd=lmbd, lmbd_s=lmbd_s, lmbd_y=lmbd_y,\\n mean_v=mean_v, kappa_s=kappa_s, kappa_y=kappa_y,\\n eta_s=eta_s, eta_y=eta_y, rho=rho, measure='Q')\\n\\n kappa_sq = kappa_s - lmbd_s * eta_s\\n kappa_yq = kappa_y - lmbd_y * eta_y\\n scale = kappa_s / kappa_sq\\n\\n self.assertEqual(param.measure, 'Q')\\n self.assertEqual(param.riskfree, riskfree)\\n self.assertEqual(param.lmbd, 0)\\n self.assertEqual(param.lmbd_s, lmbd_s)\\n self.assertEqual(param.lmbd_y, lmbd_y)\\n self.assertEqual(param.mean_v, mean_v * kappa_y / kappa_yq * scale)\\n self.assertEqual(param.kappa_s, kappa_sq)\\n self.assertEqual(param.kappa_y, kappa_yq)\\n self.assertEqual(param.eta_s, eta_s)\\n self.assertEqual(param.eta_y, eta_y * scale**.5)\\n self.assertEqual(param.rho, rho)\\n self.assertTrue(param.is_valid())\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n param.convert_to_q()\",\n \"def makeConjunction(solver, v, conjuncts):\\n cn = solver.Constraint(0, (len(conjuncts) - 1))\\n cn.SetCoefficient(v, -len(conjuncts))\\n for x in conjuncts:\\n cn.SetCoefficient(x, 1)\",\n \"def putsolutionyi(self,i_,whichsol_,y_):\\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def kspace_svd_solve(self, other, rcond = 1e-14, complex_precision = np.complex128, n_points = None, complx = False):\\n if n_points is None:\\n \\n m1n = np.max(np.abs(self.coords), axis = 0)\\n m2n = np.max(np.abs(other.coords), axis = 0)\\n \\n n_points = np.max([m1n,m2n], axis = 0) \\n\\n nx,ny,nz = 2*n_points + 1\\n\\n #print(\\\"kspace_svd_solve: \\\", nx,ny,nz)\\n m1x,m1y = self.blocks.shape[1], self.blocks.shape[2]\\n m2x,m2y = other.blocks.shape[1], other.blocks.shape[2]\\n \\n coords = np.roll(lattice_coords(n_points).reshape(nx,ny,nz, 3), -n_points, axis = (0,1,2)).reshape(nx*ny*nz, 3)\\n\\n #print(coords)\\n\\n \\n m1r = self.cget(coords).reshape(nx,ny,nz,m1x,m1y)\\n m2r = other.cget(coords).reshape(nx,ny,nz,m2x,m2y)\\n M1 = np.fft.fftn(m1r, axes = (0,1,2))\\n M2 = np.fft.fftn(m2r, axes = (0,1,2))\\n M3 = np.zeros((nx,ny,nz,m1x, m2y),dtype = np.complex128)\\n\\n\\n for c in coords:\\n #M1[c[0], c[1], c[2]] = np.dot(M1[c[0], c[1], c[2]], M2[c[0], c[1], c[2]])\\n\\n #ci = self_k.coords[i]\\n u_,s_,vh_ = np.linalg.svd(M1[c[0], c[1], c[2]])\\n b = M2[c[0], c[1], c[2]]\\n\\n t = np.abs(s_)>(rcond*np.max(np.abs(s_))) #screening\\n\\n #if s_.max()/s_.min()>10e6:\\n # print(\\\"poor condition in singular values:\\\", c, s_, t)\\n \\n \\n\\n\\n\\n pinv = np.dot(vh_[t,:].conj().T, np.dot(np.diag(s_[t]**-1), u_[:,t].conj().T))\\n x = np.dot(pinv, b)\\n\\n if np.any(t == False):\\n print(\\\"Warning (SVD): poorly conditioned JK matrix (singular values).\\\") #, s_)\\n print(\\\"Eq. :\\\", np.max(np.abs(np.dot(M1[c[0], c[1], c[2]], x)-b)))\\n\\n\\n\\n #rhs = np.dot(np.dot(np.diag(s_[t]**-1), u_[:,t].conj().T), b)\\n #x = np.linalg.solve(vh_[t,:], rhs)\\n \\n \\n \\n #svhx = np.linalg.solve(u_[:,t], b)\\n\\n #x = np.linalg.solve(np.dot(np.diag(s_[t]),vh_[t,:]), svhx )\\n\\n \\n \\\"\\\"\\\"\\n \\n U = u_[:,t]\\n S = np.diag(s_[t])\\n VH = vh_[t,:]\\n\\n SVH = np.dot(S, VH)\\n Ub = np.dot(U.conj().T, other_k.blocks[i])\\n \\\"\\\"\\\"\\n\\n M3[c[0], c[1], c[2]] = x #np.linalg.solve(SVH, Ub)\\n\\n\\n \\n \\n \\n ret = tmat()\\n if complx:\\n ret.load_nparray(np.fft.ifftn(M3.reshape(nx,ny,nz,m1x,m2y), axes = (0,1,2)).reshape(coords.shape[0], m1x,m2y), coords)\\n else:\\n ret.load_nparray(np.fft.ifftn(M3.reshape(nx,ny,nz,m1x,m2y), axes = (0,1,2)).real.reshape(coords.shape[0], m1x,m2y), coords)\\n return ret\",\n \"def putcj(self,j_,cj_): # 3\\n res = self.__obj.putcj(j_,cj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def Optimize_Sqq(qj,dt):\\n\\n # find the signal psd\\n fs=1.0/dt\\n M,L = 256,512\\n wxx,Sxx=sa.Welch_estimator(qj,fs=fs,M=M,L=L)\\n w_threshold = fs*np.pi/2\\n Sxx=Sxx[wxx<w_threshold]\\n wxx=wxx[wxx<w_threshold]\\n\\n # the psd of oscillator model is computed using Wiener-Khinchin relation\\n # and called in the objective function\\n print('S_qq matching objective fun: ls error of spectrum')\\n spec_ls_distance = lambda params: np.linalg.norm(Sxx*(Sxx - sa.Oscillator_Spectrum(params[0],params[1]/params[0],params[1],wxx)),ord=1)\\n\\n # optimize via pyswarm - v1\\n lb = [0.001, 0.001]\\n ub = [100, 500]\\n xopt, fopt = pso(spec_ls_distance, lb, ub, maxiter=10000,swarmsize=10000,minfunc=1e-10)\\n k,D = xopt[0],xopt[1]\\n\\n b = D/k\\n print('result: k='+str(k)+' b='+str(b)+' D='+str(D))\\n\\n return k,D\",\n \"def mk_q(self, xc: list, yc: list):\\n for i in range(len(xc) - 1):\\n cur = cor(xc[i], xc[i + 1])\\n self.xq.put((-cur.dist, cur))\\n cur = cor(yc[i], yc[i + 1])\\n self.yq.put((-cur.dist, cur))\\n self.rnd_mk()\",\n \"def _append_cx(self, i, j):\\n\\n if not 0 <= i < self.num_qubits or not 0 <= j < self.num_qubits:\\n raise QiskitError(\\\"CX qubits are out of bounds.\\\")\\n self.linear[j] = (self.linear[i] + self.linear[j]) % 2\\n self.shift[j] = (self.shift[i] + self.shift[j]) % 2\",\n \"def solve_Q(ih, jh):\\n # Build A and b\\n\\n # Solve for c via least squares.\\n\\n # Form C from c\\n\\n # Solve for Q and return it.\\n return None\",\n \"def putbarcj(self,j_,sub_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights_)\\n elif num_ != len(weights_):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbarcj(self.__nativep,j_,num_,_sub_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putclist(self,subj_,val_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(val_)\\n elif num_ != len(val_):\\n raise IndexError(\\\"Inconsistent length of array val\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if val_ is None:\\n raise ValueError(\\\"Argument val cannot be None\\\")\\n if val_ is None:\\n raise ValueError(\\\"Argument val may not be None\\\")\\n if isinstance(val_, numpy.ndarray) and val_.dtype is numpy.dtype(numpy.float64) and val_.flags.contiguous:\\n _val_copyarray = False\\n _val_tmp = ctypes.cast(val_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif val_ is not None:\\n _val_copyarray = True\\n _val_np_tmp = numpy.zeros(len(val_),numpy.dtype(numpy.float64))\\n _val_np_tmp[:] = val_\\n assert _val_np_tmp.flags.contiguous\\n _val_tmp = ctypes.cast(_val_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _val_copyarray = False\\n _val_tmp = None\\n \\n res = __library__.MSK_XX_putclist(self.__nativep,num_,_subj_tmp,_val_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def make_mat_cp_qe(cons_pot_mesh, quad_geo_mesh):\\n pot_faces = cons_pot_mesh.get_faces()\\n assert pot_faces.shape[0] == quad_geo_mesh.get_faces().shape[0]\\n num_faces = pot_faces.shape[0]\\n K = np.zeros((3 * num_faces, 3 * num_faces))\\n add_cp_qe_DL_terms(K, cons_pot_mesh, quad_geo_mesh)\\n add_cp_qe_RBM_terms(K, cons_pot_mesh, quad_geo_mesh)\\n return K\",\n \"def quartic_potential(x):\\n k1=1\\n k2=10\\n return (k1*x**4)-(k2*x**2)\",\n \"def Q2C(self, q):\\n\\n #q = q.squeeze();\\n C = np.empty((3,3));\\n\\tC[0,0] = (q[0]**2.0) + (q[1]**2.0) - (q[2]**2.0) - (q[3]**2.0);\\n\\tC[0,1] = 2.0 * ((q[1]*q[2]) + (q[0]*q[3]));\\n\\tC[0,2] = 2.0 * ((q[1]*q[3]) - (q[0]*q[2]));\\n\\n\\tC[1,0] = 2.0 * ((q[1]*q[2]) - (q[0]*q[3]));\\n\\tC[1,1] = (q[0]**2.0) - (q[1]**2.0) + (q[2]**2.0) - (q[3]**2.0);\\n\\tC[1,2] = 2.0 * ((q[2]*q[3]) + (q[0]*q[1]));\\n\\n\\tC[2,0] = 2.0 * ((q[1]*q[3]) + (q[0]*q[2]));\\n\\tC[2,1] = 2.0 * ((q[2]*q[3]) - (q[0]*q[1]));\\n\\tC[2,2] = (q[0]**2.0) - (q[1]**2.0) - (q[2]**2.0) + (q[3]**2.0);\\n\\n return C\",\n \"def putacol(self,j_,subj,valj): # 3\\n nzj_ = None\\n if nzj_ is None:\\n nzj_ = len(subj)\\n elif nzj_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nzj_ is None:\\n nzj_ = len(valj)\\n elif nzj_ != len(valj):\\n raise IndexError(\\\"Inconsistent length of array valj\\\")\\n if nzj_ is None: nzj_ = 0\\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valj is None: raise TypeError(\\\"Invalid type for argument valj\\\")\\n if valj is None:\\n valj_ = None\\n else:\\n try:\\n valj_ = memoryview(valj)\\n except TypeError:\\n try:\\n _tmparr_valj = array.array(\\\"d\\\",valj)\\n except TypeError:\\n raise TypeError(\\\"Argument valj has wrong type\\\")\\n else:\\n valj_ = memoryview(_tmparr_valj)\\n \\n else:\\n if valj_.format != \\\"d\\\":\\n valj_ = memoryview(array.array(\\\"d\\\",valj))\\n \\n res = self.__obj.putacol(j_,nzj_,subj_,valj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def zero_K(self):\\n # print('zero_K axnode: ', self.cell.axnode)\\n i = 0\\n for node in self.cell.axnode:\\n for seg in node:\\n if i == 0:\\n print(f\\\"KLT: {seg.klt.gbar:.6f} mho/cm2\\\")\\n print(f\\\"KCNQ: {seg.kcnq.gbar:.6f} mho/cm2\\\")\\n print(f\\\"KHT: {seg.kht.gbar:.6f} mho/cm2\\\")\\n i = 1\\n # seg.klt.gbar = 0e-3\\n seg.kcnq.gbar = 0e-3\\n # seg.kcnq.phi_m = seg.kcnq.phi_m - 20.\\n # seg.kht.gbar = 0e-3\\n # seg.kht.vshift = -20.\\n pass\",\n \"def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\\n else:\\n _skn_tmp = None\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7407992","0.73707557","0.7119913","0.67798036","0.6661862","0.5547101","0.55400264","0.5332845","0.5304892","0.5226392","0.5216021","0.5155784","0.5152715","0.51131207","0.50926286","0.50507396","0.5048387","0.5019227","0.4992005","0.497944","0.49701524","0.4944257","0.4915807","0.48931497","0.48842365","0.4846689","0.4778596","0.47593817","0.47248182","0.47172788","0.46874142","0.46798477","0.46789962","0.466166","0.46552253","0.46437842","0.46332","0.4630344","0.46214837","0.4618798","0.46044722","0.45851025","0.45844498","0.45839685","0.4569576","0.45654833","0.45617047","0.4556583","0.45489305","0.45312083","0.45183712","0.4511222","0.4508031","0.45072407","0.45037335","0.4501491","0.45007056","0.4489468","0.44779545","0.44733608","0.44667408","0.44664788","0.44663852","0.44662058","0.44640929","0.44586974","0.44437805","0.4429013","0.44246638","0.44131723","0.44051528","0.4400043","0.4390322","0.43816224","0.43794948","0.43777007","0.43745098","0.43702486","0.43591315","0.43576053","0.43567178","0.43468806","0.43427896","0.43335333","0.4328982","0.4328435","0.43280697","0.432654","0.432551","0.43253702","0.43252563","0.4324102","0.43067253","0.4306419","0.42977273","0.42905524","0.42873073","0.42836446","0.4281057","0.42781755"],"string":"[\n \"0.7407992\",\n \"0.73707557\",\n \"0.7119913\",\n \"0.67798036\",\n \"0.6661862\",\n \"0.5547101\",\n \"0.55400264\",\n \"0.5332845\",\n \"0.5304892\",\n \"0.5226392\",\n \"0.5216021\",\n \"0.5155784\",\n \"0.5152715\",\n \"0.51131207\",\n \"0.50926286\",\n \"0.50507396\",\n \"0.5048387\",\n \"0.5019227\",\n \"0.4992005\",\n \"0.497944\",\n \"0.49701524\",\n \"0.4944257\",\n \"0.4915807\",\n \"0.48931497\",\n \"0.48842365\",\n \"0.4846689\",\n \"0.4778596\",\n \"0.47593817\",\n \"0.47248182\",\n \"0.47172788\",\n \"0.46874142\",\n \"0.46798477\",\n \"0.46789962\",\n \"0.466166\",\n \"0.46552253\",\n \"0.46437842\",\n \"0.46332\",\n \"0.4630344\",\n \"0.46214837\",\n \"0.4618798\",\n \"0.46044722\",\n \"0.45851025\",\n \"0.45844498\",\n \"0.45839685\",\n \"0.4569576\",\n \"0.45654833\",\n \"0.45617047\",\n \"0.4556583\",\n \"0.45489305\",\n \"0.45312083\",\n \"0.45183712\",\n \"0.4511222\",\n \"0.4508031\",\n \"0.45072407\",\n \"0.45037335\",\n \"0.4501491\",\n \"0.45007056\",\n \"0.4489468\",\n \"0.44779545\",\n \"0.44733608\",\n \"0.44667408\",\n \"0.44664788\",\n \"0.44663852\",\n \"0.44662058\",\n \"0.44640929\",\n \"0.44586974\",\n \"0.44437805\",\n \"0.4429013\",\n \"0.44246638\",\n \"0.44131723\",\n \"0.44051528\",\n \"0.4400043\",\n \"0.4390322\",\n \"0.43816224\",\n \"0.43794948\",\n \"0.43777007\",\n \"0.43745098\",\n \"0.43702486\",\n \"0.43591315\",\n \"0.43576053\",\n \"0.43567178\",\n \"0.43468806\",\n \"0.43427896\",\n \"0.43335333\",\n \"0.4328982\",\n \"0.4328435\",\n \"0.43280697\",\n \"0.432654\",\n \"0.432551\",\n \"0.43253702\",\n \"0.43252563\",\n \"0.4324102\",\n \"0.43067253\",\n \"0.4306419\",\n \"0.42977273\",\n \"0.42905524\",\n \"0.42873073\",\n \"0.42836446\",\n \"0.4281057\",\n \"0.42781755\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76697636"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11992,"cells":{"query":{"kind":"string","value":"Replaces all quadratic terms in the objective. putqobj(self,qosubi_,qosubj_,qoval_)"},"document":{"kind":"string","value":"def putqobj(self,qosubi_,qosubj_,qoval_):\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi_)\n elif numqonz_ != len(qosubi_):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj_)\n elif numqonz_ != len(qosubj_):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval_)\n elif numqonz_ != len(qoval_):\n raise IndexError(\"Inconsistent length of array qoval\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi cannot be None\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi may not be None\")\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\n _qosubi_copyarray = False\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubi_ is not None:\n _qosubi_copyarray = True\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\n _qosubi_np_tmp[:] = qosubi_\n assert _qosubi_np_tmp.flags.contiguous\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubi_copyarray = False\n _qosubi_tmp = None\n \n if qosubj_ is None:\n raise ValueError(\"Argument qosubj cannot be None\")\n if qosubj_ is None:\n raise ValueError(\"Argument qosubj may not be None\")\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\n _qosubj_copyarray = False\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubj_ is not None:\n _qosubj_copyarray = True\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\n _qosubj_np_tmp[:] = qosubj_\n assert _qosubj_np_tmp.flags.contiguous\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubj_copyarray = False\n _qosubj_tmp = None\n \n if qoval_ is None:\n raise ValueError(\"Argument qoval cannot be None\")\n if qoval_ is None:\n raise ValueError(\"Argument qoval may not be None\")\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\n _qoval_copyarray = False\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qoval_ is not None:\n _qoval_copyarray = True\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\n _qoval_np_tmp[:] = qoval_\n assert _qoval_np_tmp.flags.contiguous\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qoval_copyarray = False\n _qoval_tmp = None\n \n res = __library__.MSK_XX_putqobj(self.__nativep,numqonz_,_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putqobj(self,qosubi,qosubj,qoval): # 3\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi)\n elif numqonz_ != len(qosubi):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj)\n elif numqonz_ != len(qosubj):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval)\n elif numqonz_ != len(qoval):\n raise IndexError(\"Inconsistent length of array qoval\")\n if numqonz_ is None: numqonz_ = 0\n if qosubi is None: raise TypeError(\"Invalid type for argument qosubi\")\n if qosubi is None:\n qosubi_ = None\n else:\n try:\n qosubi_ = memoryview(qosubi)\n except TypeError:\n try:\n _tmparr_qosubi = array.array(\"i\",qosubi)\n except TypeError:\n raise TypeError(\"Argument qosubi has wrong type\")\n else:\n qosubi_ = memoryview(_tmparr_qosubi)\n \n else:\n if qosubi_.format != \"i\":\n qosubi_ = memoryview(array.array(\"i\",qosubi))\n \n if qosubj is None: raise TypeError(\"Invalid type for argument qosubj\")\n if qosubj is None:\n qosubj_ = None\n else:\n try:\n qosubj_ = memoryview(qosubj)\n except TypeError:\n try:\n _tmparr_qosubj = array.array(\"i\",qosubj)\n except TypeError:\n raise TypeError(\"Argument qosubj has wrong type\")\n else:\n qosubj_ = memoryview(_tmparr_qosubj)\n \n else:\n if qosubj_.format != \"i\":\n qosubj_ = memoryview(array.array(\"i\",qosubj))\n \n if qoval is None: raise TypeError(\"Invalid type for argument qoval\")\n if qoval is None:\n qoval_ = None\n else:\n try:\n qoval_ = memoryview(qoval)\n except TypeError:\n try:\n _tmparr_qoval = array.array(\"d\",qoval)\n except TypeError:\n raise TypeError(\"Argument qoval has wrong type\")\n else:\n qoval_ = memoryview(_tmparr_qoval)\n \n else:\n if qoval_.format != \"d\":\n qoval_ = memoryview(array.array(\"d\",qoval))\n \n res = self.__obj.putqobj(numqonz_,qosubi_,qosubj_,qoval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqobjij(self,i_,j_,qoij_):\n res = __library__.MSK_XX_putqobjij(self.__nativep,i_,j_,qoij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqobjij(self,i_,j_,qoij_): # 3\n res = self.__obj.putqobjij(i_,j_,qoij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getqobj(self,qosubi,qosubj,qoval): # 3\n maxnumqonz_ = self.getnumqobjnz()\n if qosubi is None: raise TypeError(\"Invalid type for argument qosubi\")\n _copyback_qosubi = False\n if qosubi is None:\n qosubi_ = None\n else:\n try:\n qosubi_ = memoryview(qosubi)\n except TypeError:\n try:\n _tmparr_qosubi = array.array(\"i\",qosubi)\n except TypeError:\n raise TypeError(\"Argument qosubi has wrong type\")\n else:\n qosubi_ = memoryview(_tmparr_qosubi)\n _copyback_qosubi = True\n else:\n if qosubi_.format != \"i\":\n qosubi_ = memoryview(array.array(\"i\",qosubi))\n _copyback_qosubi = True\n if qosubi_ is not None and len(qosubi_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubi has wrong length\")\n if qosubj is None: raise TypeError(\"Invalid type for argument qosubj\")\n _copyback_qosubj = False\n if qosubj is None:\n qosubj_ = None\n else:\n try:\n qosubj_ = memoryview(qosubj)\n except TypeError:\n try:\n _tmparr_qosubj = array.array(\"i\",qosubj)\n except TypeError:\n raise TypeError(\"Argument qosubj has wrong type\")\n else:\n qosubj_ = memoryview(_tmparr_qosubj)\n _copyback_qosubj = True\n else:\n if qosubj_.format != \"i\":\n qosubj_ = memoryview(array.array(\"i\",qosubj))\n _copyback_qosubj = True\n if qosubj_ is not None and len(qosubj_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubj has wrong length\")\n if qoval is None: raise TypeError(\"Invalid type for argument qoval\")\n _copyback_qoval = False\n if qoval is None:\n qoval_ = None\n else:\n try:\n qoval_ = memoryview(qoval)\n except TypeError:\n try:\n _tmparr_qoval = array.array(\"d\",qoval)\n except TypeError:\n raise TypeError(\"Argument qoval has wrong type\")\n else:\n qoval_ = memoryview(_tmparr_qoval)\n _copyback_qoval = True\n else:\n if qoval_.format != \"d\":\n qoval_ = memoryview(array.array(\"d\",qoval))\n _copyback_qoval = True\n if qoval_ is not None and len(qoval_) != (maxnumqonz_):\n raise ValueError(\"Array argument qoval has wrong length\")\n res,resargs = self.__obj.getqobj64(maxnumqonz_,len(qosubi),qosubi_,qosubj_,qoval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqonz_return_value = resargs\n if _copyback_qoval:\n qoval[:] = _tmparr_qoval\n if _copyback_qosubj:\n qosubj[:] = _tmparr_qosubj\n if _copyback_qosubi:\n qosubi[:] = _tmparr_qosubi\n return _numqonz_return_value","def getqobj(self,qosubi_,qosubj_,qoval_):\n maxnumqonz_ = self.getnumqobjnz()\n numqonz_ = ctypes.c_int64()\n _qosubi_minlength = (maxnumqonz_)\n if (maxnumqonz_) > 0 and qosubi_ is not None and len(qosubi_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubi is not long enough: Is %d, expected %d\" % (len(qosubi_),(maxnumqonz_)))\n if isinstance(qosubi_,numpy.ndarray) and not qosubi_.flags.writeable:\n raise ValueError(\"Argument qosubi must be writable\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi may not be None\")\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\n _qosubi_copyarray = False\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubi_ is not None:\n _qosubi_copyarray = True\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\n _qosubi_np_tmp[:] = qosubi_\n assert _qosubi_np_tmp.flags.contiguous\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubi_copyarray = False\n _qosubi_tmp = None\n \n _qosubj_minlength = (maxnumqonz_)\n if (maxnumqonz_) > 0 and qosubj_ is not None and len(qosubj_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubj is not long enough: Is %d, expected %d\" % (len(qosubj_),(maxnumqonz_)))\n if isinstance(qosubj_,numpy.ndarray) and not qosubj_.flags.writeable:\n raise ValueError(\"Argument qosubj must be writable\")\n if qosubj_ is None:\n raise ValueError(\"Argument qosubj may not be None\")\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\n _qosubj_copyarray = False\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubj_ is not None:\n _qosubj_copyarray = True\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\n _qosubj_np_tmp[:] = qosubj_\n assert _qosubj_np_tmp.flags.contiguous\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubj_copyarray = False\n _qosubj_tmp = None\n \n _qoval_minlength = (maxnumqonz_)\n if (maxnumqonz_) > 0 and qoval_ is not None and len(qoval_) != (maxnumqonz_):\n raise ValueError(\"Array argument qoval is not long enough: Is %d, expected %d\" % (len(qoval_),(maxnumqonz_)))\n if isinstance(qoval_,numpy.ndarray) and not qoval_.flags.writeable:\n raise ValueError(\"Argument qoval must be writable\")\n if qoval_ is None:\n raise ValueError(\"Argument qoval may not be None\")\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\n _qoval_copyarray = False\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qoval_ is not None:\n _qoval_copyarray = True\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\n _qoval_np_tmp[:] = qoval_\n assert _qoval_np_tmp.flags.contiguous\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qoval_copyarray = False\n _qoval_tmp = None\n \n qosurp_ = ctypes.c_int64(_qosubi_minlength)\n res = __library__.MSK_XX_getqobj64(self.__nativep,maxnumqonz_,ctypes.byref(qosurp_),ctypes.byref(numqonz_),_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqonz_ = numqonz_.value\n _numqonz_return_value = numqonz_\n if _qosubi_copyarray:\n qosubi_[:] = _qosubi_np_tmp\n if _qosubj_copyarray:\n qosubj_[:] = _qosubj_np_tmp\n if _qoval_copyarray:\n qoval_[:] = _qoval_np_tmp\n return (_numqonz_return_value)","def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubk is None: raise TypeError(\"Invalid type for argument qcsubk\")\n if qcsubk is None:\n qcsubk_ = None\n else:\n try:\n qcsubk_ = memoryview(qcsubk)\n except TypeError:\n try:\n _tmparr_qcsubk = array.array(\"i\",qcsubk)\n except TypeError:\n raise TypeError(\"Argument qcsubk has wrong type\")\n else:\n qcsubk_ = memoryview(_tmparr_qcsubk)\n \n else:\n if qcsubk_.format != \"i\":\n qcsubk_ = memoryview(array.array(\"i\",qcsubk))\n \n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def convert_to_q(self):\n if self.measure == 'Q':\n warnings.warn('Parameters are already converted to Q!')\n else:\n kappa_sp = self.kappa_s\n kappa_yp = self.kappa_y\n self.kappa_s = self.kappa_s - self.lmbd_s * self.eta_s\n self.kappa_y = self.kappa_y - self.lmbd_y * self.eta_y\n self.scale = kappa_sp / self.kappa_s\n self.mean_v *= (kappa_yp / self.kappa_y * self.scale)\n self.lmbd = 0\n self.eta_y *= (self.scale**.5)\n self.measure = 'Q'\n self.update_ajd()","def putSCeval(self,\n opro = None,\n oprjo = None,\n oprfo = None,\n oprgo = None,\n oprho = None,\n oprc = None,\n opric = None,\n oprjc = None,\n oprfc = None,\n oprgc = None,\n oprhc = None):\n\n if ( opro is not None\n and oprjo is not None\n and oprfo is not None\n and oprgo is not None\n and oprho is not None):\n # we have objective.\n try:\n numnlov = len(opro)\n if ( numnlov != len(oprjo)\n or numnlov != len(oprfo)\n or numnlov != len(oprgo)\n or numnlov != len(oprho)):\n raise SCoptException(\"Arguments opro, oprjo, oprfo, oprgo and oprho have different lengths\")\n if not all([ isinstance(i,scopr) for i in opro ]):\n raise SCoptException(\"Argument opro must be an array of mosek.scopr\")\n\n _opro = array.array('i',opro)\n _oprjo = array.array('i',oprjo)\n _oprfo = array.array('d',oprfo)\n _oprgo = array.array('d',oprgo)\n _oprho = array.array('d',oprho)\n except TypeError:\n raise ValueError(\"Arguments opro, oprjo, oprfo, oprgo and oprho must be arrays\")\n else:\n numnlov = 0\n\n if ( oprc is not None\n and opric is not None\n and oprjc is not None\n and oprfc is not None\n and oprgc is not None\n and oprhc is not None):\n # we have objective.\n try:\n numnlcv = len(oprc)\n if ( numnlcv != len(opric)\n or numnlcv != len(oprjc)\n or numnlcv != len(oprfc)\n or numnlcv != len(oprgc)\n or numnlcv != len(oprhc)):\n raise ValueError(\"Arguments oprc, opric, oprjc, oprfc, oprgc and oprhc have different lengths\") \n if not all([isinstance(i,scopr) for i in oprc]):\n raise ValieError(\"Argument oprc must be an array of mosek.scopr\")\n _oprc = array.array('i',oprc)\n _opric = array.array('i',opric)\n _oprjc = array.array('i',oprjc)\n _oprfc = array.array('d',oprfc)\n _oprgc = array.array('d',oprgc)\n _oprhc = array.array('d',oprhc)\n except TypeError:\n # not 'len' operation\n raise ValueError(\"Arguments oprc, opric, oprjc, oprfc, oprgc and oprhc must be arrays\") \n else:\n numnlcv = 0\n\n if numnlov > 0 or numnlcv > 0:\n args = []\n if numnlov > 0:\n args.append(memoryview(_opro))\n args.append(memoryview(_oprjo))\n args.append(memoryview(_oprfo))\n args.append(memoryview(_oprgo))\n args.append(memoryview(_oprho))\n else:\n args.extend([ None, None, None, None, None ])\n\n if numnlcv > 0:\n args.append(memoryview(_oprc))\n args.append(memoryview(_opric))\n args.append(memoryview(_oprjc))\n args.append(memoryview(_oprfc))\n args.append(memoryview(_oprgc))\n args.append(memoryview(_oprhc))\n else:\n args.extend([ None, None, None, None, None, None ])\n\n print(len(args))\n res = self.__obj.putSCeval(*args)","def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def update_q(self):\n beta = self.EC_beta\n self.gamma_q = (self.gamma_s - self.gamma_r) * beta + (1 - beta) * self.gamma_q\n self.Sigma_q = (self.Sigma_s - self.Sigma_r) * beta + (1 - beta) * self.Sigma_q\n try:\n assert np.all(np.logical_not(np.isnan(self.gamma_q)))\n except:\n print(\"Invalid update encountered...\")","def _update_objective(self):\n # rewrap the cost if the solver has been run\n self.Finalize()\n return","def Optimize_Sqq(qj,dt):\n\n # find the signal psd\n fs=1.0/dt\n M,L = 256,512\n wxx,Sxx=sa.Welch_estimator(qj,fs=fs,M=M,L=L)\n w_threshold = fs*np.pi/2\n Sxx=Sxx[wxx<w_threshold]\n wxx=wxx[wxx<w_threshold]\n\n # the psd of oscillator model is computed using Wiener-Khinchin relation\n # and called in the objective function\n print('S_qq matching objective fun: ls error of spectrum')\n spec_ls_distance = lambda params: np.linalg.norm(Sxx*(Sxx - sa.Oscillator_Spectrum(params[0],params[1]/params[0],params[1],wxx)),ord=1)\n\n # optimize via pyswarm - v1\n lb = [0.001, 0.001]\n ub = [100, 500]\n xopt, fopt = pso(spec_ls_distance, lb, ub, maxiter=10000,swarmsize=10000,minfunc=1e-10)\n k,D = xopt[0],xopt[1]\n\n b = D/k\n print('result: k='+str(k)+' b='+str(b)+' D='+str(D))\n\n return k,D","def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def objective(self, objective):\n\n self._objective = objective","def puty(self,whichsol_,y_):\n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if y_ is None:\n raise ValueError(\"Argument y cannot be None\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_puty(self.__nativep,whichsol_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def tweak_q(self, q):\n self._q = q\n self.reset()","def putbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if barsj_ is None:\n raise ValueError(\"Argument barsj cannot be None\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def updateIMU(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray) -> np.ndarray:\n if gyr is None or not np.linalg.norm(gyr)>0:\n return q\n qEst = 0.5 * q_prod(q, [0, *gyr]) # (eq. 12)\n a_norm = np.linalg.norm(acc)\n if a_norm>0:\n a = acc/a_norm\n qw, qx, qy, qz = q/np.linalg.norm(q)\n # Gradient objective function (eq. 25) and Jacobian (eq. 26)\n f = np.array([2.0*(qx*qz - qw*qy) - a[0],\n 2.0*(qw*qx + qy*qz) - a[1],\n 2.0*(0.5-qx**2-qy**2) - a[2]]) # (eq. 25)\n J = np.array([[-2.0*qy, 2.0*qz, -2.0*qw, 2.0*qx],\n [ 2.0*qx, 2.0*qw, 2.0*qz, 2.0*qy],\n [ 0.0, -4.0*qx, -4.0*qy, 0.0 ]]) # (eq. 26)\n # Objective Function Gradient\n gradient = J.T@f # (eq. 34)\n gradient /= np.linalg.norm(gradient)\n qEst -= self.gain*gradient # (eq. 33)\n q += qEst*self.Dt # (eq. 13)\n q /= np.linalg.norm(q)\n return q","def getqobjij(self,i_,j_):\n qoij_ = ctypes.c_double()\n res = __library__.MSK_XX_getqobjij(self.__nativep,i_,j_,ctypes.byref(qoij_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n qoij_ = qoij_.value\n _qoij_return_value = qoij_\n return (_qoij_return_value)","def update_Q(self):","def calc_q_square(self):\n return self._q_x()**2 + self._q_z()**2","def update(self, q=None, l=None, u=None,\n Px=None, Px_idx=np.array([]), Ax=None, Ax_idx=np.array([])):\n\n # get problem dimensions\n (n, m) = self._model.dimensions()\n\n # check consistency of the input arguments\n if q is not None and len(q) != n:\n raise ValueError(\"q must have length n\")\n if l is not None:\n if not isinstance(l, np.ndarray):\n raise TypeError(\"l must be numpy.ndarray, not %s\" %\n type(l).__name__)\n elif len(l) != m:\n raise ValueError(\"l must have length m\")\n # Convert values to -OSQP_INFTY\n l = np.maximum(l, -_osqp.constant('OSQP_INFTY'))\n if u is not None:\n if not isinstance(u, np.ndarray):\n raise TypeError(\"u must be numpy.ndarray, not %s\" %\n type(u).__name__)\n elif len(u) != m:\n raise ValueError(\"u must have length m\")\n # Convert values to OSQP_INFTY\n u = np.minimum(u, _osqp.constant('OSQP_INFTY'))\n if Ax is None:\n if len(Ax_idx) > 0:\n raise ValueError(\"Vector Ax has not been specified\")\n else:\n if len(Ax_idx) > 0 and len(Ax) != len(Ax_idx):\n raise ValueError(\"Ax and Ax_idx must have the same lengths\")\n if Px is None:\n if len(Px_idx) > 0:\n raise ValueError(\"Vector Px has not been specified\")\n else:\n if len(Px_idx) > 0 and len(Px) != len(Px_idx):\n raise ValueError(\"Px and Px_idx must have the same lengths\")\n if q is None and l is None and u is None and Px is None and Ax is None:\n raise ValueError(\"No updatable data has been specified\")\n\n # update linear cost\n if q is not None:\n self._model.update_lin_cost(q)\n\n # update lower bound\n if l is not None and u is None:\n self._model.update_lower_bound(l)\n\n # update upper bound\n if u is not None and l is None:\n self._model.update_upper_bound(u)\n\n # update bounds\n if l is not None and u is not None:\n self._model.update_bounds(l, u)\n\n # update matrix P\n if Px is not None and Ax is None:\n self._model.update_P(Px, Px_idx, len(Px))\n\n # update matrix A\n if Ax is not None and Px is None:\n self._model.update_A(Ax, Ax_idx, len(Ax))\n\n # update matrices P and A\n if Px is not None and Ax is not None:\n self._model.update_P_A(Px, Px_idx, len(Px), Ax, Ax_idx, len(Ax))\n\n\n # TODO(bart): this will be unnecessary when the derivative will be in C\n # update problem data in self._derivative_cache\n if q is not None:\n self._derivative_cache[\"q\"] = q\n\n if l is not None:\n self._derivative_cache[\"l\"] = l\n\n if u is not None:\n self._derivative_cache[\"u\"] = u\n\n if Px is not None:\n if Px_idx.size == 0:\n self._derivative_cache[\"P\"].data = Px\n else:\n self._derivative_cache[\"P\"].data[Px_idx] = Px\n\n if Ax is not None:\n if Ax_idx.size == 0:\n self._derivative_cache[\"A\"].data = Ax\n else:\n self._derivative_cache[\"A\"].data[Ax_idx] = Ax\n\n # delete results from self._derivative_cache to prohibit\n # taking the derivative of unsolved problems\n if \"results\" in self._derivative_cache.keys():\n del self._derivative_cache[\"results\"]","def objective(self):\n pass","def test_qing(self):\n fun = get_problem('qing', self.dimension, -500, 500)\n self.assertAlmostEqual(fun(self.array10), 584.0, delta=1e-4)","def do_reduction_placzek_corrections(q,sqfg,bgd,rescale_bgd=1.0,plaz_type=None,\n gauss_damp=False,gw=20.0,qmax=None,qmin=None,\n rmin=0.0,rmax=20.0,delr=.02\n ,qminpla=10.0,qmaxpla=30.0,ndeg=2, return_correction = False,\n skip_bgd = False, return_final_sq = False, force_qmax_type='Off'):\n #first, make netsq if bgd and/or damping is present\n q = np.array(q)\n sqfg = np.array(sqfg)\n bgd = np.array(bgd)\n\n if skip_bgd:\n netsq = sqfg\n else:\n netsq = sqfg - bgd*rescale_bgd\n\n\n if gauss_damp:\n netsq = netsq*gauss(q,gw,0)\n\n\n if force_qmax_type == 'Force Data (PreCorrection)':\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n netsq -= mean_sqmax\n\n #now, apply a correction if requested\n if plaz_type != None:\n if plaz_type == 'Polynomial' or plaz_type == 'poly' or plaz_type == 'ndeg':\n sq_poly_fit = fit_ndeg_to_sq(q,netsq,ndeg=ndeg,qmin=qminpla,qmax=qmaxpla)\n this_fit = sq_poly_fit\n elif plaz_type == 'Pseudo-Voight' or plaz_type == 'pv' or plaz_type == 'hydro':\n pv_fit = fit_pv_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_fit\n elif plaz_type == 'PVoight + n0' or plaz_type == 'pvndeg0':\n pv_n0_fit = fit_pv_n0_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n0_fit\n elif plaz_type == 'PVoight + n1' or plaz_type == 'pvndeg1':\n pv_n1_fit = fit_pv_n1_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n1_fit\n elif plaz_type == 'PVoight + n2' or plaz_type == 'pvndeg2':\n pv_n2_fit = fit_pv_n2_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n2_fit\n else:\n print (\"I don't know that correction type, sorry\")\n this_fit = np.zeros(len(q))\n else:\n this_fit = np.zeros(len(q))\n\n if force_qmax_type == 'Force Data' or force_qmax_type == 'Force Both (Independent)':\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n netsq -= mean_sqmax\n if force_qmax_type == 'Force Correction' or force_qmax_type == 'Force Both (Independent)':\n qcut, sqcut = cut_data(q,this_fit,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n this_fit -= mean_sqmax\n if force_qmax_type == 'ReCorrection':\n qcut, sqcut = cut_data(q,netsq-this_fit,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n this_fit += mean_sqmax\n\n netsq = netsq - this_fit\n\n if return_correction:\n return this_fit\n\n if return_final_sq:\n return netsq\n\n #finally, generate PDF\n r,gr = make_gr_from_sq(q,netsq,qmin=qmin,qmax=qmax,rmin=rmin,rmax=rmax,delr=delr)\n\n return r,gr","def initial_Q(self, negative):\n \n ##get each values in the Q, and change their content to given number, plan to use in Q5\n for key in self.Q.iterkeys():\n self.Q[key] = float(negative)","def quo(self, a, b):\n raise NotImplementedError","def qUpdate(self,s,a,r,sPrime):\r\n #get max_a' Q(s',a')\r\n \"\"\"\r\n maxA = 0\r\n maxQ = float(\"-inf\")\r\n for aCurr in actions:\r\n qCurr = Q[(sPrime,aCurr)]\r\n if qCurr > maxQ:\r\n maxA = aCurr\r\n maxQ = qCurr\r\n \"\"\"\r\n maxQ = self.maxQ(sPrime)[0]\r\n #update Q and return it\r\n self.Q[(s,a)] = (1 - self.alpha) * self.Q[(s,a)] + self.alpha * (r + self.gamma * maxQ)","def objective(self, x):\n pass","def objective(self, x):\n pass","def q(self, q: ComType):\n if isinstance(q, complex):\n self._pwr = q\n else:\n self._pwr = complex(0, q)","def simple_q(self: Q) -> Q:\n\n self.t = sp.simplify(self.t)\n self.x = sp.simplify(self.x)\n self.y = sp.simplify(self.y)\n self.z = sp.simplify(self.z)\n return self","def setQ(self,Q):\n self.Q = Q","def getqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n maxnumqcnz_ = self.getnumqconknz((k_))\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n _copyback_qcsubi = False\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n _copyback_qcsubi = True\n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n _copyback_qcsubi = True\n if qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi has wrong length\")\n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n _copyback_qcsubj = False\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n _copyback_qcsubj = True\n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n _copyback_qcsubj = True\n if qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj has wrong length\")\n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n _copyback_qcval = False\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n _copyback_qcval = True\n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n _copyback_qcval = True\n if qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval has wrong length\")\n res,resargs = self.__obj.getqconk64(k_,maxnumqcnz_,len(qcsubi),qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqcnz_return_value = resargs\n if _copyback_qcval:\n qcval[:] = _tmparr_qcval\n if _copyback_qcsubj:\n qcsubj[:] = _tmparr_qcsubj\n if _copyback_qcsubi:\n qcsubi[:] = _tmparr_qcsubi\n return _numqcnz_return_value","def test_solve_quadratic(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.fixed = False\n iden2.x.fixed = False\n iden3.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 0)\n self.assertAlmostEqual(iden1.x.val, 1)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 3)","def _Q(self, x, y):\n\n # Calculate the [Q] coefficient matrix\n Q = array([[0, 0, 0, -2, 0, 0, -6*x, -2*y, 0, 0, -6*x*y, 0],\n [0, 0, 0, 0, 0, -2, 0, 0, -2*x, -6*y, 0, -6*x*y],\n [0, 0, 0, 0, -2, 0, 0, -4*x, -4*y, 0, -6*x**2, -6*y**2]])\n \n # Return the [Q] coefficient matrix\n return Q","def test_convert_to_q(self):\n\n riskfree = .01\n lmbd = .01\n lmbd_s = .5\n lmbd_y = .5\n mean_v = .5\n kappa_s = 1.5\n kappa_y = .5\n eta_s = .1\n eta_y = .01\n rho = -.5\n\n theta = [riskfree, mean_v, kappa_s, kappa_y, eta_s, eta_y,\n rho, lmbd, lmbd_s, lmbd_y]\n param = CentTendParam.from_theta(theta)\n param.convert_to_q()\n\n kappa_sq = kappa_s - lmbd_s * eta_s\n kappa_yq = kappa_y - lmbd_y * eta_y\n scale = kappa_s / kappa_sq\n\n self.assertEqual(param.measure, 'Q')\n self.assertEqual(param.riskfree, riskfree)\n self.assertEqual(param.lmbd, 0)\n self.assertEqual(param.lmbd_s, lmbd_s)\n self.assertEqual(param.lmbd_y, lmbd_y)\n self.assertEqual(param.mean_v, mean_v * kappa_y / kappa_yq * scale)\n self.assertEqual(param.kappa_s, kappa_sq)\n self.assertEqual(param.kappa_y, kappa_yq)\n self.assertEqual(param.eta_s, eta_s)\n self.assertEqual(param.eta_y, eta_y * scale**.5)\n self.assertEqual(param.rho, rho)\n self.assertTrue(param.is_valid())","def putbarxj(self,whichsol_,j_,barxj_):\n _barxj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj is not long enough: Is %d, expected %d\" % (len(barxj_),self.getlenbarvarj((j_))))\n if barxj_ is None:\n raise ValueError(\"Argument barxj cannot be None\")\n if barxj_ is None:\n raise ValueError(\"Argument barxj may not be None\")\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\n _barxj_copyarray = False\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxj_ is not None:\n _barxj_copyarray = True\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\n _barxj_np_tmp[:] = barxj_\n assert _barxj_np_tmp.flags.contiguous\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxj_copyarray = False\n _barxj_tmp = None\n \n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getqobjij(self,i_,j_): # 3\n res,resargs = self.__obj.getqobjij(i_,j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _qoij_return_value = resargs\n return _qoij_return_value","def test_gen_qubo_matrix(self):\n cfg = {\"problems\": {\"QK\": {\"random_P\": False}}}\n projects = np.array([\n [2, 4, 3, 5],\n [4, 5, 1, 3],\n [3, 1, 2, 2],\n [5, 3, 2, 4]\n ])\n budgets = np.array([8, 6, 5, 3])\n problem = QuadraticKnapsack(cfg, projects, budgets, 16, 10)\n matrix = problem.gen_qubo_matrix()\n want = [\n [1922.,-476.,-397.,-235., -80.,-160.,-320.,-640.],\n [-476.,1565.,-299.,-177., -60.,-120.,-240.,-480.],\n [-397.,-299.,1352.,-148., -50.,-100.,-200.,-400.],\n [-235.,-177.,-148., 874., -30., -60.,-120.,-240.],\n [ -80., -60., -50., -30., 310., -20., -40., -80.],\n [-160.,-120.,-100., -60., -20., 600., -80.,-160.],\n [-320.,-240.,-200.,-120., -40., -80.,1120.,-320.],\n [-640.,-480.,-400.,-240., -80.,-160.,-320.,1920.]\n ]\n self.assertCountEqual(matrix.tolist(), want)","def set_qxqz(self):\n self.qx = self._q_x()\n self.qz = self._q_z()","def _qij_plus(i: int, j: int):\n ia = i * 2 + 0\n ja = j * 2 + 0\n term = FermionOperator(((ja, 0), (ia, 0)), 1.0)\n return term","def _onSquaredQ(self, event):\r\n self.graph.xaxis('\\\\rm{q}^2 ', 'A^{-2}')\r\n self.set_xscale('squared')\r\n\r\n self.graph.render(self)\r\n self.subplot.figure.canvas.draw_idle()","def _qCurveToOne(self, pt1, pt2):\n assert pt2 is not None\n t = \"Q\"\n t += pointToString(pt1) + \" \"\n t += pointToString(pt2)\n self._commands.append(t)\n self._lastCommand = \"Q\"\n self._lastX, self._lastY = pt2","def test_solve_quadratic_fixed(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.val = 4\n iden2.x.val = 5\n iden3.x.val = 6\n iden1.x.name = 'x1'\n iden2.x.name = 'x2'\n iden3.x.name = 'x3'\n iden2.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 10)\n self.assertAlmostEqual(iden1.x.val, 4)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 6)","def _qij_minus(i: int, j: int):\n ib = i * 2 + 1\n jb = j * 2 + 1\n term = FermionOperator(((jb, 0), (ib, 0)), 1.0)\n return term","def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def pro_avfid_superoperator_compsubspace_phasecorrected_onlystaticqubit(U,L1,phases):\n\n Ucorrection = qtp.Qobj([[np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0, 0],\n [0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0],\n [0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0],\n [0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0, 0, 0],\n [0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0, 0],\n [0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0],\n [0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0],\n [0, 0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0],\n [0, 0, 0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0]))]],\n type='oper',\n dims=[[3, 3], [3, 3]])\n\n if U.type=='oper':\n U=Ucorrection*U\n inner = U.dag()*U_target\n part_idx = [0, 1, 3, 4] # only computational subspace\n ptrace = 0\n for i in part_idx:\n ptrace += inner[i, i]\n dim = 4 # 2 qubits comp subspace \n\n return np.real(((np.abs(ptrace))**2+dim*(1-L1))/(dim*(dim+1)))\n\n elif U.type=='super':\n U=qtp.to_super(Ucorrection)*U\n kraus_form = qtp.to_kraus(U)\n dim=4 # 2 qubits in the computational subspace\n part_idx = [0, 1, 3, 4] # only computational subspace\n psum=0\n for A_k in kraus_form:\n ptrace = 0\n inner = U_target_diffdims.dag()*A_k # otherwise dimension mismatch\n for i in part_idx:\n ptrace += inner[i, i]\n psum += (np.abs(ptrace))**2\n\n return np.real((dim*(1-L1) + psum) / (dim*(dim + 1)))","def updateMARG(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray, mag: np.ndarray) -> np.ndarray:\n if gyr is None or not np.linalg.norm(gyr)>0:\n return q\n qEst = 0.5 * q_prod(q, [0, *gyr]) # (eq. 12)\n a_norm = np.linalg.norm(acc)\n if a_norm>0:\n a = acc/a_norm\n if mag is None or not np.linalg.norm(mag)>0:\n return self.updateIMU(q, gyr, acc)\n m = mag/np.linalg.norm(mag)\n h = q_prod(q, q_prod([0, *m], q_conj(q))) # (eq. 45)\n bx = np.linalg.norm([h[1], h[2]]) # (eq. 46)\n bz = h[3]\n qw, qx, qy, qz = q/np.linalg.norm(q)\n # Gradient objective function (eq. 31) and Jacobian (eq. 32)\n f = np.array([2.0*(qx*qz - qw*qy) - a[0],\n 2.0*(qw*qx + qy*qz) - a[1],\n 2.0*(0.5-qx**2-qy**2) - a[2],\n 2.0*bx*(0.5 - qy**2 - qz**2) + 2.0*bz*(qx*qz - qw*qy) - m[0],\n 2.0*bx*(qx*qy - qw*qz) + 2.0*bz*(qw*qx + qy*qz) - m[1],\n 2.0*bx*(qw*qy + qx*qz) + 2.0*bz*(0.5 - qx**2 - qy**2) - m[2]]) # (eq. 31)\n J = np.array([[-2.0*qy, 2.0*qz, -2.0*qw, 2.0*qx ],\n [ 2.0*qx, 2.0*qw, 2.0*qz, 2.0*qy ],\n [ 0.0, -4.0*qx, -4.0*qy, 0.0 ],\n [-2.0*bz*qy, 2.0*bz*qz, -4.0*bx*qy-2.0*bz*qw, -4.0*bx*qz+2.0*bz*qx],\n [-2.0*bx*qz+2.0*bz*qx, 2.0*bx*qy+2.0*bz*qw, 2.0*bx*qx+2.0*bz*qz, -2.0*bx*qw+2.0*bz*qy],\n [ 2.0*bx*qy, 2.0*bx*qz-4.0*bz*qx, 2.0*bx*qw-4.0*bz*qy, 2.0*bx*qx ]]) # (eq. 32)\n gradient = J.T@f # (eq. 34)\n gradient /= np.linalg.norm(gradient)\n qEst -= self.gain*gradient # (eq. 33)\n q += qEst*self.Dt # (eq. 13)\n q /= np.linalg.norm(q)\n return q","def square(q_1: Q) -> Q:\n\n end_q_type = f\"{q_1.q_type}²\"\n\n qxq = _commuting_products(q_1, q_1)\n\n sq_q = Q(q_type=end_q_type, representation=q_1.representation)\n sq_q.t = qxq[\"tt\"] - qxq[\"xx+yy+zz\"]\n sq_q.x = qxq[\"tx+xt\"]\n sq_q.y = qxq[\"ty+yt\"]\n sq_q.z = qxq[\"tz+zt\"]\n\n return sq_q","def createObjective(self, solver, objective, conflict_vars_d, sched_d, conflicts_d, courses):\n\n\n # Put a little pressure on to not use slots 6 or 7\n for asl in self.vars_actualslots:\n if asl[1] == \"6\":\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_SLOT_6']) \n if asl[1] == \"7\":\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_SLOT_7']) \n\n if asl[0]==\"T\" and asl[1] in [\"4\", \"5\"]:\n if self.name in non_FAS_instructor:\n # for non-FAS faculty, favor teaching on Tuesday 3pm-5pm\n if asl[1] == \"4\":\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_FAVOR_COURSES_TU_3_TO_5'])\n else: \n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_FAVOR_COURSES_TU_3_TO_5']*3)\n else:\n # avoid teaching on Tuesday 3pm-5pm for FAS instructors\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_COURSES_TU_3_TO_5'])\n\n if asl[0]==\"F\":\n # avoid Friday teaching, to mimic faculty preferences\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_COURSES_FRIDAY']) \n \n \n \n # Add to objective function for bad course conflicts\n for other in conflicts_d.get(self.name, []):\n assert is_cross_list_canonical(other), other\n\n if other not in courses and other not in sched_d:\n # we're not scheduling the other course, so we can ignore it\n continue\n\n if other in courses and not (self.name < other):\n # we will let the processing for the other course handle this weight\n continue\n\n # create a variable that indicates if self and other conflict, add add the weight\n # to the objective function\n v_conflicts = solver.IntVar(0, 1, self.name + \" and \" + other + \" conflict\")\n objective.SetCoefficient(v_conflicts, PARAMS['WEIGHT_BAD_CONFLICT_FACTOR'] * int(conflicts_d[self.name][other]))\n\n # Record the variable so we can look at it later\n if self.name not in conflict_vars_d:\n conflict_vars_d[self.name] = {}\n assert other not in conflict_vars_d[self.name]\n conflict_vars_d[self.name][other] = v_conflicts\n \n if other not in courses:\n # the other course already has a fixed schedule.\n # Go through each actual slot and see if it intersects with other course\n disjuncts = []\n for s in self.vars_actualslots:\n my_course_time = ss.meeting_time_to_course_time([s])\n \n if sct.courses_conflict([my_course_time], sched_d[other]):\n # slots s conflicts with the time for other\n disjuncts.append(self.vars_actualslots[s])\n\n if len(disjuncts) > 0:\n makeDisjunction(solver, v_conflicts, disjuncts)\n \n if other in courses:\n # we are scheduling the other course, so we need to use its variables.\n # Create a variable for each actual slot that indicates if both self and other use that slot.\n d = courses[other]\n vs_d_same_slot = []\n for asl in self.vars_actualslots:\n if asl in d.vars_actualslots:\n # actual slot represented by vas is in both courses self and d.\n v_both_use_slot = solver.IntVar(0, 1, self.name + \" and \" + other + \" using slot \" + asl)\n makeConjunction(solver, v_both_use_slot, [self.vars_actualslots[asl], d.vars_actualslots[asl]])\n vs_d_same_slot.append(v_both_use_slot)\n\n if len(vs_d_same_slot) > 0:\n makeDisjunction(solver, v_conflicts, vs_d_same_slot)","def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)","def getqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n maxnumqcnz_ = self.getnumqconknz((k_))\n numqcnz_ = ctypes.c_int64()\n _qcsubi_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi is not long enough: Is %d, expected %d\" % (len(qcsubi_),self.getnumqconknz((k_))))\n if isinstance(qcsubi_,numpy.ndarray) and not qcsubi_.flags.writeable:\n raise ValueError(\"Argument qcsubi must be writable\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n _qcsubj_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj is not long enough: Is %d, expected %d\" % (len(qcsubj_),self.getnumqconknz((k_))))\n if isinstance(qcsubj_,numpy.ndarray) and not qcsubj_.flags.writeable:\n raise ValueError(\"Argument qcsubj must be writable\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n _qcval_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval is not long enough: Is %d, expected %d\" % (len(qcval_),self.getnumqconknz((k_))))\n if isinstance(qcval_,numpy.ndarray) and not qcval_.flags.writeable:\n raise ValueError(\"Argument qcval must be writable\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n qcsurp_ = ctypes.c_int64(_qcsubi_minlength)\n res = __library__.MSK_XX_getqconk64(self.__nativep,k_,maxnumqcnz_,ctypes.byref(qcsurp_),ctypes.byref(numqcnz_),_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqcnz_ = numqcnz_.value\n _numqcnz_return_value = numqcnz_\n if _qcsubi_copyarray:\n qcsubi_[:] = _qcsubi_np_tmp\n if _qcsubj_copyarray:\n qcsubj_[:] = _qcsubj_np_tmp\n if _qcval_copyarray:\n qcval_[:] = _qcval_np_tmp\n return (_numqcnz_return_value)","def jval(self):\n return self.q * self.model.nobs_moms","def substitute_cost(self, i, j):\n raise NotImplementedError","def ket(self: Qs) -> Qs:\n\n if self.qs_type == \"ket\":\n return self\n\n ket = conjs(deepcopy(self))\n ket.rows = self.dim\n ket.columns = 1\n\n ket.qs_type = \"ket\" if self.dim > 1 else \"scalar_q\"\n\n return ket","def quaternion_conjugate(self, q):\n\n \"\"\"\n in-place operation is an operation that changes directly the content of a given Tensor without making a copy.\n ALL operations on the tensor that operate in-place on it will have an _ postfix.\n \"\"\"\n q_star = q.new(4).fill_(-1)\n\n # leave the scalar unchanged and change signs of i, j, k number parts\n q_star[0] = 1.0\n\n return q * q_star","def q_criterion(a):\n print(\"Detection method: Q criterion\")\n Q = np.zeros((a.u.shape[0], a.u.shape[1]))\n print(a.u.shape[0], a.u.shape[1])\n #print(Q.shape)\n for i in range(a.u.shape[0]):\n for j in range(a.u.shape[1]):\n Q[i, j] = -0.5*(a.derivative['dudx'][i, j]**2 + a.derivative['dvdy'][i, j]**2) \\\n - a.derivative['dudy'][i, j] * a.derivative['dvdx'][i, j]\n return Q","def test_quintic(self):\n fun = get_problem('quintic', self.dimension, -10.0, 10.0)\n self.assertEqual(fun(self.array6), 0.0)","def Q(self, k, x):\n g = np.asarray(self.g(k, x))\n Q = g @ g.T\n return Q","def set_rhs(self):\n k = self.istore[:, 0]\n ksym = self.stencil.get_symmetric()[k]\n self.rhs[:] = self.feq[k, np.arange(k.size)] + self.feq[ksym, np.arange(k.size)]","def set_rhs(self):\n k = self.istore[:, 0]\n ksym = self.stencil.get_symmetric()[k]\n self.rhs[:] = self.feq[k, np.arange(k.size)] + self.feq[ksym, np.arange(k.size)]","def putbarsj(self,whichsol_,j_,barsj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barsj is None: raise TypeError(\"Invalid type for argument barsj\")\n if barsj is None:\n barsj_ = None\n else:\n try:\n barsj_ = memoryview(barsj)\n except TypeError:\n try:\n _tmparr_barsj = array.array(\"d\",barsj)\n except TypeError:\n raise TypeError(\"Argument barsj has wrong type\")\n else:\n barsj_ = memoryview(_tmparr_barsj)\n \n else:\n if barsj_.format != \"d\":\n barsj_ = memoryview(array.array(\"d\",barsj))\n \n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj has wrong length\")\n res = self.__obj.putbarsj(whichsol_,j_,barsj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def plot_q_values(self):\n\n sim_freq = self.conf['Simulation']['params']['frequency']\n sim_wvlgth = 1e9*consts.c / sim_freq\n leg_str = ''\n for mat, matpath in self.conf['Materials'].items():\n n, k = get_nk(matpath, sim_freq)\n mat_wv = 1e-3*sim_wvlgth / n\n mat_q = 2*np.pi/mat_wv\n leg_str += '{}: {:.2f} [rads/$\\mu$m]\\n'.format(mat, mat_q)\n leg_str = leg_str[0:-1]\n for lname, l_obj in self.layers.items():\n qarr = self.data['{}_qvals'.format(lname)]\n max_pos_freq = np.amax(qarr.real)\n max_neg_freq = np.amin(qarr.real)\n min_pos_wv = 1e3*2*np.pi/max_pos_freq\n if max_neg_freq == 0:\n min_neg_wv = 0\n else:\n min_neg_wv = 1e3*2*np.pi/max_neg_freq\n plt.figure()\n inc_q = 2*np.pi/(1e-3*sim_wvlgth)\n title = 'Layer: {}, Incident q: {:.2f} [rads/$\\mu$m]'.format(lname, inc_q)\n # title += 'Min Positive $\\\\lambda$: {:.2f} nm, '\n # title += 'Min Negative $\\\\lambda$: {:.2f} nm'\n # title = title.format(lname, sim_wvlgth, min_pos_wv, min_neg_wv)\n # title = title.format(lname, sim_wvlgth)\n plt.title(title)\n # plt.scatter(1e3*2*np.pi/qarr.real, 1e4*qarr.imag/2*np.pi, c='b', s=.5,\n # marker='o', label=leg_str)\n plt.scatter(qarr.real, qarr.imag/(2*np.pi), c='b', s=.75,\n marker='o', label=leg_str)\n # pt = (qarr[0].real, qarr[0].imag)\n # theta = np.linspace(0, 1.48, 200)\n # plt.plot(pt[0]*np.cos(theta), pt[1]/np.cos(theta), 'r--')\n plt.legend(loc='best')\n # plt.annotate(leg_str, xy=(.95,.95), xycoords='axes fraction',\n # size=14, ha='right', va='top',\n # bbox=dict(boxstyle='round', fc='w'))\n plt.xlabel('Re(q) [radians/micron]')\n plt.ylabel('Im(q) [1/microns]')\n plot_path = os.path.join(self.dir, '{}_qvals.png'.format(lname))\n plt.grid(True)\n plt.savefig(plot_path)\n plt.close()","def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij)\n elif nz_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if nz_ is None: nz_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _idx_return_value = resargs\n return _idx_return_value","def expand_q(self) -> Q:\n \"\"\"Expand each term.\"\"\"\n\n self.t = sp.expand(self.t)\n self.x = sp.expand(self.x)\n self.y = sp.expand(self.y)\n self.z = sp.expand(self.z)\n return self","def putxx(self,whichsol_,xx_):\n _xx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),self.getnumvar()))\n if xx_ is None:\n raise ValueError(\"Argument xx cannot be None\")\n if xx_ is None:\n raise ValueError(\"Argument xx may not be None\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_putxx(self.__nativep,whichsol_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def to_qubo(self, index_label=False, params=None):\n\n bqm = self.compiled_qubo.eval(params)\n q, offset = bqm.to_qubo()\n\n # Evaluate values of QUBO\n qubo = {}\n for (label1, label2), v in q.items():\n if index_label:\n i = self.label2index[label1]\n j = self.label2index[label2]\n else:\n i = label1\n j = label2\n qubo[(i, j)] = v\n\n return qubo, offset","def set_fig(cls, obj, quad):\n\t\taddr = quad.result\n\t\ttype = abs(addr) // 1000 # integer division\n\t\trelative_address = abs(addr) - (type * 1000)\n\t\tprint \"> Rel = {} - {}\".format(abs(addr), (type * 1000))\n\t\tprint \"> Set New Fig mem value: type = {}, addr = {}\".format(type, relative_address)\n\n\t\tif addr < 0:\n\t\t\tcls.heap.memory[type][abs(relative_address)] = obj\n\t\t\tprint \"> Heap memory: {}\".format(cls.heap.memory)\n\t\telse:\n\t\t\tcls.stack.peek().memory[type][relative_address] = obj\n\t\t\tprint \"> Stack memory: {}\".format(cls.stack.peek().memory)","def puty(self,whichsol_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.puty(whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def qeye_like(qobj):\n from .cy.qobjevo import QobjEvo\n if isinstance(qobj, QobjEvo):\n qobj = qobj(0)\n return Qobj(\n _data.identity_like(qobj.data), dims=qobj.dims, type=qobj.type,\n superrep=qobj.superrep, isherm=True, isunitary=True, copy=False\n )","def f(self, (k,t), (J,q,dq), **params):\n f = 0.*q\n return f","def xut2q( self, x , u , t ):\n \n # default is q = x\n \n return x","def update(self, obs, q):\n for o in obs:\n prob = np.exp(-70)\n if self.landmarks:\n # find the data association with ML\n prob, landmark_idx, ass_obs, ass_jacobian, ass_adjcov = self.find_data_association(o)\n if prob < self.TOL:\n # create new landmark\n self.create_landmark(o)\n else:\n # update corresponding EKF\n self.update_landmark(np.transpose(np.array([o])), landmark_idx, ass_obs, ass_jacobian, ass_adjcov)\n else:\n # no initial landmarks\n self.create_landmark(o)\n self.weight *= prob\n \n q.put([self]) ###","def cuttingStockKantorovich(w, q, B):\n\n model = Model(\"Naive Cutting Stock\")\n m = max(w)*max(q) # m rolls\n n = len(q) # n orders \n y = {}\n for j in range(m):\n y[j] = model.addVar(name = \"y[%s]\" % j, vtype=\"BINARY\")\n \n x = {}\n for j in range(m):\n for i in range(n):\n x[i,j] = model.addVar(name = \"x[%s,%s]\" %(i,j), lb = 0, vtype=\"INTEGER\")\n model.addCons(x[i,j] <= q[i]*y[j])\n\n for i in range(n):\n model.addCons(quicksum(x[i,j] for j in range(m)) == q[i])\n\n for j in range(m):\n model.addCons((quicksum(w[i]*x[i,j] for i in range(n)) <= B))\n\n model.setObjective(quicksum(y[j] for j in range(m)), \"minimize\")\n model.hideOutput()\n model.optimize()\n\n return model.getObjVal()","def fixB(self,i,j,value):\n if self.coeffPattern[1] == None:\n m,n=self.m,self.n\n self.coeffPattern = [[None]*n for i in range(m)]\n self.coeffPattern[1][i][j]=value\n self._updateEstimatorSize(i)","def project_curve(q):\n n,T = q.shape\n if n==2:\n dt = 0.35\n if n==3:\n dt = 0.2\n epsilon = 1e-6\n\n iter = 1\n res = ones(n)\n J = zeros((n,n))\n\n s = linspace(0,1,T)\n\n qnew = q.copy()\n qnew = qnew / sqrt(innerprod_q2(qnew,qnew))\n\n qnorm = zeros(T)\n G = zeros(n)\n C = zeros(300)\n while (norm(res) > epsilon):\n if iter > 300:\n break\n\n # Jacobian\n for i in range(0,n):\n for j in range(0,n):\n J[i,j] = 3 * trapz(qnew[i,:]*qnew[j,:],s)\n \n J += eye(n)\n\n for i in range(0,T):\n qnorm[i] = norm(qnew[:,i])\n \n # Compute the residue\n for i in range(0,n):\n G[i] = trapz(qnew[i,:]*qnorm,s)\n \n res = -G\n\n if (norm(res) < epsilon):\n break\n\n x = solve(J,res)\n C[iter] = norm(res)\n\n delG = Basis_Normal_A(qnew)\n temp = zeros((n,T))\n for i in range(0,n):\n temp += x[i]*delG[i]*dt\n \n qnew += temp\n iter += 1\n \n qnew = qnew/sqrt(innerprod_q2(qnew,qnew))\n\n return qnew","def updateIMU(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray, dt: float = None) -> np.ndarray:\n _assert_numerical_iterable(q, 'Quaternion')\n _assert_numerical_iterable(gyr, 'Tri-axial gyroscope sample')\n _assert_numerical_iterable(acc, 'Tri-axial accelerometer sample')\n dt = self.Dt if dt is None else dt\n if gyr is None or not np.linalg.norm(gyr) > 0:\n return q\n qDot = 0.5 * q_prod(q, [0, *gyr]) # (eq. 12)\n a_norm = np.linalg.norm(acc)\n if a_norm > 0:\n a = acc/a_norm\n qw, qx, qy, qz = q/np.linalg.norm(q)\n # Objective function (eq. 25)\n f = np.array([2.0*(qx*qz - qw*qy) - a[0],\n 2.0*(qw*qx + qy*qz) - a[1],\n 2.0*(0.5-qx**2-qy**2) - a[2]])\n if np.linalg.norm(f) > 0:\n # Jacobian (eq. 26)\n J = np.array([[-2.0*qy, 2.0*qz, -2.0*qw, 2.0*qx],\n [ 2.0*qx, 2.0*qw, 2.0*qz, 2.0*qy],\n [ 0.0, -4.0*qx, -4.0*qy, 0.0 ]])\n # Objective Function Gradient\n gradient = J.T@f # (eq. 34)\n gradient /= np.linalg.norm(gradient)\n qDot -= self.gain*gradient # (eq. 33)\n q += qDot*dt # (eq. 13)\n q /= np.linalg.norm(q)\n return q","def clear(self, clear_obj=True, clear_constraints=True):\n\n if clear_constraints:\n self.constraints = []\n self.bounds, self.constraints = self.const_creator.create_constraint('weight', weight_bound=(0,1), leverage=1)\n if clear_obj:\n self.objective = None","def setConstQuantityAndBetaEqState(self, pointDict, quantity, target):\n print \"setConstQuantityAndBetaEqState: \", pointDict\n assert 'ye' not in pointDict, \"You can't SPECIFY a Ye if you're \" \\\n \"setting neutrinoless beta equlibrium!\"\n self.validatePointDict(pointDict)\n assert len(pointDict) < 2, \"State overdetermined for more than 1 indVars!\"\n #todo: check quantity is valid 3D table\n\n #defines 1D root solver to use in routine\n solveRoot = scipyOptimize.brentq # solveRootBisect\n\n solveVarName = 'logtemp'\n currentSolveVar = 0.0\n currentYe = 0.25\n #previous variables used to measure convergence of solve\n # so set them to something significantly different than starting values\n previousSolveVar = 100.0\n previousYe = 100.0\n yeError = relativeError(currentYe, previousYe)\n solveVarError = relativeError(currentSolveVar, previousSolveVar)\n otherVarName = pointDict.keys()[0]\n otherVar = pointDict.values()[0]\n\n maxIters = 5\n tol = 1e-3\n\n iteration = 0\n while iteration < maxIters and yeError + solveVarError > tol/2.0:\n previousSolveVar = currentSolveVar\n previousYe = currentYe\n getSolveVar = lambda x: multidimInterp((currentYe, x, otherVar),\n [self.h5file['ye'][:],\n self.h5file[solveVarName],\n self.h5file[otherVarName]],\n self.h5file[quantity][...],\n linInterp, 2) - target\n try:\n currentSolveVar = solveRoot(getSolveVar,\n self.h5file[solveVarName][0],\n self.h5file[solveVarName][-1],\n (),tol)\n except ValueError as err:\n print \"Root for log10(T) not bracketed on entire table: \" \\\n + str(err)\n # see if lower or upper temperature bound best\n logtemp = self.h5file['logtemp']\n answer1 = multidimInterp((currentYe, logtemp[0], otherVar),\n [self.h5file['ye'][:],\n self.h5file['logtemp'],\n self.h5file['logrho']],\n self.h5file[quantity][...],\n linInterp, 2) - target\n answer2 = multidimInterp((currentYe, logtemp[-1], otherVar),\n [self.h5file['ye'][:],\n self.h5file['logtemp'],\n self.h5file['logrho']],\n self.h5file[quantity][...],\n linInterp, 2) - target\n\n if (abs(answer1) < abs(answer2)):\n currentSolveVar = self.h5file['logtemp'][0]\n print \"Recovering with lowest table value, answer: %s\" % currentSolveVar\n else:\n currentSolveVar = self.h5file['logtemp'][-1]\n print \"Recovering with highest value, answer: %s\" % currentSolveVar\n\n getYe = lambda x : multidimInterp((x, currentSolveVar, otherVar),\n [self.h5file['ye'][:],\n self.h5file[solveVarName],\n self.h5file[otherVarName]],\n self.h5file['munu'][...],\n linInterp, 2)\n #check for bracketing error in root solve for ye\n try:\n currentYe = solveRoot(getYe,\n self.h5file['ye'][0],\n self.h5file['ye'][-1], (), tol)\n except ValueError as err:\n print \"Error in scipy root solver solving for ye: \", str(err)\n currentYe = self.findYeOfMinAbsMunu((currentSolveVar, otherVar))\n print \"Recovering with findYeOfMinAbsMunu, answer: %s\" % currentYe\n #print \"currentYe: \", currentYe, \"\\tcurrentT: \", currentSolveVar\n\n yeError = relativeError(currentYe, previousYe)\n solveVarError = relativeError(currentSolveVar, previousSolveVar)\n iteration += 1\n #print \"errs: \", yeError, solveVarError\n\n newDict = pointDict.copy()\n newDict['ye'] = currentYe\n temp = numpy.power(10.0,currentSolveVar) # TODO TEMP HARD CODE\n newDict['temp'] = temp\n self.setState(newDict)\n return currentYe, temp # TODO TEMP HARD CODE","def addnewquad(zs,ze,db=0.,de=0.,et=0.,bt=0.,ts=0.,dt=0.,vx=0.,vy=0.,\n ap=0.,ax=0.,ay=0.,\n rr=0.,rl=0.,gl=0.,gp=0.,pw=0.,pa=0.,pr=0.,sl=0.,ox=0.,oy=0.,\n ph=0.,do=0.,\n glx=0.,gly=0.,axp=0.,axm=0.,ayp=0.,aym=0.,rxp=0.,rxm=0.,\n ryp=0.,rym=0.,vxp=0.,vxm=0.,vyp=0.,vym=0.,oxp=0.,oxm=0.,\n oyp=0.,oym=0.,pwl=0.,pwr=0.,pal=0.,par=0.,prl=0.,prr=0.):\n # --- Make sure that at least some of the element is in the proper range,\n # --- z >= 0., and if zlatperi != 0, z <= zlatperi.\n assert (zs < ze),\"element start must be less than element end\"\n assert (top.zlatperi == 0.) or (ze > 0.),\"element end must be greater than zero if top.zlatperi is nonzero\"\n assert (top.zlatperi == 0.) or (zs < top.zlatperi),\"element start must be less than zlatperi if top.zlatperi is nonzero\"\n\n # --- Get a dict of the input arguments and their values.\n ldict = locals()\n\n # --- Setup the lattice arrays for the insertion of the new element. If\n # --- there are already quads, then find the place where the new one is to\n # --- be inserted and shift the existing data to open up a space.\n # --- Note that this uses that same check as in resetlat, that zs != ze to\n # --- determine whether or not a quad is defined.\n ie = 0\n # --- Find which element the new one goes before.\n while (ie <= top.nquad and top.quadzs[ie] <= zs and\n top.quadzs[ie] != top.quadze[ie]):\n ie = ie + 1\n\n # --- Increase the size of the arrays if the element will go past the end\n # --- or if the array is full (i.e. the last element is used).\n if ie > top.nquad or top.quadzs[-1] != top.quadze[-1]:\n top.nquad = top.nquad + 100\n top.nqerr = top.nqerr + 100\n gchange(\"Lattice\")\n\n # --- Setup dictionary relating lattice array with input argument names.\n # --- This is done here so that the references to the lattice arrays\n # --- refer to the updated memory locations after the gchange.\n edict={'zs':top.quadzs,'ze':top.quadze,'db':top.quaddb,'de':top.quadde,\n 'et':top.quadet,'bt':top.quadbt,'ts':top.quadts,'dt':top.quaddt,\n 'vx':top.quadvx,'vy':top.quadvy,\n 'ap':top.quadap,'ax':top.quadax,'ay':top.quaday,\n 'ph':top.quadph,\n 'rr':top.quadrr,\n 'rl':top.quadrl,'gl':top.quadgl,'gp':top.quadgp,'pw':top.quadpw,\n 'pa':top.quadpa,'pr':top.quadpr,'sl':top.quadsl,'do':top.quaddo,\n 'glx':top.qdelglx,'gly':top.qdelgly,'axp':top.qdelaxp,'axm':top.qdelaxm,\n 'ayp':top.qdelayp,'aym':top.qdelaym,'rxp':top.qdelrxp,'rxm':top.qdelrxm,\n 'ryp':top.qdelryp,'rym':top.qdelrym,'vxp':top.qdelvxp,'vxm':top.qdelvxm,\n 'vyp':top.qdelvyp,'vym':top.qdelvym,'oxp':top.qdeloxp,'oxm':top.qdeloxm,\n 'oyp':top.qdeloyp,'oym':top.qdeloym,'pwl':top.qdelpwl,'pwr':top.qdelpwr,\n 'pal':top.qdelpal,'par':top.qdelpar,'prl':top.qdelprl,'prr':top.qdelprr,\n 'ox':top.qoffx,'oy':top.qoffy}\n\n # --- Shift the existing data in the arrays to open up a space for the\n # --- new element.\n if ie <= top.nquad:\n for e in edict.itervalues():\n if len(shape(e)) == 1:\n e[ie+1:] = e[ie:-1] + 0\n else:\n # --- There are two arrays which are 2-D, quadet, and quadbt.\n e[:,ie+1:] = e[:,ie:-1] + 0\n\n # --- Insert the new element. Note that edict correlates the lattice array\n # --- with the input arguments and ldict correlate the arguements with\n # --- their values.\n for (xx,e) in edict.iteritems():\n if len(shape(e)) == 1:\n e[ie] = ldict[xx]\n else:\n # --- There are two arrays which are 2-D, quadet, and quadbt.\n e[:,ie] = ldict[xx]\n\n # --- resetlat must be called before the data can be used\n top.lresetlat = true\n\n return ie","def jsonJobInfo_queuePut(self, **kwargs):\n\n str_queue = 'startQueue'\n for k,v in kwargs.items():\n if k == 'queue': str_queue = v\n\n if str_queue == 'startQueue': queue = self.queueStart\n if str_queue == 'endQueue': queue = self.queueEnd\n\n # self.dp.qprint(self.shell.d_job)\n\n queue.put(self.shell.d_job.copy())","def test_init_q(self):\n\n riskfree = .01\n lmbd = .01\n lmbd_s = .5\n lmbd_y = .5\n mean_v = .5\n kappa_s = 1.5\n kappa_y = .5\n eta_s = .1\n eta_y = .01\n rho = -.5\n\n param = CentTendParam(riskfree=riskfree,\n lmbd=lmbd, lmbd_s=lmbd_s, lmbd_y=lmbd_y,\n mean_v=mean_v, kappa_s=kappa_s, kappa_y=kappa_y,\n eta_s=eta_s, eta_y=eta_y, rho=rho, measure='Q')\n\n kappa_sq = kappa_s - lmbd_s * eta_s\n kappa_yq = kappa_y - lmbd_y * eta_y\n scale = kappa_s / kappa_sq\n\n self.assertEqual(param.measure, 'Q')\n self.assertEqual(param.riskfree, riskfree)\n self.assertEqual(param.lmbd, 0)\n self.assertEqual(param.lmbd_s, lmbd_s)\n self.assertEqual(param.lmbd_y, lmbd_y)\n self.assertEqual(param.mean_v, mean_v * kappa_y / kappa_yq * scale)\n self.assertEqual(param.kappa_s, kappa_sq)\n self.assertEqual(param.kappa_y, kappa_yq)\n self.assertEqual(param.eta_s, eta_s)\n self.assertEqual(param.eta_y, eta_y * scale**.5)\n self.assertEqual(param.rho, rho)\n self.assertTrue(param.is_valid())\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n param.convert_to_q()","def putaij(self,i_,j_,aij_):\n res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def test_quintic2(self):\n fun = get_problem('quintic', self.dimension, -10.0, 10.0)\n self.assertEqual(fun(self.array7), 0.0)","def _bucket_setitem(self, j, k, v):\n pass","def __init__(self,XXZ_,kop_,excitations_,sol_ = None):\n self.XXZ=XXZ_ #Defines the model - XXXmodel,XXZmodel,XXZmodelTrig or XXZmodelHyp\n self.levels=self.XXZ.levels #Energy levels\n self.g = kop_ #Coupling constant\n self.gamma = self.XXZ.get_c() #Gamma associated with Gaudin algebra\n self.N = excitations_ #Number of excitations\n self.n=self.XXZ.get_nlevels() #Number of single-particle levels\n self.rapidities=None; #Rapidities (have to be calculated)\n if sol_ == None:\n self.solution = None #Set of g*Lambda_i (have to be calculated)\n else: \n self.solution = np.array(sol_)\n assert(len(self.solution) == len(self.levels))\n assert(self.N <= self.n)\n self.occupation=None #Set of occupation numbers (follow from derivative of g*Lambda_i)","def save_q_functions(self, q_value, iteration):\n path = os.path.join(self.predicted_q_func_dir,\n '%06d.%s.qfunc' % (iteration, 'shovel'))\n np.save(path, q_value)","def buildQ(self):\r\n\r\n print 'Building Q ...'\r\n\r\n self.y = T.matrix('y')\r\n\r\n mlp = MLP(activations=self.hyper['q_activs'],\r\n dims=self.hyper['q_dims'],\r\n weights_init=self.hyper['q_W_init'],\r\n biases_init=Constant(0))\r\n\r\n q_parameters = mlp.apply(self.y)\r\n mlp.initialize()\r\n\r\n # self.qxgy_mu.shape == (minibatch size, num of dimension of x)\r\n self.qxgy_mu = q_parameters[:,:self.hyper['x_dim']]\r\n\r\n # self.qxgy_var.shape == (minibatch size, num of dimension of x)\r\n self.qxgy_var = T.exp( q_parameters[:,self.hyper['x_dim']:2*self.hyper['x_dim']] )\r\n\r\n # self.qwgy_mu.shape == (minibatch size, num of dimension of w)\r\n self.qwgy_mu = q_parameters[:,2*self.hyper['x_dim']:2*self.hyper['x_dim']+self.hyper['w_dim']]\r\n\r\n # self.qwgy_var.shape == (minibatch size, num of dimension of w)\r\n self.qwgy_var = T.exp( q_parameters[:,2*self.hyper['x_dim']+self.hyper['w_dim']:] )\r\n\r\n\r\n #---Will be useful to compute samples from q(x|y)---#\r\n #self.eps_x.shape == (minibatch size, # of x samples , # of dimension of x)\r\n self.eps_x = self.srng.normal((self.qxgy_mu.shape[0] ,self.hyper['L_x'] ,self.hyper['x_dim']))\r\n\r\n #self.x corresponds roughly to the function g(\\epsilon,y) (see reparametrization trick in Kingma 2014)\r\n #self.x.shape == (minibatch size, # of x samples , # of dimension of x)\r\n self.x = self.qxgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qxgy_var).dimshuffle(0,'x',1)*self.eps_x\r\n\r\n #---Will be useful to compute samples from q(w|y)---#\r\n #self.eps_w.shape == (minibatch size, # of w samples , # of dimension of w)\r\n self.eps_w = self.srng.normal((self.qwgy_mu.shape[0] ,self.hyper['L_w'] ,self.hyper['w_dim']))\r\n\r\n #self.w corresponds roughly to the function g(\\epsilon,y) (see reparametrization trick in Kingma 2014)\r\n #self.w.shape == (minibatch size, # of w samples , # of dimension of w)\r\n self.w = self.qwgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qwgy_var).dimshuffle(0,'x',1)*self.eps_w\r\n\r\n\r\n #---Building the log density q(x|y)---#\r\n little_num = 10**(-32)\r\n inside_exp = -T.sum((self.x - self.qxgy_mu.dimshuffle(0,'x',1))**2/(2*self.qxgy_var.dimshuffle(0,'x',1)), axis=2)\r\n norm_cst = (2*np.pi)**(-self.hyper['x_dim']/2.)*T.exp(T.sum(T.log(self.qxgy_var), axis=1))**(-1/2.)\r\n\r\n # shape == (minibatch size, # of x samples)\r\n qxgy = norm_cst.dimshuffle(0,'x')*T.exp(inside_exp)\r\n\r\n # shape == (minibatch size, # of x samples)\r\n self.log_qxgy = T.log(qxgy + little_num)","def putbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n \n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n \n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def q(self):\n return self.model.gmmobjective(self.params, self.weights)","def compute_lj_potential(rsq, rcsq):\n LJ_U = np.where(rsq < rcsq, 4 * epsilon * ((sigma ** 2 / rsq) ** 6 - (sigma ** 2 / rsq) ** 3), 0)\n\n return LJ_U","def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\n else:\n _skn_tmp = None\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def contractor(self, *args, **kwargs):\n vertices = copy.deepcopy(args[0])\n nrange = len(vertices[0])\n xpts = []\n ypts = []\n for i in range(nrange):\n xpts.append(vertices[0][i].value)\n ypts.append(vertices[1][i].value)\n constraint = copy.deepcopy(args[1])\n \n \n \n \n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\n \n ## the all important computation split (need to abstract this kind of thing)\n ##lhs = (np.sqrt(qxdot*qxdot + qydot*qydot)**3.) *constraint\n lhs = ( ( np.sqrt(qxdot**2 + qydot**2) )**3 )*constraint\n \n # check2 = qxdot*qyddot\n # if check2.width() < 1.e-2:\n # check2.min.value = check2.real.value\n # check2.max.value = check2.real.value\n # t1 = (lhs - check2)/qydot\n \n #\n # qyddot\n #\n check2 = qydot*qxddot\n if check2.width() < 1.e-2 and check2.contains(0.):\n check2.inf = 0.\n check2.sup = 0.\n #if qxdot.contains(0.) and abs(qxdot.min.value)>1.e-6:\n # print 'qxdot = ',qxdot\n # print 'qxdot not invertable, implement other logic please'\n if abs(float(qxdot.inf))<1.e-6:\n qxdot.inf = 1.e-10\n print 'invert qxdot'\n print 'qxdot = ', qxdot\n \n #t1 = (lhs + qydot*qxddot)/(qxdot)\n t1 = (lhs + check2)/(qxdot)\n \n t1 = t1 & qyddot # go ahead and shrink t1 to qyddot - they are logically equivalent\n total_ans = []\n useful_indices = []\n bad_indices = []\n for i in range(len(ypts)): \n min_ans = 0.\n for j in range(len(ypts)):\n if j==i:\n pass\n else:\n min_ans = (ypts[j]*float(self.localBasis[2,j])) + min_ans\n min_ans = t1 - min_ans\n if (abs(float(self.localBasis[2,i])) > 0.0):\n min_ans = min_ans/float(self.localBasis[2,i])\n useful_indices.append(i)\n else:\n bad_indices.append(i)\n total_ans.append(min_ans)\n \n new_ans = vector_AND_(ypts, total_ans)\n for i in useful_indices:\n if new_ans[i].isempty == False: # abs( new_ans[i].width() ) > 0.:\n ypts[i] = ypts[i] & new_ans[i]\n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\n else:\n print 'warning, possible constraint violation, curvature 1'\n \n ## \n ## qxdot\n ##\n check2 = qydot*qxddot\n if check2.width() < 1.e-2 and check2.contains(0.):\n check2.inf = 0.\n check2.sup = 0.\n #if qyddot.contains(0.):\n # print 'qyddot = ',qyddot\n # print 'qyddot not invertable, implement other logic please'\n \n if qyddot.contains(0.) and qyddot.width()<1.e-6:\n qxdot.inf = 0.#1.e-10\n print 'invert qyddot'\n print 'qyddot = ',qyddot\n fix = (lhs + check2)*(1./qyddot)#*(qyddot**-1.)\n fix = fix & qxdot # go ahead and shrink fix to qxdot - they are logically equivalent\n total_ans = []\n useful_indices = []\n bad_indices = []\n \n for i in range(len(xpts)): #contract on x[i]\n min_ans = 0.\n for j in range(len(xpts)): # add up all jth pieces of the dot product except i\n if j==i:\n pass\n else:\n \n min_ans = (xpts[j]*float(self.localBasis[1,j] ) ) + min_ans\n min_ans = fix - min_ans\n if (abs(float(self.localBasis[1,i]) ) >0.0 ):\n min_ans = min_ans/float(self.localBasis[1,i])\n useful_indices.append(i)\n else:\n bad_indices.append(i)\n total_ans.append(min_ans)\n \n new_ans = vector_AND_(xpts, total_ans)\n for i in useful_indices:\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\n xpts[i] = xpts[i] & new_ans[i]\n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\n else:\n print 'warning, possible constraint violation, curvature 2'\n \n \n ## switch to the other side\n \n ##\n ## contract on qydot\n ##\n check2 = qxdot*qyddot\n if check2.width() < 1.e-2 and check2.contains(0.):\n check2.inf = 0.\n check2.sup = 0.\n# if qxddot.contains(0.):\n# print 'qxddot = ',qxddot\n# print 'qxddot not invertable, implement other logic please'\n# qxddot.min.value = 0.\n if qxddot.contains(0.):\n qxddot.inf = 0.\n \n print 'invert qxddot'\n print 'qxddot = ',qxddot\n t1 = (lhs - check2)/(-qxddot)#*(-qxddot**-1)\n t1 = t1 & qydot\n total_ans = []\n useful_indices = []\n bad_indices = []\n for i in range(len(ypts)): \n min_ans = 0.\n for j in range(len(ypts)):\n if j==i:\n pass\n else:\n #print 't1 = ',t1\n #print 'ypts[{}] = {}'.format(i,ypts[i])\n #print 'localbasis[{},{}] = {}'.format(1,i,self.localBasis[1,j])\n min_ans = (ypts[j]*float(self.localBasis[1,j])) + min_ans\n min_ans = t1 - min_ans\n if (abs(float(self.localBasis[1,i])) > 0.0):\n min_ans = min_ans/float(self.localBasis[1,i])\n useful_indices.append(i)\n else:\n bad_indices.append(i)\n total_ans.append(min_ans)\n \n new_ans = vector_AND_(ypts, total_ans)\n for i in useful_indices:\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\n ypts[i] = ypts[i] & new_ans[i]\n else:\n print 'warning, possible constraint violation, curvature 3'\n \n ##contract on qxdot\n \n check2 = qxdot*qyddot\n if check2.width() < 1.e-2 and check2.contains(0.):\n check2.inf = 0.\n check2.sup = 0.\n #contract on qxddot\n# if qydot.contains(0.):\n# print 'qydot = ',qxddot\n# print 'qydot not invertable, implement other logic please'\n if qydot.contains(0.):\n qydot.inf = 0.\n print 'invert qydot'\n print 'qydot = ',qydot\n fix = (lhs - qxdot*qyddot)/(-qydot)#*(-qydot**-1)\n fix = fix & qxddot # go ahead and shrink t1 to quddot - they are logically equivalent\n total_ans = []\n useful_indices = []\n bad_indices = []\n for i in range(len(xpts)):\n min_ans = 0.\n for j in range(len(xpts)):\n if j==i:\n pass\n else:\n min_ans = (xpts[j]*float(self.localBasis[2,j] ) ) + min_ans\n min_ans = fix - min_ans\n if (abs(float(self.localBasis[2,i]) ) >0.0 ):\n min_ans = min_ans/float(self.localBasis[2,i])\n useful_indices.append(i)\n else:\n bad_indices.append(i)\n total_ans.append(min_ans)\n \n new_ans = vector_AND_(xpts, total_ans)\n for i in useful_indices:\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\n xpts[i] = xpts[i] & new_ans[i]\n else:\n print 'warning, possible constraint violation, curvature 4'\n \n for i in range(nrange):\n vertices[0][i].value = xpts[i]\n vertices[1][i].value = ypts[i]\n return vertices","def qsolve(self, options=''):\n for x in self._rhs:\n if x != 0:\n raise NotImplementedError, \"qsolve is currently only implemented for homogeneous systems (i.e., with rhs=0)\"\n out, err = self.call_4ti2('qsolve', options=options)\n qhom = ExtremalRays(self._read_file('qhom'), self)\n qfree = self._read_file('qfree')\n return (qhom, qfree)","def add_cp_qe_RBM_terms(K, cons_pot_mesh, quad_geo_mesh):\n num_faces = cons_pot_mesh.get_faces().shape[0]\n x_c = quad_geo_mesh.get_centroid()\n w = quad_geo_mesh.get_w()\n A_m = quad_geo_mesh.get_A_m()\n S_D = quad_geo_mesh.get_surface_area()\n\n for face_num in range(num_faces):\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n def v_quad(_xi, _eta, _nodes):\n return np.identity(3)\n v_sub_mat = (1. / S_D) * gq.int_over_tri_quad(v_quad, face_nodes, face_hs)\n def omega_quad(xi, eta, nodes):\n pos = geo.quadratic_interp(xi, eta, nodes)\n X = pos - x_c\n return np.einsum(\"lrs,s->lr\", geo.LC_3, X)\n tmp_omega = gq.int_over_tri_quad(\n omega_quad,\n face_nodes,\n face_hs,\n )\n tmp_arr = []\n for m in range(3):\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\"l,ls\", w[m], tmp_omega)))\n tmp_arr = np.array(tmp_arr)\n tmp_omega_mat = np.sum(tmp_arr, axis=0)\n for src_num in range(num_faces):\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += v_sub_mat\n src_center = cons_pot_mesh.get_node(src_num)\n X_0 = src_center - x_c\n omega_mat = np.einsum(\"ijk,js,k->is\", geo.LC_3, tmp_omega_mat, X_0)\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += omega_mat"],"string":"[\n \"def putqobj(self,qosubi,qosubj,qoval): # 3\\n numqonz_ = None\\n if numqonz_ is None:\\n numqonz_ = len(qosubi)\\n elif numqonz_ != len(qosubi):\\n raise IndexError(\\\"Inconsistent length of array qosubi\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qosubj)\\n elif numqonz_ != len(qosubj):\\n raise IndexError(\\\"Inconsistent length of array qosubj\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qoval)\\n elif numqonz_ != len(qoval):\\n raise IndexError(\\\"Inconsistent length of array qoval\\\")\\n if numqonz_ is None: numqonz_ = 0\\n if qosubi is None: raise TypeError(\\\"Invalid type for argument qosubi\\\")\\n if qosubi is None:\\n qosubi_ = None\\n else:\\n try:\\n qosubi_ = memoryview(qosubi)\\n except TypeError:\\n try:\\n _tmparr_qosubi = array.array(\\\"i\\\",qosubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubi has wrong type\\\")\\n else:\\n qosubi_ = memoryview(_tmparr_qosubi)\\n \\n else:\\n if qosubi_.format != \\\"i\\\":\\n qosubi_ = memoryview(array.array(\\\"i\\\",qosubi))\\n \\n if qosubj is None: raise TypeError(\\\"Invalid type for argument qosubj\\\")\\n if qosubj is None:\\n qosubj_ = None\\n else:\\n try:\\n qosubj_ = memoryview(qosubj)\\n except TypeError:\\n try:\\n _tmparr_qosubj = array.array(\\\"i\\\",qosubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubj has wrong type\\\")\\n else:\\n qosubj_ = memoryview(_tmparr_qosubj)\\n \\n else:\\n if qosubj_.format != \\\"i\\\":\\n qosubj_ = memoryview(array.array(\\\"i\\\",qosubj))\\n \\n if qoval is None: raise TypeError(\\\"Invalid type for argument qoval\\\")\\n if qoval is None:\\n qoval_ = None\\n else:\\n try:\\n qoval_ = memoryview(qoval)\\n except TypeError:\\n try:\\n _tmparr_qoval = array.array(\\\"d\\\",qoval)\\n except TypeError:\\n raise TypeError(\\\"Argument qoval has wrong type\\\")\\n else:\\n qoval_ = memoryview(_tmparr_qoval)\\n \\n else:\\n if qoval_.format != \\\"d\\\":\\n qoval_ = memoryview(array.array(\\\"d\\\",qoval))\\n \\n res = self.__obj.putqobj(numqonz_,qosubi_,qosubj_,qoval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqobjij(self,i_,j_,qoij_):\\n res = __library__.MSK_XX_putqobjij(self.__nativep,i_,j_,qoij_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqobjij(self,i_,j_,qoij_): # 3\\n res = self.__obj.putqobjij(i_,j_,qoij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getqobj(self,qosubi,qosubj,qoval): # 3\\n maxnumqonz_ = self.getnumqobjnz()\\n if qosubi is None: raise TypeError(\\\"Invalid type for argument qosubi\\\")\\n _copyback_qosubi = False\\n if qosubi is None:\\n qosubi_ = None\\n else:\\n try:\\n qosubi_ = memoryview(qosubi)\\n except TypeError:\\n try:\\n _tmparr_qosubi = array.array(\\\"i\\\",qosubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubi has wrong type\\\")\\n else:\\n qosubi_ = memoryview(_tmparr_qosubi)\\n _copyback_qosubi = True\\n else:\\n if qosubi_.format != \\\"i\\\":\\n qosubi_ = memoryview(array.array(\\\"i\\\",qosubi))\\n _copyback_qosubi = True\\n if qosubi_ is not None and len(qosubi_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qosubi has wrong length\\\")\\n if qosubj is None: raise TypeError(\\\"Invalid type for argument qosubj\\\")\\n _copyback_qosubj = False\\n if qosubj is None:\\n qosubj_ = None\\n else:\\n try:\\n qosubj_ = memoryview(qosubj)\\n except TypeError:\\n try:\\n _tmparr_qosubj = array.array(\\\"i\\\",qosubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubj has wrong type\\\")\\n else:\\n qosubj_ = memoryview(_tmparr_qosubj)\\n _copyback_qosubj = True\\n else:\\n if qosubj_.format != \\\"i\\\":\\n qosubj_ = memoryview(array.array(\\\"i\\\",qosubj))\\n _copyback_qosubj = True\\n if qosubj_ is not None and len(qosubj_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qosubj has wrong length\\\")\\n if qoval is None: raise TypeError(\\\"Invalid type for argument qoval\\\")\\n _copyback_qoval = False\\n if qoval is None:\\n qoval_ = None\\n else:\\n try:\\n qoval_ = memoryview(qoval)\\n except TypeError:\\n try:\\n _tmparr_qoval = array.array(\\\"d\\\",qoval)\\n except TypeError:\\n raise TypeError(\\\"Argument qoval has wrong type\\\")\\n else:\\n qoval_ = memoryview(_tmparr_qoval)\\n _copyback_qoval = True\\n else:\\n if qoval_.format != \\\"d\\\":\\n qoval_ = memoryview(array.array(\\\"d\\\",qoval))\\n _copyback_qoval = True\\n if qoval_ is not None and len(qoval_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qoval has wrong length\\\")\\n res,resargs = self.__obj.getqobj64(maxnumqonz_,len(qosubi),qosubi_,qosubj_,qoval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numqonz_return_value = resargs\\n if _copyback_qoval:\\n qoval[:] = _tmparr_qoval\\n if _copyback_qosubj:\\n qosubj[:] = _tmparr_qosubj\\n if _copyback_qosubi:\\n qosubi[:] = _tmparr_qosubi\\n return _numqonz_return_value\",\n \"def getqobj(self,qosubi_,qosubj_,qoval_):\\n maxnumqonz_ = self.getnumqobjnz()\\n numqonz_ = ctypes.c_int64()\\n _qosubi_minlength = (maxnumqonz_)\\n if (maxnumqonz_) > 0 and qosubi_ is not None and len(qosubi_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qosubi is not long enough: Is %d, expected %d\\\" % (len(qosubi_),(maxnumqonz_)))\\n if isinstance(qosubi_,numpy.ndarray) and not qosubi_.flags.writeable:\\n raise ValueError(\\\"Argument qosubi must be writable\\\")\\n if qosubi_ is None:\\n raise ValueError(\\\"Argument qosubi may not be None\\\")\\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\\n _qosubi_copyarray = False\\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qosubi_ is not None:\\n _qosubi_copyarray = True\\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\\n _qosubi_np_tmp[:] = qosubi_\\n assert _qosubi_np_tmp.flags.contiguous\\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qosubi_copyarray = False\\n _qosubi_tmp = None\\n \\n _qosubj_minlength = (maxnumqonz_)\\n if (maxnumqonz_) > 0 and qosubj_ is not None and len(qosubj_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qosubj is not long enough: Is %d, expected %d\\\" % (len(qosubj_),(maxnumqonz_)))\\n if isinstance(qosubj_,numpy.ndarray) and not qosubj_.flags.writeable:\\n raise ValueError(\\\"Argument qosubj must be writable\\\")\\n if qosubj_ is None:\\n raise ValueError(\\\"Argument qosubj may not be None\\\")\\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\\n _qosubj_copyarray = False\\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qosubj_ is not None:\\n _qosubj_copyarray = True\\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\\n _qosubj_np_tmp[:] = qosubj_\\n assert _qosubj_np_tmp.flags.contiguous\\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qosubj_copyarray = False\\n _qosubj_tmp = None\\n \\n _qoval_minlength = (maxnumqonz_)\\n if (maxnumqonz_) > 0 and qoval_ is not None and len(qoval_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qoval is not long enough: Is %d, expected %d\\\" % (len(qoval_),(maxnumqonz_)))\\n if isinstance(qoval_,numpy.ndarray) and not qoval_.flags.writeable:\\n raise ValueError(\\\"Argument qoval must be writable\\\")\\n if qoval_ is None:\\n raise ValueError(\\\"Argument qoval may not be None\\\")\\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\\n _qoval_copyarray = False\\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qoval_ is not None:\\n _qoval_copyarray = True\\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\\n _qoval_np_tmp[:] = qoval_\\n assert _qoval_np_tmp.flags.contiguous\\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qoval_copyarray = False\\n _qoval_tmp = None\\n \\n qosurp_ = ctypes.c_int64(_qosubi_minlength)\\n res = __library__.MSK_XX_getqobj64(self.__nativep,maxnumqonz_,ctypes.byref(qosurp_),ctypes.byref(numqonz_),_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numqonz_ = numqonz_.value\\n _numqonz_return_value = numqonz_\\n if _qosubi_copyarray:\\n qosubi_[:] = _qosubi_np_tmp\\n if _qosubj_copyarray:\\n qosubj_[:] = _qosubj_np_tmp\\n if _qoval_copyarray:\\n qoval_[:] = _qoval_np_tmp\\n return (_numqonz_return_value)\",\n \"def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubk is None: raise TypeError(\\\"Invalid type for argument qcsubk\\\")\\n if qcsubk is None:\\n qcsubk_ = None\\n else:\\n try:\\n qcsubk_ = memoryview(qcsubk)\\n except TypeError:\\n try:\\n _tmparr_qcsubk = array.array(\\\"i\\\",qcsubk)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubk has wrong type\\\")\\n else:\\n qcsubk_ = memoryview(_tmparr_qcsubk)\\n \\n else:\\n if qcsubk_.format != \\\"i\\\":\\n qcsubk_ = memoryview(array.array(\\\"i\\\",qcsubk))\\n \\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubk_ is None:\\n raise ValueError(\\\"Argument qcsubk cannot be None\\\")\\n if qcsubk_ is None:\\n raise ValueError(\\\"Argument qcsubk may not be None\\\")\\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\\n _qcsubk_copyarray = False\\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubk_ is not None:\\n _qcsubk_copyarray = True\\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\\n _qcsubk_np_tmp[:] = qcsubk_\\n assert _qcsubk_np_tmp.flags.contiguous\\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubk_copyarray = False\\n _qcsubk_tmp = None\\n \\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def convert_to_q(self):\\n if self.measure == 'Q':\\n warnings.warn('Parameters are already converted to Q!')\\n else:\\n kappa_sp = self.kappa_s\\n kappa_yp = self.kappa_y\\n self.kappa_s = self.kappa_s - self.lmbd_s * self.eta_s\\n self.kappa_y = self.kappa_y - self.lmbd_y * self.eta_y\\n self.scale = kappa_sp / self.kappa_s\\n self.mean_v *= (kappa_yp / self.kappa_y * self.scale)\\n self.lmbd = 0\\n self.eta_y *= (self.scale**.5)\\n self.measure = 'Q'\\n self.update_ajd()\",\n \"def putSCeval(self,\\n opro = None,\\n oprjo = None,\\n oprfo = None,\\n oprgo = None,\\n oprho = None,\\n oprc = None,\\n opric = None,\\n oprjc = None,\\n oprfc = None,\\n oprgc = None,\\n oprhc = None):\\n\\n if ( opro is not None\\n and oprjo is not None\\n and oprfo is not None\\n and oprgo is not None\\n and oprho is not None):\\n # we have objective.\\n try:\\n numnlov = len(opro)\\n if ( numnlov != len(oprjo)\\n or numnlov != len(oprfo)\\n or numnlov != len(oprgo)\\n or numnlov != len(oprho)):\\n raise SCoptException(\\\"Arguments opro, oprjo, oprfo, oprgo and oprho have different lengths\\\")\\n if not all([ isinstance(i,scopr) for i in opro ]):\\n raise SCoptException(\\\"Argument opro must be an array of mosek.scopr\\\")\\n\\n _opro = array.array('i',opro)\\n _oprjo = array.array('i',oprjo)\\n _oprfo = array.array('d',oprfo)\\n _oprgo = array.array('d',oprgo)\\n _oprho = array.array('d',oprho)\\n except TypeError:\\n raise ValueError(\\\"Arguments opro, oprjo, oprfo, oprgo and oprho must be arrays\\\")\\n else:\\n numnlov = 0\\n\\n if ( oprc is not None\\n and opric is not None\\n and oprjc is not None\\n and oprfc is not None\\n and oprgc is not None\\n and oprhc is not None):\\n # we have objective.\\n try:\\n numnlcv = len(oprc)\\n if ( numnlcv != len(opric)\\n or numnlcv != len(oprjc)\\n or numnlcv != len(oprfc)\\n or numnlcv != len(oprgc)\\n or numnlcv != len(oprhc)):\\n raise ValueError(\\\"Arguments oprc, opric, oprjc, oprfc, oprgc and oprhc have different lengths\\\") \\n if not all([isinstance(i,scopr) for i in oprc]):\\n raise ValieError(\\\"Argument oprc must be an array of mosek.scopr\\\")\\n _oprc = array.array('i',oprc)\\n _opric = array.array('i',opric)\\n _oprjc = array.array('i',oprjc)\\n _oprfc = array.array('d',oprfc)\\n _oprgc = array.array('d',oprgc)\\n _oprhc = array.array('d',oprhc)\\n except TypeError:\\n # not 'len' operation\\n raise ValueError(\\\"Arguments oprc, opric, oprjc, oprfc, oprgc and oprhc must be arrays\\\") \\n else:\\n numnlcv = 0\\n\\n if numnlov > 0 or numnlcv > 0:\\n args = []\\n if numnlov > 0:\\n args.append(memoryview(_opro))\\n args.append(memoryview(_oprjo))\\n args.append(memoryview(_oprfo))\\n args.append(memoryview(_oprgo))\\n args.append(memoryview(_oprho))\\n else:\\n args.extend([ None, None, None, None, None ])\\n\\n if numnlcv > 0:\\n args.append(memoryview(_oprc))\\n args.append(memoryview(_opric))\\n args.append(memoryview(_oprjc))\\n args.append(memoryview(_oprfc))\\n args.append(memoryview(_oprgc))\\n args.append(memoryview(_oprhc))\\n else:\\n args.extend([ None, None, None, None, None, None ])\\n\\n print(len(args))\\n res = self.__obj.putSCeval(*args)\",\n \"def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def update_q(self):\\n beta = self.EC_beta\\n self.gamma_q = (self.gamma_s - self.gamma_r) * beta + (1 - beta) * self.gamma_q\\n self.Sigma_q = (self.Sigma_s - self.Sigma_r) * beta + (1 - beta) * self.Sigma_q\\n try:\\n assert np.all(np.logical_not(np.isnan(self.gamma_q)))\\n except:\\n print(\\\"Invalid update encountered...\\\")\",\n \"def _update_objective(self):\\n # rewrap the cost if the solver has been run\\n self.Finalize()\\n return\",\n \"def Optimize_Sqq(qj,dt):\\n\\n # find the signal psd\\n fs=1.0/dt\\n M,L = 256,512\\n wxx,Sxx=sa.Welch_estimator(qj,fs=fs,M=M,L=L)\\n w_threshold = fs*np.pi/2\\n Sxx=Sxx[wxx<w_threshold]\\n wxx=wxx[wxx<w_threshold]\\n\\n # the psd of oscillator model is computed using Wiener-Khinchin relation\\n # and called in the objective function\\n print('S_qq matching objective fun: ls error of spectrum')\\n spec_ls_distance = lambda params: np.linalg.norm(Sxx*(Sxx - sa.Oscillator_Spectrum(params[0],params[1]/params[0],params[1],wxx)),ord=1)\\n\\n # optimize via pyswarm - v1\\n lb = [0.001, 0.001]\\n ub = [100, 500]\\n xopt, fopt = pso(spec_ls_distance, lb, ub, maxiter=10000,swarmsize=10000,minfunc=1e-10)\\n k,D = xopt[0],xopt[1]\\n\\n b = D/k\\n print('result: k='+str(k)+' b='+str(b)+' D='+str(D))\\n\\n return k,D\",\n \"def putsolutionyi(self,i_,whichsol_,y_):\\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def objective(self, objective):\\n\\n self._objective = objective\",\n \"def puty(self,whichsol_,y_):\\n _y_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),self.getnumcon()))\\n if y_ is None:\\n raise ValueError(\\\"Argument y cannot be None\\\")\\n if y_ is None:\\n raise ValueError(\\\"Argument y may not be None\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n res = __library__.MSK_XX_puty(self.__nativep,whichsol_,_y_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def tweak_q(self, q):\\n self._q = q\\n self.reset()\",\n \"def putbarsj(self,whichsol_,j_,barsj_):\\n _barsj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj is not long enough: Is %d, expected %d\\\" % (len(barsj_),self.getlenbarvarj((j_))))\\n if barsj_ is None:\\n raise ValueError(\\\"Argument barsj cannot be None\\\")\\n if barsj_ is None:\\n raise ValueError(\\\"Argument barsj may not be None\\\")\\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\\n _barsj_copyarray = False\\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barsj_ is not None:\\n _barsj_copyarray = True\\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\\n _barsj_np_tmp[:] = barsj_\\n assert _barsj_np_tmp.flags.contiguous\\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barsj_copyarray = False\\n _barsj_tmp = None\\n \\n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def updateIMU(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray) -> np.ndarray:\\n if gyr is None or not np.linalg.norm(gyr)>0:\\n return q\\n qEst = 0.5 * q_prod(q, [0, *gyr]) # (eq. 12)\\n a_norm = np.linalg.norm(acc)\\n if a_norm>0:\\n a = acc/a_norm\\n qw, qx, qy, qz = q/np.linalg.norm(q)\\n # Gradient objective function (eq. 25) and Jacobian (eq. 26)\\n f = np.array([2.0*(qx*qz - qw*qy) - a[0],\\n 2.0*(qw*qx + qy*qz) - a[1],\\n 2.0*(0.5-qx**2-qy**2) - a[2]]) # (eq. 25)\\n J = np.array([[-2.0*qy, 2.0*qz, -2.0*qw, 2.0*qx],\\n [ 2.0*qx, 2.0*qw, 2.0*qz, 2.0*qy],\\n [ 0.0, -4.0*qx, -4.0*qy, 0.0 ]]) # (eq. 26)\\n # Objective Function Gradient\\n gradient = J.T@f # (eq. 34)\\n gradient /= np.linalg.norm(gradient)\\n qEst -= self.gain*gradient # (eq. 33)\\n q += qEst*self.Dt # (eq. 13)\\n q /= np.linalg.norm(q)\\n return q\",\n \"def getqobjij(self,i_,j_):\\n qoij_ = ctypes.c_double()\\n res = __library__.MSK_XX_getqobjij(self.__nativep,i_,j_,ctypes.byref(qoij_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n qoij_ = qoij_.value\\n _qoij_return_value = qoij_\\n return (_qoij_return_value)\",\n \"def update_Q(self):\",\n \"def calc_q_square(self):\\n return self._q_x()**2 + self._q_z()**2\",\n \"def update(self, q=None, l=None, u=None,\\n Px=None, Px_idx=np.array([]), Ax=None, Ax_idx=np.array([])):\\n\\n # get problem dimensions\\n (n, m) = self._model.dimensions()\\n\\n # check consistency of the input arguments\\n if q is not None and len(q) != n:\\n raise ValueError(\\\"q must have length n\\\")\\n if l is not None:\\n if not isinstance(l, np.ndarray):\\n raise TypeError(\\\"l must be numpy.ndarray, not %s\\\" %\\n type(l).__name__)\\n elif len(l) != m:\\n raise ValueError(\\\"l must have length m\\\")\\n # Convert values to -OSQP_INFTY\\n l = np.maximum(l, -_osqp.constant('OSQP_INFTY'))\\n if u is not None:\\n if not isinstance(u, np.ndarray):\\n raise TypeError(\\\"u must be numpy.ndarray, not %s\\\" %\\n type(u).__name__)\\n elif len(u) != m:\\n raise ValueError(\\\"u must have length m\\\")\\n # Convert values to OSQP_INFTY\\n u = np.minimum(u, _osqp.constant('OSQP_INFTY'))\\n if Ax is None:\\n if len(Ax_idx) > 0:\\n raise ValueError(\\\"Vector Ax has not been specified\\\")\\n else:\\n if len(Ax_idx) > 0 and len(Ax) != len(Ax_idx):\\n raise ValueError(\\\"Ax and Ax_idx must have the same lengths\\\")\\n if Px is None:\\n if len(Px_idx) > 0:\\n raise ValueError(\\\"Vector Px has not been specified\\\")\\n else:\\n if len(Px_idx) > 0 and len(Px) != len(Px_idx):\\n raise ValueError(\\\"Px and Px_idx must have the same lengths\\\")\\n if q is None and l is None and u is None and Px is None and Ax is None:\\n raise ValueError(\\\"No updatable data has been specified\\\")\\n\\n # update linear cost\\n if q is not None:\\n self._model.update_lin_cost(q)\\n\\n # update lower bound\\n if l is not None and u is None:\\n self._model.update_lower_bound(l)\\n\\n # update upper bound\\n if u is not None and l is None:\\n self._model.update_upper_bound(u)\\n\\n # update bounds\\n if l is not None and u is not None:\\n self._model.update_bounds(l, u)\\n\\n # update matrix P\\n if Px is not None and Ax is None:\\n self._model.update_P(Px, Px_idx, len(Px))\\n\\n # update matrix A\\n if Ax is not None and Px is None:\\n self._model.update_A(Ax, Ax_idx, len(Ax))\\n\\n # update matrices P and A\\n if Px is not None and Ax is not None:\\n self._model.update_P_A(Px, Px_idx, len(Px), Ax, Ax_idx, len(Ax))\\n\\n\\n # TODO(bart): this will be unnecessary when the derivative will be in C\\n # update problem data in self._derivative_cache\\n if q is not None:\\n self._derivative_cache[\\\"q\\\"] = q\\n\\n if l is not None:\\n self._derivative_cache[\\\"l\\\"] = l\\n\\n if u is not None:\\n self._derivative_cache[\\\"u\\\"] = u\\n\\n if Px is not None:\\n if Px_idx.size == 0:\\n self._derivative_cache[\\\"P\\\"].data = Px\\n else:\\n self._derivative_cache[\\\"P\\\"].data[Px_idx] = Px\\n\\n if Ax is not None:\\n if Ax_idx.size == 0:\\n self._derivative_cache[\\\"A\\\"].data = Ax\\n else:\\n self._derivative_cache[\\\"A\\\"].data[Ax_idx] = Ax\\n\\n # delete results from self._derivative_cache to prohibit\\n # taking the derivative of unsolved problems\\n if \\\"results\\\" in self._derivative_cache.keys():\\n del self._derivative_cache[\\\"results\\\"]\",\n \"def objective(self):\\n pass\",\n \"def test_qing(self):\\n fun = get_problem('qing', self.dimension, -500, 500)\\n self.assertAlmostEqual(fun(self.array10), 584.0, delta=1e-4)\",\n \"def do_reduction_placzek_corrections(q,sqfg,bgd,rescale_bgd=1.0,plaz_type=None,\\n gauss_damp=False,gw=20.0,qmax=None,qmin=None,\\n rmin=0.0,rmax=20.0,delr=.02\\n ,qminpla=10.0,qmaxpla=30.0,ndeg=2, return_correction = False,\\n skip_bgd = False, return_final_sq = False, force_qmax_type='Off'):\\n #first, make netsq if bgd and/or damping is present\\n q = np.array(q)\\n sqfg = np.array(sqfg)\\n bgd = np.array(bgd)\\n\\n if skip_bgd:\\n netsq = sqfg\\n else:\\n netsq = sqfg - bgd*rescale_bgd\\n\\n\\n if gauss_damp:\\n netsq = netsq*gauss(q,gw,0)\\n\\n\\n if force_qmax_type == 'Force Data (PreCorrection)':\\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n netsq -= mean_sqmax\\n\\n #now, apply a correction if requested\\n if plaz_type != None:\\n if plaz_type == 'Polynomial' or plaz_type == 'poly' or plaz_type == 'ndeg':\\n sq_poly_fit = fit_ndeg_to_sq(q,netsq,ndeg=ndeg,qmin=qminpla,qmax=qmaxpla)\\n this_fit = sq_poly_fit\\n elif plaz_type == 'Pseudo-Voight' or plaz_type == 'pv' or plaz_type == 'hydro':\\n pv_fit = fit_pv_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_fit\\n elif plaz_type == 'PVoight + n0' or plaz_type == 'pvndeg0':\\n pv_n0_fit = fit_pv_n0_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_n0_fit\\n elif plaz_type == 'PVoight + n1' or plaz_type == 'pvndeg1':\\n pv_n1_fit = fit_pv_n1_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_n1_fit\\n elif plaz_type == 'PVoight + n2' or plaz_type == 'pvndeg2':\\n pv_n2_fit = fit_pv_n2_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\\n this_fit = pv_n2_fit\\n else:\\n print (\\\"I don't know that correction type, sorry\\\")\\n this_fit = np.zeros(len(q))\\n else:\\n this_fit = np.zeros(len(q))\\n\\n if force_qmax_type == 'Force Data' or force_qmax_type == 'Force Both (Independent)':\\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n netsq -= mean_sqmax\\n if force_qmax_type == 'Force Correction' or force_qmax_type == 'Force Both (Independent)':\\n qcut, sqcut = cut_data(q,this_fit,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n this_fit -= mean_sqmax\\n if force_qmax_type == 'ReCorrection':\\n qcut, sqcut = cut_data(q,netsq-this_fit,qmax-.5,qmax)\\n mean_sqmax = np.mean(sqcut)\\n this_fit += mean_sqmax\\n\\n netsq = netsq - this_fit\\n\\n if return_correction:\\n return this_fit\\n\\n if return_final_sq:\\n return netsq\\n\\n #finally, generate PDF\\n r,gr = make_gr_from_sq(q,netsq,qmin=qmin,qmax=qmax,rmin=rmin,rmax=rmax,delr=delr)\\n\\n return r,gr\",\n \"def initial_Q(self, negative):\\n \\n ##get each values in the Q, and change their content to given number, plan to use in Q5\\n for key in self.Q.iterkeys():\\n self.Q[key] = float(negative)\",\n \"def quo(self, a, b):\\n raise NotImplementedError\",\n \"def qUpdate(self,s,a,r,sPrime):\\r\\n #get max_a' Q(s',a')\\r\\n \\\"\\\"\\\"\\r\\n maxA = 0\\r\\n maxQ = float(\\\"-inf\\\")\\r\\n for aCurr in actions:\\r\\n qCurr = Q[(sPrime,aCurr)]\\r\\n if qCurr > maxQ:\\r\\n maxA = aCurr\\r\\n maxQ = qCurr\\r\\n \\\"\\\"\\\"\\r\\n maxQ = self.maxQ(sPrime)[0]\\r\\n #update Q and return it\\r\\n self.Q[(s,a)] = (1 - self.alpha) * self.Q[(s,a)] + self.alpha * (r + self.gamma * maxQ)\",\n \"def objective(self, x):\\n pass\",\n \"def objective(self, x):\\n pass\",\n \"def q(self, q: ComType):\\n if isinstance(q, complex):\\n self._pwr = q\\n else:\\n self._pwr = complex(0, q)\",\n \"def simple_q(self: Q) -> Q:\\n\\n self.t = sp.simplify(self.t)\\n self.x = sp.simplify(self.x)\\n self.y = sp.simplify(self.y)\\n self.z = sp.simplify(self.z)\\n return self\",\n \"def setQ(self,Q):\\n self.Q = Q\",\n \"def getqconk(self,k_,qcsubi,qcsubj,qcval): # 3\\n maxnumqcnz_ = self.getnumqconknz((k_))\\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n _copyback_qcsubi = False\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n _copyback_qcsubi = True\\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n _copyback_qcsubi = True\\n if qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubi has wrong length\\\")\\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n _copyback_qcsubj = False\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n _copyback_qcsubj = True\\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n _copyback_qcsubj = True\\n if qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubj has wrong length\\\")\\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n _copyback_qcval = False\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n _copyback_qcval = True\\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n _copyback_qcval = True\\n if qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcval has wrong length\\\")\\n res,resargs = self.__obj.getqconk64(k_,maxnumqcnz_,len(qcsubi),qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numqcnz_return_value = resargs\\n if _copyback_qcval:\\n qcval[:] = _tmparr_qcval\\n if _copyback_qcsubj:\\n qcsubj[:] = _tmparr_qcsubj\\n if _copyback_qcsubi:\\n qcsubi[:] = _tmparr_qcsubi\\n return _numqcnz_return_value\",\n \"def test_solve_quadratic(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.fixed = False\\n iden2.x.fixed = False\\n iden3.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 0)\\n self.assertAlmostEqual(iden1.x.val, 1)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 3)\",\n \"def _Q(self, x, y):\\n\\n # Calculate the [Q] coefficient matrix\\n Q = array([[0, 0, 0, -2, 0, 0, -6*x, -2*y, 0, 0, -6*x*y, 0],\\n [0, 0, 0, 0, 0, -2, 0, 0, -2*x, -6*y, 0, -6*x*y],\\n [0, 0, 0, 0, -2, 0, 0, -4*x, -4*y, 0, -6*x**2, -6*y**2]])\\n \\n # Return the [Q] coefficient matrix\\n return Q\",\n \"def test_convert_to_q(self):\\n\\n riskfree = .01\\n lmbd = .01\\n lmbd_s = .5\\n lmbd_y = .5\\n mean_v = .5\\n kappa_s = 1.5\\n kappa_y = .5\\n eta_s = .1\\n eta_y = .01\\n rho = -.5\\n\\n theta = [riskfree, mean_v, kappa_s, kappa_y, eta_s, eta_y,\\n rho, lmbd, lmbd_s, lmbd_y]\\n param = CentTendParam.from_theta(theta)\\n param.convert_to_q()\\n\\n kappa_sq = kappa_s - lmbd_s * eta_s\\n kappa_yq = kappa_y - lmbd_y * eta_y\\n scale = kappa_s / kappa_sq\\n\\n self.assertEqual(param.measure, 'Q')\\n self.assertEqual(param.riskfree, riskfree)\\n self.assertEqual(param.lmbd, 0)\\n self.assertEqual(param.lmbd_s, lmbd_s)\\n self.assertEqual(param.lmbd_y, lmbd_y)\\n self.assertEqual(param.mean_v, mean_v * kappa_y / kappa_yq * scale)\\n self.assertEqual(param.kappa_s, kappa_sq)\\n self.assertEqual(param.kappa_y, kappa_yq)\\n self.assertEqual(param.eta_s, eta_s)\\n self.assertEqual(param.eta_y, eta_y * scale**.5)\\n self.assertEqual(param.rho, rho)\\n self.assertTrue(param.is_valid())\",\n \"def putbarxj(self,whichsol_,j_,barxj_):\\n _barxj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj is not long enough: Is %d, expected %d\\\" % (len(barxj_),self.getlenbarvarj((j_))))\\n if barxj_ is None:\\n raise ValueError(\\\"Argument barxj cannot be None\\\")\\n if barxj_ is None:\\n raise ValueError(\\\"Argument barxj may not be None\\\")\\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\\n _barxj_copyarray = False\\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barxj_ is not None:\\n _barxj_copyarray = True\\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\\n _barxj_np_tmp[:] = barxj_\\n assert _barxj_np_tmp.flags.contiguous\\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barxj_copyarray = False\\n _barxj_tmp = None\\n \\n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getqobjij(self,i_,j_): # 3\\n res,resargs = self.__obj.getqobjij(i_,j_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _qoij_return_value = resargs\\n return _qoij_return_value\",\n \"def test_gen_qubo_matrix(self):\\n cfg = {\\\"problems\\\": {\\\"QK\\\": {\\\"random_P\\\": False}}}\\n projects = np.array([\\n [2, 4, 3, 5],\\n [4, 5, 1, 3],\\n [3, 1, 2, 2],\\n [5, 3, 2, 4]\\n ])\\n budgets = np.array([8, 6, 5, 3])\\n problem = QuadraticKnapsack(cfg, projects, budgets, 16, 10)\\n matrix = problem.gen_qubo_matrix()\\n want = [\\n [1922.,-476.,-397.,-235., -80.,-160.,-320.,-640.],\\n [-476.,1565.,-299.,-177., -60.,-120.,-240.,-480.],\\n [-397.,-299.,1352.,-148., -50.,-100.,-200.,-400.],\\n [-235.,-177.,-148., 874., -30., -60.,-120.,-240.],\\n [ -80., -60., -50., -30., 310., -20., -40., -80.],\\n [-160.,-120.,-100., -60., -20., 600., -80.,-160.],\\n [-320.,-240.,-200.,-120., -40., -80.,1120.,-320.],\\n [-640.,-480.,-400.,-240., -80.,-160.,-320.,1920.]\\n ]\\n self.assertCountEqual(matrix.tolist(), want)\",\n \"def set_qxqz(self):\\n self.qx = self._q_x()\\n self.qz = self._q_z()\",\n \"def _qij_plus(i: int, j: int):\\n ia = i * 2 + 0\\n ja = j * 2 + 0\\n term = FermionOperator(((ja, 0), (ia, 0)), 1.0)\\n return term\",\n \"def _onSquaredQ(self, event):\\r\\n self.graph.xaxis('\\\\\\\\rm{q}^2 ', 'A^{-2}')\\r\\n self.set_xscale('squared')\\r\\n\\r\\n self.graph.render(self)\\r\\n self.subplot.figure.canvas.draw_idle()\",\n \"def _qCurveToOne(self, pt1, pt2):\\n assert pt2 is not None\\n t = \\\"Q\\\"\\n t += pointToString(pt1) + \\\" \\\"\\n t += pointToString(pt2)\\n self._commands.append(t)\\n self._lastCommand = \\\"Q\\\"\\n self._lastX, self._lastY = pt2\",\n \"def test_solve_quadratic_fixed(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.val = 4\\n iden2.x.val = 5\\n iden3.x.val = 6\\n iden1.x.name = 'x1'\\n iden2.x.name = 'x2'\\n iden3.x.name = 'x3'\\n iden2.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 10)\\n self.assertAlmostEqual(iden1.x.val, 4)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 6)\",\n \"def _qij_minus(i: int, j: int):\\n ib = i * 2 + 1\\n jb = j * 2 + 1\\n term = FermionOperator(((jb, 0), (ib, 0)), 1.0)\\n return term\",\n \"def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def pro_avfid_superoperator_compsubspace_phasecorrected_onlystaticqubit(U,L1,phases):\\n\\n Ucorrection = qtp.Qobj([[np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0, 0],\\n [0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0],\\n [0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0],\\n [0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0, 0, 0],\\n [0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0, 0],\\n [0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0],\\n [0, 0, 0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0]))]],\\n type='oper',\\n dims=[[3, 3], [3, 3]])\\n\\n if U.type=='oper':\\n U=Ucorrection*U\\n inner = U.dag()*U_target\\n part_idx = [0, 1, 3, 4] # only computational subspace\\n ptrace = 0\\n for i in part_idx:\\n ptrace += inner[i, i]\\n dim = 4 # 2 qubits comp subspace \\n\\n return np.real(((np.abs(ptrace))**2+dim*(1-L1))/(dim*(dim+1)))\\n\\n elif U.type=='super':\\n U=qtp.to_super(Ucorrection)*U\\n kraus_form = qtp.to_kraus(U)\\n dim=4 # 2 qubits in the computational subspace\\n part_idx = [0, 1, 3, 4] # only computational subspace\\n psum=0\\n for A_k in kraus_form:\\n ptrace = 0\\n inner = U_target_diffdims.dag()*A_k # otherwise dimension mismatch\\n for i in part_idx:\\n ptrace += inner[i, i]\\n psum += (np.abs(ptrace))**2\\n\\n return np.real((dim*(1-L1) + psum) / (dim*(dim + 1)))\",\n \"def updateMARG(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray, mag: np.ndarray) -> np.ndarray:\\n if gyr is None or not np.linalg.norm(gyr)>0:\\n return q\\n qEst = 0.5 * q_prod(q, [0, *gyr]) # (eq. 12)\\n a_norm = np.linalg.norm(acc)\\n if a_norm>0:\\n a = acc/a_norm\\n if mag is None or not np.linalg.norm(mag)>0:\\n return self.updateIMU(q, gyr, acc)\\n m = mag/np.linalg.norm(mag)\\n h = q_prod(q, q_prod([0, *m], q_conj(q))) # (eq. 45)\\n bx = np.linalg.norm([h[1], h[2]]) # (eq. 46)\\n bz = h[3]\\n qw, qx, qy, qz = q/np.linalg.norm(q)\\n # Gradient objective function (eq. 31) and Jacobian (eq. 32)\\n f = np.array([2.0*(qx*qz - qw*qy) - a[0],\\n 2.0*(qw*qx + qy*qz) - a[1],\\n 2.0*(0.5-qx**2-qy**2) - a[2],\\n 2.0*bx*(0.5 - qy**2 - qz**2) + 2.0*bz*(qx*qz - qw*qy) - m[0],\\n 2.0*bx*(qx*qy - qw*qz) + 2.0*bz*(qw*qx + qy*qz) - m[1],\\n 2.0*bx*(qw*qy + qx*qz) + 2.0*bz*(0.5 - qx**2 - qy**2) - m[2]]) # (eq. 31)\\n J = np.array([[-2.0*qy, 2.0*qz, -2.0*qw, 2.0*qx ],\\n [ 2.0*qx, 2.0*qw, 2.0*qz, 2.0*qy ],\\n [ 0.0, -4.0*qx, -4.0*qy, 0.0 ],\\n [-2.0*bz*qy, 2.0*bz*qz, -4.0*bx*qy-2.0*bz*qw, -4.0*bx*qz+2.0*bz*qx],\\n [-2.0*bx*qz+2.0*bz*qx, 2.0*bx*qy+2.0*bz*qw, 2.0*bx*qx+2.0*bz*qz, -2.0*bx*qw+2.0*bz*qy],\\n [ 2.0*bx*qy, 2.0*bx*qz-4.0*bz*qx, 2.0*bx*qw-4.0*bz*qy, 2.0*bx*qx ]]) # (eq. 32)\\n gradient = J.T@f # (eq. 34)\\n gradient /= np.linalg.norm(gradient)\\n qEst -= self.gain*gradient # (eq. 33)\\n q += qEst*self.Dt # (eq. 13)\\n q /= np.linalg.norm(q)\\n return q\",\n \"def square(q_1: Q) -> Q:\\n\\n end_q_type = f\\\"{q_1.q_type}²\\\"\\n\\n qxq = _commuting_products(q_1, q_1)\\n\\n sq_q = Q(q_type=end_q_type, representation=q_1.representation)\\n sq_q.t = qxq[\\\"tt\\\"] - qxq[\\\"xx+yy+zz\\\"]\\n sq_q.x = qxq[\\\"tx+xt\\\"]\\n sq_q.y = qxq[\\\"ty+yt\\\"]\\n sq_q.z = qxq[\\\"tz+zt\\\"]\\n\\n return sq_q\",\n \"def createObjective(self, solver, objective, conflict_vars_d, sched_d, conflicts_d, courses):\\n\\n\\n # Put a little pressure on to not use slots 6 or 7\\n for asl in self.vars_actualslots:\\n if asl[1] == \\\"6\\\":\\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_SLOT_6']) \\n if asl[1] == \\\"7\\\":\\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_SLOT_7']) \\n\\n if asl[0]==\\\"T\\\" and asl[1] in [\\\"4\\\", \\\"5\\\"]:\\n if self.name in non_FAS_instructor:\\n # for non-FAS faculty, favor teaching on Tuesday 3pm-5pm\\n if asl[1] == \\\"4\\\":\\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_FAVOR_COURSES_TU_3_TO_5'])\\n else: \\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_FAVOR_COURSES_TU_3_TO_5']*3)\\n else:\\n # avoid teaching on Tuesday 3pm-5pm for FAS instructors\\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_COURSES_TU_3_TO_5'])\\n\\n if asl[0]==\\\"F\\\":\\n # avoid Friday teaching, to mimic faculty preferences\\n objective.SetCoefficient(self.vars_actualslots[asl], PARAMS['WEIGHT_AVOID_COURSES_FRIDAY']) \\n \\n \\n \\n # Add to objective function for bad course conflicts\\n for other in conflicts_d.get(self.name, []):\\n assert is_cross_list_canonical(other), other\\n\\n if other not in courses and other not in sched_d:\\n # we're not scheduling the other course, so we can ignore it\\n continue\\n\\n if other in courses and not (self.name < other):\\n # we will let the processing for the other course handle this weight\\n continue\\n\\n # create a variable that indicates if self and other conflict, add add the weight\\n # to the objective function\\n v_conflicts = solver.IntVar(0, 1, self.name + \\\" and \\\" + other + \\\" conflict\\\")\\n objective.SetCoefficient(v_conflicts, PARAMS['WEIGHT_BAD_CONFLICT_FACTOR'] * int(conflicts_d[self.name][other]))\\n\\n # Record the variable so we can look at it later\\n if self.name not in conflict_vars_d:\\n conflict_vars_d[self.name] = {}\\n assert other not in conflict_vars_d[self.name]\\n conflict_vars_d[self.name][other] = v_conflicts\\n \\n if other not in courses:\\n # the other course already has a fixed schedule.\\n # Go through each actual slot and see if it intersects with other course\\n disjuncts = []\\n for s in self.vars_actualslots:\\n my_course_time = ss.meeting_time_to_course_time([s])\\n \\n if sct.courses_conflict([my_course_time], sched_d[other]):\\n # slots s conflicts with the time for other\\n disjuncts.append(self.vars_actualslots[s])\\n\\n if len(disjuncts) > 0:\\n makeDisjunction(solver, v_conflicts, disjuncts)\\n \\n if other in courses:\\n # we are scheduling the other course, so we need to use its variables.\\n # Create a variable for each actual slot that indicates if both self and other use that slot.\\n d = courses[other]\\n vs_d_same_slot = []\\n for asl in self.vars_actualslots:\\n if asl in d.vars_actualslots:\\n # actual slot represented by vas is in both courses self and d.\\n v_both_use_slot = solver.IntVar(0, 1, self.name + \\\" and \\\" + other + \\\" using slot \\\" + asl)\\n makeConjunction(solver, v_both_use_slot, [self.vars_actualslots[asl], d.vars_actualslots[asl]])\\n vs_d_same_slot.append(v_both_use_slot)\\n\\n if len(vs_d_same_slot) > 0:\\n makeDisjunction(solver, v_conflicts, vs_d_same_slot)\",\n \"def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi_)\\n elif nz_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj_)\\n elif nz_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij_)\\n elif nz_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n idx_ = ctypes.c_int64()\\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n idx_ = idx_.value\\n _idx_return_value = idx_\\n return (_idx_return_value)\",\n \"def getqconk(self,k_,qcsubi_,qcsubj_,qcval_):\\n maxnumqcnz_ = self.getnumqconknz((k_))\\n numqcnz_ = ctypes.c_int64()\\n _qcsubi_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubi is not long enough: Is %d, expected %d\\\" % (len(qcsubi_),self.getnumqconknz((k_))))\\n if isinstance(qcsubi_,numpy.ndarray) and not qcsubi_.flags.writeable:\\n raise ValueError(\\\"Argument qcsubi must be writable\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n _qcsubj_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcsubj is not long enough: Is %d, expected %d\\\" % (len(qcsubj_),self.getnumqconknz((k_))))\\n if isinstance(qcsubj_,numpy.ndarray) and not qcsubj_.flags.writeable:\\n raise ValueError(\\\"Argument qcsubj must be writable\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n _qcval_minlength = self.getnumqconknz((k_))\\n if self.getnumqconknz((k_)) > 0 and qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\\n raise ValueError(\\\"Array argument qcval is not long enough: Is %d, expected %d\\\" % (len(qcval_),self.getnumqconknz((k_))))\\n if isinstance(qcval_,numpy.ndarray) and not qcval_.flags.writeable:\\n raise ValueError(\\\"Argument qcval must be writable\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n qcsurp_ = ctypes.c_int64(_qcsubi_minlength)\\n res = __library__.MSK_XX_getqconk64(self.__nativep,k_,maxnumqcnz_,ctypes.byref(qcsurp_),ctypes.byref(numqcnz_),_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numqcnz_ = numqcnz_.value\\n _numqcnz_return_value = numqcnz_\\n if _qcsubi_copyarray:\\n qcsubi_[:] = _qcsubi_np_tmp\\n if _qcsubj_copyarray:\\n qcsubj_[:] = _qcsubj_np_tmp\\n if _qcval_copyarray:\\n qcval_[:] = _qcval_np_tmp\\n return (_numqcnz_return_value)\",\n \"def jval(self):\\n return self.q * self.model.nobs_moms\",\n \"def substitute_cost(self, i, j):\\n raise NotImplementedError\",\n \"def ket(self: Qs) -> Qs:\\n\\n if self.qs_type == \\\"ket\\\":\\n return self\\n\\n ket = conjs(deepcopy(self))\\n ket.rows = self.dim\\n ket.columns = 1\\n\\n ket.qs_type = \\\"ket\\\" if self.dim > 1 else \\\"scalar_q\\\"\\n\\n return ket\",\n \"def quaternion_conjugate(self, q):\\n\\n \\\"\\\"\\\"\\n in-place operation is an operation that changes directly the content of a given Tensor without making a copy.\\n ALL operations on the tensor that operate in-place on it will have an _ postfix.\\n \\\"\\\"\\\"\\n q_star = q.new(4).fill_(-1)\\n\\n # leave the scalar unchanged and change signs of i, j, k number parts\\n q_star[0] = 1.0\\n\\n return q * q_star\",\n \"def q_criterion(a):\\n print(\\\"Detection method: Q criterion\\\")\\n Q = np.zeros((a.u.shape[0], a.u.shape[1]))\\n print(a.u.shape[0], a.u.shape[1])\\n #print(Q.shape)\\n for i in range(a.u.shape[0]):\\n for j in range(a.u.shape[1]):\\n Q[i, j] = -0.5*(a.derivative['dudx'][i, j]**2 + a.derivative['dvdy'][i, j]**2) \\\\\\n - a.derivative['dudy'][i, j] * a.derivative['dvdx'][i, j]\\n return Q\",\n \"def test_quintic(self):\\n fun = get_problem('quintic', self.dimension, -10.0, 10.0)\\n self.assertEqual(fun(self.array6), 0.0)\",\n \"def Q(self, k, x):\\n g = np.asarray(self.g(k, x))\\n Q = g @ g.T\\n return Q\",\n \"def set_rhs(self):\\n k = self.istore[:, 0]\\n ksym = self.stencil.get_symmetric()[k]\\n self.rhs[:] = self.feq[k, np.arange(k.size)] + self.feq[ksym, np.arange(k.size)]\",\n \"def set_rhs(self):\\n k = self.istore[:, 0]\\n ksym = self.stencil.get_symmetric()[k]\\n self.rhs[:] = self.feq[k, np.arange(k.size)] + self.feq[ksym, np.arange(k.size)]\",\n \"def putbarsj(self,whichsol_,j_,barsj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barsj is None: raise TypeError(\\\"Invalid type for argument barsj\\\")\\n if barsj is None:\\n barsj_ = None\\n else:\\n try:\\n barsj_ = memoryview(barsj)\\n except TypeError:\\n try:\\n _tmparr_barsj = array.array(\\\"d\\\",barsj)\\n except TypeError:\\n raise TypeError(\\\"Argument barsj has wrong type\\\")\\n else:\\n barsj_ = memoryview(_tmparr_barsj)\\n \\n else:\\n if barsj_.format != \\\"d\\\":\\n barsj_ = memoryview(array.array(\\\"d\\\",barsj))\\n \\n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj has wrong length\\\")\\n res = self.__obj.putbarsj(whichsol_,j_,barsj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsolutionyi(self,i_,whichsol_,y_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def plot_q_values(self):\\n\\n sim_freq = self.conf['Simulation']['params']['frequency']\\n sim_wvlgth = 1e9*consts.c / sim_freq\\n leg_str = ''\\n for mat, matpath in self.conf['Materials'].items():\\n n, k = get_nk(matpath, sim_freq)\\n mat_wv = 1e-3*sim_wvlgth / n\\n mat_q = 2*np.pi/mat_wv\\n leg_str += '{}: {:.2f} [rads/$\\\\mu$m]\\\\n'.format(mat, mat_q)\\n leg_str = leg_str[0:-1]\\n for lname, l_obj in self.layers.items():\\n qarr = self.data['{}_qvals'.format(lname)]\\n max_pos_freq = np.amax(qarr.real)\\n max_neg_freq = np.amin(qarr.real)\\n min_pos_wv = 1e3*2*np.pi/max_pos_freq\\n if max_neg_freq == 0:\\n min_neg_wv = 0\\n else:\\n min_neg_wv = 1e3*2*np.pi/max_neg_freq\\n plt.figure()\\n inc_q = 2*np.pi/(1e-3*sim_wvlgth)\\n title = 'Layer: {}, Incident q: {:.2f} [rads/$\\\\mu$m]'.format(lname, inc_q)\\n # title += 'Min Positive $\\\\\\\\lambda$: {:.2f} nm, '\\n # title += 'Min Negative $\\\\\\\\lambda$: {:.2f} nm'\\n # title = title.format(lname, sim_wvlgth, min_pos_wv, min_neg_wv)\\n # title = title.format(lname, sim_wvlgth)\\n plt.title(title)\\n # plt.scatter(1e3*2*np.pi/qarr.real, 1e4*qarr.imag/2*np.pi, c='b', s=.5,\\n # marker='o', label=leg_str)\\n plt.scatter(qarr.real, qarr.imag/(2*np.pi), c='b', s=.75,\\n marker='o', label=leg_str)\\n # pt = (qarr[0].real, qarr[0].imag)\\n # theta = np.linspace(0, 1.48, 200)\\n # plt.plot(pt[0]*np.cos(theta), pt[1]/np.cos(theta), 'r--')\\n plt.legend(loc='best')\\n # plt.annotate(leg_str, xy=(.95,.95), xycoords='axes fraction',\\n # size=14, ha='right', va='top',\\n # bbox=dict(boxstyle='round', fc='w'))\\n plt.xlabel('Re(q) [radians/micron]')\\n plt.ylabel('Im(q) [1/microns]')\\n plot_path = os.path.join(self.dir, '{}_qvals.png'.format(lname))\\n plt.grid(True)\\n plt.savefig(plot_path)\\n plt.close()\",\n \"def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi)\\n elif nz_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj)\\n elif nz_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij)\\n elif nz_ != len(valij):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if nz_ is None: nz_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valij is None: raise TypeError(\\\"Invalid type for argument valij\\\")\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n \\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n \\n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _idx_return_value = resargs\\n return _idx_return_value\",\n \"def expand_q(self) -> Q:\\n \\\"\\\"\\\"Expand each term.\\\"\\\"\\\"\\n\\n self.t = sp.expand(self.t)\\n self.x = sp.expand(self.x)\\n self.y = sp.expand(self.y)\\n self.z = sp.expand(self.z)\\n return self\",\n \"def putxx(self,whichsol_,xx_):\\n _xx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),self.getnumvar()))\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx cannot be None\\\")\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx may not be None\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n res = __library__.MSK_XX_putxx(self.__nativep,whichsol_,_xx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def to_qubo(self, index_label=False, params=None):\\n\\n bqm = self.compiled_qubo.eval(params)\\n q, offset = bqm.to_qubo()\\n\\n # Evaluate values of QUBO\\n qubo = {}\\n for (label1, label2), v in q.items():\\n if index_label:\\n i = self.label2index[label1]\\n j = self.label2index[label2]\\n else:\\n i = label1\\n j = label2\\n qubo[(i, j)] = v\\n\\n return qubo, offset\",\n \"def set_fig(cls, obj, quad):\\n\\t\\taddr = quad.result\\n\\t\\ttype = abs(addr) // 1000 # integer division\\n\\t\\trelative_address = abs(addr) - (type * 1000)\\n\\t\\tprint \\\"> Rel = {} - {}\\\".format(abs(addr), (type * 1000))\\n\\t\\tprint \\\"> Set New Fig mem value: type = {}, addr = {}\\\".format(type, relative_address)\\n\\n\\t\\tif addr < 0:\\n\\t\\t\\tcls.heap.memory[type][abs(relative_address)] = obj\\n\\t\\t\\tprint \\\"> Heap memory: {}\\\".format(cls.heap.memory)\\n\\t\\telse:\\n\\t\\t\\tcls.stack.peek().memory[type][relative_address] = obj\\n\\t\\t\\tprint \\\"> Stack memory: {}\\\".format(cls.stack.peek().memory)\",\n \"def puty(self,whichsol_,y): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if y is None: raise TypeError(\\\"Invalid type for argument y\\\")\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n res = self.__obj.puty(whichsol_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def qeye_like(qobj):\\n from .cy.qobjevo import QobjEvo\\n if isinstance(qobj, QobjEvo):\\n qobj = qobj(0)\\n return Qobj(\\n _data.identity_like(qobj.data), dims=qobj.dims, type=qobj.type,\\n superrep=qobj.superrep, isherm=True, isunitary=True, copy=False\\n )\",\n \"def f(self, (k,t), (J,q,dq), **params):\\n f = 0.*q\\n return f\",\n \"def xut2q( self, x , u , t ):\\n \\n # default is q = x\\n \\n return x\",\n \"def update(self, obs, q):\\n for o in obs:\\n prob = np.exp(-70)\\n if self.landmarks:\\n # find the data association with ML\\n prob, landmark_idx, ass_obs, ass_jacobian, ass_adjcov = self.find_data_association(o)\\n if prob < self.TOL:\\n # create new landmark\\n self.create_landmark(o)\\n else:\\n # update corresponding EKF\\n self.update_landmark(np.transpose(np.array([o])), landmark_idx, ass_obs, ass_jacobian, ass_adjcov)\\n else:\\n # no initial landmarks\\n self.create_landmark(o)\\n self.weight *= prob\\n \\n q.put([self]) ###\",\n \"def cuttingStockKantorovich(w, q, B):\\n\\n model = Model(\\\"Naive Cutting Stock\\\")\\n m = max(w)*max(q) # m rolls\\n n = len(q) # n orders \\n y = {}\\n for j in range(m):\\n y[j] = model.addVar(name = \\\"y[%s]\\\" % j, vtype=\\\"BINARY\\\")\\n \\n x = {}\\n for j in range(m):\\n for i in range(n):\\n x[i,j] = model.addVar(name = \\\"x[%s,%s]\\\" %(i,j), lb = 0, vtype=\\\"INTEGER\\\")\\n model.addCons(x[i,j] <= q[i]*y[j])\\n\\n for i in range(n):\\n model.addCons(quicksum(x[i,j] for j in range(m)) == q[i])\\n\\n for j in range(m):\\n model.addCons((quicksum(w[i]*x[i,j] for i in range(n)) <= B))\\n\\n model.setObjective(quicksum(y[j] for j in range(m)), \\\"minimize\\\")\\n model.hideOutput()\\n model.optimize()\\n\\n return model.getObjVal()\",\n \"def fixB(self,i,j,value):\\n if self.coeffPattern[1] == None:\\n m,n=self.m,self.n\\n self.coeffPattern = [[None]*n for i in range(m)]\\n self.coeffPattern[1][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def project_curve(q):\\n n,T = q.shape\\n if n==2:\\n dt = 0.35\\n if n==3:\\n dt = 0.2\\n epsilon = 1e-6\\n\\n iter = 1\\n res = ones(n)\\n J = zeros((n,n))\\n\\n s = linspace(0,1,T)\\n\\n qnew = q.copy()\\n qnew = qnew / sqrt(innerprod_q2(qnew,qnew))\\n\\n qnorm = zeros(T)\\n G = zeros(n)\\n C = zeros(300)\\n while (norm(res) > epsilon):\\n if iter > 300:\\n break\\n\\n # Jacobian\\n for i in range(0,n):\\n for j in range(0,n):\\n J[i,j] = 3 * trapz(qnew[i,:]*qnew[j,:],s)\\n \\n J += eye(n)\\n\\n for i in range(0,T):\\n qnorm[i] = norm(qnew[:,i])\\n \\n # Compute the residue\\n for i in range(0,n):\\n G[i] = trapz(qnew[i,:]*qnorm,s)\\n \\n res = -G\\n\\n if (norm(res) < epsilon):\\n break\\n\\n x = solve(J,res)\\n C[iter] = norm(res)\\n\\n delG = Basis_Normal_A(qnew)\\n temp = zeros((n,T))\\n for i in range(0,n):\\n temp += x[i]*delG[i]*dt\\n \\n qnew += temp\\n iter += 1\\n \\n qnew = qnew/sqrt(innerprod_q2(qnew,qnew))\\n\\n return qnew\",\n \"def updateIMU(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray, dt: float = None) -> np.ndarray:\\n _assert_numerical_iterable(q, 'Quaternion')\\n _assert_numerical_iterable(gyr, 'Tri-axial gyroscope sample')\\n _assert_numerical_iterable(acc, 'Tri-axial accelerometer sample')\\n dt = self.Dt if dt is None else dt\\n if gyr is None or not np.linalg.norm(gyr) > 0:\\n return q\\n qDot = 0.5 * q_prod(q, [0, *gyr]) # (eq. 12)\\n a_norm = np.linalg.norm(acc)\\n if a_norm > 0:\\n a = acc/a_norm\\n qw, qx, qy, qz = q/np.linalg.norm(q)\\n # Objective function (eq. 25)\\n f = np.array([2.0*(qx*qz - qw*qy) - a[0],\\n 2.0*(qw*qx + qy*qz) - a[1],\\n 2.0*(0.5-qx**2-qy**2) - a[2]])\\n if np.linalg.norm(f) > 0:\\n # Jacobian (eq. 26)\\n J = np.array([[-2.0*qy, 2.0*qz, -2.0*qw, 2.0*qx],\\n [ 2.0*qx, 2.0*qw, 2.0*qz, 2.0*qy],\\n [ 0.0, -4.0*qx, -4.0*qy, 0.0 ]])\\n # Objective Function Gradient\\n gradient = J.T@f # (eq. 34)\\n gradient /= np.linalg.norm(gradient)\\n qDot -= self.gain*gradient # (eq. 33)\\n q += qDot*dt # (eq. 13)\\n q /= np.linalg.norm(q)\\n return q\",\n \"def clear(self, clear_obj=True, clear_constraints=True):\\n\\n if clear_constraints:\\n self.constraints = []\\n self.bounds, self.constraints = self.const_creator.create_constraint('weight', weight_bound=(0,1), leverage=1)\\n if clear_obj:\\n self.objective = None\",\n \"def setConstQuantityAndBetaEqState(self, pointDict, quantity, target):\\n print \\\"setConstQuantityAndBetaEqState: \\\", pointDict\\n assert 'ye' not in pointDict, \\\"You can't SPECIFY a Ye if you're \\\" \\\\\\n \\\"setting neutrinoless beta equlibrium!\\\"\\n self.validatePointDict(pointDict)\\n assert len(pointDict) < 2, \\\"State overdetermined for more than 1 indVars!\\\"\\n #todo: check quantity is valid 3D table\\n\\n #defines 1D root solver to use in routine\\n solveRoot = scipyOptimize.brentq # solveRootBisect\\n\\n solveVarName = 'logtemp'\\n currentSolveVar = 0.0\\n currentYe = 0.25\\n #previous variables used to measure convergence of solve\\n # so set them to something significantly different than starting values\\n previousSolveVar = 100.0\\n previousYe = 100.0\\n yeError = relativeError(currentYe, previousYe)\\n solveVarError = relativeError(currentSolveVar, previousSolveVar)\\n otherVarName = pointDict.keys()[0]\\n otherVar = pointDict.values()[0]\\n\\n maxIters = 5\\n tol = 1e-3\\n\\n iteration = 0\\n while iteration < maxIters and yeError + solveVarError > tol/2.0:\\n previousSolveVar = currentSolveVar\\n previousYe = currentYe\\n getSolveVar = lambda x: multidimInterp((currentYe, x, otherVar),\\n [self.h5file['ye'][:],\\n self.h5file[solveVarName],\\n self.h5file[otherVarName]],\\n self.h5file[quantity][...],\\n linInterp, 2) - target\\n try:\\n currentSolveVar = solveRoot(getSolveVar,\\n self.h5file[solveVarName][0],\\n self.h5file[solveVarName][-1],\\n (),tol)\\n except ValueError as err:\\n print \\\"Root for log10(T) not bracketed on entire table: \\\" \\\\\\n + str(err)\\n # see if lower or upper temperature bound best\\n logtemp = self.h5file['logtemp']\\n answer1 = multidimInterp((currentYe, logtemp[0], otherVar),\\n [self.h5file['ye'][:],\\n self.h5file['logtemp'],\\n self.h5file['logrho']],\\n self.h5file[quantity][...],\\n linInterp, 2) - target\\n answer2 = multidimInterp((currentYe, logtemp[-1], otherVar),\\n [self.h5file['ye'][:],\\n self.h5file['logtemp'],\\n self.h5file['logrho']],\\n self.h5file[quantity][...],\\n linInterp, 2) - target\\n\\n if (abs(answer1) < abs(answer2)):\\n currentSolveVar = self.h5file['logtemp'][0]\\n print \\\"Recovering with lowest table value, answer: %s\\\" % currentSolveVar\\n else:\\n currentSolveVar = self.h5file['logtemp'][-1]\\n print \\\"Recovering with highest value, answer: %s\\\" % currentSolveVar\\n\\n getYe = lambda x : multidimInterp((x, currentSolveVar, otherVar),\\n [self.h5file['ye'][:],\\n self.h5file[solveVarName],\\n self.h5file[otherVarName]],\\n self.h5file['munu'][...],\\n linInterp, 2)\\n #check for bracketing error in root solve for ye\\n try:\\n currentYe = solveRoot(getYe,\\n self.h5file['ye'][0],\\n self.h5file['ye'][-1], (), tol)\\n except ValueError as err:\\n print \\\"Error in scipy root solver solving for ye: \\\", str(err)\\n currentYe = self.findYeOfMinAbsMunu((currentSolveVar, otherVar))\\n print \\\"Recovering with findYeOfMinAbsMunu, answer: %s\\\" % currentYe\\n #print \\\"currentYe: \\\", currentYe, \\\"\\\\tcurrentT: \\\", currentSolveVar\\n\\n yeError = relativeError(currentYe, previousYe)\\n solveVarError = relativeError(currentSolveVar, previousSolveVar)\\n iteration += 1\\n #print \\\"errs: \\\", yeError, solveVarError\\n\\n newDict = pointDict.copy()\\n newDict['ye'] = currentYe\\n temp = numpy.power(10.0,currentSolveVar) # TODO TEMP HARD CODE\\n newDict['temp'] = temp\\n self.setState(newDict)\\n return currentYe, temp # TODO TEMP HARD CODE\",\n \"def addnewquad(zs,ze,db=0.,de=0.,et=0.,bt=0.,ts=0.,dt=0.,vx=0.,vy=0.,\\n ap=0.,ax=0.,ay=0.,\\n rr=0.,rl=0.,gl=0.,gp=0.,pw=0.,pa=0.,pr=0.,sl=0.,ox=0.,oy=0.,\\n ph=0.,do=0.,\\n glx=0.,gly=0.,axp=0.,axm=0.,ayp=0.,aym=0.,rxp=0.,rxm=0.,\\n ryp=0.,rym=0.,vxp=0.,vxm=0.,vyp=0.,vym=0.,oxp=0.,oxm=0.,\\n oyp=0.,oym=0.,pwl=0.,pwr=0.,pal=0.,par=0.,prl=0.,prr=0.):\\n # --- Make sure that at least some of the element is in the proper range,\\n # --- z >= 0., and if zlatperi != 0, z <= zlatperi.\\n assert (zs < ze),\\\"element start must be less than element end\\\"\\n assert (top.zlatperi == 0.) or (ze > 0.),\\\"element end must be greater than zero if top.zlatperi is nonzero\\\"\\n assert (top.zlatperi == 0.) or (zs < top.zlatperi),\\\"element start must be less than zlatperi if top.zlatperi is nonzero\\\"\\n\\n # --- Get a dict of the input arguments and their values.\\n ldict = locals()\\n\\n # --- Setup the lattice arrays for the insertion of the new element. If\\n # --- there are already quads, then find the place where the new one is to\\n # --- be inserted and shift the existing data to open up a space.\\n # --- Note that this uses that same check as in resetlat, that zs != ze to\\n # --- determine whether or not a quad is defined.\\n ie = 0\\n # --- Find which element the new one goes before.\\n while (ie <= top.nquad and top.quadzs[ie] <= zs and\\n top.quadzs[ie] != top.quadze[ie]):\\n ie = ie + 1\\n\\n # --- Increase the size of the arrays if the element will go past the end\\n # --- or if the array is full (i.e. the last element is used).\\n if ie > top.nquad or top.quadzs[-1] != top.quadze[-1]:\\n top.nquad = top.nquad + 100\\n top.nqerr = top.nqerr + 100\\n gchange(\\\"Lattice\\\")\\n\\n # --- Setup dictionary relating lattice array with input argument names.\\n # --- This is done here so that the references to the lattice arrays\\n # --- refer to the updated memory locations after the gchange.\\n edict={'zs':top.quadzs,'ze':top.quadze,'db':top.quaddb,'de':top.quadde,\\n 'et':top.quadet,'bt':top.quadbt,'ts':top.quadts,'dt':top.quaddt,\\n 'vx':top.quadvx,'vy':top.quadvy,\\n 'ap':top.quadap,'ax':top.quadax,'ay':top.quaday,\\n 'ph':top.quadph,\\n 'rr':top.quadrr,\\n 'rl':top.quadrl,'gl':top.quadgl,'gp':top.quadgp,'pw':top.quadpw,\\n 'pa':top.quadpa,'pr':top.quadpr,'sl':top.quadsl,'do':top.quaddo,\\n 'glx':top.qdelglx,'gly':top.qdelgly,'axp':top.qdelaxp,'axm':top.qdelaxm,\\n 'ayp':top.qdelayp,'aym':top.qdelaym,'rxp':top.qdelrxp,'rxm':top.qdelrxm,\\n 'ryp':top.qdelryp,'rym':top.qdelrym,'vxp':top.qdelvxp,'vxm':top.qdelvxm,\\n 'vyp':top.qdelvyp,'vym':top.qdelvym,'oxp':top.qdeloxp,'oxm':top.qdeloxm,\\n 'oyp':top.qdeloyp,'oym':top.qdeloym,'pwl':top.qdelpwl,'pwr':top.qdelpwr,\\n 'pal':top.qdelpal,'par':top.qdelpar,'prl':top.qdelprl,'prr':top.qdelprr,\\n 'ox':top.qoffx,'oy':top.qoffy}\\n\\n # --- Shift the existing data in the arrays to open up a space for the\\n # --- new element.\\n if ie <= top.nquad:\\n for e in edict.itervalues():\\n if len(shape(e)) == 1:\\n e[ie+1:] = e[ie:-1] + 0\\n else:\\n # --- There are two arrays which are 2-D, quadet, and quadbt.\\n e[:,ie+1:] = e[:,ie:-1] + 0\\n\\n # --- Insert the new element. Note that edict correlates the lattice array\\n # --- with the input arguments and ldict correlate the arguements with\\n # --- their values.\\n for (xx,e) in edict.iteritems():\\n if len(shape(e)) == 1:\\n e[ie] = ldict[xx]\\n else:\\n # --- There are two arrays which are 2-D, quadet, and quadbt.\\n e[:,ie] = ldict[xx]\\n\\n # --- resetlat must be called before the data can be used\\n top.lresetlat = true\\n\\n return ie\",\n \"def jsonJobInfo_queuePut(self, **kwargs):\\n\\n str_queue = 'startQueue'\\n for k,v in kwargs.items():\\n if k == 'queue': str_queue = v\\n\\n if str_queue == 'startQueue': queue = self.queueStart\\n if str_queue == 'endQueue': queue = self.queueEnd\\n\\n # self.dp.qprint(self.shell.d_job)\\n\\n queue.put(self.shell.d_job.copy())\",\n \"def test_init_q(self):\\n\\n riskfree = .01\\n lmbd = .01\\n lmbd_s = .5\\n lmbd_y = .5\\n mean_v = .5\\n kappa_s = 1.5\\n kappa_y = .5\\n eta_s = .1\\n eta_y = .01\\n rho = -.5\\n\\n param = CentTendParam(riskfree=riskfree,\\n lmbd=lmbd, lmbd_s=lmbd_s, lmbd_y=lmbd_y,\\n mean_v=mean_v, kappa_s=kappa_s, kappa_y=kappa_y,\\n eta_s=eta_s, eta_y=eta_y, rho=rho, measure='Q')\\n\\n kappa_sq = kappa_s - lmbd_s * eta_s\\n kappa_yq = kappa_y - lmbd_y * eta_y\\n scale = kappa_s / kappa_sq\\n\\n self.assertEqual(param.measure, 'Q')\\n self.assertEqual(param.riskfree, riskfree)\\n self.assertEqual(param.lmbd, 0)\\n self.assertEqual(param.lmbd_s, lmbd_s)\\n self.assertEqual(param.lmbd_y, lmbd_y)\\n self.assertEqual(param.mean_v, mean_v * kappa_y / kappa_yq * scale)\\n self.assertEqual(param.kappa_s, kappa_sq)\\n self.assertEqual(param.kappa_y, kappa_yq)\\n self.assertEqual(param.eta_s, eta_s)\\n self.assertEqual(param.eta_y, eta_y * scale**.5)\\n self.assertEqual(param.rho, rho)\\n self.assertTrue(param.is_valid())\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n param.convert_to_q()\",\n \"def putaij(self,i_,j_,aij_):\\n res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def test_quintic2(self):\\n fun = get_problem('quintic', self.dimension, -10.0, 10.0)\\n self.assertEqual(fun(self.array7), 0.0)\",\n \"def _bucket_setitem(self, j, k, v):\\n pass\",\n \"def __init__(self,XXZ_,kop_,excitations_,sol_ = None):\\n self.XXZ=XXZ_ #Defines the model - XXXmodel,XXZmodel,XXZmodelTrig or XXZmodelHyp\\n self.levels=self.XXZ.levels #Energy levels\\n self.g = kop_ #Coupling constant\\n self.gamma = self.XXZ.get_c() #Gamma associated with Gaudin algebra\\n self.N = excitations_ #Number of excitations\\n self.n=self.XXZ.get_nlevels() #Number of single-particle levels\\n self.rapidities=None; #Rapidities (have to be calculated)\\n if sol_ == None:\\n self.solution = None #Set of g*Lambda_i (have to be calculated)\\n else: \\n self.solution = np.array(sol_)\\n assert(len(self.solution) == len(self.levels))\\n assert(self.N <= self.n)\\n self.occupation=None #Set of occupation numbers (follow from derivative of g*Lambda_i)\",\n \"def save_q_functions(self, q_value, iteration):\\n path = os.path.join(self.predicted_q_func_dir,\\n '%06d.%s.qfunc' % (iteration, 'shovel'))\\n np.save(path, q_value)\",\n \"def buildQ(self):\\r\\n\\r\\n print 'Building Q ...'\\r\\n\\r\\n self.y = T.matrix('y')\\r\\n\\r\\n mlp = MLP(activations=self.hyper['q_activs'],\\r\\n dims=self.hyper['q_dims'],\\r\\n weights_init=self.hyper['q_W_init'],\\r\\n biases_init=Constant(0))\\r\\n\\r\\n q_parameters = mlp.apply(self.y)\\r\\n mlp.initialize()\\r\\n\\r\\n # self.qxgy_mu.shape == (minibatch size, num of dimension of x)\\r\\n self.qxgy_mu = q_parameters[:,:self.hyper['x_dim']]\\r\\n\\r\\n # self.qxgy_var.shape == (minibatch size, num of dimension of x)\\r\\n self.qxgy_var = T.exp( q_parameters[:,self.hyper['x_dim']:2*self.hyper['x_dim']] )\\r\\n\\r\\n # self.qwgy_mu.shape == (minibatch size, num of dimension of w)\\r\\n self.qwgy_mu = q_parameters[:,2*self.hyper['x_dim']:2*self.hyper['x_dim']+self.hyper['w_dim']]\\r\\n\\r\\n # self.qwgy_var.shape == (minibatch size, num of dimension of w)\\r\\n self.qwgy_var = T.exp( q_parameters[:,2*self.hyper['x_dim']+self.hyper['w_dim']:] )\\r\\n\\r\\n\\r\\n #---Will be useful to compute samples from q(x|y)---#\\r\\n #self.eps_x.shape == (minibatch size, # of x samples , # of dimension of x)\\r\\n self.eps_x = self.srng.normal((self.qxgy_mu.shape[0] ,self.hyper['L_x'] ,self.hyper['x_dim']))\\r\\n\\r\\n #self.x corresponds roughly to the function g(\\\\epsilon,y) (see reparametrization trick in Kingma 2014)\\r\\n #self.x.shape == (minibatch size, # of x samples , # of dimension of x)\\r\\n self.x = self.qxgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qxgy_var).dimshuffle(0,'x',1)*self.eps_x\\r\\n\\r\\n #---Will be useful to compute samples from q(w|y)---#\\r\\n #self.eps_w.shape == (minibatch size, # of w samples , # of dimension of w)\\r\\n self.eps_w = self.srng.normal((self.qwgy_mu.shape[0] ,self.hyper['L_w'] ,self.hyper['w_dim']))\\r\\n\\r\\n #self.w corresponds roughly to the function g(\\\\epsilon,y) (see reparametrization trick in Kingma 2014)\\r\\n #self.w.shape == (minibatch size, # of w samples , # of dimension of w)\\r\\n self.w = self.qwgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qwgy_var).dimshuffle(0,'x',1)*self.eps_w\\r\\n\\r\\n\\r\\n #---Building the log density q(x|y)---#\\r\\n little_num = 10**(-32)\\r\\n inside_exp = -T.sum((self.x - self.qxgy_mu.dimshuffle(0,'x',1))**2/(2*self.qxgy_var.dimshuffle(0,'x',1)), axis=2)\\r\\n norm_cst = (2*np.pi)**(-self.hyper['x_dim']/2.)*T.exp(T.sum(T.log(self.qxgy_var), axis=1))**(-1/2.)\\r\\n\\r\\n # shape == (minibatch size, # of x samples)\\r\\n qxgy = norm_cst.dimshuffle(0,'x')*T.exp(inside_exp)\\r\\n\\r\\n # shape == (minibatch size, # of x samples)\\r\\n self.log_qxgy = T.log(qxgy + little_num)\",\n \"def putbarxj(self,whichsol_,j_,barxj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barxj is None: raise TypeError(\\\"Invalid type for argument barxj\\\")\\n if barxj is None:\\n barxj_ = None\\n else:\\n try:\\n barxj_ = memoryview(barxj)\\n except TypeError:\\n try:\\n _tmparr_barxj = array.array(\\\"d\\\",barxj)\\n except TypeError:\\n raise TypeError(\\\"Argument barxj has wrong type\\\")\\n else:\\n barxj_ = memoryview(_tmparr_barxj)\\n \\n else:\\n if barxj_.format != \\\"d\\\":\\n barxj_ = memoryview(array.array(\\\"d\\\",barxj))\\n \\n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj has wrong length\\\")\\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def q(self):\\n return self.model.gmmobjective(self.params, self.weights)\",\n \"def compute_lj_potential(rsq, rcsq):\\n LJ_U = np.where(rsq < rcsq, 4 * epsilon * ((sigma ** 2 / rsq) ** 6 - (sigma ** 2 / rsq) ** 3), 0)\\n\\n return LJ_U\",\n \"def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\\n else:\\n _skn_tmp = None\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def contractor(self, *args, **kwargs):\\n vertices = copy.deepcopy(args[0])\\n nrange = len(vertices[0])\\n xpts = []\\n ypts = []\\n for i in range(nrange):\\n xpts.append(vertices[0][i].value)\\n ypts.append(vertices[1][i].value)\\n constraint = copy.deepcopy(args[1])\\n \\n \\n \\n \\n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\\n \\n ## the all important computation split (need to abstract this kind of thing)\\n ##lhs = (np.sqrt(qxdot*qxdot + qydot*qydot)**3.) *constraint\\n lhs = ( ( np.sqrt(qxdot**2 + qydot**2) )**3 )*constraint\\n \\n # check2 = qxdot*qyddot\\n # if check2.width() < 1.e-2:\\n # check2.min.value = check2.real.value\\n # check2.max.value = check2.real.value\\n # t1 = (lhs - check2)/qydot\\n \\n #\\n # qyddot\\n #\\n check2 = qydot*qxddot\\n if check2.width() < 1.e-2 and check2.contains(0.):\\n check2.inf = 0.\\n check2.sup = 0.\\n #if qxdot.contains(0.) and abs(qxdot.min.value)>1.e-6:\\n # print 'qxdot = ',qxdot\\n # print 'qxdot not invertable, implement other logic please'\\n if abs(float(qxdot.inf))<1.e-6:\\n qxdot.inf = 1.e-10\\n print 'invert qxdot'\\n print 'qxdot = ', qxdot\\n \\n #t1 = (lhs + qydot*qxddot)/(qxdot)\\n t1 = (lhs + check2)/(qxdot)\\n \\n t1 = t1 & qyddot # go ahead and shrink t1 to qyddot - they are logically equivalent\\n total_ans = []\\n useful_indices = []\\n bad_indices = []\\n for i in range(len(ypts)): \\n min_ans = 0.\\n for j in range(len(ypts)):\\n if j==i:\\n pass\\n else:\\n min_ans = (ypts[j]*float(self.localBasis[2,j])) + min_ans\\n min_ans = t1 - min_ans\\n if (abs(float(self.localBasis[2,i])) > 0.0):\\n min_ans = min_ans/float(self.localBasis[2,i])\\n useful_indices.append(i)\\n else:\\n bad_indices.append(i)\\n total_ans.append(min_ans)\\n \\n new_ans = vector_AND_(ypts, total_ans)\\n for i in useful_indices:\\n if new_ans[i].isempty == False: # abs( new_ans[i].width() ) > 0.:\\n ypts[i] = ypts[i] & new_ans[i]\\n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\\n else:\\n print 'warning, possible constraint violation, curvature 1'\\n \\n ## \\n ## qxdot\\n ##\\n check2 = qydot*qxddot\\n if check2.width() < 1.e-2 and check2.contains(0.):\\n check2.inf = 0.\\n check2.sup = 0.\\n #if qyddot.contains(0.):\\n # print 'qyddot = ',qyddot\\n # print 'qyddot not invertable, implement other logic please'\\n \\n if qyddot.contains(0.) and qyddot.width()<1.e-6:\\n qxdot.inf = 0.#1.e-10\\n print 'invert qyddot'\\n print 'qyddot = ',qyddot\\n fix = (lhs + check2)*(1./qyddot)#*(qyddot**-1.)\\n fix = fix & qxdot # go ahead and shrink fix to qxdot - they are logically equivalent\\n total_ans = []\\n useful_indices = []\\n bad_indices = []\\n \\n for i in range(len(xpts)): #contract on x[i]\\n min_ans = 0.\\n for j in range(len(xpts)): # add up all jth pieces of the dot product except i\\n if j==i:\\n pass\\n else:\\n \\n min_ans = (xpts[j]*float(self.localBasis[1,j] ) ) + min_ans\\n min_ans = fix - min_ans\\n if (abs(float(self.localBasis[1,i]) ) >0.0 ):\\n min_ans = min_ans/float(self.localBasis[1,i])\\n useful_indices.append(i)\\n else:\\n bad_indices.append(i)\\n total_ans.append(min_ans)\\n \\n new_ans = vector_AND_(xpts, total_ans)\\n for i in useful_indices:\\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\\n xpts[i] = xpts[i] & new_ans[i]\\n qxdot,qxddot,qydot,qyddot = self.update_allq(xpts,ypts)\\n else:\\n print 'warning, possible constraint violation, curvature 2'\\n \\n \\n ## switch to the other side\\n \\n ##\\n ## contract on qydot\\n ##\\n check2 = qxdot*qyddot\\n if check2.width() < 1.e-2 and check2.contains(0.):\\n check2.inf = 0.\\n check2.sup = 0.\\n# if qxddot.contains(0.):\\n# print 'qxddot = ',qxddot\\n# print 'qxddot not invertable, implement other logic please'\\n# qxddot.min.value = 0.\\n if qxddot.contains(0.):\\n qxddot.inf = 0.\\n \\n print 'invert qxddot'\\n print 'qxddot = ',qxddot\\n t1 = (lhs - check2)/(-qxddot)#*(-qxddot**-1)\\n t1 = t1 & qydot\\n total_ans = []\\n useful_indices = []\\n bad_indices = []\\n for i in range(len(ypts)): \\n min_ans = 0.\\n for j in range(len(ypts)):\\n if j==i:\\n pass\\n else:\\n #print 't1 = ',t1\\n #print 'ypts[{}] = {}'.format(i,ypts[i])\\n #print 'localbasis[{},{}] = {}'.format(1,i,self.localBasis[1,j])\\n min_ans = (ypts[j]*float(self.localBasis[1,j])) + min_ans\\n min_ans = t1 - min_ans\\n if (abs(float(self.localBasis[1,i])) > 0.0):\\n min_ans = min_ans/float(self.localBasis[1,i])\\n useful_indices.append(i)\\n else:\\n bad_indices.append(i)\\n total_ans.append(min_ans)\\n \\n new_ans = vector_AND_(ypts, total_ans)\\n for i in useful_indices:\\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\\n ypts[i] = ypts[i] & new_ans[i]\\n else:\\n print 'warning, possible constraint violation, curvature 3'\\n \\n ##contract on qxdot\\n \\n check2 = qxdot*qyddot\\n if check2.width() < 1.e-2 and check2.contains(0.):\\n check2.inf = 0.\\n check2.sup = 0.\\n #contract on qxddot\\n# if qydot.contains(0.):\\n# print 'qydot = ',qxddot\\n# print 'qydot not invertable, implement other logic please'\\n if qydot.contains(0.):\\n qydot.inf = 0.\\n print 'invert qydot'\\n print 'qydot = ',qydot\\n fix = (lhs - qxdot*qyddot)/(-qydot)#*(-qydot**-1)\\n fix = fix & qxddot # go ahead and shrink t1 to quddot - they are logically equivalent\\n total_ans = []\\n useful_indices = []\\n bad_indices = []\\n for i in range(len(xpts)):\\n min_ans = 0.\\n for j in range(len(xpts)):\\n if j==i:\\n pass\\n else:\\n min_ans = (xpts[j]*float(self.localBasis[2,j] ) ) + min_ans\\n min_ans = fix - min_ans\\n if (abs(float(self.localBasis[2,i]) ) >0.0 ):\\n min_ans = min_ans/float(self.localBasis[2,i])\\n useful_indices.append(i)\\n else:\\n bad_indices.append(i)\\n total_ans.append(min_ans)\\n \\n new_ans = vector_AND_(xpts, total_ans)\\n for i in useful_indices:\\n if not new_ans[i].isempty: # abs( new_ans[i].width() ) > 0.:\\n xpts[i] = xpts[i] & new_ans[i]\\n else:\\n print 'warning, possible constraint violation, curvature 4'\\n \\n for i in range(nrange):\\n vertices[0][i].value = xpts[i]\\n vertices[1][i].value = ypts[i]\\n return vertices\",\n \"def qsolve(self, options=''):\\n for x in self._rhs:\\n if x != 0:\\n raise NotImplementedError, \\\"qsolve is currently only implemented for homogeneous systems (i.e., with rhs=0)\\\"\\n out, err = self.call_4ti2('qsolve', options=options)\\n qhom = ExtremalRays(self._read_file('qhom'), self)\\n qfree = self._read_file('qfree')\\n return (qhom, qfree)\",\n \"def add_cp_qe_RBM_terms(K, cons_pot_mesh, quad_geo_mesh):\\n num_faces = cons_pot_mesh.get_faces().shape[0]\\n x_c = quad_geo_mesh.get_centroid()\\n w = quad_geo_mesh.get_w()\\n A_m = quad_geo_mesh.get_A_m()\\n S_D = quad_geo_mesh.get_surface_area()\\n\\n for face_num in range(num_faces):\\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\\n face_hs = quad_geo_mesh.get_hs(face_num)\\n def v_quad(_xi, _eta, _nodes):\\n return np.identity(3)\\n v_sub_mat = (1. / S_D) * gq.int_over_tri_quad(v_quad, face_nodes, face_hs)\\n def omega_quad(xi, eta, nodes):\\n pos = geo.quadratic_interp(xi, eta, nodes)\\n X = pos - x_c\\n return np.einsum(\\\"lrs,s->lr\\\", geo.LC_3, X)\\n tmp_omega = gq.int_over_tri_quad(\\n omega_quad,\\n face_nodes,\\n face_hs,\\n )\\n tmp_arr = []\\n for m in range(3):\\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\\\"l,ls\\\", w[m], tmp_omega)))\\n tmp_arr = np.array(tmp_arr)\\n tmp_omega_mat = np.sum(tmp_arr, axis=0)\\n for src_num in range(num_faces):\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * face_num):(3 * face_num + 3)] += v_sub_mat\\n src_center = cons_pot_mesh.get_node(src_num)\\n X_0 = src_center - x_c\\n omega_mat = np.einsum(\\\"ijk,js,k->is\\\", geo.LC_3, tmp_omega_mat, X_0)\\n K[(3 * src_num):(3 * src_num + 3),\\n (3 * face_num):(3 * face_num + 3)] += omega_mat\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71988887","0.67377245","0.66842896","0.6546956","0.642287","0.5680763","0.565179","0.5649305","0.5616514","0.55763125","0.536642","0.5291595","0.5255819","0.51279503","0.51187503","0.51012385","0.50656164","0.50490975","0.5015812","0.5012745","0.50038093","0.50020003","0.49785858","0.4954533","0.49522758","0.49517247","0.4938055","0.49255264","0.49179035","0.48810005","0.48794317","0.48770165","0.48770165","0.48650885","0.48461854","0.4840384","0.48393983","0.4833685","0.48033357","0.47990337","0.47962698","0.47929004","0.47715005","0.47564095","0.47559825","0.4755195","0.4752051","0.47506097","0.4746161","0.47453073","0.47336528","0.47318336","0.47254443","0.47208497","0.47194517","0.47182614","0.47163942","0.4713247","0.47070286","0.47033295","0.46628553","0.46617827","0.46594003","0.46564716","0.46564716","0.46496293","0.4648347","0.46426073","0.46390897","0.46379152","0.46346435","0.4627597","0.4627118","0.4608536","0.4596332","0.4595824","0.45956808","0.45946822","0.45904142","0.45868027","0.4574717","0.45728147","0.45665485","0.45599398","0.4559366","0.4556794","0.45563108","0.4552832","0.45508614","0.45500872","0.45430663","0.4542859","0.45411003","0.4539157","0.4537917","0.4536774","0.45364806","0.45333058","0.45304215","0.45299208"],"string":"[\n \"0.71988887\",\n \"0.67377245\",\n \"0.66842896\",\n \"0.6546956\",\n \"0.642287\",\n \"0.5680763\",\n \"0.565179\",\n \"0.5649305\",\n \"0.5616514\",\n \"0.55763125\",\n \"0.536642\",\n \"0.5291595\",\n \"0.5255819\",\n \"0.51279503\",\n \"0.51187503\",\n \"0.51012385\",\n \"0.50656164\",\n \"0.50490975\",\n \"0.5015812\",\n \"0.5012745\",\n \"0.50038093\",\n \"0.50020003\",\n \"0.49785858\",\n \"0.4954533\",\n \"0.49522758\",\n \"0.49517247\",\n \"0.4938055\",\n \"0.49255264\",\n \"0.49179035\",\n \"0.48810005\",\n \"0.48794317\",\n \"0.48770165\",\n \"0.48770165\",\n \"0.48650885\",\n \"0.48461854\",\n \"0.4840384\",\n \"0.48393983\",\n \"0.4833685\",\n \"0.48033357\",\n \"0.47990337\",\n \"0.47962698\",\n \"0.47929004\",\n \"0.47715005\",\n \"0.47564095\",\n \"0.47559825\",\n \"0.4755195\",\n \"0.4752051\",\n \"0.47506097\",\n \"0.4746161\",\n \"0.47453073\",\n \"0.47336528\",\n \"0.47318336\",\n \"0.47254443\",\n \"0.47208497\",\n \"0.47194517\",\n \"0.47182614\",\n \"0.47163942\",\n \"0.4713247\",\n \"0.47070286\",\n \"0.47033295\",\n \"0.46628553\",\n \"0.46617827\",\n \"0.46594003\",\n \"0.46564716\",\n \"0.46564716\",\n \"0.46496293\",\n \"0.4648347\",\n \"0.46426073\",\n \"0.46390897\",\n \"0.46379152\",\n \"0.46346435\",\n \"0.4627597\",\n \"0.4627118\",\n \"0.4608536\",\n \"0.4596332\",\n \"0.4595824\",\n \"0.45956808\",\n \"0.45946822\",\n \"0.45904142\",\n \"0.45868027\",\n \"0.4574717\",\n \"0.45728147\",\n \"0.45665485\",\n \"0.45599398\",\n \"0.4559366\",\n \"0.4556794\",\n \"0.45563108\",\n \"0.4552832\",\n \"0.45508614\",\n \"0.45500872\",\n \"0.45430663\",\n \"0.4542859\",\n \"0.45411003\",\n \"0.4539157\",\n \"0.4537917\",\n \"0.4536774\",\n \"0.45364806\",\n \"0.45333058\",\n \"0.45304215\",\n \"0.45299208\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7221554"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11993,"cells":{"query":{"kind":"string","value":"Replaces one coefficient in the quadratic term in the objective. putqobjij(self,i_,j_,qoij_)"},"document":{"kind":"string","value":"def putqobjij(self,i_,j_,qoij_):\n res = __library__.MSK_XX_putqobjij(self.__nativep,i_,j_,qoij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putqobjij(self,i_,j_,qoij_): # 3\n res = self.__obj.putqobjij(i_,j_,qoij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getqobjij(self,i_,j_): # 3\n res,resargs = self.__obj.getqobjij(i_,j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _qoij_return_value = resargs\n return _qoij_return_value","def getqobjij(self,i_,j_):\n qoij_ = ctypes.c_double()\n res = __library__.MSK_XX_getqobjij(self.__nativep,i_,j_,ctypes.byref(qoij_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n qoij_ = qoij_.value\n _qoij_return_value = qoij_\n return (_qoij_return_value)","def _qij_minus(i: int, j: int):\n ib = i * 2 + 1\n jb = j * 2 + 1\n term = FermionOperator(((jb, 0), (ib, 0)), 1.0)\n return term","def _qij_0(i: int, j: int):\n ia = i * 2 + 0\n ib = i * 2 + 1\n ja = j * 2 + 0\n jb = j * 2 + 1\n term1 = FermionOperator(((ja, 0), (ib, 0)), 1.0)\n term2 = FermionOperator(((ia, 0), (jb, 0)), 1.0)\n return numpy.sqrt(0.5) * (term1 - term2)","def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _qij_plus(i: int, j: int):\n ia = i * 2 + 0\n ja = j * 2 + 0\n term = FermionOperator(((ja, 0), (ia, 0)), 1.0)\n return term","def putaij(self,i_,j_,aij_): # 3\n res = self.__obj.putaij(i_,j_,aij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putaij(self,i_,j_,aij_):\n res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def substitute_cost(self, i, j):\n raise NotImplementedError","def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqobj(self,qosubi_,qosubj_,qoval_):\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi_)\n elif numqonz_ != len(qosubi_):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj_)\n elif numqonz_ != len(qosubj_):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval_)\n elif numqonz_ != len(qoval_):\n raise IndexError(\"Inconsistent length of array qoval\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi cannot be None\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi may not be None\")\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\n _qosubi_copyarray = False\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubi_ is not None:\n _qosubi_copyarray = True\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\n _qosubi_np_tmp[:] = qosubi_\n assert _qosubi_np_tmp.flags.contiguous\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubi_copyarray = False\n _qosubi_tmp = None\n \n if qosubj_ is None:\n raise ValueError(\"Argument qosubj cannot be None\")\n if qosubj_ is None:\n raise ValueError(\"Argument qosubj may not be None\")\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\n _qosubj_copyarray = False\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubj_ is not None:\n _qosubj_copyarray = True\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\n _qosubj_np_tmp[:] = qosubj_\n assert _qosubj_np_tmp.flags.contiguous\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubj_copyarray = False\n _qosubj_tmp = None\n \n if qoval_ is None:\n raise ValueError(\"Argument qoval cannot be None\")\n if qoval_ is None:\n raise ValueError(\"Argument qoval may not be None\")\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\n _qoval_copyarray = False\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qoval_ is not None:\n _qoval_copyarray = True\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\n _qoval_np_tmp[:] = qoval_\n assert _qoval_np_tmp.flags.contiguous\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qoval_copyarray = False\n _qoval_tmp = None\n \n res = __library__.MSK_XX_putqobj(self.__nativep,numqonz_,_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def fixA(self,i,j,value):\n if self.coeffPattern[0] == None:\n m,n=self.m,self.n\n self.coeffPattern[0] = [[None]*m for i in range(m)]\n self.coeffPattern[0][i][j]=value\n self._updateEstimatorSize(i)","def fixB(self,i,j,value):\n if self.coeffPattern[1] == None:\n m,n=self.m,self.n\n self.coeffPattern = [[None]*n for i in range(m)]\n self.coeffPattern[1][i][j]=value\n self._updateEstimatorSize(i)","def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqobj(self,qosubi,qosubj,qoval): # 3\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi)\n elif numqonz_ != len(qosubi):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj)\n elif numqonz_ != len(qosubj):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval)\n elif numqonz_ != len(qoval):\n raise IndexError(\"Inconsistent length of array qoval\")\n if numqonz_ is None: numqonz_ = 0\n if qosubi is None: raise TypeError(\"Invalid type for argument qosubi\")\n if qosubi is None:\n qosubi_ = None\n else:\n try:\n qosubi_ = memoryview(qosubi)\n except TypeError:\n try:\n _tmparr_qosubi = array.array(\"i\",qosubi)\n except TypeError:\n raise TypeError(\"Argument qosubi has wrong type\")\n else:\n qosubi_ = memoryview(_tmparr_qosubi)\n \n else:\n if qosubi_.format != \"i\":\n qosubi_ = memoryview(array.array(\"i\",qosubi))\n \n if qosubj is None: raise TypeError(\"Invalid type for argument qosubj\")\n if qosubj is None:\n qosubj_ = None\n else:\n try:\n qosubj_ = memoryview(qosubj)\n except TypeError:\n try:\n _tmparr_qosubj = array.array(\"i\",qosubj)\n except TypeError:\n raise TypeError(\"Argument qosubj has wrong type\")\n else:\n qosubj_ = memoryview(_tmparr_qosubj)\n \n else:\n if qosubj_.format != \"i\":\n qosubj_ = memoryview(array.array(\"i\",qosubj))\n \n if qoval is None: raise TypeError(\"Invalid type for argument qoval\")\n if qoval is None:\n qoval_ = None\n else:\n try:\n qoval_ = memoryview(qoval)\n except TypeError:\n try:\n _tmparr_qoval = array.array(\"d\",qoval)\n except TypeError:\n raise TypeError(\"Argument qoval has wrong type\")\n else:\n qoval_ = memoryview(_tmparr_qoval)\n \n else:\n if qoval_.format != \"d\":\n qoval_ = memoryview(array.array(\"d\",qoval))\n \n res = self.__obj.putqobj(numqonz_,qosubi_,qosubj_,qoval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarxj(self,whichsol_,j_,barxj_):\n _barxj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj is not long enough: Is %d, expected %d\" % (len(barxj_),self.getlenbarvarj((j_))))\n if barxj_ is None:\n raise ValueError(\"Argument barxj cannot be None\")\n if barxj_ is None:\n raise ValueError(\"Argument barxj may not be None\")\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\n _barxj_copyarray = False\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxj_ is not None:\n _barxj_copyarray = True\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\n _barxj_np_tmp[:] = barxj_\n assert _barxj_np_tmp.flags.contiguous\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxj_copyarray = False\n _barxj_tmp = None\n \n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def ij(ij, pol, ant) :\n s.ij(pol, ij, ant)","def fixC(self,i,value):\n if self.coeffPattern[2] == None:\n m,n=self.m,self.n\n self.coeffPattern[2] = [None]*m\n self.coeffPattern[2][i]=value\n self._updateEstimatorSize(i)","def _Q(self, x, y):\n\n # Calculate the [Q] coefficient matrix\n Q = array([[0, 0, 0, -2, 0, 0, -6*x, -2*y, 0, 0, -6*x*y, 0],\n [0, 0, 0, 0, 0, -2, 0, 0, -2*x, -6*y, 0, -6*x*y],\n [0, 0, 0, 0, -2, 0, 0, -4*x, -4*y, 0, -6*x**2, -6*y**2]])\n \n # Return the [Q] coefficient matrix\n return Q","def SetIJ(self, newI, newJ, newsize=-1):\n return _table.Table_SetIJ(self, newI, newJ, newsize)","def replace_q_indices(circuit, q_nums, qr):\n\n new_circuit = qiskit.QuantumCircuit(qr)\n for op in circuit.data:\n original_qubits = op.qargs\n new_op = copy.deepcopy(op)\n new_op.qargs = [(qr, q_nums[x]) for x in [arg[1] for arg in original_qubits]]\n new_circuit.data.append(new_op)\n\n return new_circuit","def putbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if barsj_ is None:\n raise ValueError(\"Argument barsj cannot be None\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def J(self, name, q, x=None):\n\n x = self.x_zeros if x is None else x\n funcname = name + '[0,0,0]' if np.allclose(x, 0) else name\n # check for function in dictionary\n if self._J.get(funcname, None) is None:\n self._J[funcname] = self._calc_J(name=name, x=x)\n parameters = tuple(q) + tuple(x)\n return np.array(self._J[funcname](*parameters), dtype='float32')","def putbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n \n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n \n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putcj(self,j_,cj_):\n res = __library__.MSK_XX_putcj(self.__nativep,j_,cj_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def quaternion_conjugate(self, q):\n\n \"\"\"\n in-place operation is an operation that changes directly the content of a given Tensor without making a copy.\n ALL operations on the tensor that operate in-place on it will have an _ postfix.\n \"\"\"\n q_star = q.new(4).fill_(-1)\n\n # leave the scalar unchanged and change signs of i, j, k number parts\n q_star[0] = 1.0\n\n return q * q_star","def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solve_Q(ih, jh):\n # Build A and b\n\n # Solve for c via least squares.\n\n # Form C from c\n\n # Solve for Q and return it.\n return None","def putcj(self,j_,cj_): # 3\n res = self.__obj.putcj(j_,cj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)","def objective(self, objective):\n\n self._objective = objective","def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def objective(self, x):\n pass","def objective(self, x):\n pass","def fit_ij(self, i, j, **kwargs):\n assert i != j, \"i and j must be different\"\n\n # Get the layer of interest in the list of circuits\n l = self._find_layer(i, j)\n\n # Take the circuits of interest\n circuits = self._circuits[0:3]\n circuits += self._circuits[(3 + 6*l) : (3 + 6*(l+1))]\n\n # This will create an empty _data dict for the fit function\n # We are using a member field so that we can use the super() fit \n # function\n self._data = {}\n\n # Process measurement counts into probabilities\n for circ in circuits:\n # Take only the relevant qubit labels from the circuit label\n tup = literal_eval(circ.name)\n tup = (tup[i], tup[j])\n\n # Marginalize the counts for the two relevant qubits\n counts = marginal_counts(self._result.get_counts(circ), [i, j])\n\n # Populate the data\n self._data[tup] = counts\n\n # Test that all the required measurements are there\n expected_corr = product(['X', 'Y', 'Z'], ['X', 'Y', 'Z'])\n if set(self._data.keys()) != set(expected_corr):\n raise Exception(\"Could not find all the measurements required for tomography\")\n\n # Do the actual fit\n result = super().fit(**kwargs)\n\n # clear the _data field\n self._data = None\n return result","def conj(q):\n q = np.array([q[0]])\n q[0,1]=-q[0,1]\n q[0,2]=-q[0,2]\n q[0,3]=-q[0,3]\n complexconjugate = quatreal(q)\n return complexconjugate","def _qij_vec(i: int, j: int):\n return [_qij_plus(i, j), _qij_minus(i, j), _qij_0(i, j)]","def _pij(i: int, j: int):\n ia = i * 2 + 0\n ib = i * 2 + 1\n ja = j * 2 + 0\n jb = j * 2 + 1\n term1 = FermionOperator(((ja, 0), (ib, 0)), 1.0)\n term2 = FermionOperator(((ia, 0), (jb, 0)), 1.0)\n return numpy.sqrt(0.5) * (term1 + term2)","def delete_cost(self, i, j):\n raise NotImplementedError","def putbaraij(self,i_,j_,sub_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights_)\n elif num_ != len(weights_):\n raise IndexError(\"Inconsistent length of array weights\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraij(self.__nativep,i_,j_,num_,_sub_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _update_objective(self):\n # rewrap the cost if the solver has been run\n self.Finalize()\n return","def putaijlist(self,subi_,subj_,valij_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij_)\n elif num_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubk is None: raise TypeError(\"Invalid type for argument qcsubk\")\n if qcsubk is None:\n qcsubk_ = None\n else:\n try:\n qcsubk_ = memoryview(qcsubk)\n except TypeError:\n try:\n _tmparr_qcsubk = array.array(\"i\",qcsubk)\n except TypeError:\n raise TypeError(\"Argument qcsubk has wrong type\")\n else:\n qcsubk_ = memoryview(_tmparr_qcsubk)\n \n else:\n if qcsubk_.format != \"i\":\n qcsubk_ = memoryview(array.array(\"i\",qcsubk))\n \n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbaraij(self,i_,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexError(\"Inconsistent length of array weights\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n \n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n \n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n \n res = self.__obj.putbaraij(i_,j_,num_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def updateIMU(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray) -> np.ndarray:\n if gyr is None or not np.linalg.norm(gyr)>0:\n return q\n qEst = 0.5 * q_prod(q, [0, *gyr]) # (eq. 12)\n a_norm = np.linalg.norm(acc)\n if a_norm>0:\n a = acc/a_norm\n qw, qx, qy, qz = q/np.linalg.norm(q)\n # Gradient objective function (eq. 25) and Jacobian (eq. 26)\n f = np.array([2.0*(qx*qz - qw*qy) - a[0],\n 2.0*(qw*qx + qy*qz) - a[1],\n 2.0*(0.5-qx**2-qy**2) - a[2]]) # (eq. 25)\n J = np.array([[-2.0*qy, 2.0*qz, -2.0*qw, 2.0*qx],\n [ 2.0*qx, 2.0*qw, 2.0*qz, 2.0*qy],\n [ 0.0, -4.0*qx, -4.0*qy, 0.0 ]]) # (eq. 26)\n # Objective Function Gradient\n gradient = J.T@f # (eq. 34)\n gradient /= np.linalg.norm(gradient)\n qEst -= self.gain*gradient # (eq. 33)\n q += qEst*self.Dt # (eq. 13)\n q /= np.linalg.norm(q)\n return q","def setitem(self, i, j, value):\n # XXX: flint matrices do not support negative indices\n # XXX: They also raise ValueError instead of IndexError\n m, n = self.shape\n if i < 0:\n i += m\n if j < 0:\n j += n\n try:\n self.rep[i, j] = value\n except ValueError:\n raise IndexError(f\"Invalid indices ({i}, {j}) for Matrix of shape {self.shape}\")","def jot(self):\r\n\t\t\r\n\t\t# empty list?\r\n\t\tif len(self) < 1:\r\n\t\t\r\n\t\t\treturn '(0)'\r\n\t\t\r\n\t\t# go through terms\r\n\t\ts = ''\r\n\t\tfor i in self:\r\n\t\t\t\r\n\t\t\t# positive or negative\r\n\t\t\tif i.look('i') % 4 in (0,1):\r\n\t\t\t\ts += ' +('\r\n\t\t\tif i.look('i') % 4 in (2,3):\r\n\t\t\t\ts += ' -('\r\n\t\t\t\t\r\n\t\t\t# list of variables\r\n\t\t\ty = i.keys()\r\n\t\t\ty = [str(j) for j in y if j != 'i']\r\n\t\t\ty = [j for j in y if j.isalpha()]\r\n\t\t\ty = Pa._tidy(y)\r\n\t\t\t\t\r\n\t\t\t# coefficient\r\n\t\t\tn,d = i.fuse()\r\n\t\t\tif n > 1 or d > 1 or len(y) < 1:\r\n\t\t\t\ts += str(n)\r\n\t\t\tif d > 1:\r\n\t\t\t\ts += '/' + str(d)\r\n\t\t\tif i.look('i') % 4 in (1,3):\r\n\t\t\t\ts += 'i'\r\n\t\t\t\r\n\t\t\t# add variables to string\r\n\t\t\tfor k in y:\r\n\t\t\t\tif s[-1] == '(':\r\n\t\t\t\t\ts += k\r\n\t\t\t\telse:\r\n\t\t\t\t\ts += ' ' + k\r\n\t\t\t\tif i[k] != 1:\r\n\t\t\t\t\ts += str(i[k])\r\n\t\t\t\t\t\r\n\t\t\t# close\r\n\t\t\ts += ')'\r\n\t\t\t\t\r\n\t\t# remove leading ' '\r\n\t\tif s[0] == ' ':\r\n\t\t\ts = s[1:]\r\n\t\t\t\t\r\n\t\t# remove leading +\r\n\t\tif s[0] == '+':\r\n\t\t\ts = s[1:]\r\n\t\t\t\t\r\n\t\treturn s","def convert_to_q(self):\n if self.measure == 'Q':\n warnings.warn('Parameters are already converted to Q!')\n else:\n kappa_sp = self.kappa_s\n kappa_yp = self.kappa_y\n self.kappa_s = self.kappa_s - self.lmbd_s * self.eta_s\n self.kappa_y = self.kappa_y - self.lmbd_y * self.eta_y\n self.scale = kappa_sp / self.kappa_s\n self.mean_v *= (kappa_yp / self.kappa_y * self.scale)\n self.lmbd = 0\n self.eta_y *= (self.scale**.5)\n self.measure = 'Q'\n self.update_ajd()","def Q_i(params):\n Q = params['Q'].value\n Qe = Q_e(params)\n return (Q ** -1 - np.real(Qe ** -1)) ** -1","def _append_cx(self, i, j):\n\n if not 0 <= i < self.num_qubits or not 0 <= j < self.num_qubits:\n raise QiskitError(\"CX qubits are out of bounds.\")\n self.linear[j] = (self.linear[i] + self.linear[j]) % 2\n self.shift[j] = (self.shift[i] + self.shift[j]) % 2","def qmincon(self, q=None):\n\n def sumsqr(A):\n return np.sum(A**2)\n\n def cost(x, ub, lb, qm, N):\n return sumsqr(\n (2 * (N @ x + qm) - ub - lb) / (ub - lb))\n\n q = getmatrix(q, (None, self.n))\n\n qstar = np.zeros((q.shape[0], self.n))\n error = np.zeros(q.shape[0])\n success = np.zeros(q.shape[0])\n\n lb = self.qlim[0, :]\n ub = self.qlim[1, :]\n\n for k, qk in enumerate(q):\n\n J = self.jacobe(qk)\n\n N = null(J)\n\n x0 = np.zeros(N.shape[1])\n A = np.r_[N, -N]\n b = np.r_[ub - qk, qk - lb].reshape(A.shape[0],)\n\n con = LinearConstraint(A, -np.inf, b)\n\n res = minimize(\n lambda x: cost(x, ub, lb, qk, N),\n x0, constraints=con)\n\n qstar[k, :] = qk + N @ res.x\n error[k] = res.fun\n success[k] = res.success\n\n if q.shape[0] == 1:\n return qstar[0, :], success[0], error[0]\n else:\n return qstar, success, error","def objective(self):\n pass","def putbarsj(self,whichsol_,j_,barsj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barsj is None: raise TypeError(\"Invalid type for argument barsj\")\n if barsj is None:\n barsj_ = None\n else:\n try:\n barsj_ = memoryview(barsj)\n except TypeError:\n try:\n _tmparr_barsj = array.array(\"d\",barsj)\n except TypeError:\n raise TypeError(\"Argument barsj has wrong type\")\n else:\n barsj_ = memoryview(_tmparr_barsj)\n \n else:\n if barsj_.format != \"d\":\n barsj_ = memoryview(array.array(\"d\",barsj))\n \n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj has wrong length\")\n res = self.__obj.putbarsj(whichsol_,j_,barsj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij)\n elif nz_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if nz_ is None: nz_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _idx_return_value = resargs\n return _idx_return_value","def UBM_update_double_prime(self, j_ind, k_ind, alpha_1, alpha_2, beta_1, beta_2, Gamma, Lambda, Omega):\n #set nodeType to \"-1,1\"\n self.nodeType = \"-11\"\n\n\n lambda_entry = Lambda[0, 1]\n gamma_entry = Gamma[0, 1]\n #two new hidden nodes\n self.n_h += 2\n\n #store old parameters\n old_b_v = np.copy(self.b_v)\n old_b_h = np.copy(self.b_h)\n\n old_w_hv = np.copy(self.w_hv)\n\n\n #update visible biases\n self.b_v[j_ind] = alpha_1\n self.b_v[k_ind] = alpha_2\n\n #update hidden biases\n self.b_h = np.zeros(self.n_h, dtype=complex)\n self.b_h[:-2] = old_b_h\n self.b_h[-2] = beta_1 + old_b_v[j_ind]\n self.b_h[-1] = beta_2 + old_b_v[k_ind]\n\n #update weight_matrix\n \"here could be the reason why successiive 2-qubit gates don't work! \"\n if hasattr(self, \"updated\"):\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\n\n self.w_hv[:-2, :j_ind] = old_w_hv[:, :j_ind]\n self.w_hv[:-2, j_ind+1:k_ind] = old_w_hv[:, j_ind+1:k_ind]\n self.w_hv[:-2, k_ind+1:] = old_w_hv[:, k_ind+1:]\n\n self.w_hv[-2, :] = self.w_Z[j_ind, :]\n self.w_hv[-1, :] = self.w_Z[k_ind, :]\n #replace the Z-entries by unitary parameters\n self.w_hv[-2, j_ind] = Omega[0, 0]\n self.w_hv[-1, j_ind] = Omega[1, 0]\n self.w_hv[-2, k_ind] = Omega[0, 1]\n self.w_hv[-1, k_ind] = Omega[1, 1]\n else:\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\n self.w_hv[:-2, :j_ind] = old_w_hv[:, :j_ind]\n self.w_hv[:-2, j_ind+1:k_ind] = old_w_hv[:, j_ind+1:k_ind]\n self.w_hv[:-2, k_ind+1:] = old_w_hv[:, k_ind+1:]\n self.w_hv[-2, j_ind] = Omega[0, 0]\n self.w_hv[-1, j_ind] = Omega[1, 0]\n self.w_hv[-2, k_ind] = Omega[0, 1]\n self.w_hv[-1, k_ind] = Omega[1, 1]\n\n\n\n\n\n if hasattr(self, \"updated\"):\n print(\"already updated\")\n old_w_X = np.copy(self.w_X)\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\n self.w_X[-2, :-2] = old_w_hv[:, j_ind].T\n self.w_X[-1, :-2] = old_w_hv[:, k_ind].T\n self.w_X[:-2, -2] = old_w_hv[:, j_ind]\n self.w_X[:-2, -1] = old_w_hv[:, k_ind]\n self.w_X[-1, -2] = gamma_entry + self.w_Z[j_ind, k_ind]\n self.w_X[-2, -1] = gamma_entry + self.w_Z[j_ind, k_ind]\n self.w_X[:-2, :-2] = old_w_X\n\n\n self.w_Z[j_ind, k_ind] = lambda_entry\n self.w_Z[k_ind, j_ind] = lambda_entry\n\n\n else:\n print(\"First RBM update \")\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\n self.w_X[-2, :-2] = old_w_hv[:, j_ind].T\n self.w_X[-1, :-2] = old_w_hv[:, k_ind].T\n self.w_X[:-2, -2] = old_w_hv[:, j_ind]\n self.w_X[:-2, -1] = old_w_hv[:, k_ind]\n self.w_X[-1, -2] = gamma_entry\n self.w_X[-2, -1] = gamma_entry\n\n\n self.w_Z = np.zeros((self.n_v, self.n_v), dtype=complex)\n self.w_Z[j_ind, k_ind] = lambda_entry\n self.w_Z[k_ind, j_ind] = lambda_entry\n\n\n\n\n self.updated = True","def jacobian(self, x):\n pass","def vjij(Vj, Ij, pol, ant) :\n s.vj(pol,Vj, ant)\n sleep(0.5)\n s.ij(pol,Ij, ant)","def putaijlist(self,subi,subj,valij): # 3\n num_ = None\n if num_ is None:\n num_ = len(subi)\n elif num_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij)\n elif num_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if num_ is None: num_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res = self.__obj.putaijlist64(num_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getqobj(self,qosubi,qosubj,qoval): # 3\n maxnumqonz_ = self.getnumqobjnz()\n if qosubi is None: raise TypeError(\"Invalid type for argument qosubi\")\n _copyback_qosubi = False\n if qosubi is None:\n qosubi_ = None\n else:\n try:\n qosubi_ = memoryview(qosubi)\n except TypeError:\n try:\n _tmparr_qosubi = array.array(\"i\",qosubi)\n except TypeError:\n raise TypeError(\"Argument qosubi has wrong type\")\n else:\n qosubi_ = memoryview(_tmparr_qosubi)\n _copyback_qosubi = True\n else:\n if qosubi_.format != \"i\":\n qosubi_ = memoryview(array.array(\"i\",qosubi))\n _copyback_qosubi = True\n if qosubi_ is not None and len(qosubi_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubi has wrong length\")\n if qosubj is None: raise TypeError(\"Invalid type for argument qosubj\")\n _copyback_qosubj = False\n if qosubj is None:\n qosubj_ = None\n else:\n try:\n qosubj_ = memoryview(qosubj)\n except TypeError:\n try:\n _tmparr_qosubj = array.array(\"i\",qosubj)\n except TypeError:\n raise TypeError(\"Argument qosubj has wrong type\")\n else:\n qosubj_ = memoryview(_tmparr_qosubj)\n _copyback_qosubj = True\n else:\n if qosubj_.format != \"i\":\n qosubj_ = memoryview(array.array(\"i\",qosubj))\n _copyback_qosubj = True\n if qosubj_ is not None and len(qosubj_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubj has wrong length\")\n if qoval is None: raise TypeError(\"Invalid type for argument qoval\")\n _copyback_qoval = False\n if qoval is None:\n qoval_ = None\n else:\n try:\n qoval_ = memoryview(qoval)\n except TypeError:\n try:\n _tmparr_qoval = array.array(\"d\",qoval)\n except TypeError:\n raise TypeError(\"Argument qoval has wrong type\")\n else:\n qoval_ = memoryview(_tmparr_qoval)\n _copyback_qoval = True\n else:\n if qoval_.format != \"d\":\n qoval_ = memoryview(array.array(\"d\",qoval))\n _copyback_qoval = True\n if qoval_ is not None and len(qoval_) != (maxnumqonz_):\n raise ValueError(\"Array argument qoval has wrong length\")\n res,resargs = self.__obj.getqobj64(maxnumqonz_,len(qosubi),qosubi_,qosubj_,qoval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqonz_return_value = resargs\n if _copyback_qoval:\n qoval[:] = _tmparr_qoval\n if _copyback_qosubj:\n qosubj[:] = _tmparr_qosubj\n if _copyback_qosubi:\n qosubi[:] = _tmparr_qosubi\n return _numqonz_return_value","def project_curve(q):\n n,T = q.shape\n if n==2:\n dt = 0.35\n if n==3:\n dt = 0.2\n epsilon = 1e-6\n\n iter = 1\n res = ones(n)\n J = zeros((n,n))\n\n s = linspace(0,1,T)\n\n qnew = q.copy()\n qnew = qnew / sqrt(innerprod_q2(qnew,qnew))\n\n qnorm = zeros(T)\n G = zeros(n)\n C = zeros(300)\n while (norm(res) > epsilon):\n if iter > 300:\n break\n\n # Jacobian\n for i in range(0,n):\n for j in range(0,n):\n J[i,j] = 3 * trapz(qnew[i,:]*qnew[j,:],s)\n \n J += eye(n)\n\n for i in range(0,T):\n qnorm[i] = norm(qnew[:,i])\n \n # Compute the residue\n for i in range(0,n):\n G[i] = trapz(qnew[i,:]*qnorm,s)\n \n res = -G\n\n if (norm(res) < epsilon):\n break\n\n x = solve(J,res)\n C[iter] = norm(res)\n\n delG = Basis_Normal_A(qnew)\n temp = zeros((n,T))\n for i in range(0,n):\n temp += x[i]*delG[i]*dt\n \n qnew += temp\n iter += 1\n \n qnew = qnew/sqrt(innerprod_q2(qnew,qnew))\n\n return qnew","def test_solve_quadratic(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.fixed = False\n iden2.x.fixed = False\n iden3.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 0)\n self.assertAlmostEqual(iden1.x.val, 1)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 3)","def getaij(self,i_,j_):\n aij_ = ctypes.c_double()\n res = __library__.MSK_XX_getaij(self.__nativep,i_,j_,ctypes.byref(aij_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n aij_ = aij_.value\n _aij_return_value = aij_\n return (_aij_return_value)","def getqobj(self,qosubi_,qosubj_,qoval_):\n maxnumqonz_ = self.getnumqobjnz()\n numqonz_ = ctypes.c_int64()\n _qosubi_minlength = (maxnumqonz_)\n if (maxnumqonz_) > 0 and qosubi_ is not None and len(qosubi_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubi is not long enough: Is %d, expected %d\" % (len(qosubi_),(maxnumqonz_)))\n if isinstance(qosubi_,numpy.ndarray) and not qosubi_.flags.writeable:\n raise ValueError(\"Argument qosubi must be writable\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi may not be None\")\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\n _qosubi_copyarray = False\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubi_ is not None:\n _qosubi_copyarray = True\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\n _qosubi_np_tmp[:] = qosubi_\n assert _qosubi_np_tmp.flags.contiguous\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubi_copyarray = False\n _qosubi_tmp = None\n \n _qosubj_minlength = (maxnumqonz_)\n if (maxnumqonz_) > 0 and qosubj_ is not None and len(qosubj_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubj is not long enough: Is %d, expected %d\" % (len(qosubj_),(maxnumqonz_)))\n if isinstance(qosubj_,numpy.ndarray) and not qosubj_.flags.writeable:\n raise ValueError(\"Argument qosubj must be writable\")\n if qosubj_ is None:\n raise ValueError(\"Argument qosubj may not be None\")\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\n _qosubj_copyarray = False\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubj_ is not None:\n _qosubj_copyarray = True\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\n _qosubj_np_tmp[:] = qosubj_\n assert _qosubj_np_tmp.flags.contiguous\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubj_copyarray = False\n _qosubj_tmp = None\n \n _qoval_minlength = (maxnumqonz_)\n if (maxnumqonz_) > 0 and qoval_ is not None and len(qoval_) != (maxnumqonz_):\n raise ValueError(\"Array argument qoval is not long enough: Is %d, expected %d\" % (len(qoval_),(maxnumqonz_)))\n if isinstance(qoval_,numpy.ndarray) and not qoval_.flags.writeable:\n raise ValueError(\"Argument qoval must be writable\")\n if qoval_ is None:\n raise ValueError(\"Argument qoval may not be None\")\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\n _qoval_copyarray = False\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qoval_ is not None:\n _qoval_copyarray = True\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\n _qoval_np_tmp[:] = qoval_\n assert _qoval_np_tmp.flags.contiguous\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qoval_copyarray = False\n _qoval_tmp = None\n \n qosurp_ = ctypes.c_int64(_qosubi_minlength)\n res = __library__.MSK_XX_getqobj64(self.__nativep,maxnumqonz_,ctypes.byref(qosurp_),ctypes.byref(numqonz_),_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqonz_ = numqonz_.value\n _numqonz_return_value = numqonz_\n if _qosubi_copyarray:\n qosubi_[:] = _qosubi_np_tmp\n if _qosubj_copyarray:\n qosubj_[:] = _qosubj_np_tmp\n if _qoval_copyarray:\n qoval_[:] = _qoval_np_tmp\n return (_numqonz_return_value)","def getsparsesymmat(self,idx_,subi_,subj_,valij_):\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _subi_minlength = (maxlen_)\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxlen_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (maxlen_)\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxlen_)))\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\n raise ValueError(\"Argument subj must be writable\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = (maxlen_)\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),(maxlen_)))\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\n raise ValueError(\"Argument valij must be writable\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valij_copyarray:\n valij_[:] = _valij_np_tmp","def qeye_like(qobj):\n from .cy.qobjevo import QobjEvo\n if isinstance(qobj, QobjEvo):\n qobj = qobj(0)\n return Qobj(\n _data.identity_like(qobj.data), dims=qobj.dims, type=qobj.type,\n superrep=qobj.superrep, isherm=True, isunitary=True, copy=False\n )","def add(self, i, j):\n \n # use running average to update CoM coordinates.\n self._x = (self._x * self._P + i) / (self._P + 1)\n self._y = (self._y * self._P + j) / (self._P + 1)\n # increment mass\n self._P += 1","def calc_Hij(self):\n\t\n\tself.Hij = np.zeros((3,3, 3,3, self.N),dtype=np.complex_)\n\t\n\tself.Hij[0,0,:,:,:] = self.Hpij*self.ep[0,0] + self.Hcij*self.ec[0,0]\n\tself.Hij[0,1,:,:,:] = self.Hpij*self.ep[0,1] + self.Hcij*self.ec[0,1]\n\tself.Hij[0,2,:,:,:] = self.Hpij*self.ep[0,2] + self.Hcij*self.ec[0,2]\n\t\n\tself.Hij[1,0,:,:,:] = self.Hpij*self.ep[1,0] + self.Hcij*self.ec[1,0] \n\tself.Hij[1,1,:,:,:] = self.Hpij*self.ep[1,1] + self.Hcij*self.ec[1,1]\n\tself.Hij[1,2,:,:,:] = self.Hpij*self.ep[1,2] + self.Hcij*self.ec[1,2]\n\t\n\tself.Hij[2,0,:,:,:] = self.Hpij*self.ep[2,0] + self.Hcij*self.ec[2,0] \n\tself.Hij[2,1,:,:,:] = self.Hpij*self.ep[2,1] + self.Hcij*self.ec[2,1] \n\tself.Hij[2,2,:,:,:] = self.Hpij*self.ep[2,2] + self.Hcij*self.ec[2,2]\n\t\n\treturn","def putbarcj(self,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexError(\"Inconsistent length of array weights\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n \n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n \n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n \n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def a_ij(s, p, i=1, j=1): # (Validated)\n from math import sqrt\n if i == j:\n return s.c[i]['a'] # Return pure paramater\n else: # find mixture aij i =/= j\n return (1 - p.m['k'][i][j]) * sqrt(s.c[i]['a'] * s.c[j]['a'])","def getaij(self,i_,j_): # 3\n res,resargs = self.__obj.getaij(i_,j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _aij_return_value = resargs\n return _aij_return_value","def _repr_(self):\n return \"Jacobian of %s\"%self.__curve","def problem_reduction_single(self, i, val):\n y_update = - val * self.A.getcol(i).toarray().flatten()\n self.y += y_update\n self.A = sparse.hstack([self.A[:, :i], self.A[:, i + 1:]], format='csr')\n z_index = self.mask.searchsorted(i)\n self.mask = np.insert(self.mask, z_index, i)\n self.z = np.insert(self.z, z_index, val)","def Iq(q, lorentz_scale, porod_scale, cor_length, porod_exp, lorentz_exp):\n with errstate(divide='ignore'):\n porod = porod_scale / q**porod_exp\n lorentz = lorentz_scale / (1.0 + (q * cor_length)**lorentz_exp)\n inten = porod + lorentz\n return inten","def quad_interp(x,y,xi) :\n f = interp1d(x,y,kind='quadratic')\n yi = f(xi)\n \n return yi","def ikcon(self, T, q0=None):\n\n if not isinstance(T, SE3):\n T = SE3(T)\n\n trajn = len(T)\n\n try:\n if q0 is not None:\n q0 = getvector(q0, self.n, 'row')\n else:\n q0 = np.zeros((trajn, self.n))\n except ValueError:\n verifymatrix(q0, (trajn, self.n))\n\n # create output variables\n qstar = np.zeros((trajn, self.n))\n error = []\n exitflag = []\n\n omega = np.diag([1, 1, 1, 3 / self.reach])\n\n def cost(q, T, omega):\n return np.sum(\n (\n (np.linalg.pinv(T.A) @ self.fkine(q).A - np.eye(4)) @\n omega) ** 2\n )\n\n bnds = Bounds(self.qlim[0, :], self.qlim[1, :])\n\n for i in range(trajn):\n Ti = T[i]\n res = minimize(\n lambda q: cost(q, Ti, omega),\n q0[i, :], bounds=bnds, options={'gtol': 1e-6})\n qstar[i, :] = res.x\n error.append(res.fun)\n exitflag.append(res.success)\n\n if trajn > 1:\n return qstar, exitflag, error\n else:\n return qstar[0, :], exitflag[0], error[0]","def eoq_soco(I,F,h,d,w,W):\n model = Model(\"EOQ model using SOCO\")\n\n T,c = {},{}\n for i in I:\n T[i] = model.addVar(vtype=\"C\", name=\"T(%s)\"%i) # cycle time for item i\n c[i] = model.addVar(vtype=\"C\", name=\"c(%s)\"%i) # total cost for item i\n\n for i in I:\n model.addCons(F[i] <= c[i]*T[i])\n\n model.addCons(quicksum(w[i]*d[i]*T[i] for i in I) <= W)\n\n model.setObjective(quicksum(c[i] + h[i]*d[i]*T[i]*0.5 for i in I), \"minimize\")\n\n model.data = T,c\n return model","def insert_cost(self, i, j):\n raise NotImplementedError","def Mq_inv(self, q):\n\t\traise NotImplementedError","def setExtraMatrixAnimCurve(self, i, j, animcurve, view) -> None:\n ...","def Qc(I, dT, a, b, c, d, e, f, g, h, i, k):\n x1 = I # I\n x2 = dT # dT\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\n return m * x2 + b","def conj(self, o): \n return (o.inv()) * self * o","def set_rhs(self):\n k = self.istore[:, 0]\n ksym = self.stencil.get_symmetric()[k]\n self.rhs[:] = self.feq[k, np.arange(k.size)] + self.feq[ksym, np.arange(k.size)]","def set_rhs(self):\n k = self.istore[:, 0]\n ksym = self.stencil.get_symmetric()[k]\n self.rhs[:] = self.feq[k, np.arange(k.size)] + self.feq[ksym, np.arange(k.size)]","def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\n self.coeff[self._core.index_alpha(key[0]),\n self._core.index_beta(key[1])] = value","def Optimize_Sqq(qj,dt):\n\n # find the signal psd\n fs=1.0/dt\n M,L = 256,512\n wxx,Sxx=sa.Welch_estimator(qj,fs=fs,M=M,L=L)\n w_threshold = fs*np.pi/2\n Sxx=Sxx[wxx<w_threshold]\n wxx=wxx[wxx<w_threshold]\n\n # the psd of oscillator model is computed using Wiener-Khinchin relation\n # and called in the objective function\n print('S_qq matching objective fun: ls error of spectrum')\n spec_ls_distance = lambda params: np.linalg.norm(Sxx*(Sxx - sa.Oscillator_Spectrum(params[0],params[1]/params[0],params[1],wxx)),ord=1)\n\n # optimize via pyswarm - v1\n lb = [0.001, 0.001]\n ub = [100, 500]\n xopt, fopt = pso(spec_ls_distance, lb, ub, maxiter=10000,swarmsize=10000,minfunc=1e-10)\n k,D = xopt[0],xopt[1]\n\n b = D/k\n print('result: k='+str(k)+' b='+str(b)+' D='+str(D))\n\n return k,D","def puty(self,whichsol_,y_):\n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if y_ is None:\n raise ValueError(\"Argument y cannot be None\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_puty(self.__nativep,whichsol_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def calculate_strain(self):\n\t\n\tself.Hpij = np.zeros((3,3,self.N),dtype=np.complex_)\n\tself.Hcij = np.zeros((3,3,self.N),dtype=np.complex_)\n\t\n\tcalc_Hcp_ij(self)\t\n\n\tconstruct_basis_tensors(self)\n\n\tcalc_Hij(self)\n\t\n\tcontract_tenors(self)\n\n\treturn","def putbarcj(self,j_,sub_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights_)\n elif num_ != len(weights_):\n raise IndexError(\"Inconsistent length of array weights\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbarcj(self.__nativep,j_,num_,_sub_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def pl_mi(i,j, first=False):\n if (-1)**(i+j)>0:\n if first:\n return \"\"\n else:\n return \"+\"\n else:\n return \"-\"","def switch(self, i, j0, j1):\n if not self._mutable:\n raise ValueError(\"this constellation is immutable.\"\n \" Take a mutable copy first.\")\n S = SymmetricGroup(list(range(self.degree())))\n tr = S((j0, j1))\n i = int(i)\n if i < 0 or i >= len(self._g):\n raise ValueError(\"index out of range\")\n\n ii = i + 1\n if ii == len(self._g):\n ii = 0\n self._g[i] = self._g[i] * tr\n self._g[ii] = tr * self._g[ii]","def setCoefficient(self, *args):\n return _libsbml.FluxObjective_setCoefficient(self, *args)","def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_valij = False\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n _copyback_valij = True\n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n _copyback_valij = True\n if valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij has wrong length\")\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_valij:\n valij[:] = _tmparr_valij\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi","def qrot(I,sym):\n T = s.Symbol(\"T\")\n if type(I) == list:\n return (((s.pi * I[0] * I[1] * I[2])**(1/2))/sym) * ((8 * s.pi**2 * k * T) / (h**2))**(3/2)\n else:\n return (((s.pi * I)**(1/2))/sym) * ((8 * s.pi**2 * k * T) / (h**2))**(3/2)","def __imul__(self, obj):\n # calls __mul__\n tmp = self * obj\n self.data = tmp.data\n self.m, self.n = tmp.dim\n return self","def quad_interp(xi,yi):\n\n # check inputs and print error message if not valid:\n\n error_message = \"xi and yi should have type numpy.ndarray\"\n assert (type(xi) is np.ndarray) and (type(yi) is np.ndarray), error_message\n\n error_message = \"xi and yi should have length 3\"\n assert len(xi)==3 and len(yi)==3, error_message\n\n error_message = \"it is not possible to have more than one point in the with the same xi\"\n assert (len(np.unique(xi)) == len(xi)), error_message\n\n # Set up linear system to interpolate through data points:\n\n A = np.array([[1, 1, 1], xi, xi**2]).T\n b = yi\n\n c = solve(A,b)\n \n return c","def make_sq(mlat, dAB, *J):\n if (len(J)!=4):\n print(\"Number of paramaters are exceeded 5!\")\n NN = 2*mlat\n \n tau = np.zeros((NN,NN), dtype=complex)\n h = np.zeros((NN,NN), dtype=complex)\n \n for i in range(mlat-1):\n if (i%2==0):\n h[i,i] = dAB/2. # on-site energy\n h[mlat+i,mlat+i] = -dAB/2. # on-site energy \n h[i, mlat+i] = J[0]\n h[i, i+1] = J[1]\n h[mlat+i, mlat+i+1] = J[3]\n #\n tau[mlat+i, i] = J[2]\n elif (i%2==1):\n h[i,i] = -dAB/2. # on-site energy\n h[mlat+i,mlat+i] = dAB/2. # on-site energy \n h[i, mlat+i] = J[2]\n h[i, i+1] = J[3]\n h[mlat+i, mlat+i+1] = J[1]\n #\n tau[mlat+i, i] = J[0]\n\n # End of loop over lattice sites\n\n # The upper edge site\n if (mlat-1 % 2==0):\n h[mlat-1, mlat-1] = dAB/2. # on-site energy\n h[NN-1,NN-1] = -dAB/2. # on-site energy \n h[mlat-1, NN-1] = J[0]\n #\n tau[NN-1, mlat-1] = J[2]\n elif (mlat-1 % 2==1):\n h[mlat-1, mlat-1] = -dAB/2. # on-site energy\n h[NN-1,NN-1] = dAB/2. # on-site energy \n h[mlat-1, NN-1] = J[2]\n #\n tau[NN-1, mlat-1] = J[0] \n \n h = h + h.conj().T # make it hermitian\n return h, tau","def compare_J_terms(m, nix, srcclass=None, analytic_class=None, calced_class=None, \n only_calced_Cterms=False, fx=None):\n \n if fx is None:\n fx = fixtures.fixture_from_model(m)\n \n if srcclass is None:\n srcclass = FullSingleFieldSource\n \n if analytic_class is None:\n analytic_class = analyticsolution.NoPhaseBunchDaviesSolution\n if calced_class is None:\n calced_class = calcedsolution.NoPhaseBunchDaviesCalced\n \n asol = analytic_class(fx, srcclass)\n csol = calced_class(fx, srcclass)\n \n #Need to make analytic solution use 128 bit floats to avoid overruns\n asol.srceqns.k = np.float128(asol.srceqns.k)\n \n \n #Get background values\n bgvars = m.yresult[nix, 0:3, 0]\n a = m.ainit*np.exp(m.tresult[nix])\n #Get potentials\n potentials = m.potentials(np.array([bgvars[0]]), m.pot_params)\n \n #Set alpha and beta\n alpha = 1/(a*np.sqrt(2))\n beta = a*bgvars[2]\n \n dp1 = csol.get_dp1(csol.srceqns.fullk, alpha=alpha)\n dp1dot = csol.get_dp1dot(csol.srceqns.fullk, alpha=alpha, beta=beta)\n \n #Calculate dphi(q) and dphi(k-q)\n dp1_q = dp1[:csol.srceqns.k.shape[-1]]\n dp1dot_q = dp1dot[:csol.srceqns.k.shape[-1]] \n \n theta_terms = csol.srceqns.getthetaterms(dp1, dp1dot)\n csol.srceqns.k = np.float128(csol.srceqns.k)\n csol.srceqns.fullk = np.float128(csol.srceqns.fullk)\n \n calced_Cterms = csol.calculate_Cterms(bgvars, a, potentials) \n if only_calced_Cterms:\n analytic_Cterms = calced_Cterms\n else:\n analytic_Cterms = asol.calculate_Cterms(bgvars, a, potentials)\n \n results = {}\n \n for Jkey in csol.J_terms.iterkeys():\n afunc = asol.J_terms[Jkey]\n cfunc = csol.J_terms[Jkey]\n analytic_result = afunc(asol.srceqns.k, analytic_Cterms, alpha=alpha, beta=beta)\n calced_result = cfunc(theta_terms, dp1_q, dp1dot_q, calced_Cterms)\n diff = analytic_result - calced_result\n err = np.abs(diff)/np.abs(analytic_result)\n results[Jkey] = (diff, err, analytic_result, calced_result)\n \n return results"],"string":"[\n \"def putqobjij(self,i_,j_,qoij_): # 3\\n res = self.__obj.putqobjij(i_,j_,qoij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getqobjij(self,i_,j_): # 3\\n res,resargs = self.__obj.getqobjij(i_,j_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _qoij_return_value = resargs\\n return _qoij_return_value\",\n \"def getqobjij(self,i_,j_):\\n qoij_ = ctypes.c_double()\\n res = __library__.MSK_XX_getqobjij(self.__nativep,i_,j_,ctypes.byref(qoij_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n qoij_ = qoij_.value\\n _qoij_return_value = qoij_\\n return (_qoij_return_value)\",\n \"def _qij_minus(i: int, j: int):\\n ib = i * 2 + 1\\n jb = j * 2 + 1\\n term = FermionOperator(((jb, 0), (ib, 0)), 1.0)\\n return term\",\n \"def _qij_0(i: int, j: int):\\n ia = i * 2 + 0\\n ib = i * 2 + 1\\n ja = j * 2 + 0\\n jb = j * 2 + 1\\n term1 = FermionOperator(((ja, 0), (ib, 0)), 1.0)\\n term2 = FermionOperator(((ia, 0), (jb, 0)), 1.0)\\n return numpy.sqrt(0.5) * (term1 - term2)\",\n \"def putsolutionyi(self,i_,whichsol_,y_):\\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _qij_plus(i: int, j: int):\\n ia = i * 2 + 0\\n ja = j * 2 + 0\\n term = FermionOperator(((ja, 0), (ia, 0)), 1.0)\\n return term\",\n \"def putaij(self,i_,j_,aij_): # 3\\n res = self.__obj.putaij(i_,j_,aij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putaij(self,i_,j_,aij_):\\n res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def substitute_cost(self, i, j):\\n raise NotImplementedError\",\n \"def putsolutionyi(self,i_,whichsol_,y_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqobj(self,qosubi_,qosubj_,qoval_):\\n numqonz_ = None\\n if numqonz_ is None:\\n numqonz_ = len(qosubi_)\\n elif numqonz_ != len(qosubi_):\\n raise IndexError(\\\"Inconsistent length of array qosubi\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qosubj_)\\n elif numqonz_ != len(qosubj_):\\n raise IndexError(\\\"Inconsistent length of array qosubj\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qoval_)\\n elif numqonz_ != len(qoval_):\\n raise IndexError(\\\"Inconsistent length of array qoval\\\")\\n if qosubi_ is None:\\n raise ValueError(\\\"Argument qosubi cannot be None\\\")\\n if qosubi_ is None:\\n raise ValueError(\\\"Argument qosubi may not be None\\\")\\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\\n _qosubi_copyarray = False\\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qosubi_ is not None:\\n _qosubi_copyarray = True\\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\\n _qosubi_np_tmp[:] = qosubi_\\n assert _qosubi_np_tmp.flags.contiguous\\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qosubi_copyarray = False\\n _qosubi_tmp = None\\n \\n if qosubj_ is None:\\n raise ValueError(\\\"Argument qosubj cannot be None\\\")\\n if qosubj_ is None:\\n raise ValueError(\\\"Argument qosubj may not be None\\\")\\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\\n _qosubj_copyarray = False\\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qosubj_ is not None:\\n _qosubj_copyarray = True\\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\\n _qosubj_np_tmp[:] = qosubj_\\n assert _qosubj_np_tmp.flags.contiguous\\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qosubj_copyarray = False\\n _qosubj_tmp = None\\n \\n if qoval_ is None:\\n raise ValueError(\\\"Argument qoval cannot be None\\\")\\n if qoval_ is None:\\n raise ValueError(\\\"Argument qoval may not be None\\\")\\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\\n _qoval_copyarray = False\\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qoval_ is not None:\\n _qoval_copyarray = True\\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\\n _qoval_np_tmp[:] = qoval_\\n assert _qoval_np_tmp.flags.contiguous\\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qoval_copyarray = False\\n _qoval_tmp = None\\n \\n res = __library__.MSK_XX_putqobj(self.__nativep,numqonz_,_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def fixA(self,i,j,value):\\n if self.coeffPattern[0] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[0] = [[None]*m for i in range(m)]\\n self.coeffPattern[0][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def fixB(self,i,j,value):\\n if self.coeffPattern[1] == None:\\n m,n=self.m,self.n\\n self.coeffPattern = [[None]*n for i in range(m)]\\n self.coeffPattern[1][i][j]=value\\n self._updateEstimatorSize(i)\",\n \"def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqobj(self,qosubi,qosubj,qoval): # 3\\n numqonz_ = None\\n if numqonz_ is None:\\n numqonz_ = len(qosubi)\\n elif numqonz_ != len(qosubi):\\n raise IndexError(\\\"Inconsistent length of array qosubi\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qosubj)\\n elif numqonz_ != len(qosubj):\\n raise IndexError(\\\"Inconsistent length of array qosubj\\\")\\n if numqonz_ is None:\\n numqonz_ = len(qoval)\\n elif numqonz_ != len(qoval):\\n raise IndexError(\\\"Inconsistent length of array qoval\\\")\\n if numqonz_ is None: numqonz_ = 0\\n if qosubi is None: raise TypeError(\\\"Invalid type for argument qosubi\\\")\\n if qosubi is None:\\n qosubi_ = None\\n else:\\n try:\\n qosubi_ = memoryview(qosubi)\\n except TypeError:\\n try:\\n _tmparr_qosubi = array.array(\\\"i\\\",qosubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubi has wrong type\\\")\\n else:\\n qosubi_ = memoryview(_tmparr_qosubi)\\n \\n else:\\n if qosubi_.format != \\\"i\\\":\\n qosubi_ = memoryview(array.array(\\\"i\\\",qosubi))\\n \\n if qosubj is None: raise TypeError(\\\"Invalid type for argument qosubj\\\")\\n if qosubj is None:\\n qosubj_ = None\\n else:\\n try:\\n qosubj_ = memoryview(qosubj)\\n except TypeError:\\n try:\\n _tmparr_qosubj = array.array(\\\"i\\\",qosubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubj has wrong type\\\")\\n else:\\n qosubj_ = memoryview(_tmparr_qosubj)\\n \\n else:\\n if qosubj_.format != \\\"i\\\":\\n qosubj_ = memoryview(array.array(\\\"i\\\",qosubj))\\n \\n if qoval is None: raise TypeError(\\\"Invalid type for argument qoval\\\")\\n if qoval is None:\\n qoval_ = None\\n else:\\n try:\\n qoval_ = memoryview(qoval)\\n except TypeError:\\n try:\\n _tmparr_qoval = array.array(\\\"d\\\",qoval)\\n except TypeError:\\n raise TypeError(\\\"Argument qoval has wrong type\\\")\\n else:\\n qoval_ = memoryview(_tmparr_qoval)\\n \\n else:\\n if qoval_.format != \\\"d\\\":\\n qoval_ = memoryview(array.array(\\\"d\\\",qoval))\\n \\n res = self.__obj.putqobj(numqonz_,qosubi_,qosubj_,qoval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarxj(self,whichsol_,j_,barxj_):\\n _barxj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj is not long enough: Is %d, expected %d\\\" % (len(barxj_),self.getlenbarvarj((j_))))\\n if barxj_ is None:\\n raise ValueError(\\\"Argument barxj cannot be None\\\")\\n if barxj_ is None:\\n raise ValueError(\\\"Argument barxj may not be None\\\")\\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\\n _barxj_copyarray = False\\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barxj_ is not None:\\n _barxj_copyarray = True\\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\\n _barxj_np_tmp[:] = barxj_\\n assert _barxj_np_tmp.flags.contiguous\\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barxj_copyarray = False\\n _barxj_tmp = None\\n \\n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def ij(ij, pol, ant) :\\n s.ij(pol, ij, ant)\",\n \"def fixC(self,i,value):\\n if self.coeffPattern[2] == None:\\n m,n=self.m,self.n\\n self.coeffPattern[2] = [None]*m\\n self.coeffPattern[2][i]=value\\n self._updateEstimatorSize(i)\",\n \"def _Q(self, x, y):\\n\\n # Calculate the [Q] coefficient matrix\\n Q = array([[0, 0, 0, -2, 0, 0, -6*x, -2*y, 0, 0, -6*x*y, 0],\\n [0, 0, 0, 0, 0, -2, 0, 0, -2*x, -6*y, 0, -6*x*y],\\n [0, 0, 0, 0, -2, 0, 0, -4*x, -4*y, 0, -6*x**2, -6*y**2]])\\n \\n # Return the [Q] coefficient matrix\\n return Q\",\n \"def SetIJ(self, newI, newJ, newsize=-1):\\n return _table.Table_SetIJ(self, newI, newJ, newsize)\",\n \"def replace_q_indices(circuit, q_nums, qr):\\n\\n new_circuit = qiskit.QuantumCircuit(qr)\\n for op in circuit.data:\\n original_qubits = op.qargs\\n new_op = copy.deepcopy(op)\\n new_op.qargs = [(qr, q_nums[x]) for x in [arg[1] for arg in original_qubits]]\\n new_circuit.data.append(new_op)\\n\\n return new_circuit\",\n \"def putbarsj(self,whichsol_,j_,barsj_):\\n _barsj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj is not long enough: Is %d, expected %d\\\" % (len(barsj_),self.getlenbarvarj((j_))))\\n if barsj_ is None:\\n raise ValueError(\\\"Argument barsj cannot be None\\\")\\n if barsj_ is None:\\n raise ValueError(\\\"Argument barsj may not be None\\\")\\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\\n _barsj_copyarray = False\\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barsj_ is not None:\\n _barsj_copyarray = True\\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\\n _barsj_np_tmp[:] = barsj_\\n assert _barsj_np_tmp.flags.contiguous\\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barsj_copyarray = False\\n _barsj_tmp = None\\n \\n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def J(self, name, q, x=None):\\n\\n x = self.x_zeros if x is None else x\\n funcname = name + '[0,0,0]' if np.allclose(x, 0) else name\\n # check for function in dictionary\\n if self._J.get(funcname, None) is None:\\n self._J[funcname] = self._calc_J(name=name, x=x)\\n parameters = tuple(q) + tuple(x)\\n return np.array(self._J[funcname](*parameters), dtype='float32')\",\n \"def putbarxj(self,whichsol_,j_,barxj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barxj is None: raise TypeError(\\\"Invalid type for argument barxj\\\")\\n if barxj is None:\\n barxj_ = None\\n else:\\n try:\\n barxj_ = memoryview(barxj)\\n except TypeError:\\n try:\\n _tmparr_barxj = array.array(\\\"d\\\",barxj)\\n except TypeError:\\n raise TypeError(\\\"Argument barxj has wrong type\\\")\\n else:\\n barxj_ = memoryview(_tmparr_barxj)\\n \\n else:\\n if barxj_.format != \\\"d\\\":\\n barxj_ = memoryview(array.array(\\\"d\\\",barxj))\\n \\n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj has wrong length\\\")\\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putcj(self,j_,cj_):\\n res = __library__.MSK_XX_putcj(self.__nativep,j_,cj_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def quaternion_conjugate(self, q):\\n\\n \\\"\\\"\\\"\\n in-place operation is an operation that changes directly the content of a given Tensor without making a copy.\\n ALL operations on the tensor that operate in-place on it will have an _ postfix.\\n \\\"\\\"\\\"\\n q_star = q.new(4).fill_(-1)\\n\\n # leave the scalar unchanged and change signs of i, j, k number parts\\n q_star[0] = 1.0\\n\\n return q * q_star\",\n \"def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi_)\\n elif numqcnz_ != len(qcsubi_):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj_)\\n elif numqcnz_ != len(qcsubj_):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval_)\\n elif numqcnz_ != len(qcval_):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if qcsubk_ is None:\\n raise ValueError(\\\"Argument qcsubk cannot be None\\\")\\n if qcsubk_ is None:\\n raise ValueError(\\\"Argument qcsubk may not be None\\\")\\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\\n _qcsubk_copyarray = False\\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubk_ is not None:\\n _qcsubk_copyarray = True\\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\\n _qcsubk_np_tmp[:] = qcsubk_\\n assert _qcsubk_np_tmp.flags.contiguous\\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubk_copyarray = False\\n _qcsubk_tmp = None\\n \\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi cannot be None\\\")\\n if qcsubi_ is None:\\n raise ValueError(\\\"Argument qcsubi may not be None\\\")\\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubi_ is not None:\\n _qcsubi_copyarray = True\\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\\n _qcsubi_np_tmp[:] = qcsubi_\\n assert _qcsubi_np_tmp.flags.contiguous\\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubi_copyarray = False\\n _qcsubi_tmp = None\\n \\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj cannot be None\\\")\\n if qcsubj_ is None:\\n raise ValueError(\\\"Argument qcsubj may not be None\\\")\\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qcsubj_ is not None:\\n _qcsubj_copyarray = True\\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\\n _qcsubj_np_tmp[:] = qcsubj_\\n assert _qcsubj_np_tmp.flags.contiguous\\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qcsubj_copyarray = False\\n _qcsubj_tmp = None\\n \\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval cannot be None\\\")\\n if qcval_ is None:\\n raise ValueError(\\\"Argument qcval may not be None\\\")\\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\\n _qcval_copyarray = False\\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qcval_ is not None:\\n _qcval_copyarray = True\\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\\n _qcval_np_tmp[:] = qcval_\\n assert _qcval_np_tmp.flags.contiguous\\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qcval_copyarray = False\\n _qcval_tmp = None\\n \\n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solve_Q(ih, jh):\\n # Build A and b\\n\\n # Solve for c via least squares.\\n\\n # Form C from c\\n\\n # Solve for Q and return it.\\n return None\",\n \"def putcj(self,j_,cj_): # 3\\n res = self.__obj.putcj(j_,cj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi_)\\n elif nz_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj_)\\n elif nz_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij_)\\n elif nz_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n idx_ = ctypes.c_int64()\\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n idx_ = idx_.value\\n _idx_return_value = idx_\\n return (_idx_return_value)\",\n \"def objective(self, objective):\\n\\n self._objective = objective\",\n \"def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def objective(self, x):\\n pass\",\n \"def objective(self, x):\\n pass\",\n \"def fit_ij(self, i, j, **kwargs):\\n assert i != j, \\\"i and j must be different\\\"\\n\\n # Get the layer of interest in the list of circuits\\n l = self._find_layer(i, j)\\n\\n # Take the circuits of interest\\n circuits = self._circuits[0:3]\\n circuits += self._circuits[(3 + 6*l) : (3 + 6*(l+1))]\\n\\n # This will create an empty _data dict for the fit function\\n # We are using a member field so that we can use the super() fit \\n # function\\n self._data = {}\\n\\n # Process measurement counts into probabilities\\n for circ in circuits:\\n # Take only the relevant qubit labels from the circuit label\\n tup = literal_eval(circ.name)\\n tup = (tup[i], tup[j])\\n\\n # Marginalize the counts for the two relevant qubits\\n counts = marginal_counts(self._result.get_counts(circ), [i, j])\\n\\n # Populate the data\\n self._data[tup] = counts\\n\\n # Test that all the required measurements are there\\n expected_corr = product(['X', 'Y', 'Z'], ['X', 'Y', 'Z'])\\n if set(self._data.keys()) != set(expected_corr):\\n raise Exception(\\\"Could not find all the measurements required for tomography\\\")\\n\\n # Do the actual fit\\n result = super().fit(**kwargs)\\n\\n # clear the _data field\\n self._data = None\\n return result\",\n \"def conj(q):\\n q = np.array([q[0]])\\n q[0,1]=-q[0,1]\\n q[0,2]=-q[0,2]\\n q[0,3]=-q[0,3]\\n complexconjugate = quatreal(q)\\n return complexconjugate\",\n \"def _qij_vec(i: int, j: int):\\n return [_qij_plus(i, j), _qij_minus(i, j), _qij_0(i, j)]\",\n \"def _pij(i: int, j: int):\\n ia = i * 2 + 0\\n ib = i * 2 + 1\\n ja = j * 2 + 0\\n jb = j * 2 + 1\\n term1 = FermionOperator(((ja, 0), (ib, 0)), 1.0)\\n term2 = FermionOperator(((ia, 0), (jb, 0)), 1.0)\\n return numpy.sqrt(0.5) * (term1 + term2)\",\n \"def delete_cost(self, i, j):\\n raise NotImplementedError\",\n \"def putbaraij(self,i_,j_,sub_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights_)\\n elif num_ != len(weights_):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbaraij(self.__nativep,i_,j_,num_,_sub_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _update_objective(self):\\n # rewrap the cost if the solver has been run\\n self.Finalize()\\n return\",\n \"def putaijlist(self,subi_,subj_,valij_):\\n num_ = None\\n if num_ is None:\\n num_ = len(subi_)\\n elif num_ != len(subi_):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj_)\\n elif num_ != len(subj_):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(valij_)\\n elif num_ != len(valij_):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi cannot be None\\\")\\n if subi_ is None:\\n raise ValueError(\\\"Argument subi may not be None\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n if subj_ is None:\\n raise ValueError(\\\"Argument subj cannot be None\\\")\\n if subj_ is None:\\n raise ValueError(\\\"Argument subj may not be None\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n if valij_ is None:\\n raise ValueError(\\\"Argument valij cannot be None\\\")\\n if valij_ is None:\\n raise ValueError(\\\"Argument valij may not be None\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\\n numqcnz_ = None\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubi)\\n elif numqcnz_ != len(qcsubi):\\n raise IndexError(\\\"Inconsistent length of array qcsubi\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcsubj)\\n elif numqcnz_ != len(qcsubj):\\n raise IndexError(\\\"Inconsistent length of array qcsubj\\\")\\n if numqcnz_ is None:\\n numqcnz_ = len(qcval)\\n elif numqcnz_ != len(qcval):\\n raise IndexError(\\\"Inconsistent length of array qcval\\\")\\n if numqcnz_ is None: numqcnz_ = 0\\n if qcsubk is None: raise TypeError(\\\"Invalid type for argument qcsubk\\\")\\n if qcsubk is None:\\n qcsubk_ = None\\n else:\\n try:\\n qcsubk_ = memoryview(qcsubk)\\n except TypeError:\\n try:\\n _tmparr_qcsubk = array.array(\\\"i\\\",qcsubk)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubk has wrong type\\\")\\n else:\\n qcsubk_ = memoryview(_tmparr_qcsubk)\\n \\n else:\\n if qcsubk_.format != \\\"i\\\":\\n qcsubk_ = memoryview(array.array(\\\"i\\\",qcsubk))\\n \\n if qcsubi is None: raise TypeError(\\\"Invalid type for argument qcsubi\\\")\\n if qcsubi is None:\\n qcsubi_ = None\\n else:\\n try:\\n qcsubi_ = memoryview(qcsubi)\\n except TypeError:\\n try:\\n _tmparr_qcsubi = array.array(\\\"i\\\",qcsubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubi has wrong type\\\")\\n else:\\n qcsubi_ = memoryview(_tmparr_qcsubi)\\n \\n else:\\n if qcsubi_.format != \\\"i\\\":\\n qcsubi_ = memoryview(array.array(\\\"i\\\",qcsubi))\\n \\n if qcsubj is None: raise TypeError(\\\"Invalid type for argument qcsubj\\\")\\n if qcsubj is None:\\n qcsubj_ = None\\n else:\\n try:\\n qcsubj_ = memoryview(qcsubj)\\n except TypeError:\\n try:\\n _tmparr_qcsubj = array.array(\\\"i\\\",qcsubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qcsubj has wrong type\\\")\\n else:\\n qcsubj_ = memoryview(_tmparr_qcsubj)\\n \\n else:\\n if qcsubj_.format != \\\"i\\\":\\n qcsubj_ = memoryview(array.array(\\\"i\\\",qcsubj))\\n \\n if qcval is None: raise TypeError(\\\"Invalid type for argument qcval\\\")\\n if qcval is None:\\n qcval_ = None\\n else:\\n try:\\n qcval_ = memoryview(qcval)\\n except TypeError:\\n try:\\n _tmparr_qcval = array.array(\\\"d\\\",qcval)\\n except TypeError:\\n raise TypeError(\\\"Argument qcval has wrong type\\\")\\n else:\\n qcval_ = memoryview(_tmparr_qcval)\\n \\n else:\\n if qcval_.format != \\\"d\\\":\\n qcval_ = memoryview(array.array(\\\"d\\\",qcval))\\n \\n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbaraij(self,i_,j_,sub,weights): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights)\\n elif num_ != len(weights):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"q\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"q\\\":\\n sub_ = memoryview(array.array(\\\"q\\\",sub))\\n \\n if weights is None: raise TypeError(\\\"Invalid type for argument weights\\\")\\n if weights is None:\\n weights_ = None\\n else:\\n try:\\n weights_ = memoryview(weights)\\n except TypeError:\\n try:\\n _tmparr_weights = array.array(\\\"d\\\",weights)\\n except TypeError:\\n raise TypeError(\\\"Argument weights has wrong type\\\")\\n else:\\n weights_ = memoryview(_tmparr_weights)\\n \\n else:\\n if weights_.format != \\\"d\\\":\\n weights_ = memoryview(array.array(\\\"d\\\",weights))\\n \\n res = self.__obj.putbaraij(i_,j_,num_,sub_,weights_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def updateIMU(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray) -> np.ndarray:\\n if gyr is None or not np.linalg.norm(gyr)>0:\\n return q\\n qEst = 0.5 * q_prod(q, [0, *gyr]) # (eq. 12)\\n a_norm = np.linalg.norm(acc)\\n if a_norm>0:\\n a = acc/a_norm\\n qw, qx, qy, qz = q/np.linalg.norm(q)\\n # Gradient objective function (eq. 25) and Jacobian (eq. 26)\\n f = np.array([2.0*(qx*qz - qw*qy) - a[0],\\n 2.0*(qw*qx + qy*qz) - a[1],\\n 2.0*(0.5-qx**2-qy**2) - a[2]]) # (eq. 25)\\n J = np.array([[-2.0*qy, 2.0*qz, -2.0*qw, 2.0*qx],\\n [ 2.0*qx, 2.0*qw, 2.0*qz, 2.0*qy],\\n [ 0.0, -4.0*qx, -4.0*qy, 0.0 ]]) # (eq. 26)\\n # Objective Function Gradient\\n gradient = J.T@f # (eq. 34)\\n gradient /= np.linalg.norm(gradient)\\n qEst -= self.gain*gradient # (eq. 33)\\n q += qEst*self.Dt # (eq. 13)\\n q /= np.linalg.norm(q)\\n return q\",\n \"def setitem(self, i, j, value):\\n # XXX: flint matrices do not support negative indices\\n # XXX: They also raise ValueError instead of IndexError\\n m, n = self.shape\\n if i < 0:\\n i += m\\n if j < 0:\\n j += n\\n try:\\n self.rep[i, j] = value\\n except ValueError:\\n raise IndexError(f\\\"Invalid indices ({i}, {j}) for Matrix of shape {self.shape}\\\")\",\n \"def jot(self):\\r\\n\\t\\t\\r\\n\\t\\t# empty list?\\r\\n\\t\\tif len(self) < 1:\\r\\n\\t\\t\\r\\n\\t\\t\\treturn '(0)'\\r\\n\\t\\t\\r\\n\\t\\t# go through terms\\r\\n\\t\\ts = ''\\r\\n\\t\\tfor i in self:\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# positive or negative\\r\\n\\t\\t\\tif i.look('i') % 4 in (0,1):\\r\\n\\t\\t\\t\\ts += ' +('\\r\\n\\t\\t\\tif i.look('i') % 4 in (2,3):\\r\\n\\t\\t\\t\\ts += ' -('\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t# list of variables\\r\\n\\t\\t\\ty = i.keys()\\r\\n\\t\\t\\ty = [str(j) for j in y if j != 'i']\\r\\n\\t\\t\\ty = [j for j in y if j.isalpha()]\\r\\n\\t\\t\\ty = Pa._tidy(y)\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t# coefficient\\r\\n\\t\\t\\tn,d = i.fuse()\\r\\n\\t\\t\\tif n > 1 or d > 1 or len(y) < 1:\\r\\n\\t\\t\\t\\ts += str(n)\\r\\n\\t\\t\\tif d > 1:\\r\\n\\t\\t\\t\\ts += '/' + str(d)\\r\\n\\t\\t\\tif i.look('i') % 4 in (1,3):\\r\\n\\t\\t\\t\\ts += 'i'\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# add variables to string\\r\\n\\t\\t\\tfor k in y:\\r\\n\\t\\t\\t\\tif s[-1] == '(':\\r\\n\\t\\t\\t\\t\\ts += k\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\ts += ' ' + k\\r\\n\\t\\t\\t\\tif i[k] != 1:\\r\\n\\t\\t\\t\\t\\ts += str(i[k])\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t# close\\r\\n\\t\\t\\ts += ')'\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t# remove leading ' '\\r\\n\\t\\tif s[0] == ' ':\\r\\n\\t\\t\\ts = s[1:]\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t# remove leading +\\r\\n\\t\\tif s[0] == '+':\\r\\n\\t\\t\\ts = s[1:]\\r\\n\\t\\t\\t\\t\\r\\n\\t\\treturn s\",\n \"def convert_to_q(self):\\n if self.measure == 'Q':\\n warnings.warn('Parameters are already converted to Q!')\\n else:\\n kappa_sp = self.kappa_s\\n kappa_yp = self.kappa_y\\n self.kappa_s = self.kappa_s - self.lmbd_s * self.eta_s\\n self.kappa_y = self.kappa_y - self.lmbd_y * self.eta_y\\n self.scale = kappa_sp / self.kappa_s\\n self.mean_v *= (kappa_yp / self.kappa_y * self.scale)\\n self.lmbd = 0\\n self.eta_y *= (self.scale**.5)\\n self.measure = 'Q'\\n self.update_ajd()\",\n \"def Q_i(params):\\n Q = params['Q'].value\\n Qe = Q_e(params)\\n return (Q ** -1 - np.real(Qe ** -1)) ** -1\",\n \"def _append_cx(self, i, j):\\n\\n if not 0 <= i < self.num_qubits or not 0 <= j < self.num_qubits:\\n raise QiskitError(\\\"CX qubits are out of bounds.\\\")\\n self.linear[j] = (self.linear[i] + self.linear[j]) % 2\\n self.shift[j] = (self.shift[i] + self.shift[j]) % 2\",\n \"def qmincon(self, q=None):\\n\\n def sumsqr(A):\\n return np.sum(A**2)\\n\\n def cost(x, ub, lb, qm, N):\\n return sumsqr(\\n (2 * (N @ x + qm) - ub - lb) / (ub - lb))\\n\\n q = getmatrix(q, (None, self.n))\\n\\n qstar = np.zeros((q.shape[0], self.n))\\n error = np.zeros(q.shape[0])\\n success = np.zeros(q.shape[0])\\n\\n lb = self.qlim[0, :]\\n ub = self.qlim[1, :]\\n\\n for k, qk in enumerate(q):\\n\\n J = self.jacobe(qk)\\n\\n N = null(J)\\n\\n x0 = np.zeros(N.shape[1])\\n A = np.r_[N, -N]\\n b = np.r_[ub - qk, qk - lb].reshape(A.shape[0],)\\n\\n con = LinearConstraint(A, -np.inf, b)\\n\\n res = minimize(\\n lambda x: cost(x, ub, lb, qk, N),\\n x0, constraints=con)\\n\\n qstar[k, :] = qk + N @ res.x\\n error[k] = res.fun\\n success[k] = res.success\\n\\n if q.shape[0] == 1:\\n return qstar[0, :], success[0], error[0]\\n else:\\n return qstar, success, error\",\n \"def objective(self):\\n pass\",\n \"def putbarsj(self,whichsol_,j_,barsj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barsj is None: raise TypeError(\\\"Invalid type for argument barsj\\\")\\n if barsj is None:\\n barsj_ = None\\n else:\\n try:\\n barsj_ = memoryview(barsj)\\n except TypeError:\\n try:\\n _tmparr_barsj = array.array(\\\"d\\\",barsj)\\n except TypeError:\\n raise TypeError(\\\"Argument barsj has wrong type\\\")\\n else:\\n barsj_ = memoryview(_tmparr_barsj)\\n \\n else:\\n if barsj_.format != \\\"d\\\":\\n barsj_ = memoryview(array.array(\\\"d\\\",barsj))\\n \\n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj has wrong length\\\")\\n res = self.__obj.putbarsj(whichsol_,j_,barsj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\\n nz_ = None\\n if nz_ is None:\\n nz_ = len(subi)\\n elif nz_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if nz_ is None:\\n nz_ = len(subj)\\n elif nz_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if nz_ is None:\\n nz_ = len(valij)\\n elif nz_ != len(valij):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if nz_ is None: nz_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valij is None: raise TypeError(\\\"Invalid type for argument valij\\\")\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n \\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n \\n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _idx_return_value = resargs\\n return _idx_return_value\",\n \"def UBM_update_double_prime(self, j_ind, k_ind, alpha_1, alpha_2, beta_1, beta_2, Gamma, Lambda, Omega):\\n #set nodeType to \\\"-1,1\\\"\\n self.nodeType = \\\"-11\\\"\\n\\n\\n lambda_entry = Lambda[0, 1]\\n gamma_entry = Gamma[0, 1]\\n #two new hidden nodes\\n self.n_h += 2\\n\\n #store old parameters\\n old_b_v = np.copy(self.b_v)\\n old_b_h = np.copy(self.b_h)\\n\\n old_w_hv = np.copy(self.w_hv)\\n\\n\\n #update visible biases\\n self.b_v[j_ind] = alpha_1\\n self.b_v[k_ind] = alpha_2\\n\\n #update hidden biases\\n self.b_h = np.zeros(self.n_h, dtype=complex)\\n self.b_h[:-2] = old_b_h\\n self.b_h[-2] = beta_1 + old_b_v[j_ind]\\n self.b_h[-1] = beta_2 + old_b_v[k_ind]\\n\\n #update weight_matrix\\n \\\"here could be the reason why successiive 2-qubit gates don't work! \\\"\\n if hasattr(self, \\\"updated\\\"):\\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\\n\\n self.w_hv[:-2, :j_ind] = old_w_hv[:, :j_ind]\\n self.w_hv[:-2, j_ind+1:k_ind] = old_w_hv[:, j_ind+1:k_ind]\\n self.w_hv[:-2, k_ind+1:] = old_w_hv[:, k_ind+1:]\\n\\n self.w_hv[-2, :] = self.w_Z[j_ind, :]\\n self.w_hv[-1, :] = self.w_Z[k_ind, :]\\n #replace the Z-entries by unitary parameters\\n self.w_hv[-2, j_ind] = Omega[0, 0]\\n self.w_hv[-1, j_ind] = Omega[1, 0]\\n self.w_hv[-2, k_ind] = Omega[0, 1]\\n self.w_hv[-1, k_ind] = Omega[1, 1]\\n else:\\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\\n self.w_hv[:-2, :j_ind] = old_w_hv[:, :j_ind]\\n self.w_hv[:-2, j_ind+1:k_ind] = old_w_hv[:, j_ind+1:k_ind]\\n self.w_hv[:-2, k_ind+1:] = old_w_hv[:, k_ind+1:]\\n self.w_hv[-2, j_ind] = Omega[0, 0]\\n self.w_hv[-1, j_ind] = Omega[1, 0]\\n self.w_hv[-2, k_ind] = Omega[0, 1]\\n self.w_hv[-1, k_ind] = Omega[1, 1]\\n\\n\\n\\n\\n\\n if hasattr(self, \\\"updated\\\"):\\n print(\\\"already updated\\\")\\n old_w_X = np.copy(self.w_X)\\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\\n self.w_X[-2, :-2] = old_w_hv[:, j_ind].T\\n self.w_X[-1, :-2] = old_w_hv[:, k_ind].T\\n self.w_X[:-2, -2] = old_w_hv[:, j_ind]\\n self.w_X[:-2, -1] = old_w_hv[:, k_ind]\\n self.w_X[-1, -2] = gamma_entry + self.w_Z[j_ind, k_ind]\\n self.w_X[-2, -1] = gamma_entry + self.w_Z[j_ind, k_ind]\\n self.w_X[:-2, :-2] = old_w_X\\n\\n\\n self.w_Z[j_ind, k_ind] = lambda_entry\\n self.w_Z[k_ind, j_ind] = lambda_entry\\n\\n\\n else:\\n print(\\\"First RBM update \\\")\\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\\n self.w_X[-2, :-2] = old_w_hv[:, j_ind].T\\n self.w_X[-1, :-2] = old_w_hv[:, k_ind].T\\n self.w_X[:-2, -2] = old_w_hv[:, j_ind]\\n self.w_X[:-2, -1] = old_w_hv[:, k_ind]\\n self.w_X[-1, -2] = gamma_entry\\n self.w_X[-2, -1] = gamma_entry\\n\\n\\n self.w_Z = np.zeros((self.n_v, self.n_v), dtype=complex)\\n self.w_Z[j_ind, k_ind] = lambda_entry\\n self.w_Z[k_ind, j_ind] = lambda_entry\\n\\n\\n\\n\\n self.updated = True\",\n \"def jacobian(self, x):\\n pass\",\n \"def vjij(Vj, Ij, pol, ant) :\\n s.vj(pol,Vj, ant)\\n sleep(0.5)\\n s.ij(pol,Ij, ant)\",\n \"def putaijlist(self,subi,subj,valij): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(subi)\\n elif num_ != len(subi):\\n raise IndexError(\\\"Inconsistent length of array subi\\\")\\n if num_ is None:\\n num_ = len(subj)\\n elif num_ != len(subj):\\n raise IndexError(\\\"Inconsistent length of array subj\\\")\\n if num_ is None:\\n num_ = len(valij)\\n elif num_ != len(valij):\\n raise IndexError(\\\"Inconsistent length of array valij\\\")\\n if num_ is None: num_ = 0\\n if subi is None: raise TypeError(\\\"Invalid type for argument subi\\\")\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n \\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n \\n if subj is None: raise TypeError(\\\"Invalid type for argument subj\\\")\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n \\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n \\n if valij is None: raise TypeError(\\\"Invalid type for argument valij\\\")\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n \\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n \\n res = self.__obj.putaijlist64(num_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getqobj(self,qosubi,qosubj,qoval): # 3\\n maxnumqonz_ = self.getnumqobjnz()\\n if qosubi is None: raise TypeError(\\\"Invalid type for argument qosubi\\\")\\n _copyback_qosubi = False\\n if qosubi is None:\\n qosubi_ = None\\n else:\\n try:\\n qosubi_ = memoryview(qosubi)\\n except TypeError:\\n try:\\n _tmparr_qosubi = array.array(\\\"i\\\",qosubi)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubi has wrong type\\\")\\n else:\\n qosubi_ = memoryview(_tmparr_qosubi)\\n _copyback_qosubi = True\\n else:\\n if qosubi_.format != \\\"i\\\":\\n qosubi_ = memoryview(array.array(\\\"i\\\",qosubi))\\n _copyback_qosubi = True\\n if qosubi_ is not None and len(qosubi_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qosubi has wrong length\\\")\\n if qosubj is None: raise TypeError(\\\"Invalid type for argument qosubj\\\")\\n _copyback_qosubj = False\\n if qosubj is None:\\n qosubj_ = None\\n else:\\n try:\\n qosubj_ = memoryview(qosubj)\\n except TypeError:\\n try:\\n _tmparr_qosubj = array.array(\\\"i\\\",qosubj)\\n except TypeError:\\n raise TypeError(\\\"Argument qosubj has wrong type\\\")\\n else:\\n qosubj_ = memoryview(_tmparr_qosubj)\\n _copyback_qosubj = True\\n else:\\n if qosubj_.format != \\\"i\\\":\\n qosubj_ = memoryview(array.array(\\\"i\\\",qosubj))\\n _copyback_qosubj = True\\n if qosubj_ is not None and len(qosubj_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qosubj has wrong length\\\")\\n if qoval is None: raise TypeError(\\\"Invalid type for argument qoval\\\")\\n _copyback_qoval = False\\n if qoval is None:\\n qoval_ = None\\n else:\\n try:\\n qoval_ = memoryview(qoval)\\n except TypeError:\\n try:\\n _tmparr_qoval = array.array(\\\"d\\\",qoval)\\n except TypeError:\\n raise TypeError(\\\"Argument qoval has wrong type\\\")\\n else:\\n qoval_ = memoryview(_tmparr_qoval)\\n _copyback_qoval = True\\n else:\\n if qoval_.format != \\\"d\\\":\\n qoval_ = memoryview(array.array(\\\"d\\\",qoval))\\n _copyback_qoval = True\\n if qoval_ is not None and len(qoval_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qoval has wrong length\\\")\\n res,resargs = self.__obj.getqobj64(maxnumqonz_,len(qosubi),qosubi_,qosubj_,qoval_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numqonz_return_value = resargs\\n if _copyback_qoval:\\n qoval[:] = _tmparr_qoval\\n if _copyback_qosubj:\\n qosubj[:] = _tmparr_qosubj\\n if _copyback_qosubi:\\n qosubi[:] = _tmparr_qosubi\\n return _numqonz_return_value\",\n \"def project_curve(q):\\n n,T = q.shape\\n if n==2:\\n dt = 0.35\\n if n==3:\\n dt = 0.2\\n epsilon = 1e-6\\n\\n iter = 1\\n res = ones(n)\\n J = zeros((n,n))\\n\\n s = linspace(0,1,T)\\n\\n qnew = q.copy()\\n qnew = qnew / sqrt(innerprod_q2(qnew,qnew))\\n\\n qnorm = zeros(T)\\n G = zeros(n)\\n C = zeros(300)\\n while (norm(res) > epsilon):\\n if iter > 300:\\n break\\n\\n # Jacobian\\n for i in range(0,n):\\n for j in range(0,n):\\n J[i,j] = 3 * trapz(qnew[i,:]*qnew[j,:],s)\\n \\n J += eye(n)\\n\\n for i in range(0,T):\\n qnorm[i] = norm(qnew[:,i])\\n \\n # Compute the residue\\n for i in range(0,n):\\n G[i] = trapz(qnew[i,:]*qnorm,s)\\n \\n res = -G\\n\\n if (norm(res) < epsilon):\\n break\\n\\n x = solve(J,res)\\n C[iter] = norm(res)\\n\\n delG = Basis_Normal_A(qnew)\\n temp = zeros((n,T))\\n for i in range(0,n):\\n temp += x[i]*delG[i]*dt\\n \\n qnew += temp\\n iter += 1\\n \\n qnew = qnew/sqrt(innerprod_q2(qnew,qnew))\\n\\n return qnew\",\n \"def test_solve_quadratic(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.fixed = False\\n iden2.x.fixed = False\\n iden3.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 0)\\n self.assertAlmostEqual(iden1.x.val, 1)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 3)\",\n \"def getaij(self,i_,j_):\\n aij_ = ctypes.c_double()\\n res = __library__.MSK_XX_getaij(self.__nativep,i_,j_,ctypes.byref(aij_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n aij_ = aij_.value\\n _aij_return_value = aij_\\n return (_aij_return_value)\",\n \"def getqobj(self,qosubi_,qosubj_,qoval_):\\n maxnumqonz_ = self.getnumqobjnz()\\n numqonz_ = ctypes.c_int64()\\n _qosubi_minlength = (maxnumqonz_)\\n if (maxnumqonz_) > 0 and qosubi_ is not None and len(qosubi_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qosubi is not long enough: Is %d, expected %d\\\" % (len(qosubi_),(maxnumqonz_)))\\n if isinstance(qosubi_,numpy.ndarray) and not qosubi_.flags.writeable:\\n raise ValueError(\\\"Argument qosubi must be writable\\\")\\n if qosubi_ is None:\\n raise ValueError(\\\"Argument qosubi may not be None\\\")\\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\\n _qosubi_copyarray = False\\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qosubi_ is not None:\\n _qosubi_copyarray = True\\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\\n _qosubi_np_tmp[:] = qosubi_\\n assert _qosubi_np_tmp.flags.contiguous\\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qosubi_copyarray = False\\n _qosubi_tmp = None\\n \\n _qosubj_minlength = (maxnumqonz_)\\n if (maxnumqonz_) > 0 and qosubj_ is not None and len(qosubj_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qosubj is not long enough: Is %d, expected %d\\\" % (len(qosubj_),(maxnumqonz_)))\\n if isinstance(qosubj_,numpy.ndarray) and not qosubj_.flags.writeable:\\n raise ValueError(\\\"Argument qosubj must be writable\\\")\\n if qosubj_ is None:\\n raise ValueError(\\\"Argument qosubj may not be None\\\")\\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\\n _qosubj_copyarray = False\\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif qosubj_ is not None:\\n _qosubj_copyarray = True\\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\\n _qosubj_np_tmp[:] = qosubj_\\n assert _qosubj_np_tmp.flags.contiguous\\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _qosubj_copyarray = False\\n _qosubj_tmp = None\\n \\n _qoval_minlength = (maxnumqonz_)\\n if (maxnumqonz_) > 0 and qoval_ is not None and len(qoval_) != (maxnumqonz_):\\n raise ValueError(\\\"Array argument qoval is not long enough: Is %d, expected %d\\\" % (len(qoval_),(maxnumqonz_)))\\n if isinstance(qoval_,numpy.ndarray) and not qoval_.flags.writeable:\\n raise ValueError(\\\"Argument qoval must be writable\\\")\\n if qoval_ is None:\\n raise ValueError(\\\"Argument qoval may not be None\\\")\\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\\n _qoval_copyarray = False\\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif qoval_ is not None:\\n _qoval_copyarray = True\\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\\n _qoval_np_tmp[:] = qoval_\\n assert _qoval_np_tmp.flags.contiguous\\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _qoval_copyarray = False\\n _qoval_tmp = None\\n \\n qosurp_ = ctypes.c_int64(_qosubi_minlength)\\n res = __library__.MSK_XX_getqobj64(self.__nativep,maxnumqonz_,ctypes.byref(qosurp_),ctypes.byref(numqonz_),_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n numqonz_ = numqonz_.value\\n _numqonz_return_value = numqonz_\\n if _qosubi_copyarray:\\n qosubi_[:] = _qosubi_np_tmp\\n if _qosubj_copyarray:\\n qosubj_[:] = _qosubj_np_tmp\\n if _qoval_copyarray:\\n qoval_[:] = _qoval_np_tmp\\n return (_numqonz_return_value)\",\n \"def getsparsesymmat(self,idx_,subi_,subj_,valij_):\\n maxlen_ = self.getsymmatinfo((idx_))[1]\\n _subi_minlength = (maxlen_)\\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxlen_)))\\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\\n raise ValueError(\\\"Argument subi must be writable\\\")\\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\\n _subi_copyarray = False\\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subi_ is not None:\\n _subi_copyarray = True\\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\\n _subi_np_tmp[:] = subi_\\n assert _subi_np_tmp.flags.contiguous\\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subi_copyarray = False\\n _subi_tmp = None\\n \\n _subj_minlength = (maxlen_)\\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxlen_)))\\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\\n raise ValueError(\\\"Argument subj must be writable\\\")\\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\\n _subj_copyarray = False\\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif subj_ is not None:\\n _subj_copyarray = True\\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\\n _subj_np_tmp[:] = subj_\\n assert _subj_np_tmp.flags.contiguous\\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _subj_copyarray = False\\n _subj_tmp = None\\n \\n _valij_minlength = (maxlen_)\\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\\n raise ValueError(\\\"Array argument valij is not long enough: Is %d, expected %d\\\" % (len(valij_),(maxlen_)))\\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\\n raise ValueError(\\\"Argument valij must be writable\\\")\\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\\n _valij_copyarray = False\\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif valij_ is not None:\\n _valij_copyarray = True\\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\\n _valij_np_tmp[:] = valij_\\n assert _valij_np_tmp.flags.contiguous\\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _valij_copyarray = False\\n _valij_tmp = None\\n \\n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _subi_copyarray:\\n subi_[:] = _subi_np_tmp\\n if _subj_copyarray:\\n subj_[:] = _subj_np_tmp\\n if _valij_copyarray:\\n valij_[:] = _valij_np_tmp\",\n \"def qeye_like(qobj):\\n from .cy.qobjevo import QobjEvo\\n if isinstance(qobj, QobjEvo):\\n qobj = qobj(0)\\n return Qobj(\\n _data.identity_like(qobj.data), dims=qobj.dims, type=qobj.type,\\n superrep=qobj.superrep, isherm=True, isunitary=True, copy=False\\n )\",\n \"def add(self, i, j):\\n \\n # use running average to update CoM coordinates.\\n self._x = (self._x * self._P + i) / (self._P + 1)\\n self._y = (self._y * self._P + j) / (self._P + 1)\\n # increment mass\\n self._P += 1\",\n \"def calc_Hij(self):\\n\\t\\n\\tself.Hij = np.zeros((3,3, 3,3, self.N),dtype=np.complex_)\\n\\t\\n\\tself.Hij[0,0,:,:,:] = self.Hpij*self.ep[0,0] + self.Hcij*self.ec[0,0]\\n\\tself.Hij[0,1,:,:,:] = self.Hpij*self.ep[0,1] + self.Hcij*self.ec[0,1]\\n\\tself.Hij[0,2,:,:,:] = self.Hpij*self.ep[0,2] + self.Hcij*self.ec[0,2]\\n\\t\\n\\tself.Hij[1,0,:,:,:] = self.Hpij*self.ep[1,0] + self.Hcij*self.ec[1,0] \\n\\tself.Hij[1,1,:,:,:] = self.Hpij*self.ep[1,1] + self.Hcij*self.ec[1,1]\\n\\tself.Hij[1,2,:,:,:] = self.Hpij*self.ep[1,2] + self.Hcij*self.ec[1,2]\\n\\t\\n\\tself.Hij[2,0,:,:,:] = self.Hpij*self.ep[2,0] + self.Hcij*self.ec[2,0] \\n\\tself.Hij[2,1,:,:,:] = self.Hpij*self.ep[2,1] + self.Hcij*self.ec[2,1] \\n\\tself.Hij[2,2,:,:,:] = self.Hpij*self.ep[2,2] + self.Hcij*self.ec[2,2]\\n\\t\\n\\treturn\",\n \"def putbarcj(self,j_,sub,weights): # 3\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights)\\n elif num_ != len(weights):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"q\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"q\\\":\\n sub_ = memoryview(array.array(\\\"q\\\",sub))\\n \\n if weights is None: raise TypeError(\\\"Invalid type for argument weights\\\")\\n if weights is None:\\n weights_ = None\\n else:\\n try:\\n weights_ = memoryview(weights)\\n except TypeError:\\n try:\\n _tmparr_weights = array.array(\\\"d\\\",weights)\\n except TypeError:\\n raise TypeError(\\\"Argument weights has wrong type\\\")\\n else:\\n weights_ = memoryview(_tmparr_weights)\\n \\n else:\\n if weights_.format != \\\"d\\\":\\n weights_ = memoryview(array.array(\\\"d\\\",weights))\\n \\n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def a_ij(s, p, i=1, j=1): # (Validated)\\n from math import sqrt\\n if i == j:\\n return s.c[i]['a'] # Return pure paramater\\n else: # find mixture aij i =/= j\\n return (1 - p.m['k'][i][j]) * sqrt(s.c[i]['a'] * s.c[j]['a'])\",\n \"def getaij(self,i_,j_): # 3\\n res,resargs = self.__obj.getaij(i_,j_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _aij_return_value = resargs\\n return _aij_return_value\",\n \"def _repr_(self):\\n return \\\"Jacobian of %s\\\"%self.__curve\",\n \"def problem_reduction_single(self, i, val):\\n y_update = - val * self.A.getcol(i).toarray().flatten()\\n self.y += y_update\\n self.A = sparse.hstack([self.A[:, :i], self.A[:, i + 1:]], format='csr')\\n z_index = self.mask.searchsorted(i)\\n self.mask = np.insert(self.mask, z_index, i)\\n self.z = np.insert(self.z, z_index, val)\",\n \"def Iq(q, lorentz_scale, porod_scale, cor_length, porod_exp, lorentz_exp):\\n with errstate(divide='ignore'):\\n porod = porod_scale / q**porod_exp\\n lorentz = lorentz_scale / (1.0 + (q * cor_length)**lorentz_exp)\\n inten = porod + lorentz\\n return inten\",\n \"def quad_interp(x,y,xi) :\\n f = interp1d(x,y,kind='quadratic')\\n yi = f(xi)\\n \\n return yi\",\n \"def ikcon(self, T, q0=None):\\n\\n if not isinstance(T, SE3):\\n T = SE3(T)\\n\\n trajn = len(T)\\n\\n try:\\n if q0 is not None:\\n q0 = getvector(q0, self.n, 'row')\\n else:\\n q0 = np.zeros((trajn, self.n))\\n except ValueError:\\n verifymatrix(q0, (trajn, self.n))\\n\\n # create output variables\\n qstar = np.zeros((trajn, self.n))\\n error = []\\n exitflag = []\\n\\n omega = np.diag([1, 1, 1, 3 / self.reach])\\n\\n def cost(q, T, omega):\\n return np.sum(\\n (\\n (np.linalg.pinv(T.A) @ self.fkine(q).A - np.eye(4)) @\\n omega) ** 2\\n )\\n\\n bnds = Bounds(self.qlim[0, :], self.qlim[1, :])\\n\\n for i in range(trajn):\\n Ti = T[i]\\n res = minimize(\\n lambda q: cost(q, Ti, omega),\\n q0[i, :], bounds=bnds, options={'gtol': 1e-6})\\n qstar[i, :] = res.x\\n error.append(res.fun)\\n exitflag.append(res.success)\\n\\n if trajn > 1:\\n return qstar, exitflag, error\\n else:\\n return qstar[0, :], exitflag[0], error[0]\",\n \"def eoq_soco(I,F,h,d,w,W):\\n model = Model(\\\"EOQ model using SOCO\\\")\\n\\n T,c = {},{}\\n for i in I:\\n T[i] = model.addVar(vtype=\\\"C\\\", name=\\\"T(%s)\\\"%i) # cycle time for item i\\n c[i] = model.addVar(vtype=\\\"C\\\", name=\\\"c(%s)\\\"%i) # total cost for item i\\n\\n for i in I:\\n model.addCons(F[i] <= c[i]*T[i])\\n\\n model.addCons(quicksum(w[i]*d[i]*T[i] for i in I) <= W)\\n\\n model.setObjective(quicksum(c[i] + h[i]*d[i]*T[i]*0.5 for i in I), \\\"minimize\\\")\\n\\n model.data = T,c\\n return model\",\n \"def insert_cost(self, i, j):\\n raise NotImplementedError\",\n \"def Mq_inv(self, q):\\n\\t\\traise NotImplementedError\",\n \"def setExtraMatrixAnimCurve(self, i, j, animcurve, view) -> None:\\n ...\",\n \"def Qc(I, dT, a, b, c, d, e, f, g, h, i, k):\\n x1 = I # I\\n x2 = dT # dT\\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\\n return m * x2 + b\",\n \"def conj(self, o): \\n return (o.inv()) * self * o\",\n \"def set_rhs(self):\\n k = self.istore[:, 0]\\n ksym = self.stencil.get_symmetric()[k]\\n self.rhs[:] = self.feq[k, np.arange(k.size)] + self.feq[ksym, np.arange(k.size)]\",\n \"def set_rhs(self):\\n k = self.istore[:, 0]\\n ksym = self.stencil.get_symmetric()[k]\\n self.rhs[:] = self.feq[k, np.arange(k.size)] + self.feq[ksym, np.arange(k.size)]\",\n \"def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\\n self.coeff[self._core.index_alpha(key[0]),\\n self._core.index_beta(key[1])] = value\",\n \"def Optimize_Sqq(qj,dt):\\n\\n # find the signal psd\\n fs=1.0/dt\\n M,L = 256,512\\n wxx,Sxx=sa.Welch_estimator(qj,fs=fs,M=M,L=L)\\n w_threshold = fs*np.pi/2\\n Sxx=Sxx[wxx<w_threshold]\\n wxx=wxx[wxx<w_threshold]\\n\\n # the psd of oscillator model is computed using Wiener-Khinchin relation\\n # and called in the objective function\\n print('S_qq matching objective fun: ls error of spectrum')\\n spec_ls_distance = lambda params: np.linalg.norm(Sxx*(Sxx - sa.Oscillator_Spectrum(params[0],params[1]/params[0],params[1],wxx)),ord=1)\\n\\n # optimize via pyswarm - v1\\n lb = [0.001, 0.001]\\n ub = [100, 500]\\n xopt, fopt = pso(spec_ls_distance, lb, ub, maxiter=10000,swarmsize=10000,minfunc=1e-10)\\n k,D = xopt[0],xopt[1]\\n\\n b = D/k\\n print('result: k='+str(k)+' b='+str(b)+' D='+str(D))\\n\\n return k,D\",\n \"def puty(self,whichsol_,y_):\\n _y_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),self.getnumcon()))\\n if y_ is None:\\n raise ValueError(\\\"Argument y cannot be None\\\")\\n if y_ is None:\\n raise ValueError(\\\"Argument y may not be None\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n res = __library__.MSK_XX_puty(self.__nativep,whichsol_,_y_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def calculate_strain(self):\\n\\t\\n\\tself.Hpij = np.zeros((3,3,self.N),dtype=np.complex_)\\n\\tself.Hcij = np.zeros((3,3,self.N),dtype=np.complex_)\\n\\t\\n\\tcalc_Hcp_ij(self)\\t\\n\\n\\tconstruct_basis_tensors(self)\\n\\n\\tcalc_Hij(self)\\n\\t\\n\\tcontract_tenors(self)\\n\\n\\treturn\",\n \"def putbarcj(self,j_,sub_,weights_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None:\\n num_ = len(weights_)\\n elif num_ != len(weights_):\\n raise IndexError(\\\"Inconsistent length of array weights\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n if weights_ is None:\\n raise ValueError(\\\"Argument weights cannot be None\\\")\\n if weights_ is None:\\n raise ValueError(\\\"Argument weights may not be None\\\")\\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\\n _weights_copyarray = False\\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif weights_ is not None:\\n _weights_copyarray = True\\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\\n _weights_np_tmp[:] = weights_\\n assert _weights_np_tmp.flags.contiguous\\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _weights_copyarray = False\\n _weights_tmp = None\\n \\n res = __library__.MSK_XX_putbarcj(self.__nativep,j_,num_,_sub_tmp,_weights_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def pl_mi(i,j, first=False):\\n if (-1)**(i+j)>0:\\n if first:\\n return \\\"\\\"\\n else:\\n return \\\"+\\\"\\n else:\\n return \\\"-\\\"\",\n \"def switch(self, i, j0, j1):\\n if not self._mutable:\\n raise ValueError(\\\"this constellation is immutable.\\\"\\n \\\" Take a mutable copy first.\\\")\\n S = SymmetricGroup(list(range(self.degree())))\\n tr = S((j0, j1))\\n i = int(i)\\n if i < 0 or i >= len(self._g):\\n raise ValueError(\\\"index out of range\\\")\\n\\n ii = i + 1\\n if ii == len(self._g):\\n ii = 0\\n self._g[i] = self._g[i] * tr\\n self._g[ii] = tr * self._g[ii]\",\n \"def setCoefficient(self, *args):\\n return _libsbml.FluxObjective_setCoefficient(self, *args)\",\n \"def getsparsesymmat(self,idx_,subi,subj,valij): # 3\\n maxlen_ = self.getsymmatinfo((idx_))[1]\\n _copyback_subi = False\\n if subi is None:\\n subi_ = None\\n else:\\n try:\\n subi_ = memoryview(subi)\\n except TypeError:\\n try:\\n _tmparr_subi = array.array(\\\"i\\\",subi)\\n except TypeError:\\n raise TypeError(\\\"Argument subi has wrong type\\\")\\n else:\\n subi_ = memoryview(_tmparr_subi)\\n _copyback_subi = True\\n else:\\n if subi_.format != \\\"i\\\":\\n subi_ = memoryview(array.array(\\\"i\\\",subi))\\n _copyback_subi = True\\n if subi_ is not None and len(subi_) != (maxlen_):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n _copyback_subj = False\\n if subj is None:\\n subj_ = None\\n else:\\n try:\\n subj_ = memoryview(subj)\\n except TypeError:\\n try:\\n _tmparr_subj = array.array(\\\"i\\\",subj)\\n except TypeError:\\n raise TypeError(\\\"Argument subj has wrong type\\\")\\n else:\\n subj_ = memoryview(_tmparr_subj)\\n _copyback_subj = True\\n else:\\n if subj_.format != \\\"i\\\":\\n subj_ = memoryview(array.array(\\\"i\\\",subj))\\n _copyback_subj = True\\n if subj_ is not None and len(subj_) != (maxlen_):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n _copyback_valij = False\\n if valij is None:\\n valij_ = None\\n else:\\n try:\\n valij_ = memoryview(valij)\\n except TypeError:\\n try:\\n _tmparr_valij = array.array(\\\"d\\\",valij)\\n except TypeError:\\n raise TypeError(\\\"Argument valij has wrong type\\\")\\n else:\\n valij_ = memoryview(_tmparr_valij)\\n _copyback_valij = True\\n else:\\n if valij_.format != \\\"d\\\":\\n valij_ = memoryview(array.array(\\\"d\\\",valij))\\n _copyback_valij = True\\n if valij_ is not None and len(valij_) != (maxlen_):\\n raise ValueError(\\\"Array argument valij has wrong length\\\")\\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_valij:\\n valij[:] = _tmparr_valij\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n if _copyback_subi:\\n subi[:] = _tmparr_subi\",\n \"def qrot(I,sym):\\n T = s.Symbol(\\\"T\\\")\\n if type(I) == list:\\n return (((s.pi * I[0] * I[1] * I[2])**(1/2))/sym) * ((8 * s.pi**2 * k * T) / (h**2))**(3/2)\\n else:\\n return (((s.pi * I)**(1/2))/sym) * ((8 * s.pi**2 * k * T) / (h**2))**(3/2)\",\n \"def __imul__(self, obj):\\n # calls __mul__\\n tmp = self * obj\\n self.data = tmp.data\\n self.m, self.n = tmp.dim\\n return self\",\n \"def quad_interp(xi,yi):\\n\\n # check inputs and print error message if not valid:\\n\\n error_message = \\\"xi and yi should have type numpy.ndarray\\\"\\n assert (type(xi) is np.ndarray) and (type(yi) is np.ndarray), error_message\\n\\n error_message = \\\"xi and yi should have length 3\\\"\\n assert len(xi)==3 and len(yi)==3, error_message\\n\\n error_message = \\\"it is not possible to have more than one point in the with the same xi\\\"\\n assert (len(np.unique(xi)) == len(xi)), error_message\\n\\n # Set up linear system to interpolate through data points:\\n\\n A = np.array([[1, 1, 1], xi, xi**2]).T\\n b = yi\\n\\n c = solve(A,b)\\n \\n return c\",\n \"def make_sq(mlat, dAB, *J):\\n if (len(J)!=4):\\n print(\\\"Number of paramaters are exceeded 5!\\\")\\n NN = 2*mlat\\n \\n tau = np.zeros((NN,NN), dtype=complex)\\n h = np.zeros((NN,NN), dtype=complex)\\n \\n for i in range(mlat-1):\\n if (i%2==0):\\n h[i,i] = dAB/2. # on-site energy\\n h[mlat+i,mlat+i] = -dAB/2. # on-site energy \\n h[i, mlat+i] = J[0]\\n h[i, i+1] = J[1]\\n h[mlat+i, mlat+i+1] = J[3]\\n #\\n tau[mlat+i, i] = J[2]\\n elif (i%2==1):\\n h[i,i] = -dAB/2. # on-site energy\\n h[mlat+i,mlat+i] = dAB/2. # on-site energy \\n h[i, mlat+i] = J[2]\\n h[i, i+1] = J[3]\\n h[mlat+i, mlat+i+1] = J[1]\\n #\\n tau[mlat+i, i] = J[0]\\n\\n # End of loop over lattice sites\\n\\n # The upper edge site\\n if (mlat-1 % 2==0):\\n h[mlat-1, mlat-1] = dAB/2. # on-site energy\\n h[NN-1,NN-1] = -dAB/2. # on-site energy \\n h[mlat-1, NN-1] = J[0]\\n #\\n tau[NN-1, mlat-1] = J[2]\\n elif (mlat-1 % 2==1):\\n h[mlat-1, mlat-1] = -dAB/2. # on-site energy\\n h[NN-1,NN-1] = dAB/2. # on-site energy \\n h[mlat-1, NN-1] = J[2]\\n #\\n tau[NN-1, mlat-1] = J[0] \\n \\n h = h + h.conj().T # make it hermitian\\n return h, tau\",\n \"def compare_J_terms(m, nix, srcclass=None, analytic_class=None, calced_class=None, \\n only_calced_Cterms=False, fx=None):\\n \\n if fx is None:\\n fx = fixtures.fixture_from_model(m)\\n \\n if srcclass is None:\\n srcclass = FullSingleFieldSource\\n \\n if analytic_class is None:\\n analytic_class = analyticsolution.NoPhaseBunchDaviesSolution\\n if calced_class is None:\\n calced_class = calcedsolution.NoPhaseBunchDaviesCalced\\n \\n asol = analytic_class(fx, srcclass)\\n csol = calced_class(fx, srcclass)\\n \\n #Need to make analytic solution use 128 bit floats to avoid overruns\\n asol.srceqns.k = np.float128(asol.srceqns.k)\\n \\n \\n #Get background values\\n bgvars = m.yresult[nix, 0:3, 0]\\n a = m.ainit*np.exp(m.tresult[nix])\\n #Get potentials\\n potentials = m.potentials(np.array([bgvars[0]]), m.pot_params)\\n \\n #Set alpha and beta\\n alpha = 1/(a*np.sqrt(2))\\n beta = a*bgvars[2]\\n \\n dp1 = csol.get_dp1(csol.srceqns.fullk, alpha=alpha)\\n dp1dot = csol.get_dp1dot(csol.srceqns.fullk, alpha=alpha, beta=beta)\\n \\n #Calculate dphi(q) and dphi(k-q)\\n dp1_q = dp1[:csol.srceqns.k.shape[-1]]\\n dp1dot_q = dp1dot[:csol.srceqns.k.shape[-1]] \\n \\n theta_terms = csol.srceqns.getthetaterms(dp1, dp1dot)\\n csol.srceqns.k = np.float128(csol.srceqns.k)\\n csol.srceqns.fullk = np.float128(csol.srceqns.fullk)\\n \\n calced_Cterms = csol.calculate_Cterms(bgvars, a, potentials) \\n if only_calced_Cterms:\\n analytic_Cterms = calced_Cterms\\n else:\\n analytic_Cterms = asol.calculate_Cterms(bgvars, a, potentials)\\n \\n results = {}\\n \\n for Jkey in csol.J_terms.iterkeys():\\n afunc = asol.J_terms[Jkey]\\n cfunc = csol.J_terms[Jkey]\\n analytic_result = afunc(asol.srceqns.k, analytic_Cterms, alpha=alpha, beta=beta)\\n calced_result = cfunc(theta_terms, dp1_q, dp1dot_q, calced_Cterms)\\n diff = analytic_result - calced_result\\n err = np.abs(diff)/np.abs(analytic_result)\\n results[Jkey] = (diff, err, analytic_result, calced_result)\\n \\n return results\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8022443","0.67813957","0.659472","0.62387276","0.6219436","0.61874425","0.6092477","0.6030711","0.6007447","0.58423615","0.58029187","0.5546479","0.5543163","0.54854363","0.54592514","0.54140776","0.5403207","0.54002947","0.537518","0.5340782","0.5334495","0.53016627","0.527868","0.52648073","0.52392924","0.52378243","0.5234838","0.52327704","0.5232115","0.52215046","0.52198225","0.5217989","0.520391","0.5198449","0.5196362","0.519441","0.519441","0.5192794","0.5180831","0.5180257","0.5172358","0.5169769","0.51625204","0.5123069","0.5120559","0.51122","0.5075653","0.50680554","0.50622207","0.5025804","0.5013788","0.49861562","0.49797773","0.4964045","0.49619612","0.4942442","0.49414682","0.49310505","0.49306932","0.4929997","0.4923508","0.4912614","0.4902244","0.48916474","0.48871562","0.48747617","0.4862351","0.48607886","0.48470137","0.48439544","0.48233992","0.48228028","0.48222893","0.4821339","0.48084614","0.4803671","0.4803598","0.47960824","0.47944662","0.47899875","0.4789446","0.4789216","0.47881538","0.47794092","0.4779111","0.4779111","0.47671345","0.47623858","0.47534615","0.47488463","0.4747059","0.47459045","0.47442392","0.47429642","0.47405827","0.47267315","0.47156155","0.47099435","0.47071174","0.47021946"],"string":"[\n \"0.8022443\",\n \"0.67813957\",\n \"0.659472\",\n \"0.62387276\",\n \"0.6219436\",\n \"0.61874425\",\n \"0.6092477\",\n \"0.6030711\",\n \"0.6007447\",\n \"0.58423615\",\n \"0.58029187\",\n \"0.5546479\",\n \"0.5543163\",\n \"0.54854363\",\n \"0.54592514\",\n \"0.54140776\",\n \"0.5403207\",\n \"0.54002947\",\n \"0.537518\",\n \"0.5340782\",\n \"0.5334495\",\n \"0.53016627\",\n \"0.527868\",\n \"0.52648073\",\n \"0.52392924\",\n \"0.52378243\",\n \"0.5234838\",\n \"0.52327704\",\n \"0.5232115\",\n \"0.52215046\",\n \"0.52198225\",\n \"0.5217989\",\n \"0.520391\",\n \"0.5198449\",\n \"0.5196362\",\n \"0.519441\",\n \"0.519441\",\n \"0.5192794\",\n \"0.5180831\",\n \"0.5180257\",\n \"0.5172358\",\n \"0.5169769\",\n \"0.51625204\",\n \"0.5123069\",\n \"0.5120559\",\n \"0.51122\",\n \"0.5075653\",\n \"0.50680554\",\n \"0.50622207\",\n \"0.5025804\",\n \"0.5013788\",\n \"0.49861562\",\n \"0.49797773\",\n \"0.4964045\",\n \"0.49619612\",\n \"0.4942442\",\n \"0.49414682\",\n \"0.49310505\",\n \"0.49306932\",\n \"0.4929997\",\n \"0.4923508\",\n \"0.4912614\",\n \"0.4902244\",\n \"0.48916474\",\n \"0.48871562\",\n \"0.48747617\",\n \"0.4862351\",\n \"0.48607886\",\n \"0.48470137\",\n \"0.48439544\",\n \"0.48233992\",\n \"0.48228028\",\n \"0.48222893\",\n \"0.4821339\",\n \"0.48084614\",\n \"0.4803671\",\n \"0.4803598\",\n \"0.47960824\",\n \"0.47944662\",\n \"0.47899875\",\n \"0.4789446\",\n \"0.4789216\",\n \"0.47881538\",\n \"0.47794092\",\n \"0.4779111\",\n \"0.4779111\",\n \"0.47671345\",\n \"0.47623858\",\n \"0.47534615\",\n \"0.47488463\",\n \"0.4747059\",\n \"0.47459045\",\n \"0.47442392\",\n \"0.47429642\",\n \"0.47405827\",\n \"0.47267315\",\n \"0.47156155\",\n \"0.47099435\",\n \"0.47071174\",\n \"0.47021946\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78803056"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11994,"cells":{"query":{"kind":"string","value":"Inserts a solution. putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)"},"document":{"kind":"string","value":"def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\n else:\n _skn_tmp = None\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n \n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n \n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if not isinstance(sk_,stakey): raise TypeError(\"Argument sk has wrong type\")\n res = self.__obj.putsolutioni(accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskx(self,whichsol_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskx(whichsol_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskc(self,whichsol_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None: raise TypeError(\"Invalid type for argument skc\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskc(whichsol_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n _copyback_skn = False\n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n _copyback_skn = True\n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n _copyback_skn = True\n if skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn has wrong length\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n _copyback_xx = False\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n _copyback_xx = True\n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n _copyback_xx = True\n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n _copyback_suc = False\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n _copyback_suc = True\n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n _copyback_suc = True\n if suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc has wrong length\")\n _copyback_slx = False\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n _copyback_slx = True\n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n _copyback_slx = True\n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n _copyback_sux = False\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n _copyback_sux = True\n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n _copyback_sux = True\n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n _copyback_snx = False\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n _copyback_snx = True\n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n _copyback_snx = True\n if snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx has wrong length\")\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value,_solsta_return_value = resargs\n if _copyback_snx:\n snx[:] = _tmparr_snx\n if _copyback_sux:\n sux[:] = _tmparr_sux\n if _copyback_slx:\n slx[:] = _tmparr_slx\n if _copyback_suc:\n suc[:] = _tmparr_suc\n if _copyback_slc:\n slc[:] = _tmparr_slc\n if _copyback_y:\n y[:] = _tmparr_y\n if _copyback_xx:\n xx[:] = _tmparr_xx\n if _copyback_xc:\n xc[:] = _tmparr_xc\n if _copyback_skn:\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\n if _copyback_skx:\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\n _solsta_return_value = solsta(_solsta_return_value)\n _prosta_return_value = prosta(_prosta_return_value)\n return _prosta_return_value,_solsta_return_value","def getsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n prosta_ = ctypes.c_int32()\n solsta_ = ctypes.c_int32()\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\n raise ValueError(\"Argument skc must be writable\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\n else:\n _skc_tmp = None\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\n raise ValueError(\"Argument skx must be writable\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\n else:\n _skx_tmp = None\n _skn_minlength = self.getnumcone()\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn is not long enough: Is %d, expected %d\" % (len(skn_),self.getnumcone()))\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\n raise ValueError(\"Argument skn must be writable\")\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\n else:\n _skn_tmp = None\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n _xx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),self.getnumvar()))\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\n raise ValueError(\"Argument xx must be writable\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\n raise ValueError(\"Argument y must be writable\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\n raise ValueError(\"Argument slc must be writable\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n _suc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),self.getnumcon()))\n if isinstance(suc_,numpy.ndarray) and not suc_.flags.writeable:\n raise ValueError(\"Argument suc must be writable\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n _slx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),self.getnumvar()))\n if isinstance(slx_,numpy.ndarray) and not slx_.flags.writeable:\n raise ValueError(\"Argument slx must be writable\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\n raise ValueError(\"Argument sux must be writable\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n _snx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx is not long enough: Is %d, expected %d\" % (len(snx_),self.getnumvar()))\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\n raise ValueError(\"Argument snx must be writable\")\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_getsolution(self.__nativep,whichsol_,ctypes.byref(prosta_),ctypes.byref(solsta_),_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value = prosta(prosta_.value)\n _solsta_return_value = solsta(solsta_.value)\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp\n if _xx_copyarray:\n xx_[:] = _xx_np_tmp\n if _y_copyarray:\n y_[:] = _y_np_tmp\n if _slc_copyarray:\n slc_[:] = _slc_np_tmp\n if _suc_copyarray:\n suc_[:] = _suc_np_tmp\n if _slx_copyarray:\n slx_[:] = _slx_np_tmp\n if _sux_copyarray:\n sux_[:] = _sux_np_tmp\n if _snx_copyarray:\n snx_[:] = _snx_np_tmp\n return (_prosta_return_value,_solsta_return_value)","def putslc(self,whichsol_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slc is None: raise TypeError(\"Invalid type for argument slc\")\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.putslc(whichsol_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskc(self,whichsol_,skc_):\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if skc_ is None:\n raise ValueError(\"Argument skc cannot be None\")\n if skc_ is None:\n raise ValueError(\"Argument skc may not be None\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskx(self,whichsol_,skx_):\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskx(self.__nativep,whichsol_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putslc(self,whichsol_,slc_):\n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslc(self.__nativep,whichsol_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsuc(self,whichsol_,suc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if suc is None: raise TypeError(\"Invalid type for argument suc\")\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc has wrong length\")\n res = self.__obj.putsuc(whichsol_,suc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putslx(self,whichsol_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslx(whichsol_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsnx(self,whichsol_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsnx(whichsol_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsuc(self,whichsol_,suc_):\n _suc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),self.getnumcon()))\n if suc_ is None:\n raise ValueError(\"Argument suc cannot be None\")\n if suc_ is None:\n raise ValueError(\"Argument suc may not be None\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n res = __library__.MSK_XX_putsuc(self.__nativep,whichsol_,_suc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def sketch_of_solution(self,sol=None):\n raise NotImplementedError","def updatesolutioninfo(self,whichsol_):\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_solution(self, solution):\n if self.check_solution(solution):\n self._solution = solution\n self.solution_status = 'OK'\n else:\n self._solution = None\n self.solution_status = 'X'","def putslx(self,whichsol_,slx_):\n _slx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),self.getnumvar()))\n if slx_ is None:\n raise ValueError(\"Argument slx cannot be None\")\n if slx_ is None:\n raise ValueError(\"Argument slx may not be None\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n res = __library__.MSK_XX_putslx(self.__nativep,whichsol_,_slx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsnx(self,whichsol_,sux_):\n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if sux_ is None:\n raise ValueError(\"Argument sux cannot be None\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_putsnx(self.__nativep,whichsol_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def writesolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.writesolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsucslice(self,whichsol_,first_,last_,suc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if suc is None: raise TypeError(\"Invalid type for argument suc\")\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if suc_ is not None and len(suc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument suc has wrong length\")\n res = self.__obj.putsucslice(whichsol_,first_,last_,suc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_solution(self, paths):\n assert self.benchmark.status[\"state\"] == \"RUNNING\", print(\n f\"Benchmark seems to be inactive. state: {self.benchmark.status(['state'])}\")\n assert self.benchmark.status[\"data\"][\"problem_states\"][self.batch_pos] == 0, print(\n \"Problem seems to be already solved\")\n\n self.paths = paths\n self.time = time()-self.start_time\n self.benchmark.status[\"data\"][\"problem_states\"][self.batch_pos] = 1\n\n if all(self.benchmark.status[\"data\"][\"problem_states\"]):\n self.status = {\"state\": \"SUBMITTING\", \"data\": None}\n self.benchmark.submit()","def putxx(self,whichsol_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xx is None: raise TypeError(\"Invalid type for argument xx\")\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.putxx(whichsol_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsucslice(self,whichsol_,first_,last_,suc_):\n _suc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and suc_ is not None and len(suc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),((last_) - (first_))))\n if suc_ is None:\n raise ValueError(\"Argument suc cannot be None\")\n if suc_ is None:\n raise ValueError(\"Argument suc may not be None\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n res = __library__.MSK_XX_putsucslice(self.__nativep,whichsol_,first_,last_,_suc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def writesolution(self,whichsol_,filename_):\n if isinstance(filename_,unicode):\n filename_ = filename_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_writesolution(self.__nativep,whichsol_,filename_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskcslice(self,whichsol_,first_,last_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskcslice(whichsol_,first_,last_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solutiondef(self,whichsol_):\n isdef_ = ctypes.c_int32()\n res = __library__.MSK_XX_solutiondef(self.__nativep,whichsol_,ctypes.byref(isdef_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n isdef_ = isdef_.value\n _isdef_return_value = isdef_\n return (_isdef_return_value)","def deletesolution(self,whichsol_):\n res = __library__.MSK_XX_deletesolution(self.__nativep,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putxc(self,whichsol_,xc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xc is None: raise TypeError(\"Invalid type for argument xc\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n res = self.__obj.putxc(whichsol_,xc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_xc:\n xc[:] = _tmparr_xc","def getskc(self,whichsol_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.getskc(whichsol_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])","def getskx(self,whichsol_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.getskx(whichsol_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_skx:\n for __tmp_var_0 in range(len(skx_)): skx[__tmp_var_0] = stakey(_tmparr_skx[__tmp_var_0])","def updatesolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.updatesolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def analyzesolution(self,whichstream_,whichsol_):\n res = __library__.MSK_XX_analyzesolution(self.__nativep,whichstream_,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskxslice(self,whichsol_,first_,last_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskxslice(whichsol_,first_,last_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsolutioni(self,accmode_,i_,whichsol_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioni(accmode_,i_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value = resargs\n _sk_return_value = stakey(_sk_return_value)\n return _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value","def score_solution(g, s):\n pass","def putsux(self,whichsol_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsux(whichsol_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskcslice(self,whichsol_,first_,last_,skc_):\n _skc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),((last_) - (first_))))\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskcslice(self.__nativep,whichsol_,first_,last_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putslcslice(self,whichsol_,first_,last_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slc is None: raise TypeError(\"Invalid type for argument slc\")\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.putslcslice(whichsol_,first_,last_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsolsta(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolsta(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _solsta_return_value = resargs\n _solsta_return_value = solsta(_solsta_return_value)\n return _solsta_return_value","def solution(self,pos):\n\t\tif pos == 0:\n\t\t\treturn self.solve(pos)\n\n\t\tif not self.mem.has_key(pos-1):\n\t\t\tself.mem[pos-1] = self.solution(pos-1)\n\t\tif not self.mem.has_key(pos):\n\t\t\tself.mem[pos] = self.mem[pos-1] + self.solve(pos)\n\t\tif pos == len(self.objective)-1 and not self.eob():\n\t\t\t\"\"\"Ya obtuvimos la solucion y aun hay elementos en la base que deben ser\n\t\t\tdescartados\"\"\"\n\t\t\tself.mem[pos] += self.terminar()\n\t\treturn self.mem[pos]","def notify_solution(self, sol):\n self._solutions.append(sol)","def insertion_neighborhood(solution, local_optimum=True, tie_breaking=False):\n current_makespan = 0\n improve = True\n\n # If parameter local_optimum is true: repeat while solution is improved\n while improve:\n improve = False\n # Select random elements without repetition - O(N) implementation\n not_tested = solution.sequence.copy()\n shuffle(not_tested)\n for removed_job in not_tested:\n solution.sequence.remove(removed_job)\n # If first iteration then calculate makespan\n if current_makespan == 0:\n current_makespan = solution.calculate_makespan()\n # Insert job in best position and check improvement\n solution.insert_best_position(removed_job, tie_breaking)\n if solution.makespan < current_makespan:\n improve = True\n current_makespan = solution.makespan\n if local_optimum == False:\n break","def test_Schur_Sp_solve():\n mat_A = load_matrix_step_noslip()\n petsc_options = initialize_petsc_options()\n b, x = create_petsc_vecs(mat_A)\n\n solver_info = LS.ModelInfo('interlaced', 3)\n schur_approx = LS.Schur_Sp(mat_A,\n '',\n solver_info=solver_info)\n ksp_obj = initialize_schur_ksp_obj(mat_A, schur_approx)\n ksp_obj.solve(b,x)\n\n assert ksp_obj.converged == True\n assert ksp_obj.reason == 2\n assert float(ksp_obj.norm) < 1.0e-5\n assert ksp_obj.its == 63","def update_solution(self, new_plan, use_cat=True, soc=True):\n if not new_plan.path:\n self.sol = TimeUncertaintySolution.empty_solution(self.sol.nodes_generated)\n self.sol.paths[new_plan.agent] = new_plan\n return\n if not use_cat:\n self.sol.paths[new_plan.agent] = new_plan\n self.sol.create_movement_tuples(agents=[new_plan.agent])\n return\n\n self.sol.paths[new_plan.agent] = new_plan\n new_moves = self.sol.add_stationary_moves()\n self.sol.create_movement_tuples(agents=[new_plan.agent])\n self.update_conflict_avoidance_table(new_plan.agent, new_moves)\n self.sol.compute_solution_cost(sum_of_costs=soc) # compute the cost","def solutiondef(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.solutiondef(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _isdef_return_value = resargs\n return _isdef_return_value","def putskxslice(self,whichsol_,first_,last_,skx_):\n _skx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),((last_) - (first_))))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def createsolution(self,rows):\r\n l = [0,0]\r\n if not self.findzero(l):\r\n return True\r\n x = l[0]\r\n y = l[1]\r\n for i in range(1,10):\r\n if self.check(i, x, y):\r\n rows[x][y] = i\r\n if self.createsolution(rows):\r\n return True\r\n rows[x][y] = 0\r\n return False","def solve(self, solver):\n solver.solve()","def putslcslice(self,whichsol_,first_,last_,slc_):\n _slc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),((last_) - (first_))))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solve(team, chal, request):\n provided_key = request.form['key'].strip()\n solve = Solves(teamid=team.id, chalid=chal.id, ip=utils.get_ip(req=request), flag=provided_key)\n db.session.add(solve)\n db.session.commit()\n db.session.close()","def putsux(self,whichsol_,sux_):\n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if sux_ is None:\n raise ValueError(\"Argument sux cannot be None\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_putsux(self.__nativep,whichsol_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solveSudoku(self, board) -> None:\n self.board = board\n self.backTrace(0,0)","def main():\n\n # first lets test with a already created csp:\n csp = create_map_csp()\n solution = backtracking(csp)\n #solution2,assigned = minimum_remaining_values(csp)\n print(solution)\n #print assigned\n\n # and now with our own generated sudoku CSP\n \"\"\"sudokus = read_sudokus()\n csp = create_sudoku_csp(sudokus[1])\n solution = backtracking(csp)\n print sudoku_csp_to_array(solution)\n\"\"\"","def update(self, solution):\n self.heuristic_path = [i for i in self.initial_path if i in solution]\n self.heuristic_cost = self.pathCost(self.heuristic_path)","def solve_puzzle(self):\r\n \r\n counter = 0\r\n rows = self._height-1\r\n cols = self._width-1\r\n # print rows, cols\r\n # print 'The greed has %s rows and %s coloumn indexes' %(rows, cols) \r\n solution_move = ''\r\n if self.get_number(0,0) == 0 and \\\r\n self.get_number(0,1) == 1:\r\n # print 'Congrads Puxxle is Aolved at start!!!!!'\r\n return ''\r\n #appropriate_number = (self._height * self._width) - 1\r\n appropriate_number = (rows+1) * (cols+1) -1\r\n # print 'First appropriate_number=',appropriate_number\r\n # print \"Grid first tile that we will solwing has value =\", self._grid[rows][cols]\r\n \r\n while counter < 300:\r\n counter +=1\r\n # print self\r\n #appropriate_number = (rows+1) * (cols+1) -1\r\n # print 'Appropriate number in loop=',appropriate_number\r\n # print 'We are solving %s index_row and %s index_col' %(rows, cols) \r\n ####Case when we use solve_interior_tile\r\n if rows > 1 and cols > 0:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n cols -= 1\r\n appropriate_number -=1\r\n else:\r\n # print 'We are solving interior tile', (rows, cols)\r\n solution_move += self.solve_interior_tile(rows, cols)\r\n # print 'Solution move=', solution_move\r\n cols -= 1\r\n #### Case when we use solve_col0_tile\r\n elif rows > 1 and cols == 0:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n rows -= 1\r\n cols = self._width-1\r\n appropriate_number -=1\r\n else:\r\n # print 'We are solwing tile 0 in row', rows\r\n # print 'Appropriate number here ='\r\n solution_move += self.solve_col0_tile(rows)\r\n # print 'Solution move=', solution_move\r\n rows -=1\r\n cols = self._width-1\r\n\r\n\r\n #### Cases when we use solve_row0_tile\r\n elif rows == 1 and cols > 1:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n rows -= 1\r\n #cols = self._width-1\r\n appropriate_number -= self._width\r\n\r\n else:\r\n # print 'Solving upper 2 rows right side'\r\n solution_move += self.solve_row1_tile(cols)\r\n rows -=1\r\n appropriate_number -= self._width\r\n #### Cases when we use solve_row1_tile \r\n if rows < 1 and cols > 1:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n rows += 1\r\n cols -= 1\r\n appropriate_number +=self._width-1\r\n else:\r\n # print '(1,J) tile solved, lets solwe tile (0,j) in tile',(rows,cols)\r\n # print 'Greed after move solve_row1_tile'\r\n # print self\r\n solution_move += self.solve_row0_tile(cols)\r\n rows +=1\r\n cols -=1\r\n appropriate_number +=self._width-1\r\n\r\n\r\n #### Case when we use solve_2x2\r\n elif rows <= 1 and cols <= 1:\r\n # print 'We are solving 2x2 puzzle'\r\n solution_move += self.solve_2x2()\r\n if self._grid[0][0] == 0 and \\\r\n self._grid[0][1] == 1:\r\n # print 'Congrads Puxxle is SOLVED!!!!!'\r\n break\r\n\r\n\r\n\r\n\r\n if counter > 100:\r\n # print 'COUNTER BREAK'\r\n break\r\n # print solution_move, len(solution_move)\r\n return solution_move\r\n\r\n\r\n\r\n\r\n\r\n\r\n # for row in solution_greed._grid[::-1]:\r\n # print solution_greed._grid\r\n # print 'Row =',row\r\n \r\n # if solution_greed._grid.index(row) > 1:\r\n # print \"Case when we solwing Interior and Tile0 part\"\r\n \r\n\r\n # for col in solution_greed._grid[solution_greed._grid.index(row)][::-1]:\r\n # print 'Coloumn value=', col\r\n #print row[0]\r\n # if col !=row[0]:\r\n # print 'Case when we use just Interior tile solution'\r\n # print solution_greed._grid.index(row)\r\n # print row.index(col)\r\n \r\n # solution += solution_greed.solve_interior_tile(solution_greed._grid.index(row) , row.index(col))\r\n # print 'Solution =', solution\r\n # print self \r\n # print solution_greed._grid\r\n # elif col ==row[0]:\r\n # print 'Case when we use just Col0 solution'\r\n\r\n # else:\r\n # print 'Case when we solwing first two rows'\r\n\r\n #return \"\"\r","def update_current_sol_and_cost(self,sol=None):\n\n # Update current sol if argument given\n if sol is not None:\n self.current_sol = sol\n \n # Update residual and cost\n try:\n self.residual = self.sketch_reweighted - self.sketch_of_solution(self.current_sol)\n self.current_sol_cost = np.linalg.norm(self.residual)\n except AttributeError: # We are here if self.current_sol does not exist yet\n self.current_sol, self.residual = None, self.sketch_reweighted\n self.current_sol_cost = np.inf","def solve(self):\n # check for jacobian and set it if present and to be used\n if self.use_sparse:\n if self._use_jac and hasattr(self.problem,'sparse_jac'):\n jac = self.problem.sparse_jac\n else:\n jac = None\n else:\n if self._use_jac and hasattr(self.problem,'jac'):\n jac = self.problem.jac\n else:\n jac = None\n \n # Initialize solver and solve \n \n solved = False\n local_min = False\n\n res = N.zeros(self.x0.__len__())\n while (not solved) and self.reg_count < 2:\n try:\n if self._use_fscale:\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,self.fscale)\n else:\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,None)\n start = time.clock()\n res = self.solver.KINSOL_solve(not self._use_ls)\n stop = time.clock()\n self.exec_time += (stop - start)\n solved = True\n except KINError as error:\n if error.value == 42:\n # Try the heuristic\n if hasattr(self.problem, 'get_heuristic_x0'):\n print \"----------------------------------------------------\"\n print \" Solver stuck with zero step-length.\"\n print \"----------------------------------------------------\"\n print \"The following variables have start value zero\"\n print \"and min set to zero causing the zero step-lenght.\"\n print \"These settings are either set by default or by user.\"\n print \"\"\n\n self.x0 = self.problem.get_heuristic_x0()\n self.reg_count += 1\n \n print \"\"\n print \"This setting (start and min to zero) can often\"\n print \"cause problem when initializing the system. \"\n print \"\"\n print \"To avoid this the above variables have\"\n print \"their start attributes reset to one.\"\n print \"\"\n print \"Trying to solve the system again...\"\n else:\n raise KINSOL_Exception(\"Regularization failed due to constraints, tried getting heuristic initial guess but failed.\")\n \n\n elif (error.value == 2):\n print \"---------------------------------------------------------\"\n print \"\"\n print \" !!! WARNING !!!\"\n print \"\"\n print \" KINSOL has returned a result but the algorithm has converged\"\n print \" to a local minima, the initial values are NOT consistant!\"\n print \"\"\n print \"---------------------------------------------------------\"\n solved = True\n local_min = True\n else:\n # Other error, send onward as exception\n self.problem.check_constraints(res)\n raise KINSOL_Exception(error.msg[error.value])\n \n if not solved:\n self.solver.Free_KINSOL()\n raise KINSOL_Exception(\"Algorithm exited solution loop without finding a solution, please contact Assimulo support.\")\n\n if self.check_with_model:\n self.problem.check_constraints(res)\n if not local_min:\n print \"Problem sent to KINSOL solved.\"\n \n return res","def getskc(self,whichsol_,skc_):\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\n raise ValueError(\"Argument skc must be writable\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_getskc(self.__nativep,whichsol_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]","def getslc(self,whichsol_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slc is None: raise TypeError(\"Invalid type for argument slc\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.getslc(whichsol_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_slc:\n slc[:] = _tmparr_slc","def test_solve_quadratic(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.fixed = False\n iden2.x.fixed = False\n iden3.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 0)\n self.assertAlmostEqual(iden1.x.val, 1)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 3)","def add_test(self, sol_src, run_test_type=False):\n\n if sol_src in self.sol_srcs: # already added this solution\n return\n if run_test_type:\n env = dict(List=List, Dict=Dict, Set=Set)\n time0 = time.perf_counter()\n my_exec(sol_src + \"\\n\" + \"answer = sol()\", env, description=self.name)\n\n sol_val = env[\"answer\"]\n\n assert sol_val is not None, \"sol returned None\"\n\n assert type_check(run_test_type, sol_val)\n\n answer = decode(encode(sol_val))\n assert answer == sol_val, \"encode/decode round trip failed\"\n\n env2 = dict(answer=answer, List=List, Dict=Dict, Set=Set) # in case they mucked with env\n my_exec(self.src + \"\\n\" + \"assert sat(answer)\", env2, description=self.name)\n dur = time.perf_counter() - time0\n if dur > (DEFAULT_TIMEOUT if self.timeout is None else self.timeout * DEFAULT_TIMEOUT):\n utils.warn(f\"Took {dur}s to test {self.name} (self.timeout={self.timeout})\")\n self.sol_srcs.append(sol_src)","def add_to_player_sq(self, key, player_sq):\n current_selected_sq = self.board.unused_squares_dict[key]\n print(\"current selected sq ---->\", current_selected_sq)\n print(\"BEFORE player selected_sq: \", player_sq)\n player_sq.add(current_selected_sq) # player 1 = {1}\n print(\"AFTER player selected_sq: \", player_sq)","def test_solve_quadratic_fixed(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.val = 4\n iden2.x.val = 5\n iden3.x.val = 6\n iden1.x.name = 'x1'\n iden2.x.name = 'x2'\n iden3.x.name = 'x3'\n iden2.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 10)\n self.assertAlmostEqual(iden1.x.val, 4)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 6)","def getsolsta(self,whichsol_):\n solsta_ = ctypes.c_int32()\n res = __library__.MSK_XX_getsolsta(self.__nativep,whichsol_,ctypes.byref(solsta_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _solsta_return_value = solsta(solsta_.value)\n return (_solsta_return_value)","def solve(num_wizards, num_constraints, wizards, constraints): \n global wiz_const\n wiz_const = mapConstraints(wizards, constraints)\n partial_soltns = []\n\n # counter for priority queue since it doesn't allow \n # identical priorities\n k = 0\n\n # list of wizards sorted by lowest to highest degree\n sorted_wiz = sortWizByConsts(wiz_const)\n wiz_rankings = {wiz: i for i, wiz in enumerate(sorted_wiz)}\n\n const_set = set(map(tuple, constraints))\n for i in range(4) : \n heapq.heappush(partial_soltns, (0, k, nx.DiGraph(), const_set.copy()))\n k += 1\n\n print(\"setup done, commencing solving\")\n\n while len(partial_soltns) : \n\n # for partial_soltn, const_set in partial_soltns : \n# partial_soltns.remove(partial_soltn)\n num_seen, _, partial_soltn, const_set = heapq.heappop(partial_soltns)\n const = findNextConst(partial_soltn, const_set, wiz_rankings)\n print(\"seen \" + str(len(partial_soltn)) + \"\\t num partial_solutions\\t\" + str(len(partial_soltns)))\n try : \n const_set.remove(const)\n except KeyError : \n print(\"BAD SHIT\")\n pass\n possible_arrangements = [(const[0], const[1], const[2]),\n (const[2], const[0], const[1]), \n (const[2], const[1], const[0]),\n (const[1], const[0], const[2])]\n for arr in possible_arrangements:\n soltn = partial_soltn.copy()\n a, b, c = arr\n if not (soltn.has_node(a) and soltn.has_node(b) and nx.has_path(soltn, a, b)) : \n soltn.add_edge(a, b)\n if not (soltn.has_node(b) and soltn.has_node(c) and nx.has_path(soltn, b, c)) : \n soltn.add_edge(b, c)\n # see if we violated any other constraints (seen or not seen)\n is_valid, num_wiz = validNumWiz(soltn, const_set)\n\n if is_valid and len(list(nx.simple_cycles(soltn))) == 0 :\n heapq.heappush(partial_soltns, (-len(soltn), k, soltn, const_set.copy()))\n k += 1\n # are we done?\n if num_wiz == num_wizards :\n print(\"FINAL SOLUTION (found without processing all constraints but validating against them)\")\n ordering = list(nx.topological_sort(soltn))\n finishEverything(ordering, constraints)\n return ordering\n if foundCompleteOrdering(heapq.heappop(partial_soltns)) : \n print(\"FINAL SOLUTION\")\n ordering = list(nx.topological_sort(soltn))\n finishEverything(ordering, constraints)\n return ordering\n print(\"NO SOLUTION FOUND\")\n return \"\"","def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)","def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)","def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)","def add_poss(self, sqs=None, gates=None):\n if sqs:\n self.orig_sqs.update(set(sqs))\n if gates:\n self.orig_gates.update(set(gates))\n self.recalc()\n self.reset_hints()","def getinfeasiblesubproblem(self,whichsol_):\n inftask_ = ctypes.c_void_p()\n res = __library__.MSK_XX_getinfeasiblesubproblem(self.__nativep,whichsol_,ctypes.byref(inftask_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _inftask_return_value = Task(nativep = inftask_)\n return (_inftask_return_value)","def puty(self,whichsol_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.puty(whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def deletesolution(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.deletesolution(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def PuzzleSolved(tabl, ches, columns=0, possibleMoves=[], points=[]):\n if columns == tabl:\n possibleMoves.append(points[:])\n return(possibleMoves)\n for moves in range(tabl):\n ches[columns][moves] = 1\n if (solveQPuzzle(ches)):\n score = [columns, moves]\n points.append(score)\n PuzzleSolved(tabl, ches, columns + 1, possibleMoves, points)\n points.remove(score)\n ches[columns][moves] = 0\n return(possibleMoves)","def solve(self):\n new_puzzle = self._puzzle.clone()\n self._solution = new_puzzle.solve_puzzle()\n del new_puzzle\n pass","def solve_board(self):\n\n self.fill_board()\n\n if self.bts_solver():\n for i in self.sudoku_board.keys():\n self.file.write(str(self.sudoku_board[i]))\n self.file.write(\" BTS\")\n print(\"Solution Found!\")","def solve(self, sid: str, operation: str, result: Any) -> bool:\n\n # Check if the operation solved is present in the queue\n # and if it should be solved by the actual client.\n op = [o for c, o in self.queue if o[1] == operation and c == sid]\n\n # If not, return False,\n # to express an error.\n if len(op) == 0:\n return False\n\n # Extract the queue instance from the list of candidates.\n # (it shouldn't contain more that one element...).\n op = op[0]\n\n # Remove the instance from the queue.\n self.queue = [q for q in self.queue if q != (sid, op)]\n\n # Finally insert the result record into the database.\n # This table holds only the id of the unsolved operations,\n # to join all together run the query located at `scripts/results.sql`.\n run_sql(\n \"INSERT INTO results (client, operation, result) VALUES (?, ?, ?)\",\n (sid, op[0], result),\n )\n\n # Return True to express the successfull\n # marking of the operation as completed.\n return True","def putxc(self,whichsol_,xc_):\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp","def putxx(self,whichsol_,xx_):\n _xx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),self.getnumvar()))\n if xx_ is None:\n raise ValueError(\"Argument xx cannot be None\")\n if xx_ is None:\n raise ValueError(\"Argument xx may not be None\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_putxx(self.__nativep,whichsol_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getskx(self,whichsol_,skx_):\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\n raise ValueError(\"Argument skx must be writable\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_getskx(self.__nativep,whichsol_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]","def putsnxslice(self,whichsol_,first_,last_,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if snx is None: raise TypeError(\"Invalid type for argument snx\")\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n if snx_ is not None and len(snx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument snx has wrong length\")\n res = self.__obj.putsnxslice(whichsol_,first_,last_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_new_solved_problem(self, user_id, task_id, cost=1):\n cur = self.connection.cursor()\n\n # At first get solved problems array\n data = self.get_user_data(user_id)\n score, solved_problems = data[\"score\"], data[\"solved_problems\"]\n\n if solved_problems == \"\": # If this user have never asked\n solved_problems = repr([task_id])\n else:\n s_p_list = json.loads(solved_problems)\n s_p_list.append(task_id)\n solved_problems = repr(s_p_list)\n\n add_point_q = f\"\"\"\n UPDATE users\n SET score = {score + cost}, solved_problems = '{solved_problems}'\n WHERE user_id = {user_id}\n \"\"\"\n cur.execute(add_point_q)\n self.connection.commit()\n log.info(f\"User [ID: {user_id}] add new solved problem: {task_id} with cost={cost}\")\n return score + cost","def getskn(self,whichsol_,skn_):\n _skn_minlength = self.getnumcone()\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn is not long enough: Is %d, expected %d\" % (len(skn_),self.getnumcone()))\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\n raise ValueError(\"Argument skn must be writable\")\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\n else:\n _skn_tmp = None\n res = __library__.MSK_XX_getskn(self.__nativep,whichsol_,_skn_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]","def modify_hints_based_on_allsols(self):\n #~ if self.hashid=='92580481c330e7057171d5f5b0f5ba84x003x006':\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\n if self.all_sols is None or self.all_sols=='timeout':\n #should restore all hints!\n #untested, or timeout so we know nothing\n self.reset_hints()\n return\n if self.all_sols==[]:\n #has no sol. so start/end must be here.\n self.reset_hints()\n return\n hints={}\n for sq in self.sqs:\n hints[sq]=set()\n for gsq in self.gatesqs:\n hints[gsq]=set()\n for s in self.all_sols:\n for sg in s:\n start,sindv,path,covered,end=sg\n covered=('IN',)+(start,)+covered+('OUT',)\n if start==end:\n hints[start].add(('OUT','OUT'))\n else:\n for ii,sq in enumerate(covered):\n if sq=='IN':\n continue\n if sq=='OUT':\n continue\n indv=makevec2(covered[ii-1],covered[ii])\n outdv=makevec2(covered[ii],covered[ii+1])\n hints[sq].add((indv,outdv))\n\n for sq in self.sqs:\n #~ print self.hints(sq)\n #~ print hints[sq]\n #~ print self._hints[sq]\n bad=self.intersect_hint(sq, hints[sq])\n #~ if len(self.all_sols)==1:\n #~ if len(self._hints[sq])!=1:\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\n if bad:return 1\n for gsq in self.gatesqs:\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\n bad=self.intersect_enter_hint(gsq, hints[gsq])\n if bad:return bad\n #should return the actual bad sq - it may be better!\n bad=self.intersect_exit_hint(gsq, hints[gsq])\n if bad:return bad\n #~ if len(hints[gsq])==1:\n #~ if len(self._hints[gsq])!=1:\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'","def getsuc(self,whichsol_,suc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if suc is None: raise TypeError(\"Invalid type for argument suc\")\n _copyback_suc = False\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n _copyback_suc = True\n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n _copyback_suc = True\n if suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc has wrong length\")\n res = self.__obj.getsuc(whichsol_,suc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_suc:\n suc[:] = _tmparr_suc","def complie_soldity():\n assert(os.path.exists(src_entry) and os.path.isfile(src_entry) )\n\n commands = [\n [SOLC, \"--optimize\", \"--bin\", \"--overwrite\", \"-o\", os.path.relpath(bin_dir), os.path.relpath(src_entry) ]\n , [SOLC, \"--optimize\", \"--ast\", \"--overwrite\", \"-o\", os.path.relpath(ast_dir), os.path.relpath(src_entry) ]\n , [SOLC, \"--optimize\", \"--abi\", \"--overwrite\", \"-o\", os.path.relpath(dst_dir), os.path.relpath(src_entry) ]\n ]\n # commands = [\n # [SOLC, \"--optimize\", \"--bin\", \"-o\", bin_dir, os.path.relpath(src_entry) ]\n # , [SOLC, \"--optimize\", \"--ast\", \"-o\", ast_dir, os.path.relpath(src_entry) ]\n # , [SOLC, \"--optimize\", \"--abi\", \"-o\", dst_dir, os.path.relpath(src_entry) ]\n # ]\n\n print(\"======================Complie Solidity Language=========================\")\n for cmd in commands:\n command = \" \".join(cmd)\n print(command)\n os.system(command)\n \n os.system(\"cp %s/* %s\" %(bin_dir, dst_dir))\n\n # result = map(lambda cmd: os.system(\" \".join(cmd)), commands )\n # print(result)","def putslxslice(self,whichsol_,first_,last_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslxslice(whichsol_,first_,last_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _get_solution(self, x_0, sol, k_fb, k_fb_perf_0, sol_verbose=False,\n crashed=False, feas_tol=1e-6, q_0=None, k_fb_0=None):\n\n success = True\n feasible = True\n if crashed:\n feasible = False\n\n if self.verbosity > 1:\n print(\"Optimization crashed, infeasible soluion!\")\n else:\n g_res = np.array(sol[\"g\"]).squeeze()\n\n # This is not sufficient, since casadi gives out wrong feasibility values\n if np.any(np.array(self.lbg) - feas_tol > g_res) or np.any(\n np.array(self.ubg) + feas_tol < g_res):\n feasible = False\n\n x_opt = sol[\"x\"]\n self.has_openloop = True\n\n if self.opt_x0:\n x_0 = x_opt[:self.n_s]\n x_opt = x_opt[self.n_s:, :]\n\n # get indices of the respective variables\n n_u_0 = self.n_u\n n_u_perf = 0\n if self.n_perf > 1:\n n_u_perf = (self.n_perf - self.r) * self.n_u\n n_k_ff = (self.n_safe - 1) * self.n_u\n\n c = 0\n idx_u_0 = np.arange(n_u_0)\n c += n_u_0\n idx_u_perf = np.arange(c, c + n_u_perf)\n c += n_u_perf\n idx_k_ff = np.arange(c, c + n_k_ff)\n c += n_k_ff\n\n u_apply = np.array(cas_reshape(x_opt[idx_u_0], (1, self.n_u)))\n k_ff_perf = np.array(\n cas_reshape(x_opt[idx_u_perf], (self.n_perf - self.r, self.n_u)))\n\n k_ff_safe = np.array(\n cas_reshape(x_opt[idx_k_ff], (self.n_safe - 1, self.n_u)))\n k_ff_safe_all = np.vstack((u_apply, k_ff_safe))\n\n k_fb_safe_output = array_of_vec_to_array_of_mat(np.copy(k_fb), self.n_u,\n self.n_s)\n\n p_safe, q_safe, gp_sigma_pred_safe_all = self.get_safety_trajectory_openloop(x_0, u_apply,\n np.copy(k_fb),\n k_ff_safe, q_0, k_fb_0)\n\n p_safe = np.array(p_safe)\n q_safe = np.array(q_safe)\n\n if self.verbosity > 1:\n print(\"=== Safe Trajectory: ===\")\n print(\"Centers:\")\n print(p_safe)\n print(\"Shape matrices:\")\n print(q_safe)\n print(\"Safety controls:\")\n print(u_apply)\n print(k_ff_safe)\n\n k_fb_perf_traj_eval = np.empty((0, self.n_s * self.n_u))\n k_ff_perf_traj_eval = np.empty((0, self.n_u))\n if self.n_safe > 1:\n k_fb_perf_traj_eval = np.vstack(\n (k_fb_perf_traj_eval, k_fb[:self.r - 1, :]))\n k_ff_perf_traj_eval = np.vstack(\n (k_ff_perf_traj_eval, k_ff_safe[:self.r - 1, :]))\n if self.n_perf > self.r:\n k_fb_perf_traj_eval = np.vstack((k_fb_perf_traj_eval,\n np.matlib.repmat(k_fb_perf_0,\n self.n_perf - self.r,\n 1)))\n k_ff_perf_traj_eval = np.vstack((k_ff_perf_traj_eval, k_ff_perf))\n\n if self.n_perf > 1:\n mu_perf, sigma_perf = self._f_multistep_perf_eval(x_0.squeeze(),\n u_apply,\n k_fb_perf_traj_eval,\n k_ff_perf_traj_eval)\n\n if self.verbosity > 1:\n print(\"=== Performance Trajectory: ===\")\n print(\"Mu perf:\")\n print(mu_perf)\n print(\"Peformance controls:\")\n print(k_ff_perf_traj_eval)\n\n feasible, _ = self.eval_safety_constraints(p_safe, q_safe)\n\n if self.rhc and feasible:\n self.k_ff_safe = k_ff_safe\n self.k_ff_perf = k_ff_perf\n self.p_safe = p_safe\n self.k_fb_safe_all = np.copy(k_fb)\n self.u_apply = u_apply\n self.k_fb_perf_0 = k_fb_perf_0\n\n if feasible:\n self.n_fail = 0\n\n if not feasible:\n self.n_fail += 1\n q_all = None\n k_fb_safe_output = None\n k_ff_all = None\n p_safe = None\n q_safe = None\n g_res = None\n\n if self.n_fail >= self.n_safe:\n # Too many infeasible solutions -> switch to safe controller\n if self.verbosity > 1:\n print(\n \"Infeasible solution. Too many infeasible solutions, switching to safe controller\")\n u_apply = self.safe_policy(x_0)\n k_ff_safe_all = u_apply\n else:\n # can apply previous solution\n if self.verbosity > 1:\n print((\n \"Infeasible solution. Switching to previous solution, n_fail = {}, n_safe = {}\".format(\n self.n_fail, self.n_safe)))\n if sol_verbose:\n u_apply, k_fb_safe_output, k_ff_safe_all, p_safe = self.get_old_solution(\n x_0, get_ctrl_traj=True)\n else:\n u_apply = self.get_old_solution(x_0)\n k_ff_safe_all = u_apply\n\n if sol_verbose:\n return x_0, u_apply, feasible, success, k_fb_safe_output, k_ff_safe_all, p_safe, q_safe, sol, gp_sigma_pred_safe_all\n\n return x_0, u_apply, success","def lns_sa(self, sol_in, veh_type_in, cost_in):\r\n\r\n itr_cost = []\r\n solu = copy.deepcopy(sol_in)\r\n solu_type = copy.deepcopy(veh_type_in)\r\n best_solu = sol_in\r\n best_val = cost_in\r\n tabu_list = []\r\n random.seed(10)\r\n itr = 0\r\n temp = initial_temp\r\n t_run = time.time()\r\n while temp > stop_temp:\r\n itr += 1\r\n print(itr)\r\n if itr <= 0:\r\n sa_lns = True # use sa or lns\r\n else:\r\n sa_lns = False\r\n c = random.randint(1, store_num - 1) # randomly generated moving customer\r\n while c in tabu_list:\r\n c = random.randint(1, store_num - 1) # randint(a, b), both a and b are selectable\r\n c_loc = cust_loc(solu, c)\r\n\r\n if len(solu[c_loc[0]]) < 4: # customer number less than 2, can only implement shift1 and exchange1 operator\r\n wheel_value1 = random.uniform(0, 1)\r\n if wheel_value1 < 0.45:\r\n self.shift_1_cust(solu, c, c_loc, temp, solu_type, sa_lns)\r\n elif wheel_value1 < 0.9:\r\n self.exchange_1_cust(solu, c, c_loc, temp, solu_type, sa_lns)\r\n else:\r\n self.two_opt(solu, c, c_loc, temp, solu_type, sa_lns)\r\n\r\n # customer number more than 2, can implement all operators\r\n elif len(solu[c_loc[0]]) >= 4 and c_loc[1] <= len(solu[c_loc[0]]) - 3:\r\n wheel_value2 = random.uniform(0, 1)\r\n if wheel_value2 < 0.2:\r\n self.shift_1_cust(solu, c, c_loc, temp, solu_type, sa_lns)\r\n elif wheel_value2 < 0.4:\r\n self.shift_2_cust(solu, c, c_loc, temp, solu_type, sa_lns)\r\n elif wheel_value2 < 0.6:\r\n self.exchange_1_cust(solu, c, c_loc, temp, solu_type, sa_lns)\r\n elif wheel_value2 < 0.8:\r\n self.exchange_2_cust(solu, c, c_loc, temp, solu_type, sa_lns)\r\n else:\r\n self.two_opt(solu, c, c_loc, temp, solu_type, sa_lns)\r\n\r\n\r\n if itr % 100 == 0: # implement two-exchange operator every 200 iteration\r\n self.two_exchange_sol(solu, temp, solu_type, sa_lns)\r\n\r\n\r\n temp -= delta\r\n tabu_list.append(c)\r\n if len(tabu_list) > 100:\r\n tabu_list.pop(0)\r\n\r\n\r\n cost_i = of.print_result(solu, solu_type, False)\r\n # print(solu_type)\r\n itr_cost.append(cost_i)\r\n if cost_i < best_val:\r\n best_solu = solu\r\n best_val = cost_i\r\n\r\n\r\n t_run = time.time()\r\n\r\n # Adjust0: delete [0, 0] routes\r\n adjust_sol0 = []\r\n for route0 in best_solu:\r\n if len(route0) <= 2: # [0, 0] route\r\n continue\r\n else:\r\n adjust_sol0.append(route0)\r\n\r\n # Adjust1: use small vehicle if posiible\r\n adjust_type = []\r\n for route1 in adjust_sol0:\r\n adjust_type.append(route_type(route1))\r\n\r\n\r\n\r\n return adjust_sol0, adjust_type, best_val, itr_cost","def saddle_point(I):\n #--- FILL ME IN ---\n\n m, n = I.shape\n\n #compute the inputs to the function lstsq\n\n #get sci\n sci = I.reshape(m*n, 1)\n #get A\n A = []\n for y in range(n):\n for x in range(m):\n #print((x,y))\n #print([x*x, x*y, y*y, x, y, 1])\n A.append([x*x, x*y, y*y, x, y, 1])\n\n A = np.array(A)\n \n parms = np.linalg.lstsq(A,sci)[0]\n #print(parms)\n r1 = np.array([[2*parms[0][0], parms[1][0]], \n [parms[1][0], 2*parms[2][0]]])\n r1 = np.linalg.inv(r1)\n r2 = np.array([[parms[3][0]], \n [parms[4][0]]])\n\n pt = np.negative(np.matmul(r1, r2))\n\n #------------------\n\n return pt","def add_unknown_strcuture(self, name, cospsi = None,\n corr = None, cl = None):\n if cospsi is not None:\n # if cospsi and corr are both given, use them \n if corr is None:\n pass\n else:\n if len(cospsi.shape) == 1:\n cospsi = np.array( [cospsi] * self.n_q )\n try:\n assert(corr.shape[0] == self.n_q)\n assert(corr.shape[-1] == cospsi.shape[-1])\n except AssertionError:\n print(\"EEROR: corr and cospsi shape mismatch\")\n return\n\n corr, cospsi = self._check_cospsi( corr, cospsi )\n cl = self._compute_component_legendre_projection(corr, cospsi)\n self.guess_cl.update({name: cl})\n \n elif cl is not None:\n # if cl is given\n try:\n assert( cl.shape[0] == self.lmax+1)\n assert( cl.shape[-1] == self.n_q)\n except AssertionError:\n print(\"ERORR: cl shape mismatch\")\n\n self.guess_cl.update( {name:cl} )","def guess(x, y, k, s, w=None):\n return splrep(x, y, w, k=k, s=s)","def solute(self, puzzle):\r\n \"\"\"suppose that ax = c, where a is a matrix, c and x are vectors.\"\"\"\r\n \"\"\"The aim is to figure out x, which indicates the solution.\"\"\"\r\n A, a, c = [], [], []\r\n for i in range(puzzle.row):\r\n for j in range(puzzle.column):\r\n # create a puzzle.row * puzzle.column by puzzle.row * puzzle.column matrix.\r\n # each column represents a cell in the puzzle.\r\n # each row represents the changed cell if column c is selected.\r\n if puzzle.lights[i][j] == -1:\r\n c.append(1)\r\n else:\r\n c.append(0)\r\n for m in range(puzzle.row):\r\n for n in range(puzzle.column):\r\n if self.is_adjecent([m, n], [i, j]):\r\n # if [m, n] is adjecent to [i, j], then a[ij][mn] should be 1.\r\n a.append(1)\r\n else:\r\n a.append(0)\r\n a.append(c[i * puzzle.column + j])\r\n A.append(a)\r\n a = []\r\n\r\n self.eliminate(A)\r\n x = [item[len(item) - 1] for item in A]\r\n # x is the last column of A.\r\n # if x[i] is 1, cell i should be selected.\r\n i = 0\r\n for m in range(puzzle.row):\r\n for n in range(puzzle.column):\r\n if x[i] == 1:\r\n puzzle.selection.add((m, n))\r\n i += 1\r\n\r\n return puzzle.selection","def getslc(self,whichsol_,slc_):\n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\n raise ValueError(\"Argument slc must be writable\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_getslc(self.__nativep,whichsol_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _slc_copyarray:\n slc_[:] = _slc_np_tmp","def solve_(self, x, y, board, path):\n if self.SOLVED:\n return\n if self.is_done(board):\n self.print_path(path)\n self.SOLVED = True\n return\n for new_x, new_y in self.next_click(x, y, board):\n if new_x is None or new_y is None:\n return\n new_board = self.click(new_x, new_y, board)\n self.solve_(\n x=0, y=0,\n board=new_board,\n path=path + [((new_x, new_y), new_board)]\n )","def add_sudoku_constraints(sg):\n for y in range(6):\n sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for x in range(6)]))\n for x in range(6):\n sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for y in range(6)]))\n for z in range(6):\n top = (z // 2) * 2\n left = (z % 2) * 3\n cells = [sg.grid[Point(y, x)] for y in range(top, top + 2) for x in range(left, left + 3)]\n sg.solver.add(Distinct(*cells))"],"string":"[\n \"def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skn is None:\\n skn_ = None\\n else:\\n try:\\n skn_ = memoryview(skn)\\n except TypeError:\\n try:\\n _tmparr_skn = array.array(\\\"i\\\",skn)\\n except TypeError:\\n raise TypeError(\\\"Argument skn has wrong type\\\")\\n else:\\n skn_ = memoryview(_tmparr_skn)\\n \\n else:\\n if skn_.format != \\\"i\\\":\\n skn_ = memoryview(array.array(\\\"i\\\",skn))\\n \\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n \\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n \\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n \\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n \\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n \\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n \\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n \\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n \\n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if not isinstance(sk_,stakey): raise TypeError(\\\"Argument sk has wrong type\\\")\\n res = self.__obj.putsolutioni(accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskx(self,whichsol_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skx is None: raise TypeError(\\\"Invalid type for argument skx\\\")\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.putskx(whichsol_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskc(self,whichsol_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None: raise TypeError(\\\"Invalid type for argument skc\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.putskc(whichsol_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skc = False\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n _copyback_skc = True\\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n _copyback_skc = True\\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n _copyback_skx = False\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n _copyback_skx = True\\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n _copyback_skx = True\\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n _copyback_skn = False\\n if skn is None:\\n skn_ = None\\n else:\\n try:\\n skn_ = memoryview(skn)\\n except TypeError:\\n try:\\n _tmparr_skn = array.array(\\\"i\\\",skn)\\n except TypeError:\\n raise TypeError(\\\"Argument skn has wrong type\\\")\\n else:\\n skn_ = memoryview(_tmparr_skn)\\n _copyback_skn = True\\n else:\\n if skn_.format != \\\"i\\\":\\n skn_ = memoryview(array.array(\\\"i\\\",skn))\\n _copyback_skn = True\\n if skn_ is not None and len(skn_) != self.getnumcone():\\n raise ValueError(\\\"Array argument skn has wrong length\\\")\\n _copyback_xc = False\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n _copyback_xc = True\\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n _copyback_xc = True\\n if xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n _copyback_xx = False\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n _copyback_xx = True\\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n _copyback_xx = True\\n if xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n _copyback_y = False\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n _copyback_y = True\\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n _copyback_y = True\\n if y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n _copyback_slc = False\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n _copyback_slc = True\\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n _copyback_slc = True\\n if slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n _copyback_suc = False\\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n _copyback_suc = True\\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n _copyback_suc = True\\n if suc_ is not None and len(suc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument suc has wrong length\\\")\\n _copyback_slx = False\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n _copyback_slx = True\\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n _copyback_slx = True\\n if slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n _copyback_sux = False\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n _copyback_sux = True\\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n _copyback_sux = True\\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n _copyback_snx = False\\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n _copyback_snx = True\\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n _copyback_snx = True\\n if snx_ is not None and len(snx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument snx has wrong length\\\")\\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _prosta_return_value,_solsta_return_value = resargs\\n if _copyback_snx:\\n snx[:] = _tmparr_snx\\n if _copyback_sux:\\n sux[:] = _tmparr_sux\\n if _copyback_slx:\\n slx[:] = _tmparr_slx\\n if _copyback_suc:\\n suc[:] = _tmparr_suc\\n if _copyback_slc:\\n slc[:] = _tmparr_slc\\n if _copyback_y:\\n y[:] = _tmparr_y\\n if _copyback_xx:\\n xx[:] = _tmparr_xx\\n if _copyback_xc:\\n xc[:] = _tmparr_xc\\n if _copyback_skn:\\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\\n if _copyback_skx:\\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\\n if _copyback_skc:\\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\\n _solsta_return_value = solsta(_solsta_return_value)\\n _prosta_return_value = prosta(_prosta_return_value)\\n return _prosta_return_value,_solsta_return_value\",\n \"def getsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\\n prosta_ = ctypes.c_int32()\\n solsta_ = ctypes.c_int32()\\n _skc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),self.getnumcon()))\\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\\n raise ValueError(\\\"Argument skc must be writable\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\\n else:\\n _skc_tmp = None\\n _skx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),self.getnumvar()))\\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\\n raise ValueError(\\\"Argument skx must be writable\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\\n else:\\n _skx_tmp = None\\n _skn_minlength = self.getnumcone()\\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\\n raise ValueError(\\\"Array argument skn is not long enough: Is %d, expected %d\\\" % (len(skn_),self.getnumcone()))\\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\\n raise ValueError(\\\"Argument skn must be writable\\\")\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\\n else:\\n _skn_tmp = None\\n _xc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),self.getnumcon()))\\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\\n raise ValueError(\\\"Argument xc must be writable\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n _xx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),self.getnumvar()))\\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\\n raise ValueError(\\\"Argument xx must be writable\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n _y_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),self.getnumcon()))\\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\\n raise ValueError(\\\"Argument y must be writable\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n _slc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),self.getnumcon()))\\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\\n raise ValueError(\\\"Argument slc must be writable\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n _suc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and suc_ is not None and len(suc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument suc is not long enough: Is %d, expected %d\\\" % (len(suc_),self.getnumcon()))\\n if isinstance(suc_,numpy.ndarray) and not suc_.flags.writeable:\\n raise ValueError(\\\"Argument suc must be writable\\\")\\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n _slx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx is not long enough: Is %d, expected %d\\\" % (len(slx_),self.getnumvar()))\\n if isinstance(slx_,numpy.ndarray) and not slx_.flags.writeable:\\n raise ValueError(\\\"Argument slx must be writable\\\")\\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n _sux_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),self.getnumvar()))\\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\\n raise ValueError(\\\"Argument sux must be writable\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n _snx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and snx_ is not None and len(snx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument snx is not long enough: Is %d, expected %d\\\" % (len(snx_),self.getnumvar()))\\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\\n raise ValueError(\\\"Argument snx must be writable\\\")\\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_getsolution(self.__nativep,whichsol_,ctypes.byref(prosta_),ctypes.byref(solsta_),_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _prosta_return_value = prosta(prosta_.value)\\n _solsta_return_value = solsta(solsta_.value)\\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]\\n if _xc_copyarray:\\n xc_[:] = _xc_np_tmp\\n if _xx_copyarray:\\n xx_[:] = _xx_np_tmp\\n if _y_copyarray:\\n y_[:] = _y_np_tmp\\n if _slc_copyarray:\\n slc_[:] = _slc_np_tmp\\n if _suc_copyarray:\\n suc_[:] = _suc_np_tmp\\n if _slx_copyarray:\\n slx_[:] = _slx_np_tmp\\n if _sux_copyarray:\\n sux_[:] = _sux_np_tmp\\n if _snx_copyarray:\\n snx_[:] = _snx_np_tmp\\n return (_prosta_return_value,_solsta_return_value)\",\n \"def putslc(self,whichsol_,slc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slc is None: raise TypeError(\\\"Invalid type for argument slc\\\")\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n \\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n \\n if slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n res = self.__obj.putslc(whichsol_,slc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskc(self,whichsol_,skc_):\\n _skc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),self.getnumcon()))\\n if skc_ is None:\\n raise ValueError(\\\"Argument skc cannot be None\\\")\\n if skc_ is None:\\n raise ValueError(\\\"Argument skc may not be None\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsolutionyi(self,i_,whichsol_,y_):\\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskx(self,whichsol_,skx_):\\n _skx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),self.getnumvar()))\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx cannot be None\\\")\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx may not be None\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_putskx(self.__nativep,whichsol_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putslc(self,whichsol_,slc_):\\n _slc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),self.getnumcon()))\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc cannot be None\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_putslc(self.__nativep,whichsol_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsolutionyi(self,i_,whichsol_,y_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsuc(self,whichsol_,suc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if suc is None: raise TypeError(\\\"Invalid type for argument suc\\\")\\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n \\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n \\n if suc_ is not None and len(suc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument suc has wrong length\\\")\\n res = self.__obj.putsuc(whichsol_,suc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putslx(self,whichsol_,slx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slx is None: raise TypeError(\\\"Invalid type for argument slx\\\")\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n res = self.__obj.putslx(whichsol_,slx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsnx(self,whichsol_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if sux is None: raise TypeError(\\\"Invalid type for argument sux\\\")\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.putsnx(whichsol_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsuc(self,whichsol_,suc_):\\n _suc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and suc_ is not None and len(suc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument suc is not long enough: Is %d, expected %d\\\" % (len(suc_),self.getnumcon()))\\n if suc_ is None:\\n raise ValueError(\\\"Argument suc cannot be None\\\")\\n if suc_ is None:\\n raise ValueError(\\\"Argument suc may not be None\\\")\\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n res = __library__.MSK_XX_putsuc(self.__nativep,whichsol_,_suc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def sketch_of_solution(self,sol=None):\\n raise NotImplementedError\",\n \"def updatesolutioninfo(self,whichsol_):\\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_solution(self, solution):\\n if self.check_solution(solution):\\n self._solution = solution\\n self.solution_status = 'OK'\\n else:\\n self._solution = None\\n self.solution_status = 'X'\",\n \"def putslx(self,whichsol_,slx_):\\n _slx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx is not long enough: Is %d, expected %d\\\" % (len(slx_),self.getnumvar()))\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx cannot be None\\\")\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx may not be None\\\")\\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n res = __library__.MSK_XX_putslx(self.__nativep,whichsol_,_slx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsnx(self,whichsol_,sux_):\\n _sux_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),self.getnumvar()))\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux cannot be None\\\")\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux may not be None\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_putsnx(self.__nativep,whichsol_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def writesolution(self,whichsol_,filename_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.writesolution(whichsol_,filename_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsucslice(self,whichsol_,first_,last_,suc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if suc is None: raise TypeError(\\\"Invalid type for argument suc\\\")\\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n \\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n \\n if suc_ is not None and len(suc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument suc has wrong length\\\")\\n res = self.__obj.putsucslice(whichsol_,first_,last_,suc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_solution(self, paths):\\n assert self.benchmark.status[\\\"state\\\"] == \\\"RUNNING\\\", print(\\n f\\\"Benchmark seems to be inactive. state: {self.benchmark.status(['state'])}\\\")\\n assert self.benchmark.status[\\\"data\\\"][\\\"problem_states\\\"][self.batch_pos] == 0, print(\\n \\\"Problem seems to be already solved\\\")\\n\\n self.paths = paths\\n self.time = time()-self.start_time\\n self.benchmark.status[\\\"data\\\"][\\\"problem_states\\\"][self.batch_pos] = 1\\n\\n if all(self.benchmark.status[\\\"data\\\"][\\\"problem_states\\\"]):\\n self.status = {\\\"state\\\": \\\"SUBMITTING\\\", \\\"data\\\": None}\\n self.benchmark.submit()\",\n \"def putxx(self,whichsol_,xx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xx is None: raise TypeError(\\\"Invalid type for argument xx\\\")\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n res = self.__obj.putxx(whichsol_,xx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsucslice(self,whichsol_,first_,last_,suc_):\\n _suc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and suc_ is not None and len(suc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument suc is not long enough: Is %d, expected %d\\\" % (len(suc_),((last_) - (first_))))\\n if suc_ is None:\\n raise ValueError(\\\"Argument suc cannot be None\\\")\\n if suc_ is None:\\n raise ValueError(\\\"Argument suc may not be None\\\")\\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n res = __library__.MSK_XX_putsucslice(self.__nativep,whichsol_,first_,last_,_suc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def writesolution(self,whichsol_,filename_):\\n if isinstance(filename_,unicode):\\n filename_ = filename_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_writesolution(self.__nativep,whichsol_,filename_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskcslice(self,whichsol_,first_,last_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skc_ is not None and len(skc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.putskcslice(whichsol_,first_,last_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solutiondef(self,whichsol_):\\n isdef_ = ctypes.c_int32()\\n res = __library__.MSK_XX_solutiondef(self.__nativep,whichsol_,ctypes.byref(isdef_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n isdef_ = isdef_.value\\n _isdef_return_value = isdef_\\n return (_isdef_return_value)\",\n \"def deletesolution(self,whichsol_):\\n res = __library__.MSK_XX_deletesolution(self.__nativep,whichsol_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putxc(self,whichsol_,xc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xc is None: raise TypeError(\\\"Invalid type for argument xc\\\")\\n _copyback_xc = False\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n _copyback_xc = True\\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n _copyback_xc = True\\n if xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n res = self.__obj.putxc(whichsol_,xc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_xc:\\n xc[:] = _tmparr_xc\",\n \"def getskc(self,whichsol_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skc = False\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n _copyback_skc = True\\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n _copyback_skc = True\\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.getskc(whichsol_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_skc:\\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\",\n \"def getskx(self,whichsol_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skx = False\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n _copyback_skx = True\\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n _copyback_skx = True\\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.getskx(whichsol_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_skx:\\n for __tmp_var_0 in range(len(skx_)): skx[__tmp_var_0] = stakey(_tmparr_skx[__tmp_var_0])\",\n \"def updatesolutioninfo(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.updatesolutioninfo(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def analyzesolution(self,whichstream_,whichsol_):\\n res = __library__.MSK_XX_analyzesolution(self.__nativep,whichstream_,whichsol_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskxslice(self,whichsol_,first_,last_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skx is None: raise TypeError(\\\"Invalid type for argument skx\\\")\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skx_ is not None and len(skx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.putskxslice(whichsol_,first_,last_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsolutioni(self,accmode_,i_,whichsol_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getsolutioni(accmode_,i_,whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value = resargs\\n _sk_return_value = stakey(_sk_return_value)\\n return _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value\",\n \"def score_solution(g, s):\\n pass\",\n \"def putsux(self,whichsol_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if sux is None: raise TypeError(\\\"Invalid type for argument sux\\\")\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.putsux(whichsol_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskcslice(self,whichsol_,first_,last_,skc_):\\n _skc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and skc_ is not None and len(skc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),((last_) - (first_))))\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_putskcslice(self.__nativep,whichsol_,first_,last_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putslcslice(self,whichsol_,first_,last_,slc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slc is None: raise TypeError(\\\"Invalid type for argument slc\\\")\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n \\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n \\n if slc_ is not None and len(slc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n res = self.__obj.putslcslice(whichsol_,first_,last_,slc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsolsta(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getsolsta(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _solsta_return_value = resargs\\n _solsta_return_value = solsta(_solsta_return_value)\\n return _solsta_return_value\",\n \"def solution(self,pos):\\n\\t\\tif pos == 0:\\n\\t\\t\\treturn self.solve(pos)\\n\\n\\t\\tif not self.mem.has_key(pos-1):\\n\\t\\t\\tself.mem[pos-1] = self.solution(pos-1)\\n\\t\\tif not self.mem.has_key(pos):\\n\\t\\t\\tself.mem[pos] = self.mem[pos-1] + self.solve(pos)\\n\\t\\tif pos == len(self.objective)-1 and not self.eob():\\n\\t\\t\\t\\\"\\\"\\\"Ya obtuvimos la solucion y aun hay elementos en la base que deben ser\\n\\t\\t\\tdescartados\\\"\\\"\\\"\\n\\t\\t\\tself.mem[pos] += self.terminar()\\n\\t\\treturn self.mem[pos]\",\n \"def notify_solution(self, sol):\\n self._solutions.append(sol)\",\n \"def insertion_neighborhood(solution, local_optimum=True, tie_breaking=False):\\n current_makespan = 0\\n improve = True\\n\\n # If parameter local_optimum is true: repeat while solution is improved\\n while improve:\\n improve = False\\n # Select random elements without repetition - O(N) implementation\\n not_tested = solution.sequence.copy()\\n shuffle(not_tested)\\n for removed_job in not_tested:\\n solution.sequence.remove(removed_job)\\n # If first iteration then calculate makespan\\n if current_makespan == 0:\\n current_makespan = solution.calculate_makespan()\\n # Insert job in best position and check improvement\\n solution.insert_best_position(removed_job, tie_breaking)\\n if solution.makespan < current_makespan:\\n improve = True\\n current_makespan = solution.makespan\\n if local_optimum == False:\\n break\",\n \"def test_Schur_Sp_solve():\\n mat_A = load_matrix_step_noslip()\\n petsc_options = initialize_petsc_options()\\n b, x = create_petsc_vecs(mat_A)\\n\\n solver_info = LS.ModelInfo('interlaced', 3)\\n schur_approx = LS.Schur_Sp(mat_A,\\n '',\\n solver_info=solver_info)\\n ksp_obj = initialize_schur_ksp_obj(mat_A, schur_approx)\\n ksp_obj.solve(b,x)\\n\\n assert ksp_obj.converged == True\\n assert ksp_obj.reason == 2\\n assert float(ksp_obj.norm) < 1.0e-5\\n assert ksp_obj.its == 63\",\n \"def update_solution(self, new_plan, use_cat=True, soc=True):\\n if not new_plan.path:\\n self.sol = TimeUncertaintySolution.empty_solution(self.sol.nodes_generated)\\n self.sol.paths[new_plan.agent] = new_plan\\n return\\n if not use_cat:\\n self.sol.paths[new_plan.agent] = new_plan\\n self.sol.create_movement_tuples(agents=[new_plan.agent])\\n return\\n\\n self.sol.paths[new_plan.agent] = new_plan\\n new_moves = self.sol.add_stationary_moves()\\n self.sol.create_movement_tuples(agents=[new_plan.agent])\\n self.update_conflict_avoidance_table(new_plan.agent, new_moves)\\n self.sol.compute_solution_cost(sum_of_costs=soc) # compute the cost\",\n \"def solutiondef(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.solutiondef(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _isdef_return_value = resargs\\n return _isdef_return_value\",\n \"def putskxslice(self,whichsol_,first_,last_,skx_):\\n _skx_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),((last_) - (first_))))\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx cannot be None\\\")\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx may not be None\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_putskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def createsolution(self,rows):\\r\\n l = [0,0]\\r\\n if not self.findzero(l):\\r\\n return True\\r\\n x = l[0]\\r\\n y = l[1]\\r\\n for i in range(1,10):\\r\\n if self.check(i, x, y):\\r\\n rows[x][y] = i\\r\\n if self.createsolution(rows):\\r\\n return True\\r\\n rows[x][y] = 0\\r\\n return False\",\n \"def solve(self, solver):\\n solver.solve()\",\n \"def putslcslice(self,whichsol_,first_,last_,slc_):\\n _slc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),((last_) - (first_))))\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc cannot be None\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solve(team, chal, request):\\n provided_key = request.form['key'].strip()\\n solve = Solves(teamid=team.id, chalid=chal.id, ip=utils.get_ip(req=request), flag=provided_key)\\n db.session.add(solve)\\n db.session.commit()\\n db.session.close()\",\n \"def putsux(self,whichsol_,sux_):\\n _sux_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),self.getnumvar()))\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux cannot be None\\\")\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux may not be None\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_putsux(self.__nativep,whichsol_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solveSudoku(self, board) -> None:\\n self.board = board\\n self.backTrace(0,0)\",\n \"def main():\\n\\n # first lets test with a already created csp:\\n csp = create_map_csp()\\n solution = backtracking(csp)\\n #solution2,assigned = minimum_remaining_values(csp)\\n print(solution)\\n #print assigned\\n\\n # and now with our own generated sudoku CSP\\n \\\"\\\"\\\"sudokus = read_sudokus()\\n csp = create_sudoku_csp(sudokus[1])\\n solution = backtracking(csp)\\n print sudoku_csp_to_array(solution)\\n\\\"\\\"\\\"\",\n \"def update(self, solution):\\n self.heuristic_path = [i for i in self.initial_path if i in solution]\\n self.heuristic_cost = self.pathCost(self.heuristic_path)\",\n \"def solve_puzzle(self):\\r\\n \\r\\n counter = 0\\r\\n rows = self._height-1\\r\\n cols = self._width-1\\r\\n # print rows, cols\\r\\n # print 'The greed has %s rows and %s coloumn indexes' %(rows, cols) \\r\\n solution_move = ''\\r\\n if self.get_number(0,0) == 0 and \\\\\\r\\n self.get_number(0,1) == 1:\\r\\n # print 'Congrads Puxxle is Aolved at start!!!!!'\\r\\n return ''\\r\\n #appropriate_number = (self._height * self._width) - 1\\r\\n appropriate_number = (rows+1) * (cols+1) -1\\r\\n # print 'First appropriate_number=',appropriate_number\\r\\n # print \\\"Grid first tile that we will solwing has value =\\\", self._grid[rows][cols]\\r\\n \\r\\n while counter < 300:\\r\\n counter +=1\\r\\n # print self\\r\\n #appropriate_number = (rows+1) * (cols+1) -1\\r\\n # print 'Appropriate number in loop=',appropriate_number\\r\\n # print 'We are solving %s index_row and %s index_col' %(rows, cols) \\r\\n ####Case when we use solve_interior_tile\\r\\n if rows > 1 and cols > 0:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n cols -= 1\\r\\n appropriate_number -=1\\r\\n else:\\r\\n # print 'We are solving interior tile', (rows, cols)\\r\\n solution_move += self.solve_interior_tile(rows, cols)\\r\\n # print 'Solution move=', solution_move\\r\\n cols -= 1\\r\\n #### Case when we use solve_col0_tile\\r\\n elif rows > 1 and cols == 0:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows -= 1\\r\\n cols = self._width-1\\r\\n appropriate_number -=1\\r\\n else:\\r\\n # print 'We are solwing tile 0 in row', rows\\r\\n # print 'Appropriate number here ='\\r\\n solution_move += self.solve_col0_tile(rows)\\r\\n # print 'Solution move=', solution_move\\r\\n rows -=1\\r\\n cols = self._width-1\\r\\n\\r\\n\\r\\n #### Cases when we use solve_row0_tile\\r\\n elif rows == 1 and cols > 1:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows -= 1\\r\\n #cols = self._width-1\\r\\n appropriate_number -= self._width\\r\\n\\r\\n else:\\r\\n # print 'Solving upper 2 rows right side'\\r\\n solution_move += self.solve_row1_tile(cols)\\r\\n rows -=1\\r\\n appropriate_number -= self._width\\r\\n #### Cases when we use solve_row1_tile \\r\\n if rows < 1 and cols > 1:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows += 1\\r\\n cols -= 1\\r\\n appropriate_number +=self._width-1\\r\\n else:\\r\\n # print '(1,J) tile solved, lets solwe tile (0,j) in tile',(rows,cols)\\r\\n # print 'Greed after move solve_row1_tile'\\r\\n # print self\\r\\n solution_move += self.solve_row0_tile(cols)\\r\\n rows +=1\\r\\n cols -=1\\r\\n appropriate_number +=self._width-1\\r\\n\\r\\n\\r\\n #### Case when we use solve_2x2\\r\\n elif rows <= 1 and cols <= 1:\\r\\n # print 'We are solving 2x2 puzzle'\\r\\n solution_move += self.solve_2x2()\\r\\n if self._grid[0][0] == 0 and \\\\\\r\\n self._grid[0][1] == 1:\\r\\n # print 'Congrads Puxxle is SOLVED!!!!!'\\r\\n break\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n if counter > 100:\\r\\n # print 'COUNTER BREAK'\\r\\n break\\r\\n # print solution_move, len(solution_move)\\r\\n return solution_move\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n # for row in solution_greed._grid[::-1]:\\r\\n # print solution_greed._grid\\r\\n # print 'Row =',row\\r\\n \\r\\n # if solution_greed._grid.index(row) > 1:\\r\\n # print \\\"Case when we solwing Interior and Tile0 part\\\"\\r\\n \\r\\n\\r\\n # for col in solution_greed._grid[solution_greed._grid.index(row)][::-1]:\\r\\n # print 'Coloumn value=', col\\r\\n #print row[0]\\r\\n # if col !=row[0]:\\r\\n # print 'Case when we use just Interior tile solution'\\r\\n # print solution_greed._grid.index(row)\\r\\n # print row.index(col)\\r\\n \\r\\n # solution += solution_greed.solve_interior_tile(solution_greed._grid.index(row) , row.index(col))\\r\\n # print 'Solution =', solution\\r\\n # print self \\r\\n # print solution_greed._grid\\r\\n # elif col ==row[0]:\\r\\n # print 'Case when we use just Col0 solution'\\r\\n\\r\\n # else:\\r\\n # print 'Case when we solwing first two rows'\\r\\n\\r\\n #return \\\"\\\"\\r\",\n \"def update_current_sol_and_cost(self,sol=None):\\n\\n # Update current sol if argument given\\n if sol is not None:\\n self.current_sol = sol\\n \\n # Update residual and cost\\n try:\\n self.residual = self.sketch_reweighted - self.sketch_of_solution(self.current_sol)\\n self.current_sol_cost = np.linalg.norm(self.residual)\\n except AttributeError: # We are here if self.current_sol does not exist yet\\n self.current_sol, self.residual = None, self.sketch_reweighted\\n self.current_sol_cost = np.inf\",\n \"def solve(self):\\n # check for jacobian and set it if present and to be used\\n if self.use_sparse:\\n if self._use_jac and hasattr(self.problem,'sparse_jac'):\\n jac = self.problem.sparse_jac\\n else:\\n jac = None\\n else:\\n if self._use_jac and hasattr(self.problem,'jac'):\\n jac = self.problem.jac\\n else:\\n jac = None\\n \\n # Initialize solver and solve \\n \\n solved = False\\n local_min = False\\n\\n res = N.zeros(self.x0.__len__())\\n while (not solved) and self.reg_count < 2:\\n try:\\n if self._use_fscale:\\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,self.fscale)\\n else:\\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,None)\\n start = time.clock()\\n res = self.solver.KINSOL_solve(not self._use_ls)\\n stop = time.clock()\\n self.exec_time += (stop - start)\\n solved = True\\n except KINError as error:\\n if error.value == 42:\\n # Try the heuristic\\n if hasattr(self.problem, 'get_heuristic_x0'):\\n print \\\"----------------------------------------------------\\\"\\n print \\\" Solver stuck with zero step-length.\\\"\\n print \\\"----------------------------------------------------\\\"\\n print \\\"The following variables have start value zero\\\"\\n print \\\"and min set to zero causing the zero step-lenght.\\\"\\n print \\\"These settings are either set by default or by user.\\\"\\n print \\\"\\\"\\n\\n self.x0 = self.problem.get_heuristic_x0()\\n self.reg_count += 1\\n \\n print \\\"\\\"\\n print \\\"This setting (start and min to zero) can often\\\"\\n print \\\"cause problem when initializing the system. \\\"\\n print \\\"\\\"\\n print \\\"To avoid this the above variables have\\\"\\n print \\\"their start attributes reset to one.\\\"\\n print \\\"\\\"\\n print \\\"Trying to solve the system again...\\\"\\n else:\\n raise KINSOL_Exception(\\\"Regularization failed due to constraints, tried getting heuristic initial guess but failed.\\\")\\n \\n\\n elif (error.value == 2):\\n print \\\"---------------------------------------------------------\\\"\\n print \\\"\\\"\\n print \\\" !!! WARNING !!!\\\"\\n print \\\"\\\"\\n print \\\" KINSOL has returned a result but the algorithm has converged\\\"\\n print \\\" to a local minima, the initial values are NOT consistant!\\\"\\n print \\\"\\\"\\n print \\\"---------------------------------------------------------\\\"\\n solved = True\\n local_min = True\\n else:\\n # Other error, send onward as exception\\n self.problem.check_constraints(res)\\n raise KINSOL_Exception(error.msg[error.value])\\n \\n if not solved:\\n self.solver.Free_KINSOL()\\n raise KINSOL_Exception(\\\"Algorithm exited solution loop without finding a solution, please contact Assimulo support.\\\")\\n\\n if self.check_with_model:\\n self.problem.check_constraints(res)\\n if not local_min:\\n print \\\"Problem sent to KINSOL solved.\\\"\\n \\n return res\",\n \"def getskc(self,whichsol_,skc_):\\n _skc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),self.getnumcon()))\\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\\n raise ValueError(\\\"Argument skc must be writable\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_getskc(self.__nativep,whichsol_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\",\n \"def getslc(self,whichsol_,slc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slc is None: raise TypeError(\\\"Invalid type for argument slc\\\")\\n _copyback_slc = False\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n _copyback_slc = True\\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n _copyback_slc = True\\n if slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n res = self.__obj.getslc(whichsol_,slc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_slc:\\n slc[:] = _tmparr_slc\",\n \"def test_solve_quadratic(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.fixed = False\\n iden2.x.fixed = False\\n iden3.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 0)\\n self.assertAlmostEqual(iden1.x.val, 1)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 3)\",\n \"def add_test(self, sol_src, run_test_type=False):\\n\\n if sol_src in self.sol_srcs: # already added this solution\\n return\\n if run_test_type:\\n env = dict(List=List, Dict=Dict, Set=Set)\\n time0 = time.perf_counter()\\n my_exec(sol_src + \\\"\\\\n\\\" + \\\"answer = sol()\\\", env, description=self.name)\\n\\n sol_val = env[\\\"answer\\\"]\\n\\n assert sol_val is not None, \\\"sol returned None\\\"\\n\\n assert type_check(run_test_type, sol_val)\\n\\n answer = decode(encode(sol_val))\\n assert answer == sol_val, \\\"encode/decode round trip failed\\\"\\n\\n env2 = dict(answer=answer, List=List, Dict=Dict, Set=Set) # in case they mucked with env\\n my_exec(self.src + \\\"\\\\n\\\" + \\\"assert sat(answer)\\\", env2, description=self.name)\\n dur = time.perf_counter() - time0\\n if dur > (DEFAULT_TIMEOUT if self.timeout is None else self.timeout * DEFAULT_TIMEOUT):\\n utils.warn(f\\\"Took {dur}s to test {self.name} (self.timeout={self.timeout})\\\")\\n self.sol_srcs.append(sol_src)\",\n \"def add_to_player_sq(self, key, player_sq):\\n current_selected_sq = self.board.unused_squares_dict[key]\\n print(\\\"current selected sq ---->\\\", current_selected_sq)\\n print(\\\"BEFORE player selected_sq: \\\", player_sq)\\n player_sq.add(current_selected_sq) # player 1 = {1}\\n print(\\\"AFTER player selected_sq: \\\", player_sq)\",\n \"def test_solve_quadratic_fixed(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.val = 4\\n iden2.x.val = 5\\n iden3.x.val = 6\\n iden1.x.name = 'x1'\\n iden2.x.name = 'x2'\\n iden3.x.name = 'x3'\\n iden2.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 10)\\n self.assertAlmostEqual(iden1.x.val, 4)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 6)\",\n \"def getsolsta(self,whichsol_):\\n solsta_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getsolsta(self.__nativep,whichsol_,ctypes.byref(solsta_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _solsta_return_value = solsta(solsta_.value)\\n return (_solsta_return_value)\",\n \"def solve(num_wizards, num_constraints, wizards, constraints): \\n global wiz_const\\n wiz_const = mapConstraints(wizards, constraints)\\n partial_soltns = []\\n\\n # counter for priority queue since it doesn't allow \\n # identical priorities\\n k = 0\\n\\n # list of wizards sorted by lowest to highest degree\\n sorted_wiz = sortWizByConsts(wiz_const)\\n wiz_rankings = {wiz: i for i, wiz in enumerate(sorted_wiz)}\\n\\n const_set = set(map(tuple, constraints))\\n for i in range(4) : \\n heapq.heappush(partial_soltns, (0, k, nx.DiGraph(), const_set.copy()))\\n k += 1\\n\\n print(\\\"setup done, commencing solving\\\")\\n\\n while len(partial_soltns) : \\n\\n # for partial_soltn, const_set in partial_soltns : \\n# partial_soltns.remove(partial_soltn)\\n num_seen, _, partial_soltn, const_set = heapq.heappop(partial_soltns)\\n const = findNextConst(partial_soltn, const_set, wiz_rankings)\\n print(\\\"seen \\\" + str(len(partial_soltn)) + \\\"\\\\t num partial_solutions\\\\t\\\" + str(len(partial_soltns)))\\n try : \\n const_set.remove(const)\\n except KeyError : \\n print(\\\"BAD SHIT\\\")\\n pass\\n possible_arrangements = [(const[0], const[1], const[2]),\\n (const[2], const[0], const[1]), \\n (const[2], const[1], const[0]),\\n (const[1], const[0], const[2])]\\n for arr in possible_arrangements:\\n soltn = partial_soltn.copy()\\n a, b, c = arr\\n if not (soltn.has_node(a) and soltn.has_node(b) and nx.has_path(soltn, a, b)) : \\n soltn.add_edge(a, b)\\n if not (soltn.has_node(b) and soltn.has_node(c) and nx.has_path(soltn, b, c)) : \\n soltn.add_edge(b, c)\\n # see if we violated any other constraints (seen or not seen)\\n is_valid, num_wiz = validNumWiz(soltn, const_set)\\n\\n if is_valid and len(list(nx.simple_cycles(soltn))) == 0 :\\n heapq.heappush(partial_soltns, (-len(soltn), k, soltn, const_set.copy()))\\n k += 1\\n # are we done?\\n if num_wiz == num_wizards :\\n print(\\\"FINAL SOLUTION (found without processing all constraints but validating against them)\\\")\\n ordering = list(nx.topological_sort(soltn))\\n finishEverything(ordering, constraints)\\n return ordering\\n if foundCompleteOrdering(heapq.heappop(partial_soltns)) : \\n print(\\\"FINAL SOLUTION\\\")\\n ordering = list(nx.topological_sort(soltn))\\n finishEverything(ordering, constraints)\\n return ordering\\n print(\\\"NO SOLUTION FOUND\\\")\\n return \\\"\\\"\",\n \"def solve(self):\\n print(\\\"Attempting to solve problem instance with {} constraints\\\".format(len(self.constraints)))\\n self.formulation.solve(solver='SCS')\\n print(self.formulation.status)\",\n \"def solve(self):\\n print(\\\"Attempting to solve problem instance with {} constraints\\\".format(len(self.constraints)))\\n self.formulation.solve(solver='SCS')\\n print(self.formulation.status)\",\n \"def solve(self):\\n print(\\\"Attempting to solve problem instance with {} constraints\\\".format(len(self.constraints)))\\n self.formulation.solve(solver='SCS')\\n print(self.formulation.status)\",\n \"def add_poss(self, sqs=None, gates=None):\\n if sqs:\\n self.orig_sqs.update(set(sqs))\\n if gates:\\n self.orig_gates.update(set(gates))\\n self.recalc()\\n self.reset_hints()\",\n \"def getinfeasiblesubproblem(self,whichsol_):\\n inftask_ = ctypes.c_void_p()\\n res = __library__.MSK_XX_getinfeasiblesubproblem(self.__nativep,whichsol_,ctypes.byref(inftask_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _inftask_return_value = Task(nativep = inftask_)\\n return (_inftask_return_value)\",\n \"def puty(self,whichsol_,y): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if y is None: raise TypeError(\\\"Invalid type for argument y\\\")\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n res = self.__obj.puty(whichsol_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def deletesolution(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.deletesolution(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def PuzzleSolved(tabl, ches, columns=0, possibleMoves=[], points=[]):\\n if columns == tabl:\\n possibleMoves.append(points[:])\\n return(possibleMoves)\\n for moves in range(tabl):\\n ches[columns][moves] = 1\\n if (solveQPuzzle(ches)):\\n score = [columns, moves]\\n points.append(score)\\n PuzzleSolved(tabl, ches, columns + 1, possibleMoves, points)\\n points.remove(score)\\n ches[columns][moves] = 0\\n return(possibleMoves)\",\n \"def solve(self):\\n new_puzzle = self._puzzle.clone()\\n self._solution = new_puzzle.solve_puzzle()\\n del new_puzzle\\n pass\",\n \"def solve_board(self):\\n\\n self.fill_board()\\n\\n if self.bts_solver():\\n for i in self.sudoku_board.keys():\\n self.file.write(str(self.sudoku_board[i]))\\n self.file.write(\\\" BTS\\\")\\n print(\\\"Solution Found!\\\")\",\n \"def solve(self, sid: str, operation: str, result: Any) -> bool:\\n\\n # Check if the operation solved is present in the queue\\n # and if it should be solved by the actual client.\\n op = [o for c, o in self.queue if o[1] == operation and c == sid]\\n\\n # If not, return False,\\n # to express an error.\\n if len(op) == 0:\\n return False\\n\\n # Extract the queue instance from the list of candidates.\\n # (it shouldn't contain more that one element...).\\n op = op[0]\\n\\n # Remove the instance from the queue.\\n self.queue = [q for q in self.queue if q != (sid, op)]\\n\\n # Finally insert the result record into the database.\\n # This table holds only the id of the unsolved operations,\\n # to join all together run the query located at `scripts/results.sql`.\\n run_sql(\\n \\\"INSERT INTO results (client, operation, result) VALUES (?, ?, ?)\\\",\\n (sid, op[0], result),\\n )\\n\\n # Return True to express the successfull\\n # marking of the operation as completed.\\n return True\",\n \"def putxc(self,whichsol_,xc_):\\n _xc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),self.getnumcon()))\\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\\n raise ValueError(\\\"Argument xc must be writable\\\")\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc may not be None\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _xc_copyarray:\\n xc_[:] = _xc_np_tmp\",\n \"def putxx(self,whichsol_,xx_):\\n _xx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),self.getnumvar()))\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx cannot be None\\\")\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx may not be None\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n res = __library__.MSK_XX_putxx(self.__nativep,whichsol_,_xx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getskx(self,whichsol_,skx_):\\n _skx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),self.getnumvar()))\\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\\n raise ValueError(\\\"Argument skx must be writable\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_getskx(self.__nativep,whichsol_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\",\n \"def putsnxslice(self,whichsol_,first_,last_,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if snx is None: raise TypeError(\\\"Invalid type for argument snx\\\")\\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n \\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n \\n if snx_ is not None and len(snx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument snx has wrong length\\\")\\n res = self.__obj.putsnxslice(whichsol_,first_,last_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_new_solved_problem(self, user_id, task_id, cost=1):\\n cur = self.connection.cursor()\\n\\n # At first get solved problems array\\n data = self.get_user_data(user_id)\\n score, solved_problems = data[\\\"score\\\"], data[\\\"solved_problems\\\"]\\n\\n if solved_problems == \\\"\\\": # If this user have never asked\\n solved_problems = repr([task_id])\\n else:\\n s_p_list = json.loads(solved_problems)\\n s_p_list.append(task_id)\\n solved_problems = repr(s_p_list)\\n\\n add_point_q = f\\\"\\\"\\\"\\n UPDATE users\\n SET score = {score + cost}, solved_problems = '{solved_problems}'\\n WHERE user_id = {user_id}\\n \\\"\\\"\\\"\\n cur.execute(add_point_q)\\n self.connection.commit()\\n log.info(f\\\"User [ID: {user_id}] add new solved problem: {task_id} with cost={cost}\\\")\\n return score + cost\",\n \"def getskn(self,whichsol_,skn_):\\n _skn_minlength = self.getnumcone()\\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\\n raise ValueError(\\\"Array argument skn is not long enough: Is %d, expected %d\\\" % (len(skn_),self.getnumcone()))\\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\\n raise ValueError(\\\"Argument skn must be writable\\\")\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\\n else:\\n _skn_tmp = None\\n res = __library__.MSK_XX_getskn(self.__nativep,whichsol_,_skn_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]\",\n \"def modify_hints_based_on_allsols(self):\\n #~ if self.hashid=='92580481c330e7057171d5f5b0f5ba84x003x006':\\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\\n if self.all_sols is None or self.all_sols=='timeout':\\n #should restore all hints!\\n #untested, or timeout so we know nothing\\n self.reset_hints()\\n return\\n if self.all_sols==[]:\\n #has no sol. so start/end must be here.\\n self.reset_hints()\\n return\\n hints={}\\n for sq in self.sqs:\\n hints[sq]=set()\\n for gsq in self.gatesqs:\\n hints[gsq]=set()\\n for s in self.all_sols:\\n for sg in s:\\n start,sindv,path,covered,end=sg\\n covered=('IN',)+(start,)+covered+('OUT',)\\n if start==end:\\n hints[start].add(('OUT','OUT'))\\n else:\\n for ii,sq in enumerate(covered):\\n if sq=='IN':\\n continue\\n if sq=='OUT':\\n continue\\n indv=makevec2(covered[ii-1],covered[ii])\\n outdv=makevec2(covered[ii],covered[ii+1])\\n hints[sq].add((indv,outdv))\\n\\n for sq in self.sqs:\\n #~ print self.hints(sq)\\n #~ print hints[sq]\\n #~ print self._hints[sq]\\n bad=self.intersect_hint(sq, hints[sq])\\n #~ if len(self.all_sols)==1:\\n #~ if len(self._hints[sq])!=1:\\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\\n if bad:return 1\\n for gsq in self.gatesqs:\\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\\n bad=self.intersect_enter_hint(gsq, hints[gsq])\\n if bad:return bad\\n #should return the actual bad sq - it may be better!\\n bad=self.intersect_exit_hint(gsq, hints[gsq])\\n if bad:return bad\\n #~ if len(hints[gsq])==1:\\n #~ if len(self._hints[gsq])!=1:\\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\",\n \"def getsuc(self,whichsol_,suc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if suc is None: raise TypeError(\\\"Invalid type for argument suc\\\")\\n _copyback_suc = False\\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n _copyback_suc = True\\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n _copyback_suc = True\\n if suc_ is not None and len(suc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument suc has wrong length\\\")\\n res = self.__obj.getsuc(whichsol_,suc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_suc:\\n suc[:] = _tmparr_suc\",\n \"def complie_soldity():\\n assert(os.path.exists(src_entry) and os.path.isfile(src_entry) )\\n\\n commands = [\\n [SOLC, \\\"--optimize\\\", \\\"--bin\\\", \\\"--overwrite\\\", \\\"-o\\\", os.path.relpath(bin_dir), os.path.relpath(src_entry) ]\\n , [SOLC, \\\"--optimize\\\", \\\"--ast\\\", \\\"--overwrite\\\", \\\"-o\\\", os.path.relpath(ast_dir), os.path.relpath(src_entry) ]\\n , [SOLC, \\\"--optimize\\\", \\\"--abi\\\", \\\"--overwrite\\\", \\\"-o\\\", os.path.relpath(dst_dir), os.path.relpath(src_entry) ]\\n ]\\n # commands = [\\n # [SOLC, \\\"--optimize\\\", \\\"--bin\\\", \\\"-o\\\", bin_dir, os.path.relpath(src_entry) ]\\n # , [SOLC, \\\"--optimize\\\", \\\"--ast\\\", \\\"-o\\\", ast_dir, os.path.relpath(src_entry) ]\\n # , [SOLC, \\\"--optimize\\\", \\\"--abi\\\", \\\"-o\\\", dst_dir, os.path.relpath(src_entry) ]\\n # ]\\n\\n print(\\\"======================Complie Solidity Language=========================\\\")\\n for cmd in commands:\\n command = \\\" \\\".join(cmd)\\n print(command)\\n os.system(command)\\n \\n os.system(\\\"cp %s/* %s\\\" %(bin_dir, dst_dir))\\n\\n # result = map(lambda cmd: os.system(\\\" \\\".join(cmd)), commands )\\n # print(result)\",\n \"def putslxslice(self,whichsol_,first_,last_,slx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slx is None: raise TypeError(\\\"Invalid type for argument slx\\\")\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if slx_ is not None and len(slx_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n res = self.__obj.putslxslice(whichsol_,first_,last_,slx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _get_solution(self, x_0, sol, k_fb, k_fb_perf_0, sol_verbose=False,\\n crashed=False, feas_tol=1e-6, q_0=None, k_fb_0=None):\\n\\n success = True\\n feasible = True\\n if crashed:\\n feasible = False\\n\\n if self.verbosity > 1:\\n print(\\\"Optimization crashed, infeasible soluion!\\\")\\n else:\\n g_res = np.array(sol[\\\"g\\\"]).squeeze()\\n\\n # This is not sufficient, since casadi gives out wrong feasibility values\\n if np.any(np.array(self.lbg) - feas_tol > g_res) or np.any(\\n np.array(self.ubg) + feas_tol < g_res):\\n feasible = False\\n\\n x_opt = sol[\\\"x\\\"]\\n self.has_openloop = True\\n\\n if self.opt_x0:\\n x_0 = x_opt[:self.n_s]\\n x_opt = x_opt[self.n_s:, :]\\n\\n # get indices of the respective variables\\n n_u_0 = self.n_u\\n n_u_perf = 0\\n if self.n_perf > 1:\\n n_u_perf = (self.n_perf - self.r) * self.n_u\\n n_k_ff = (self.n_safe - 1) * self.n_u\\n\\n c = 0\\n idx_u_0 = np.arange(n_u_0)\\n c += n_u_0\\n idx_u_perf = np.arange(c, c + n_u_perf)\\n c += n_u_perf\\n idx_k_ff = np.arange(c, c + n_k_ff)\\n c += n_k_ff\\n\\n u_apply = np.array(cas_reshape(x_opt[idx_u_0], (1, self.n_u)))\\n k_ff_perf = np.array(\\n cas_reshape(x_opt[idx_u_perf], (self.n_perf - self.r, self.n_u)))\\n\\n k_ff_safe = np.array(\\n cas_reshape(x_opt[idx_k_ff], (self.n_safe - 1, self.n_u)))\\n k_ff_safe_all = np.vstack((u_apply, k_ff_safe))\\n\\n k_fb_safe_output = array_of_vec_to_array_of_mat(np.copy(k_fb), self.n_u,\\n self.n_s)\\n\\n p_safe, q_safe, gp_sigma_pred_safe_all = self.get_safety_trajectory_openloop(x_0, u_apply,\\n np.copy(k_fb),\\n k_ff_safe, q_0, k_fb_0)\\n\\n p_safe = np.array(p_safe)\\n q_safe = np.array(q_safe)\\n\\n if self.verbosity > 1:\\n print(\\\"=== Safe Trajectory: ===\\\")\\n print(\\\"Centers:\\\")\\n print(p_safe)\\n print(\\\"Shape matrices:\\\")\\n print(q_safe)\\n print(\\\"Safety controls:\\\")\\n print(u_apply)\\n print(k_ff_safe)\\n\\n k_fb_perf_traj_eval = np.empty((0, self.n_s * self.n_u))\\n k_ff_perf_traj_eval = np.empty((0, self.n_u))\\n if self.n_safe > 1:\\n k_fb_perf_traj_eval = np.vstack(\\n (k_fb_perf_traj_eval, k_fb[:self.r - 1, :]))\\n k_ff_perf_traj_eval = np.vstack(\\n (k_ff_perf_traj_eval, k_ff_safe[:self.r - 1, :]))\\n if self.n_perf > self.r:\\n k_fb_perf_traj_eval = np.vstack((k_fb_perf_traj_eval,\\n np.matlib.repmat(k_fb_perf_0,\\n self.n_perf - self.r,\\n 1)))\\n k_ff_perf_traj_eval = np.vstack((k_ff_perf_traj_eval, k_ff_perf))\\n\\n if self.n_perf > 1:\\n mu_perf, sigma_perf = self._f_multistep_perf_eval(x_0.squeeze(),\\n u_apply,\\n k_fb_perf_traj_eval,\\n k_ff_perf_traj_eval)\\n\\n if self.verbosity > 1:\\n print(\\\"=== Performance Trajectory: ===\\\")\\n print(\\\"Mu perf:\\\")\\n print(mu_perf)\\n print(\\\"Peformance controls:\\\")\\n print(k_ff_perf_traj_eval)\\n\\n feasible, _ = self.eval_safety_constraints(p_safe, q_safe)\\n\\n if self.rhc and feasible:\\n self.k_ff_safe = k_ff_safe\\n self.k_ff_perf = k_ff_perf\\n self.p_safe = p_safe\\n self.k_fb_safe_all = np.copy(k_fb)\\n self.u_apply = u_apply\\n self.k_fb_perf_0 = k_fb_perf_0\\n\\n if feasible:\\n self.n_fail = 0\\n\\n if not feasible:\\n self.n_fail += 1\\n q_all = None\\n k_fb_safe_output = None\\n k_ff_all = None\\n p_safe = None\\n q_safe = None\\n g_res = None\\n\\n if self.n_fail >= self.n_safe:\\n # Too many infeasible solutions -> switch to safe controller\\n if self.verbosity > 1:\\n print(\\n \\\"Infeasible solution. Too many infeasible solutions, switching to safe controller\\\")\\n u_apply = self.safe_policy(x_0)\\n k_ff_safe_all = u_apply\\n else:\\n # can apply previous solution\\n if self.verbosity > 1:\\n print((\\n \\\"Infeasible solution. Switching to previous solution, n_fail = {}, n_safe = {}\\\".format(\\n self.n_fail, self.n_safe)))\\n if sol_verbose:\\n u_apply, k_fb_safe_output, k_ff_safe_all, p_safe = self.get_old_solution(\\n x_0, get_ctrl_traj=True)\\n else:\\n u_apply = self.get_old_solution(x_0)\\n k_ff_safe_all = u_apply\\n\\n if sol_verbose:\\n return x_0, u_apply, feasible, success, k_fb_safe_output, k_ff_safe_all, p_safe, q_safe, sol, gp_sigma_pred_safe_all\\n\\n return x_0, u_apply, success\",\n \"def lns_sa(self, sol_in, veh_type_in, cost_in):\\r\\n\\r\\n itr_cost = []\\r\\n solu = copy.deepcopy(sol_in)\\r\\n solu_type = copy.deepcopy(veh_type_in)\\r\\n best_solu = sol_in\\r\\n best_val = cost_in\\r\\n tabu_list = []\\r\\n random.seed(10)\\r\\n itr = 0\\r\\n temp = initial_temp\\r\\n t_run = time.time()\\r\\n while temp > stop_temp:\\r\\n itr += 1\\r\\n print(itr)\\r\\n if itr <= 0:\\r\\n sa_lns = True # use sa or lns\\r\\n else:\\r\\n sa_lns = False\\r\\n c = random.randint(1, store_num - 1) # randomly generated moving customer\\r\\n while c in tabu_list:\\r\\n c = random.randint(1, store_num - 1) # randint(a, b), both a and b are selectable\\r\\n c_loc = cust_loc(solu, c)\\r\\n\\r\\n if len(solu[c_loc[0]]) < 4: # customer number less than 2, can only implement shift1 and exchange1 operator\\r\\n wheel_value1 = random.uniform(0, 1)\\r\\n if wheel_value1 < 0.45:\\r\\n self.shift_1_cust(solu, c, c_loc, temp, solu_type, sa_lns)\\r\\n elif wheel_value1 < 0.9:\\r\\n self.exchange_1_cust(solu, c, c_loc, temp, solu_type, sa_lns)\\r\\n else:\\r\\n self.two_opt(solu, c, c_loc, temp, solu_type, sa_lns)\\r\\n\\r\\n # customer number more than 2, can implement all operators\\r\\n elif len(solu[c_loc[0]]) >= 4 and c_loc[1] <= len(solu[c_loc[0]]) - 3:\\r\\n wheel_value2 = random.uniform(0, 1)\\r\\n if wheel_value2 < 0.2:\\r\\n self.shift_1_cust(solu, c, c_loc, temp, solu_type, sa_lns)\\r\\n elif wheel_value2 < 0.4:\\r\\n self.shift_2_cust(solu, c, c_loc, temp, solu_type, sa_lns)\\r\\n elif wheel_value2 < 0.6:\\r\\n self.exchange_1_cust(solu, c, c_loc, temp, solu_type, sa_lns)\\r\\n elif wheel_value2 < 0.8:\\r\\n self.exchange_2_cust(solu, c, c_loc, temp, solu_type, sa_lns)\\r\\n else:\\r\\n self.two_opt(solu, c, c_loc, temp, solu_type, sa_lns)\\r\\n\\r\\n\\r\\n if itr % 100 == 0: # implement two-exchange operator every 200 iteration\\r\\n self.two_exchange_sol(solu, temp, solu_type, sa_lns)\\r\\n\\r\\n\\r\\n temp -= delta\\r\\n tabu_list.append(c)\\r\\n if len(tabu_list) > 100:\\r\\n tabu_list.pop(0)\\r\\n\\r\\n\\r\\n cost_i = of.print_result(solu, solu_type, False)\\r\\n # print(solu_type)\\r\\n itr_cost.append(cost_i)\\r\\n if cost_i < best_val:\\r\\n best_solu = solu\\r\\n best_val = cost_i\\r\\n\\r\\n\\r\\n t_run = time.time()\\r\\n\\r\\n # Adjust0: delete [0, 0] routes\\r\\n adjust_sol0 = []\\r\\n for route0 in best_solu:\\r\\n if len(route0) <= 2: # [0, 0] route\\r\\n continue\\r\\n else:\\r\\n adjust_sol0.append(route0)\\r\\n\\r\\n # Adjust1: use small vehicle if posiible\\r\\n adjust_type = []\\r\\n for route1 in adjust_sol0:\\r\\n adjust_type.append(route_type(route1))\\r\\n\\r\\n\\r\\n\\r\\n return adjust_sol0, adjust_type, best_val, itr_cost\",\n \"def saddle_point(I):\\n #--- FILL ME IN ---\\n\\n m, n = I.shape\\n\\n #compute the inputs to the function lstsq\\n\\n #get sci\\n sci = I.reshape(m*n, 1)\\n #get A\\n A = []\\n for y in range(n):\\n for x in range(m):\\n #print((x,y))\\n #print([x*x, x*y, y*y, x, y, 1])\\n A.append([x*x, x*y, y*y, x, y, 1])\\n\\n A = np.array(A)\\n \\n parms = np.linalg.lstsq(A,sci)[0]\\n #print(parms)\\n r1 = np.array([[2*parms[0][0], parms[1][0]], \\n [parms[1][0], 2*parms[2][0]]])\\n r1 = np.linalg.inv(r1)\\n r2 = np.array([[parms[3][0]], \\n [parms[4][0]]])\\n\\n pt = np.negative(np.matmul(r1, r2))\\n\\n #------------------\\n\\n return pt\",\n \"def add_unknown_strcuture(self, name, cospsi = None,\\n corr = None, cl = None):\\n if cospsi is not None:\\n # if cospsi and corr are both given, use them \\n if corr is None:\\n pass\\n else:\\n if len(cospsi.shape) == 1:\\n cospsi = np.array( [cospsi] * self.n_q )\\n try:\\n assert(corr.shape[0] == self.n_q)\\n assert(corr.shape[-1] == cospsi.shape[-1])\\n except AssertionError:\\n print(\\\"EEROR: corr and cospsi shape mismatch\\\")\\n return\\n\\n corr, cospsi = self._check_cospsi( corr, cospsi )\\n cl = self._compute_component_legendre_projection(corr, cospsi)\\n self.guess_cl.update({name: cl})\\n \\n elif cl is not None:\\n # if cl is given\\n try:\\n assert( cl.shape[0] == self.lmax+1)\\n assert( cl.shape[-1] == self.n_q)\\n except AssertionError:\\n print(\\\"ERORR: cl shape mismatch\\\")\\n\\n self.guess_cl.update( {name:cl} )\",\n \"def guess(x, y, k, s, w=None):\\n return splrep(x, y, w, k=k, s=s)\",\n \"def solute(self, puzzle):\\r\\n \\\"\\\"\\\"suppose that ax = c, where a is a matrix, c and x are vectors.\\\"\\\"\\\"\\r\\n \\\"\\\"\\\"The aim is to figure out x, which indicates the solution.\\\"\\\"\\\"\\r\\n A, a, c = [], [], []\\r\\n for i in range(puzzle.row):\\r\\n for j in range(puzzle.column):\\r\\n # create a puzzle.row * puzzle.column by puzzle.row * puzzle.column matrix.\\r\\n # each column represents a cell in the puzzle.\\r\\n # each row represents the changed cell if column c is selected.\\r\\n if puzzle.lights[i][j] == -1:\\r\\n c.append(1)\\r\\n else:\\r\\n c.append(0)\\r\\n for m in range(puzzle.row):\\r\\n for n in range(puzzle.column):\\r\\n if self.is_adjecent([m, n], [i, j]):\\r\\n # if [m, n] is adjecent to [i, j], then a[ij][mn] should be 1.\\r\\n a.append(1)\\r\\n else:\\r\\n a.append(0)\\r\\n a.append(c[i * puzzle.column + j])\\r\\n A.append(a)\\r\\n a = []\\r\\n\\r\\n self.eliminate(A)\\r\\n x = [item[len(item) - 1] for item in A]\\r\\n # x is the last column of A.\\r\\n # if x[i] is 1, cell i should be selected.\\r\\n i = 0\\r\\n for m in range(puzzle.row):\\r\\n for n in range(puzzle.column):\\r\\n if x[i] == 1:\\r\\n puzzle.selection.add((m, n))\\r\\n i += 1\\r\\n\\r\\n return puzzle.selection\",\n \"def getslc(self,whichsol_,slc_):\\n _slc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),self.getnumcon()))\\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\\n raise ValueError(\\\"Argument slc must be writable\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_getslc(self.__nativep,whichsol_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _slc_copyarray:\\n slc_[:] = _slc_np_tmp\",\n \"def solve_(self, x, y, board, path):\\n if self.SOLVED:\\n return\\n if self.is_done(board):\\n self.print_path(path)\\n self.SOLVED = True\\n return\\n for new_x, new_y in self.next_click(x, y, board):\\n if new_x is None or new_y is None:\\n return\\n new_board = self.click(new_x, new_y, board)\\n self.solve_(\\n x=0, y=0,\\n board=new_board,\\n path=path + [((new_x, new_y), new_board)]\\n )\",\n \"def add_sudoku_constraints(sg):\\n for y in range(6):\\n sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for x in range(6)]))\\n for x in range(6):\\n sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for y in range(6)]))\\n for z in range(6):\\n top = (z // 2) * 2\\n left = (z % 2) * 3\\n cells = [sg.grid[Point(y, x)] for y in range(top, top + 2) for x in range(left, left + 3)]\\n sg.solver.add(Distinct(*cells))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.83637935","0.7895491","0.7831769","0.7328144","0.7040539","0.7033978","0.6997567","0.698759","0.66940373","0.6660308","0.66146725","0.6513136","0.65105414","0.6361307","0.621909","0.62079835","0.6107939","0.6081402","0.59675616","0.582417","0.57879984","0.5774321","0.5663592","0.56333345","0.56253827","0.560339","0.5600451","0.5568607","0.5548993","0.55291","0.55207026","0.5495911","0.546431","0.54306513","0.54178387","0.5386085","0.53808343","0.53578967","0.53502685","0.53325295","0.5326968","0.5262451","0.52620053","0.525537","0.5231046","0.51792663","0.5178211","0.5162546","0.5149735","0.51491153","0.5114933","0.50895745","0.5087581","0.50849617","0.5081003","0.5079005","0.5077146","0.5059458","0.5050404","0.50272155","0.5023701","0.5023363","0.50197244","0.50014275","0.4996938","0.4996619","0.49891704","0.49806693","0.49687278","0.49671733","0.49616882","0.49616882","0.49616882","0.49506685","0.49383664","0.49241832","0.49210474","0.49182975","0.49073148","0.49060786","0.48982623","0.48772413","0.48691043","0.48669758","0.4866742","0.48598292","0.48264164","0.48238674","0.48110133","0.48081964","0.4797249","0.47902954","0.47806257","0.4776494","0.47763297","0.47604612","0.47599983","0.47592136","0.4754594","0.47525507"],"string":"[\n \"0.83637935\",\n \"0.7895491\",\n \"0.7831769\",\n \"0.7328144\",\n \"0.7040539\",\n \"0.7033978\",\n \"0.6997567\",\n \"0.698759\",\n \"0.66940373\",\n \"0.6660308\",\n \"0.66146725\",\n \"0.6513136\",\n \"0.65105414\",\n \"0.6361307\",\n \"0.621909\",\n \"0.62079835\",\n \"0.6107939\",\n \"0.6081402\",\n \"0.59675616\",\n \"0.582417\",\n \"0.57879984\",\n \"0.5774321\",\n \"0.5663592\",\n \"0.56333345\",\n \"0.56253827\",\n \"0.560339\",\n \"0.5600451\",\n \"0.5568607\",\n \"0.5548993\",\n \"0.55291\",\n \"0.55207026\",\n \"0.5495911\",\n \"0.546431\",\n \"0.54306513\",\n \"0.54178387\",\n \"0.5386085\",\n \"0.53808343\",\n \"0.53578967\",\n \"0.53502685\",\n \"0.53325295\",\n \"0.5326968\",\n \"0.5262451\",\n \"0.52620053\",\n \"0.525537\",\n \"0.5231046\",\n \"0.51792663\",\n \"0.5178211\",\n \"0.5162546\",\n \"0.5149735\",\n \"0.51491153\",\n \"0.5114933\",\n \"0.50895745\",\n \"0.5087581\",\n \"0.50849617\",\n \"0.5081003\",\n \"0.5079005\",\n \"0.5077146\",\n \"0.5059458\",\n \"0.5050404\",\n \"0.50272155\",\n \"0.5023701\",\n \"0.5023363\",\n \"0.50197244\",\n \"0.50014275\",\n \"0.4996938\",\n \"0.4996619\",\n \"0.49891704\",\n \"0.49806693\",\n \"0.49687278\",\n \"0.49671733\",\n \"0.49616882\",\n \"0.49616882\",\n \"0.49616882\",\n \"0.49506685\",\n \"0.49383664\",\n \"0.49241832\",\n \"0.49210474\",\n \"0.49182975\",\n \"0.49073148\",\n \"0.49060786\",\n \"0.48982623\",\n \"0.48772413\",\n \"0.48691043\",\n \"0.48669758\",\n \"0.4866742\",\n \"0.48598292\",\n \"0.48264164\",\n \"0.48238674\",\n \"0.48110133\",\n \"0.48081964\",\n \"0.4797249\",\n \"0.47902954\",\n \"0.47806257\",\n \"0.4776494\",\n \"0.47763297\",\n \"0.47604612\",\n \"0.47599983\",\n \"0.47592136\",\n \"0.4754594\",\n \"0.47525507\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7681796"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":11995,"cells":{"query":{"kind":"string","value":"Sets the primal and dual solution information for a single constraint. putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_)"},"document":{"kind":"string","value":"def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if not isinstance(sk_,stakey): raise TypeError(\"Argument sk has wrong type\")\n res = self.__obj.putsolutioni(accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n \n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n \n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n prosta_ = ctypes.c_int32()\n solsta_ = ctypes.c_int32()\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\n raise ValueError(\"Argument skc must be writable\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\n else:\n _skc_tmp = None\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\n raise ValueError(\"Argument skx must be writable\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\n else:\n _skx_tmp = None\n _skn_minlength = self.getnumcone()\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn is not long enough: Is %d, expected %d\" % (len(skn_),self.getnumcone()))\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\n raise ValueError(\"Argument skn must be writable\")\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\n else:\n _skn_tmp = None\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n _xx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),self.getnumvar()))\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\n raise ValueError(\"Argument xx must be writable\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\n raise ValueError(\"Argument y must be writable\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\n raise ValueError(\"Argument slc must be writable\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n _suc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),self.getnumcon()))\n if isinstance(suc_,numpy.ndarray) and not suc_.flags.writeable:\n raise ValueError(\"Argument suc must be writable\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n _slx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),self.getnumvar()))\n if isinstance(slx_,numpy.ndarray) and not slx_.flags.writeable:\n raise ValueError(\"Argument slx must be writable\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\n raise ValueError(\"Argument sux must be writable\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n _snx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx is not long enough: Is %d, expected %d\" % (len(snx_),self.getnumvar()))\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\n raise ValueError(\"Argument snx must be writable\")\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_getsolution(self.__nativep,whichsol_,ctypes.byref(prosta_),ctypes.byref(solsta_),_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value = prosta(prosta_.value)\n _solsta_return_value = solsta(solsta_.value)\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp\n if _xx_copyarray:\n xx_[:] = _xx_np_tmp\n if _y_copyarray:\n y_[:] = _y_np_tmp\n if _slc_copyarray:\n slc_[:] = _slc_np_tmp\n if _suc_copyarray:\n suc_[:] = _suc_np_tmp\n if _slx_copyarray:\n slx_[:] = _slx_np_tmp\n if _sux_copyarray:\n sux_[:] = _sux_np_tmp\n if _snx_copyarray:\n snx_[:] = _snx_np_tmp\n return (_prosta_return_value,_solsta_return_value)","def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\n else:\n _skn_tmp = None\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n _copyback_skn = False\n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n _copyback_skn = True\n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n _copyback_skn = True\n if skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn has wrong length\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n _copyback_xx = False\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n _copyback_xx = True\n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n _copyback_xx = True\n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n _copyback_suc = False\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n _copyback_suc = True\n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n _copyback_suc = True\n if suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc has wrong length\")\n _copyback_slx = False\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n _copyback_slx = True\n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n _copyback_slx = True\n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n _copyback_sux = False\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n _copyback_sux = True\n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n _copyback_sux = True\n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n _copyback_snx = False\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n _copyback_snx = True\n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n _copyback_snx = True\n if snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx has wrong length\")\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value,_solsta_return_value = resargs\n if _copyback_snx:\n snx[:] = _tmparr_snx\n if _copyback_sux:\n sux[:] = _tmparr_sux\n if _copyback_slx:\n slx[:] = _tmparr_slx\n if _copyback_suc:\n suc[:] = _tmparr_suc\n if _copyback_slc:\n slc[:] = _tmparr_slc\n if _copyback_y:\n y[:] = _tmparr_y\n if _copyback_xx:\n xx[:] = _tmparr_xx\n if _copyback_xc:\n xc[:] = _tmparr_xc\n if _copyback_skn:\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\n if _copyback_skx:\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\n _solsta_return_value = solsta(_solsta_return_value)\n _prosta_return_value = prosta(_prosta_return_value)\n return _prosta_return_value,_solsta_return_value","def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def updatesolutioninfo(self,whichsol_):\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskc(self,whichsol_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None: raise TypeError(\"Invalid type for argument skc\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskc(whichsol_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def updatesolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.updatesolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskc(self,whichsol_,skc_):\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if skc_ is None:\n raise ValueError(\"Argument skc cannot be None\")\n if skc_ is None:\n raise ValueError(\"Argument skc may not be None\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def sketch_of_solution(self,sol=None):\n raise NotImplementedError","def putslc(self,whichsol_,slc_):\n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslc(self.__nativep,whichsol_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskx(self,whichsol_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskx(whichsol_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putslc(self,whichsol_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slc is None: raise TypeError(\"Invalid type for argument slc\")\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.putslc(whichsol_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)","def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)","def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)","def getsolutioni(self,accmode_,i_,whichsol_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioni(accmode_,i_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value = resargs\n _sk_return_value = stakey(_sk_return_value)\n return _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value","def putskx(self,whichsol_,skx_):\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskx(self.__nativep,whichsol_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setup_solver(self):\n option = Options()\n if logger.getEffectiveLevel() == logging.DEBUG:\n # option.printLevel = PrintLevel.HIGH\n option.printLevel = PrintLevel.NONE\n else:\n option.printLevel = PrintLevel.NONE\n self.solver_minimizing = SQProblem(self.nV, self.nC)\n self.solver_minimizing.setOptions(option)\n self.solver_maximizing = SQProblem(self.nV, self.nC)\n self.solver_maximizing.setOptions(option)\n\n self.solver_minimizing_recent_index = -2\n self.solver_maximizing_recent_index = -2","def putslx(self,whichsol_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslx(whichsol_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsolutioninfo(self,whichsol_):\n pobj_ = ctypes.c_double()\n pviolcon_ = ctypes.c_double()\n pviolvar_ = ctypes.c_double()\n pviolbarvar_ = ctypes.c_double()\n pviolcone_ = ctypes.c_double()\n pviolitg_ = ctypes.c_double()\n dobj_ = ctypes.c_double()\n dviolcon_ = ctypes.c_double()\n dviolvar_ = ctypes.c_double()\n dviolbarvar_ = ctypes.c_double()\n dviolcone_ = ctypes.c_double()\n res = __library__.MSK_XX_getsolutioninfo(self.__nativep,whichsol_,ctypes.byref(pobj_),ctypes.byref(pviolcon_),ctypes.byref(pviolvar_),ctypes.byref(pviolbarvar_),ctypes.byref(pviolcone_),ctypes.byref(pviolitg_),ctypes.byref(dobj_),ctypes.byref(dviolcon_),ctypes.byref(dviolvar_),ctypes.byref(dviolbarvar_),ctypes.byref(dviolcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n pobj_ = pobj_.value\n _pobj_return_value = pobj_\n pviolcon_ = pviolcon_.value\n _pviolcon_return_value = pviolcon_\n pviolvar_ = pviolvar_.value\n _pviolvar_return_value = pviolvar_\n pviolbarvar_ = pviolbarvar_.value\n _pviolbarvar_return_value = pviolbarvar_\n pviolcone_ = pviolcone_.value\n _pviolcone_return_value = pviolcone_\n pviolitg_ = pviolitg_.value\n _pviolitg_return_value = pviolitg_\n dobj_ = dobj_.value\n _dobj_return_value = dobj_\n dviolcon_ = dviolcon_.value\n _dviolcon_return_value = dviolcon_\n dviolvar_ = dviolvar_.value\n _dviolvar_return_value = dviolvar_\n dviolbarvar_ = dviolbarvar_.value\n _dviolbarvar_return_value = dviolbarvar_\n dviolcone_ = dviolcone_.value\n _dviolcone_return_value = dviolcone_\n return (_pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value)","def getsolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value = resargs\n return _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value","def putslx(self,whichsol_,slx_):\n _slx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),self.getnumvar()))\n if slx_ is None:\n raise ValueError(\"Argument slx cannot be None\")\n if slx_ is None:\n raise ValueError(\"Argument slx may not be None\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n res = __library__.MSK_XX_putslx(self.__nativep,whichsol_,_slx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solutiondef(self,whichsol_):\n isdef_ = ctypes.c_int32()\n res = __library__.MSK_XX_solutiondef(self.__nativep,whichsol_,ctypes.byref(isdef_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n isdef_ = isdef_.value\n _isdef_return_value = isdef_\n return (_isdef_return_value)","def update_current_sol_and_cost(self,sol=None):\n\n # Update current sol if argument given\n if sol is not None:\n self.current_sol = sol\n \n # Update residual and cost\n try:\n self.residual = self.sketch_reweighted - self.sketch_of_solution(self.current_sol)\n self.current_sol_cost = np.linalg.norm(self.residual)\n except AttributeError: # We are here if self.current_sol does not exist yet\n self.current_sol, self.residual = None, self.sketch_reweighted\n self.current_sol_cost = np.inf","def notify_solution(self, sol):\n pass # pragma: no cover","def getskc(self,whichsol_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.getskc(whichsol_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])","def analyzesolution(self,whichstream_,whichsol_):\n res = __library__.MSK_XX_analyzesolution(self.__nativep,whichstream_,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getskc(self,whichsol_,skc_):\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\n raise ValueError(\"Argument skc must be writable\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_getskc(self.__nativep,whichsol_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]","def getinfeasiblesubproblem(self,whichsol_):\n inftask_ = ctypes.c_void_p()\n res = __library__.MSK_XX_getinfeasiblesubproblem(self.__nativep,whichsol_,ctypes.byref(inftask_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _inftask_return_value = Task(nativep = inftask_)\n return (_inftask_return_value)","def putsnx(self,whichsol_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsnx(whichsol_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_sudoku_constraints(sg):\n for y in range(6):\n sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for x in range(6)]))\n for x in range(6):\n sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for y in range(6)]))\n for z in range(6):\n top = (z // 2) * 2\n left = (z % 2) * 3\n cells = [sg.grid[Point(y, x)] for y in range(top, top + 2) for x in range(left, left + 3)]\n sg.solver.add(Distinct(*cells))","def writesolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.writesolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solve(self):\n # check for jacobian and set it if present and to be used\n if self.use_sparse:\n if self._use_jac and hasattr(self.problem,'sparse_jac'):\n jac = self.problem.sparse_jac\n else:\n jac = None\n else:\n if self._use_jac and hasattr(self.problem,'jac'):\n jac = self.problem.jac\n else:\n jac = None\n \n # Initialize solver and solve \n \n solved = False\n local_min = False\n\n res = N.zeros(self.x0.__len__())\n while (not solved) and self.reg_count < 2:\n try:\n if self._use_fscale:\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,self.fscale)\n else:\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,None)\n start = time.clock()\n res = self.solver.KINSOL_solve(not self._use_ls)\n stop = time.clock()\n self.exec_time += (stop - start)\n solved = True\n except KINError as error:\n if error.value == 42:\n # Try the heuristic\n if hasattr(self.problem, 'get_heuristic_x0'):\n print \"----------------------------------------------------\"\n print \" Solver stuck with zero step-length.\"\n print \"----------------------------------------------------\"\n print \"The following variables have start value zero\"\n print \"and min set to zero causing the zero step-lenght.\"\n print \"These settings are either set by default or by user.\"\n print \"\"\n\n self.x0 = self.problem.get_heuristic_x0()\n self.reg_count += 1\n \n print \"\"\n print \"This setting (start and min to zero) can often\"\n print \"cause problem when initializing the system. \"\n print \"\"\n print \"To avoid this the above variables have\"\n print \"their start attributes reset to one.\"\n print \"\"\n print \"Trying to solve the system again...\"\n else:\n raise KINSOL_Exception(\"Regularization failed due to constraints, tried getting heuristic initial guess but failed.\")\n \n\n elif (error.value == 2):\n print \"---------------------------------------------------------\"\n print \"\"\n print \" !!! WARNING !!!\"\n print \"\"\n print \" KINSOL has returned a result but the algorithm has converged\"\n print \" to a local minima, the initial values are NOT consistant!\"\n print \"\"\n print \"---------------------------------------------------------\"\n solved = True\n local_min = True\n else:\n # Other error, send onward as exception\n self.problem.check_constraints(res)\n raise KINSOL_Exception(error.msg[error.value])\n \n if not solved:\n self.solver.Free_KINSOL()\n raise KINSOL_Exception(\"Algorithm exited solution loop without finding a solution, please contact Assimulo support.\")\n\n if self.check_with_model:\n self.problem.check_constraints(res)\n if not local_min:\n print \"Problem sent to KINSOL solved.\"\n \n return res","def getslc(self,whichsol_,slc_):\n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\n raise ValueError(\"Argument slc must be writable\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_getslc(self.__nativep,whichsol_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _slc_copyarray:\n slc_[:] = _slc_np_tmp","def putsnx(self,whichsol_,sux_):\n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if sux_ is None:\n raise ValueError(\"Argument sux cannot be None\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_putsnx(self.__nativep,whichsol_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def modify_hints_based_on_allsols(self):\n #~ if self.hashid=='92580481c330e7057171d5f5b0f5ba84x003x006':\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\n if self.all_sols is None or self.all_sols=='timeout':\n #should restore all hints!\n #untested, or timeout so we know nothing\n self.reset_hints()\n return\n if self.all_sols==[]:\n #has no sol. so start/end must be here.\n self.reset_hints()\n return\n hints={}\n for sq in self.sqs:\n hints[sq]=set()\n for gsq in self.gatesqs:\n hints[gsq]=set()\n for s in self.all_sols:\n for sg in s:\n start,sindv,path,covered,end=sg\n covered=('IN',)+(start,)+covered+('OUT',)\n if start==end:\n hints[start].add(('OUT','OUT'))\n else:\n for ii,sq in enumerate(covered):\n if sq=='IN':\n continue\n if sq=='OUT':\n continue\n indv=makevec2(covered[ii-1],covered[ii])\n outdv=makevec2(covered[ii],covered[ii+1])\n hints[sq].add((indv,outdv))\n\n for sq in self.sqs:\n #~ print self.hints(sq)\n #~ print hints[sq]\n #~ print self._hints[sq]\n bad=self.intersect_hint(sq, hints[sq])\n #~ if len(self.all_sols)==1:\n #~ if len(self._hints[sq])!=1:\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\n if bad:return 1\n for gsq in self.gatesqs:\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\n bad=self.intersect_enter_hint(gsq, hints[gsq])\n if bad:return bad\n #should return the actual bad sq - it may be better!\n bad=self.intersect_exit_hint(gsq, hints[gsq])\n if bad:return bad\n #~ if len(hints[gsq])==1:\n #~ if len(self._hints[gsq])!=1:\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'","def writesolution(self,whichsol_,filename_):\n if isinstance(filename_,unicode):\n filename_ = filename_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_writesolution(self.__nativep,whichsol_,filename_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def notify_solution(self, s):\n pass # pragma: no cover","def pk_cs(self, snr=30, headroom = 0):\n # Initialize\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\n # loop over frequencies\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\n # print(self.pk.shape)\n for jf, k0 in enumerate(self.controls.k0):\n # get the scaled version of the propagating directions\n k_vec = k0 * self.dir\n # Form the sensing matrix\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\n H = h_mtx.astype(complex)\n # measured data\n pm = self.pres_s[:,jf].astype(complex)\n # Performing constrained optmization cvxpy\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True)\n # Create the problem\n epsilon = 10**(-(snr-headroom)/10)\n objective = cp.Minimize(cp.pnorm(x_cvx, p=1))\n constraints = [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon]#[H*x == pm]\n # Create the problem and solve\n problem = cp.Problem(objective, constraints)\n problem.solve(solver=cp.SCS, verbose=True) \n self.pk[:,jf] = x_cvx.value\n bar.update(1)\n bar.close()\n return self.pk","def solutiondef(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.solutiondef(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _isdef_return_value = resargs\n return _isdef_return_value","def pk_constrained(self, snr=30, headroom = 0):\n # Initialize\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\n # loop over frequencies\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\n for jf, k0 in enumerate(self.controls.k0):\n # get the scaled version of the propagating directions\n k_vec = k0 * self.dir\n # Form the sensing matrix\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\n H = h_mtx.astype(complex) # cvxpy does not accept floats, apparently\n # measured data\n pm = self.pres_s[:,jf].astype(complex)\n # Performing constrained optmization cvxpy\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True) # create x variable\n # Create the problem\n epsilon = 10**(-(snr-headroom)/10)\n problem = cp.Problem(cp.Minimize(cp.norm2(x_cvx)**2),\n [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon])\n problem.solve(solver=cp.SCS, verbose=False)\n self.pk[:,jf] = x_cvx.value\n bar.update(1)\n bar.close()","def notify_solution(self, sol):\n self._solutions.append(sol)","def getskx(self,whichsol_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.getskx(whichsol_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_skx:\n for __tmp_var_0 in range(len(skx_)): skx[__tmp_var_0] = stakey(_tmparr_skx[__tmp_var_0])","def deletesolution(self,whichsol_):\n res = __library__.MSK_XX_deletesolution(self.__nativep,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getslc(self,whichsol_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slc is None: raise TypeError(\"Invalid type for argument slc\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.getslc(whichsol_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_slc:\n slc[:] = _tmparr_slc","def solve(self, solver):\n solver.solve()","def _get_solution(self, x_0, sol, k_fb, k_fb_perf_0, sol_verbose=False,\n crashed=False, feas_tol=1e-6, q_0=None, k_fb_0=None):\n\n success = True\n feasible = True\n if crashed:\n feasible = False\n\n if self.verbosity > 1:\n print(\"Optimization crashed, infeasible soluion!\")\n else:\n g_res = np.array(sol[\"g\"]).squeeze()\n\n # This is not sufficient, since casadi gives out wrong feasibility values\n if np.any(np.array(self.lbg) - feas_tol > g_res) or np.any(\n np.array(self.ubg) + feas_tol < g_res):\n feasible = False\n\n x_opt = sol[\"x\"]\n self.has_openloop = True\n\n if self.opt_x0:\n x_0 = x_opt[:self.n_s]\n x_opt = x_opt[self.n_s:, :]\n\n # get indices of the respective variables\n n_u_0 = self.n_u\n n_u_perf = 0\n if self.n_perf > 1:\n n_u_perf = (self.n_perf - self.r) * self.n_u\n n_k_ff = (self.n_safe - 1) * self.n_u\n\n c = 0\n idx_u_0 = np.arange(n_u_0)\n c += n_u_0\n idx_u_perf = np.arange(c, c + n_u_perf)\n c += n_u_perf\n idx_k_ff = np.arange(c, c + n_k_ff)\n c += n_k_ff\n\n u_apply = np.array(cas_reshape(x_opt[idx_u_0], (1, self.n_u)))\n k_ff_perf = np.array(\n cas_reshape(x_opt[idx_u_perf], (self.n_perf - self.r, self.n_u)))\n\n k_ff_safe = np.array(\n cas_reshape(x_opt[idx_k_ff], (self.n_safe - 1, self.n_u)))\n k_ff_safe_all = np.vstack((u_apply, k_ff_safe))\n\n k_fb_safe_output = array_of_vec_to_array_of_mat(np.copy(k_fb), self.n_u,\n self.n_s)\n\n p_safe, q_safe, gp_sigma_pred_safe_all = self.get_safety_trajectory_openloop(x_0, u_apply,\n np.copy(k_fb),\n k_ff_safe, q_0, k_fb_0)\n\n p_safe = np.array(p_safe)\n q_safe = np.array(q_safe)\n\n if self.verbosity > 1:\n print(\"=== Safe Trajectory: ===\")\n print(\"Centers:\")\n print(p_safe)\n print(\"Shape matrices:\")\n print(q_safe)\n print(\"Safety controls:\")\n print(u_apply)\n print(k_ff_safe)\n\n k_fb_perf_traj_eval = np.empty((0, self.n_s * self.n_u))\n k_ff_perf_traj_eval = np.empty((0, self.n_u))\n if self.n_safe > 1:\n k_fb_perf_traj_eval = np.vstack(\n (k_fb_perf_traj_eval, k_fb[:self.r - 1, :]))\n k_ff_perf_traj_eval = np.vstack(\n (k_ff_perf_traj_eval, k_ff_safe[:self.r - 1, :]))\n if self.n_perf > self.r:\n k_fb_perf_traj_eval = np.vstack((k_fb_perf_traj_eval,\n np.matlib.repmat(k_fb_perf_0,\n self.n_perf - self.r,\n 1)))\n k_ff_perf_traj_eval = np.vstack((k_ff_perf_traj_eval, k_ff_perf))\n\n if self.n_perf > 1:\n mu_perf, sigma_perf = self._f_multistep_perf_eval(x_0.squeeze(),\n u_apply,\n k_fb_perf_traj_eval,\n k_ff_perf_traj_eval)\n\n if self.verbosity > 1:\n print(\"=== Performance Trajectory: ===\")\n print(\"Mu perf:\")\n print(mu_perf)\n print(\"Peformance controls:\")\n print(k_ff_perf_traj_eval)\n\n feasible, _ = self.eval_safety_constraints(p_safe, q_safe)\n\n if self.rhc and feasible:\n self.k_ff_safe = k_ff_safe\n self.k_ff_perf = k_ff_perf\n self.p_safe = p_safe\n self.k_fb_safe_all = np.copy(k_fb)\n self.u_apply = u_apply\n self.k_fb_perf_0 = k_fb_perf_0\n\n if feasible:\n self.n_fail = 0\n\n if not feasible:\n self.n_fail += 1\n q_all = None\n k_fb_safe_output = None\n k_ff_all = None\n p_safe = None\n q_safe = None\n g_res = None\n\n if self.n_fail >= self.n_safe:\n # Too many infeasible solutions -> switch to safe controller\n if self.verbosity > 1:\n print(\n \"Infeasible solution. Too many infeasible solutions, switching to safe controller\")\n u_apply = self.safe_policy(x_0)\n k_ff_safe_all = u_apply\n else:\n # can apply previous solution\n if self.verbosity > 1:\n print((\n \"Infeasible solution. Switching to previous solution, n_fail = {}, n_safe = {}\".format(\n self.n_fail, self.n_safe)))\n if sol_verbose:\n u_apply, k_fb_safe_output, k_ff_safe_all, p_safe = self.get_old_solution(\n x_0, get_ctrl_traj=True)\n else:\n u_apply = self.get_old_solution(x_0)\n k_ff_safe_all = u_apply\n\n if sol_verbose:\n return x_0, u_apply, feasible, success, k_fb_safe_output, k_ff_safe_all, p_safe, q_safe, sol, gp_sigma_pred_safe_all\n\n return x_0, u_apply, success","def getpviolcon(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolcon(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def set_coeffs(self, sol):\n # TODO: look for bugs here!\n self.log_debug(\"Set spline coefficients\")\n\n # task: find which of the free parameters (coeffs) belong to which spline object\n sol_bak = sol.copy()\n subs = dict()\n\n # iterate over the OrderedDict {'x1': [cx1_..., ...], 'u1': [cu1_...]}\n for k, v in list(self.indep_vars.items()):\n i = len(v)\n # TODO: improve comment\n subs[k] = sol[:i] # set numerical value to symbolical value\n sol = sol[i:] ##:: sol = []\n \n if self._parameters['use_chains']:\n for var in self.sys.states + self.sys.inputs:\n for ic in self._chains:\n if var in ic: ##:: ('x1','x2','u1') and ('x3','x4')\n subs[var] = subs[ic.upper] ##:: elements in the same chain have the same coefficients (number, not symbol).\n \n # set numerical coefficients for each spline and derivative\n # TODO: handle `!!`-comments after talking to yx \n ##!! spline_key_plus_k = self.splines.keys().append('k')\n for k in list(self.splines.keys()): ##:: ['x1','x3']\n self.splines[k].set_coefficients(free_coeffs=subs[k])\n ##:: self._indep_vars = free_coeffs (self.splines[k]._indep_coeffs=free_coeffs) makes symbols changing into numbers. {'x1': <Spline object>, 'x3': <Spline object>}, Spline._P[k] saves the polynomial.\n \n # yet another dictionary for solution and coeffs\n# ##!! indep_vars['z_par'] = np.array([sp.symbols('k')])\n# ##!! self.indep_vars = indep_vars\n\n coeffs_sol = OrderedDict()\n\n # used for indexing\n i = 0\n j = 0\n\n for k, v in list(self.indep_vars.items()):\n ##:: ['x1': array([0.12,0.13,...,]), 'x3':...] symbols change into numbers\n j += len(v)\n coeffs_sol[k] = sol_bak[i:j]\n i = j\n\n self.coeffs_sol = coeffs_sol\n ##:: {'x1': array([ 25.94485709, 16.38313857, -35.65010072, ..., 2.28427004, 2.82974712, 1.88490863]), 'x3': array([-34.33884269, 45.13959025, 1.3272378 , -4.15546318,# 5.3863866 , -5.39286006, -8.86559812, -6.11620983, -2.95630206])}\n\n ##!! return self.coeffs_sol['z_par'].tolist()","def getpviolcon(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getpviolcon(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol","def apply(self, problem):\n data, inv_data = super(CVXOPT, self).apply(problem)\n # Convert A, b, G, h, c to CVXOPT matrices.\n if data[s.A] is not None:\n data[s.A] = intf.sparse2cvxopt(data[s.A])\n if data[s.G] is not None:\n data[s.G] = intf.sparse2cvxopt(data[s.G])\n if data[s.B] is not None:\n data[s.B] = intf.dense2cvxopt(data[s.B])\n if data[s.H] is not None:\n data[s.H] = intf.dense2cvxopt(data[s.H])\n if data[s.C] is not None:\n data[s.C] = intf.dense2cvxopt(data[s.C])\n return data, inv_data","def sparse_options(default_solver='spsolve',\n default_least_squares_solver='least_squares_lsmr' if HAVE_SCIPY_LSMR else 'least_squares_generic_lsmr',\n bicgstab_tol=1e-15,\n bicgstab_maxiter=None,\n spilu_drop_tol=1e-4,\n spilu_fill_factor=10,\n spilu_drop_rule='basic,area',\n spilu_permc_spec='COLAMD',\n spsolve_permc_spec='COLAMD',\n spsolve_keep_factorization=True,\n lgmres_tol=1e-5,\n lgmres_maxiter=1000,\n lgmres_inner_m=39,\n lgmres_outer_k=3,\n least_squares_lsmr_damp=0.0,\n least_squares_lsmr_atol=1e-6,\n least_squares_lsmr_btol=1e-6,\n least_squares_lsmr_conlim=1e8,\n least_squares_lsmr_maxiter=None,\n least_squares_lsmr_show=False,\n least_squares_lsqr_damp=0.0,\n least_squares_lsqr_atol=1e-6,\n least_squares_lsqr_btol=1e-6,\n least_squares_lsqr_conlim=1e8,\n least_squares_lsqr_iter_lim=None,\n least_squares_lsqr_show=False,\n pyamg_tol=1e-5,\n pyamg_maxiter=400,\n pyamg_verb=False,\n pyamg_rs_strength=('classical', {'theta': 0.25}),\n pyamg_rs_CF='RS',\n pyamg_rs_presmoother=('gauss_seidel', {'sweep': 'symmetric'}),\n pyamg_rs_postsmoother=('gauss_seidel', {'sweep': 'symmetric'}),\n pyamg_rs_max_levels=10,\n pyamg_rs_max_coarse=500,\n pyamg_rs_coarse_solver='pinv2',\n pyamg_rs_cycle='V',\n pyamg_rs_accel=None,\n pyamg_rs_tol=1e-5,\n pyamg_rs_maxiter=100,\n pyamg_sa_symmetry='hermitian',\n pyamg_sa_strength='symmetric',\n pyamg_sa_aggregate='standard',\n pyamg_sa_smooth=('jacobi', {'omega': 4.0/3.0}),\n pyamg_sa_presmoother=('block_gauss_seidel', {'sweep': 'symmetric'}),\n pyamg_sa_postsmoother=('block_gauss_seidel', {'sweep': 'symmetric'}),\n pyamg_sa_improve_candidates=[('block_gauss_seidel', {'sweep': 'symmetric', 'iterations': 4}), None],\n pyamg_sa_max_levels=10,\n pyamg_sa_max_coarse=500,\n pyamg_sa_diagonal_dominance=False,\n pyamg_sa_coarse_solver='pinv2',\n pyamg_sa_cycle='V',\n pyamg_sa_accel=None,\n pyamg_sa_tol=1e-5,\n pyamg_sa_maxiter=100):\n\n assert default_least_squares_solver.startswith('least_squares')\n\n opts = (('bicgstab_spilu', {'type': 'bicgstab_spilu',\n 'tol': bicgstab_tol,\n 'maxiter': bicgstab_maxiter,\n 'spilu_drop_tol': spilu_drop_tol,\n 'spilu_fill_factor': spilu_fill_factor,\n 'spilu_drop_rule': spilu_drop_rule,\n 'spilu_permc_spec': spilu_permc_spec}),\n ('bicgstab', {'type': 'bicgstab',\n 'tol': bicgstab_tol,\n 'maxiter': bicgstab_maxiter}),\n ('spsolve', {'type': 'spsolve',\n 'permc_spec': spsolve_permc_spec,\n 'keep_factorization': spsolve_keep_factorization}),\n ('lgmres', {'type': 'lgmres',\n 'tol': lgmres_tol,\n 'maxiter': lgmres_maxiter,\n 'inner_m': lgmres_inner_m,\n 'outer_k': lgmres_outer_k}),\n ('least_squares_lsqr', {'type': 'least_squares_lsqr',\n 'damp': least_squares_lsqr_damp,\n 'atol': least_squares_lsqr_atol,\n 'btol': least_squares_lsqr_btol,\n 'conlim': least_squares_lsqr_conlim,\n 'iter_lim': least_squares_lsqr_iter_lim,\n 'show': least_squares_lsqr_show}))\n\n if HAVE_SCIPY_LSMR:\n opts += (('least_squares_lsmr', {'type': 'least_squares_lsmr',\n 'damp': least_squares_lsmr_damp,\n 'atol': least_squares_lsmr_atol,\n 'btol': least_squares_lsmr_btol,\n 'conlim': least_squares_lsmr_conlim,\n 'maxiter': least_squares_lsmr_maxiter,\n 'show': least_squares_lsmr_show}),)\n\n if HAVE_PYAMG:\n opts += (('pyamg', {'type': 'pyamg',\n 'tol': pyamg_tol,\n 'maxiter': pyamg_maxiter}),\n ('pyamg-rs', {'type': 'pyamg-rs',\n 'strength': pyamg_rs_strength,\n 'CF': pyamg_rs_CF,\n 'presmoother': pyamg_rs_presmoother,\n 'postsmoother': pyamg_rs_postsmoother,\n 'max_levels': pyamg_rs_max_levels,\n 'max_coarse': pyamg_rs_max_coarse,\n 'coarse_solver': pyamg_rs_coarse_solver,\n 'cycle': pyamg_rs_cycle,\n 'accel': pyamg_rs_accel,\n 'tol': pyamg_rs_tol,\n 'maxiter': pyamg_rs_maxiter}),\n ('pyamg-sa', {'type': 'pyamg-sa',\n 'symmetry': pyamg_sa_symmetry,\n 'strength': pyamg_sa_strength,\n 'aggregate': pyamg_sa_aggregate,\n 'smooth': pyamg_sa_smooth,\n 'presmoother': pyamg_sa_presmoother,\n 'postsmoother': pyamg_sa_postsmoother,\n 'improve_candidates': pyamg_sa_improve_candidates,\n 'max_levels': pyamg_sa_max_levels,\n 'max_coarse': pyamg_sa_max_coarse,\n 'diagonal_dominance': pyamg_sa_diagonal_dominance,\n 'coarse_solver': pyamg_sa_coarse_solver,\n 'cycle': pyamg_sa_cycle,\n 'accel': pyamg_sa_accel,\n 'tol': pyamg_sa_tol,\n 'maxiter': pyamg_sa_maxiter}))\n opts = OrderedDict(opts)\n opts.update(genericsolvers.options())\n def_opt = opts.pop(default_solver)\n if default_least_squares_solver != default_solver:\n def_ls_opt = opts.pop(default_least_squares_solver)\n ordered_opts = OrderedDict(((default_solver, def_opt),\n (default_least_squares_solver, def_ls_opt)))\n else:\n ordered_opts = OrderedDict(((default_solver, def_opt),))\n ordered_opts.update(opts)\n return ordered_opts","def test_solve_quadratic(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.fixed = False\n iden2.x.fixed = False\n iden3.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 0)\n self.assertAlmostEqual(iden1.x.val, 1)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 3)","def getdviolcon(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getdviolcon(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def update_solution(self, new_plan, use_cat=True, soc=True):\n if not new_plan.path:\n self.sol = TimeUncertaintySolution.empty_solution(self.sol.nodes_generated)\n self.sol.paths[new_plan.agent] = new_plan\n return\n if not use_cat:\n self.sol.paths[new_plan.agent] = new_plan\n self.sol.create_movement_tuples(agents=[new_plan.agent])\n return\n\n self.sol.paths[new_plan.agent] = new_plan\n new_moves = self.sol.add_stationary_moves()\n self.sol.create_movement_tuples(agents=[new_plan.agent])\n self.update_conflict_avoidance_table(new_plan.agent, new_moves)\n self.sol.compute_solution_cost(sum_of_costs=soc) # compute the cost","def cvstem0(self,xs,ps,alp):\n epsilon = self.epsilon\n Ncv = np.size(xs,0)\n n = self.n\n I = np.identity(n)\n Ws = []\n for k in range(Ncv):\n Ws.append(cp.Variable((n,n),PSD=True))\n nu = cp.Variable(nonneg=True)\n chi = cp.Variable(nonneg=True)\n errtxt = \"https://github.com/AstroHiro/ncm#troubleshooting\"\n if len(sig(self.Afun).parameters) == 1:\n fun1 = self.Afun\n self.Afun = lambda x,p: fun1(x)\n if (self.iEC == \"est\") and (len(sig(self.Cfun).parameters) == 1):\n fun2 = self.Cfun\n self.Cfun = lambda x,p: fun2(x)\n if self.iEC == \"est\":\n Af = self.Afun\n Cf = self.Cfun\n J = (self.d1_over*self.b_over*chi\\\n +self.d2_over*self.c_over*self.g_over*nu)/alp\n elif self.iEC == \"con\":\n Af = lambda x,p: self.Afun(x,p).T\n Cf = lambda x,p: self.h_or_g(x,p).T\n J = self.d1_over*self.b_over*chi/alp+self.d2_over*nu\n else:\n raise ValueError('Invalid iEC: iEC = \"est\" or \"con\"')\n constraints = []\n for k in range(Ncv):\n x = xs[k,:]\n p = ps[k,:]\n Ax = Af(x,p)\n Cx = Cf(x,p)\n W = Ws[k]\n constraints += [chi*I-W >> 0,W-I >> 0]\n constraints += [-2*alp*W-((W-I)/self.dt+W@Ax+Ax.T@W-2*nu*Cx.T@Cx)\\\n >> epsilon*I]\n prob = cp.Problem(cp.Minimize(J),constraints)\n prob.solve(solver=cp.MOSEK)\n cvx_status = prob.status\n if cvx_status in [\"infeasible\",\"infeasible_inaccurate\"]:\n raise ValueError(\"Problem infeasible: see \"+errtxt)\n elif cvx_status in [\"unbounded\",\"unbounded_inaccurate\"]:\n raise ValueError(\"Problem unbounded: \"+errtxt)\n Wsout = []\n for k in range(Ncv):\n Wk = Ws[k].value/nu.value\n Wsout.append(Wk)\n self.Ws = Wsout\n self.nu = nu.value\n self.chi = chi.value\n self.Jcv = prob.value\n self.cvx_status = cvx_status\n pass","def test_solve_quadratic_fixed(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.val = 4\n iden2.x.val = 5\n iden3.x.val = 6\n iden1.x.name = 'x1'\n iden2.x.name = 'x2'\n iden3.x.name = 'x3'\n iden2.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 10)\n self.assertAlmostEqual(iden1.x.val, 4)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 6)","def putxc(self,whichsol_,xc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xc is None: raise TypeError(\"Invalid type for argument xc\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n res = self.__obj.putxc(whichsol_,xc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_xc:\n xc[:] = _tmparr_xc","def setup(\n self,\n method: str = \"SLSQP\",\n tol: Union[None, float] = None,\n options: Union[None, Dict] = None,\n ):\n\n # Input check\n if self.opt_type in CONSTRAINED_OPT and (\n method not in ScipyMinimizeSolver.methods_handle_constraints\n ):\n raise TypeError(\n f\"optimization problem has constraints, the method '{method}' is not suitable\"\n )\n\n # Setup class attributes\n\n ## Container for the statistics.\n self._stats = None\n\n ## Method name.\n self.method = method\n\n # Setup minimize input parameters\n\n ## Input to the minimize method\n self.minimize_input = {\n \"fun\": self.f,\n \"method\": method,\n \"x0\": self.x0.toarray().flatten(),\n }\n\n if tol is not None:\n self.minimize_input[\"tol\"] = tol\n\n if options is not None:\n self.minimize_input[\"options\"] = options\n\n if method in ScipyMinimizeSolver.methods_req_jac:\n self.minimize_input[\"jac\"] = self.jac\n\n if method in ScipyMinimizeSolver.methods_req_hess:\n self.minimize_input[\"hess\"] = self.hess\n\n ## Constraints definition passed to the minimize method.\n self._constraints = {}\n if method in ScipyMinimizeSolver.methods_handle_constraints:\n if method != \"trust-constr\":\n if self.opt_type in CONSTRAINED_OPT:\n self._constraints[\"constr\"] = {\n \"type\": \"ineq\",\n \"fun\": self.v,\n \"jac\": self.dv,\n }\n else:\n if self.opt.nk:\n self._constraints[\"k\"] = LinearConstraint(\n A=csc_matrix(self.opt.M(self.p).toarray()),\n lb=-self.opt.c(self.p).toarray().flatten(),\n ub=self.opt.inf * np.ones(self.opt.nk),\n )\n\n if self.opt.na:\n eq = -self.opt.b(self.p).toarray().flatten()\n self._constraints[\"a\"] = LinearConstraint(\n A=csc_matrix(self.opt.A(self.p).toarray()),\n lb=eq,\n ub=eq,\n )\n\n if self.opt.ng:\n self._constraints[\"g\"] = NonlinearConstraint(\n fun=self.g,\n lb=np.zeros(self.opt.ng),\n ub=self.opt.inf * np.ones(self.opt.ng),\n jac=self.dg,\n hess=self.ddg,\n )\n\n if self.opt.nh:\n self._constraints[\"h\"] = NonlinearConstraint(\n fun=self.h,\n lb=np.zeros(self.opt.nh),\n ub=np.zeros(self.opt.nh),\n jac=self.dh,\n hess=self.ddh,\n )\n\n return self","def getsolsta(self,whichsol_):\n solsta_ = ctypes.c_int32()\n res = __library__.MSK_XX_getsolsta(self.__nativep,whichsol_,ctypes.byref(solsta_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _solsta_return_value = solsta(solsta_.value)\n return (_solsta_return_value)","def getdviolcon(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getdviolcon(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol","def putsux(self,whichsol_,sux_):\n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if sux_ is None:\n raise ValueError(\"Argument sux cannot be None\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_putsux(self.__nativep,whichsol_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solveSudoku(self, board) -> None:\n self.board = board\n self.backTrace(0,0)","def prepare_ss_condor_job(self, pool_type, pool_address, number_of_jobs, subtask_index=1, rank='0', extraArgs=''):\n #New: only prepares a single job which allows multiple jobs to be queued\n #We must change the ownership of each of the copasi files to the user running this script\n #\n #We assume that we have write privileges on each of the files through our group, but don't have permission to actually change ownership (must be superuser to do this)\n #Thus, we workaround this by copying the original file, deleting the original, and moving the copy back to the original filename\n \n# import shutil\n# for i in range(len(self.get_optimization_parameters())):\n# for max in (0, 1):\n# copasi_file = os.path.join(self.path, 'auto_copasi_%d.cps' % (2*i + max))\n# temp_file = os.path.join(self.path, 'temp.cps')\n# shutil.copy2(copasi_file, temp_file)\n# os.remove(copasi_file)\n# os.rename(temp_file, copasi_file)\n# os.chmod(copasi_file, 0664) #Set as group readable and writable\n \n ############\n #Build the appropriate .job files for the sensitivity optimization task, write them to disk, and make a note of their locations\n condor_jobs = []\n \n copasi_file = 'auto_copasi_%d.$(Process).cps' % subtask_index\n output_file = 'output_%d.$(Process).txt' % subtask_index\n \n \n \n if pool_type == 'ec2':\n binary_dir = '/usr/local/bin'\n transfer_executable = 'NO'\n else:\n binary_dir, binary = os.path.split(settings.COPASI_LOCAL_BINARY)\n transfer_executable = 'YES'\n \n \n condor_job_string = Template(condor_spec.raw_condor_job_string).substitute(copasiFile=copasi_file, \n otherFiles='',\n rank=rank,\n binary_dir = binary_dir,\n transfer_executable = transfer_executable,\n pool_type = pool_type,\n pool_address = pool_address,\n subtask=str(subtask_index),\n n = number_of_jobs,\n outputFile = output_file,\n extraArgs='',\n )\n \n condor_job_filename = 'auto_condor_%d.job'%subtask_index\n condor_job_full_filename = os.path.join(self.path, condor_job_filename)\n condor_file = open(condor_job_full_filename, 'w')\n condor_file.write(condor_job_string)\n condor_file.close()\n\n return condor_job_filename","def solve(self):\n\n # Set up display header if verbose operation enabled\n if self.opt['Verbose']:\n hdr = 'Itn DFidX PriResX DuaResX DFidG' + \\\n ' ResG '\n print(hdr)\n print('-' * len(hdr))\n\n # Main iteration loop\n for n in range(self.opt['MaxMainIter']):\n\n # At start of 2nd iteration, set the numbers of inner\n # iterations for the X and G solvers from the options\n # object for the outer solver\n if n == 1:\n self.slvX.opt['MaxMainIter'] = self.opt['XslvIter']\n self.slvG.opt['MaxMainIter'] = self.opt['GslvIter']\n\n # Run the configured number of iterations of the X (CSC)\n # solver and assign the result to X\n self.X = self.slvX.solve()\n\n # Compute the sum of the subpixel shifts of X\n Xhs = np.sum(fftconv(self.H, self.X.squeeze(), axes=(0, 1)),\n axis=-1)\n\n # Set the convolution kernel in the deconvolution solver\n # to the sum of the subpixel shifts of X\n self.slvG.setG(Xhs)\n # Run the configured number of iterations of the G\n # (deconvolution) solver and crop the result to obtain the\n # updated g\n self.g = self.slvG.solve()[0:self.gshp[0], 0:self.gshp[1]]\n\n # Construct a new dictionary for the X (CSC) solver from\n # the updated psf g\n self.D, self.dn = self.getD(self.g)\n self.slvX.setdict(self.D[..., np.newaxis, np.newaxis, :])\n\n # Display iteration statistics if verbose operation enabled\n if self.opt['Verbose']:\n itsX = self.slvX.getitstat()\n itsG = self.slvG.getitstat()\n fmt = '%3d %.3e %.3e %.3e %.3e %.3e'\n tpl = (n, itsX.DFid[-1], itsX.PrimalRsdl[-1],\n itsX.DualRsdl[-1], itsG.DFid[-1], itsG.Rsdl[-1])\n print(fmt % tpl)\n\n # Return the (normalised) psf estimate g\n return self.g / np.linalg.norm(self.g)","def add_solution(self, solution):\n if self.check_solution(solution):\n self._solution = solution\n self.solution_status = 'OK'\n else:\n self._solution = None\n self.solution_status = 'X'","def show_solution(self,show):\r\n self.showSolution = show","def setup(self, solver_name: str, solver_options: Dict = {}):\n # Setup problem\n x = self.opt.decision_variables.vec()\n p = self.opt.parameters.vec()\n\n problem = {\n \"x\": x,\n \"p\": p,\n \"f\": self.opt.f(x, p),\n }\n\n # Setup constraints\n\n ## Lower bound on constraints.\n self._lbg = None\n\n ## Upper bound on constraints\n self._ubg = None\n\n if self.opt_type in CONSTRAINED_OPT:\n problem[\"g\"] = self.opt.v(x, p)\n self._lbg = self.opt.lbv\n self._ubg = self.opt.ubv\n\n # Get solver interface\n if (solver_name in self.qp_solvers) and not self.opt.has_discrete_variables():\n sol = cs.qpsol\n elif (solver_name in self.nlp_solvers) or (solver_name in self.mi_solvers):\n sol = cs.nlpsol\n else:\n raise ValueError(\n f\"solver '{solver_name}' does not support this problem type\"\n )\n\n # Check for discrete variables\n if self.opt.has_discrete_variables():\n solver_options[\"discrete\"] = self.opt.decision_variables.discrete()\n\n # Initialize solver\n\n ## Instance of the CasADi solver.\n self._solver = sol(\"solver\", solver_name, problem, solver_options)\n\n return self","def optim_solve(\n self, x0: devices.PrimaryWeights = None, global_search: bool = False, **kwargs\n ) -> scipy.optimize.OptimizeResult:\n print(f'{\" optim_solve \":~^60s}')\n self._assert_problem_is_valid()\n if self._background is None:\n bounds = self.bounds * 2\n print(\"> No background specified, will optimise background.\")\n else:\n bounds = self.bounds\n\n if np.inf in self._target_contrast:\n print(\"> Aiming to maximise contrast.\")\n\n elif -np.inf in self._target_contrast:\n print(\"> Aiming to minimize contrast.\")\n\n constraints = [\n {\"type\": \"eq\", \"fun\": self.silencing_constraint, \"tol\": 1e-04}\n ]\n\n if x0 is None:\n x0 = self.initial_guess_x0()\n \n if not global_search: # Local minimization\n\n default_options = {\"iprint\": 2, \"disp\": True, \"ftol\": 1e-08}\n options = kwargs.pop(\"options\", default_options)\n\n print(\"> Performing local optimization with SLSQP.\")\n result = scipy.optimize.minimize(\n fun=self.objective_function,\n x0=x0,\n method=\"SLSQP\",\n bounds=bounds,\n constraints=constraints,\n options=options,\n **kwargs,\n )\n\n elif global_search: # Global minimization\n print(\n \"> Performing global optimization with basinhopping and SLSQP\"\n )\n\n # Configure global defaults\n disp = kwargs.pop(\"disp\", True)\n # Configure local defaults\n default_minimizer_kwargs = {\n \"method\": \"SLSQP\",\n \"constraints\": constraints,\n \"bounds\": bounds,\n \"options\": {\"iprint\": 2, \"disp\": False},\n }\n minimizer_kwargs = kwargs.pop(\n \"minimizer_kwargs\", default_minimizer_kwargs\n )\n\n # Do optimization\n result = scipy.optimize.basinhopping(\n func=self.objective_function,\n x0=x0,\n minimizer_kwargs=minimizer_kwargs,\n disp=disp,\n **kwargs,\n )\n\n return result","def getskx(self,whichsol_,skx_):\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\n raise ValueError(\"Argument skx must be writable\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_getskx(self.__nativep,whichsol_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]","def getskn(self,whichsol_,skn_):\n _skn_minlength = self.getnumcone()\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn is not long enough: Is %d, expected %d\" % (len(skn_),self.getnumcone()))\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\n raise ValueError(\"Argument skn must be writable\")\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\n else:\n _skn_tmp = None\n res = __library__.MSK_XX_getskn(self.__nativep,whichsol_,_skn_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]","def __init__(self, data_p, data_q, solver=\"SCS\"):\n self.data_P = data_p\n self.data_Q = data_q\n self.solver = solver\n self.warning_msg = None\n\n self.n = len(data_p)\n\n # Hold values *separate* from cvxpy variables as we sometimes need to revert\n # cvxpy optimizations which actually move values in a way that gives a *worse*\n # objective function.\n self.p = _np.array(self.data_P) / _np.sum(self.data_P)\n self.q = _np.array(self.data_Q) / _np.sum(self.data_Q)\n\n # cvxpy parameters\n self.T = _cp.Parameter(shape=(self.n, self.n), nonneg=True, value=_np.eye(self.n))\n self.alpha = _cp.Parameter(nonneg=True, value=1.0)\n\n self.max_logl = max_log_likelihood(data_p) + max_log_likelihood(data_q)\n self._build_problem()","def getsolsta(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolsta(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _solsta_return_value = resargs\n _solsta_return_value = solsta(_solsta_return_value)\n return _solsta_return_value","def create_sudoku_csp(sudoku):\n print sudoku\n constraints = []\n variables = []\n tupel_list = np.empty((9,9), dtype = csp.Variable)\n domain = range(1,10)#['1','2','3','4','5','6','7','8','9']\n for i,row in enumerate(sudoku):\n for j,field in enumerate(row):\n if field == 0:\n field = None\n \"\"\" do = domain\n else:\n do = [field]\"\"\"\n var = csp.Variable((i,j),domain)\n var.set_value(field)\n variables.append(var)\n tupel_list[i][j]=var \n \n for i in range(0,9):\n for j in range(0,9):\n for r in range(i+1,9):\n constraints.append(csp.UnequalConstraint(tupel_list[i][j],tupel_list[r][j]))\n for l in range(j+1,9):\n constraints.append(csp.UnequalConstraint(tupel_list[i][j],tupel_list[i][l]))\n for outer in range(0,3):\n for inner in range(0,3):\n if outer is not i % 3 and inner is not j % 3:\n constraints.append(csp.UnequalConstraint(tupel_list[i][j],tupel_list[3*(i/3)+outer][3*(j/3)+inner]))\n return csp.ConstrainedSatisfactionProblem(variables,constraints)","def update(self, solution):\n self.heuristic_path = [i for i in self.initial_path if i in solution]\n self.heuristic_cost = self.pathCost(self.heuristic_path)","def set_sparsity(self,use_sparse):\n \n if hasattr(self.problem,'sparse_jac'):\n self.use_sparse = use_sparse\n else:\n raise KINSOL_Exception(\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\")","def getpviolcones(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolcones(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def putsux(self,whichsol_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsux(whichsol_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def SolveSCP(self):\n\n t0 = time()\n\n # Some predicates\n Lu_min = 0.\n niters_max = self._maxiters\n maxfracchange = self._maxfracchange\n\n # initialization, resetting ...\n self.reset_all() # including _u_naught(), first application\n scp_min = self.greedy()\n\n # re-initialization iteration; col fixing ignored for the moment\n niters = 0\n f_change = _largenumber\n while (f_change>maxfracchange) and (niters<niters_max):\n # re-initialize u\n if (np.mod(niters, 2)==0): \n self.reset_u(random=True)\n else:\n self.reset_u()\n u_tmp, Lu_tmp = self.subgradient() # find a near-optimal solution \n u, Lu = self.subgradient() # rerun subgradient to get a set of Lagrangian multipliers\n\n scp_all = np.zeros(self._subg_nsteps)\n for i in np.arange(self._subg_nsteps):\n #self.reset_s()\n self.s = np.copy(self.f)\n scp_all[i] = self.greedy(u=u[:,i])\n\n # check if the solution is gettting better\n imin_tmp = (np.where(scp_all==np.amin(scp_all)))[0]\n imin = imin_tmp[np.argmax(Lu[imin_tmp])]\n imax = np.argmax(Lu)\n if (np.mod(niters, 5)==0):\n print(\"This Best solution: UB={0}, LB={1}, UB1={2}, LB1={3}\".format(scp_all[imin], Lu[imin], scp_all[imax], Lu[imax]))\n if (niters==0) or ((scp_all[imin]<=scp_min) and ((Lu[imin]-Lu_min)>-(np.fabs(Lu_min)*self._LB_maxfracchange))):\n scp_min = scp_all[imin]\n u_min = np.copy(u[:,imin])\n Lu_min = Lu[imin]\n self.stepsize = _stepsize\n\n LB = Lu_min\n\n # final step, needs to get u_min back\n self.u = np.copy(u_min)\n self.s = np.copy(self.f)\n UB = self.greedy()\n\n # Which is better? absolute change or fractional change? \n # Both are fine, but cost should be normalized over the mean/median.\n GAP = (UB-LB)/np.fabs(UB)\n f_change = GAP\n if (np.mod(niters, 5)==0):\n print(\"Current Best Solution: UB={0}, LB={1}, change={2}% @ niters={3}\".format(UB,LB,f_change*100.,niters))\n niters = niters + 1\n if (niters == niters_max): \n #warnings.warn(\"Iteration reaches maximum = {0}\".format(niters))\n print(\"Iteration in re-initialization reaches maximum number = {0}\".format(niters))\n\n # Need to remove redundant columns\n # self.remove_redundant() # this itself is NP-hard ...\n\n print(\"Current Best Solution: UB={0}, LB={1}, change={2}% @ niters={3}\".format(UB,LB,f_change*100.,niters))\n print(\"Final Best solution: {0}\".format(UB))\n time_used = (time()-t0)/60.\n print(\"Took {0:.3f} minutes to reach current solution.\".format(time_used))\n\n return (UB,time_used)","def set_solver(self, solver):\n self.solver = solver\n solver.associate_with_model(self)\n self.set_free_variable(0) # A reasonable initial assumption; can be overridden by simulations","def check_sol (statess, bigX, littleX, bigY, littleY):\n\t\tcheckstates = copy.deepcopy(statess)\n\t\tcheckstates[bigX] = littleX\n\t\tcheckstates[bigY] = littleY\n\t\treturnval = constraint_generator(checkstates).get((bigX,bigY), False)\n\t\treturn returnval","def optimize(self):\n\n self.logger.info(\"Solving with Dynamic Slope Scaling Procedure in Julia :\")\n optimization_start = time.time()\n\n # 1. Preprocess for old network graph\n if self.old_network_graph is not None:\n\n # DSSP on old network\n old_network_obj = sum(list(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).values()))-1e-5\n try:\n self.check_infeasibility(self.old_network_graph, old_network_obj)\n except DHCOptimizerException as e:\n e.data = \"Invalid existing network: \" + e.data\n raise e\n\n flows, obj_val = self.optimize_with_dssp_julia(self.old_network_graph, old_network_obj, set())\n self.logger.info(\"Optimization phase time: %.2fs\" % (time.time() - optimization_start))\n solution_old_graph = self.build_solution_graph(self.old_network_graph, flows)\n\n if self.modify_old_network:\n\n # Add max capacity on old edges\n self.old_capacity = deepcopy(flows)\n old_buildings = list(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).values())\n for key in flows:\n if (key[1],key[0],0) not in self.old_capacity and key[1] not in old_buildings:\n self.old_capacity[(key[1],key[0],0)] = self.old_capacity[key]\n\n # Add Imaginary edges\n for edge in self.old_capacity:\n if self.optimization_graph.has_edge(*edge):\n\n # add nodes\n if not self.optimization_graph.has_node(config.IM_PREFIX+edge[0]):\n self.optimization_graph.add_node(config.IM_PREFIX+edge[0])\n self.optimization_graph.nodes[config.IM_PREFIX+edge[0]][config.GPD_GEO_KEY] = \\\n self.optimization_graph.nodes[edge[0]][config.GPD_GEO_KEY]\n if not self.optimization_graph.has_node(config.IM_PREFIX+edge[1]):\n self.optimization_graph.add_node(config.IM_PREFIX+edge[1])\n self.optimization_graph.nodes[config.IM_PREFIX+edge[1]][config.GPD_GEO_KEY] = \\\n self.optimization_graph.nodes[edge[1]][config.GPD_GEO_KEY]\n # add edges\n if not self.optimization_graph.has_edge(edge[0],config.IM_PREFIX+edge[0]):\n self.optimization_graph.add_edge(edge[0],config.IM_PREFIX+edge[0])\n if not self.optimization_graph.has_edge(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1]):\n self.optimization_graph.add_edge(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1])\n if not self.optimization_graph.has_edge(config.IM_PREFIX+edge[1],edge[1]):\n self.optimization_graph.add_edge(config.IM_PREFIX+edge[1],edge[1])\n\n # put cost\n self.optimization_graph.edges[(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1],0)][config.EDGE_COST_KEY] = \\\n self.optimization_graph.edges[(edge[0],edge[1],0)][config.EDGE_COST_KEY]\n self.optimization_graph.edges[(edge[0],edge[1],0)][config.EDGE_COST_KEY] = 1e-5\n self.optimization_graph.edges[(edge[0],config.IM_PREFIX+edge[0],0)][config.EDGE_COST_KEY] = 1e-5\n self.optimization_graph.edges[(config.IM_PREFIX+edge[1],edge[1],0)][config.EDGE_COST_KEY] = 1e-5\n\n else:\n # if we don't modify the old network, we have to change the capacity of the supplies\n already_consummed = {}\n for edge in solution_old_graph.edges():\n if solution_old_graph.nodes[edge[0]].get(config.NODE_TYPE_KEY) == config.SUPPLY_NODE_TYPE:\n already_consummed[edge[0]] = already_consummed.get(edge[0], 0) + \\\n solution_old_graph.edges[edge][config.SOLUTION_POWER_FLOW_KEY]\n for source in already_consummed:\n if already_consummed[source] <= self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY]:\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] -= already_consummed[source]\n self.network_objective -= already_consummed[source]\n else:\n self.network_objective -= self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY]\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] = 0\n\n # Remove edges from old network\n edges_to_remove = set()\n for e in self.optimization_graph.edges():\n if self.old_network_graph.has_edge(*e) or self.old_network_graph.has_edge(e[1],e[0]):\n edges_to_remove.add(e)\n self.optimization_graph.remove_edges_from(edges_to_remove)\n\n # Remove isolated buildings of optimization graph\n isolated_to_remove = set()\n for e in self.old_network_graph.edges():\n if e[0] in self.old_network_graph.nodes() and \\\n self.optimization_graph.nodes[e[1]].get(config.NODE_TYPE_KEY) == config.BUILDING_NODE_TYPE:\n isolated_to_remove.add(e)\n self.optimization_graph.remove_edges_from(isolated_to_remove)\n\n # Remove buildings from old network\n for n, data in self.old_network_graph.nodes(data=True):\n if data.get(config.NODE_TYPE_KEY) == config.BUILDING_NODE_TYPE:\n self.optimization_graph.remove_node(n)\n\n # Re-link sources\n sources = set()\n for n, data in self.optimization_graph.nodes(data=True):\n if data.get(config.NODE_TYPE_KEY) == config.SUPPLY_NODE_TYPE:\n sources.add(n)\n source_graph = self.optimization_graph.subgraph(sources).copy()\n self.optimization_graph.remove_nodes_from(sources)\n gnx.remove_isolates(self.optimization_graph)\n node_filter = lambda n: self.optimization_graph.nodes.get(n,{}).get(config.NODE_TYPE_KEY) != config.BUILDING_NODE_TYPE\n gnx.spatial_points_merge(self.optimization_graph, source_graph.nodes_to_gdf(), node_filter=node_filter, inplace=True)\n\n # fill missing information\n gnx.fill_edges_missing_geometry_attributes(self.optimization_graph)\n gnx.fill_length_attribute(self.optimization_graph, config.EDGE_LENGTH_KEY, only_missing=True)\n gnx.fill_length_attribute(self.optimization_graph, config.EDGE_COST_KEY, only_missing=True)\n for e in self.optimization_graph.edges(keys=True):\n self.optimization_graph.edges[e][config.LEASTCOST_COEF_KEY] = \\\n self.optimization_graph.edges[e].get(config.LEASTCOST_COEF_KEY,0)\n\n\n\n # 2. Process the DSSP on optimization graph\n self.check_is_ready()\n self.check_infeasibility(self.optimization_graph, self.network_objective)\n\n if self.old_network_graph is not None and self.modify_old_network:\n old_buildings = set(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).keys())\n else:\n old_buildings = set()\n flows, obj_val = self.optimize_with_dssp_julia(self.optimization_graph, self.network_objective, old_buildings,postprocess= (not self.modify_old_network))\n self.logger.info(\"Optimization phase time: %.2fs\" % (time.time() - optimization_start))\n self.solution_graph = self.build_solution_graph(self.optimization_graph, flows, self.connected)\n\n # 3. Postprocess for old network graph\n if self.old_network_graph is not None:\n \n if self.modify_old_network:\n # Put the right supply capacity and cost\n for edge in self.old_capacity:\n if self.solution_graph.has_edge(edge[0],edge[1]):\n self.solution_graph.edges[(edge[0],edge[1])][config.EDGE_COST_KEY] = \\\n self.optimization_graph.edges[(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1],0)][config.EDGE_COST_KEY]\n \n # Remove imaginary edges\n imaginary_nodes_to_remove = set()\n nodes_to_relabel = {}\n for edge in self.solution_graph.edges():\n if str(edge[0]).startswith(config.IM_PREFIX) and str(edge[1]).startswith(config.IM_PREFIX):\n real_edge = edge[0][len(config.IM_PREFIX):],edge[1][len(config.IM_PREFIX):]\n self.old_capacity[(real_edge[0], real_edge[1], 0)] = pd.np.inf\n self.old_capacity[(real_edge[1], real_edge[0], 0)] = pd.np.inf\n if not self.solution_graph.has_edge(*real_edge):\n for i in range(2):\n nodes_to_relabel[edge[i]] = real_edge[i]\n else:\n self.solution_graph.edges[real_edge[0],real_edge[1]][config.SOLUTION_POWER_FLOW_KEY] += \\\n self.solution_graph.edges[edge].get(config.SOLUTION_POWER_FLOW_KEY,0)\n imaginary_nodes_to_remove.add(edge[0])\n imaginary_nodes_to_remove.add(edge[1])\n elif str(edge[0]).startswith(config.IM_PREFIX):\n imaginary_nodes_to_remove.add(edge[0])\n elif str(edge[1]).startswith(config.IM_PREFIX):\n imaginary_nodes_to_remove.add(edge[1])\n\n nx.relabel_nodes(self.solution_graph, nodes_to_relabel, copy=False)\n self.solution_graph.remove_nodes_from(list(imaginary_nodes_to_remove))\n for node in nodes_to_relabel.values():\n if self.solution_graph.has_edge(node, node):\n self.solution_graph.remove_edge(node, node)\n\n else:\n for source in nx.get_node_attributes(self.solution_graph, config.SUPPLY_POWER_CAPACITY_KEY):\n self.solution_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] += already_consummed.get(source,0)\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] += already_consummed.get(source,0)\n\n return flows, obj_val","def putxx(self,whichsol_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xx is None: raise TypeError(\"Invalid type for argument xx\")\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.putxx(whichsol_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def onesolutionsummary(self,whichstream_,whichsol_):\n res = __library__.MSK_XX_onesolutionsummary(self.__nativep,whichstream_,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def add_unknown_strcuture(self, name, cospsi = None,\n corr = None, cl = None):\n if cospsi is not None:\n # if cospsi and corr are both given, use them \n if corr is None:\n pass\n else:\n if len(cospsi.shape) == 1:\n cospsi = np.array( [cospsi] * self.n_q )\n try:\n assert(corr.shape[0] == self.n_q)\n assert(corr.shape[-1] == cospsi.shape[-1])\n except AssertionError:\n print(\"EEROR: corr and cospsi shape mismatch\")\n return\n\n corr, cospsi = self._check_cospsi( corr, cospsi )\n cl = self._compute_component_legendre_projection(corr, cospsi)\n self.guess_cl.update({name: cl})\n \n elif cl is not None:\n # if cl is given\n try:\n assert( cl.shape[0] == self.lmax+1)\n assert( cl.shape[-1] == self.n_q)\n except AssertionError:\n print(\"ERORR: cl shape mismatch\")\n\n self.guess_cl.update( {name:cl} )","def testSimpleTSVC(self):\n puzzle = PuzzleFactory.createSingleConstraintPuzzle(set([1,2]), 2, TotalSumValueConstraint)\n \n for cg in puzzle.getConstraintGroups():\n for constraint in cg.getConstraints():\n constraint.setTotalValue(3);\n constraint.applyConstraint()\n \n possibleValues = puzzle.grid.getCells()[1].getPossibleValues()\n self.assertTrue(1 in possibleValues, \"Incorrectly removed value from constraint\")\n self.assertTrue(2 in possibleValues, \"Incorrectly removed value from constraint\")\n \n puzzle.grid.getCells()[0].setValue(1)\n for cg in puzzle.getConstraintGroups():\n for constraint in cg.getConstraints():\n constraint.applyConstraint()\n possibleValues = puzzle.grid.getCells()[1].getPossibleValues()\n \n self.assertTrue(1 not in possibleValues, \"Incorrectly kept value from constraint\")\n self.assertTrue(2 in possibleValues, \"Incorrectly removed value from constraint\")","def setRepairCost(self):\n # first take into account the ship hull which is based on internal structure points\n ratio = 1.0 - (self.currentISP/self.myShipHull.maxISP)\n CR = int(self.myShipHull.costCR*ratio)\n AL = int(self.myShipHull.costAL*ratio)\n EC = int(self.myShipHull.costEC*ratio)\n IA = int(self.myShipHull.costIA*ratio)\n\n # compare to ship design, add costs of replacement\n for position, myQuad in self.quads.iteritems():\n designQuad = self.myDesign.quads[position]\n weaponsInQuad = []\n # look for missing components\n for componentID in designQuad.components.keys():\n if componentID not in myQuad.components:\n missingComponent = designQuad.components[componentID]\n if missingComponent.weaponID == '':\n # regular component\n CR += missingComponent.myComponentData.costCR\n AL += missingComponent.myComponentData.costAL\n EC += missingComponent.myComponentData.costEC\n IA += missingComponent.myComponentData.costIA\n elif missingComponent.weaponID not in weaponsInQuad:\n # component part of weapon, weapon must be replaced\n weaponsInQuad.append(missingComponent.weaponID)\n\n # go through weapons that were damaged in this quadrant\n for weaponID in weaponsInQuad:\n damagedWeapon = designQuad.weapons[weaponID]\n CR += damagedWeapon.myWeaponData.costCR\n AL += damagedWeapon.myWeaponData.costAL\n EC += damagedWeapon.myWeaponData.costEC\n IA += damagedWeapon.myWeaponData.costIA\n\n self.repairCost = [CR,AL,EC,IA]","def putxc(self,whichsol_,xc_):\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp","def getpviolcones(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getpviolcones(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol","def solve_CBC(self, lp):\n\t\tif not self.executable(self.path[1]):\n\t\t\traise \"PuLP: cannot execute \"+self.path[1]\n\t\tif not self.keepFiles:\n\t\t\tpid = os.getpid()\n\t\t\ttmpLp = os.path.join(self.tmpDir, \"%d-pulp.mps\" % pid)\n\t\t\ttmpSol = os.path.join(self.tmpDir, \"%d-pulp.sol\" % pid)\n\t\telse:\n\t\t\ttmpLp = lp.name+\"-pulp.mps\"\n\t\t\ttmpSol = lp.name+\"-pulp.sol\"\n##\t\tvs, variablesNames, constraintsNames, objectiveName = lp.writeMPS(tmpLp, rename = 1)\r\n\t\tvs = lp.writeMPS(tmpLp, rename = 0)\n\t\tif not self.msg:\n\t\t\tcbc = os.popen(self.path[1]+\" - > /dev/null 2> /dev/null\",\"w\")\n\t\telse:\n\t\t\tcbc = os.popen(self.path[1]+\" -\",\"w\")\n\t\tcbc.write(\"import \"+tmpLp+\"\\n\")\n\t\tif self.presolve:\n\t\t\tcbc.write(\"presolve on\\n\")\n\t\tcbc.write(\"strong %d\\n\" % self.strong)\n\t\tif self.cuts:\n\t\t\tcbc.write(\"gomory on\\n\")\n\t\t\tcbc.write(\"oddhole on\\n\")\n\t\t\tcbc.write(\"knapsack on\\n\")\n\t\t\tcbc.write(\"probing on\\n\")\n\t\tfor option in self.options:\n\t\t\tcbc.write(option+\"\\n\")\n\t\tif lp.sense == LpMinimize:\n\t\t\tcbc.write(\"min\\n\")\n\t\telse:\n\t\t\tcbc.write(\"max\\n\")\n\t\tif self.mip:\n\t\t\tcbc.write(\"branch\\n\")\n\t\telse:\n\t\t\tcbc.write(\"initialSolve\\n\")\n\t\tcbc.write(\"solution \"+tmpSol+\"\\n\")\n\t\tcbc.write(\"quit\\n\")\n\t\tif cbc.close() != None:\n\t\t\traise \"PuLP: Error while trying to execute \"+self.path[1]\n\t\tif not os.path.exists(tmpSol):\n\t\t\traise \"PuLP: Error while executing \"+self.path[1]\n\t\tlp.status, values = self.readsol_CBC(tmpSol, lp, vs)\n\t\tlp.assign(values)\n\t\tif not self.keepFiles:\n\t\t\ttry: os.remove(tmpLp)\n\t\t\texcept: pass\n\t\t\ttry: os.remove(tmpSol)\n\t\t\texcept: pass\n\t\treturn lp.status","def solute(self, puzzle):\r\n \"\"\"suppose that ax = c, where a is a matrix, c and x are vectors.\"\"\"\r\n \"\"\"The aim is to figure out x, which indicates the solution.\"\"\"\r\n A, a, c = [], [], []\r\n for i in range(puzzle.row):\r\n for j in range(puzzle.column):\r\n # create a puzzle.row * puzzle.column by puzzle.row * puzzle.column matrix.\r\n # each column represents a cell in the puzzle.\r\n # each row represents the changed cell if column c is selected.\r\n if puzzle.lights[i][j] == -1:\r\n c.append(1)\r\n else:\r\n c.append(0)\r\n for m in range(puzzle.row):\r\n for n in range(puzzle.column):\r\n if self.is_adjecent([m, n], [i, j]):\r\n # if [m, n] is adjecent to [i, j], then a[ij][mn] should be 1.\r\n a.append(1)\r\n else:\r\n a.append(0)\r\n a.append(c[i * puzzle.column + j])\r\n A.append(a)\r\n a = []\r\n\r\n self.eliminate(A)\r\n x = [item[len(item) - 1] for item in A]\r\n # x is the last column of A.\r\n # if x[i] is 1, cell i should be selected.\r\n i = 0\r\n for m in range(puzzle.row):\r\n for n in range(puzzle.column):\r\n if x[i] == 1:\r\n puzzle.selection.add((m, n))\r\n i += 1\r\n\r\n return puzzle.selection","def set_sparse_signals(self):\n\t\n\t\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\n\t\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\n\t\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\tparams_dSs,\tseed=self.seed_dSs)\n\t\t\n\t\t# Replace components with conflicting background odor \n\t\tif self.Kk_split is not None and self.Kk_split != 0:\n\t\t\tassert 0 <= self.Kk_split <= self.Kk, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires Kk_split\" \\\n\t\t\t\t\" to be non-negative and less than or equal to Kk.\"\n\t\t\tassert self.mu_dSs_2 is not None \\\n\t\t\t\tand self.sigma_dSs_2 is not None, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires that\" \\\n\t\t\t\t\" mu_dSs_2 and sigma_dSs_2 are set.\"\n\n\t\t\tsp.random.seed(self.seed_dSs)\n\t\t\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \n\t\t\t\t\t\t\t\t\t\t\treplace=False)\n\t\t\tfor idx_2 in self.idxs_2:\n\t\t\t\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \n\t\t\t\t\t\t\t\t\t\t\t\t\tself.sigma_dSs_2)\n\t\telse:\n\t\t\tself.idxs_2 = []\n\t\t\tself.Kk_split = 0\n\t\t\t\n\t\t# Ss0 is the ideal (learned) background stimulus without noise\n\t\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.idxs, params_Ss0,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tseed=self.seed_Ss0)\n\t\t\n\t\tself.Ss = self.dSs + self.Ss0_noisy","def fit(self):\n if self.cost_func.hessian:\n self._popt = ral_nlls.solve(self.initial_params,\n self.cost_func.eval_r,\n self.cost_func.jac_res,\n self.hes_eval,\n options=self._options,\n lower_bounds=self.param_ranges[0],\n upper_bounds=self.param_ranges[1])[0]\n else:\n self._popt = ral_nlls.solve(self.initial_params,\n self.cost_func.eval_r,\n self.cost_func.jac_res,\n options=self._options,\n lower_bounds=self.param_ranges[0],\n upper_bounds=self.param_ranges[1])[0]\n self._status = 0 if self._popt is not None else 1","def set_params(self, solver):\n params = yicespy.yices_new_param_record()\n yicespy.yices_default_params_for_context(solver.yices, params)\n for k,v in self.solver_options.items():\n rv = yicespy.yices_set_param(params, k, v)\n if rv != 0:\n raise PysmtValueError(\"Error setting the option '%s=%s'\" % (k,v))\n solver.yices_params = params","def putsucslice(self,whichsol_,first_,last_,suc_):\n _suc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and suc_ is not None and len(suc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),((last_) - (first_))))\n if suc_ is None:\n raise ValueError(\"Argument suc cannot be None\")\n if suc_ is None:\n raise ValueError(\"Argument suc may not be None\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n res = __library__.MSK_XX_putsucslice(self.__nativep,whichsol_,first_,last_,_suc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __init__(self, n_safe, ssm, opt_env, wx_cost, wu_cost, beta_safety=2.5,\n rhc=True,\n safe_policy=None, opt_perf_trajectory={}, lin_trafo_gp_input=None, opts_solver=None, verbosity=0):\n self.rhc = rhc\n self.ssm = ssm\n self.ssm_forward = ssm.get_forward_model_casadi(True)\n self.n_safe = n_safe\n self.n_fail = self.n_safe # initialize s.t. there is no backup strategy\n self.n_s = self.ssm.num_states\n self.n_u = self.ssm.num_actions\n self.has_openloop = False\n self.opts_solver = opts_solver\n\n self.safe_policy = safe_policy\n\n self.cost_func = None # This is updated wheenver the solver is newly initialized (possibly again with None)\n\n self._set_attributes_from_dict(ATTR_NAMES_ENV, DEFAULT_OPT_ENV, opt_env)\n\n self.lin_trafo_gp_input = lin_trafo_gp_input\n if self.lin_trafo_gp_input is None:\n self.lin_trafo_gp_input = np.eye(self.n_s)\n\n if self.h_mat_obs is None:\n m_obs_mat = 0\n else:\n m_obs_mat, n_s_obs = np.shape(self.h_mat_obs)\n assert n_s_obs == self.n_s, \" Wrong shape of obstacle matrix\"\n assert np.shape(self.h_obs) == (m_obs_mat,\n 1), \" Shapes of obstacle linear inequality matrix/vector must match \"\n self.m_obs = m_obs_mat\n\n m_safe_mat, n_s_safe = np.shape(self.h_mat_safe)\n assert n_s_safe == self.n_s, \" Wrong shape of safety matrix\"\n assert np.shape(self.h_safe) == (\n m_safe_mat,\n 1), \" Shapes of safety linear inequality matrix/vector must match \"\n self.m_safe = m_safe_mat\n\n # init safety constraints evaluator\n p_cas = MX.sym('p', (self.n_s, self.n_u))\n q_cas = MX.sym('q', (self.n_s, self.n_s))\n g_val_term_cas = lin_ellipsoid_safety_distance(p_cas, q_cas, self.h_mat_safe,\n self.h_safe)\n self.g_term_cas = cas.Function(\"g_term\", [p_cas, q_cas], [g_val_term_cas])\n\n if not self.h_mat_obs is None:\n g_val_interm_cas = lin_ellipsoid_safety_distance(p_cas, q_cas,\n self.h_mat_obs, self.h_obs)\n self.g_interm_cas = cas.Function(\"g_interm\", [p_cas, q_cas],\n [g_val_term_cas])\n\n self.has_ctrl_bounds = False\n\n if not self.ctrl_bounds is None:\n self.has_ctrl_bounds = True\n assert np.shape(self.ctrl_bounds) == (self.n_u, 2), \"\"\"control bounds need\n to be of shape n_u x 2 with i,0 lower bound and i,1 upper bound per dimension\"\"\"\n\n self.wx_cost = wx_cost\n self.wu_cost = wu_cost\n self.wx_feedback = wx_cost\n self.wu_feedback = 1 * wu_cost\n\n self.do_shift_solution = True\n self.solver_initialized = False\n\n self.beta_safety = beta_safety\n self.verbosity = verbosity\n\n # SET ALL ATTRIBUTES FOR THE ENVIRONMENT\n\n self.lin_prior = False\n self.a = np.eye(self.n_s)\n self.b = np.zeros((self.n_s, self.n_u))\n if not self.lin_model is None:\n self.a, self.b = self.lin_model\n self.lin_prior = True\n if self.safe_policy is None:\n # no safe policy specified? Use lqr as safe policy\n K = self.get_lqr_feedback()\n self.safe_policy = lambda x: np.dot(K, x)\n\n # if self.performance_trajectory is None:\n # self.performance_trajectory = mean_equivalent\n self._set_attributes_from_dict(ATTR_NAMES_PERF, DEFAULT_OPT_PERF,\n opt_perf_trajectory)\n self._set_perf_trajectory(self.type_perf_traj)\n\n self.k_fb_all = None\n if self.safe_policy is None:\n warnings.warn(\"No SafePolicy!\")\n\n # init safe"],"string":"[\n \"def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if not isinstance(sk_,stakey): raise TypeError(\\\"Argument sk has wrong type\\\")\\n res = self.__obj.putsolutioni(accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skn is None:\\n skn_ = None\\n else:\\n try:\\n skn_ = memoryview(skn)\\n except TypeError:\\n try:\\n _tmparr_skn = array.array(\\\"i\\\",skn)\\n except TypeError:\\n raise TypeError(\\\"Argument skn has wrong type\\\")\\n else:\\n skn_ = memoryview(_tmparr_skn)\\n \\n else:\\n if skn_.format != \\\"i\\\":\\n skn_ = memoryview(array.array(\\\"i\\\",skn))\\n \\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n \\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n \\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n \\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n \\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n \\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n \\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n \\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n \\n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\\n prosta_ = ctypes.c_int32()\\n solsta_ = ctypes.c_int32()\\n _skc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),self.getnumcon()))\\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\\n raise ValueError(\\\"Argument skc must be writable\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\\n else:\\n _skc_tmp = None\\n _skx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),self.getnumvar()))\\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\\n raise ValueError(\\\"Argument skx must be writable\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\\n else:\\n _skx_tmp = None\\n _skn_minlength = self.getnumcone()\\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\\n raise ValueError(\\\"Array argument skn is not long enough: Is %d, expected %d\\\" % (len(skn_),self.getnumcone()))\\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\\n raise ValueError(\\\"Argument skn must be writable\\\")\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\\n else:\\n _skn_tmp = None\\n _xc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),self.getnumcon()))\\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\\n raise ValueError(\\\"Argument xc must be writable\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n _xx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),self.getnumvar()))\\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\\n raise ValueError(\\\"Argument xx must be writable\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n _y_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),self.getnumcon()))\\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\\n raise ValueError(\\\"Argument y must be writable\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n _slc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),self.getnumcon()))\\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\\n raise ValueError(\\\"Argument slc must be writable\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n _suc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and suc_ is not None and len(suc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument suc is not long enough: Is %d, expected %d\\\" % (len(suc_),self.getnumcon()))\\n if isinstance(suc_,numpy.ndarray) and not suc_.flags.writeable:\\n raise ValueError(\\\"Argument suc must be writable\\\")\\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n _slx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx is not long enough: Is %d, expected %d\\\" % (len(slx_),self.getnumvar()))\\n if isinstance(slx_,numpy.ndarray) and not slx_.flags.writeable:\\n raise ValueError(\\\"Argument slx must be writable\\\")\\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n _sux_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),self.getnumvar()))\\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\\n raise ValueError(\\\"Argument sux must be writable\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n _snx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and snx_ is not None and len(snx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument snx is not long enough: Is %d, expected %d\\\" % (len(snx_),self.getnumvar()))\\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\\n raise ValueError(\\\"Argument snx must be writable\\\")\\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_getsolution(self.__nativep,whichsol_,ctypes.byref(prosta_),ctypes.byref(solsta_),_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _prosta_return_value = prosta(prosta_.value)\\n _solsta_return_value = solsta(solsta_.value)\\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]\\n if _xc_copyarray:\\n xc_[:] = _xc_np_tmp\\n if _xx_copyarray:\\n xx_[:] = _xx_np_tmp\\n if _y_copyarray:\\n y_[:] = _y_np_tmp\\n if _slc_copyarray:\\n slc_[:] = _slc_np_tmp\\n if _suc_copyarray:\\n suc_[:] = _suc_np_tmp\\n if _slx_copyarray:\\n slx_[:] = _slx_np_tmp\\n if _sux_copyarray:\\n sux_[:] = _sux_np_tmp\\n if _snx_copyarray:\\n snx_[:] = _snx_np_tmp\\n return (_prosta_return_value,_solsta_return_value)\",\n \"def putsolutionyi(self,i_,whichsol_,y_):\\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\\n else:\\n _skn_tmp = None\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skc = False\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n _copyback_skc = True\\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n _copyback_skc = True\\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n _copyback_skx = False\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n _copyback_skx = True\\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n _copyback_skx = True\\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n _copyback_skn = False\\n if skn is None:\\n skn_ = None\\n else:\\n try:\\n skn_ = memoryview(skn)\\n except TypeError:\\n try:\\n _tmparr_skn = array.array(\\\"i\\\",skn)\\n except TypeError:\\n raise TypeError(\\\"Argument skn has wrong type\\\")\\n else:\\n skn_ = memoryview(_tmparr_skn)\\n _copyback_skn = True\\n else:\\n if skn_.format != \\\"i\\\":\\n skn_ = memoryview(array.array(\\\"i\\\",skn))\\n _copyback_skn = True\\n if skn_ is not None and len(skn_) != self.getnumcone():\\n raise ValueError(\\\"Array argument skn has wrong length\\\")\\n _copyback_xc = False\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n _copyback_xc = True\\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n _copyback_xc = True\\n if xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n _copyback_xx = False\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n _copyback_xx = True\\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n _copyback_xx = True\\n if xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n _copyback_y = False\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n _copyback_y = True\\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n _copyback_y = True\\n if y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n _copyback_slc = False\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n _copyback_slc = True\\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n _copyback_slc = True\\n if slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n _copyback_suc = False\\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n _copyback_suc = True\\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n _copyback_suc = True\\n if suc_ is not None and len(suc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument suc has wrong length\\\")\\n _copyback_slx = False\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n _copyback_slx = True\\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n _copyback_slx = True\\n if slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n _copyback_sux = False\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n _copyback_sux = True\\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n _copyback_sux = True\\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n _copyback_snx = False\\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n _copyback_snx = True\\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n _copyback_snx = True\\n if snx_ is not None and len(snx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument snx has wrong length\\\")\\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _prosta_return_value,_solsta_return_value = resargs\\n if _copyback_snx:\\n snx[:] = _tmparr_snx\\n if _copyback_sux:\\n sux[:] = _tmparr_sux\\n if _copyback_slx:\\n slx[:] = _tmparr_slx\\n if _copyback_suc:\\n suc[:] = _tmparr_suc\\n if _copyback_slc:\\n slc[:] = _tmparr_slc\\n if _copyback_y:\\n y[:] = _tmparr_y\\n if _copyback_xx:\\n xx[:] = _tmparr_xx\\n if _copyback_xc:\\n xc[:] = _tmparr_xc\\n if _copyback_skn:\\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\\n if _copyback_skx:\\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\\n if _copyback_skc:\\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\\n _solsta_return_value = solsta(_solsta_return_value)\\n _prosta_return_value = prosta(_prosta_return_value)\\n return _prosta_return_value,_solsta_return_value\",\n \"def putsolutionyi(self,i_,whichsol_,y_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def updatesolutioninfo(self,whichsol_):\\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskc(self,whichsol_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None: raise TypeError(\\\"Invalid type for argument skc\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.putskc(whichsol_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def updatesolutioninfo(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.updatesolutioninfo(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskc(self,whichsol_,skc_):\\n _skc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),self.getnumcon()))\\n if skc_ is None:\\n raise ValueError(\\\"Argument skc cannot be None\\\")\\n if skc_ is None:\\n raise ValueError(\\\"Argument skc may not be None\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def sketch_of_solution(self,sol=None):\\n raise NotImplementedError\",\n \"def putslc(self,whichsol_,slc_):\\n _slc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),self.getnumcon()))\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc cannot be None\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_putslc(self.__nativep,whichsol_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskx(self,whichsol_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skx is None: raise TypeError(\\\"Invalid type for argument skx\\\")\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.putskx(whichsol_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putslc(self,whichsol_,slc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slc is None: raise TypeError(\\\"Invalid type for argument slc\\\")\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n \\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n \\n if slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n res = self.__obj.putslc(whichsol_,slc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solve(self):\\n print(\\\"Attempting to solve problem instance with {} constraints\\\".format(len(self.constraints)))\\n self.formulation.solve(solver='SCS')\\n print(self.formulation.status)\",\n \"def solve(self):\\n print(\\\"Attempting to solve problem instance with {} constraints\\\".format(len(self.constraints)))\\n self.formulation.solve(solver='SCS')\\n print(self.formulation.status)\",\n \"def solve(self):\\n print(\\\"Attempting to solve problem instance with {} constraints\\\".format(len(self.constraints)))\\n self.formulation.solve(solver='SCS')\\n print(self.formulation.status)\",\n \"def getsolutioni(self,accmode_,i_,whichsol_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getsolutioni(accmode_,i_,whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value = resargs\\n _sk_return_value = stakey(_sk_return_value)\\n return _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value\",\n \"def putskx(self,whichsol_,skx_):\\n _skx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),self.getnumvar()))\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx cannot be None\\\")\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx may not be None\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_putskx(self.__nativep,whichsol_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setup_solver(self):\\n option = Options()\\n if logger.getEffectiveLevel() == logging.DEBUG:\\n # option.printLevel = PrintLevel.HIGH\\n option.printLevel = PrintLevel.NONE\\n else:\\n option.printLevel = PrintLevel.NONE\\n self.solver_minimizing = SQProblem(self.nV, self.nC)\\n self.solver_minimizing.setOptions(option)\\n self.solver_maximizing = SQProblem(self.nV, self.nC)\\n self.solver_maximizing.setOptions(option)\\n\\n self.solver_minimizing_recent_index = -2\\n self.solver_maximizing_recent_index = -2\",\n \"def putslx(self,whichsol_,slx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slx is None: raise TypeError(\\\"Invalid type for argument slx\\\")\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n res = self.__obj.putslx(whichsol_,slx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsolutioninfo(self,whichsol_):\\n pobj_ = ctypes.c_double()\\n pviolcon_ = ctypes.c_double()\\n pviolvar_ = ctypes.c_double()\\n pviolbarvar_ = ctypes.c_double()\\n pviolcone_ = ctypes.c_double()\\n pviolitg_ = ctypes.c_double()\\n dobj_ = ctypes.c_double()\\n dviolcon_ = ctypes.c_double()\\n dviolvar_ = ctypes.c_double()\\n dviolbarvar_ = ctypes.c_double()\\n dviolcone_ = ctypes.c_double()\\n res = __library__.MSK_XX_getsolutioninfo(self.__nativep,whichsol_,ctypes.byref(pobj_),ctypes.byref(pviolcon_),ctypes.byref(pviolvar_),ctypes.byref(pviolbarvar_),ctypes.byref(pviolcone_),ctypes.byref(pviolitg_),ctypes.byref(dobj_),ctypes.byref(dviolcon_),ctypes.byref(dviolvar_),ctypes.byref(dviolbarvar_),ctypes.byref(dviolcone_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n pobj_ = pobj_.value\\n _pobj_return_value = pobj_\\n pviolcon_ = pviolcon_.value\\n _pviolcon_return_value = pviolcon_\\n pviolvar_ = pviolvar_.value\\n _pviolvar_return_value = pviolvar_\\n pviolbarvar_ = pviolbarvar_.value\\n _pviolbarvar_return_value = pviolbarvar_\\n pviolcone_ = pviolcone_.value\\n _pviolcone_return_value = pviolcone_\\n pviolitg_ = pviolitg_.value\\n _pviolitg_return_value = pviolitg_\\n dobj_ = dobj_.value\\n _dobj_return_value = dobj_\\n dviolcon_ = dviolcon_.value\\n _dviolcon_return_value = dviolcon_\\n dviolvar_ = dviolvar_.value\\n _dviolvar_return_value = dviolvar_\\n dviolbarvar_ = dviolbarvar_.value\\n _dviolbarvar_return_value = dviolbarvar_\\n dviolcone_ = dviolcone_.value\\n _dviolcone_return_value = dviolcone_\\n return (_pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value)\",\n \"def getsolutioninfo(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getsolutioninfo(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value = resargs\\n return _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value\",\n \"def putslx(self,whichsol_,slx_):\\n _slx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx is not long enough: Is %d, expected %d\\\" % (len(slx_),self.getnumvar()))\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx cannot be None\\\")\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx may not be None\\\")\\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n res = __library__.MSK_XX_putslx(self.__nativep,whichsol_,_slx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solutiondef(self,whichsol_):\\n isdef_ = ctypes.c_int32()\\n res = __library__.MSK_XX_solutiondef(self.__nativep,whichsol_,ctypes.byref(isdef_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n isdef_ = isdef_.value\\n _isdef_return_value = isdef_\\n return (_isdef_return_value)\",\n \"def update_current_sol_and_cost(self,sol=None):\\n\\n # Update current sol if argument given\\n if sol is not None:\\n self.current_sol = sol\\n \\n # Update residual and cost\\n try:\\n self.residual = self.sketch_reweighted - self.sketch_of_solution(self.current_sol)\\n self.current_sol_cost = np.linalg.norm(self.residual)\\n except AttributeError: # We are here if self.current_sol does not exist yet\\n self.current_sol, self.residual = None, self.sketch_reweighted\\n self.current_sol_cost = np.inf\",\n \"def notify_solution(self, sol):\\n pass # pragma: no cover\",\n \"def getskc(self,whichsol_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skc = False\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n _copyback_skc = True\\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n _copyback_skc = True\\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.getskc(whichsol_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_skc:\\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\",\n \"def analyzesolution(self,whichstream_,whichsol_):\\n res = __library__.MSK_XX_analyzesolution(self.__nativep,whichstream_,whichsol_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getskc(self,whichsol_,skc_):\\n _skc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),self.getnumcon()))\\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\\n raise ValueError(\\\"Argument skc must be writable\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\\n else:\\n _skc_tmp = None\\n res = __library__.MSK_XX_getskc(self.__nativep,whichsol_,_skc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\",\n \"def getinfeasiblesubproblem(self,whichsol_):\\n inftask_ = ctypes.c_void_p()\\n res = __library__.MSK_XX_getinfeasiblesubproblem(self.__nativep,whichsol_,ctypes.byref(inftask_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _inftask_return_value = Task(nativep = inftask_)\\n return (_inftask_return_value)\",\n \"def putsnx(self,whichsol_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if sux is None: raise TypeError(\\\"Invalid type for argument sux\\\")\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.putsnx(whichsol_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_sudoku_constraints(sg):\\n for y in range(6):\\n sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for x in range(6)]))\\n for x in range(6):\\n sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for y in range(6)]))\\n for z in range(6):\\n top = (z // 2) * 2\\n left = (z % 2) * 3\\n cells = [sg.grid[Point(y, x)] for y in range(top, top + 2) for x in range(left, left + 3)]\\n sg.solver.add(Distinct(*cells))\",\n \"def writesolution(self,whichsol_,filename_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.writesolution(whichsol_,filename_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solve(self):\\n # check for jacobian and set it if present and to be used\\n if self.use_sparse:\\n if self._use_jac and hasattr(self.problem,'sparse_jac'):\\n jac = self.problem.sparse_jac\\n else:\\n jac = None\\n else:\\n if self._use_jac and hasattr(self.problem,'jac'):\\n jac = self.problem.jac\\n else:\\n jac = None\\n \\n # Initialize solver and solve \\n \\n solved = False\\n local_min = False\\n\\n res = N.zeros(self.x0.__len__())\\n while (not solved) and self.reg_count < 2:\\n try:\\n if self._use_fscale:\\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,self.fscale)\\n else:\\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,None)\\n start = time.clock()\\n res = self.solver.KINSOL_solve(not self._use_ls)\\n stop = time.clock()\\n self.exec_time += (stop - start)\\n solved = True\\n except KINError as error:\\n if error.value == 42:\\n # Try the heuristic\\n if hasattr(self.problem, 'get_heuristic_x0'):\\n print \\\"----------------------------------------------------\\\"\\n print \\\" Solver stuck with zero step-length.\\\"\\n print \\\"----------------------------------------------------\\\"\\n print \\\"The following variables have start value zero\\\"\\n print \\\"and min set to zero causing the zero step-lenght.\\\"\\n print \\\"These settings are either set by default or by user.\\\"\\n print \\\"\\\"\\n\\n self.x0 = self.problem.get_heuristic_x0()\\n self.reg_count += 1\\n \\n print \\\"\\\"\\n print \\\"This setting (start and min to zero) can often\\\"\\n print \\\"cause problem when initializing the system. \\\"\\n print \\\"\\\"\\n print \\\"To avoid this the above variables have\\\"\\n print \\\"their start attributes reset to one.\\\"\\n print \\\"\\\"\\n print \\\"Trying to solve the system again...\\\"\\n else:\\n raise KINSOL_Exception(\\\"Regularization failed due to constraints, tried getting heuristic initial guess but failed.\\\")\\n \\n\\n elif (error.value == 2):\\n print \\\"---------------------------------------------------------\\\"\\n print \\\"\\\"\\n print \\\" !!! WARNING !!!\\\"\\n print \\\"\\\"\\n print \\\" KINSOL has returned a result but the algorithm has converged\\\"\\n print \\\" to a local minima, the initial values are NOT consistant!\\\"\\n print \\\"\\\"\\n print \\\"---------------------------------------------------------\\\"\\n solved = True\\n local_min = True\\n else:\\n # Other error, send onward as exception\\n self.problem.check_constraints(res)\\n raise KINSOL_Exception(error.msg[error.value])\\n \\n if not solved:\\n self.solver.Free_KINSOL()\\n raise KINSOL_Exception(\\\"Algorithm exited solution loop without finding a solution, please contact Assimulo support.\\\")\\n\\n if self.check_with_model:\\n self.problem.check_constraints(res)\\n if not local_min:\\n print \\\"Problem sent to KINSOL solved.\\\"\\n \\n return res\",\n \"def getslc(self,whichsol_,slc_):\\n _slc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),self.getnumcon()))\\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\\n raise ValueError(\\\"Argument slc must be writable\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_getslc(self.__nativep,whichsol_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _slc_copyarray:\\n slc_[:] = _slc_np_tmp\",\n \"def putsnx(self,whichsol_,sux_):\\n _sux_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),self.getnumvar()))\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux cannot be None\\\")\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux may not be None\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_putsnx(self.__nativep,whichsol_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def modify_hints_based_on_allsols(self):\\n #~ if self.hashid=='92580481c330e7057171d5f5b0f5ba84x003x006':\\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\\n if self.all_sols is None or self.all_sols=='timeout':\\n #should restore all hints!\\n #untested, or timeout so we know nothing\\n self.reset_hints()\\n return\\n if self.all_sols==[]:\\n #has no sol. so start/end must be here.\\n self.reset_hints()\\n return\\n hints={}\\n for sq in self.sqs:\\n hints[sq]=set()\\n for gsq in self.gatesqs:\\n hints[gsq]=set()\\n for s in self.all_sols:\\n for sg in s:\\n start,sindv,path,covered,end=sg\\n covered=('IN',)+(start,)+covered+('OUT',)\\n if start==end:\\n hints[start].add(('OUT','OUT'))\\n else:\\n for ii,sq in enumerate(covered):\\n if sq=='IN':\\n continue\\n if sq=='OUT':\\n continue\\n indv=makevec2(covered[ii-1],covered[ii])\\n outdv=makevec2(covered[ii],covered[ii+1])\\n hints[sq].add((indv,outdv))\\n\\n for sq in self.sqs:\\n #~ print self.hints(sq)\\n #~ print hints[sq]\\n #~ print self._hints[sq]\\n bad=self.intersect_hint(sq, hints[sq])\\n #~ if len(self.all_sols)==1:\\n #~ if len(self._hints[sq])!=1:\\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\\n if bad:return 1\\n for gsq in self.gatesqs:\\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\\n bad=self.intersect_enter_hint(gsq, hints[gsq])\\n if bad:return bad\\n #should return the actual bad sq - it may be better!\\n bad=self.intersect_exit_hint(gsq, hints[gsq])\\n if bad:return bad\\n #~ if len(hints[gsq])==1:\\n #~ if len(self._hints[gsq])!=1:\\n #~ import ipdb;ipdb.set_trace();print 'ipdb!'\",\n \"def writesolution(self,whichsol_,filename_):\\n if isinstance(filename_,unicode):\\n filename_ = filename_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_writesolution(self.__nativep,whichsol_,filename_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def notify_solution(self, s):\\n pass # pragma: no cover\",\n \"def pk_cs(self, snr=30, headroom = 0):\\n # Initialize\\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\\n # loop over frequencies\\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\\n # print(self.pk.shape)\\n for jf, k0 in enumerate(self.controls.k0):\\n # get the scaled version of the propagating directions\\n k_vec = k0 * self.dir\\n # Form the sensing matrix\\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\\n H = h_mtx.astype(complex)\\n # measured data\\n pm = self.pres_s[:,jf].astype(complex)\\n # Performing constrained optmization cvxpy\\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True)\\n # Create the problem\\n epsilon = 10**(-(snr-headroom)/10)\\n objective = cp.Minimize(cp.pnorm(x_cvx, p=1))\\n constraints = [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon]#[H*x == pm]\\n # Create the problem and solve\\n problem = cp.Problem(objective, constraints)\\n problem.solve(solver=cp.SCS, verbose=True) \\n self.pk[:,jf] = x_cvx.value\\n bar.update(1)\\n bar.close()\\n return self.pk\",\n \"def solutiondef(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.solutiondef(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _isdef_return_value = resargs\\n return _isdef_return_value\",\n \"def pk_constrained(self, snr=30, headroom = 0):\\n # Initialize\\n self.pk = np.zeros((self.n_waves, len(self.controls.k0)), dtype=np.csingle)\\n # loop over frequencies\\n bar = tqdm(total = len(self.controls.k0), desc = 'Calculating Constrained Optim.')\\n for jf, k0 in enumerate(self.controls.k0):\\n # get the scaled version of the propagating directions\\n k_vec = k0 * self.dir\\n # Form the sensing matrix\\n h_mtx = np.exp(1j*self.receivers.coord @ k_vec.T)\\n H = h_mtx.astype(complex) # cvxpy does not accept floats, apparently\\n # measured data\\n pm = self.pres_s[:,jf].astype(complex)\\n # Performing constrained optmization cvxpy\\n x_cvx = cp.Variable(h_mtx.shape[1], complex = True) # create x variable\\n # Create the problem\\n epsilon = 10**(-(snr-headroom)/10)\\n problem = cp.Problem(cp.Minimize(cp.norm2(x_cvx)**2),\\n [cp.pnorm(pm - cp.matmul(H, x_cvx), p=2) <= epsilon])\\n problem.solve(solver=cp.SCS, verbose=False)\\n self.pk[:,jf] = x_cvx.value\\n bar.update(1)\\n bar.close()\",\n \"def notify_solution(self, sol):\\n self._solutions.append(sol)\",\n \"def getskx(self,whichsol_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skx = False\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n _copyback_skx = True\\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n _copyback_skx = True\\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.getskx(whichsol_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_skx:\\n for __tmp_var_0 in range(len(skx_)): skx[__tmp_var_0] = stakey(_tmparr_skx[__tmp_var_0])\",\n \"def deletesolution(self,whichsol_):\\n res = __library__.MSK_XX_deletesolution(self.__nativep,whichsol_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getslc(self,whichsol_,slc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slc is None: raise TypeError(\\\"Invalid type for argument slc\\\")\\n _copyback_slc = False\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n _copyback_slc = True\\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n _copyback_slc = True\\n if slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n res = self.__obj.getslc(whichsol_,slc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_slc:\\n slc[:] = _tmparr_slc\",\n \"def solve(self, solver):\\n solver.solve()\",\n \"def _get_solution(self, x_0, sol, k_fb, k_fb_perf_0, sol_verbose=False,\\n crashed=False, feas_tol=1e-6, q_0=None, k_fb_0=None):\\n\\n success = True\\n feasible = True\\n if crashed:\\n feasible = False\\n\\n if self.verbosity > 1:\\n print(\\\"Optimization crashed, infeasible soluion!\\\")\\n else:\\n g_res = np.array(sol[\\\"g\\\"]).squeeze()\\n\\n # This is not sufficient, since casadi gives out wrong feasibility values\\n if np.any(np.array(self.lbg) - feas_tol > g_res) or np.any(\\n np.array(self.ubg) + feas_tol < g_res):\\n feasible = False\\n\\n x_opt = sol[\\\"x\\\"]\\n self.has_openloop = True\\n\\n if self.opt_x0:\\n x_0 = x_opt[:self.n_s]\\n x_opt = x_opt[self.n_s:, :]\\n\\n # get indices of the respective variables\\n n_u_0 = self.n_u\\n n_u_perf = 0\\n if self.n_perf > 1:\\n n_u_perf = (self.n_perf - self.r) * self.n_u\\n n_k_ff = (self.n_safe - 1) * self.n_u\\n\\n c = 0\\n idx_u_0 = np.arange(n_u_0)\\n c += n_u_0\\n idx_u_perf = np.arange(c, c + n_u_perf)\\n c += n_u_perf\\n idx_k_ff = np.arange(c, c + n_k_ff)\\n c += n_k_ff\\n\\n u_apply = np.array(cas_reshape(x_opt[idx_u_0], (1, self.n_u)))\\n k_ff_perf = np.array(\\n cas_reshape(x_opt[idx_u_perf], (self.n_perf - self.r, self.n_u)))\\n\\n k_ff_safe = np.array(\\n cas_reshape(x_opt[idx_k_ff], (self.n_safe - 1, self.n_u)))\\n k_ff_safe_all = np.vstack((u_apply, k_ff_safe))\\n\\n k_fb_safe_output = array_of_vec_to_array_of_mat(np.copy(k_fb), self.n_u,\\n self.n_s)\\n\\n p_safe, q_safe, gp_sigma_pred_safe_all = self.get_safety_trajectory_openloop(x_0, u_apply,\\n np.copy(k_fb),\\n k_ff_safe, q_0, k_fb_0)\\n\\n p_safe = np.array(p_safe)\\n q_safe = np.array(q_safe)\\n\\n if self.verbosity > 1:\\n print(\\\"=== Safe Trajectory: ===\\\")\\n print(\\\"Centers:\\\")\\n print(p_safe)\\n print(\\\"Shape matrices:\\\")\\n print(q_safe)\\n print(\\\"Safety controls:\\\")\\n print(u_apply)\\n print(k_ff_safe)\\n\\n k_fb_perf_traj_eval = np.empty((0, self.n_s * self.n_u))\\n k_ff_perf_traj_eval = np.empty((0, self.n_u))\\n if self.n_safe > 1:\\n k_fb_perf_traj_eval = np.vstack(\\n (k_fb_perf_traj_eval, k_fb[:self.r - 1, :]))\\n k_ff_perf_traj_eval = np.vstack(\\n (k_ff_perf_traj_eval, k_ff_safe[:self.r - 1, :]))\\n if self.n_perf > self.r:\\n k_fb_perf_traj_eval = np.vstack((k_fb_perf_traj_eval,\\n np.matlib.repmat(k_fb_perf_0,\\n self.n_perf - self.r,\\n 1)))\\n k_ff_perf_traj_eval = np.vstack((k_ff_perf_traj_eval, k_ff_perf))\\n\\n if self.n_perf > 1:\\n mu_perf, sigma_perf = self._f_multistep_perf_eval(x_0.squeeze(),\\n u_apply,\\n k_fb_perf_traj_eval,\\n k_ff_perf_traj_eval)\\n\\n if self.verbosity > 1:\\n print(\\\"=== Performance Trajectory: ===\\\")\\n print(\\\"Mu perf:\\\")\\n print(mu_perf)\\n print(\\\"Peformance controls:\\\")\\n print(k_ff_perf_traj_eval)\\n\\n feasible, _ = self.eval_safety_constraints(p_safe, q_safe)\\n\\n if self.rhc and feasible:\\n self.k_ff_safe = k_ff_safe\\n self.k_ff_perf = k_ff_perf\\n self.p_safe = p_safe\\n self.k_fb_safe_all = np.copy(k_fb)\\n self.u_apply = u_apply\\n self.k_fb_perf_0 = k_fb_perf_0\\n\\n if feasible:\\n self.n_fail = 0\\n\\n if not feasible:\\n self.n_fail += 1\\n q_all = None\\n k_fb_safe_output = None\\n k_ff_all = None\\n p_safe = None\\n q_safe = None\\n g_res = None\\n\\n if self.n_fail >= self.n_safe:\\n # Too many infeasible solutions -> switch to safe controller\\n if self.verbosity > 1:\\n print(\\n \\\"Infeasible solution. Too many infeasible solutions, switching to safe controller\\\")\\n u_apply = self.safe_policy(x_0)\\n k_ff_safe_all = u_apply\\n else:\\n # can apply previous solution\\n if self.verbosity > 1:\\n print((\\n \\\"Infeasible solution. Switching to previous solution, n_fail = {}, n_safe = {}\\\".format(\\n self.n_fail, self.n_safe)))\\n if sol_verbose:\\n u_apply, k_fb_safe_output, k_ff_safe_all, p_safe = self.get_old_solution(\\n x_0, get_ctrl_traj=True)\\n else:\\n u_apply = self.get_old_solution(x_0)\\n k_ff_safe_all = u_apply\\n\\n if sol_verbose:\\n return x_0, u_apply, feasible, success, k_fb_safe_output, k_ff_safe_all, p_safe, q_safe, sol, gp_sigma_pred_safe_all\\n\\n return x_0, u_apply, success\",\n \"def getpviolcon(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getpviolcon(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def set_coeffs(self, sol):\\n # TODO: look for bugs here!\\n self.log_debug(\\\"Set spline coefficients\\\")\\n\\n # task: find which of the free parameters (coeffs) belong to which spline object\\n sol_bak = sol.copy()\\n subs = dict()\\n\\n # iterate over the OrderedDict {'x1': [cx1_..., ...], 'u1': [cu1_...]}\\n for k, v in list(self.indep_vars.items()):\\n i = len(v)\\n # TODO: improve comment\\n subs[k] = sol[:i] # set numerical value to symbolical value\\n sol = sol[i:] ##:: sol = []\\n \\n if self._parameters['use_chains']:\\n for var in self.sys.states + self.sys.inputs:\\n for ic in self._chains:\\n if var in ic: ##:: ('x1','x2','u1') and ('x3','x4')\\n subs[var] = subs[ic.upper] ##:: elements in the same chain have the same coefficients (number, not symbol).\\n \\n # set numerical coefficients for each spline and derivative\\n # TODO: handle `!!`-comments after talking to yx \\n ##!! spline_key_plus_k = self.splines.keys().append('k')\\n for k in list(self.splines.keys()): ##:: ['x1','x3']\\n self.splines[k].set_coefficients(free_coeffs=subs[k])\\n ##:: self._indep_vars = free_coeffs (self.splines[k]._indep_coeffs=free_coeffs) makes symbols changing into numbers. {'x1': <Spline object>, 'x3': <Spline object>}, Spline._P[k] saves the polynomial.\\n \\n # yet another dictionary for solution and coeffs\\n# ##!! indep_vars['z_par'] = np.array([sp.symbols('k')])\\n# ##!! self.indep_vars = indep_vars\\n\\n coeffs_sol = OrderedDict()\\n\\n # used for indexing\\n i = 0\\n j = 0\\n\\n for k, v in list(self.indep_vars.items()):\\n ##:: ['x1': array([0.12,0.13,...,]), 'x3':...] symbols change into numbers\\n j += len(v)\\n coeffs_sol[k] = sol_bak[i:j]\\n i = j\\n\\n self.coeffs_sol = coeffs_sol\\n ##:: {'x1': array([ 25.94485709, 16.38313857, -35.65010072, ..., 2.28427004, 2.82974712, 1.88490863]), 'x3': array([-34.33884269, 45.13959025, 1.3272378 , -4.15546318,# 5.3863866 , -5.39286006, -8.86559812, -6.11620983, -2.95630206])}\\n\\n ##!! return self.coeffs_sol['z_par'].tolist()\",\n \"def getpviolcon(self,whichsol_,sub,viol): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if viol is None: raise TypeError(\\\"Invalid type for argument viol\\\")\\n _copyback_viol = False\\n if viol is None:\\n viol_ = None\\n else:\\n try:\\n viol_ = memoryview(viol)\\n except TypeError:\\n try:\\n _tmparr_viol = array.array(\\\"d\\\",viol)\\n except TypeError:\\n raise TypeError(\\\"Argument viol has wrong type\\\")\\n else:\\n viol_ = memoryview(_tmparr_viol)\\n _copyback_viol = True\\n else:\\n if viol_.format != \\\"d\\\":\\n viol_ = memoryview(array.array(\\\"d\\\",viol))\\n _copyback_viol = True\\n if viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol has wrong length\\\")\\n res = self.__obj.getpviolcon(whichsol_,num_,sub_,viol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_viol:\\n viol[:] = _tmparr_viol\",\n \"def apply(self, problem):\\n data, inv_data = super(CVXOPT, self).apply(problem)\\n # Convert A, b, G, h, c to CVXOPT matrices.\\n if data[s.A] is not None:\\n data[s.A] = intf.sparse2cvxopt(data[s.A])\\n if data[s.G] is not None:\\n data[s.G] = intf.sparse2cvxopt(data[s.G])\\n if data[s.B] is not None:\\n data[s.B] = intf.dense2cvxopt(data[s.B])\\n if data[s.H] is not None:\\n data[s.H] = intf.dense2cvxopt(data[s.H])\\n if data[s.C] is not None:\\n data[s.C] = intf.dense2cvxopt(data[s.C])\\n return data, inv_data\",\n \"def sparse_options(default_solver='spsolve',\\n default_least_squares_solver='least_squares_lsmr' if HAVE_SCIPY_LSMR else 'least_squares_generic_lsmr',\\n bicgstab_tol=1e-15,\\n bicgstab_maxiter=None,\\n spilu_drop_tol=1e-4,\\n spilu_fill_factor=10,\\n spilu_drop_rule='basic,area',\\n spilu_permc_spec='COLAMD',\\n spsolve_permc_spec='COLAMD',\\n spsolve_keep_factorization=True,\\n lgmres_tol=1e-5,\\n lgmres_maxiter=1000,\\n lgmres_inner_m=39,\\n lgmres_outer_k=3,\\n least_squares_lsmr_damp=0.0,\\n least_squares_lsmr_atol=1e-6,\\n least_squares_lsmr_btol=1e-6,\\n least_squares_lsmr_conlim=1e8,\\n least_squares_lsmr_maxiter=None,\\n least_squares_lsmr_show=False,\\n least_squares_lsqr_damp=0.0,\\n least_squares_lsqr_atol=1e-6,\\n least_squares_lsqr_btol=1e-6,\\n least_squares_lsqr_conlim=1e8,\\n least_squares_lsqr_iter_lim=None,\\n least_squares_lsqr_show=False,\\n pyamg_tol=1e-5,\\n pyamg_maxiter=400,\\n pyamg_verb=False,\\n pyamg_rs_strength=('classical', {'theta': 0.25}),\\n pyamg_rs_CF='RS',\\n pyamg_rs_presmoother=('gauss_seidel', {'sweep': 'symmetric'}),\\n pyamg_rs_postsmoother=('gauss_seidel', {'sweep': 'symmetric'}),\\n pyamg_rs_max_levels=10,\\n pyamg_rs_max_coarse=500,\\n pyamg_rs_coarse_solver='pinv2',\\n pyamg_rs_cycle='V',\\n pyamg_rs_accel=None,\\n pyamg_rs_tol=1e-5,\\n pyamg_rs_maxiter=100,\\n pyamg_sa_symmetry='hermitian',\\n pyamg_sa_strength='symmetric',\\n pyamg_sa_aggregate='standard',\\n pyamg_sa_smooth=('jacobi', {'omega': 4.0/3.0}),\\n pyamg_sa_presmoother=('block_gauss_seidel', {'sweep': 'symmetric'}),\\n pyamg_sa_postsmoother=('block_gauss_seidel', {'sweep': 'symmetric'}),\\n pyamg_sa_improve_candidates=[('block_gauss_seidel', {'sweep': 'symmetric', 'iterations': 4}), None],\\n pyamg_sa_max_levels=10,\\n pyamg_sa_max_coarse=500,\\n pyamg_sa_diagonal_dominance=False,\\n pyamg_sa_coarse_solver='pinv2',\\n pyamg_sa_cycle='V',\\n pyamg_sa_accel=None,\\n pyamg_sa_tol=1e-5,\\n pyamg_sa_maxiter=100):\\n\\n assert default_least_squares_solver.startswith('least_squares')\\n\\n opts = (('bicgstab_spilu', {'type': 'bicgstab_spilu',\\n 'tol': bicgstab_tol,\\n 'maxiter': bicgstab_maxiter,\\n 'spilu_drop_tol': spilu_drop_tol,\\n 'spilu_fill_factor': spilu_fill_factor,\\n 'spilu_drop_rule': spilu_drop_rule,\\n 'spilu_permc_spec': spilu_permc_spec}),\\n ('bicgstab', {'type': 'bicgstab',\\n 'tol': bicgstab_tol,\\n 'maxiter': bicgstab_maxiter}),\\n ('spsolve', {'type': 'spsolve',\\n 'permc_spec': spsolve_permc_spec,\\n 'keep_factorization': spsolve_keep_factorization}),\\n ('lgmres', {'type': 'lgmres',\\n 'tol': lgmres_tol,\\n 'maxiter': lgmres_maxiter,\\n 'inner_m': lgmres_inner_m,\\n 'outer_k': lgmres_outer_k}),\\n ('least_squares_lsqr', {'type': 'least_squares_lsqr',\\n 'damp': least_squares_lsqr_damp,\\n 'atol': least_squares_lsqr_atol,\\n 'btol': least_squares_lsqr_btol,\\n 'conlim': least_squares_lsqr_conlim,\\n 'iter_lim': least_squares_lsqr_iter_lim,\\n 'show': least_squares_lsqr_show}))\\n\\n if HAVE_SCIPY_LSMR:\\n opts += (('least_squares_lsmr', {'type': 'least_squares_lsmr',\\n 'damp': least_squares_lsmr_damp,\\n 'atol': least_squares_lsmr_atol,\\n 'btol': least_squares_lsmr_btol,\\n 'conlim': least_squares_lsmr_conlim,\\n 'maxiter': least_squares_lsmr_maxiter,\\n 'show': least_squares_lsmr_show}),)\\n\\n if HAVE_PYAMG:\\n opts += (('pyamg', {'type': 'pyamg',\\n 'tol': pyamg_tol,\\n 'maxiter': pyamg_maxiter}),\\n ('pyamg-rs', {'type': 'pyamg-rs',\\n 'strength': pyamg_rs_strength,\\n 'CF': pyamg_rs_CF,\\n 'presmoother': pyamg_rs_presmoother,\\n 'postsmoother': pyamg_rs_postsmoother,\\n 'max_levels': pyamg_rs_max_levels,\\n 'max_coarse': pyamg_rs_max_coarse,\\n 'coarse_solver': pyamg_rs_coarse_solver,\\n 'cycle': pyamg_rs_cycle,\\n 'accel': pyamg_rs_accel,\\n 'tol': pyamg_rs_tol,\\n 'maxiter': pyamg_rs_maxiter}),\\n ('pyamg-sa', {'type': 'pyamg-sa',\\n 'symmetry': pyamg_sa_symmetry,\\n 'strength': pyamg_sa_strength,\\n 'aggregate': pyamg_sa_aggregate,\\n 'smooth': pyamg_sa_smooth,\\n 'presmoother': pyamg_sa_presmoother,\\n 'postsmoother': pyamg_sa_postsmoother,\\n 'improve_candidates': pyamg_sa_improve_candidates,\\n 'max_levels': pyamg_sa_max_levels,\\n 'max_coarse': pyamg_sa_max_coarse,\\n 'diagonal_dominance': pyamg_sa_diagonal_dominance,\\n 'coarse_solver': pyamg_sa_coarse_solver,\\n 'cycle': pyamg_sa_cycle,\\n 'accel': pyamg_sa_accel,\\n 'tol': pyamg_sa_tol,\\n 'maxiter': pyamg_sa_maxiter}))\\n opts = OrderedDict(opts)\\n opts.update(genericsolvers.options())\\n def_opt = opts.pop(default_solver)\\n if default_least_squares_solver != default_solver:\\n def_ls_opt = opts.pop(default_least_squares_solver)\\n ordered_opts = OrderedDict(((default_solver, def_opt),\\n (default_least_squares_solver, def_ls_opt)))\\n else:\\n ordered_opts = OrderedDict(((default_solver, def_opt),))\\n ordered_opts.update(opts)\\n return ordered_opts\",\n \"def test_solve_quadratic(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.fixed = False\\n iden2.x.fixed = False\\n iden3.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 0)\\n self.assertAlmostEqual(iden1.x.val, 1)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 3)\",\n \"def getdviolcon(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getdviolcon(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def update_solution(self, new_plan, use_cat=True, soc=True):\\n if not new_plan.path:\\n self.sol = TimeUncertaintySolution.empty_solution(self.sol.nodes_generated)\\n self.sol.paths[new_plan.agent] = new_plan\\n return\\n if not use_cat:\\n self.sol.paths[new_plan.agent] = new_plan\\n self.sol.create_movement_tuples(agents=[new_plan.agent])\\n return\\n\\n self.sol.paths[new_plan.agent] = new_plan\\n new_moves = self.sol.add_stationary_moves()\\n self.sol.create_movement_tuples(agents=[new_plan.agent])\\n self.update_conflict_avoidance_table(new_plan.agent, new_moves)\\n self.sol.compute_solution_cost(sum_of_costs=soc) # compute the cost\",\n \"def cvstem0(self,xs,ps,alp):\\n epsilon = self.epsilon\\n Ncv = np.size(xs,0)\\n n = self.n\\n I = np.identity(n)\\n Ws = []\\n for k in range(Ncv):\\n Ws.append(cp.Variable((n,n),PSD=True))\\n nu = cp.Variable(nonneg=True)\\n chi = cp.Variable(nonneg=True)\\n errtxt = \\\"https://github.com/AstroHiro/ncm#troubleshooting\\\"\\n if len(sig(self.Afun).parameters) == 1:\\n fun1 = self.Afun\\n self.Afun = lambda x,p: fun1(x)\\n if (self.iEC == \\\"est\\\") and (len(sig(self.Cfun).parameters) == 1):\\n fun2 = self.Cfun\\n self.Cfun = lambda x,p: fun2(x)\\n if self.iEC == \\\"est\\\":\\n Af = self.Afun\\n Cf = self.Cfun\\n J = (self.d1_over*self.b_over*chi\\\\\\n +self.d2_over*self.c_over*self.g_over*nu)/alp\\n elif self.iEC == \\\"con\\\":\\n Af = lambda x,p: self.Afun(x,p).T\\n Cf = lambda x,p: self.h_or_g(x,p).T\\n J = self.d1_over*self.b_over*chi/alp+self.d2_over*nu\\n else:\\n raise ValueError('Invalid iEC: iEC = \\\"est\\\" or \\\"con\\\"')\\n constraints = []\\n for k in range(Ncv):\\n x = xs[k,:]\\n p = ps[k,:]\\n Ax = Af(x,p)\\n Cx = Cf(x,p)\\n W = Ws[k]\\n constraints += [chi*I-W >> 0,W-I >> 0]\\n constraints += [-2*alp*W-((W-I)/self.dt+W@Ax+Ax.T@W-2*nu*Cx.T@Cx)\\\\\\n >> epsilon*I]\\n prob = cp.Problem(cp.Minimize(J),constraints)\\n prob.solve(solver=cp.MOSEK)\\n cvx_status = prob.status\\n if cvx_status in [\\\"infeasible\\\",\\\"infeasible_inaccurate\\\"]:\\n raise ValueError(\\\"Problem infeasible: see \\\"+errtxt)\\n elif cvx_status in [\\\"unbounded\\\",\\\"unbounded_inaccurate\\\"]:\\n raise ValueError(\\\"Problem unbounded: \\\"+errtxt)\\n Wsout = []\\n for k in range(Ncv):\\n Wk = Ws[k].value/nu.value\\n Wsout.append(Wk)\\n self.Ws = Wsout\\n self.nu = nu.value\\n self.chi = chi.value\\n self.Jcv = prob.value\\n self.cvx_status = cvx_status\\n pass\",\n \"def test_solve_quadratic_fixed(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.val = 4\\n iden2.x.val = 5\\n iden3.x.val = 6\\n iden1.x.name = 'x1'\\n iden2.x.name = 'x2'\\n iden3.x.name = 'x3'\\n iden2.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 10)\\n self.assertAlmostEqual(iden1.x.val, 4)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 6)\",\n \"def putxc(self,whichsol_,xc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xc is None: raise TypeError(\\\"Invalid type for argument xc\\\")\\n _copyback_xc = False\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n _copyback_xc = True\\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n _copyback_xc = True\\n if xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n res = self.__obj.putxc(whichsol_,xc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_xc:\\n xc[:] = _tmparr_xc\",\n \"def setup(\\n self,\\n method: str = \\\"SLSQP\\\",\\n tol: Union[None, float] = None,\\n options: Union[None, Dict] = None,\\n ):\\n\\n # Input check\\n if self.opt_type in CONSTRAINED_OPT and (\\n method not in ScipyMinimizeSolver.methods_handle_constraints\\n ):\\n raise TypeError(\\n f\\\"optimization problem has constraints, the method '{method}' is not suitable\\\"\\n )\\n\\n # Setup class attributes\\n\\n ## Container for the statistics.\\n self._stats = None\\n\\n ## Method name.\\n self.method = method\\n\\n # Setup minimize input parameters\\n\\n ## Input to the minimize method\\n self.minimize_input = {\\n \\\"fun\\\": self.f,\\n \\\"method\\\": method,\\n \\\"x0\\\": self.x0.toarray().flatten(),\\n }\\n\\n if tol is not None:\\n self.minimize_input[\\\"tol\\\"] = tol\\n\\n if options is not None:\\n self.minimize_input[\\\"options\\\"] = options\\n\\n if method in ScipyMinimizeSolver.methods_req_jac:\\n self.minimize_input[\\\"jac\\\"] = self.jac\\n\\n if method in ScipyMinimizeSolver.methods_req_hess:\\n self.minimize_input[\\\"hess\\\"] = self.hess\\n\\n ## Constraints definition passed to the minimize method.\\n self._constraints = {}\\n if method in ScipyMinimizeSolver.methods_handle_constraints:\\n if method != \\\"trust-constr\\\":\\n if self.opt_type in CONSTRAINED_OPT:\\n self._constraints[\\\"constr\\\"] = {\\n \\\"type\\\": \\\"ineq\\\",\\n \\\"fun\\\": self.v,\\n \\\"jac\\\": self.dv,\\n }\\n else:\\n if self.opt.nk:\\n self._constraints[\\\"k\\\"] = LinearConstraint(\\n A=csc_matrix(self.opt.M(self.p).toarray()),\\n lb=-self.opt.c(self.p).toarray().flatten(),\\n ub=self.opt.inf * np.ones(self.opt.nk),\\n )\\n\\n if self.opt.na:\\n eq = -self.opt.b(self.p).toarray().flatten()\\n self._constraints[\\\"a\\\"] = LinearConstraint(\\n A=csc_matrix(self.opt.A(self.p).toarray()),\\n lb=eq,\\n ub=eq,\\n )\\n\\n if self.opt.ng:\\n self._constraints[\\\"g\\\"] = NonlinearConstraint(\\n fun=self.g,\\n lb=np.zeros(self.opt.ng),\\n ub=self.opt.inf * np.ones(self.opt.ng),\\n jac=self.dg,\\n hess=self.ddg,\\n )\\n\\n if self.opt.nh:\\n self._constraints[\\\"h\\\"] = NonlinearConstraint(\\n fun=self.h,\\n lb=np.zeros(self.opt.nh),\\n ub=np.zeros(self.opt.nh),\\n jac=self.dh,\\n hess=self.ddh,\\n )\\n\\n return self\",\n \"def getsolsta(self,whichsol_):\\n solsta_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getsolsta(self.__nativep,whichsol_,ctypes.byref(solsta_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _solsta_return_value = solsta(solsta_.value)\\n return (_solsta_return_value)\",\n \"def getdviolcon(self,whichsol_,sub,viol): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if viol is None: raise TypeError(\\\"Invalid type for argument viol\\\")\\n _copyback_viol = False\\n if viol is None:\\n viol_ = None\\n else:\\n try:\\n viol_ = memoryview(viol)\\n except TypeError:\\n try:\\n _tmparr_viol = array.array(\\\"d\\\",viol)\\n except TypeError:\\n raise TypeError(\\\"Argument viol has wrong type\\\")\\n else:\\n viol_ = memoryview(_tmparr_viol)\\n _copyback_viol = True\\n else:\\n if viol_.format != \\\"d\\\":\\n viol_ = memoryview(array.array(\\\"d\\\",viol))\\n _copyback_viol = True\\n if viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol has wrong length\\\")\\n res = self.__obj.getdviolcon(whichsol_,num_,sub_,viol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_viol:\\n viol[:] = _tmparr_viol\",\n \"def putsux(self,whichsol_,sux_):\\n _sux_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),self.getnumvar()))\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux cannot be None\\\")\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux may not be None\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_putsux(self.__nativep,whichsol_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solveSudoku(self, board) -> None:\\n self.board = board\\n self.backTrace(0,0)\",\n \"def prepare_ss_condor_job(self, pool_type, pool_address, number_of_jobs, subtask_index=1, rank='0', extraArgs=''):\\n #New: only prepares a single job which allows multiple jobs to be queued\\n #We must change the ownership of each of the copasi files to the user running this script\\n #\\n #We assume that we have write privileges on each of the files through our group, but don't have permission to actually change ownership (must be superuser to do this)\\n #Thus, we workaround this by copying the original file, deleting the original, and moving the copy back to the original filename\\n \\n# import shutil\\n# for i in range(len(self.get_optimization_parameters())):\\n# for max in (0, 1):\\n# copasi_file = os.path.join(self.path, 'auto_copasi_%d.cps' % (2*i + max))\\n# temp_file = os.path.join(self.path, 'temp.cps')\\n# shutil.copy2(copasi_file, temp_file)\\n# os.remove(copasi_file)\\n# os.rename(temp_file, copasi_file)\\n# os.chmod(copasi_file, 0664) #Set as group readable and writable\\n \\n ############\\n #Build the appropriate .job files for the sensitivity optimization task, write them to disk, and make a note of their locations\\n condor_jobs = []\\n \\n copasi_file = 'auto_copasi_%d.$(Process).cps' % subtask_index\\n output_file = 'output_%d.$(Process).txt' % subtask_index\\n \\n \\n \\n if pool_type == 'ec2':\\n binary_dir = '/usr/local/bin'\\n transfer_executable = 'NO'\\n else:\\n binary_dir, binary = os.path.split(settings.COPASI_LOCAL_BINARY)\\n transfer_executable = 'YES'\\n \\n \\n condor_job_string = Template(condor_spec.raw_condor_job_string).substitute(copasiFile=copasi_file, \\n otherFiles='',\\n rank=rank,\\n binary_dir = binary_dir,\\n transfer_executable = transfer_executable,\\n pool_type = pool_type,\\n pool_address = pool_address,\\n subtask=str(subtask_index),\\n n = number_of_jobs,\\n outputFile = output_file,\\n extraArgs='',\\n )\\n \\n condor_job_filename = 'auto_condor_%d.job'%subtask_index\\n condor_job_full_filename = os.path.join(self.path, condor_job_filename)\\n condor_file = open(condor_job_full_filename, 'w')\\n condor_file.write(condor_job_string)\\n condor_file.close()\\n\\n return condor_job_filename\",\n \"def solve(self):\\n\\n # Set up display header if verbose operation enabled\\n if self.opt['Verbose']:\\n hdr = 'Itn DFidX PriResX DuaResX DFidG' + \\\\\\n ' ResG '\\n print(hdr)\\n print('-' * len(hdr))\\n\\n # Main iteration loop\\n for n in range(self.opt['MaxMainIter']):\\n\\n # At start of 2nd iteration, set the numbers of inner\\n # iterations for the X and G solvers from the options\\n # object for the outer solver\\n if n == 1:\\n self.slvX.opt['MaxMainIter'] = self.opt['XslvIter']\\n self.slvG.opt['MaxMainIter'] = self.opt['GslvIter']\\n\\n # Run the configured number of iterations of the X (CSC)\\n # solver and assign the result to X\\n self.X = self.slvX.solve()\\n\\n # Compute the sum of the subpixel shifts of X\\n Xhs = np.sum(fftconv(self.H, self.X.squeeze(), axes=(0, 1)),\\n axis=-1)\\n\\n # Set the convolution kernel in the deconvolution solver\\n # to the sum of the subpixel shifts of X\\n self.slvG.setG(Xhs)\\n # Run the configured number of iterations of the G\\n # (deconvolution) solver and crop the result to obtain the\\n # updated g\\n self.g = self.slvG.solve()[0:self.gshp[0], 0:self.gshp[1]]\\n\\n # Construct a new dictionary for the X (CSC) solver from\\n # the updated psf g\\n self.D, self.dn = self.getD(self.g)\\n self.slvX.setdict(self.D[..., np.newaxis, np.newaxis, :])\\n\\n # Display iteration statistics if verbose operation enabled\\n if self.opt['Verbose']:\\n itsX = self.slvX.getitstat()\\n itsG = self.slvG.getitstat()\\n fmt = '%3d %.3e %.3e %.3e %.3e %.3e'\\n tpl = (n, itsX.DFid[-1], itsX.PrimalRsdl[-1],\\n itsX.DualRsdl[-1], itsG.DFid[-1], itsG.Rsdl[-1])\\n print(fmt % tpl)\\n\\n # Return the (normalised) psf estimate g\\n return self.g / np.linalg.norm(self.g)\",\n \"def add_solution(self, solution):\\n if self.check_solution(solution):\\n self._solution = solution\\n self.solution_status = 'OK'\\n else:\\n self._solution = None\\n self.solution_status = 'X'\",\n \"def show_solution(self,show):\\r\\n self.showSolution = show\",\n \"def setup(self, solver_name: str, solver_options: Dict = {}):\\n # Setup problem\\n x = self.opt.decision_variables.vec()\\n p = self.opt.parameters.vec()\\n\\n problem = {\\n \\\"x\\\": x,\\n \\\"p\\\": p,\\n \\\"f\\\": self.opt.f(x, p),\\n }\\n\\n # Setup constraints\\n\\n ## Lower bound on constraints.\\n self._lbg = None\\n\\n ## Upper bound on constraints\\n self._ubg = None\\n\\n if self.opt_type in CONSTRAINED_OPT:\\n problem[\\\"g\\\"] = self.opt.v(x, p)\\n self._lbg = self.opt.lbv\\n self._ubg = self.opt.ubv\\n\\n # Get solver interface\\n if (solver_name in self.qp_solvers) and not self.opt.has_discrete_variables():\\n sol = cs.qpsol\\n elif (solver_name in self.nlp_solvers) or (solver_name in self.mi_solvers):\\n sol = cs.nlpsol\\n else:\\n raise ValueError(\\n f\\\"solver '{solver_name}' does not support this problem type\\\"\\n )\\n\\n # Check for discrete variables\\n if self.opt.has_discrete_variables():\\n solver_options[\\\"discrete\\\"] = self.opt.decision_variables.discrete()\\n\\n # Initialize solver\\n\\n ## Instance of the CasADi solver.\\n self._solver = sol(\\\"solver\\\", solver_name, problem, solver_options)\\n\\n return self\",\n \"def optim_solve(\\n self, x0: devices.PrimaryWeights = None, global_search: bool = False, **kwargs\\n ) -> scipy.optimize.OptimizeResult:\\n print(f'{\\\" optim_solve \\\":~^60s}')\\n self._assert_problem_is_valid()\\n if self._background is None:\\n bounds = self.bounds * 2\\n print(\\\"> No background specified, will optimise background.\\\")\\n else:\\n bounds = self.bounds\\n\\n if np.inf in self._target_contrast:\\n print(\\\"> Aiming to maximise contrast.\\\")\\n\\n elif -np.inf in self._target_contrast:\\n print(\\\"> Aiming to minimize contrast.\\\")\\n\\n constraints = [\\n {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": self.silencing_constraint, \\\"tol\\\": 1e-04}\\n ]\\n\\n if x0 is None:\\n x0 = self.initial_guess_x0()\\n \\n if not global_search: # Local minimization\\n\\n default_options = {\\\"iprint\\\": 2, \\\"disp\\\": True, \\\"ftol\\\": 1e-08}\\n options = kwargs.pop(\\\"options\\\", default_options)\\n\\n print(\\\"> Performing local optimization with SLSQP.\\\")\\n result = scipy.optimize.minimize(\\n fun=self.objective_function,\\n x0=x0,\\n method=\\\"SLSQP\\\",\\n bounds=bounds,\\n constraints=constraints,\\n options=options,\\n **kwargs,\\n )\\n\\n elif global_search: # Global minimization\\n print(\\n \\\"> Performing global optimization with basinhopping and SLSQP\\\"\\n )\\n\\n # Configure global defaults\\n disp = kwargs.pop(\\\"disp\\\", True)\\n # Configure local defaults\\n default_minimizer_kwargs = {\\n \\\"method\\\": \\\"SLSQP\\\",\\n \\\"constraints\\\": constraints,\\n \\\"bounds\\\": bounds,\\n \\\"options\\\": {\\\"iprint\\\": 2, \\\"disp\\\": False},\\n }\\n minimizer_kwargs = kwargs.pop(\\n \\\"minimizer_kwargs\\\", default_minimizer_kwargs\\n )\\n\\n # Do optimization\\n result = scipy.optimize.basinhopping(\\n func=self.objective_function,\\n x0=x0,\\n minimizer_kwargs=minimizer_kwargs,\\n disp=disp,\\n **kwargs,\\n )\\n\\n return result\",\n \"def getskx(self,whichsol_,skx_):\\n _skx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),self.getnumvar()))\\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\\n raise ValueError(\\\"Argument skx must be writable\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_getskx(self.__nativep,whichsol_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\",\n \"def getskn(self,whichsol_,skn_):\\n _skn_minlength = self.getnumcone()\\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\\n raise ValueError(\\\"Array argument skn is not long enough: Is %d, expected %d\\\" % (len(skn_),self.getnumcone()))\\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\\n raise ValueError(\\\"Argument skn must be writable\\\")\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\\n else:\\n _skn_tmp = None\\n res = __library__.MSK_XX_getskn(self.__nativep,whichsol_,_skn_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]\",\n \"def __init__(self, data_p, data_q, solver=\\\"SCS\\\"):\\n self.data_P = data_p\\n self.data_Q = data_q\\n self.solver = solver\\n self.warning_msg = None\\n\\n self.n = len(data_p)\\n\\n # Hold values *separate* from cvxpy variables as we sometimes need to revert\\n # cvxpy optimizations which actually move values in a way that gives a *worse*\\n # objective function.\\n self.p = _np.array(self.data_P) / _np.sum(self.data_P)\\n self.q = _np.array(self.data_Q) / _np.sum(self.data_Q)\\n\\n # cvxpy parameters\\n self.T = _cp.Parameter(shape=(self.n, self.n), nonneg=True, value=_np.eye(self.n))\\n self.alpha = _cp.Parameter(nonneg=True, value=1.0)\\n\\n self.max_logl = max_log_likelihood(data_p) + max_log_likelihood(data_q)\\n self._build_problem()\",\n \"def getsolsta(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getsolsta(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _solsta_return_value = resargs\\n _solsta_return_value = solsta(_solsta_return_value)\\n return _solsta_return_value\",\n \"def create_sudoku_csp(sudoku):\\n print sudoku\\n constraints = []\\n variables = []\\n tupel_list = np.empty((9,9), dtype = csp.Variable)\\n domain = range(1,10)#['1','2','3','4','5','6','7','8','9']\\n for i,row in enumerate(sudoku):\\n for j,field in enumerate(row):\\n if field == 0:\\n field = None\\n \\\"\\\"\\\" do = domain\\n else:\\n do = [field]\\\"\\\"\\\"\\n var = csp.Variable((i,j),domain)\\n var.set_value(field)\\n variables.append(var)\\n tupel_list[i][j]=var \\n \\n for i in range(0,9):\\n for j in range(0,9):\\n for r in range(i+1,9):\\n constraints.append(csp.UnequalConstraint(tupel_list[i][j],tupel_list[r][j]))\\n for l in range(j+1,9):\\n constraints.append(csp.UnequalConstraint(tupel_list[i][j],tupel_list[i][l]))\\n for outer in range(0,3):\\n for inner in range(0,3):\\n if outer is not i % 3 and inner is not j % 3:\\n constraints.append(csp.UnequalConstraint(tupel_list[i][j],tupel_list[3*(i/3)+outer][3*(j/3)+inner]))\\n return csp.ConstrainedSatisfactionProblem(variables,constraints)\",\n \"def update(self, solution):\\n self.heuristic_path = [i for i in self.initial_path if i in solution]\\n self.heuristic_cost = self.pathCost(self.heuristic_path)\",\n \"def set_sparsity(self,use_sparse):\\n \\n if hasattr(self.problem,'sparse_jac'):\\n self.use_sparse = use_sparse\\n else:\\n raise KINSOL_Exception(\\\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\\\")\",\n \"def getpviolcones(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getpviolcones(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def putsux(self,whichsol_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if sux is None: raise TypeError(\\\"Invalid type for argument sux\\\")\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.putsux(whichsol_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def SolveSCP(self):\\n\\n t0 = time()\\n\\n # Some predicates\\n Lu_min = 0.\\n niters_max = self._maxiters\\n maxfracchange = self._maxfracchange\\n\\n # initialization, resetting ...\\n self.reset_all() # including _u_naught(), first application\\n scp_min = self.greedy()\\n\\n # re-initialization iteration; col fixing ignored for the moment\\n niters = 0\\n f_change = _largenumber\\n while (f_change>maxfracchange) and (niters<niters_max):\\n # re-initialize u\\n if (np.mod(niters, 2)==0): \\n self.reset_u(random=True)\\n else:\\n self.reset_u()\\n u_tmp, Lu_tmp = self.subgradient() # find a near-optimal solution \\n u, Lu = self.subgradient() # rerun subgradient to get a set of Lagrangian multipliers\\n\\n scp_all = np.zeros(self._subg_nsteps)\\n for i in np.arange(self._subg_nsteps):\\n #self.reset_s()\\n self.s = np.copy(self.f)\\n scp_all[i] = self.greedy(u=u[:,i])\\n\\n # check if the solution is gettting better\\n imin_tmp = (np.where(scp_all==np.amin(scp_all)))[0]\\n imin = imin_tmp[np.argmax(Lu[imin_tmp])]\\n imax = np.argmax(Lu)\\n if (np.mod(niters, 5)==0):\\n print(\\\"This Best solution: UB={0}, LB={1}, UB1={2}, LB1={3}\\\".format(scp_all[imin], Lu[imin], scp_all[imax], Lu[imax]))\\n if (niters==0) or ((scp_all[imin]<=scp_min) and ((Lu[imin]-Lu_min)>-(np.fabs(Lu_min)*self._LB_maxfracchange))):\\n scp_min = scp_all[imin]\\n u_min = np.copy(u[:,imin])\\n Lu_min = Lu[imin]\\n self.stepsize = _stepsize\\n\\n LB = Lu_min\\n\\n # final step, needs to get u_min back\\n self.u = np.copy(u_min)\\n self.s = np.copy(self.f)\\n UB = self.greedy()\\n\\n # Which is better? absolute change or fractional change? \\n # Both are fine, but cost should be normalized over the mean/median.\\n GAP = (UB-LB)/np.fabs(UB)\\n f_change = GAP\\n if (np.mod(niters, 5)==0):\\n print(\\\"Current Best Solution: UB={0}, LB={1}, change={2}% @ niters={3}\\\".format(UB,LB,f_change*100.,niters))\\n niters = niters + 1\\n if (niters == niters_max): \\n #warnings.warn(\\\"Iteration reaches maximum = {0}\\\".format(niters))\\n print(\\\"Iteration in re-initialization reaches maximum number = {0}\\\".format(niters))\\n\\n # Need to remove redundant columns\\n # self.remove_redundant() # this itself is NP-hard ...\\n\\n print(\\\"Current Best Solution: UB={0}, LB={1}, change={2}% @ niters={3}\\\".format(UB,LB,f_change*100.,niters))\\n print(\\\"Final Best solution: {0}\\\".format(UB))\\n time_used = (time()-t0)/60.\\n print(\\\"Took {0:.3f} minutes to reach current solution.\\\".format(time_used))\\n\\n return (UB,time_used)\",\n \"def set_solver(self, solver):\\n self.solver = solver\\n solver.associate_with_model(self)\\n self.set_free_variable(0) # A reasonable initial assumption; can be overridden by simulations\",\n \"def check_sol (statess, bigX, littleX, bigY, littleY):\\n\\t\\tcheckstates = copy.deepcopy(statess)\\n\\t\\tcheckstates[bigX] = littleX\\n\\t\\tcheckstates[bigY] = littleY\\n\\t\\treturnval = constraint_generator(checkstates).get((bigX,bigY), False)\\n\\t\\treturn returnval\",\n \"def optimize(self):\\n\\n self.logger.info(\\\"Solving with Dynamic Slope Scaling Procedure in Julia :\\\")\\n optimization_start = time.time()\\n\\n # 1. Preprocess for old network graph\\n if self.old_network_graph is not None:\\n\\n # DSSP on old network\\n old_network_obj = sum(list(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).values()))-1e-5\\n try:\\n self.check_infeasibility(self.old_network_graph, old_network_obj)\\n except DHCOptimizerException as e:\\n e.data = \\\"Invalid existing network: \\\" + e.data\\n raise e\\n\\n flows, obj_val = self.optimize_with_dssp_julia(self.old_network_graph, old_network_obj, set())\\n self.logger.info(\\\"Optimization phase time: %.2fs\\\" % (time.time() - optimization_start))\\n solution_old_graph = self.build_solution_graph(self.old_network_graph, flows)\\n\\n if self.modify_old_network:\\n\\n # Add max capacity on old edges\\n self.old_capacity = deepcopy(flows)\\n old_buildings = list(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).values())\\n for key in flows:\\n if (key[1],key[0],0) not in self.old_capacity and key[1] not in old_buildings:\\n self.old_capacity[(key[1],key[0],0)] = self.old_capacity[key]\\n\\n # Add Imaginary edges\\n for edge in self.old_capacity:\\n if self.optimization_graph.has_edge(*edge):\\n\\n # add nodes\\n if not self.optimization_graph.has_node(config.IM_PREFIX+edge[0]):\\n self.optimization_graph.add_node(config.IM_PREFIX+edge[0])\\n self.optimization_graph.nodes[config.IM_PREFIX+edge[0]][config.GPD_GEO_KEY] = \\\\\\n self.optimization_graph.nodes[edge[0]][config.GPD_GEO_KEY]\\n if not self.optimization_graph.has_node(config.IM_PREFIX+edge[1]):\\n self.optimization_graph.add_node(config.IM_PREFIX+edge[1])\\n self.optimization_graph.nodes[config.IM_PREFIX+edge[1]][config.GPD_GEO_KEY] = \\\\\\n self.optimization_graph.nodes[edge[1]][config.GPD_GEO_KEY]\\n # add edges\\n if not self.optimization_graph.has_edge(edge[0],config.IM_PREFIX+edge[0]):\\n self.optimization_graph.add_edge(edge[0],config.IM_PREFIX+edge[0])\\n if not self.optimization_graph.has_edge(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1]):\\n self.optimization_graph.add_edge(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1])\\n if not self.optimization_graph.has_edge(config.IM_PREFIX+edge[1],edge[1]):\\n self.optimization_graph.add_edge(config.IM_PREFIX+edge[1],edge[1])\\n\\n # put cost\\n self.optimization_graph.edges[(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1],0)][config.EDGE_COST_KEY] = \\\\\\n self.optimization_graph.edges[(edge[0],edge[1],0)][config.EDGE_COST_KEY]\\n self.optimization_graph.edges[(edge[0],edge[1],0)][config.EDGE_COST_KEY] = 1e-5\\n self.optimization_graph.edges[(edge[0],config.IM_PREFIX+edge[0],0)][config.EDGE_COST_KEY] = 1e-5\\n self.optimization_graph.edges[(config.IM_PREFIX+edge[1],edge[1],0)][config.EDGE_COST_KEY] = 1e-5\\n\\n else:\\n # if we don't modify the old network, we have to change the capacity of the supplies\\n already_consummed = {}\\n for edge in solution_old_graph.edges():\\n if solution_old_graph.nodes[edge[0]].get(config.NODE_TYPE_KEY) == config.SUPPLY_NODE_TYPE:\\n already_consummed[edge[0]] = already_consummed.get(edge[0], 0) + \\\\\\n solution_old_graph.edges[edge][config.SOLUTION_POWER_FLOW_KEY]\\n for source in already_consummed:\\n if already_consummed[source] <= self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY]:\\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] -= already_consummed[source]\\n self.network_objective -= already_consummed[source]\\n else:\\n self.network_objective -= self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY]\\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] = 0\\n\\n # Remove edges from old network\\n edges_to_remove = set()\\n for e in self.optimization_graph.edges():\\n if self.old_network_graph.has_edge(*e) or self.old_network_graph.has_edge(e[1],e[0]):\\n edges_to_remove.add(e)\\n self.optimization_graph.remove_edges_from(edges_to_remove)\\n\\n # Remove isolated buildings of optimization graph\\n isolated_to_remove = set()\\n for e in self.old_network_graph.edges():\\n if e[0] in self.old_network_graph.nodes() and \\\\\\n self.optimization_graph.nodes[e[1]].get(config.NODE_TYPE_KEY) == config.BUILDING_NODE_TYPE:\\n isolated_to_remove.add(e)\\n self.optimization_graph.remove_edges_from(isolated_to_remove)\\n\\n # Remove buildings from old network\\n for n, data in self.old_network_graph.nodes(data=True):\\n if data.get(config.NODE_TYPE_KEY) == config.BUILDING_NODE_TYPE:\\n self.optimization_graph.remove_node(n)\\n\\n # Re-link sources\\n sources = set()\\n for n, data in self.optimization_graph.nodes(data=True):\\n if data.get(config.NODE_TYPE_KEY) == config.SUPPLY_NODE_TYPE:\\n sources.add(n)\\n source_graph = self.optimization_graph.subgraph(sources).copy()\\n self.optimization_graph.remove_nodes_from(sources)\\n gnx.remove_isolates(self.optimization_graph)\\n node_filter = lambda n: self.optimization_graph.nodes.get(n,{}).get(config.NODE_TYPE_KEY) != config.BUILDING_NODE_TYPE\\n gnx.spatial_points_merge(self.optimization_graph, source_graph.nodes_to_gdf(), node_filter=node_filter, inplace=True)\\n\\n # fill missing information\\n gnx.fill_edges_missing_geometry_attributes(self.optimization_graph)\\n gnx.fill_length_attribute(self.optimization_graph, config.EDGE_LENGTH_KEY, only_missing=True)\\n gnx.fill_length_attribute(self.optimization_graph, config.EDGE_COST_KEY, only_missing=True)\\n for e in self.optimization_graph.edges(keys=True):\\n self.optimization_graph.edges[e][config.LEASTCOST_COEF_KEY] = \\\\\\n self.optimization_graph.edges[e].get(config.LEASTCOST_COEF_KEY,0)\\n\\n\\n\\n # 2. Process the DSSP on optimization graph\\n self.check_is_ready()\\n self.check_infeasibility(self.optimization_graph, self.network_objective)\\n\\n if self.old_network_graph is not None and self.modify_old_network:\\n old_buildings = set(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).keys())\\n else:\\n old_buildings = set()\\n flows, obj_val = self.optimize_with_dssp_julia(self.optimization_graph, self.network_objective, old_buildings,postprocess= (not self.modify_old_network))\\n self.logger.info(\\\"Optimization phase time: %.2fs\\\" % (time.time() - optimization_start))\\n self.solution_graph = self.build_solution_graph(self.optimization_graph, flows, self.connected)\\n\\n # 3. Postprocess for old network graph\\n if self.old_network_graph is not None:\\n \\n if self.modify_old_network:\\n # Put the right supply capacity and cost\\n for edge in self.old_capacity:\\n if self.solution_graph.has_edge(edge[0],edge[1]):\\n self.solution_graph.edges[(edge[0],edge[1])][config.EDGE_COST_KEY] = \\\\\\n self.optimization_graph.edges[(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1],0)][config.EDGE_COST_KEY]\\n \\n # Remove imaginary edges\\n imaginary_nodes_to_remove = set()\\n nodes_to_relabel = {}\\n for edge in self.solution_graph.edges():\\n if str(edge[0]).startswith(config.IM_PREFIX) and str(edge[1]).startswith(config.IM_PREFIX):\\n real_edge = edge[0][len(config.IM_PREFIX):],edge[1][len(config.IM_PREFIX):]\\n self.old_capacity[(real_edge[0], real_edge[1], 0)] = pd.np.inf\\n self.old_capacity[(real_edge[1], real_edge[0], 0)] = pd.np.inf\\n if not self.solution_graph.has_edge(*real_edge):\\n for i in range(2):\\n nodes_to_relabel[edge[i]] = real_edge[i]\\n else:\\n self.solution_graph.edges[real_edge[0],real_edge[1]][config.SOLUTION_POWER_FLOW_KEY] += \\\\\\n self.solution_graph.edges[edge].get(config.SOLUTION_POWER_FLOW_KEY,0)\\n imaginary_nodes_to_remove.add(edge[0])\\n imaginary_nodes_to_remove.add(edge[1])\\n elif str(edge[0]).startswith(config.IM_PREFIX):\\n imaginary_nodes_to_remove.add(edge[0])\\n elif str(edge[1]).startswith(config.IM_PREFIX):\\n imaginary_nodes_to_remove.add(edge[1])\\n\\n nx.relabel_nodes(self.solution_graph, nodes_to_relabel, copy=False)\\n self.solution_graph.remove_nodes_from(list(imaginary_nodes_to_remove))\\n for node in nodes_to_relabel.values():\\n if self.solution_graph.has_edge(node, node):\\n self.solution_graph.remove_edge(node, node)\\n\\n else:\\n for source in nx.get_node_attributes(self.solution_graph, config.SUPPLY_POWER_CAPACITY_KEY):\\n self.solution_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] += already_consummed.get(source,0)\\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] += already_consummed.get(source,0)\\n\\n return flows, obj_val\",\n \"def putxx(self,whichsol_,xx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xx is None: raise TypeError(\\\"Invalid type for argument xx\\\")\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n res = self.__obj.putxx(whichsol_,xx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def onesolutionsummary(self,whichstream_,whichsol_):\\n res = __library__.MSK_XX_onesolutionsummary(self.__nativep,whichstream_,whichsol_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def add_unknown_strcuture(self, name, cospsi = None,\\n corr = None, cl = None):\\n if cospsi is not None:\\n # if cospsi and corr are both given, use them \\n if corr is None:\\n pass\\n else:\\n if len(cospsi.shape) == 1:\\n cospsi = np.array( [cospsi] * self.n_q )\\n try:\\n assert(corr.shape[0] == self.n_q)\\n assert(corr.shape[-1] == cospsi.shape[-1])\\n except AssertionError:\\n print(\\\"EEROR: corr and cospsi shape mismatch\\\")\\n return\\n\\n corr, cospsi = self._check_cospsi( corr, cospsi )\\n cl = self._compute_component_legendre_projection(corr, cospsi)\\n self.guess_cl.update({name: cl})\\n \\n elif cl is not None:\\n # if cl is given\\n try:\\n assert( cl.shape[0] == self.lmax+1)\\n assert( cl.shape[-1] == self.n_q)\\n except AssertionError:\\n print(\\\"ERORR: cl shape mismatch\\\")\\n\\n self.guess_cl.update( {name:cl} )\",\n \"def testSimpleTSVC(self):\\n puzzle = PuzzleFactory.createSingleConstraintPuzzle(set([1,2]), 2, TotalSumValueConstraint)\\n \\n for cg in puzzle.getConstraintGroups():\\n for constraint in cg.getConstraints():\\n constraint.setTotalValue(3);\\n constraint.applyConstraint()\\n \\n possibleValues = puzzle.grid.getCells()[1].getPossibleValues()\\n self.assertTrue(1 in possibleValues, \\\"Incorrectly removed value from constraint\\\")\\n self.assertTrue(2 in possibleValues, \\\"Incorrectly removed value from constraint\\\")\\n \\n puzzle.grid.getCells()[0].setValue(1)\\n for cg in puzzle.getConstraintGroups():\\n for constraint in cg.getConstraints():\\n constraint.applyConstraint()\\n possibleValues = puzzle.grid.getCells()[1].getPossibleValues()\\n \\n self.assertTrue(1 not in possibleValues, \\\"Incorrectly kept value from constraint\\\")\\n self.assertTrue(2 in possibleValues, \\\"Incorrectly removed value from constraint\\\")\",\n \"def setRepairCost(self):\\n # first take into account the ship hull which is based on internal structure points\\n ratio = 1.0 - (self.currentISP/self.myShipHull.maxISP)\\n CR = int(self.myShipHull.costCR*ratio)\\n AL = int(self.myShipHull.costAL*ratio)\\n EC = int(self.myShipHull.costEC*ratio)\\n IA = int(self.myShipHull.costIA*ratio)\\n\\n # compare to ship design, add costs of replacement\\n for position, myQuad in self.quads.iteritems():\\n designQuad = self.myDesign.quads[position]\\n weaponsInQuad = []\\n # look for missing components\\n for componentID in designQuad.components.keys():\\n if componentID not in myQuad.components:\\n missingComponent = designQuad.components[componentID]\\n if missingComponent.weaponID == '':\\n # regular component\\n CR += missingComponent.myComponentData.costCR\\n AL += missingComponent.myComponentData.costAL\\n EC += missingComponent.myComponentData.costEC\\n IA += missingComponent.myComponentData.costIA\\n elif missingComponent.weaponID not in weaponsInQuad:\\n # component part of weapon, weapon must be replaced\\n weaponsInQuad.append(missingComponent.weaponID)\\n\\n # go through weapons that were damaged in this quadrant\\n for weaponID in weaponsInQuad:\\n damagedWeapon = designQuad.weapons[weaponID]\\n CR += damagedWeapon.myWeaponData.costCR\\n AL += damagedWeapon.myWeaponData.costAL\\n EC += damagedWeapon.myWeaponData.costEC\\n IA += damagedWeapon.myWeaponData.costIA\\n\\n self.repairCost = [CR,AL,EC,IA]\",\n \"def putxc(self,whichsol_,xc_):\\n _xc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),self.getnumcon()))\\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\\n raise ValueError(\\\"Argument xc must be writable\\\")\\n if xc_ is None:\\n raise ValueError(\\\"Argument xc may not be None\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _xc_copyarray:\\n xc_[:] = _xc_np_tmp\",\n \"def getpviolcones(self,whichsol_,sub,viol): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if viol is None: raise TypeError(\\\"Invalid type for argument viol\\\")\\n _copyback_viol = False\\n if viol is None:\\n viol_ = None\\n else:\\n try:\\n viol_ = memoryview(viol)\\n except TypeError:\\n try:\\n _tmparr_viol = array.array(\\\"d\\\",viol)\\n except TypeError:\\n raise TypeError(\\\"Argument viol has wrong type\\\")\\n else:\\n viol_ = memoryview(_tmparr_viol)\\n _copyback_viol = True\\n else:\\n if viol_.format != \\\"d\\\":\\n viol_ = memoryview(array.array(\\\"d\\\",viol))\\n _copyback_viol = True\\n if viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol has wrong length\\\")\\n res = self.__obj.getpviolcones(whichsol_,num_,sub_,viol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_viol:\\n viol[:] = _tmparr_viol\",\n \"def solve_CBC(self, lp):\\n\\t\\tif not self.executable(self.path[1]):\\n\\t\\t\\traise \\\"PuLP: cannot execute \\\"+self.path[1]\\n\\t\\tif not self.keepFiles:\\n\\t\\t\\tpid = os.getpid()\\n\\t\\t\\ttmpLp = os.path.join(self.tmpDir, \\\"%d-pulp.mps\\\" % pid)\\n\\t\\t\\ttmpSol = os.path.join(self.tmpDir, \\\"%d-pulp.sol\\\" % pid)\\n\\t\\telse:\\n\\t\\t\\ttmpLp = lp.name+\\\"-pulp.mps\\\"\\n\\t\\t\\ttmpSol = lp.name+\\\"-pulp.sol\\\"\\n##\\t\\tvs, variablesNames, constraintsNames, objectiveName = lp.writeMPS(tmpLp, rename = 1)\\r\\n\\t\\tvs = lp.writeMPS(tmpLp, rename = 0)\\n\\t\\tif not self.msg:\\n\\t\\t\\tcbc = os.popen(self.path[1]+\\\" - > /dev/null 2> /dev/null\\\",\\\"w\\\")\\n\\t\\telse:\\n\\t\\t\\tcbc = os.popen(self.path[1]+\\\" -\\\",\\\"w\\\")\\n\\t\\tcbc.write(\\\"import \\\"+tmpLp+\\\"\\\\n\\\")\\n\\t\\tif self.presolve:\\n\\t\\t\\tcbc.write(\\\"presolve on\\\\n\\\")\\n\\t\\tcbc.write(\\\"strong %d\\\\n\\\" % self.strong)\\n\\t\\tif self.cuts:\\n\\t\\t\\tcbc.write(\\\"gomory on\\\\n\\\")\\n\\t\\t\\tcbc.write(\\\"oddhole on\\\\n\\\")\\n\\t\\t\\tcbc.write(\\\"knapsack on\\\\n\\\")\\n\\t\\t\\tcbc.write(\\\"probing on\\\\n\\\")\\n\\t\\tfor option in self.options:\\n\\t\\t\\tcbc.write(option+\\\"\\\\n\\\")\\n\\t\\tif lp.sense == LpMinimize:\\n\\t\\t\\tcbc.write(\\\"min\\\\n\\\")\\n\\t\\telse:\\n\\t\\t\\tcbc.write(\\\"max\\\\n\\\")\\n\\t\\tif self.mip:\\n\\t\\t\\tcbc.write(\\\"branch\\\\n\\\")\\n\\t\\telse:\\n\\t\\t\\tcbc.write(\\\"initialSolve\\\\n\\\")\\n\\t\\tcbc.write(\\\"solution \\\"+tmpSol+\\\"\\\\n\\\")\\n\\t\\tcbc.write(\\\"quit\\\\n\\\")\\n\\t\\tif cbc.close() != None:\\n\\t\\t\\traise \\\"PuLP: Error while trying to execute \\\"+self.path[1]\\n\\t\\tif not os.path.exists(tmpSol):\\n\\t\\t\\traise \\\"PuLP: Error while executing \\\"+self.path[1]\\n\\t\\tlp.status, values = self.readsol_CBC(tmpSol, lp, vs)\\n\\t\\tlp.assign(values)\\n\\t\\tif not self.keepFiles:\\n\\t\\t\\ttry: os.remove(tmpLp)\\n\\t\\t\\texcept: pass\\n\\t\\t\\ttry: os.remove(tmpSol)\\n\\t\\t\\texcept: pass\\n\\t\\treturn lp.status\",\n \"def solute(self, puzzle):\\r\\n \\\"\\\"\\\"suppose that ax = c, where a is a matrix, c and x are vectors.\\\"\\\"\\\"\\r\\n \\\"\\\"\\\"The aim is to figure out x, which indicates the solution.\\\"\\\"\\\"\\r\\n A, a, c = [], [], []\\r\\n for i in range(puzzle.row):\\r\\n for j in range(puzzle.column):\\r\\n # create a puzzle.row * puzzle.column by puzzle.row * puzzle.column matrix.\\r\\n # each column represents a cell in the puzzle.\\r\\n # each row represents the changed cell if column c is selected.\\r\\n if puzzle.lights[i][j] == -1:\\r\\n c.append(1)\\r\\n else:\\r\\n c.append(0)\\r\\n for m in range(puzzle.row):\\r\\n for n in range(puzzle.column):\\r\\n if self.is_adjecent([m, n], [i, j]):\\r\\n # if [m, n] is adjecent to [i, j], then a[ij][mn] should be 1.\\r\\n a.append(1)\\r\\n else:\\r\\n a.append(0)\\r\\n a.append(c[i * puzzle.column + j])\\r\\n A.append(a)\\r\\n a = []\\r\\n\\r\\n self.eliminate(A)\\r\\n x = [item[len(item) - 1] for item in A]\\r\\n # x is the last column of A.\\r\\n # if x[i] is 1, cell i should be selected.\\r\\n i = 0\\r\\n for m in range(puzzle.row):\\r\\n for n in range(puzzle.column):\\r\\n if x[i] == 1:\\r\\n puzzle.selection.add((m, n))\\r\\n i += 1\\r\\n\\r\\n return puzzle.selection\",\n \"def set_sparse_signals(self):\\n\\t\\n\\t\\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\\n\\t\\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\\n\\t\\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tparams_dSs,\\tseed=self.seed_dSs)\\n\\t\\t\\n\\t\\t# Replace components with conflicting background odor \\n\\t\\tif self.Kk_split is not None and self.Kk_split != 0:\\n\\t\\t\\tassert 0 <= self.Kk_split <= self.Kk, \\\\\\n\\t\\t\\t\\t\\\"Splitting sparse signal into two levels requires Kk_split\\\" \\\\\\n\\t\\t\\t\\t\\\" to be non-negative and less than or equal to Kk.\\\"\\n\\t\\t\\tassert self.mu_dSs_2 is not None \\\\\\n\\t\\t\\t\\tand self.sigma_dSs_2 is not None, \\\\\\n\\t\\t\\t\\t\\\"Splitting sparse signal into two levels requires that\\\" \\\\\\n\\t\\t\\t\\t\\\" mu_dSs_2 and sigma_dSs_2 are set.\\\"\\n\\n\\t\\t\\tsp.random.seed(self.seed_dSs)\\n\\t\\t\\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\treplace=False)\\n\\t\\t\\tfor idx_2 in self.idxs_2:\\n\\t\\t\\t\\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.sigma_dSs_2)\\n\\t\\telse:\\n\\t\\t\\tself.idxs_2 = []\\n\\t\\t\\tself.Kk_split = 0\\n\\t\\t\\t\\n\\t\\t# Ss0 is the ideal (learned) background stimulus without noise\\n\\t\\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.idxs, params_Ss0,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Ss0)\\n\\t\\t\\n\\t\\tself.Ss = self.dSs + self.Ss0_noisy\",\n \"def fit(self):\\n if self.cost_func.hessian:\\n self._popt = ral_nlls.solve(self.initial_params,\\n self.cost_func.eval_r,\\n self.cost_func.jac_res,\\n self.hes_eval,\\n options=self._options,\\n lower_bounds=self.param_ranges[0],\\n upper_bounds=self.param_ranges[1])[0]\\n else:\\n self._popt = ral_nlls.solve(self.initial_params,\\n self.cost_func.eval_r,\\n self.cost_func.jac_res,\\n options=self._options,\\n lower_bounds=self.param_ranges[0],\\n upper_bounds=self.param_ranges[1])[0]\\n self._status = 0 if self._popt is not None else 1\",\n \"def set_params(self, solver):\\n params = yicespy.yices_new_param_record()\\n yicespy.yices_default_params_for_context(solver.yices, params)\\n for k,v in self.solver_options.items():\\n rv = yicespy.yices_set_param(params, k, v)\\n if rv != 0:\\n raise PysmtValueError(\\\"Error setting the option '%s=%s'\\\" % (k,v))\\n solver.yices_params = params\",\n \"def putsucslice(self,whichsol_,first_,last_,suc_):\\n _suc_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and suc_ is not None and len(suc_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument suc is not long enough: Is %d, expected %d\\\" % (len(suc_),((last_) - (first_))))\\n if suc_ is None:\\n raise ValueError(\\\"Argument suc cannot be None\\\")\\n if suc_ is None:\\n raise ValueError(\\\"Argument suc may not be None\\\")\\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n res = __library__.MSK_XX_putsucslice(self.__nativep,whichsol_,first_,last_,_suc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __init__(self, n_safe, ssm, opt_env, wx_cost, wu_cost, beta_safety=2.5,\\n rhc=True,\\n safe_policy=None, opt_perf_trajectory={}, lin_trafo_gp_input=None, opts_solver=None, verbosity=0):\\n self.rhc = rhc\\n self.ssm = ssm\\n self.ssm_forward = ssm.get_forward_model_casadi(True)\\n self.n_safe = n_safe\\n self.n_fail = self.n_safe # initialize s.t. there is no backup strategy\\n self.n_s = self.ssm.num_states\\n self.n_u = self.ssm.num_actions\\n self.has_openloop = False\\n self.opts_solver = opts_solver\\n\\n self.safe_policy = safe_policy\\n\\n self.cost_func = None # This is updated wheenver the solver is newly initialized (possibly again with None)\\n\\n self._set_attributes_from_dict(ATTR_NAMES_ENV, DEFAULT_OPT_ENV, opt_env)\\n\\n self.lin_trafo_gp_input = lin_trafo_gp_input\\n if self.lin_trafo_gp_input is None:\\n self.lin_trafo_gp_input = np.eye(self.n_s)\\n\\n if self.h_mat_obs is None:\\n m_obs_mat = 0\\n else:\\n m_obs_mat, n_s_obs = np.shape(self.h_mat_obs)\\n assert n_s_obs == self.n_s, \\\" Wrong shape of obstacle matrix\\\"\\n assert np.shape(self.h_obs) == (m_obs_mat,\\n 1), \\\" Shapes of obstacle linear inequality matrix/vector must match \\\"\\n self.m_obs = m_obs_mat\\n\\n m_safe_mat, n_s_safe = np.shape(self.h_mat_safe)\\n assert n_s_safe == self.n_s, \\\" Wrong shape of safety matrix\\\"\\n assert np.shape(self.h_safe) == (\\n m_safe_mat,\\n 1), \\\" Shapes of safety linear inequality matrix/vector must match \\\"\\n self.m_safe = m_safe_mat\\n\\n # init safety constraints evaluator\\n p_cas = MX.sym('p', (self.n_s, self.n_u))\\n q_cas = MX.sym('q', (self.n_s, self.n_s))\\n g_val_term_cas = lin_ellipsoid_safety_distance(p_cas, q_cas, self.h_mat_safe,\\n self.h_safe)\\n self.g_term_cas = cas.Function(\\\"g_term\\\", [p_cas, q_cas], [g_val_term_cas])\\n\\n if not self.h_mat_obs is None:\\n g_val_interm_cas = lin_ellipsoid_safety_distance(p_cas, q_cas,\\n self.h_mat_obs, self.h_obs)\\n self.g_interm_cas = cas.Function(\\\"g_interm\\\", [p_cas, q_cas],\\n [g_val_term_cas])\\n\\n self.has_ctrl_bounds = False\\n\\n if not self.ctrl_bounds is None:\\n self.has_ctrl_bounds = True\\n assert np.shape(self.ctrl_bounds) == (self.n_u, 2), \\\"\\\"\\\"control bounds need\\n to be of shape n_u x 2 with i,0 lower bound and i,1 upper bound per dimension\\\"\\\"\\\"\\n\\n self.wx_cost = wx_cost\\n self.wu_cost = wu_cost\\n self.wx_feedback = wx_cost\\n self.wu_feedback = 1 * wu_cost\\n\\n self.do_shift_solution = True\\n self.solver_initialized = False\\n\\n self.beta_safety = beta_safety\\n self.verbosity = verbosity\\n\\n # SET ALL ATTRIBUTES FOR THE ENVIRONMENT\\n\\n self.lin_prior = False\\n self.a = np.eye(self.n_s)\\n self.b = np.zeros((self.n_s, self.n_u))\\n if not self.lin_model is None:\\n self.a, self.b = self.lin_model\\n self.lin_prior = True\\n if self.safe_policy is None:\\n # no safe policy specified? Use lqr as safe policy\\n K = self.get_lqr_feedback()\\n self.safe_policy = lambda x: np.dot(K, x)\\n\\n # if self.performance_trajectory is None:\\n # self.performance_trajectory = mean_equivalent\\n self._set_attributes_from_dict(ATTR_NAMES_PERF, DEFAULT_OPT_PERF,\\n opt_perf_trajectory)\\n self._set_perf_trajectory(self.type_perf_traj)\\n\\n self.k_fb_all = None\\n if self.safe_policy is None:\\n warnings.warn(\\\"No SafePolicy!\\\")\\n\\n # init safe\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7256664","0.7085556","0.70574284","0.67001754","0.66156536","0.6531177","0.6481275","0.63412815","0.63247937","0.6043454","0.5931599","0.5860473","0.58587015","0.57669955","0.57536566","0.5601438","0.55334353","0.55334353","0.55334353","0.5502079","0.54629934","0.5458374","0.542206","0.5412008","0.5379738","0.53786117","0.53472835","0.5208965","0.51494455","0.51446486","0.5118194","0.5093227","0.50612605","0.504554","0.50322294","0.5030368","0.50093985","0.49899182","0.49824977","0.49524426","0.49382088","0.49351594","0.49307895","0.4919634","0.49091995","0.49015906","0.48637438","0.4842622","0.48332128","0.48208466","0.48038903","0.4796855","0.47962767","0.47861898","0.47847214","0.47836456","0.47594273","0.47574693","0.47478026","0.4746294","0.47398362","0.4730608","0.47238928","0.47076088","0.4706043","0.47008726","0.46922156","0.4689041","0.46860898","0.46830004","0.4671689","0.46679533","0.46561986","0.46474853","0.4637876","0.46342784","0.46279302","0.462754","0.46202517","0.46153843","0.46098483","0.46003395","0.4598952","0.4582518","0.45722848","0.45701262","0.4564087","0.45570725","0.45522368","0.4546243","0.45392954","0.45350838","0.45335114","0.45318782","0.45296726","0.45296612","0.45238596","0.45035866","0.45034102","0.44997555"],"string":"[\n \"0.7256664\",\n \"0.7085556\",\n \"0.70574284\",\n \"0.67001754\",\n \"0.66156536\",\n \"0.6531177\",\n \"0.6481275\",\n \"0.63412815\",\n \"0.63247937\",\n \"0.6043454\",\n \"0.5931599\",\n \"0.5860473\",\n \"0.58587015\",\n \"0.57669955\",\n \"0.57536566\",\n \"0.5601438\",\n \"0.55334353\",\n \"0.55334353\",\n \"0.55334353\",\n \"0.5502079\",\n \"0.54629934\",\n \"0.5458374\",\n \"0.542206\",\n \"0.5412008\",\n \"0.5379738\",\n \"0.53786117\",\n \"0.53472835\",\n \"0.5208965\",\n \"0.51494455\",\n \"0.51446486\",\n \"0.5118194\",\n \"0.5093227\",\n \"0.50612605\",\n \"0.504554\",\n \"0.50322294\",\n \"0.5030368\",\n \"0.50093985\",\n \"0.49899182\",\n \"0.49824977\",\n \"0.49524426\",\n \"0.49382088\",\n \"0.49351594\",\n \"0.49307895\",\n \"0.4919634\",\n \"0.49091995\",\n \"0.49015906\",\n \"0.48637438\",\n \"0.4842622\",\n \"0.48332128\",\n \"0.48208466\",\n \"0.48038903\",\n \"0.4796855\",\n \"0.47962767\",\n \"0.47861898\",\n \"0.47847214\",\n \"0.47836456\",\n \"0.47594273\",\n \"0.47574693\",\n \"0.47478026\",\n \"0.4746294\",\n \"0.47398362\",\n \"0.4730608\",\n \"0.47238928\",\n \"0.47076088\",\n \"0.4706043\",\n \"0.47008726\",\n \"0.46922156\",\n \"0.4689041\",\n \"0.46860898\",\n \"0.46830004\",\n \"0.4671689\",\n \"0.46679533\",\n \"0.46561986\",\n \"0.46474853\",\n \"0.4637876\",\n \"0.46342784\",\n \"0.46279302\",\n \"0.462754\",\n \"0.46202517\",\n \"0.46153843\",\n \"0.46098483\",\n \"0.46003395\",\n \"0.4598952\",\n \"0.4582518\",\n \"0.45722848\",\n \"0.45701262\",\n \"0.4564087\",\n \"0.45570725\",\n \"0.45522368\",\n \"0.4546243\",\n \"0.45392954\",\n \"0.45350838\",\n \"0.45335114\",\n \"0.45318782\",\n \"0.45296726\",\n \"0.45296612\",\n \"0.45238596\",\n \"0.45035866\",\n \"0.45034102\",\n \"0.44997555\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8645917"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11996,"cells":{"query":{"kind":"string","value":"Sets the primal and dual solution information for a single variable. putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_)"},"document":{"kind":"string","value":"def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n \n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n \n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if not isinstance(sk_,stakey): raise TypeError(\"Argument sk has wrong type\")\n res = self.__obj.putsolutioni(accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\n else:\n _skn_tmp = None\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putskx(self,whichsol_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskx(whichsol_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n \n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n \n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if barsj_ is None:\n raise ValueError(\"Argument barsj cannot be None\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def updatesolutioninfo(self,whichsol_):\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarsj(self,whichsol_,j_,barsj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barsj is None: raise TypeError(\"Invalid type for argument barsj\")\n if barsj is None:\n barsj_ = None\n else:\n try:\n barsj_ = memoryview(barsj)\n except TypeError:\n try:\n _tmparr_barsj = array.array(\"d\",barsj)\n except TypeError:\n raise TypeError(\"Argument barsj has wrong type\")\n else:\n barsj_ = memoryview(_tmparr_barsj)\n \n else:\n if barsj_.format != \"d\":\n barsj_ = memoryview(array.array(\"d\",barsj))\n \n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj has wrong length\")\n res = self.__obj.putbarsj(whichsol_,j_,barsj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsnx(self,whichsol_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsnx(whichsol_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n _copyback_skn = False\n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n _copyback_skn = True\n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n _copyback_skn = True\n if skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn has wrong length\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n _copyback_xx = False\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n _copyback_xx = True\n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n _copyback_xx = True\n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n _copyback_suc = False\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n _copyback_suc = True\n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n _copyback_suc = True\n if suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc has wrong length\")\n _copyback_slx = False\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n _copyback_slx = True\n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n _copyback_slx = True\n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n _copyback_sux = False\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n _copyback_sux = True\n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n _copyback_sux = True\n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n _copyback_snx = False\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n _copyback_snx = True\n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n _copyback_snx = True\n if snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx has wrong length\")\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value,_solsta_return_value = resargs\n if _copyback_snx:\n snx[:] = _tmparr_snx\n if _copyback_sux:\n sux[:] = _tmparr_sux\n if _copyback_slx:\n slx[:] = _tmparr_slx\n if _copyback_suc:\n suc[:] = _tmparr_suc\n if _copyback_slc:\n slc[:] = _tmparr_slc\n if _copyback_y:\n y[:] = _tmparr_y\n if _copyback_xx:\n xx[:] = _tmparr_xx\n if _copyback_xc:\n xc[:] = _tmparr_xc\n if _copyback_skn:\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\n if _copyback_skx:\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\n _solsta_return_value = solsta(_solsta_return_value)\n _prosta_return_value = prosta(_prosta_return_value)\n return _prosta_return_value,_solsta_return_value","def putslx(self,whichsol_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslx(whichsol_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsnx(self,whichsol_,sux_):\n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if sux_ is None:\n raise ValueError(\"Argument sux cannot be None\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_putsnx(self.__nativep,whichsol_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def updatesolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.updatesolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putbarxj(self,whichsol_,j_,barxj_):\n _barxj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj is not long enough: Is %d, expected %d\" % (len(barxj_),self.getlenbarvarj((j_))))\n if barxj_ is None:\n raise ValueError(\"Argument barxj cannot be None\")\n if barxj_ is None:\n raise ValueError(\"Argument barxj may not be None\")\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\n _barxj_copyarray = False\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxj_ is not None:\n _barxj_copyarray = True\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\n _barxj_np_tmp[:] = barxj_\n assert _barxj_np_tmp.flags.contiguous\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxj_copyarray = False\n _barxj_tmp = None\n \n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvarbound(self,j_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putslx(self,whichsol_,slx_):\n _slx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),self.getnumvar()))\n if slx_ is None:\n raise ValueError(\"Argument slx cannot be None\")\n if slx_ is None:\n raise ValueError(\"Argument slx may not be None\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n res = __library__.MSK_XX_putslx(self.__nativep,whichsol_,_slx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n prosta_ = ctypes.c_int32()\n solsta_ = ctypes.c_int32()\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\n raise ValueError(\"Argument skc must be writable\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\n else:\n _skc_tmp = None\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\n raise ValueError(\"Argument skx must be writable\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\n else:\n _skx_tmp = None\n _skn_minlength = self.getnumcone()\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn is not long enough: Is %d, expected %d\" % (len(skn_),self.getnumcone()))\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\n raise ValueError(\"Argument skn must be writable\")\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\n else:\n _skn_tmp = None\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n _xx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),self.getnumvar()))\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\n raise ValueError(\"Argument xx must be writable\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\n raise ValueError(\"Argument y must be writable\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\n raise ValueError(\"Argument slc must be writable\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n _suc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),self.getnumcon()))\n if isinstance(suc_,numpy.ndarray) and not suc_.flags.writeable:\n raise ValueError(\"Argument suc must be writable\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n _slx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),self.getnumvar()))\n if isinstance(slx_,numpy.ndarray) and not slx_.flags.writeable:\n raise ValueError(\"Argument slx must be writable\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\n raise ValueError(\"Argument sux must be writable\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n _snx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx is not long enough: Is %d, expected %d\" % (len(snx_),self.getnumvar()))\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\n raise ValueError(\"Argument snx must be writable\")\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_getsolution(self.__nativep,whichsol_,ctypes.byref(prosta_),ctypes.byref(solsta_),_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value = prosta(prosta_.value)\n _solsta_return_value = solsta(solsta_.value)\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp\n if _xx_copyarray:\n xx_[:] = _xx_np_tmp\n if _y_copyarray:\n y_[:] = _y_np_tmp\n if _slc_copyarray:\n slc_[:] = _slc_np_tmp\n if _suc_copyarray:\n suc_[:] = _suc_np_tmp\n if _slx_copyarray:\n slx_[:] = _slx_np_tmp\n if _sux_copyarray:\n sux_[:] = _sux_np_tmp\n if _snx_copyarray:\n snx_[:] = _snx_np_tmp\n return (_prosta_return_value,_solsta_return_value)","def putskx(self,whichsol_,skx_):\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskx(self.__nativep,whichsol_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvartype(self,j_,vartype_): # 3\n if not isinstance(vartype_,variabletype): raise TypeError(\"Argument vartype has wrong type\")\n res = self.__obj.putvartype(j_,vartype_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putvartype(self,j_,vartype_):\n res = __library__.MSK_XX_putvartype(self.__nativep,j_,vartype_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_sparsity(self,use_sparse):\n \n if hasattr(self.problem,'sparse_jac'):\n self.use_sparse = use_sparse\n else:\n raise KINSOL_Exception(\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\")","def writesolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.writesolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _var_sol(self, var: Union[LpVariable, Var]) -> float:\n\n return value(var) if self.optimizer == 'pulp' else var.x","def setup_solver(self):\n option = Options()\n if logger.getEffectiveLevel() == logging.DEBUG:\n # option.printLevel = PrintLevel.HIGH\n option.printLevel = PrintLevel.NONE\n else:\n option.printLevel = PrintLevel.NONE\n self.solver_minimizing = SQProblem(self.nV, self.nC)\n self.solver_minimizing.setOptions(option)\n self.solver_maximizing = SQProblem(self.nV, self.nC)\n self.solver_maximizing.setOptions(option)\n\n self.solver_minimizing_recent_index = -2\n self.solver_maximizing_recent_index = -2","def _parse_var_initsol(self,varname) :\n\t\tinitsol = self.ss.constraint.initsol\n\t\tparams = getattr(initsol,varname)\n\t\tnvars = len(self.ss.variables) # num of variables\n\n\t\tif varname in ('alpha','beta') : \n\t\t\tself.initsol[varname] = np.ones(nvars)\n\t\t\tkeys = params.keys()\n\t\t\tself.initsol[varname][:] = params['defaultInitialValue']\n\t\t\tfor key in keys : \n\t\t\t\tif re.match(varname+'_\\d+',key)\t:\n\t\t\t\t\tidx = int(key.split('_')[1])\n\t\t\t\t\tself.initsol[varname][idx-1] = params[key]\n\t\telif varname in ('g','h') :\n\t\t\tself.initsol[varname] = np.ones([nvars,nvars])\n\t\t\tkeys = params.keys()\n\t\t\tself.initsol[varname][:] = params['defaultInitialValue']\n\t\t\tfor key in keys : \n\t\t\t\tif re.match(varname+'_\\d+_\\d+',key)\t:\n\t\t\t\t\tidr,idc = map(int,(key.split('_')[1:3]))\n\t\t\t\t\tself.initsol[varname][idr-1][idc-1] = params[key]\n\t\t\n\t\telse :\n\t\t\tlogging.error(\"Unrecognized varname %s quitting..\" \\\n\t\t\t%(varname))\n\t\t\tsys.exit(1)","def solve(self):\n # check for jacobian and set it if present and to be used\n if self.use_sparse:\n if self._use_jac and hasattr(self.problem,'sparse_jac'):\n jac = self.problem.sparse_jac\n else:\n jac = None\n else:\n if self._use_jac and hasattr(self.problem,'jac'):\n jac = self.problem.jac\n else:\n jac = None\n \n # Initialize solver and solve \n \n solved = False\n local_min = False\n\n res = N.zeros(self.x0.__len__())\n while (not solved) and self.reg_count < 2:\n try:\n if self._use_fscale:\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,self.fscale)\n else:\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,None)\n start = time.clock()\n res = self.solver.KINSOL_solve(not self._use_ls)\n stop = time.clock()\n self.exec_time += (stop - start)\n solved = True\n except KINError as error:\n if error.value == 42:\n # Try the heuristic\n if hasattr(self.problem, 'get_heuristic_x0'):\n print \"----------------------------------------------------\"\n print \" Solver stuck with zero step-length.\"\n print \"----------------------------------------------------\"\n print \"The following variables have start value zero\"\n print \"and min set to zero causing the zero step-lenght.\"\n print \"These settings are either set by default or by user.\"\n print \"\"\n\n self.x0 = self.problem.get_heuristic_x0()\n self.reg_count += 1\n \n print \"\"\n print \"This setting (start and min to zero) can often\"\n print \"cause problem when initializing the system. \"\n print \"\"\n print \"To avoid this the above variables have\"\n print \"their start attributes reset to one.\"\n print \"\"\n print \"Trying to solve the system again...\"\n else:\n raise KINSOL_Exception(\"Regularization failed due to constraints, tried getting heuristic initial guess but failed.\")\n \n\n elif (error.value == 2):\n print \"---------------------------------------------------------\"\n print \"\"\n print \" !!! WARNING !!!\"\n print \"\"\n print \" KINSOL has returned a result but the algorithm has converged\"\n print \" to a local minima, the initial values are NOT consistant!\"\n print \"\"\n print \"---------------------------------------------------------\"\n solved = True\n local_min = True\n else:\n # Other error, send onward as exception\n self.problem.check_constraints(res)\n raise KINSOL_Exception(error.msg[error.value])\n \n if not solved:\n self.solver.Free_KINSOL()\n raise KINSOL_Exception(\"Algorithm exited solution loop without finding a solution, please contact Assimulo support.\")\n\n if self.check_with_model:\n self.problem.check_constraints(res)\n if not local_min:\n print \"Problem sent to KINSOL solved.\"\n \n return res","def Solve(self,iter_val=0):\n\n ### Save Files before solve ###\n self.fprint(\"Saving Input Data\",special=\"header\")\n if \"mesh\" in self.params.output:\n self.problem.dom.Save(val=iter_val)\n if \"initial_guess\" in self.params.output:\n self.problem.bd.SaveInitialGuess(val=iter_val)\n if \"height\" in self.params.output and self.problem.dom.dim == 3:\n self.problem.bd.SaveHeight(val=iter_val)\n if \"turbine_force\" in self.params.output:\n self.problem.farm.SaveRotorDisks(val=iter_val)\n self.fprint(\"Finished\",special=\"footer\")\n\n ####################################################################\n ### This is the better way to define a nonlinear problem but it \n ### doesn't play nice with dolfin_adjoint\n # ### Define Jacobian ###\n # dU = TrialFunction(self.problem.fs.W)\n # J = derivative(self.problem.F, self.problem.up_next, dU)\n\n # ### Setup nonlinear solver ###\n # nonlinear_problem = NonlinearVariationalProblem(self.problem.F, self.problem.up_next, self.problem.bd.bcs, J)\n # nonlinear_solver = NonlinearVariationalSolver(nonlinear_problem)\n\n # ### Set some parameters ###\n # solver_parameters = nonlinear_solver.parameters\n # solver_parameters[\"nonlinear_solver\"] = \"snes\"\n # solver_parameters[\"snes_solver\"][\"linear_solver\"] = \"mumps\"\n # solver_parameters[\"snes_solver\"][\"maximum_iterations\"] = 50\n # solver_parameters[\"snes_solver\"][\"error_on_nonconvergence\"] = False\n # solver_parameters[\"snes_solver\"][\"line_search\"] = \"bt\" # Available: basic, bt, cp, l2, nleqerr\n\n ### Solve the problem ###\n # self.fprint(\"Solving\",special=\"header\")\n # start = time.time()\n # iters, converged = nonlinear_solver.solve()\n # stop = time.time()\n # self.fprint(\"Total Nonlinear Iterations: {:d}\".format(iters))\n # self.fprint(\"Converged Successfully: {0}\".format(converged))\n ####################################################################\n\n\n nonlinear_solver = self.params[\"solver\"].get(\"nonlinear_solver\", \"snes\")\n relaxation = self.params[\"solver\"].get(\"newton_relaxation\", 1.0)\n\n self.fprint(\"Solving with {0}\".format(nonlinear_solver))\n if nonlinear_solver == \"newton\":\n self.fprint(\"Relaxation parameter = {: 1.2f}\".format(relaxation))\n\n newton_options = {\"relaxation_parameter\": relaxation,\n \"maximum_iterations\": 40,\n \"linear_solver\": \"mumps\",\n \"absolute_tolerance\": 1e-6,\n \"relative_tolerance\": 1e-5}\n \n solver_parameters = {\"nonlinear_solver\": \"newton\",\n \"newton_solver\": newton_options}\n\n elif nonlinear_solver == \"snes\":\n # ### Add some helper functions to solver options ###\n solver_parameters = {\"nonlinear_solver\": \"snes\",\n \"snes_solver\": {\n \"linear_solver\": \"mumps\", \n \"maximum_iterations\": 40,\n \"error_on_nonconvergence\": True,\n \"line_search\": \"bt\",\n }}\n \n else:\n raise ValueError(\"Unknown nonlinear solver type: {0}\".format(nonlinear_solver))\n\n ### Start the Solve Process ###\n self.fprint(\"Solving\",special=\"header\")\n start = time.time()\n \n # ### Solve the Baseline Problem ###\n # solve(self.problem.F_sans_tf == 0, self.problem.up_next, self.problem.bd.bcs, solver_parameters=solver_parameters, **self.extra_kwarg)\n\n # ### Store the Baseline and Assign for the real solve ###\n # self.up_baseline = self.problem.up_next.copy(deepcopy=True)\n # self.problem.up_next.assign(self.up_baseline)\n\n ### Solve the real problem ###\n solve(self.problem.F == 0, self.problem.up_next, self.problem.bd.bcs, solver_parameters=solver_parameters)\n stop = time.time()\n self.fprint(\"Solve Complete: {:1.2f} s\".format(stop-start),special=\"footer\")\n # self.u_next,self.p_next = self.problem.up_next.split(True)\n self.u_next,self.p_next = split(self.problem.up_next)\n # self.nu_T = project(self.problem.nu_T,self.problem.fs.Q,solver_type='mumps',**self.extra_kwarg)\n self.nu_T = None\n\n\n ### Save solutions ###\n if \"solution\" in self.params.output:\n self.fprint(\"Saving Solution\",special=\"header\")\n self.Save(val=iter_val)\n self.fprint(\"Finished\",special=\"footer\")\n\n ### calculate the power for each turbine ###\n ###################################\n ### Fix how angle is transfered ###\n ###################################\n if self.optimizing or self.save_power:\n self.J += -self.CalculatePowerFunctional((iter_val-self.problem.dom.init_wind)) \n\n # self.fprint(\"Speed Percent of Inflow Speed\")\n # ps = []\n # for i in range(6):\n # HH = self.problem.farm.HH[0]\n # RD = self.problem.farm.RD[0]\n # x_val = (i+1)*RD\n # vel = self.problem.up_next([x_val,0,HH])\n # vel = vel[0:3]\n # nom = np.linalg.norm(vel)\n # perc = nom/self.problem.bd.HH_vel\n # ps.append(perc)\n # self.fprint(\"Speed Percent at (\"+repr(int(x_val))+\", 0, \"+repr(HH)+\"): \"+repr(perc))\n # print(ps)","def chgvarbound(self,j_,lower_,finite_,value_): # 3\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if isinstance(barsj_,numpy.ndarray) and not barsj_.flags.writeable:\n raise ValueError(\"Argument barsj must be writable\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_getbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barsj_copyarray:\n barsj_[:] = _barsj_np_tmp","def putvarbound(self,j_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getpviolvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getpviolvar(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol","def putaij(self,i_,j_,aij_): # 3\n res = self.__obj.putaij(i_,j_,aij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setGasState(self, j):\n nsp = self.gas.nSpecies()\n y = zeros(nsp, 'd')\n for n in range(nsp):\n nm = self.gas.speciesName(n)\n y[n] = self.solution(nm, j)\n self.gas.setState_TPY(self.T(j), self.pressure, y)","def set_solver(self, solver):\n self.solver = solver\n solver.associate_with_model(self)\n self.set_free_variable(0) # A reasonable initial assumption; can be overridden by simulations","def getpviolvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def putxx(self,whichsol_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xx is None: raise TypeError(\"Invalid type for argument xx\")\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.putxx(whichsol_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def sketch_of_solution(self,sol=None):\n raise NotImplementedError","def solutiondef(self,whichsol_):\n isdef_ = ctypes.c_int32()\n res = __library__.MSK_XX_solutiondef(self.__nativep,whichsol_,ctypes.byref(isdef_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n isdef_ = isdef_.value\n _isdef_return_value = isdef_\n return (_isdef_return_value)","def writesolution(self,whichsol_,filename_):\n if isinstance(filename_,unicode):\n filename_ = filename_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_writesolution(self.__nativep,whichsol_,filename_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getdviolvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getdviolvar(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol","def vj(vj, pol, ant) :\n s.vj(pol, vj, ant)","def putvarname(self,j_,name_): # 3\n res = self.__obj.putvarname(j_,name_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getbarsj(self,whichsol_,j_,barsj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barsj is None: raise TypeError(\"Invalid type for argument barsj\")\n _copyback_barsj = False\n if barsj is None:\n barsj_ = None\n else:\n try:\n barsj_ = memoryview(barsj)\n except TypeError:\n try:\n _tmparr_barsj = array.array(\"d\",barsj)\n except TypeError:\n raise TypeError(\"Argument barsj has wrong type\")\n else:\n barsj_ = memoryview(_tmparr_barsj)\n _copyback_barsj = True\n else:\n if barsj_.format != \"d\":\n barsj_ = memoryview(array.array(\"d\",barsj))\n _copyback_barsj = True\n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj has wrong length\")\n res = self.__obj.getbarsj(whichsol_,j_,barsj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_barsj:\n barsj[:] = _tmparr_barsj","def getsolutioninfo(self,whichsol_):\n pobj_ = ctypes.c_double()\n pviolcon_ = ctypes.c_double()\n pviolvar_ = ctypes.c_double()\n pviolbarvar_ = ctypes.c_double()\n pviolcone_ = ctypes.c_double()\n pviolitg_ = ctypes.c_double()\n dobj_ = ctypes.c_double()\n dviolcon_ = ctypes.c_double()\n dviolvar_ = ctypes.c_double()\n dviolbarvar_ = ctypes.c_double()\n dviolcone_ = ctypes.c_double()\n res = __library__.MSK_XX_getsolutioninfo(self.__nativep,whichsol_,ctypes.byref(pobj_),ctypes.byref(pviolcon_),ctypes.byref(pviolvar_),ctypes.byref(pviolbarvar_),ctypes.byref(pviolcone_),ctypes.byref(pviolitg_),ctypes.byref(dobj_),ctypes.byref(dviolcon_),ctypes.byref(dviolvar_),ctypes.byref(dviolbarvar_),ctypes.byref(dviolcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n pobj_ = pobj_.value\n _pobj_return_value = pobj_\n pviolcon_ = pviolcon_.value\n _pviolcon_return_value = pviolcon_\n pviolvar_ = pviolvar_.value\n _pviolvar_return_value = pviolvar_\n pviolbarvar_ = pviolbarvar_.value\n _pviolbarvar_return_value = pviolbarvar_\n pviolcone_ = pviolcone_.value\n _pviolcone_return_value = pviolcone_\n pviolitg_ = pviolitg_.value\n _pviolitg_return_value = pviolitg_\n dobj_ = dobj_.value\n _dobj_return_value = dobj_\n dviolcon_ = dviolcon_.value\n _dviolcon_return_value = dviolcon_\n dviolvar_ = dviolvar_.value\n _dviolvar_return_value = dviolvar_\n dviolbarvar_ = dviolbarvar_.value\n _dviolbarvar_return_value = dviolbarvar_\n dviolcone_ = dviolcone_.value\n _dviolcone_return_value = dviolcone_\n return (_pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value)","def chgvarbound(self,j_,lower_,finite_,value_):\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsux(self,whichsol_,sux_):\n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if sux_ is None:\n raise ValueError(\"Argument sux cannot be None\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_putsux(self.__nativep,whichsol_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getdviolvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getdviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def set_jac_usage(self,use_jac):\n if type(use_jac).__name__ == 'bool':\n self._use_jac = use_jac\n else:\n raise KINSOL_Exception(\"The variable sent to 'set_jac_usage' must be a boolean.\")","def readSolution(solution):\n g = solution\n __data.g = g\n __data.nsp = g.n_species","def SetNestedSolver(self, solver):\n self._solver = solver\n return","def getbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n _copyback_barxj = False\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n _copyback_barxj = True\n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n _copyback_barxj = True\n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.getbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_barxj:\n barxj[:] = _tmparr_barxj","def solve(self, solver):\n solver.solve()","def getbarxj(self,whichsol_,j_,barxj_):\n _barxj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj is not long enough: Is %d, expected %d\" % (len(barxj_),self.getlenbarvarj((j_))))\n if isinstance(barxj_,numpy.ndarray) and not barxj_.flags.writeable:\n raise ValueError(\"Argument barxj must be writable\")\n if barxj_ is None:\n raise ValueError(\"Argument barxj may not be None\")\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\n _barxj_copyarray = False\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxj_ is not None:\n _barxj_copyarray = True\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\n _barxj_np_tmp[:] = barxj_\n assert _barxj_np_tmp.flags.contiguous\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxj_copyarray = False\n _barxj_tmp = None\n \n res = __library__.MSK_XX_getbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barxj_copyarray:\n barxj_[:] = _barxj_np_tmp","def getsolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value = resargs\n return _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value","def _solve(self, solver):\n self.prob.solve(solver)\n if self.prob.status <= 0:\n raise Exception(\"Infeasible Solution.\")\n return {pid for pid, variable \n in self.player_vars.iteritems()\n if variable.varValue}","def putvarname(self,j_,name_):\n if isinstance(name_,unicode):\n name_ = name_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putvarname(self.__nativep,j_,name_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsnx(self,whichsol_,snx_):\n _snx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx is not long enough: Is %d, expected %d\" % (len(snx_),self.getnumvar()))\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\n raise ValueError(\"Argument snx must be writable\")\n if snx_ is None:\n raise ValueError(\"Argument snx may not be None\")\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_getsnx(self.__nativep,whichsol_,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _snx_copyarray:\n snx_[:] = _snx_np_tmp","def UEVAS_var2sol(self,var,loop_dom,conditions=None):\n\t\tself.UEVAS['sol'][return_version(self.prefix+var,self.UEVAS['sol'])] = \t{'dom': f\"[{', '.join(self.shock_gm.database[loop_dom].names+self.model.out_db[var].index.names)}]\",\n\t\t\t\t\t\t\t\t\t\t\t \t\t \t\t\t\t\t\t\t'cond': \"\" if conditions is None else f\"$({conditions})\",\n\t\t\t\t\t\t\t\t\t\t\t \t\t \t\t\t\t\t\t\t'var': var}","def put_nodal_var(self, time_step, nodal_var_index, num_nodes,\n nodal_var_vals):\n ierr = exolib.py_expnv(self.exoid, time_step + self._o,\n nodal_var_index + self._o, num_nodes,\n nodal_var_vals)\n if ierr:\n raise ExodusIIWriterError(\"Error putting nodal vars\")","def putskc(self,whichsol_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None: raise TypeError(\"Invalid type for argument skc\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskc(whichsol_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def vjij(Vj, Ij, pol, ant) :\n s.vj(pol,Vj, ant)\n sleep(0.5)\n s.ij(pol,Ij, ant)","def getpviolbarvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def putsux(self,whichsol_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsux(whichsol_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def run(self, problem):\n\n self.pyopt_solution = None\n rel = problem.root._probdata.relevance\n\n # Metadata Setup\n self.metadata = create_local_meta(None, self.options['optimizer'])\n self.iter_count = 0\n update_local_meta(self.metadata, (self.iter_count,))\n\n # Initial Run\n with problem.root._dircontext:\n problem.root.solve_nonlinear(metadata=self.metadata)\n\n opt_prob = Optimization(self.options['title'], self._objfunc)\n\n # Add all parameters\n param_meta = self.get_desvar_metadata()\n self.indep_list = indep_list = list(param_meta)\n param_vals = self.get_desvars()\n\n for name, meta in iteritems(param_meta):\n opt_prob.addVarGroup(name, meta['size'], type='c',\n value=param_vals[name],\n lower=meta['lower'], upper=meta['upper'])\n\n opt_prob.finalizeDesignVariables()\n\n # Figure out parameter subsparsity for paramcomp index connections.\n # sub_param_conns is empty unless there are some index conns.\n # full_param_conns gets filled with the connections to the entire\n # parameter so that those params can be filtered out of the sparse\n # set if the full path is also relevant\n sub_param_conns = {}\n full_param_conns = {}\n for name in indep_list:\n pathname = problem.root.unknowns.metadata(name)['pathname']\n sub_param_conns[name] = {}\n full_param_conns[name] = set()\n for target, info in iteritems(problem.root.connections):\n src, indices = info\n if src == pathname:\n if indices is not None:\n # Need to map the connection indices onto the desvar\n # indices if both are declared.\n dv_idx = param_meta[name].get('indices')\n indices = set(indices)\n if dv_idx is not None:\n indices.intersection_update(dv_idx)\n ldv_idx = list(dv_idx)\n mapped_idx = [ldv_idx.index(item) for item in indices]\n sub_param_conns[name][target] = mapped_idx\n else:\n sub_param_conns[name][target] = indices\n else:\n full_param_conns[name].add(target)\n\n # Add all objectives\n objs = self.get_objectives()\n self.quantities = list(objs)\n self.sparsity = OrderedDict()\n self.sub_sparsity = OrderedDict()\n for name in objs:\n opt_prob.addObj(name)\n self.sparsity[name] = self.indep_list\n\n # Calculate and save gradient for any linear constraints.\n lcons = self.get_constraints(lintype='linear').keys()\n if len(lcons) > 0:\n self.lin_jacs = problem.calc_gradient(indep_list, lcons,\n return_format='dict')\n #print(\"Linear Gradient\")\n #print(self.lin_jacs)\n\n # Add all equality constraints\n econs = self.get_constraints(ctype='eq', lintype='nonlinear')\n con_meta = self.get_constraint_metadata()\n self.quantities += list(econs)\n\n self.active_tols = {}\n for name in self.get_constraints(ctype='eq'):\n meta = con_meta[name]\n size = meta['size']\n lower = upper = meta['equals']\n\n # Sparsify Jacobian via relevance\n rels = rel.relevant[name]\n wrt = rels.intersection(indep_list)\n self.sparsity[name] = wrt\n\n if meta['linear']:\n opt_prob.addConGroup(name, size, lower=lower, upper=upper,\n linear=True, wrt=wrt,\n jac=self.lin_jacs[name])\n else:\n\n jac = self._build_sparse(name, wrt, size, param_vals,\n sub_param_conns, full_param_conns, rels)\n opt_prob.addConGroup(name, size, lower=lower, upper=upper,\n wrt=wrt, jac=jac)\n\n active_tol = meta.get('active_tol')\n if active_tol:\n self.active_tols[name] = active_tol\n\n # Add all inequality constraints\n incons = self.get_constraints(ctype='ineq', lintype='nonlinear')\n self.quantities += list(incons)\n\n for name in self.get_constraints(ctype='ineq'):\n meta = con_meta[name]\n size = meta['size']\n\n # Bounds - double sided is supported\n lower = meta['lower']\n upper = meta['upper']\n\n # Sparsify Jacobian via relevance\n rels = rel.relevant[name]\n wrt = rels.intersection(indep_list)\n self.sparsity[name] = wrt\n\n if meta['linear']:\n opt_prob.addConGroup(name, size, upper=upper, lower=lower,\n linear=True, wrt=wrt,\n jac=self.lin_jacs[name])\n else:\n\n jac = self._build_sparse(name, wrt, size, param_vals,\n sub_param_conns, full_param_conns, rels)\n opt_prob.addConGroup(name, size, upper=upper, lower=lower,\n wrt=wrt, jac=jac)\n\n active_tol = meta.get('active_tol')\n if active_tol is not None:\n self.active_tols[name] = active_tol\n\n # Instantiate the requested optimizer\n optimizer = self.options['optimizer']\n try:\n _tmp = __import__('pyoptsparse', globals(), locals(), [optimizer], 0)\n opt = getattr(_tmp, optimizer)()\n except ImportError:\n msg = \"Optimizer %s is not available in this installation.\" % \\\n optimizer\n raise ImportError(msg)\n\n #Set optimization options\n for option, value in self.opt_settings.items():\n opt.setOption(option, value)\n\n self._problem = problem\n self.opt_prob = opt_prob\n\n # Execute the optimization problem\n if self.options['gradient method'] == 'pyopt_fd':\n\n # Use pyOpt's internal finite difference\n fd_step = problem.root.deriv_options['step_size']\n sol = opt(opt_prob, sens='FD', sensStep=fd_step, storeHistory=self.hist_file)\n\n elif self.options['gradient method'] == 'snopt_fd':\n if self.options['optimizer']=='SNOPT':\n\n # Use SNOPT's internal finite difference\n fd_step = problem.root.deriv_options['step_size']\n sol = opt(opt_prob, sens=None, sensStep=fd_step, storeHistory=self.hist_file)\n\n else:\n msg = \"SNOPT's internal finite difference can only be used with SNOPT\"\n raise Exception(msg)\n else:\n\n # Use OpenMDAO's differentiator for the gradient\n sol = opt(opt_prob, sens=self._gradfunc, storeHistory=self.hist_file)\n\n self._problem = None\n\n # Print results\n if self.options['print_results']:\n print(sol)\n\n # Pull optimal parameters back into framework and re-run, so that\n # framework is left in the right final state\n dv_dict = sol.getDVs()\n for name in indep_list:\n val = dv_dict[name]\n self.set_desvar(name, val)\n\n with self.root._dircontext:\n self.root.solve_nonlinear(metadata=self.metadata)\n\n # Save the most recent solution.\n self.pyopt_solution = sol\n try:\n exit_status = sol.optInform['value']\n self.exit_flag = 1\n if exit_status > 2: # bad\n self.exit_flag = 0\n except KeyError: #nothing is here, so something bad happened!\n self.exit_flag = 0","def test_set_su(self):\n s = State(substance=\"water\")\n s.su = Q_(3028.9867985920914, \"J/(kg*K)\"), Q_(1013250.0, \"J/kg\")\n # Pylance does not support NumPy ufuncs\n assert np.isclose(s.T, Q_(373.1242958476843, \"K\")) # type: ignore\n assert np.isclose(s.p, Q_(101325.0, \"Pa\")) # type: ignore\n assert np.isclose(s.su[0], Q_(3028.9867985920914, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.su[1], Q_(1013250.0, \"J/kg\")) # type: ignore\n assert np.isclose(s.u, Q_(1013250, \"J/kg\")) # type: ignore\n assert np.isclose(s.s, Q_(3028.9867985920914, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.v, Q_(0.4772010021515822, \"m**3/kg\")) # type: ignore\n assert np.isclose(s.h, Q_(1061602.391543017, \"J/kg\")) # type: ignore\n assert np.isclose(s.x, Q_(0.28475636946248034, \"dimensionless\")) # type: ignore","def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)","def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)","def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)","def test_solution_usage(self, test_data):\n for finput in test_data[\"EKFSLAM.EKFSLAM.h_jac\"][:1]:\n params = finput\n\n solution.used[\"EKFSLAM.EKFSLAM.h_jac\"] = False\n\n EKFSLAM.EKFSLAM.h_jac(**params)\n\n assert not solution.used[\"EKFSLAM.EKFSLAM.h_jac\"], \"The function uses the solution\"","def test_set_sv(self):\n s = State(substance=\"water\")\n s.sv = Q_(3028.9867985920914, \"J/(kg*K)\"), Q_(0.4772010021515822, \"m**3/kg\")\n # Pylance does not support NumPy ufuncs\n assert np.isclose(s.T, Q_(373.1242958476843, \"K\")) # type: ignore\n assert np.isclose(s.p, Q_(101325.0, \"Pa\")) # type: ignore\n assert np.isclose(s.sv[0], Q_(3028.9867985920914, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.sv[1], Q_(0.4772010021515822, \"m**3/kg\")) # type: ignore\n assert np.isclose(s.u, Q_(1013250.0, \"J/kg\")) # type: ignore\n assert np.isclose(s.s, Q_(3028.9867985920914, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.v, Q_(0.4772010021515822, \"m**3/kg\")) # type: ignore\n assert np.isclose(s.h, Q_(1061602.391543017, \"J/kg\")) # type: ignore\n assert np.isclose(s.x, Q_(0.28475636946248034, \"dimensionless\")) # type: ignore","def putaij(self,i_,j_,aij_):\n res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def substitute_cost(self, i, j):\n raise NotImplementedError","def add_solution(self, solution):\n if self.check_solution(solution):\n self._solution = solution\n self.solution_status = 'OK'\n else:\n self._solution = None\n self.solution_status = 'X'","def show_solution(self,show):\r\n self.showSolution = show","def actualSolve(self, lp):\n if not self.executable(self.path):\n raise PulpSolverError(\"PuLP: cannot execute \" + self.path)\n tmpLp, tmpSol, tmpCmd, tmpAttr, tmpStart = self.create_tmp_files(\n lp.name, \"lp\", \"prt\", \"cmd\", \"attr\", \"slx\"\n )\n variables = lp.writeLP(tmpLp, writeSOS=1, mip=self.mip)\n if self.optionsDict.get(\"warmStart\", False):\n start = [(v.name, v.value()) for v in variables if v.value() is not None]\n self.writeslxsol(tmpStart, start)\n # Explicitly capture some attributes so that we can easily get\n # information about the solution.\n attrNames = []\n if _ismip(lp) and self.mip:\n attrNames.extend([\"mipobjval\", \"bestbound\", \"mipstatus\"])\n statusmap = {\n 0: constants.LpStatusUndefined, # XPRS_MIP_NOT_LOADED\n 1: constants.LpStatusUndefined, # XPRS_MIP_LP_NOT_OPTIMAL\n 2: constants.LpStatusUndefined, # XPRS_MIP_LP_OPTIMAL\n 3: constants.LpStatusUndefined, # XPRS_MIP_NO_SOL_FOUND\n 4: constants.LpStatusUndefined, # XPRS_MIP_SOLUTION\n 5: constants.LpStatusInfeasible, # XPRS_MIP_INFEAS\n 6: constants.LpStatusOptimal, # XPRS_MIP_OPTIMAL\n 7: constants.LpStatusUndefined, # XPRS_MIP_UNBOUNDED\n }\n statuskey = \"mipstatus\"\n else:\n attrNames.extend([\"lpobjval\", \"lpstatus\"])\n statusmap = {\n 0: constants.LpStatusNotSolved, # XPRS_LP_UNSTARTED\n 1: constants.LpStatusOptimal, # XPRS_LP_OPTIMAL\n 2: constants.LpStatusInfeasible, # XPRS_LP_INFEAS\n 3: constants.LpStatusUndefined, # XPRS_LP_CUTOFF\n 4: constants.LpStatusUndefined, # XPRS_LP_UNFINISHED\n 5: constants.LpStatusUnbounded, # XPRS_LP_UNBOUNDED\n 6: constants.LpStatusUndefined, # XPRS_LP_CUTOFF_IN_DUAL\n 7: constants.LpStatusNotSolved, # XPRS_LP_UNSOLVED\n 8: constants.LpStatusUndefined, # XPRS_LP_NONCONVEX\n }\n statuskey = \"lpstatus\"\n with open(tmpCmd, \"w\") as cmd:\n if not self.msg:\n cmd.write(\"OUTPUTLOG=0\\n\")\n # The readprob command must be in lower case for correct filename handling\n cmd.write(\"readprob \" + self.quote_path(tmpLp) + \"\\n\")\n if self.timeLimit is not None:\n cmd.write(\"MAXTIME=%d\\n\" % self.timeLimit)\n targetGap = self.optionsDict.get(\"gapRel\")\n if targetGap is not None:\n cmd.write(f\"MIPRELSTOP={targetGap:f}\\n\")\n heurFreq = self.optionsDict.get(\"heurFreq\")\n if heurFreq is not None:\n cmd.write(\"HEURFREQ=%d\\n\" % heurFreq)\n heurStra = self.optionsDict.get(\"heurStra\")\n if heurStra is not None:\n cmd.write(\"HEURSTRATEGY=%d\\n\" % heurStra)\n coverCuts = self.optionsDict.get(\"coverCuts\")\n if coverCuts is not None:\n cmd.write(\"COVERCUTS=%d\\n\" % coverCuts)\n preSolve = self.optionsDict.get(\"preSolve\")\n if preSolve is not None:\n cmd.write(\"PRESOLVE=%d\\n\" % preSolve)\n if self.optionsDict.get(\"warmStart\", False):\n cmd.write(\"readslxsol \" + self.quote_path(tmpStart) + \"\\n\")\n for option in self.options:\n cmd.write(option + \"\\n\")\n if _ismip(lp) and self.mip:\n cmd.write(\"mipoptimize\\n\")\n else:\n cmd.write(\"lpoptimize\\n\")\n # The writeprtsol command must be in lower case for correct filename handling\n cmd.write(\"writeprtsol \" + self.quote_path(tmpSol) + \"\\n\")\n cmd.write(\n f\"set fh [open {self.quote_path(tmpAttr)} w]; list\\n\"\n ) # `list` to suppress output\n\n for attr in attrNames:\n cmd.write(f'puts $fh \"{attr}=${attr}\"\\n')\n cmd.write(\"close $fh\\n\")\n cmd.write(\"QUIT\\n\")\n with open(tmpCmd) as cmd:\n consume = False\n subout = None\n suberr = None\n if not self.msg:\n # Xpress writes a banner before we can disable output. So\n # we have to explicitly consume the banner.\n if sys.hexversion >= 0x03030000:\n subout = subprocess.DEVNULL\n suberr = subprocess.DEVNULL\n else:\n # We could also use open(os.devnull, 'w') but then we\n # would be responsible for closing the file.\n subout = subprocess.PIPE\n suberr = subprocess.STDOUT\n consume = True\n xpress = subprocess.Popen(\n [self.path, lp.name],\n shell=True,\n stdin=cmd,\n stdout=subout,\n stderr=suberr,\n universal_newlines=True,\n )\n if consume:\n # Special case in which messages are disabled and we have\n # to consume any output\n for _ in xpress.stdout:\n pass\n\n if xpress.wait() != 0:\n raise PulpSolverError(\"PuLP: Error while executing \" + self.path)\n values, redcost, slacks, duals, attrs = self.readsol(tmpSol, tmpAttr)\n self.delete_tmp_files(tmpLp, tmpSol, tmpCmd, tmpAttr)\n status = statusmap.get(attrs.get(statuskey, -1), constants.LpStatusUndefined)\n lp.assignVarsVals(values)\n lp.assignVarsDj(redcost)\n lp.assignConsSlack(slacks)\n lp.assignConsPi(duals)\n lp.assignStatus(status)\n return status","def getsnx(self,whichsol_,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if snx is None: raise TypeError(\"Invalid type for argument snx\")\n _copyback_snx = False\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n _copyback_snx = True\n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n _copyback_snx = True\n if snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx has wrong length\")\n res = self.__obj.getsnx(whichsol_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_snx:\n snx[:] = _tmparr_snx","def solutiondef(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.solutiondef(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _isdef_return_value = resargs\n return _isdef_return_value","def getskx(self,whichsol_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.getskx(whichsol_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_skx:\n for __tmp_var_0 in range(len(skx_)): skx[__tmp_var_0] = stakey(_tmparr_skx[__tmp_var_0])","def putqobjij(self,i_,j_,qoij_): # 3\n res = self.__obj.putqobjij(i_,j_,qoij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_node_value(self, val, i: int, j: int, key: str = 'target') -> None:\n if key not in self._dist:\n raise KeyError('key parameter must be a valid distribution: ' +\n '[\\'initial\\', \\'current\\', \\'target\\']')\n\n elif not self._status['target'] or not self._status['initial']:\n raise ValueError(\n 'No initial or target distribution has been defined.')\n\n mat_i, mat_j = to_matrix(self._param['n_v'], np.array([i, j]))\n self._dist[key][mat_i, mat_j] = val","def putslc(self,whichsol_,slc_):\n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslc(self.__nativep,whichsol_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putslc(self,whichsol_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slc is None: raise TypeError(\"Invalid type for argument slc\")\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.putslc(whichsol_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getdimbarvarj(self,j_): # 3\n res,resargs = self.__obj.getdimbarvarj(j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dimbarvarj_return_value = resargs\n return _dimbarvarj_return_value","def getdviolbarvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getdviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def putxx(self,whichsol_,xx_):\n _xx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),self.getnumvar()))\n if xx_ is None:\n raise ValueError(\"Argument xx cannot be None\")\n if xx_ is None:\n raise ValueError(\"Argument xx may not be None\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_putxx(self.__nativep,whichsol_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def __solve(self) -> None:\n pyo.TransformationFactory(\"contrib.detect_fixed_vars\").apply_to(self.model) # type: ignore\n pyo.TransformationFactory(\"contrib.deactivate_trivial_constraints\").apply_to(self.model) # type: ignore\n\n # initialise the solver object\n self._logger.debug(\"[ModelSolver] Solver object initiated...\")\n solver = Config.OPTIMISATION_MODEL_CONFIG['SOLVER_TYPE']\n opt = pyo.SolverFactory(solver)\n if Config.OPTIMISATION_MODEL_CONFIG['SOLVER_OPTION'].get(solver) is not None:\n for k, v in Config.OPTIMISATION_MODEL_CONFIG['SOLVER_OPTION'].get(solver).items():\n opt.options[k] = v\n\n try:\n start_time = datetime.now()\n self._logger.debug(\"[ModelSolver] Solver starting...\")\n results = opt.solve(self.model, tee=True)\n self.results = results\n end_time = datetime.now()\n self._logger.info(f\"[ModelSolver] Solver completed in {end_time - start_time}.\")\n except Exception as e:\n raise Exception(f\"Model optimisation failed with {solver} with error message {e}.\")\n\n if (results.solver.status == SolverStatus.ok) and (results.solver.termination_condition == TerminationCondition.optimal):\n self._logger.info(\"Solution is feasible and optimal\")\n results.write()\n elif results.solver.termination_condition == TerminationCondition.infeasible:\n raise ValueError(\"Model optimisation resulted into an infeasible solution\")\n\n self.model.optimised = True","def _tkvar_set(self,param_name,val):\n self.debug(\"_tkvar_set(%s,%s)\"%(param_name,val))\n val = self._object2string(param_name,val)\n tkvar = self._tkvars[param_name]\n tkvar._original_set(val) # trace not called because we're already in trace,\n # and tk disables trace activation during trace","def calculate_dvec_spatial_fixed_j(self, jorb: int) -> 'Nparray':\n return self._calculate_dvec_spatial_with_coeff_fixed_j(self.coeff, jorb)","def set_solve_with_search_next(self, swsn):\n self.context.solver.solve_with_search_next = swsn","def getsux(self,whichsol_,sux_):\n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\n raise ValueError(\"Argument sux must be writable\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_getsux(self.__nativep,whichsol_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _sux_copyarray:\n sux_[:] = _sux_np_tmp","def _update_local_solution(self, x: np.ndarray, z: np.ndarray, z_neigh: dict, rho: float, **kwargs):\r\n self.z_neigh = z_neigh\r\n\r\n # update dual variables\r\n self.lambd[self.agent.id] += rho * (x - z)\r\n for j, z_j in z_neigh.items():\r\n self.lambd[j] += rho * (x - z_j)\r\n\r\n # update primal variables\r\n self.x = x\r\n self.z = z","def newVarnode(self, sz: int, addr: ghidra.program.model.address.Address) -> ghidra.program.model.pcode.Varnode:\n ...","def set_variables(self,dv,scale=False):\n\n if type(dv) == np.ndarray:\n dv = dv.tolist()\n\n var_list = self.get_variables()\n\n for ivar, var in enumerate(var_list):\n if type(dv[0]) == Variable:\n var.value = dv[ivar].value * var.scaling if scale else dv[ivar].value\n else:\n value = dv.pop(0)\n var.value = value * var.scaling if scale else value\n\n # Make sure the coupled variables get set too\n self._enforce_coupling()","def getpviolbarvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getpviolbarvar(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol","def test_solve_quadratic_fixed(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.val = 4\n iden2.x.val = 5\n iden3.x.val = 6\n iden1.x.name = 'x1'\n iden2.x.name = 'x2'\n iden3.x.name = 'x3'\n iden2.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 10)\n self.assertAlmostEqual(iden1.x.val, 4)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 6)","def optimize(self):\n\n self.logger.info(\"Solving with Dynamic Slope Scaling Procedure in Julia :\")\n optimization_start = time.time()\n\n # 1. Preprocess for old network graph\n if self.old_network_graph is not None:\n\n # DSSP on old network\n old_network_obj = sum(list(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).values()))-1e-5\n try:\n self.check_infeasibility(self.old_network_graph, old_network_obj)\n except DHCOptimizerException as e:\n e.data = \"Invalid existing network: \" + e.data\n raise e\n\n flows, obj_val = self.optimize_with_dssp_julia(self.old_network_graph, old_network_obj, set())\n self.logger.info(\"Optimization phase time: %.2fs\" % (time.time() - optimization_start))\n solution_old_graph = self.build_solution_graph(self.old_network_graph, flows)\n\n if self.modify_old_network:\n\n # Add max capacity on old edges\n self.old_capacity = deepcopy(flows)\n old_buildings = list(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).values())\n for key in flows:\n if (key[1],key[0],0) not in self.old_capacity and key[1] not in old_buildings:\n self.old_capacity[(key[1],key[0],0)] = self.old_capacity[key]\n\n # Add Imaginary edges\n for edge in self.old_capacity:\n if self.optimization_graph.has_edge(*edge):\n\n # add nodes\n if not self.optimization_graph.has_node(config.IM_PREFIX+edge[0]):\n self.optimization_graph.add_node(config.IM_PREFIX+edge[0])\n self.optimization_graph.nodes[config.IM_PREFIX+edge[0]][config.GPD_GEO_KEY] = \\\n self.optimization_graph.nodes[edge[0]][config.GPD_GEO_KEY]\n if not self.optimization_graph.has_node(config.IM_PREFIX+edge[1]):\n self.optimization_graph.add_node(config.IM_PREFIX+edge[1])\n self.optimization_graph.nodes[config.IM_PREFIX+edge[1]][config.GPD_GEO_KEY] = \\\n self.optimization_graph.nodes[edge[1]][config.GPD_GEO_KEY]\n # add edges\n if not self.optimization_graph.has_edge(edge[0],config.IM_PREFIX+edge[0]):\n self.optimization_graph.add_edge(edge[0],config.IM_PREFIX+edge[0])\n if not self.optimization_graph.has_edge(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1]):\n self.optimization_graph.add_edge(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1])\n if not self.optimization_graph.has_edge(config.IM_PREFIX+edge[1],edge[1]):\n self.optimization_graph.add_edge(config.IM_PREFIX+edge[1],edge[1])\n\n # put cost\n self.optimization_graph.edges[(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1],0)][config.EDGE_COST_KEY] = \\\n self.optimization_graph.edges[(edge[0],edge[1],0)][config.EDGE_COST_KEY]\n self.optimization_graph.edges[(edge[0],edge[1],0)][config.EDGE_COST_KEY] = 1e-5\n self.optimization_graph.edges[(edge[0],config.IM_PREFIX+edge[0],0)][config.EDGE_COST_KEY] = 1e-5\n self.optimization_graph.edges[(config.IM_PREFIX+edge[1],edge[1],0)][config.EDGE_COST_KEY] = 1e-5\n\n else:\n # if we don't modify the old network, we have to change the capacity of the supplies\n already_consummed = {}\n for edge in solution_old_graph.edges():\n if solution_old_graph.nodes[edge[0]].get(config.NODE_TYPE_KEY) == config.SUPPLY_NODE_TYPE:\n already_consummed[edge[0]] = already_consummed.get(edge[0], 0) + \\\n solution_old_graph.edges[edge][config.SOLUTION_POWER_FLOW_KEY]\n for source in already_consummed:\n if already_consummed[source] <= self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY]:\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] -= already_consummed[source]\n self.network_objective -= already_consummed[source]\n else:\n self.network_objective -= self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY]\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] = 0\n\n # Remove edges from old network\n edges_to_remove = set()\n for e in self.optimization_graph.edges():\n if self.old_network_graph.has_edge(*e) or self.old_network_graph.has_edge(e[1],e[0]):\n edges_to_remove.add(e)\n self.optimization_graph.remove_edges_from(edges_to_remove)\n\n # Remove isolated buildings of optimization graph\n isolated_to_remove = set()\n for e in self.old_network_graph.edges():\n if e[0] in self.old_network_graph.nodes() and \\\n self.optimization_graph.nodes[e[1]].get(config.NODE_TYPE_KEY) == config.BUILDING_NODE_TYPE:\n isolated_to_remove.add(e)\n self.optimization_graph.remove_edges_from(isolated_to_remove)\n\n # Remove buildings from old network\n for n, data in self.old_network_graph.nodes(data=True):\n if data.get(config.NODE_TYPE_KEY) == config.BUILDING_NODE_TYPE:\n self.optimization_graph.remove_node(n)\n\n # Re-link sources\n sources = set()\n for n, data in self.optimization_graph.nodes(data=True):\n if data.get(config.NODE_TYPE_KEY) == config.SUPPLY_NODE_TYPE:\n sources.add(n)\n source_graph = self.optimization_graph.subgraph(sources).copy()\n self.optimization_graph.remove_nodes_from(sources)\n gnx.remove_isolates(self.optimization_graph)\n node_filter = lambda n: self.optimization_graph.nodes.get(n,{}).get(config.NODE_TYPE_KEY) != config.BUILDING_NODE_TYPE\n gnx.spatial_points_merge(self.optimization_graph, source_graph.nodes_to_gdf(), node_filter=node_filter, inplace=True)\n\n # fill missing information\n gnx.fill_edges_missing_geometry_attributes(self.optimization_graph)\n gnx.fill_length_attribute(self.optimization_graph, config.EDGE_LENGTH_KEY, only_missing=True)\n gnx.fill_length_attribute(self.optimization_graph, config.EDGE_COST_KEY, only_missing=True)\n for e in self.optimization_graph.edges(keys=True):\n self.optimization_graph.edges[e][config.LEASTCOST_COEF_KEY] = \\\n self.optimization_graph.edges[e].get(config.LEASTCOST_COEF_KEY,0)\n\n\n\n # 2. Process the DSSP on optimization graph\n self.check_is_ready()\n self.check_infeasibility(self.optimization_graph, self.network_objective)\n\n if self.old_network_graph is not None and self.modify_old_network:\n old_buildings = set(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).keys())\n else:\n old_buildings = set()\n flows, obj_val = self.optimize_with_dssp_julia(self.optimization_graph, self.network_objective, old_buildings,postprocess= (not self.modify_old_network))\n self.logger.info(\"Optimization phase time: %.2fs\" % (time.time() - optimization_start))\n self.solution_graph = self.build_solution_graph(self.optimization_graph, flows, self.connected)\n\n # 3. Postprocess for old network graph\n if self.old_network_graph is not None:\n \n if self.modify_old_network:\n # Put the right supply capacity and cost\n for edge in self.old_capacity:\n if self.solution_graph.has_edge(edge[0],edge[1]):\n self.solution_graph.edges[(edge[0],edge[1])][config.EDGE_COST_KEY] = \\\n self.optimization_graph.edges[(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1],0)][config.EDGE_COST_KEY]\n \n # Remove imaginary edges\n imaginary_nodes_to_remove = set()\n nodes_to_relabel = {}\n for edge in self.solution_graph.edges():\n if str(edge[0]).startswith(config.IM_PREFIX) and str(edge[1]).startswith(config.IM_PREFIX):\n real_edge = edge[0][len(config.IM_PREFIX):],edge[1][len(config.IM_PREFIX):]\n self.old_capacity[(real_edge[0], real_edge[1], 0)] = pd.np.inf\n self.old_capacity[(real_edge[1], real_edge[0], 0)] = pd.np.inf\n if not self.solution_graph.has_edge(*real_edge):\n for i in range(2):\n nodes_to_relabel[edge[i]] = real_edge[i]\n else:\n self.solution_graph.edges[real_edge[0],real_edge[1]][config.SOLUTION_POWER_FLOW_KEY] += \\\n self.solution_graph.edges[edge].get(config.SOLUTION_POWER_FLOW_KEY,0)\n imaginary_nodes_to_remove.add(edge[0])\n imaginary_nodes_to_remove.add(edge[1])\n elif str(edge[0]).startswith(config.IM_PREFIX):\n imaginary_nodes_to_remove.add(edge[0])\n elif str(edge[1]).startswith(config.IM_PREFIX):\n imaginary_nodes_to_remove.add(edge[1])\n\n nx.relabel_nodes(self.solution_graph, nodes_to_relabel, copy=False)\n self.solution_graph.remove_nodes_from(list(imaginary_nodes_to_remove))\n for node in nodes_to_relabel.values():\n if self.solution_graph.has_edge(node, node):\n self.solution_graph.remove_edge(node, node)\n\n else:\n for source in nx.get_node_attributes(self.solution_graph, config.SUPPLY_POWER_CAPACITY_KEY):\n self.solution_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] += already_consummed.get(source,0)\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] += already_consummed.get(source,0)\n\n return flows, obj_val","def Save(self,val=0):\n u,p = self.problem.up_next.split(True,**self.extra_kwarg)\n if self.first_save:\n self.u_file = self.params.Save(u,\"velocity\",subfolder=\"solutions/\",val=val)\n self.p_file = self.params.Save(p,\"pressure\",subfolder=\"solutions/\",val=val)\n # self.nuT_file = self.params.Save(self.nu_T,\"eddy_viscosity\",subfolder=\"solutions/\",val=val)\n self.first_save = False\n else:\n self.params.Save(u,\"velocity\",subfolder=\"solutions/\",val=val,file=self.u_file)\n self.params.Save(p,\"pressure\",subfolder=\"solutions/\",val=val,file=self.p_file)\n # self.params.Save(self.nu_T,\"eddy_viscosity\",subfolder=\"solutions/\",val=val,file=self.nuT_file)"],"string":"[\n \"def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skn is None:\\n skn_ = None\\n else:\\n try:\\n skn_ = memoryview(skn)\\n except TypeError:\\n try:\\n _tmparr_skn = array.array(\\\"i\\\",skn)\\n except TypeError:\\n raise TypeError(\\\"Argument skn has wrong type\\\")\\n else:\\n skn_ = memoryview(_tmparr_skn)\\n \\n else:\\n if skn_.format != \\\"i\\\":\\n skn_ = memoryview(array.array(\\\"i\\\",skn))\\n \\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n \\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n \\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n \\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n \\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n \\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n \\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n \\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n \\n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if not isinstance(sk_,stakey): raise TypeError(\\\"Argument sk has wrong type\\\")\\n res = self.__obj.putsolutioni(accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsolutionyi(self,i_,whichsol_,y_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsolutionyi(self,i_,whichsol_,y_):\\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\\n else:\\n _skn_tmp = None\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putskx(self,whichsol_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skx is None: raise TypeError(\\\"Invalid type for argument skx\\\")\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.putskx(whichsol_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarxj(self,whichsol_,j_,barxj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barxj is None: raise TypeError(\\\"Invalid type for argument barxj\\\")\\n if barxj is None:\\n barxj_ = None\\n else:\\n try:\\n barxj_ = memoryview(barxj)\\n except TypeError:\\n try:\\n _tmparr_barxj = array.array(\\\"d\\\",barxj)\\n except TypeError:\\n raise TypeError(\\\"Argument barxj has wrong type\\\")\\n else:\\n barxj_ = memoryview(_tmparr_barxj)\\n \\n else:\\n if barxj_.format != \\\"d\\\":\\n barxj_ = memoryview(array.array(\\\"d\\\",barxj))\\n \\n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj has wrong length\\\")\\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarsj(self,whichsol_,j_,barsj_):\\n _barsj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj is not long enough: Is %d, expected %d\\\" % (len(barsj_),self.getlenbarvarj((j_))))\\n if barsj_ is None:\\n raise ValueError(\\\"Argument barsj cannot be None\\\")\\n if barsj_ is None:\\n raise ValueError(\\\"Argument barsj may not be None\\\")\\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\\n _barsj_copyarray = False\\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barsj_ is not None:\\n _barsj_copyarray = True\\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\\n _barsj_np_tmp[:] = barsj_\\n assert _barsj_np_tmp.flags.contiguous\\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barsj_copyarray = False\\n _barsj_tmp = None\\n \\n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def updatesolutioninfo(self,whichsol_):\\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarsj(self,whichsol_,j_,barsj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barsj is None: raise TypeError(\\\"Invalid type for argument barsj\\\")\\n if barsj is None:\\n barsj_ = None\\n else:\\n try:\\n barsj_ = memoryview(barsj)\\n except TypeError:\\n try:\\n _tmparr_barsj = array.array(\\\"d\\\",barsj)\\n except TypeError:\\n raise TypeError(\\\"Argument barsj has wrong type\\\")\\n else:\\n barsj_ = memoryview(_tmparr_barsj)\\n \\n else:\\n if barsj_.format != \\\"d\\\":\\n barsj_ = memoryview(array.array(\\\"d\\\",barsj))\\n \\n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj has wrong length\\\")\\n res = self.__obj.putbarsj(whichsol_,j_,barsj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsnx(self,whichsol_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if sux is None: raise TypeError(\\\"Invalid type for argument sux\\\")\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.putsnx(whichsol_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skc = False\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n _copyback_skc = True\\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n _copyback_skc = True\\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n _copyback_skx = False\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n _copyback_skx = True\\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n _copyback_skx = True\\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n _copyback_skn = False\\n if skn is None:\\n skn_ = None\\n else:\\n try:\\n skn_ = memoryview(skn)\\n except TypeError:\\n try:\\n _tmparr_skn = array.array(\\\"i\\\",skn)\\n except TypeError:\\n raise TypeError(\\\"Argument skn has wrong type\\\")\\n else:\\n skn_ = memoryview(_tmparr_skn)\\n _copyback_skn = True\\n else:\\n if skn_.format != \\\"i\\\":\\n skn_ = memoryview(array.array(\\\"i\\\",skn))\\n _copyback_skn = True\\n if skn_ is not None and len(skn_) != self.getnumcone():\\n raise ValueError(\\\"Array argument skn has wrong length\\\")\\n _copyback_xc = False\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n _copyback_xc = True\\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n _copyback_xc = True\\n if xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n _copyback_xx = False\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n _copyback_xx = True\\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n _copyback_xx = True\\n if xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n _copyback_y = False\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n _copyback_y = True\\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n _copyback_y = True\\n if y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n _copyback_slc = False\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n _copyback_slc = True\\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n _copyback_slc = True\\n if slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n _copyback_suc = False\\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n _copyback_suc = True\\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n _copyback_suc = True\\n if suc_ is not None and len(suc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument suc has wrong length\\\")\\n _copyback_slx = False\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n _copyback_slx = True\\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n _copyback_slx = True\\n if slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n _copyback_sux = False\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n _copyback_sux = True\\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n _copyback_sux = True\\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n _copyback_snx = False\\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n _copyback_snx = True\\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n _copyback_snx = True\\n if snx_ is not None and len(snx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument snx has wrong length\\\")\\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _prosta_return_value,_solsta_return_value = resargs\\n if _copyback_snx:\\n snx[:] = _tmparr_snx\\n if _copyback_sux:\\n sux[:] = _tmparr_sux\\n if _copyback_slx:\\n slx[:] = _tmparr_slx\\n if _copyback_suc:\\n suc[:] = _tmparr_suc\\n if _copyback_slc:\\n slc[:] = _tmparr_slc\\n if _copyback_y:\\n y[:] = _tmparr_y\\n if _copyback_xx:\\n xx[:] = _tmparr_xx\\n if _copyback_xc:\\n xc[:] = _tmparr_xc\\n if _copyback_skn:\\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\\n if _copyback_skx:\\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\\n if _copyback_skc:\\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\\n _solsta_return_value = solsta(_solsta_return_value)\\n _prosta_return_value = prosta(_prosta_return_value)\\n return _prosta_return_value,_solsta_return_value\",\n \"def putslx(self,whichsol_,slx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slx is None: raise TypeError(\\\"Invalid type for argument slx\\\")\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n res = self.__obj.putslx(whichsol_,slx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsnx(self,whichsol_,sux_):\\n _sux_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),self.getnumvar()))\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux cannot be None\\\")\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux may not be None\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_putsnx(self.__nativep,whichsol_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def updatesolutioninfo(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.updatesolutioninfo(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putbarxj(self,whichsol_,j_,barxj_):\\n _barxj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj is not long enough: Is %d, expected %d\\\" % (len(barxj_),self.getlenbarvarj((j_))))\\n if barxj_ is None:\\n raise ValueError(\\\"Argument barxj cannot be None\\\")\\n if barxj_ is None:\\n raise ValueError(\\\"Argument barxj may not be None\\\")\\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\\n _barxj_copyarray = False\\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barxj_ is not None:\\n _barxj_copyarray = True\\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\\n _barxj_np_tmp[:] = barxj_\\n assert _barxj_np_tmp.flags.contiguous\\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barxj_copyarray = False\\n _barxj_tmp = None\\n \\n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvarbound(self,j_,bkx_,blx_,bux_):\\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putslx(self,whichsol_,slx_):\\n _slx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx is not long enough: Is %d, expected %d\\\" % (len(slx_),self.getnumvar()))\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx cannot be None\\\")\\n if slx_ is None:\\n raise ValueError(\\\"Argument slx may not be None\\\")\\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n res = __library__.MSK_XX_putslx(self.__nativep,whichsol_,_slx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\\n prosta_ = ctypes.c_int32()\\n solsta_ = ctypes.c_int32()\\n _skc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc is not long enough: Is %d, expected %d\\\" % (len(skc_),self.getnumcon()))\\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\\n raise ValueError(\\\"Argument skc must be writable\\\")\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\\n else:\\n _skc_tmp = None\\n _skx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),self.getnumvar()))\\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\\n raise ValueError(\\\"Argument skx must be writable\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\\n else:\\n _skx_tmp = None\\n _skn_minlength = self.getnumcone()\\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\\n raise ValueError(\\\"Array argument skn is not long enough: Is %d, expected %d\\\" % (len(skn_),self.getnumcone()))\\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\\n raise ValueError(\\\"Argument skn must be writable\\\")\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\\n else:\\n _skn_tmp = None\\n _xc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc is not long enough: Is %d, expected %d\\\" % (len(xc_),self.getnumcon()))\\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\\n raise ValueError(\\\"Argument xc must be writable\\\")\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n _xx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),self.getnumvar()))\\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\\n raise ValueError(\\\"Argument xx must be writable\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n _y_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),self.getnumcon()))\\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\\n raise ValueError(\\\"Argument y must be writable\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n _slc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),self.getnumcon()))\\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\\n raise ValueError(\\\"Argument slc must be writable\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n _suc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and suc_ is not None and len(suc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument suc is not long enough: Is %d, expected %d\\\" % (len(suc_),self.getnumcon()))\\n if isinstance(suc_,numpy.ndarray) and not suc_.flags.writeable:\\n raise ValueError(\\\"Argument suc must be writable\\\")\\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n _slx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx is not long enough: Is %d, expected %d\\\" % (len(slx_),self.getnumvar()))\\n if isinstance(slx_,numpy.ndarray) and not slx_.flags.writeable:\\n raise ValueError(\\\"Argument slx must be writable\\\")\\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n _sux_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),self.getnumvar()))\\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\\n raise ValueError(\\\"Argument sux must be writable\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n _snx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and snx_ is not None and len(snx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument snx is not long enough: Is %d, expected %d\\\" % (len(snx_),self.getnumvar()))\\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\\n raise ValueError(\\\"Argument snx must be writable\\\")\\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_getsolution(self.__nativep,whichsol_,ctypes.byref(prosta_),ctypes.byref(solsta_),_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _prosta_return_value = prosta(prosta_.value)\\n _solsta_return_value = solsta(solsta_.value)\\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]\\n if _xc_copyarray:\\n xc_[:] = _xc_np_tmp\\n if _xx_copyarray:\\n xx_[:] = _xx_np_tmp\\n if _y_copyarray:\\n y_[:] = _y_np_tmp\\n if _slc_copyarray:\\n slc_[:] = _slc_np_tmp\\n if _suc_copyarray:\\n suc_[:] = _suc_np_tmp\\n if _slx_copyarray:\\n slx_[:] = _slx_np_tmp\\n if _sux_copyarray:\\n sux_[:] = _sux_np_tmp\\n if _snx_copyarray:\\n snx_[:] = _snx_np_tmp\\n return (_prosta_return_value,_solsta_return_value)\",\n \"def putskx(self,whichsol_,skx_):\\n _skx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx is not long enough: Is %d, expected %d\\\" % (len(skx_),self.getnumvar()))\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx cannot be None\\\")\\n if skx_ is None:\\n raise ValueError(\\\"Argument skx may not be None\\\")\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n res = __library__.MSK_XX_putskx(self.__nativep,whichsol_,_skx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvartype(self,j_,vartype_): # 3\\n if not isinstance(vartype_,variabletype): raise TypeError(\\\"Argument vartype has wrong type\\\")\\n res = self.__obj.putvartype(j_,vartype_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putvartype(self,j_,vartype_):\\n res = __library__.MSK_XX_putvartype(self.__nativep,j_,vartype_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_sparsity(self,use_sparse):\\n \\n if hasattr(self.problem,'sparse_jac'):\\n self.use_sparse = use_sparse\\n else:\\n raise KINSOL_Exception(\\\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\\\")\",\n \"def writesolution(self,whichsol_,filename_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.writesolution(whichsol_,filename_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _var_sol(self, var: Union[LpVariable, Var]) -> float:\\n\\n return value(var) if self.optimizer == 'pulp' else var.x\",\n \"def setup_solver(self):\\n option = Options()\\n if logger.getEffectiveLevel() == logging.DEBUG:\\n # option.printLevel = PrintLevel.HIGH\\n option.printLevel = PrintLevel.NONE\\n else:\\n option.printLevel = PrintLevel.NONE\\n self.solver_minimizing = SQProblem(self.nV, self.nC)\\n self.solver_minimizing.setOptions(option)\\n self.solver_maximizing = SQProblem(self.nV, self.nC)\\n self.solver_maximizing.setOptions(option)\\n\\n self.solver_minimizing_recent_index = -2\\n self.solver_maximizing_recent_index = -2\",\n \"def _parse_var_initsol(self,varname) :\\n\\t\\tinitsol = self.ss.constraint.initsol\\n\\t\\tparams = getattr(initsol,varname)\\n\\t\\tnvars = len(self.ss.variables) # num of variables\\n\\n\\t\\tif varname in ('alpha','beta') : \\n\\t\\t\\tself.initsol[varname] = np.ones(nvars)\\n\\t\\t\\tkeys = params.keys()\\n\\t\\t\\tself.initsol[varname][:] = params['defaultInitialValue']\\n\\t\\t\\tfor key in keys : \\n\\t\\t\\t\\tif re.match(varname+'_\\\\d+',key)\\t:\\n\\t\\t\\t\\t\\tidx = int(key.split('_')[1])\\n\\t\\t\\t\\t\\tself.initsol[varname][idx-1] = params[key]\\n\\t\\telif varname in ('g','h') :\\n\\t\\t\\tself.initsol[varname] = np.ones([nvars,nvars])\\n\\t\\t\\tkeys = params.keys()\\n\\t\\t\\tself.initsol[varname][:] = params['defaultInitialValue']\\n\\t\\t\\tfor key in keys : \\n\\t\\t\\t\\tif re.match(varname+'_\\\\d+_\\\\d+',key)\\t:\\n\\t\\t\\t\\t\\tidr,idc = map(int,(key.split('_')[1:3]))\\n\\t\\t\\t\\t\\tself.initsol[varname][idr-1][idc-1] = params[key]\\n\\t\\t\\n\\t\\telse :\\n\\t\\t\\tlogging.error(\\\"Unrecognized varname %s quitting..\\\" \\\\\\n\\t\\t\\t%(varname))\\n\\t\\t\\tsys.exit(1)\",\n \"def solve(self):\\n # check for jacobian and set it if present and to be used\\n if self.use_sparse:\\n if self._use_jac and hasattr(self.problem,'sparse_jac'):\\n jac = self.problem.sparse_jac\\n else:\\n jac = None\\n else:\\n if self._use_jac and hasattr(self.problem,'jac'):\\n jac = self.problem.jac\\n else:\\n jac = None\\n \\n # Initialize solver and solve \\n \\n solved = False\\n local_min = False\\n\\n res = N.zeros(self.x0.__len__())\\n while (not solved) and self.reg_count < 2:\\n try:\\n if self._use_fscale:\\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,self.fscale)\\n else:\\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,None)\\n start = time.clock()\\n res = self.solver.KINSOL_solve(not self._use_ls)\\n stop = time.clock()\\n self.exec_time += (stop - start)\\n solved = True\\n except KINError as error:\\n if error.value == 42:\\n # Try the heuristic\\n if hasattr(self.problem, 'get_heuristic_x0'):\\n print \\\"----------------------------------------------------\\\"\\n print \\\" Solver stuck with zero step-length.\\\"\\n print \\\"----------------------------------------------------\\\"\\n print \\\"The following variables have start value zero\\\"\\n print \\\"and min set to zero causing the zero step-lenght.\\\"\\n print \\\"These settings are either set by default or by user.\\\"\\n print \\\"\\\"\\n\\n self.x0 = self.problem.get_heuristic_x0()\\n self.reg_count += 1\\n \\n print \\\"\\\"\\n print \\\"This setting (start and min to zero) can often\\\"\\n print \\\"cause problem when initializing the system. \\\"\\n print \\\"\\\"\\n print \\\"To avoid this the above variables have\\\"\\n print \\\"their start attributes reset to one.\\\"\\n print \\\"\\\"\\n print \\\"Trying to solve the system again...\\\"\\n else:\\n raise KINSOL_Exception(\\\"Regularization failed due to constraints, tried getting heuristic initial guess but failed.\\\")\\n \\n\\n elif (error.value == 2):\\n print \\\"---------------------------------------------------------\\\"\\n print \\\"\\\"\\n print \\\" !!! WARNING !!!\\\"\\n print \\\"\\\"\\n print \\\" KINSOL has returned a result but the algorithm has converged\\\"\\n print \\\" to a local minima, the initial values are NOT consistant!\\\"\\n print \\\"\\\"\\n print \\\"---------------------------------------------------------\\\"\\n solved = True\\n local_min = True\\n else:\\n # Other error, send onward as exception\\n self.problem.check_constraints(res)\\n raise KINSOL_Exception(error.msg[error.value])\\n \\n if not solved:\\n self.solver.Free_KINSOL()\\n raise KINSOL_Exception(\\\"Algorithm exited solution loop without finding a solution, please contact Assimulo support.\\\")\\n\\n if self.check_with_model:\\n self.problem.check_constraints(res)\\n if not local_min:\\n print \\\"Problem sent to KINSOL solved.\\\"\\n \\n return res\",\n \"def Solve(self,iter_val=0):\\n\\n ### Save Files before solve ###\\n self.fprint(\\\"Saving Input Data\\\",special=\\\"header\\\")\\n if \\\"mesh\\\" in self.params.output:\\n self.problem.dom.Save(val=iter_val)\\n if \\\"initial_guess\\\" in self.params.output:\\n self.problem.bd.SaveInitialGuess(val=iter_val)\\n if \\\"height\\\" in self.params.output and self.problem.dom.dim == 3:\\n self.problem.bd.SaveHeight(val=iter_val)\\n if \\\"turbine_force\\\" in self.params.output:\\n self.problem.farm.SaveRotorDisks(val=iter_val)\\n self.fprint(\\\"Finished\\\",special=\\\"footer\\\")\\n\\n ####################################################################\\n ### This is the better way to define a nonlinear problem but it \\n ### doesn't play nice with dolfin_adjoint\\n # ### Define Jacobian ###\\n # dU = TrialFunction(self.problem.fs.W)\\n # J = derivative(self.problem.F, self.problem.up_next, dU)\\n\\n # ### Setup nonlinear solver ###\\n # nonlinear_problem = NonlinearVariationalProblem(self.problem.F, self.problem.up_next, self.problem.bd.bcs, J)\\n # nonlinear_solver = NonlinearVariationalSolver(nonlinear_problem)\\n\\n # ### Set some parameters ###\\n # solver_parameters = nonlinear_solver.parameters\\n # solver_parameters[\\\"nonlinear_solver\\\"] = \\\"snes\\\"\\n # solver_parameters[\\\"snes_solver\\\"][\\\"linear_solver\\\"] = \\\"mumps\\\"\\n # solver_parameters[\\\"snes_solver\\\"][\\\"maximum_iterations\\\"] = 50\\n # solver_parameters[\\\"snes_solver\\\"][\\\"error_on_nonconvergence\\\"] = False\\n # solver_parameters[\\\"snes_solver\\\"][\\\"line_search\\\"] = \\\"bt\\\" # Available: basic, bt, cp, l2, nleqerr\\n\\n ### Solve the problem ###\\n # self.fprint(\\\"Solving\\\",special=\\\"header\\\")\\n # start = time.time()\\n # iters, converged = nonlinear_solver.solve()\\n # stop = time.time()\\n # self.fprint(\\\"Total Nonlinear Iterations: {:d}\\\".format(iters))\\n # self.fprint(\\\"Converged Successfully: {0}\\\".format(converged))\\n ####################################################################\\n\\n\\n nonlinear_solver = self.params[\\\"solver\\\"].get(\\\"nonlinear_solver\\\", \\\"snes\\\")\\n relaxation = self.params[\\\"solver\\\"].get(\\\"newton_relaxation\\\", 1.0)\\n\\n self.fprint(\\\"Solving with {0}\\\".format(nonlinear_solver))\\n if nonlinear_solver == \\\"newton\\\":\\n self.fprint(\\\"Relaxation parameter = {: 1.2f}\\\".format(relaxation))\\n\\n newton_options = {\\\"relaxation_parameter\\\": relaxation,\\n \\\"maximum_iterations\\\": 40,\\n \\\"linear_solver\\\": \\\"mumps\\\",\\n \\\"absolute_tolerance\\\": 1e-6,\\n \\\"relative_tolerance\\\": 1e-5}\\n \\n solver_parameters = {\\\"nonlinear_solver\\\": \\\"newton\\\",\\n \\\"newton_solver\\\": newton_options}\\n\\n elif nonlinear_solver == \\\"snes\\\":\\n # ### Add some helper functions to solver options ###\\n solver_parameters = {\\\"nonlinear_solver\\\": \\\"snes\\\",\\n \\\"snes_solver\\\": {\\n \\\"linear_solver\\\": \\\"mumps\\\", \\n \\\"maximum_iterations\\\": 40,\\n \\\"error_on_nonconvergence\\\": True,\\n \\\"line_search\\\": \\\"bt\\\",\\n }}\\n \\n else:\\n raise ValueError(\\\"Unknown nonlinear solver type: {0}\\\".format(nonlinear_solver))\\n\\n ### Start the Solve Process ###\\n self.fprint(\\\"Solving\\\",special=\\\"header\\\")\\n start = time.time()\\n \\n # ### Solve the Baseline Problem ###\\n # solve(self.problem.F_sans_tf == 0, self.problem.up_next, self.problem.bd.bcs, solver_parameters=solver_parameters, **self.extra_kwarg)\\n\\n # ### Store the Baseline and Assign for the real solve ###\\n # self.up_baseline = self.problem.up_next.copy(deepcopy=True)\\n # self.problem.up_next.assign(self.up_baseline)\\n\\n ### Solve the real problem ###\\n solve(self.problem.F == 0, self.problem.up_next, self.problem.bd.bcs, solver_parameters=solver_parameters)\\n stop = time.time()\\n self.fprint(\\\"Solve Complete: {:1.2f} s\\\".format(stop-start),special=\\\"footer\\\")\\n # self.u_next,self.p_next = self.problem.up_next.split(True)\\n self.u_next,self.p_next = split(self.problem.up_next)\\n # self.nu_T = project(self.problem.nu_T,self.problem.fs.Q,solver_type='mumps',**self.extra_kwarg)\\n self.nu_T = None\\n\\n\\n ### Save solutions ###\\n if \\\"solution\\\" in self.params.output:\\n self.fprint(\\\"Saving Solution\\\",special=\\\"header\\\")\\n self.Save(val=iter_val)\\n self.fprint(\\\"Finished\\\",special=\\\"footer\\\")\\n\\n ### calculate the power for each turbine ###\\n ###################################\\n ### Fix how angle is transfered ###\\n ###################################\\n if self.optimizing or self.save_power:\\n self.J += -self.CalculatePowerFunctional((iter_val-self.problem.dom.init_wind)) \\n\\n # self.fprint(\\\"Speed Percent of Inflow Speed\\\")\\n # ps = []\\n # for i in range(6):\\n # HH = self.problem.farm.HH[0]\\n # RD = self.problem.farm.RD[0]\\n # x_val = (i+1)*RD\\n # vel = self.problem.up_next([x_val,0,HH])\\n # vel = vel[0:3]\\n # nom = np.linalg.norm(vel)\\n # perc = nom/self.problem.bd.HH_vel\\n # ps.append(perc)\\n # self.fprint(\\\"Speed Percent at (\\\"+repr(int(x_val))+\\\", 0, \\\"+repr(HH)+\\\"): \\\"+repr(perc))\\n # print(ps)\",\n \"def chgvarbound(self,j_,lower_,finite_,value_): # 3\\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getbarsj(self,whichsol_,j_,barsj_):\\n _barsj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj is not long enough: Is %d, expected %d\\\" % (len(barsj_),self.getlenbarvarj((j_))))\\n if isinstance(barsj_,numpy.ndarray) and not barsj_.flags.writeable:\\n raise ValueError(\\\"Argument barsj must be writable\\\")\\n if barsj_ is None:\\n raise ValueError(\\\"Argument barsj may not be None\\\")\\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\\n _barsj_copyarray = False\\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barsj_ is not None:\\n _barsj_copyarray = True\\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\\n _barsj_np_tmp[:] = barsj_\\n assert _barsj_np_tmp.flags.contiguous\\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barsj_copyarray = False\\n _barsj_tmp = None\\n \\n res = __library__.MSK_XX_getbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _barsj_copyarray:\\n barsj_[:] = _barsj_np_tmp\",\n \"def putvarbound(self,j_,bk_,bl_,bu_): # 3\\n if not isinstance(bk_,boundkey): raise TypeError(\\\"Argument bk has wrong type\\\")\\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getpviolvar(self,whichsol_,sub,viol): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if viol is None: raise TypeError(\\\"Invalid type for argument viol\\\")\\n _copyback_viol = False\\n if viol is None:\\n viol_ = None\\n else:\\n try:\\n viol_ = memoryview(viol)\\n except TypeError:\\n try:\\n _tmparr_viol = array.array(\\\"d\\\",viol)\\n except TypeError:\\n raise TypeError(\\\"Argument viol has wrong type\\\")\\n else:\\n viol_ = memoryview(_tmparr_viol)\\n _copyback_viol = True\\n else:\\n if viol_.format != \\\"d\\\":\\n viol_ = memoryview(array.array(\\\"d\\\",viol))\\n _copyback_viol = True\\n if viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol has wrong length\\\")\\n res = self.__obj.getpviolvar(whichsol_,num_,sub_,viol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_viol:\\n viol[:] = _tmparr_viol\",\n \"def putaij(self,i_,j_,aij_): # 3\\n res = self.__obj.putaij(i_,j_,aij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setGasState(self, j):\\n nsp = self.gas.nSpecies()\\n y = zeros(nsp, 'd')\\n for n in range(nsp):\\n nm = self.gas.speciesName(n)\\n y[n] = self.solution(nm, j)\\n self.gas.setState_TPY(self.T(j), self.pressure, y)\",\n \"def set_solver(self, solver):\\n self.solver = solver\\n solver.associate_with_model(self)\\n self.set_free_variable(0) # A reasonable initial assumption; can be overridden by simulations\",\n \"def getpviolvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getpviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def putxx(self,whichsol_,xx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xx is None: raise TypeError(\\\"Invalid type for argument xx\\\")\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n res = self.__obj.putxx(whichsol_,xx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def sketch_of_solution(self,sol=None):\\n raise NotImplementedError\",\n \"def solutiondef(self,whichsol_):\\n isdef_ = ctypes.c_int32()\\n res = __library__.MSK_XX_solutiondef(self.__nativep,whichsol_,ctypes.byref(isdef_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n isdef_ = isdef_.value\\n _isdef_return_value = isdef_\\n return (_isdef_return_value)\",\n \"def writesolution(self,whichsol_,filename_):\\n if isinstance(filename_,unicode):\\n filename_ = filename_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_writesolution(self.__nativep,whichsol_,filename_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getdviolvar(self,whichsol_,sub,viol): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if viol is None: raise TypeError(\\\"Invalid type for argument viol\\\")\\n _copyback_viol = False\\n if viol is None:\\n viol_ = None\\n else:\\n try:\\n viol_ = memoryview(viol)\\n except TypeError:\\n try:\\n _tmparr_viol = array.array(\\\"d\\\",viol)\\n except TypeError:\\n raise TypeError(\\\"Argument viol has wrong type\\\")\\n else:\\n viol_ = memoryview(_tmparr_viol)\\n _copyback_viol = True\\n else:\\n if viol_.format != \\\"d\\\":\\n viol_ = memoryview(array.array(\\\"d\\\",viol))\\n _copyback_viol = True\\n if viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol has wrong length\\\")\\n res = self.__obj.getdviolvar(whichsol_,num_,sub_,viol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_viol:\\n viol[:] = _tmparr_viol\",\n \"def vj(vj, pol, ant) :\\n s.vj(pol, vj, ant)\",\n \"def putvarname(self,j_,name_): # 3\\n res = self.__obj.putvarname(j_,name_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getbarsj(self,whichsol_,j_,barsj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barsj is None: raise TypeError(\\\"Invalid type for argument barsj\\\")\\n _copyback_barsj = False\\n if barsj is None:\\n barsj_ = None\\n else:\\n try:\\n barsj_ = memoryview(barsj)\\n except TypeError:\\n try:\\n _tmparr_barsj = array.array(\\\"d\\\",barsj)\\n except TypeError:\\n raise TypeError(\\\"Argument barsj has wrong type\\\")\\n else:\\n barsj_ = memoryview(_tmparr_barsj)\\n _copyback_barsj = True\\n else:\\n if barsj_.format != \\\"d\\\":\\n barsj_ = memoryview(array.array(\\\"d\\\",barsj))\\n _copyback_barsj = True\\n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barsj has wrong length\\\")\\n res = self.__obj.getbarsj(whichsol_,j_,barsj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_barsj:\\n barsj[:] = _tmparr_barsj\",\n \"def getsolutioninfo(self,whichsol_):\\n pobj_ = ctypes.c_double()\\n pviolcon_ = ctypes.c_double()\\n pviolvar_ = ctypes.c_double()\\n pviolbarvar_ = ctypes.c_double()\\n pviolcone_ = ctypes.c_double()\\n pviolitg_ = ctypes.c_double()\\n dobj_ = ctypes.c_double()\\n dviolcon_ = ctypes.c_double()\\n dviolvar_ = ctypes.c_double()\\n dviolbarvar_ = ctypes.c_double()\\n dviolcone_ = ctypes.c_double()\\n res = __library__.MSK_XX_getsolutioninfo(self.__nativep,whichsol_,ctypes.byref(pobj_),ctypes.byref(pviolcon_),ctypes.byref(pviolvar_),ctypes.byref(pviolbarvar_),ctypes.byref(pviolcone_),ctypes.byref(pviolitg_),ctypes.byref(dobj_),ctypes.byref(dviolcon_),ctypes.byref(dviolvar_),ctypes.byref(dviolbarvar_),ctypes.byref(dviolcone_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n pobj_ = pobj_.value\\n _pobj_return_value = pobj_\\n pviolcon_ = pviolcon_.value\\n _pviolcon_return_value = pviolcon_\\n pviolvar_ = pviolvar_.value\\n _pviolvar_return_value = pviolvar_\\n pviolbarvar_ = pviolbarvar_.value\\n _pviolbarvar_return_value = pviolbarvar_\\n pviolcone_ = pviolcone_.value\\n _pviolcone_return_value = pviolcone_\\n pviolitg_ = pviolitg_.value\\n _pviolitg_return_value = pviolitg_\\n dobj_ = dobj_.value\\n _dobj_return_value = dobj_\\n dviolcon_ = dviolcon_.value\\n _dviolcon_return_value = dviolcon_\\n dviolvar_ = dviolvar_.value\\n _dviolvar_return_value = dviolvar_\\n dviolbarvar_ = dviolbarvar_.value\\n _dviolbarvar_return_value = dviolbarvar_\\n dviolcone_ = dviolcone_.value\\n _dviolcone_return_value = dviolcone_\\n return (_pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value)\",\n \"def chgvarbound(self,j_,lower_,finite_,value_):\\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsux(self,whichsol_,sux_):\\n _sux_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),self.getnumvar()))\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux cannot be None\\\")\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux may not be None\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_putsux(self.__nativep,whichsol_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getdviolvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getdviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def set_jac_usage(self,use_jac):\\n if type(use_jac).__name__ == 'bool':\\n self._use_jac = use_jac\\n else:\\n raise KINSOL_Exception(\\\"The variable sent to 'set_jac_usage' must be a boolean.\\\")\",\n \"def readSolution(solution):\\n g = solution\\n __data.g = g\\n __data.nsp = g.n_species\",\n \"def SetNestedSolver(self, solver):\\n self._solver = solver\\n return\",\n \"def getbarxj(self,whichsol_,j_,barxj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barxj is None: raise TypeError(\\\"Invalid type for argument barxj\\\")\\n _copyback_barxj = False\\n if barxj is None:\\n barxj_ = None\\n else:\\n try:\\n barxj_ = memoryview(barxj)\\n except TypeError:\\n try:\\n _tmparr_barxj = array.array(\\\"d\\\",barxj)\\n except TypeError:\\n raise TypeError(\\\"Argument barxj has wrong type\\\")\\n else:\\n barxj_ = memoryview(_tmparr_barxj)\\n _copyback_barxj = True\\n else:\\n if barxj_.format != \\\"d\\\":\\n barxj_ = memoryview(array.array(\\\"d\\\",barxj))\\n _copyback_barxj = True\\n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj has wrong length\\\")\\n res = self.__obj.getbarxj(whichsol_,j_,barxj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_barxj:\\n barxj[:] = _tmparr_barxj\",\n \"def solve(self, solver):\\n solver.solve()\",\n \"def getbarxj(self,whichsol_,j_,barxj_):\\n _barxj_minlength = self.getlenbarvarj((j_))\\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj is not long enough: Is %d, expected %d\\\" % (len(barxj_),self.getlenbarvarj((j_))))\\n if isinstance(barxj_,numpy.ndarray) and not barxj_.flags.writeable:\\n raise ValueError(\\\"Argument barxj must be writable\\\")\\n if barxj_ is None:\\n raise ValueError(\\\"Argument barxj may not be None\\\")\\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\\n _barxj_copyarray = False\\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif barxj_ is not None:\\n _barxj_copyarray = True\\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\\n _barxj_np_tmp[:] = barxj_\\n assert _barxj_np_tmp.flags.contiguous\\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _barxj_copyarray = False\\n _barxj_tmp = None\\n \\n res = __library__.MSK_XX_getbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _barxj_copyarray:\\n barxj_[:] = _barxj_np_tmp\",\n \"def getsolutioninfo(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getsolutioninfo(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value = resargs\\n return _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value\",\n \"def _solve(self, solver):\\n self.prob.solve(solver)\\n if self.prob.status <= 0:\\n raise Exception(\\\"Infeasible Solution.\\\")\\n return {pid for pid, variable \\n in self.player_vars.iteritems()\\n if variable.varValue}\",\n \"def putvarname(self,j_,name_):\\n if isinstance(name_,unicode):\\n name_ = name_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putvarname(self.__nativep,j_,name_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsnx(self,whichsol_,snx_):\\n _snx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and snx_ is not None and len(snx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument snx is not long enough: Is %d, expected %d\\\" % (len(snx_),self.getnumvar()))\\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\\n raise ValueError(\\\"Argument snx must be writable\\\")\\n if snx_ is None:\\n raise ValueError(\\\"Argument snx may not be None\\\")\\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_getsnx(self.__nativep,whichsol_,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _snx_copyarray:\\n snx_[:] = _snx_np_tmp\",\n \"def UEVAS_var2sol(self,var,loop_dom,conditions=None):\\n\\t\\tself.UEVAS['sol'][return_version(self.prefix+var,self.UEVAS['sol'])] = \\t{'dom': f\\\"[{', '.join(self.shock_gm.database[loop_dom].names+self.model.out_db[var].index.names)}]\\\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \\t\\t \\t\\t\\t\\t\\t\\t\\t'cond': \\\"\\\" if conditions is None else f\\\"$({conditions})\\\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \\t\\t \\t\\t\\t\\t\\t\\t\\t'var': var}\",\n \"def put_nodal_var(self, time_step, nodal_var_index, num_nodes,\\n nodal_var_vals):\\n ierr = exolib.py_expnv(self.exoid, time_step + self._o,\\n nodal_var_index + self._o, num_nodes,\\n nodal_var_vals)\\n if ierr:\\n raise ExodusIIWriterError(\\\"Error putting nodal vars\\\")\",\n \"def putskc(self,whichsol_,skc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None: raise TypeError(\\\"Invalid type for argument skc\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n res = self.__obj.putskc(whichsol_,skc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def vjij(Vj, Ij, pol, ant) :\\n s.vj(pol,Vj, ant)\\n sleep(0.5)\\n s.ij(pol,Ij, ant)\",\n \"def getpviolbarvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getpviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def putsux(self,whichsol_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if sux is None: raise TypeError(\\\"Invalid type for argument sux\\\")\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.putsux(whichsol_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def run(self, problem):\\n\\n self.pyopt_solution = None\\n rel = problem.root._probdata.relevance\\n\\n # Metadata Setup\\n self.metadata = create_local_meta(None, self.options['optimizer'])\\n self.iter_count = 0\\n update_local_meta(self.metadata, (self.iter_count,))\\n\\n # Initial Run\\n with problem.root._dircontext:\\n problem.root.solve_nonlinear(metadata=self.metadata)\\n\\n opt_prob = Optimization(self.options['title'], self._objfunc)\\n\\n # Add all parameters\\n param_meta = self.get_desvar_metadata()\\n self.indep_list = indep_list = list(param_meta)\\n param_vals = self.get_desvars()\\n\\n for name, meta in iteritems(param_meta):\\n opt_prob.addVarGroup(name, meta['size'], type='c',\\n value=param_vals[name],\\n lower=meta['lower'], upper=meta['upper'])\\n\\n opt_prob.finalizeDesignVariables()\\n\\n # Figure out parameter subsparsity for paramcomp index connections.\\n # sub_param_conns is empty unless there are some index conns.\\n # full_param_conns gets filled with the connections to the entire\\n # parameter so that those params can be filtered out of the sparse\\n # set if the full path is also relevant\\n sub_param_conns = {}\\n full_param_conns = {}\\n for name in indep_list:\\n pathname = problem.root.unknowns.metadata(name)['pathname']\\n sub_param_conns[name] = {}\\n full_param_conns[name] = set()\\n for target, info in iteritems(problem.root.connections):\\n src, indices = info\\n if src == pathname:\\n if indices is not None:\\n # Need to map the connection indices onto the desvar\\n # indices if both are declared.\\n dv_idx = param_meta[name].get('indices')\\n indices = set(indices)\\n if dv_idx is not None:\\n indices.intersection_update(dv_idx)\\n ldv_idx = list(dv_idx)\\n mapped_idx = [ldv_idx.index(item) for item in indices]\\n sub_param_conns[name][target] = mapped_idx\\n else:\\n sub_param_conns[name][target] = indices\\n else:\\n full_param_conns[name].add(target)\\n\\n # Add all objectives\\n objs = self.get_objectives()\\n self.quantities = list(objs)\\n self.sparsity = OrderedDict()\\n self.sub_sparsity = OrderedDict()\\n for name in objs:\\n opt_prob.addObj(name)\\n self.sparsity[name] = self.indep_list\\n\\n # Calculate and save gradient for any linear constraints.\\n lcons = self.get_constraints(lintype='linear').keys()\\n if len(lcons) > 0:\\n self.lin_jacs = problem.calc_gradient(indep_list, lcons,\\n return_format='dict')\\n #print(\\\"Linear Gradient\\\")\\n #print(self.lin_jacs)\\n\\n # Add all equality constraints\\n econs = self.get_constraints(ctype='eq', lintype='nonlinear')\\n con_meta = self.get_constraint_metadata()\\n self.quantities += list(econs)\\n\\n self.active_tols = {}\\n for name in self.get_constraints(ctype='eq'):\\n meta = con_meta[name]\\n size = meta['size']\\n lower = upper = meta['equals']\\n\\n # Sparsify Jacobian via relevance\\n rels = rel.relevant[name]\\n wrt = rels.intersection(indep_list)\\n self.sparsity[name] = wrt\\n\\n if meta['linear']:\\n opt_prob.addConGroup(name, size, lower=lower, upper=upper,\\n linear=True, wrt=wrt,\\n jac=self.lin_jacs[name])\\n else:\\n\\n jac = self._build_sparse(name, wrt, size, param_vals,\\n sub_param_conns, full_param_conns, rels)\\n opt_prob.addConGroup(name, size, lower=lower, upper=upper,\\n wrt=wrt, jac=jac)\\n\\n active_tol = meta.get('active_tol')\\n if active_tol:\\n self.active_tols[name] = active_tol\\n\\n # Add all inequality constraints\\n incons = self.get_constraints(ctype='ineq', lintype='nonlinear')\\n self.quantities += list(incons)\\n\\n for name in self.get_constraints(ctype='ineq'):\\n meta = con_meta[name]\\n size = meta['size']\\n\\n # Bounds - double sided is supported\\n lower = meta['lower']\\n upper = meta['upper']\\n\\n # Sparsify Jacobian via relevance\\n rels = rel.relevant[name]\\n wrt = rels.intersection(indep_list)\\n self.sparsity[name] = wrt\\n\\n if meta['linear']:\\n opt_prob.addConGroup(name, size, upper=upper, lower=lower,\\n linear=True, wrt=wrt,\\n jac=self.lin_jacs[name])\\n else:\\n\\n jac = self._build_sparse(name, wrt, size, param_vals,\\n sub_param_conns, full_param_conns, rels)\\n opt_prob.addConGroup(name, size, upper=upper, lower=lower,\\n wrt=wrt, jac=jac)\\n\\n active_tol = meta.get('active_tol')\\n if active_tol is not None:\\n self.active_tols[name] = active_tol\\n\\n # Instantiate the requested optimizer\\n optimizer = self.options['optimizer']\\n try:\\n _tmp = __import__('pyoptsparse', globals(), locals(), [optimizer], 0)\\n opt = getattr(_tmp, optimizer)()\\n except ImportError:\\n msg = \\\"Optimizer %s is not available in this installation.\\\" % \\\\\\n optimizer\\n raise ImportError(msg)\\n\\n #Set optimization options\\n for option, value in self.opt_settings.items():\\n opt.setOption(option, value)\\n\\n self._problem = problem\\n self.opt_prob = opt_prob\\n\\n # Execute the optimization problem\\n if self.options['gradient method'] == 'pyopt_fd':\\n\\n # Use pyOpt's internal finite difference\\n fd_step = problem.root.deriv_options['step_size']\\n sol = opt(opt_prob, sens='FD', sensStep=fd_step, storeHistory=self.hist_file)\\n\\n elif self.options['gradient method'] == 'snopt_fd':\\n if self.options['optimizer']=='SNOPT':\\n\\n # Use SNOPT's internal finite difference\\n fd_step = problem.root.deriv_options['step_size']\\n sol = opt(opt_prob, sens=None, sensStep=fd_step, storeHistory=self.hist_file)\\n\\n else:\\n msg = \\\"SNOPT's internal finite difference can only be used with SNOPT\\\"\\n raise Exception(msg)\\n else:\\n\\n # Use OpenMDAO's differentiator for the gradient\\n sol = opt(opt_prob, sens=self._gradfunc, storeHistory=self.hist_file)\\n\\n self._problem = None\\n\\n # Print results\\n if self.options['print_results']:\\n print(sol)\\n\\n # Pull optimal parameters back into framework and re-run, so that\\n # framework is left in the right final state\\n dv_dict = sol.getDVs()\\n for name in indep_list:\\n val = dv_dict[name]\\n self.set_desvar(name, val)\\n\\n with self.root._dircontext:\\n self.root.solve_nonlinear(metadata=self.metadata)\\n\\n # Save the most recent solution.\\n self.pyopt_solution = sol\\n try:\\n exit_status = sol.optInform['value']\\n self.exit_flag = 1\\n if exit_status > 2: # bad\\n self.exit_flag = 0\\n except KeyError: #nothing is here, so something bad happened!\\n self.exit_flag = 0\",\n \"def test_set_su(self):\\n s = State(substance=\\\"water\\\")\\n s.su = Q_(3028.9867985920914, \\\"J/(kg*K)\\\"), Q_(1013250.0, \\\"J/kg\\\")\\n # Pylance does not support NumPy ufuncs\\n assert np.isclose(s.T, Q_(373.1242958476843, \\\"K\\\")) # type: ignore\\n assert np.isclose(s.p, Q_(101325.0, \\\"Pa\\\")) # type: ignore\\n assert np.isclose(s.su[0], Q_(3028.9867985920914, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.su[1], Q_(1013250.0, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.u, Q_(1013250, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.s, Q_(3028.9867985920914, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.v, Q_(0.4772010021515822, \\\"m**3/kg\\\")) # type: ignore\\n assert np.isclose(s.h, Q_(1061602.391543017, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.x, Q_(0.28475636946248034, \\\"dimensionless\\\")) # type: ignore\",\n \"def solve(self):\\n print(\\\"Attempting to solve problem instance with {} constraints\\\".format(len(self.constraints)))\\n self.formulation.solve(solver='SCS')\\n print(self.formulation.status)\",\n \"def solve(self):\\n print(\\\"Attempting to solve problem instance with {} constraints\\\".format(len(self.constraints)))\\n self.formulation.solve(solver='SCS')\\n print(self.formulation.status)\",\n \"def solve(self):\\n print(\\\"Attempting to solve problem instance with {} constraints\\\".format(len(self.constraints)))\\n self.formulation.solve(solver='SCS')\\n print(self.formulation.status)\",\n \"def test_solution_usage(self, test_data):\\n for finput in test_data[\\\"EKFSLAM.EKFSLAM.h_jac\\\"][:1]:\\n params = finput\\n\\n solution.used[\\\"EKFSLAM.EKFSLAM.h_jac\\\"] = False\\n\\n EKFSLAM.EKFSLAM.h_jac(**params)\\n\\n assert not solution.used[\\\"EKFSLAM.EKFSLAM.h_jac\\\"], \\\"The function uses the solution\\\"\",\n \"def test_set_sv(self):\\n s = State(substance=\\\"water\\\")\\n s.sv = Q_(3028.9867985920914, \\\"J/(kg*K)\\\"), Q_(0.4772010021515822, \\\"m**3/kg\\\")\\n # Pylance does not support NumPy ufuncs\\n assert np.isclose(s.T, Q_(373.1242958476843, \\\"K\\\")) # type: ignore\\n assert np.isclose(s.p, Q_(101325.0, \\\"Pa\\\")) # type: ignore\\n assert np.isclose(s.sv[0], Q_(3028.9867985920914, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.sv[1], Q_(0.4772010021515822, \\\"m**3/kg\\\")) # type: ignore\\n assert np.isclose(s.u, Q_(1013250.0, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.s, Q_(3028.9867985920914, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.v, Q_(0.4772010021515822, \\\"m**3/kg\\\")) # type: ignore\\n assert np.isclose(s.h, Q_(1061602.391543017, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.x, Q_(0.28475636946248034, \\\"dimensionless\\\")) # type: ignore\",\n \"def putaij(self,i_,j_,aij_):\\n res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def substitute_cost(self, i, j):\\n raise NotImplementedError\",\n \"def add_solution(self, solution):\\n if self.check_solution(solution):\\n self._solution = solution\\n self.solution_status = 'OK'\\n else:\\n self._solution = None\\n self.solution_status = 'X'\",\n \"def show_solution(self,show):\\r\\n self.showSolution = show\",\n \"def actualSolve(self, lp):\\n if not self.executable(self.path):\\n raise PulpSolverError(\\\"PuLP: cannot execute \\\" + self.path)\\n tmpLp, tmpSol, tmpCmd, tmpAttr, tmpStart = self.create_tmp_files(\\n lp.name, \\\"lp\\\", \\\"prt\\\", \\\"cmd\\\", \\\"attr\\\", \\\"slx\\\"\\n )\\n variables = lp.writeLP(tmpLp, writeSOS=1, mip=self.mip)\\n if self.optionsDict.get(\\\"warmStart\\\", False):\\n start = [(v.name, v.value()) for v in variables if v.value() is not None]\\n self.writeslxsol(tmpStart, start)\\n # Explicitly capture some attributes so that we can easily get\\n # information about the solution.\\n attrNames = []\\n if _ismip(lp) and self.mip:\\n attrNames.extend([\\\"mipobjval\\\", \\\"bestbound\\\", \\\"mipstatus\\\"])\\n statusmap = {\\n 0: constants.LpStatusUndefined, # XPRS_MIP_NOT_LOADED\\n 1: constants.LpStatusUndefined, # XPRS_MIP_LP_NOT_OPTIMAL\\n 2: constants.LpStatusUndefined, # XPRS_MIP_LP_OPTIMAL\\n 3: constants.LpStatusUndefined, # XPRS_MIP_NO_SOL_FOUND\\n 4: constants.LpStatusUndefined, # XPRS_MIP_SOLUTION\\n 5: constants.LpStatusInfeasible, # XPRS_MIP_INFEAS\\n 6: constants.LpStatusOptimal, # XPRS_MIP_OPTIMAL\\n 7: constants.LpStatusUndefined, # XPRS_MIP_UNBOUNDED\\n }\\n statuskey = \\\"mipstatus\\\"\\n else:\\n attrNames.extend([\\\"lpobjval\\\", \\\"lpstatus\\\"])\\n statusmap = {\\n 0: constants.LpStatusNotSolved, # XPRS_LP_UNSTARTED\\n 1: constants.LpStatusOptimal, # XPRS_LP_OPTIMAL\\n 2: constants.LpStatusInfeasible, # XPRS_LP_INFEAS\\n 3: constants.LpStatusUndefined, # XPRS_LP_CUTOFF\\n 4: constants.LpStatusUndefined, # XPRS_LP_UNFINISHED\\n 5: constants.LpStatusUnbounded, # XPRS_LP_UNBOUNDED\\n 6: constants.LpStatusUndefined, # XPRS_LP_CUTOFF_IN_DUAL\\n 7: constants.LpStatusNotSolved, # XPRS_LP_UNSOLVED\\n 8: constants.LpStatusUndefined, # XPRS_LP_NONCONVEX\\n }\\n statuskey = \\\"lpstatus\\\"\\n with open(tmpCmd, \\\"w\\\") as cmd:\\n if not self.msg:\\n cmd.write(\\\"OUTPUTLOG=0\\\\n\\\")\\n # The readprob command must be in lower case for correct filename handling\\n cmd.write(\\\"readprob \\\" + self.quote_path(tmpLp) + \\\"\\\\n\\\")\\n if self.timeLimit is not None:\\n cmd.write(\\\"MAXTIME=%d\\\\n\\\" % self.timeLimit)\\n targetGap = self.optionsDict.get(\\\"gapRel\\\")\\n if targetGap is not None:\\n cmd.write(f\\\"MIPRELSTOP={targetGap:f}\\\\n\\\")\\n heurFreq = self.optionsDict.get(\\\"heurFreq\\\")\\n if heurFreq is not None:\\n cmd.write(\\\"HEURFREQ=%d\\\\n\\\" % heurFreq)\\n heurStra = self.optionsDict.get(\\\"heurStra\\\")\\n if heurStra is not None:\\n cmd.write(\\\"HEURSTRATEGY=%d\\\\n\\\" % heurStra)\\n coverCuts = self.optionsDict.get(\\\"coverCuts\\\")\\n if coverCuts is not None:\\n cmd.write(\\\"COVERCUTS=%d\\\\n\\\" % coverCuts)\\n preSolve = self.optionsDict.get(\\\"preSolve\\\")\\n if preSolve is not None:\\n cmd.write(\\\"PRESOLVE=%d\\\\n\\\" % preSolve)\\n if self.optionsDict.get(\\\"warmStart\\\", False):\\n cmd.write(\\\"readslxsol \\\" + self.quote_path(tmpStart) + \\\"\\\\n\\\")\\n for option in self.options:\\n cmd.write(option + \\\"\\\\n\\\")\\n if _ismip(lp) and self.mip:\\n cmd.write(\\\"mipoptimize\\\\n\\\")\\n else:\\n cmd.write(\\\"lpoptimize\\\\n\\\")\\n # The writeprtsol command must be in lower case for correct filename handling\\n cmd.write(\\\"writeprtsol \\\" + self.quote_path(tmpSol) + \\\"\\\\n\\\")\\n cmd.write(\\n f\\\"set fh [open {self.quote_path(tmpAttr)} w]; list\\\\n\\\"\\n ) # `list` to suppress output\\n\\n for attr in attrNames:\\n cmd.write(f'puts $fh \\\"{attr}=${attr}\\\"\\\\n')\\n cmd.write(\\\"close $fh\\\\n\\\")\\n cmd.write(\\\"QUIT\\\\n\\\")\\n with open(tmpCmd) as cmd:\\n consume = False\\n subout = None\\n suberr = None\\n if not self.msg:\\n # Xpress writes a banner before we can disable output. So\\n # we have to explicitly consume the banner.\\n if sys.hexversion >= 0x03030000:\\n subout = subprocess.DEVNULL\\n suberr = subprocess.DEVNULL\\n else:\\n # We could also use open(os.devnull, 'w') but then we\\n # would be responsible for closing the file.\\n subout = subprocess.PIPE\\n suberr = subprocess.STDOUT\\n consume = True\\n xpress = subprocess.Popen(\\n [self.path, lp.name],\\n shell=True,\\n stdin=cmd,\\n stdout=subout,\\n stderr=suberr,\\n universal_newlines=True,\\n )\\n if consume:\\n # Special case in which messages are disabled and we have\\n # to consume any output\\n for _ in xpress.stdout:\\n pass\\n\\n if xpress.wait() != 0:\\n raise PulpSolverError(\\\"PuLP: Error while executing \\\" + self.path)\\n values, redcost, slacks, duals, attrs = self.readsol(tmpSol, tmpAttr)\\n self.delete_tmp_files(tmpLp, tmpSol, tmpCmd, tmpAttr)\\n status = statusmap.get(attrs.get(statuskey, -1), constants.LpStatusUndefined)\\n lp.assignVarsVals(values)\\n lp.assignVarsDj(redcost)\\n lp.assignConsSlack(slacks)\\n lp.assignConsPi(duals)\\n lp.assignStatus(status)\\n return status\",\n \"def getsnx(self,whichsol_,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if snx is None: raise TypeError(\\\"Invalid type for argument snx\\\")\\n _copyback_snx = False\\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n _copyback_snx = True\\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n _copyback_snx = True\\n if snx_ is not None and len(snx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument snx has wrong length\\\")\\n res = self.__obj.getsnx(whichsol_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_snx:\\n snx[:] = _tmparr_snx\",\n \"def solutiondef(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.solutiondef(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _isdef_return_value = resargs\\n return _isdef_return_value\",\n \"def getskx(self,whichsol_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skx = False\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n _copyback_skx = True\\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n _copyback_skx = True\\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.getskx(whichsol_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_skx:\\n for __tmp_var_0 in range(len(skx_)): skx[__tmp_var_0] = stakey(_tmparr_skx[__tmp_var_0])\",\n \"def putqobjij(self,i_,j_,qoij_): # 3\\n res = self.__obj.putqobjij(i_,j_,qoij_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_node_value(self, val, i: int, j: int, key: str = 'target') -> None:\\n if key not in self._dist:\\n raise KeyError('key parameter must be a valid distribution: ' +\\n '[\\\\'initial\\\\', \\\\'current\\\\', \\\\'target\\\\']')\\n\\n elif not self._status['target'] or not self._status['initial']:\\n raise ValueError(\\n 'No initial or target distribution has been defined.')\\n\\n mat_i, mat_j = to_matrix(self._param['n_v'], np.array([i, j]))\\n self._dist[key][mat_i, mat_j] = val\",\n \"def putslc(self,whichsol_,slc_):\\n _slc_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc is not long enough: Is %d, expected %d\\\" % (len(slc_),self.getnumcon()))\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc cannot be None\\\")\\n if slc_ is None:\\n raise ValueError(\\\"Argument slc may not be None\\\")\\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n res = __library__.MSK_XX_putslc(self.__nativep,whichsol_,_slc_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putslc(self,whichsol_,slc): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slc is None: raise TypeError(\\\"Invalid type for argument slc\\\")\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n \\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n \\n if slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n res = self.__obj.putslc(whichsol_,slc_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getdimbarvarj(self,j_): # 3\\n res,resargs = self.__obj.getdimbarvarj(j_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _dimbarvarj_return_value = resargs\\n return _dimbarvarj_return_value\",\n \"def getdviolbarvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getdviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def putxx(self,whichsol_,xx_):\\n _xx_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx is not long enough: Is %d, expected %d\\\" % (len(xx_),self.getnumvar()))\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx cannot be None\\\")\\n if xx_ is None:\\n raise ValueError(\\\"Argument xx may not be None\\\")\\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n res = __library__.MSK_XX_putxx(self.__nativep,whichsol_,_xx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def __solve(self) -> None:\\n pyo.TransformationFactory(\\\"contrib.detect_fixed_vars\\\").apply_to(self.model) # type: ignore\\n pyo.TransformationFactory(\\\"contrib.deactivate_trivial_constraints\\\").apply_to(self.model) # type: ignore\\n\\n # initialise the solver object\\n self._logger.debug(\\\"[ModelSolver] Solver object initiated...\\\")\\n solver = Config.OPTIMISATION_MODEL_CONFIG['SOLVER_TYPE']\\n opt = pyo.SolverFactory(solver)\\n if Config.OPTIMISATION_MODEL_CONFIG['SOLVER_OPTION'].get(solver) is not None:\\n for k, v in Config.OPTIMISATION_MODEL_CONFIG['SOLVER_OPTION'].get(solver).items():\\n opt.options[k] = v\\n\\n try:\\n start_time = datetime.now()\\n self._logger.debug(\\\"[ModelSolver] Solver starting...\\\")\\n results = opt.solve(self.model, tee=True)\\n self.results = results\\n end_time = datetime.now()\\n self._logger.info(f\\\"[ModelSolver] Solver completed in {end_time - start_time}.\\\")\\n except Exception as e:\\n raise Exception(f\\\"Model optimisation failed with {solver} with error message {e}.\\\")\\n\\n if (results.solver.status == SolverStatus.ok) and (results.solver.termination_condition == TerminationCondition.optimal):\\n self._logger.info(\\\"Solution is feasible and optimal\\\")\\n results.write()\\n elif results.solver.termination_condition == TerminationCondition.infeasible:\\n raise ValueError(\\\"Model optimisation resulted into an infeasible solution\\\")\\n\\n self.model.optimised = True\",\n \"def _tkvar_set(self,param_name,val):\\n self.debug(\\\"_tkvar_set(%s,%s)\\\"%(param_name,val))\\n val = self._object2string(param_name,val)\\n tkvar = self._tkvars[param_name]\\n tkvar._original_set(val) # trace not called because we're already in trace,\\n # and tk disables trace activation during trace\",\n \"def calculate_dvec_spatial_fixed_j(self, jorb: int) -> 'Nparray':\\n return self._calculate_dvec_spatial_with_coeff_fixed_j(self.coeff, jorb)\",\n \"def set_solve_with_search_next(self, swsn):\\n self.context.solver.solve_with_search_next = swsn\",\n \"def getsux(self,whichsol_,sux_):\\n _sux_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux is not long enough: Is %d, expected %d\\\" % (len(sux_),self.getnumvar()))\\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\\n raise ValueError(\\\"Argument sux must be writable\\\")\\n if sux_ is None:\\n raise ValueError(\\\"Argument sux may not be None\\\")\\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n res = __library__.MSK_XX_getsux(self.__nativep,whichsol_,_sux_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _sux_copyarray:\\n sux_[:] = _sux_np_tmp\",\n \"def _update_local_solution(self, x: np.ndarray, z: np.ndarray, z_neigh: dict, rho: float, **kwargs):\\r\\n self.z_neigh = z_neigh\\r\\n\\r\\n # update dual variables\\r\\n self.lambd[self.agent.id] += rho * (x - z)\\r\\n for j, z_j in z_neigh.items():\\r\\n self.lambd[j] += rho * (x - z_j)\\r\\n\\r\\n # update primal variables\\r\\n self.x = x\\r\\n self.z = z\",\n \"def newVarnode(self, sz: int, addr: ghidra.program.model.address.Address) -> ghidra.program.model.pcode.Varnode:\\n ...\",\n \"def set_variables(self,dv,scale=False):\\n\\n if type(dv) == np.ndarray:\\n dv = dv.tolist()\\n\\n var_list = self.get_variables()\\n\\n for ivar, var in enumerate(var_list):\\n if type(dv[0]) == Variable:\\n var.value = dv[ivar].value * var.scaling if scale else dv[ivar].value\\n else:\\n value = dv.pop(0)\\n var.value = value * var.scaling if scale else value\\n\\n # Make sure the coupled variables get set too\\n self._enforce_coupling()\",\n \"def getpviolbarvar(self,whichsol_,sub,viol): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if viol is None: raise TypeError(\\\"Invalid type for argument viol\\\")\\n _copyback_viol = False\\n if viol is None:\\n viol_ = None\\n else:\\n try:\\n viol_ = memoryview(viol)\\n except TypeError:\\n try:\\n _tmparr_viol = array.array(\\\"d\\\",viol)\\n except TypeError:\\n raise TypeError(\\\"Argument viol has wrong type\\\")\\n else:\\n viol_ = memoryview(_tmparr_viol)\\n _copyback_viol = True\\n else:\\n if viol_.format != \\\"d\\\":\\n viol_ = memoryview(array.array(\\\"d\\\",viol))\\n _copyback_viol = True\\n if viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol has wrong length\\\")\\n res = self.__obj.getpviolbarvar(whichsol_,num_,sub_,viol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_viol:\\n viol[:] = _tmparr_viol\",\n \"def test_solve_quadratic_fixed(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.val = 4\\n iden2.x.val = 5\\n iden3.x.val = 6\\n iden1.x.name = 'x1'\\n iden2.x.name = 'x2'\\n iden3.x.name = 'x3'\\n iden2.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 10)\\n self.assertAlmostEqual(iden1.x.val, 4)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 6)\",\n \"def optimize(self):\\n\\n self.logger.info(\\\"Solving with Dynamic Slope Scaling Procedure in Julia :\\\")\\n optimization_start = time.time()\\n\\n # 1. Preprocess for old network graph\\n if self.old_network_graph is not None:\\n\\n # DSSP on old network\\n old_network_obj = sum(list(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).values()))-1e-5\\n try:\\n self.check_infeasibility(self.old_network_graph, old_network_obj)\\n except DHCOptimizerException as e:\\n e.data = \\\"Invalid existing network: \\\" + e.data\\n raise e\\n\\n flows, obj_val = self.optimize_with_dssp_julia(self.old_network_graph, old_network_obj, set())\\n self.logger.info(\\\"Optimization phase time: %.2fs\\\" % (time.time() - optimization_start))\\n solution_old_graph = self.build_solution_graph(self.old_network_graph, flows)\\n\\n if self.modify_old_network:\\n\\n # Add max capacity on old edges\\n self.old_capacity = deepcopy(flows)\\n old_buildings = list(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).values())\\n for key in flows:\\n if (key[1],key[0],0) not in self.old_capacity and key[1] not in old_buildings:\\n self.old_capacity[(key[1],key[0],0)] = self.old_capacity[key]\\n\\n # Add Imaginary edges\\n for edge in self.old_capacity:\\n if self.optimization_graph.has_edge(*edge):\\n\\n # add nodes\\n if not self.optimization_graph.has_node(config.IM_PREFIX+edge[0]):\\n self.optimization_graph.add_node(config.IM_PREFIX+edge[0])\\n self.optimization_graph.nodes[config.IM_PREFIX+edge[0]][config.GPD_GEO_KEY] = \\\\\\n self.optimization_graph.nodes[edge[0]][config.GPD_GEO_KEY]\\n if not self.optimization_graph.has_node(config.IM_PREFIX+edge[1]):\\n self.optimization_graph.add_node(config.IM_PREFIX+edge[1])\\n self.optimization_graph.nodes[config.IM_PREFIX+edge[1]][config.GPD_GEO_KEY] = \\\\\\n self.optimization_graph.nodes[edge[1]][config.GPD_GEO_KEY]\\n # add edges\\n if not self.optimization_graph.has_edge(edge[0],config.IM_PREFIX+edge[0]):\\n self.optimization_graph.add_edge(edge[0],config.IM_PREFIX+edge[0])\\n if not self.optimization_graph.has_edge(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1]):\\n self.optimization_graph.add_edge(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1])\\n if not self.optimization_graph.has_edge(config.IM_PREFIX+edge[1],edge[1]):\\n self.optimization_graph.add_edge(config.IM_PREFIX+edge[1],edge[1])\\n\\n # put cost\\n self.optimization_graph.edges[(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1],0)][config.EDGE_COST_KEY] = \\\\\\n self.optimization_graph.edges[(edge[0],edge[1],0)][config.EDGE_COST_KEY]\\n self.optimization_graph.edges[(edge[0],edge[1],0)][config.EDGE_COST_KEY] = 1e-5\\n self.optimization_graph.edges[(edge[0],config.IM_PREFIX+edge[0],0)][config.EDGE_COST_KEY] = 1e-5\\n self.optimization_graph.edges[(config.IM_PREFIX+edge[1],edge[1],0)][config.EDGE_COST_KEY] = 1e-5\\n\\n else:\\n # if we don't modify the old network, we have to change the capacity of the supplies\\n already_consummed = {}\\n for edge in solution_old_graph.edges():\\n if solution_old_graph.nodes[edge[0]].get(config.NODE_TYPE_KEY) == config.SUPPLY_NODE_TYPE:\\n already_consummed[edge[0]] = already_consummed.get(edge[0], 0) + \\\\\\n solution_old_graph.edges[edge][config.SOLUTION_POWER_FLOW_KEY]\\n for source in already_consummed:\\n if already_consummed[source] <= self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY]:\\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] -= already_consummed[source]\\n self.network_objective -= already_consummed[source]\\n else:\\n self.network_objective -= self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY]\\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] = 0\\n\\n # Remove edges from old network\\n edges_to_remove = set()\\n for e in self.optimization_graph.edges():\\n if self.old_network_graph.has_edge(*e) or self.old_network_graph.has_edge(e[1],e[0]):\\n edges_to_remove.add(e)\\n self.optimization_graph.remove_edges_from(edges_to_remove)\\n\\n # Remove isolated buildings of optimization graph\\n isolated_to_remove = set()\\n for e in self.old_network_graph.edges():\\n if e[0] in self.old_network_graph.nodes() and \\\\\\n self.optimization_graph.nodes[e[1]].get(config.NODE_TYPE_KEY) == config.BUILDING_NODE_TYPE:\\n isolated_to_remove.add(e)\\n self.optimization_graph.remove_edges_from(isolated_to_remove)\\n\\n # Remove buildings from old network\\n for n, data in self.old_network_graph.nodes(data=True):\\n if data.get(config.NODE_TYPE_KEY) == config.BUILDING_NODE_TYPE:\\n self.optimization_graph.remove_node(n)\\n\\n # Re-link sources\\n sources = set()\\n for n, data in self.optimization_graph.nodes(data=True):\\n if data.get(config.NODE_TYPE_KEY) == config.SUPPLY_NODE_TYPE:\\n sources.add(n)\\n source_graph = self.optimization_graph.subgraph(sources).copy()\\n self.optimization_graph.remove_nodes_from(sources)\\n gnx.remove_isolates(self.optimization_graph)\\n node_filter = lambda n: self.optimization_graph.nodes.get(n,{}).get(config.NODE_TYPE_KEY) != config.BUILDING_NODE_TYPE\\n gnx.spatial_points_merge(self.optimization_graph, source_graph.nodes_to_gdf(), node_filter=node_filter, inplace=True)\\n\\n # fill missing information\\n gnx.fill_edges_missing_geometry_attributes(self.optimization_graph)\\n gnx.fill_length_attribute(self.optimization_graph, config.EDGE_LENGTH_KEY, only_missing=True)\\n gnx.fill_length_attribute(self.optimization_graph, config.EDGE_COST_KEY, only_missing=True)\\n for e in self.optimization_graph.edges(keys=True):\\n self.optimization_graph.edges[e][config.LEASTCOST_COEF_KEY] = \\\\\\n self.optimization_graph.edges[e].get(config.LEASTCOST_COEF_KEY,0)\\n\\n\\n\\n # 2. Process the DSSP on optimization graph\\n self.check_is_ready()\\n self.check_infeasibility(self.optimization_graph, self.network_objective)\\n\\n if self.old_network_graph is not None and self.modify_old_network:\\n old_buildings = set(nx.get_node_attributes(self.old_network_graph, config.BUILDING_CONSUMPTION_KEY).keys())\\n else:\\n old_buildings = set()\\n flows, obj_val = self.optimize_with_dssp_julia(self.optimization_graph, self.network_objective, old_buildings,postprocess= (not self.modify_old_network))\\n self.logger.info(\\\"Optimization phase time: %.2fs\\\" % (time.time() - optimization_start))\\n self.solution_graph = self.build_solution_graph(self.optimization_graph, flows, self.connected)\\n\\n # 3. Postprocess for old network graph\\n if self.old_network_graph is not None:\\n \\n if self.modify_old_network:\\n # Put the right supply capacity and cost\\n for edge in self.old_capacity:\\n if self.solution_graph.has_edge(edge[0],edge[1]):\\n self.solution_graph.edges[(edge[0],edge[1])][config.EDGE_COST_KEY] = \\\\\\n self.optimization_graph.edges[(config.IM_PREFIX+edge[0],config.IM_PREFIX+edge[1],0)][config.EDGE_COST_KEY]\\n \\n # Remove imaginary edges\\n imaginary_nodes_to_remove = set()\\n nodes_to_relabel = {}\\n for edge in self.solution_graph.edges():\\n if str(edge[0]).startswith(config.IM_PREFIX) and str(edge[1]).startswith(config.IM_PREFIX):\\n real_edge = edge[0][len(config.IM_PREFIX):],edge[1][len(config.IM_PREFIX):]\\n self.old_capacity[(real_edge[0], real_edge[1], 0)] = pd.np.inf\\n self.old_capacity[(real_edge[1], real_edge[0], 0)] = pd.np.inf\\n if not self.solution_graph.has_edge(*real_edge):\\n for i in range(2):\\n nodes_to_relabel[edge[i]] = real_edge[i]\\n else:\\n self.solution_graph.edges[real_edge[0],real_edge[1]][config.SOLUTION_POWER_FLOW_KEY] += \\\\\\n self.solution_graph.edges[edge].get(config.SOLUTION_POWER_FLOW_KEY,0)\\n imaginary_nodes_to_remove.add(edge[0])\\n imaginary_nodes_to_remove.add(edge[1])\\n elif str(edge[0]).startswith(config.IM_PREFIX):\\n imaginary_nodes_to_remove.add(edge[0])\\n elif str(edge[1]).startswith(config.IM_PREFIX):\\n imaginary_nodes_to_remove.add(edge[1])\\n\\n nx.relabel_nodes(self.solution_graph, nodes_to_relabel, copy=False)\\n self.solution_graph.remove_nodes_from(list(imaginary_nodes_to_remove))\\n for node in nodes_to_relabel.values():\\n if self.solution_graph.has_edge(node, node):\\n self.solution_graph.remove_edge(node, node)\\n\\n else:\\n for source in nx.get_node_attributes(self.solution_graph, config.SUPPLY_POWER_CAPACITY_KEY):\\n self.solution_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] += already_consummed.get(source,0)\\n self.optimization_graph.nodes[source][config.SUPPLY_POWER_CAPACITY_KEY] += already_consummed.get(source,0)\\n\\n return flows, obj_val\",\n \"def Save(self,val=0):\\n u,p = self.problem.up_next.split(True,**self.extra_kwarg)\\n if self.first_save:\\n self.u_file = self.params.Save(u,\\\"velocity\\\",subfolder=\\\"solutions/\\\",val=val)\\n self.p_file = self.params.Save(p,\\\"pressure\\\",subfolder=\\\"solutions/\\\",val=val)\\n # self.nuT_file = self.params.Save(self.nu_T,\\\"eddy_viscosity\\\",subfolder=\\\"solutions/\\\",val=val)\\n self.first_save = False\\n else:\\n self.params.Save(u,\\\"velocity\\\",subfolder=\\\"solutions/\\\",val=val,file=self.u_file)\\n self.params.Save(p,\\\"pressure\\\",subfolder=\\\"solutions/\\\",val=val,file=self.p_file)\\n # self.params.Save(self.nu_T,\\\"eddy_viscosity\\\",subfolder=\\\"solutions/\\\",val=val,file=self.nuT_file)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6384697","0.59763306","0.5868142","0.58261186","0.58098394","0.5764213","0.5686661","0.56294215","0.55897075","0.555756","0.55094","0.54587734","0.5450689","0.5421204","0.5396719","0.5390872","0.53769356","0.53164923","0.52948296","0.52874386","0.52444196","0.51348186","0.513479","0.51198614","0.5118228","0.5117997","0.50903535","0.50476485","0.50258005","0.5002769","0.4993314","0.49915802","0.49874622","0.4980678","0.49690902","0.49529886","0.4944692","0.4930765","0.4926388","0.4912962","0.49049127","0.48853943","0.48836303","0.48798177","0.48796168","0.48738426","0.48571074","0.48423678","0.48357746","0.48261422","0.48117992","0.47861198","0.47689503","0.47505355","0.4743992","0.47438064","0.47383732","0.47335666","0.47075158","0.4707435","0.46956557","0.46878737","0.46815833","0.46782017","0.46751773","0.46735907","0.4655286","0.46524912","0.46403298","0.46403298","0.46403298","0.46350008","0.46317136","0.46312332","0.46241015","0.4622388","0.4606744","0.46060848","0.46035412","0.4592499","0.45885488","0.4581407","0.4578413","0.45771936","0.45594808","0.4555995","0.45548686","0.4538305","0.4528887","0.45190638","0.44981682","0.44943747","0.44690177","0.44511676","0.44395393","0.44377384","0.44337723","0.44328395","0.44323778","0.4430756"],"string":"[\n \"0.6384697\",\n \"0.59763306\",\n \"0.5868142\",\n \"0.58261186\",\n \"0.58098394\",\n \"0.5764213\",\n \"0.5686661\",\n \"0.56294215\",\n \"0.55897075\",\n \"0.555756\",\n \"0.55094\",\n \"0.54587734\",\n \"0.5450689\",\n \"0.5421204\",\n \"0.5396719\",\n \"0.5390872\",\n \"0.53769356\",\n \"0.53164923\",\n \"0.52948296\",\n \"0.52874386\",\n \"0.52444196\",\n \"0.51348186\",\n \"0.513479\",\n \"0.51198614\",\n \"0.5118228\",\n \"0.5117997\",\n \"0.50903535\",\n \"0.50476485\",\n \"0.50258005\",\n \"0.5002769\",\n \"0.4993314\",\n \"0.49915802\",\n \"0.49874622\",\n \"0.4980678\",\n \"0.49690902\",\n \"0.49529886\",\n \"0.4944692\",\n \"0.4930765\",\n \"0.4926388\",\n \"0.4912962\",\n \"0.49049127\",\n \"0.48853943\",\n \"0.48836303\",\n \"0.48798177\",\n \"0.48796168\",\n \"0.48738426\",\n \"0.48571074\",\n \"0.48423678\",\n \"0.48357746\",\n \"0.48261422\",\n \"0.48117992\",\n \"0.47861198\",\n \"0.47689503\",\n \"0.47505355\",\n \"0.4743992\",\n \"0.47438064\",\n \"0.47383732\",\n \"0.47335666\",\n \"0.47075158\",\n \"0.4707435\",\n \"0.46956557\",\n \"0.46878737\",\n \"0.46815833\",\n \"0.46782017\",\n \"0.46751773\",\n \"0.46735907\",\n \"0.4655286\",\n \"0.46524912\",\n \"0.46403298\",\n \"0.46403298\",\n \"0.46403298\",\n \"0.46350008\",\n \"0.46317136\",\n \"0.46312332\",\n \"0.46241015\",\n \"0.4622388\",\n \"0.4606744\",\n \"0.46060848\",\n \"0.46035412\",\n \"0.4592499\",\n \"0.45885488\",\n \"0.4581407\",\n \"0.4578413\",\n \"0.45771936\",\n \"0.45594808\",\n \"0.4555995\",\n \"0.45548686\",\n \"0.4538305\",\n \"0.4528887\",\n \"0.45190638\",\n \"0.44981682\",\n \"0.44943747\",\n \"0.44690177\",\n \"0.44511676\",\n \"0.44395393\",\n \"0.44377384\",\n \"0.44337723\",\n \"0.44328395\",\n \"0.44323778\",\n \"0.4430756\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.888349"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11997,"cells":{"query":{"kind":"string","value":"Inputs the dual variable of a solution. putsolutionyi(self,i_,whichsol_,y_)"},"document":{"kind":"string","value":"def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def puty(self,whichsol_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.puty(whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def puty(self,whichsol_,y_):\n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if y_ is None:\n raise ValueError(\"Argument y cannot be None\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_puty(self.__nativep,whichsol_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def gety(self,whichsol_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.gety(whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_y:\n y[:] = _tmparr_y","def gety(self,whichsol_,y_):\n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\n raise ValueError(\"Argument y must be writable\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_gety(self.__nativep,whichsol_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _y_copyarray:\n y_[:] = _y_np_tmp","def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solve(self, x, y):\n\t\tx = np.concatenate((np.ones([x.shape[0], 1]), x), axis=1)\n\t\txtx = np.dot(x.T, x)\n\t\txty = np.dot(y, x)\n\t\tself.w = np.dot(np.linalg.inv(xtx), xty.T)","def y_constraint(q, xy):\n y = ( self.L[0]*np.sin(q[0]) + self.L[1]*np.sin(q[0]+q[1]) + \n self.L[2]*np.sin(q[0]+q[1]+q[2]) + self.L[3]*np.sin(np.sum(q)) ) - xy[1]\n return y","def updatesolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.updatesolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def analytic_solution(self, x, y):\n return (1/np.sinh(2*np.pi))*np.sinh(2*np.pi*y)*np.sin(2*np.pi*x)","def make_solver(y, x, ylim, xlim, invertible_fitter=None):\n cb_y = lambdify(x, y)\n cb_dydx = lambdify(x, y.diff(x))\n\n y0 = ylim[0]\n if invertible_fitter:\n pass\n # TODO:\n # fit parameterized invertible function\n # calculated the invese and use as guess\n fitexpr, params = invertible_fitter\n cb_fitexpr = lambdify(x, fitexpr)\n else:\n DxDy = (xlim[1]-xlim[0])/(ylim[1]-ylim[0])\n def inv_y(y, abstol=1e-13, itermax=30, conv=None):\n \"\"\"\n Returns x and error estimate thereof\n \"\"\"\n if invertible_fitter:\n pass\n else:\n x_ = y0+y*DxDy # guess (linear over xspan)\n dy = cb_y(x_)-y\n i=0\n dx=0.0 # could skip while-loop\n while abs(dy) > abstol and i < itermax:\n dx = -dy/cb_dydx(x_)\n x_ += dx\n dy = cb_y(x_)-y\n i += 1\n if conv != None: conv.append(dx)\n if i==itermax:\n raise RuntimeError(\"Did not converge\")\n return x_, abs(dx)\n\n return inv_y","def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n \n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n \n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def writesolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.writesolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def updatesolutioninfo(self,whichsol_):\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def yxal(self, i):\n return self.y[i]","def DirectSolve(self, y):\n if y >= -self.R:\n a1 = self.apath[y+1]\n if y == -2:\n a2 = 0\n else:\n a2 = self.apath[y+2] \n def constraints(a):\n c0 = (1+self.r)*a + self.b - a1\n c1 = (1+self.r)*a1 + self.b - a2\n return self.uc(c0,0)-self.beta*self.uc(c1,0)*(1+self.r)\n a, n = fsolve(constraints, a1), 0\n c = (1+self.r)*a + self.b - a1\n else:\n a1 = self.apath[y+1]\n a2 = self.apath[y+2]\n if y == -(self.R+1):\n n1 = 0\n c1 = (1+self.r)*a1 + self.b - a2\n else:\n n1 = self.npath[y+1]\n c1 = (1+self.r)*a1 + (1-self.tau)*self.w*n1 - a2\n def constraints((a0,n0)):\n c0 = (1+self.r)*a0 + (1-self.tau)*self.w*n0 - a1\n return self.uc(c0,n0) - self.beta*self.uc(c1,n1)*(1+self.r),\\\n (1-self.tau)*self.w*self.uc(c0,n0) + self.un(c0,n0)\n a, n = fsolve(constraints,(a1,n1))\n c = (1+self.r)*a + (1-self.tau)*self.w*n - a1\n return a, n, c","def y(self, y):\n if type(y) is not int:\n raise TypeError(\"y must be an integer\")\n if y < 0:\n raise ValueError(\"y must be >= 0\")\n self.__y = y","def y(self, y):\n if type(y) is not int:\n raise TypeError(\"y must be an integer\")\n if y < 0:\n raise ValueError(\"y must be >= 0\")\n self.__y = y","def solve(self, solver):\n solver.solve()","def y(self, y):\n if type(y) is not int:\n raise TypeError(\"y must be an integer\")\n elif y < 0:\n raise ValueError(\"y must be >= 0\")\n else:\n self.__y = y","def set_params(self, solver):\n params = yicespy.yices_new_param_record()\n yicespy.yices_default_params_for_context(solver.yices, params)\n for k,v in self.solver_options.items():\n rv = yicespy.yices_set_param(params, k, v)\n if rv != 0:\n raise PysmtValueError(\"Error setting the option '%s=%s'\" % (k,v))\n solver.yices_params = params","def getdviolvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getdviolvar(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol","def putslx(self,whichsol_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslx(whichsol_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def solve_nonlinear(self, params, unknowns, resids):\n\n x = params['x']\n a = self.a\n b = self.b\n c = self.c\n\n unknowns['y'] = a*x**2 + b*x + c","def setBetaEqState(self, pointDict, useThisYeIfSolveFails=None):\n assert isinstance(pointDict, dict)\n assert 'ye' not in pointDict, \"You can't SPECIFY a Ye if you're \" \\\n \"setting neutrinoless beta equlibrium!\"\n self.validatePointDict(pointDict)\n assert len(pointDict) < 3, \"State overdetermined for more than 2 indVars!\"\n\n #defines 1D root solver to use in routine\n solveRoot = scipyOptimize.brentq # solveRootBisect\n\n\n #ASSUME 2 INDEPENENT VARIABLES ARE rho & temp\n logtemp = pointDict['logtemp']\n logrho = pointDict['logrho']\n\n tol = 1.e-6\n getYe = lambda x : multidimInterp((x, logtemp, logrho),\n [self.h5file['ye'][:],\n self.h5file['logtemp'],\n self.h5file['logrho']],\n self.h5file['munu'][...],\n linInterp, 2)\n #check for bracketing error in root solve for ye\n try:\n currentYe = solveRoot(getYe,\n self.h5file['ye'][0],\n self.h5file['ye'][-1], (), tol)\n except ValueError as err:\n print \"Error in scipy root solver solving for ye: \", str(err)\n if useThisYeIfSolveFails is None:\n currentYe = self.findYeOfMinAbsMunu((logtemp, logrho))\n print \"Recovering with findYeOfMinAbsMunu, answer: %s\" % currentYe\n else:\n currentYe = useThisYeIfSolveFails\n print \"Setting Ye to useThisYeIfSolveFails, answer: %s\" % currentYe\n\n newDict = pointDict.copy()\n newDict['ye'] = currentYe\n self.setState(newDict)\n return currentYe","def solve_nonlinear(self, params, unknowns, resids):\n\n x = params['x']\n m = self.slope\n b = self.intercept\n\n unknowns['y'] = m*x + b","def y(x,xi):\n return np.exp(-xi)-np.exp(-xi)*(x-xi)","def getdviolbarvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getdviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def y(self, y):\n if y is None:\n raise ValueError(\"Invalid value for `y`, must not be `None`\") # noqa: E501\n\n self._y = y","def y(self, y):\n if y is None:\n raise ValueError(\"Invalid value for `y`, must not be `None`\") # noqa: E501\n\n self._y = y","def __set_y__(self,y):\n\n # Input vaidation\n try:\n y = int(y)\n except:\n raise ValueError('H Bridge direction is not valid')\n \n if(y != 0 and y != 1 and y != -1):\n raise ValueError('H Bridge direction is not valid')\n \n self.direction['y'] = y\n self.HBridges['y'].SetDirection(y)","def getdviolvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getdviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def getpviolvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getpviolvar(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol","def learned_RHS(t,y,q,x,desc):\n \n \n Ux_mat = create_Ux_mat(x)\n Uxx_mat = create_Uxx_mat(x)\n\n return (q[desc.index('u_{x}')]*Ux_mat.dot(y) + \n q[desc.index('u_{xx}')]*Uxx_mat.dot(y) +\n q[desc.index('u^2')]*y**2 +\n q[desc.index('u')]*y + \n q[desc.index('u^2u_{x}')]*(y**2)*Ux_mat.dot(y) + \n q[desc.index('uu_{x}')]*y*Ux_mat.dot(y) + \n q[desc.index('u^2u_{xx}')]*(y**2)*Uxx_mat.dot(y) + \n q[desc.index('uu_{xx}')]*y*Uxx_mat.dot(y) + \n q[desc.index('u_{x}^2')]*Ux_mat.dot(y)**2)","def getsolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value = resargs\n return _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value","def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\n else:\n _skn_tmp = None\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def userToPlotY(y): \n return dislin.nyposn(y)","def yvar ( self ) :\n return self.__yvar","def getpviolbarvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def getpviolvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp","def getsolutioni(self,accmode_,i_,whichsol_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioni(accmode_,i_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value = resargs\n _sk_return_value = stakey(_sk_return_value)\n return _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value","def getdviolbarvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getdviolbarvar(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol","def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if not isinstance(sk_,stakey): raise TypeError(\"Argument sk has wrong type\")\n res = self.__obj.putsolutioni(accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def y(self, value):\n self.validate_input(y=value)\n self.__y = value","def putyslice(self,whichsol_,first_,last_,y_):\n _y_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),((last_) - (first_))))\n if y_ is None:\n raise ValueError(\"Argument y cannot be None\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_putyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n _copyback_skn = False\n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n _copyback_skn = True\n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n _copyback_skn = True\n if skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn has wrong length\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n _copyback_xx = False\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n _copyback_xx = True\n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n _copyback_xx = True\n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n _copyback_suc = False\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n _copyback_suc = True\n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n _copyback_suc = True\n if suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc has wrong length\")\n _copyback_slx = False\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n _copyback_slx = True\n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n _copyback_slx = True\n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n _copyback_sux = False\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n _copyback_sux = True\n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n _copyback_sux = True\n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n _copyback_snx = False\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n _copyback_snx = True\n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n _copyback_snx = True\n if snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx has wrong length\")\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value,_solsta_return_value = resargs\n if _copyback_snx:\n snx[:] = _tmparr_snx\n if _copyback_sux:\n sux[:] = _tmparr_sux\n if _copyback_slx:\n slx[:] = _tmparr_slx\n if _copyback_suc:\n suc[:] = _tmparr_suc\n if _copyback_slc:\n slc[:] = _tmparr_slc\n if _copyback_y:\n y[:] = _tmparr_y\n if _copyback_xx:\n xx[:] = _tmparr_xx\n if _copyback_xc:\n xc[:] = _tmparr_xc\n if _copyback_skn:\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\n if _copyback_skx:\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\n _solsta_return_value = solsta(_solsta_return_value)\n _prosta_return_value = prosta(_prosta_return_value)\n return _prosta_return_value,_solsta_return_value","def setSolverTau(*argv):","def putyslice(self,whichsol_,first_,last_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.putyslice(whichsol_,first_,last_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def RHS(y,t):\r\n\r\n return np.multiply(A.dot(y),ones-y)-beta*y","def dualy(self, arg, **kwargs):\n ax = self.alty(**kwargs)\n self._dualy_arg = arg\n self._dualy_overrides()\n return ax","def SE_Vx_Vy(self, x, y):\n\t\tif self.V[x] == self.V[y]:\n\t\t\tself.IP += 2","def ij2xy(self, i=None, j=None):\n pass","def y(self, x):\n return x","def getsolutioninfo(self,whichsol_):\n pobj_ = ctypes.c_double()\n pviolcon_ = ctypes.c_double()\n pviolvar_ = ctypes.c_double()\n pviolbarvar_ = ctypes.c_double()\n pviolcone_ = ctypes.c_double()\n pviolitg_ = ctypes.c_double()\n dobj_ = ctypes.c_double()\n dviolcon_ = ctypes.c_double()\n dviolvar_ = ctypes.c_double()\n dviolbarvar_ = ctypes.c_double()\n dviolcone_ = ctypes.c_double()\n res = __library__.MSK_XX_getsolutioninfo(self.__nativep,whichsol_,ctypes.byref(pobj_),ctypes.byref(pviolcon_),ctypes.byref(pviolvar_),ctypes.byref(pviolbarvar_),ctypes.byref(pviolcone_),ctypes.byref(pviolitg_),ctypes.byref(dobj_),ctypes.byref(dviolcon_),ctypes.byref(dviolvar_),ctypes.byref(dviolbarvar_),ctypes.byref(dviolcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n pobj_ = pobj_.value\n _pobj_return_value = pobj_\n pviolcon_ = pviolcon_.value\n _pviolcon_return_value = pviolcon_\n pviolvar_ = pviolvar_.value\n _pviolvar_return_value = pviolvar_\n pviolbarvar_ = pviolbarvar_.value\n _pviolbarvar_return_value = pviolbarvar_\n pviolcone_ = pviolcone_.value\n _pviolcone_return_value = pviolcone_\n pviolitg_ = pviolitg_.value\n _pviolitg_return_value = pviolitg_\n dobj_ = dobj_.value\n _dobj_return_value = dobj_\n dviolcon_ = dviolcon_.value\n _dviolcon_return_value = dviolcon_\n dviolvar_ = dviolvar_.value\n _dviolvar_return_value = dviolvar_\n dviolbarvar_ = dviolbarvar_.value\n _dviolbarvar_return_value = dviolbarvar_\n dviolcone_ = dviolcone_.value\n _dviolcone_return_value = dviolcone_\n return (_pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value)","def getpviolbarvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getpviolbarvar(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol","def _prepare_y(self,y):\n y_i = np.ones(y.shape, dtype=np.float64, order=\"C\")\n y_i[y != 1] = -1.0\n return y_i","def getdualsolutionnorms(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getdualsolutionnorms(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nrmy_return_value,_nrmslc_return_value,_nrmsuc_return_value,_nrmslx_return_value,_nrmsux_return_value,_nrmsnx_return_value,_nrmbars_return_value = resargs\n return _nrmy_return_value,_nrmslc_return_value,_nrmsuc_return_value,_nrmslx_return_value,_nrmsux_return_value,_nrmsnx_return_value,_nrmbars_return_value","def solutiondef(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.solutiondef(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _isdef_return_value = resargs\n return _isdef_return_value","def _solve(self):\n B = sp.linalg.solve(self._XtX, self._XtY, assume_a='pos', overwrite_a=False, overwrite_b=False)\n self.coef_ = B[1:]\n self.intercept_ = B[0]\n self.is_fitted_ = True","def y_constraint(q,ee_pos):\n y = (self.length[0]*np.sin(q[0]) + self.length[1]*np.sin(q[0]+q[1]) +\n self.length[2]*np.sin(np.sum(q))) - self.ee_pos[1]\n return y","def OR_Vx_Vy(self, x, y):\n\t\tself.V[x] |= self.V[y]","def get_sol(self):","def readsolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.readsolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def evaluate_rhs(self, t, y, ydot=np.empty(0)):\n raise NotImplementedError","def putbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n \n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n \n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def fit(self, X, y):\n # If solver is 'lstsq' use ordinary least squares optimization method.\n if self.solver == 'lstsq':\n # Make sure inputs are numpy arrays.\n X = np.array(X)\n y = np.array(y)\n # Add x_0 = 1 to each instance for the bias term.\n X = np.c_[np.ones((X.shape[0], 1)), X]\n # Scipy implementation of least squares.\n self.coef_, residues, rank, singular = lstsq(X, y)\n\n return self\n\n elif self.solver == 'bgd': \n super(LinearRegression, self).fit(X, y)","def g_solving_subproblem_of_ALR(self,vehicle_id):\r\n global_LB = -10000\r\n global_UB = 10000\r\n iteration_for_RSP = 20\r\n optimal_solution_for_RSP = None\r\n self.multiplier_v = 0.5\r\n\r\n # solve the expected shortest path problem\r\n self.g_dynamic_programming_algorithm(vehicle_id, 3)\r\n\r\n # obtain the variance\r\n y_ =self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\r\n\r\n for k in range(iteration_for_RSP):\r\n # print(k)\r\n LB = 0\r\n # step 2: solve decomposed dual problems\r\n # Part I: subproblem of x\r\n self.g_dynamic_programming_algorithm(vehicle_id, 1)\r\n LB += self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_searching\r\n\r\n # Part II: subproblem of y\r\n obj_of_y_ = self.reliability * (y_) ** 0.5 - self.multiplier_v * y_\r\n if obj_of_y_ > 0:\r\n y = 0\r\n LB += 0\r\n else:\r\n y = y_\r\n LB += obj_of_y_\r\n\r\n # generate an upper bound\r\n variance = self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\r\n Label_cost_for_lagrangian_mean = self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_searching_mean\r\n UB = Label_cost_for_lagrangian_mean + self.reliability * (variance) ** 0.5\r\n\r\n # print(\"UB:{}\".format(UB))\r\n # print(\"LB:{}\".format(LB))\r\n\r\n # UB and LB update\r\n if LB > global_LB:\r\n global_LB = LB\r\n\r\n if UB < global_UB:\r\n global_UB = UB\r\n optimal_solution_for_RSP = self.g_ending_state_vector[vehicle_id].VSStateVector[0]\r\n\r\n # step 3: update multipliers\r\n if variance- y != 0:\r\n self.multiplier_v+= (global_UB - LB) / (variance-y)\r\n # if self.multiplier_v<0:\r\n # self.multiplier_v=1\r\n # print(self.multiplier_v)\r\n\r\n # step 4: termination condition test\r\n if global_UB != 0:\r\n gap = abs((global_UB - global_LB) / global_UB)\r\n # print(gap)\r\n if gap < 0.02:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP, global_LB\r\n else:\r\n if global_UB - global_LB == 0:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP, global_LB\r\n\r\n if k == iteration_for_RSP - 1:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP, global_LB","def set_y(self, new_y):\r\n self.y = new_y","def xy2ij(self, x, y=None):\n pass","def solve_nonlinear(self, params, unknowns, resids):\n\n x = params['x']\n y = params['y']\n\n unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0","def g_solving_subproblem_of_LR(self,vehicle_id):\r\n global_LB=-10000\r\n global_UB=10000\r\n iteration_for_RSP=20\r\n optimal_solution_for_RSP=None\r\n optimal_value_y=0\r\n self.multiplier_v=0.5\r\n\r\n #solve the expected shortest path problem\r\n self.g_dynamic_programming_algorithm(vehicle_id, 4)\r\n #obtain the variance\r\n y_=self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\r\n\r\n for k in range(iteration_for_RSP):\r\n # print(k)\r\n LB=0\r\n # step 2: solve decomposed dual problems\r\n # Part I: subproblem of x\r\n self.g_dynamic_programming_algorithm(vehicle_id, 2)\r\n LB+=self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_lagrangian\r\n\r\n # Part II: subproblem of y\r\n obj_of_y_ = self.reliability * (y_) ** 0.5 - self.multiplier_v * y_\r\n if obj_of_y_ > 0:\r\n y = 0\r\n LB += 0\r\n else:\r\n y = y_\r\n LB += obj_of_y_\r\n # generate an upper bound\r\n variance = self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\r\n Label_cost_for_lagrangian_mean=self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_lagrangian_mean\r\n UB=Label_cost_for_lagrangian_mean+self.reliability*(variance)**0.5\r\n\r\n # print(\"UB:{}\".format(UB))\r\n # print(\"LB:{}\".format(LB))\r\n\r\n # UB and LB update\r\n if LB > global_LB:\r\n global_LB = LB\r\n optimal_solution_for_RSP = self.g_ending_state_vector[vehicle_id].VSStateVector\r\n optimal_value_y = y\r\n\r\n if UB < global_UB:\r\n global_UB = UB\r\n\r\n\r\n # step 3: update multipliers\r\n if variance-y!= 0:\r\n self.multiplier_v+= (global_UB - LB) / (variance-y)\r\n # if self.multiplier_v<0:\r\n # self.multiplier_v=1\r\n # print(self.multiplier_v)\r\n\r\n # step 4: termination condition test\r\n if global_UB != 0:\r\n gap = abs((global_UB-global_LB) / global_UB)\r\n # print(gap)\r\n if gap < 0.02:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP, optimal_value_y,global_LB,global_UB\r\n else:\r\n if global_UB - global_LB == 0:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP,optimal_value_y,global_LB,global_UB\r\n\r\n if k == iteration_for_RSP - 1:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP,optimal_value_y,global_LB,global_UB","def putxx(self,whichsol_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xx is None: raise TypeError(\"Invalid type for argument xx\")\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.putxx(whichsol_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getdualobj(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getdualobj(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dualobj_return_value = resargs\n return _dualobj_return_value","def y2(self, nx, ny, x_des):\n\n c_d, a1, output = self.component_dependency['y_2'], self.dependency_matrix, []\n for i in range(ny):\n [sum_i, row] = [[], a1[4 * ny + nx + i]]\n sum_i.append(np.sum(row))\n [assign, y] = [c_d[i], []]\n # x_des = np.random.random_sample(4 * nx + 5 * ny) # this is an instance of the design vector\n [y.append(self.aer_int.y2_int([x_des[k]], assign - 1)) for k in range(4 * nx + 5 * ny) if row[k] == 1]\n output.append(np.sum(y) * 1 / sum_i)\n\n return output","def LD_Vx_Vy(self, x, y):\n\t\tself.V[x] = self.V[y]","def putskx(self,whichsol_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskx(whichsol_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def f(self,y,psi):\r\n raise NotImplementedError","def getyslice(self,whichsol_,first_,last_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.getyslice(whichsol_,first_,last_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_y:\n y[:] = _tmparr_y","def inv(self,yval,*args,**kwargs):\n import scipy.optimize\n\n if not np.isscalar(yval):\n raise ModelTypeError('generic 1D inverter can only accept scalar inputs')\n\n if len(args) == 0:\n args=[0]\n method = kwargs.pop('method','newton')\n elif len(args) == 1:\n method = kwargs.pop('method','newton')\n else:\n method = kwargs.pop('method','brentq')\n\n if isinstance(method,basestring):\n f = getattr(scipy.optimize,method)\n else:\n f = method\n\n if kwargs.pop('abs',False):\n g = lambda(x):np.abs(self(x)-yval)\n else:\n g = lambda(x):self(x)-yval\n\n return f(g,*args,**kwargs)","def test_solve_quadratic(self):\n iden1 = Identity()\n iden2 = Identity()\n iden3 = Identity()\n iden1.x.fixed = False\n iden2.x.fixed = False\n iden3.x.fixed = False\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\n prob = LeastSquaresProblem([term1, term2, term3])\n prob.solve()\n self.assertAlmostEqual(prob.objective, 0)\n self.assertAlmostEqual(iden1.x.val, 1)\n self.assertAlmostEqual(iden2.x.val, 2)\n self.assertAlmostEqual(iden3.x.val, 3)","def invert(self, solution, inverse_data):\n status = solution['status']\n\n primal_vars = None\n dual_vars = None\n if status in s.SOLUTION_PRESENT:\n opt_val = solution['value'] + inverse_data[s.OFFSET]\n primal_vars = {inverse_data[self.VAR_ID]: solution['primal']}\n return Solution(status, opt_val, primal_vars, dual_vars, {})\n else:\n return failure_solution(status)","def y(self, number):\n self.validate_int(\"y\", number)\n if number < 0:\n raise ValueError(\"y must be >= 0\")\n self.__y = number","def set_solver(self, solver):\n self.solver = solver\n solver.associate_with_model(self)\n self.set_free_variable(0) # A reasonable initial assumption; can be overridden by simulations","def solve(self, x_0, dual_x_0):\n # Sanitize the inputs\n if type(x_0) is not np.ndarray or type(dual_x_0) is not np.ndarray:\n x_0 = np.array(x_0)\n dual_x_0 = np.array(dual_x_0)\n # Make sure that the arrays are column vectors\n x_0 = x_0.reshape(-1, 1)\n dual_x_0 = dual_x_0.reshape(-1, 1)\n\n print (\"Starting SQP minimization...\")\n [x, dual_x, exit_info] = self.globalized_sqp(x_0, dual_x_0)\n conv_criteria = exit_info['val']\n\n print (exit_info['msg'])\n print (\"Exiting with ||grad[L]|| = {0:e}\".format(conv_criteria))\n print (\"x = {0}\".format(x.reshape(-1)))\n print (\"dual_x = {0}\".format(dual_x.reshape(-1)))\n\n return [x, dual_x]","def Solve(self,iter_val=0):\n\n ### Save Files before solve ###\n self.fprint(\"Saving Input Data\",special=\"header\")\n if \"mesh\" in self.params.output:\n self.problem.dom.Save(val=iter_val)\n if \"initial_guess\" in self.params.output:\n self.problem.bd.SaveInitialGuess(val=iter_val)\n if \"height\" in self.params.output and self.problem.dom.dim == 3:\n self.problem.bd.SaveHeight(val=iter_val)\n if \"turbine_force\" in self.params.output:\n self.problem.farm.SaveRotorDisks(val=iter_val)\n self.fprint(\"Finished\",special=\"footer\")\n\n ####################################################################\n ### This is the better way to define a nonlinear problem but it \n ### doesn't play nice with dolfin_adjoint\n # ### Define Jacobian ###\n # dU = TrialFunction(self.problem.fs.W)\n # J = derivative(self.problem.F, self.problem.up_next, dU)\n\n # ### Setup nonlinear solver ###\n # nonlinear_problem = NonlinearVariationalProblem(self.problem.F, self.problem.up_next, self.problem.bd.bcs, J)\n # nonlinear_solver = NonlinearVariationalSolver(nonlinear_problem)\n\n # ### Set some parameters ###\n # solver_parameters = nonlinear_solver.parameters\n # solver_parameters[\"nonlinear_solver\"] = \"snes\"\n # solver_parameters[\"snes_solver\"][\"linear_solver\"] = \"mumps\"\n # solver_parameters[\"snes_solver\"][\"maximum_iterations\"] = 50\n # solver_parameters[\"snes_solver\"][\"error_on_nonconvergence\"] = False\n # solver_parameters[\"snes_solver\"][\"line_search\"] = \"bt\" # Available: basic, bt, cp, l2, nleqerr\n\n ### Solve the problem ###\n # self.fprint(\"Solving\",special=\"header\")\n # start = time.time()\n # iters, converged = nonlinear_solver.solve()\n # stop = time.time()\n # self.fprint(\"Total Nonlinear Iterations: {:d}\".format(iters))\n # self.fprint(\"Converged Successfully: {0}\".format(converged))\n ####################################################################\n\n\n nonlinear_solver = self.params[\"solver\"].get(\"nonlinear_solver\", \"snes\")\n relaxation = self.params[\"solver\"].get(\"newton_relaxation\", 1.0)\n\n self.fprint(\"Solving with {0}\".format(nonlinear_solver))\n if nonlinear_solver == \"newton\":\n self.fprint(\"Relaxation parameter = {: 1.2f}\".format(relaxation))\n\n newton_options = {\"relaxation_parameter\": relaxation,\n \"maximum_iterations\": 40,\n \"linear_solver\": \"mumps\",\n \"absolute_tolerance\": 1e-6,\n \"relative_tolerance\": 1e-5}\n \n solver_parameters = {\"nonlinear_solver\": \"newton\",\n \"newton_solver\": newton_options}\n\n elif nonlinear_solver == \"snes\":\n # ### Add some helper functions to solver options ###\n solver_parameters = {\"nonlinear_solver\": \"snes\",\n \"snes_solver\": {\n \"linear_solver\": \"mumps\", \n \"maximum_iterations\": 40,\n \"error_on_nonconvergence\": True,\n \"line_search\": \"bt\",\n }}\n \n else:\n raise ValueError(\"Unknown nonlinear solver type: {0}\".format(nonlinear_solver))\n\n ### Start the Solve Process ###\n self.fprint(\"Solving\",special=\"header\")\n start = time.time()\n \n # ### Solve the Baseline Problem ###\n # solve(self.problem.F_sans_tf == 0, self.problem.up_next, self.problem.bd.bcs, solver_parameters=solver_parameters, **self.extra_kwarg)\n\n # ### Store the Baseline and Assign for the real solve ###\n # self.up_baseline = self.problem.up_next.copy(deepcopy=True)\n # self.problem.up_next.assign(self.up_baseline)\n\n ### Solve the real problem ###\n solve(self.problem.F == 0, self.problem.up_next, self.problem.bd.bcs, solver_parameters=solver_parameters)\n stop = time.time()\n self.fprint(\"Solve Complete: {:1.2f} s\".format(stop-start),special=\"footer\")\n # self.u_next,self.p_next = self.problem.up_next.split(True)\n self.u_next,self.p_next = split(self.problem.up_next)\n # self.nu_T = project(self.problem.nu_T,self.problem.fs.Q,solver_type='mumps',**self.extra_kwarg)\n self.nu_T = None\n\n\n ### Save solutions ###\n if \"solution\" in self.params.output:\n self.fprint(\"Saving Solution\",special=\"header\")\n self.Save(val=iter_val)\n self.fprint(\"Finished\",special=\"footer\")\n\n ### calculate the power for each turbine ###\n ###################################\n ### Fix how angle is transfered ###\n ###################################\n if self.optimizing or self.save_power:\n self.J += -self.CalculatePowerFunctional((iter_val-self.problem.dom.init_wind)) \n\n # self.fprint(\"Speed Percent of Inflow Speed\")\n # ps = []\n # for i in range(6):\n # HH = self.problem.farm.HH[0]\n # RD = self.problem.farm.RD[0]\n # x_val = (i+1)*RD\n # vel = self.problem.up_next([x_val,0,HH])\n # vel = vel[0:3]\n # nom = np.linalg.norm(vel)\n # perc = nom/self.problem.bd.HH_vel\n # ps.append(perc)\n # self.fprint(\"Speed Percent at (\"+repr(int(x_val))+\", 0, \"+repr(HH)+\"): \"+repr(perc))\n # print(ps)","def deletesolution(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.deletesolution(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putsnx(self,whichsol_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsnx(whichsol_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def y ( self ) :\n return self.yvar","def solve_nonlinear(self, params, unknowns, resids):\n pass","def solve_nonlinear(self, params, unknowns, resids):\n pass","def set_xyvxvy(self, y):\n self.x = y[0]\n self.y = y[1]\n self.vx = y[2]\n self.vy = y[3]","def y(self, value):\n self.data_validator(\"y\", value)\n self.__y = value","def solve_nonlinear(self, params, unknowns, resids):\n\n x = hash(params['x'])\n y = params['y']\n\n unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0","def y(self, value):\n if not isinstance(value, int):\n raise TypeError(\"y must be an integer\")\n if value < 0:\n raise ValueError(\"y must be >= 0\")\n self.__y = value","def solve_series(\n self,\n x0,\n params,\n varied_data,\n varied_idx,\n internal_x0=None,\n solver=None,\n propagate=True,\n **kwargs\n ):\n if self.x_by_name and isinstance(x0, dict):\n x0 = [x0[k] for k in self.names]\n if self.par_by_name:\n if isinstance(params, dict):\n params = [params[k] for k in self.param_names]\n if isinstance(varied_idx, str):\n varied_idx = self.param_names.index(varied_idx)\n\n new_params = np.atleast_1d(np.array(params, dtype=np.float64))\n xout = np.empty((len(varied_data), len(x0)))\n self.internal_xout = np.empty_like(xout)\n self.internal_params_out = np.empty((len(varied_data), len(new_params)))\n info_dicts = []\n new_x0 = np.array(x0, dtype=np.float64) # copy\n conds = kwargs.get(\"initial_conditions\", None) # see ConditionalNeqSys\n for idx, value in enumerate(varied_data):\n try:\n new_params[varied_idx] = value\n except TypeError:\n new_params = value # e.g. type(new_params) == int\n if conds is not None:\n kwargs[\"initial_conditions\"] = conds\n x, info_dict = self.solve(new_x0, new_params, internal_x0, solver, **kwargs)\n if propagate:\n if info_dict[\"success\"]:\n try:\n # See ChainedNeqSys.solve\n new_x0 = info_dict[\"x_vecs\"][0]\n internal_x0 = info_dict[\"internal_x_vecs\"][0]\n conds = info_dict[\"intermediate_info\"][0].get(\n \"conditions\", None\n )\n except:\n new_x0 = x\n internal_x0 = None\n conds = info_dict.get(\"conditions\", None)\n xout[idx, :] = x\n self.internal_xout[idx, :] = self.internal_x\n self.internal_params_out[idx, :] = self.internal_params\n info_dicts.append(info_dict)\n return xout, info_dicts","def y(self, value):\n if isinstance(value, int) is False:\n raise TypeError(\"y must be an integer\")\n if value < 0:\n raise ValueError(\"y must be >= 0\")\n self.__y = value","def lstsqnumpy(A,y):\r\n N=A.shape[1]\r\n b = np.linalg.lstsq(A,y)\r\n A = b[0][1::2]\r\n B = b[0][2::2]\r\n \r\n return A+1j*B, b[0][0::N]","def solve_i():\r\n x = np.array([ -2.1, -1.45, -1.3, -0.2, 0.1, 0.15, 0.8, 1.1, 1.5, 2.8, 3.8 ])\r\n y = np.array([0.012155, 0.122151, 0.184520, 0.960789, 0.990050, 0.977751,\r\n 0.527292, 0.298197, 0.105399, 3.936690E-4, 5.355348E-7])\r\n # find and plot both interpolations and the oiginal points\r\n plt.figure(1)\r\n cubic_interpol(x,y)\r\n lin_interpol(x,y)\r\n plt.plot(x, y, 'rx', ms = 10, label = 'Points')\r\n # plot settings\r\n plt.title('Cubic & Linear Interpolation Given Points')\r\n plt.xlabel('x',fontsize = 14)\r\n plt.ylabel('y',fontsize = 14)\r\n plt.legend()","def integrate(self, y0, t0, tend, N=None, abstol=1e-5, reltol=1e-5):\n from ode45 import ode45\n vfun = lambda t,y: self.rhs.Applyf(y)\n vslot = (t0, tend)\n vinit = y0\n t,Y,stats = ode45(vfun,vslot,vinit,abstol=abstol,reltol=reltol,stats=True)\n self.stats = stats\n return t,Y"],"string":"[\n \"def putsolutionyi(self,i_,whichsol_,y_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def puty(self,whichsol_,y): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if y is None: raise TypeError(\\\"Invalid type for argument y\\\")\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n res = self.__obj.puty(whichsol_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def puty(self,whichsol_,y_):\\n _y_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),self.getnumcon()))\\n if y_ is None:\\n raise ValueError(\\\"Argument y cannot be None\\\")\\n if y_ is None:\\n raise ValueError(\\\"Argument y may not be None\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n res = __library__.MSK_XX_puty(self.__nativep,whichsol_,_y_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def gety(self,whichsol_,y): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if y is None: raise TypeError(\\\"Invalid type for argument y\\\")\\n _copyback_y = False\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n _copyback_y = True\\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n _copyback_y = True\\n if y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n res = self.__obj.gety(whichsol_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_y:\\n y[:] = _tmparr_y\",\n \"def gety(self,whichsol_,y_):\\n _y_minlength = self.getnumcon()\\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),self.getnumcon()))\\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\\n raise ValueError(\\\"Argument y must be writable\\\")\\n if y_ is None:\\n raise ValueError(\\\"Argument y may not be None\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n res = __library__.MSK_XX_gety(self.__nativep,whichsol_,_y_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _y_copyarray:\\n y_[:] = _y_np_tmp\",\n \"def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solve(self, x, y):\\n\\t\\tx = np.concatenate((np.ones([x.shape[0], 1]), x), axis=1)\\n\\t\\txtx = np.dot(x.T, x)\\n\\t\\txty = np.dot(y, x)\\n\\t\\tself.w = np.dot(np.linalg.inv(xtx), xty.T)\",\n \"def y_constraint(q, xy):\\n y = ( self.L[0]*np.sin(q[0]) + self.L[1]*np.sin(q[0]+q[1]) + \\n self.L[2]*np.sin(q[0]+q[1]+q[2]) + self.L[3]*np.sin(np.sum(q)) ) - xy[1]\\n return y\",\n \"def updatesolutioninfo(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.updatesolutioninfo(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def analytic_solution(self, x, y):\\n return (1/np.sinh(2*np.pi))*np.sinh(2*np.pi*y)*np.sin(2*np.pi*x)\",\n \"def make_solver(y, x, ylim, xlim, invertible_fitter=None):\\n cb_y = lambdify(x, y)\\n cb_dydx = lambdify(x, y.diff(x))\\n\\n y0 = ylim[0]\\n if invertible_fitter:\\n pass\\n # TODO:\\n # fit parameterized invertible function\\n # calculated the invese and use as guess\\n fitexpr, params = invertible_fitter\\n cb_fitexpr = lambdify(x, fitexpr)\\n else:\\n DxDy = (xlim[1]-xlim[0])/(ylim[1]-ylim[0])\\n def inv_y(y, abstol=1e-13, itermax=30, conv=None):\\n \\\"\\\"\\\"\\n Returns x and error estimate thereof\\n \\\"\\\"\\\"\\n if invertible_fitter:\\n pass\\n else:\\n x_ = y0+y*DxDy # guess (linear over xspan)\\n dy = cb_y(x_)-y\\n i=0\\n dx=0.0 # could skip while-loop\\n while abs(dy) > abstol and i < itermax:\\n dx = -dy/cb_dydx(x_)\\n x_ += dx\\n dy = cb_y(x_)-y\\n i += 1\\n if conv != None: conv.append(dx)\\n if i==itermax:\\n raise RuntimeError(\\\"Did not converge\\\")\\n return x_, abs(dx)\\n\\n return inv_y\",\n \"def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n \\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n \\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skn is None:\\n skn_ = None\\n else:\\n try:\\n skn_ = memoryview(skn)\\n except TypeError:\\n try:\\n _tmparr_skn = array.array(\\\"i\\\",skn)\\n except TypeError:\\n raise TypeError(\\\"Argument skn has wrong type\\\")\\n else:\\n skn_ = memoryview(_tmparr_skn)\\n \\n else:\\n if skn_.format != \\\"i\\\":\\n skn_ = memoryview(array.array(\\\"i\\\",skn))\\n \\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n \\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n \\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n \\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n \\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n \\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n \\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n \\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n \\n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def writesolution(self,whichsol_,filename_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.writesolution(whichsol_,filename_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def updatesolutioninfo(self,whichsol_):\\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def yxal(self, i):\\n return self.y[i]\",\n \"def DirectSolve(self, y):\\n if y >= -self.R:\\n a1 = self.apath[y+1]\\n if y == -2:\\n a2 = 0\\n else:\\n a2 = self.apath[y+2] \\n def constraints(a):\\n c0 = (1+self.r)*a + self.b - a1\\n c1 = (1+self.r)*a1 + self.b - a2\\n return self.uc(c0,0)-self.beta*self.uc(c1,0)*(1+self.r)\\n a, n = fsolve(constraints, a1), 0\\n c = (1+self.r)*a + self.b - a1\\n else:\\n a1 = self.apath[y+1]\\n a2 = self.apath[y+2]\\n if y == -(self.R+1):\\n n1 = 0\\n c1 = (1+self.r)*a1 + self.b - a2\\n else:\\n n1 = self.npath[y+1]\\n c1 = (1+self.r)*a1 + (1-self.tau)*self.w*n1 - a2\\n def constraints((a0,n0)):\\n c0 = (1+self.r)*a0 + (1-self.tau)*self.w*n0 - a1\\n return self.uc(c0,n0) - self.beta*self.uc(c1,n1)*(1+self.r),\\\\\\n (1-self.tau)*self.w*self.uc(c0,n0) + self.un(c0,n0)\\n a, n = fsolve(constraints,(a1,n1))\\n c = (1+self.r)*a + (1-self.tau)*self.w*n - a1\\n return a, n, c\",\n \"def y(self, y):\\n if type(y) is not int:\\n raise TypeError(\\\"y must be an integer\\\")\\n if y < 0:\\n raise ValueError(\\\"y must be >= 0\\\")\\n self.__y = y\",\n \"def y(self, y):\\n if type(y) is not int:\\n raise TypeError(\\\"y must be an integer\\\")\\n if y < 0:\\n raise ValueError(\\\"y must be >= 0\\\")\\n self.__y = y\",\n \"def solve(self, solver):\\n solver.solve()\",\n \"def y(self, y):\\n if type(y) is not int:\\n raise TypeError(\\\"y must be an integer\\\")\\n elif y < 0:\\n raise ValueError(\\\"y must be >= 0\\\")\\n else:\\n self.__y = y\",\n \"def set_params(self, solver):\\n params = yicespy.yices_new_param_record()\\n yicespy.yices_default_params_for_context(solver.yices, params)\\n for k,v in self.solver_options.items():\\n rv = yicespy.yices_set_param(params, k, v)\\n if rv != 0:\\n raise PysmtValueError(\\\"Error setting the option '%s=%s'\\\" % (k,v))\\n solver.yices_params = params\",\n \"def getdviolvar(self,whichsol_,sub,viol): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if viol is None: raise TypeError(\\\"Invalid type for argument viol\\\")\\n _copyback_viol = False\\n if viol is None:\\n viol_ = None\\n else:\\n try:\\n viol_ = memoryview(viol)\\n except TypeError:\\n try:\\n _tmparr_viol = array.array(\\\"d\\\",viol)\\n except TypeError:\\n raise TypeError(\\\"Argument viol has wrong type\\\")\\n else:\\n viol_ = memoryview(_tmparr_viol)\\n _copyback_viol = True\\n else:\\n if viol_.format != \\\"d\\\":\\n viol_ = memoryview(array.array(\\\"d\\\",viol))\\n _copyback_viol = True\\n if viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol has wrong length\\\")\\n res = self.__obj.getdviolvar(whichsol_,num_,sub_,viol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_viol:\\n viol[:] = _tmparr_viol\",\n \"def putslx(self,whichsol_,slx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if slx is None: raise TypeError(\\\"Invalid type for argument slx\\\")\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n \\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n \\n if slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n res = self.__obj.putslx(whichsol_,slx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def solve_nonlinear(self, params, unknowns, resids):\\n\\n x = params['x']\\n a = self.a\\n b = self.b\\n c = self.c\\n\\n unknowns['y'] = a*x**2 + b*x + c\",\n \"def setBetaEqState(self, pointDict, useThisYeIfSolveFails=None):\\n assert isinstance(pointDict, dict)\\n assert 'ye' not in pointDict, \\\"You can't SPECIFY a Ye if you're \\\" \\\\\\n \\\"setting neutrinoless beta equlibrium!\\\"\\n self.validatePointDict(pointDict)\\n assert len(pointDict) < 3, \\\"State overdetermined for more than 2 indVars!\\\"\\n\\n #defines 1D root solver to use in routine\\n solveRoot = scipyOptimize.brentq # solveRootBisect\\n\\n\\n #ASSUME 2 INDEPENENT VARIABLES ARE rho & temp\\n logtemp = pointDict['logtemp']\\n logrho = pointDict['logrho']\\n\\n tol = 1.e-6\\n getYe = lambda x : multidimInterp((x, logtemp, logrho),\\n [self.h5file['ye'][:],\\n self.h5file['logtemp'],\\n self.h5file['logrho']],\\n self.h5file['munu'][...],\\n linInterp, 2)\\n #check for bracketing error in root solve for ye\\n try:\\n currentYe = solveRoot(getYe,\\n self.h5file['ye'][0],\\n self.h5file['ye'][-1], (), tol)\\n except ValueError as err:\\n print \\\"Error in scipy root solver solving for ye: \\\", str(err)\\n if useThisYeIfSolveFails is None:\\n currentYe = self.findYeOfMinAbsMunu((logtemp, logrho))\\n print \\\"Recovering with findYeOfMinAbsMunu, answer: %s\\\" % currentYe\\n else:\\n currentYe = useThisYeIfSolveFails\\n print \\\"Setting Ye to useThisYeIfSolveFails, answer: %s\\\" % currentYe\\n\\n newDict = pointDict.copy()\\n newDict['ye'] = currentYe\\n self.setState(newDict)\\n return currentYe\",\n \"def solve_nonlinear(self, params, unknowns, resids):\\n\\n x = params['x']\\n m = self.slope\\n b = self.intercept\\n\\n unknowns['y'] = m*x + b\",\n \"def y(x,xi):\\n return np.exp(-xi)-np.exp(-xi)*(x-xi)\",\n \"def getdviolbarvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getdviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def y(self, y):\\n if y is None:\\n raise ValueError(\\\"Invalid value for `y`, must not be `None`\\\") # noqa: E501\\n\\n self._y = y\",\n \"def y(self, y):\\n if y is None:\\n raise ValueError(\\\"Invalid value for `y`, must not be `None`\\\") # noqa: E501\\n\\n self._y = y\",\n \"def __set_y__(self,y):\\n\\n # Input vaidation\\n try:\\n y = int(y)\\n except:\\n raise ValueError('H Bridge direction is not valid')\\n \\n if(y != 0 and y != 1 and y != -1):\\n raise ValueError('H Bridge direction is not valid')\\n \\n self.direction['y'] = y\\n self.HBridges['y'].SetDirection(y)\",\n \"def getdviolvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getdviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def getpviolvar(self,whichsol_,sub,viol): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if viol is None: raise TypeError(\\\"Invalid type for argument viol\\\")\\n _copyback_viol = False\\n if viol is None:\\n viol_ = None\\n else:\\n try:\\n viol_ = memoryview(viol)\\n except TypeError:\\n try:\\n _tmparr_viol = array.array(\\\"d\\\",viol)\\n except TypeError:\\n raise TypeError(\\\"Argument viol has wrong type\\\")\\n else:\\n viol_ = memoryview(_tmparr_viol)\\n _copyback_viol = True\\n else:\\n if viol_.format != \\\"d\\\":\\n viol_ = memoryview(array.array(\\\"d\\\",viol))\\n _copyback_viol = True\\n if viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol has wrong length\\\")\\n res = self.__obj.getpviolvar(whichsol_,num_,sub_,viol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_viol:\\n viol[:] = _tmparr_viol\",\n \"def learned_RHS(t,y,q,x,desc):\\n \\n \\n Ux_mat = create_Ux_mat(x)\\n Uxx_mat = create_Uxx_mat(x)\\n\\n return (q[desc.index('u_{x}')]*Ux_mat.dot(y) + \\n q[desc.index('u_{xx}')]*Uxx_mat.dot(y) +\\n q[desc.index('u^2')]*y**2 +\\n q[desc.index('u')]*y + \\n q[desc.index('u^2u_{x}')]*(y**2)*Ux_mat.dot(y) + \\n q[desc.index('uu_{x}')]*y*Ux_mat.dot(y) + \\n q[desc.index('u^2u_{xx}')]*(y**2)*Uxx_mat.dot(y) + \\n q[desc.index('uu_{xx}')]*y*Uxx_mat.dot(y) + \\n q[desc.index('u_{x}^2')]*Ux_mat.dot(y)**2)\",\n \"def getsolutioninfo(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getsolutioninfo(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value = resargs\\n return _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value\",\n \"def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\\n if skc_ is not None:\\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\\n else:\\n _skc_tmp = None\\n if skx_ is not None:\\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\\n else:\\n _skx_tmp = None\\n if skn_ is not None:\\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\\n else:\\n _skn_tmp = None\\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\\n _xc_copyarray = False\\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xc_ is not None:\\n _xc_copyarray = True\\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\\n _xc_np_tmp[:] = xc_\\n assert _xc_np_tmp.flags.contiguous\\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xc_copyarray = False\\n _xc_tmp = None\\n \\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\\n _xx_copyarray = False\\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif xx_ is not None:\\n _xx_copyarray = True\\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\\n _xx_np_tmp[:] = xx_\\n assert _xx_np_tmp.flags.contiguous\\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _xx_copyarray = False\\n _xx_tmp = None\\n \\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\\n _slc_copyarray = False\\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slc_ is not None:\\n _slc_copyarray = True\\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\\n _slc_np_tmp[:] = slc_\\n assert _slc_np_tmp.flags.contiguous\\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slc_copyarray = False\\n _slc_tmp = None\\n \\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\\n _suc_copyarray = False\\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif suc_ is not None:\\n _suc_copyarray = True\\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\\n _suc_np_tmp[:] = suc_\\n assert _suc_np_tmp.flags.contiguous\\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _suc_copyarray = False\\n _suc_tmp = None\\n \\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\\n _slx_copyarray = False\\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif slx_ is not None:\\n _slx_copyarray = True\\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\\n _slx_np_tmp[:] = slx_\\n assert _slx_np_tmp.flags.contiguous\\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _slx_copyarray = False\\n _slx_tmp = None\\n \\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\\n _sux_copyarray = False\\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif sux_ is not None:\\n _sux_copyarray = True\\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\\n _sux_np_tmp[:] = sux_\\n assert _sux_np_tmp.flags.contiguous\\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _sux_copyarray = False\\n _sux_tmp = None\\n \\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\\n _snx_copyarray = False\\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif snx_ is not None:\\n _snx_copyarray = True\\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\\n _snx_np_tmp[:] = snx_\\n assert _snx_np_tmp.flags.contiguous\\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _snx_copyarray = False\\n _snx_tmp = None\\n \\n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def userToPlotY(y): \\n return dislin.nyposn(y)\",\n \"def yvar ( self ) :\\n return self.__yvar\",\n \"def getpviolbarvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getpviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def getpviolvar(self,whichsol_,sub_,viol_):\\n num_ = None\\n if num_ is None:\\n num_ = len(sub_)\\n elif num_ != len(sub_):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub cannot be None\\\")\\n if sub_ is None:\\n raise ValueError(\\\"Argument sub may not be None\\\")\\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\\n _sub_copyarray = False\\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n elif sub_ is not None:\\n _sub_copyarray = True\\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\\n _sub_np_tmp[:] = sub_\\n assert _sub_np_tmp.flags.contiguous\\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\\n else:\\n _sub_copyarray = False\\n _sub_tmp = None\\n \\n _viol_minlength = (num_)\\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol is not long enough: Is %d, expected %d\\\" % (len(viol_),(num_)))\\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\\n raise ValueError(\\\"Argument viol must be writable\\\")\\n if viol_ is None:\\n raise ValueError(\\\"Argument viol may not be None\\\")\\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\\n _viol_copyarray = False\\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif viol_ is not None:\\n _viol_copyarray = True\\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\\n _viol_np_tmp[:] = viol_\\n assert _viol_np_tmp.flags.contiguous\\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _viol_copyarray = False\\n _viol_tmp = None\\n \\n res = __library__.MSK_XX_getpviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _viol_copyarray:\\n viol_[:] = _viol_np_tmp\",\n \"def getsolutioni(self,accmode_,i_,whichsol_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getsolutioni(accmode_,i_,whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value = resargs\\n _sk_return_value = stakey(_sk_return_value)\\n return _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value\",\n \"def getdviolbarvar(self,whichsol_,sub,viol): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if viol is None: raise TypeError(\\\"Invalid type for argument viol\\\")\\n _copyback_viol = False\\n if viol is None:\\n viol_ = None\\n else:\\n try:\\n viol_ = memoryview(viol)\\n except TypeError:\\n try:\\n _tmparr_viol = array.array(\\\"d\\\",viol)\\n except TypeError:\\n raise TypeError(\\\"Argument viol has wrong type\\\")\\n else:\\n viol_ = memoryview(_tmparr_viol)\\n _copyback_viol = True\\n else:\\n if viol_.format != \\\"d\\\":\\n viol_ = memoryview(array.array(\\\"d\\\",viol))\\n _copyback_viol = True\\n if viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol has wrong length\\\")\\n res = self.__obj.getdviolbarvar(whichsol_,num_,sub_,viol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_viol:\\n viol[:] = _tmparr_viol\",\n \"def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if not isinstance(sk_,stakey): raise TypeError(\\\"Argument sk has wrong type\\\")\\n res = self.__obj.putsolutioni(accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def y(self, value):\\n self.validate_input(y=value)\\n self.__y = value\",\n \"def putyslice(self,whichsol_,first_,last_,y_):\\n _y_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y is not long enough: Is %d, expected %d\\\" % (len(y_),((last_) - (first_))))\\n if y_ is None:\\n raise ValueError(\\\"Argument y cannot be None\\\")\\n if y_ is None:\\n raise ValueError(\\\"Argument y may not be None\\\")\\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\\n _y_copyarray = False\\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif y_ is not None:\\n _y_copyarray = True\\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\\n _y_np_tmp[:] = y_\\n assert _y_np_tmp.flags.contiguous\\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _y_copyarray = False\\n _y_tmp = None\\n \\n res = __library__.MSK_XX_putyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_skc = False\\n if skc is None:\\n skc_ = None\\n else:\\n try:\\n skc_ = memoryview(skc)\\n except TypeError:\\n try:\\n _tmparr_skc = array.array(\\\"i\\\",skc)\\n except TypeError:\\n raise TypeError(\\\"Argument skc has wrong type\\\")\\n else:\\n skc_ = memoryview(_tmparr_skc)\\n _copyback_skc = True\\n else:\\n if skc_.format != \\\"i\\\":\\n skc_ = memoryview(array.array(\\\"i\\\",skc))\\n _copyback_skc = True\\n if skc_ is not None and len(skc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument skc has wrong length\\\")\\n _copyback_skx = False\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n _copyback_skx = True\\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n _copyback_skx = True\\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n _copyback_skn = False\\n if skn is None:\\n skn_ = None\\n else:\\n try:\\n skn_ = memoryview(skn)\\n except TypeError:\\n try:\\n _tmparr_skn = array.array(\\\"i\\\",skn)\\n except TypeError:\\n raise TypeError(\\\"Argument skn has wrong type\\\")\\n else:\\n skn_ = memoryview(_tmparr_skn)\\n _copyback_skn = True\\n else:\\n if skn_.format != \\\"i\\\":\\n skn_ = memoryview(array.array(\\\"i\\\",skn))\\n _copyback_skn = True\\n if skn_ is not None and len(skn_) != self.getnumcone():\\n raise ValueError(\\\"Array argument skn has wrong length\\\")\\n _copyback_xc = False\\n if xc is None:\\n xc_ = None\\n else:\\n try:\\n xc_ = memoryview(xc)\\n except TypeError:\\n try:\\n _tmparr_xc = array.array(\\\"d\\\",xc)\\n except TypeError:\\n raise TypeError(\\\"Argument xc has wrong type\\\")\\n else:\\n xc_ = memoryview(_tmparr_xc)\\n _copyback_xc = True\\n else:\\n if xc_.format != \\\"d\\\":\\n xc_ = memoryview(array.array(\\\"d\\\",xc))\\n _copyback_xc = True\\n if xc_ is not None and len(xc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument xc has wrong length\\\")\\n _copyback_xx = False\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n _copyback_xx = True\\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n _copyback_xx = True\\n if xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n _copyback_y = False\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n _copyback_y = True\\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n _copyback_y = True\\n if y_ is not None and len(y_) != self.getnumcon():\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n _copyback_slc = False\\n if slc is None:\\n slc_ = None\\n else:\\n try:\\n slc_ = memoryview(slc)\\n except TypeError:\\n try:\\n _tmparr_slc = array.array(\\\"d\\\",slc)\\n except TypeError:\\n raise TypeError(\\\"Argument slc has wrong type\\\")\\n else:\\n slc_ = memoryview(_tmparr_slc)\\n _copyback_slc = True\\n else:\\n if slc_.format != \\\"d\\\":\\n slc_ = memoryview(array.array(\\\"d\\\",slc))\\n _copyback_slc = True\\n if slc_ is not None and len(slc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument slc has wrong length\\\")\\n _copyback_suc = False\\n if suc is None:\\n suc_ = None\\n else:\\n try:\\n suc_ = memoryview(suc)\\n except TypeError:\\n try:\\n _tmparr_suc = array.array(\\\"d\\\",suc)\\n except TypeError:\\n raise TypeError(\\\"Argument suc has wrong type\\\")\\n else:\\n suc_ = memoryview(_tmparr_suc)\\n _copyback_suc = True\\n else:\\n if suc_.format != \\\"d\\\":\\n suc_ = memoryview(array.array(\\\"d\\\",suc))\\n _copyback_suc = True\\n if suc_ is not None and len(suc_) != self.getnumcon():\\n raise ValueError(\\\"Array argument suc has wrong length\\\")\\n _copyback_slx = False\\n if slx is None:\\n slx_ = None\\n else:\\n try:\\n slx_ = memoryview(slx)\\n except TypeError:\\n try:\\n _tmparr_slx = array.array(\\\"d\\\",slx)\\n except TypeError:\\n raise TypeError(\\\"Argument slx has wrong type\\\")\\n else:\\n slx_ = memoryview(_tmparr_slx)\\n _copyback_slx = True\\n else:\\n if slx_.format != \\\"d\\\":\\n slx_ = memoryview(array.array(\\\"d\\\",slx))\\n _copyback_slx = True\\n if slx_ is not None and len(slx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument slx has wrong length\\\")\\n _copyback_sux = False\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n _copyback_sux = True\\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n _copyback_sux = True\\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n _copyback_snx = False\\n if snx is None:\\n snx_ = None\\n else:\\n try:\\n snx_ = memoryview(snx)\\n except TypeError:\\n try:\\n _tmparr_snx = array.array(\\\"d\\\",snx)\\n except TypeError:\\n raise TypeError(\\\"Argument snx has wrong type\\\")\\n else:\\n snx_ = memoryview(_tmparr_snx)\\n _copyback_snx = True\\n else:\\n if snx_.format != \\\"d\\\":\\n snx_ = memoryview(array.array(\\\"d\\\",snx))\\n _copyback_snx = True\\n if snx_ is not None and len(snx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument snx has wrong length\\\")\\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _prosta_return_value,_solsta_return_value = resargs\\n if _copyback_snx:\\n snx[:] = _tmparr_snx\\n if _copyback_sux:\\n sux[:] = _tmparr_sux\\n if _copyback_slx:\\n slx[:] = _tmparr_slx\\n if _copyback_suc:\\n suc[:] = _tmparr_suc\\n if _copyback_slc:\\n slc[:] = _tmparr_slc\\n if _copyback_y:\\n y[:] = _tmparr_y\\n if _copyback_xx:\\n xx[:] = _tmparr_xx\\n if _copyback_xc:\\n xc[:] = _tmparr_xc\\n if _copyback_skn:\\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\\n if _copyback_skx:\\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\\n if _copyback_skc:\\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\\n _solsta_return_value = solsta(_solsta_return_value)\\n _prosta_return_value = prosta(_prosta_return_value)\\n return _prosta_return_value,_solsta_return_value\",\n \"def setSolverTau(*argv):\",\n \"def putyslice(self,whichsol_,first_,last_,y): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if y is None: raise TypeError(\\\"Invalid type for argument y\\\")\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n \\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n \\n if y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n res = self.__obj.putyslice(whichsol_,first_,last_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def RHS(y,t):\\r\\n\\r\\n return np.multiply(A.dot(y),ones-y)-beta*y\",\n \"def dualy(self, arg, **kwargs):\\n ax = self.alty(**kwargs)\\n self._dualy_arg = arg\\n self._dualy_overrides()\\n return ax\",\n \"def SE_Vx_Vy(self, x, y):\\n\\t\\tif self.V[x] == self.V[y]:\\n\\t\\t\\tself.IP += 2\",\n \"def ij2xy(self, i=None, j=None):\\n pass\",\n \"def y(self, x):\\n return x\",\n \"def getsolutioninfo(self,whichsol_):\\n pobj_ = ctypes.c_double()\\n pviolcon_ = ctypes.c_double()\\n pviolvar_ = ctypes.c_double()\\n pviolbarvar_ = ctypes.c_double()\\n pviolcone_ = ctypes.c_double()\\n pviolitg_ = ctypes.c_double()\\n dobj_ = ctypes.c_double()\\n dviolcon_ = ctypes.c_double()\\n dviolvar_ = ctypes.c_double()\\n dviolbarvar_ = ctypes.c_double()\\n dviolcone_ = ctypes.c_double()\\n res = __library__.MSK_XX_getsolutioninfo(self.__nativep,whichsol_,ctypes.byref(pobj_),ctypes.byref(pviolcon_),ctypes.byref(pviolvar_),ctypes.byref(pviolbarvar_),ctypes.byref(pviolcone_),ctypes.byref(pviolitg_),ctypes.byref(dobj_),ctypes.byref(dviolcon_),ctypes.byref(dviolvar_),ctypes.byref(dviolbarvar_),ctypes.byref(dviolcone_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n pobj_ = pobj_.value\\n _pobj_return_value = pobj_\\n pviolcon_ = pviolcon_.value\\n _pviolcon_return_value = pviolcon_\\n pviolvar_ = pviolvar_.value\\n _pviolvar_return_value = pviolvar_\\n pviolbarvar_ = pviolbarvar_.value\\n _pviolbarvar_return_value = pviolbarvar_\\n pviolcone_ = pviolcone_.value\\n _pviolcone_return_value = pviolcone_\\n pviolitg_ = pviolitg_.value\\n _pviolitg_return_value = pviolitg_\\n dobj_ = dobj_.value\\n _dobj_return_value = dobj_\\n dviolcon_ = dviolcon_.value\\n _dviolcon_return_value = dviolcon_\\n dviolvar_ = dviolvar_.value\\n _dviolvar_return_value = dviolvar_\\n dviolbarvar_ = dviolbarvar_.value\\n _dviolbarvar_return_value = dviolbarvar_\\n dviolcone_ = dviolcone_.value\\n _dviolcone_return_value = dviolcone_\\n return (_pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value)\",\n \"def getpviolbarvar(self,whichsol_,sub,viol): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n num_ = None\\n if num_ is None:\\n num_ = len(sub)\\n elif num_ != len(sub):\\n raise IndexError(\\\"Inconsistent length of array sub\\\")\\n if num_ is None: num_ = 0\\n if sub is None: raise TypeError(\\\"Invalid type for argument sub\\\")\\n if sub is None:\\n sub_ = None\\n else:\\n try:\\n sub_ = memoryview(sub)\\n except TypeError:\\n try:\\n _tmparr_sub = array.array(\\\"i\\\",sub)\\n except TypeError:\\n raise TypeError(\\\"Argument sub has wrong type\\\")\\n else:\\n sub_ = memoryview(_tmparr_sub)\\n \\n else:\\n if sub_.format != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n \\n if viol is None: raise TypeError(\\\"Invalid type for argument viol\\\")\\n _copyback_viol = False\\n if viol is None:\\n viol_ = None\\n else:\\n try:\\n viol_ = memoryview(viol)\\n except TypeError:\\n try:\\n _tmparr_viol = array.array(\\\"d\\\",viol)\\n except TypeError:\\n raise TypeError(\\\"Argument viol has wrong type\\\")\\n else:\\n viol_ = memoryview(_tmparr_viol)\\n _copyback_viol = True\\n else:\\n if viol_.format != \\\"d\\\":\\n viol_ = memoryview(array.array(\\\"d\\\",viol))\\n _copyback_viol = True\\n if viol_ is not None and len(viol_) != (num_):\\n raise ValueError(\\\"Array argument viol has wrong length\\\")\\n res = self.__obj.getpviolbarvar(whichsol_,num_,sub_,viol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_viol:\\n viol[:] = _tmparr_viol\",\n \"def _prepare_y(self,y):\\n y_i = np.ones(y.shape, dtype=np.float64, order=\\\"C\\\")\\n y_i[y != 1] = -1.0\\n return y_i\",\n \"def getdualsolutionnorms(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getdualsolutionnorms(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nrmy_return_value,_nrmslc_return_value,_nrmsuc_return_value,_nrmslx_return_value,_nrmsux_return_value,_nrmsnx_return_value,_nrmbars_return_value = resargs\\n return _nrmy_return_value,_nrmslc_return_value,_nrmsuc_return_value,_nrmslx_return_value,_nrmsux_return_value,_nrmsnx_return_value,_nrmbars_return_value\",\n \"def solutiondef(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.solutiondef(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _isdef_return_value = resargs\\n return _isdef_return_value\",\n \"def _solve(self):\\n B = sp.linalg.solve(self._XtX, self._XtY, assume_a='pos', overwrite_a=False, overwrite_b=False)\\n self.coef_ = B[1:]\\n self.intercept_ = B[0]\\n self.is_fitted_ = True\",\n \"def y_constraint(q,ee_pos):\\n y = (self.length[0]*np.sin(q[0]) + self.length[1]*np.sin(q[0]+q[1]) +\\n self.length[2]*np.sin(np.sum(q))) - self.ee_pos[1]\\n return y\",\n \"def OR_Vx_Vy(self, x, y):\\n\\t\\tself.V[x] |= self.V[y]\",\n \"def get_sol(self):\",\n \"def readsolution(self,whichsol_,filename_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.readsolution(whichsol_,filename_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def evaluate_rhs(self, t, y, ydot=np.empty(0)):\\n raise NotImplementedError\",\n \"def putbarxj(self,whichsol_,j_,barxj): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if barxj is None: raise TypeError(\\\"Invalid type for argument barxj\\\")\\n if barxj is None:\\n barxj_ = None\\n else:\\n try:\\n barxj_ = memoryview(barxj)\\n except TypeError:\\n try:\\n _tmparr_barxj = array.array(\\\"d\\\",barxj)\\n except TypeError:\\n raise TypeError(\\\"Argument barxj has wrong type\\\")\\n else:\\n barxj_ = memoryview(_tmparr_barxj)\\n \\n else:\\n if barxj_.format != \\\"d\\\":\\n barxj_ = memoryview(array.array(\\\"d\\\",barxj))\\n \\n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\\n raise ValueError(\\\"Array argument barxj has wrong length\\\")\\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def fit(self, X, y):\\n # If solver is 'lstsq' use ordinary least squares optimization method.\\n if self.solver == 'lstsq':\\n # Make sure inputs are numpy arrays.\\n X = np.array(X)\\n y = np.array(y)\\n # Add x_0 = 1 to each instance for the bias term.\\n X = np.c_[np.ones((X.shape[0], 1)), X]\\n # Scipy implementation of least squares.\\n self.coef_, residues, rank, singular = lstsq(X, y)\\n\\n return self\\n\\n elif self.solver == 'bgd': \\n super(LinearRegression, self).fit(X, y)\",\n \"def g_solving_subproblem_of_ALR(self,vehicle_id):\\r\\n global_LB = -10000\\r\\n global_UB = 10000\\r\\n iteration_for_RSP = 20\\r\\n optimal_solution_for_RSP = None\\r\\n self.multiplier_v = 0.5\\r\\n\\r\\n # solve the expected shortest path problem\\r\\n self.g_dynamic_programming_algorithm(vehicle_id, 3)\\r\\n\\r\\n # obtain the variance\\r\\n y_ =self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\\r\\n\\r\\n for k in range(iteration_for_RSP):\\r\\n # print(k)\\r\\n LB = 0\\r\\n # step 2: solve decomposed dual problems\\r\\n # Part I: subproblem of x\\r\\n self.g_dynamic_programming_algorithm(vehicle_id, 1)\\r\\n LB += self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_searching\\r\\n\\r\\n # Part II: subproblem of y\\r\\n obj_of_y_ = self.reliability * (y_) ** 0.5 - self.multiplier_v * y_\\r\\n if obj_of_y_ > 0:\\r\\n y = 0\\r\\n LB += 0\\r\\n else:\\r\\n y = y_\\r\\n LB += obj_of_y_\\r\\n\\r\\n # generate an upper bound\\r\\n variance = self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\\r\\n Label_cost_for_lagrangian_mean = self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_searching_mean\\r\\n UB = Label_cost_for_lagrangian_mean + self.reliability * (variance) ** 0.5\\r\\n\\r\\n # print(\\\"UB:{}\\\".format(UB))\\r\\n # print(\\\"LB:{}\\\".format(LB))\\r\\n\\r\\n # UB and LB update\\r\\n if LB > global_LB:\\r\\n global_LB = LB\\r\\n\\r\\n if UB < global_UB:\\r\\n global_UB = UB\\r\\n optimal_solution_for_RSP = self.g_ending_state_vector[vehicle_id].VSStateVector[0]\\r\\n\\r\\n # step 3: update multipliers\\r\\n if variance- y != 0:\\r\\n self.multiplier_v+= (global_UB - LB) / (variance-y)\\r\\n # if self.multiplier_v<0:\\r\\n # self.multiplier_v=1\\r\\n # print(self.multiplier_v)\\r\\n\\r\\n # step 4: termination condition test\\r\\n if global_UB != 0:\\r\\n gap = abs((global_UB - global_LB) / global_UB)\\r\\n # print(gap)\\r\\n if gap < 0.02:\\r\\n print(\\\"iteration{}\\\".format(k + 1))\\r\\n print(self.multiplier_v)\\r\\n print(global_LB, global_UB)\\r\\n return optimal_solution_for_RSP, global_LB\\r\\n else:\\r\\n if global_UB - global_LB == 0:\\r\\n print(\\\"iteration{}\\\".format(k + 1))\\r\\n print(self.multiplier_v)\\r\\n print(global_LB, global_UB)\\r\\n return optimal_solution_for_RSP, global_LB\\r\\n\\r\\n if k == iteration_for_RSP - 1:\\r\\n print(\\\"iteration{}\\\".format(k + 1))\\r\\n print(self.multiplier_v)\\r\\n print(global_LB, global_UB)\\r\\n return optimal_solution_for_RSP, global_LB\",\n \"def set_y(self, new_y):\\r\\n self.y = new_y\",\n \"def xy2ij(self, x, y=None):\\n pass\",\n \"def solve_nonlinear(self, params, unknowns, resids):\\n\\n x = params['x']\\n y = params['y']\\n\\n unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0\",\n \"def g_solving_subproblem_of_LR(self,vehicle_id):\\r\\n global_LB=-10000\\r\\n global_UB=10000\\r\\n iteration_for_RSP=20\\r\\n optimal_solution_for_RSP=None\\r\\n optimal_value_y=0\\r\\n self.multiplier_v=0.5\\r\\n\\r\\n #solve the expected shortest path problem\\r\\n self.g_dynamic_programming_algorithm(vehicle_id, 4)\\r\\n #obtain the variance\\r\\n y_=self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\\r\\n\\r\\n for k in range(iteration_for_RSP):\\r\\n # print(k)\\r\\n LB=0\\r\\n # step 2: solve decomposed dual problems\\r\\n # Part I: subproblem of x\\r\\n self.g_dynamic_programming_algorithm(vehicle_id, 2)\\r\\n LB+=self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_lagrangian\\r\\n\\r\\n # Part II: subproblem of y\\r\\n obj_of_y_ = self.reliability * (y_) ** 0.5 - self.multiplier_v * y_\\r\\n if obj_of_y_ > 0:\\r\\n y = 0\\r\\n LB += 0\\r\\n else:\\r\\n y = y_\\r\\n LB += obj_of_y_\\r\\n # generate an upper bound\\r\\n variance = self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\\r\\n Label_cost_for_lagrangian_mean=self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_lagrangian_mean\\r\\n UB=Label_cost_for_lagrangian_mean+self.reliability*(variance)**0.5\\r\\n\\r\\n # print(\\\"UB:{}\\\".format(UB))\\r\\n # print(\\\"LB:{}\\\".format(LB))\\r\\n\\r\\n # UB and LB update\\r\\n if LB > global_LB:\\r\\n global_LB = LB\\r\\n optimal_solution_for_RSP = self.g_ending_state_vector[vehicle_id].VSStateVector\\r\\n optimal_value_y = y\\r\\n\\r\\n if UB < global_UB:\\r\\n global_UB = UB\\r\\n\\r\\n\\r\\n # step 3: update multipliers\\r\\n if variance-y!= 0:\\r\\n self.multiplier_v+= (global_UB - LB) / (variance-y)\\r\\n # if self.multiplier_v<0:\\r\\n # self.multiplier_v=1\\r\\n # print(self.multiplier_v)\\r\\n\\r\\n # step 4: termination condition test\\r\\n if global_UB != 0:\\r\\n gap = abs((global_UB-global_LB) / global_UB)\\r\\n # print(gap)\\r\\n if gap < 0.02:\\r\\n print(\\\"iteration{}\\\".format(k + 1))\\r\\n print(self.multiplier_v)\\r\\n print(global_LB, global_UB)\\r\\n return optimal_solution_for_RSP, optimal_value_y,global_LB,global_UB\\r\\n else:\\r\\n if global_UB - global_LB == 0:\\r\\n print(\\\"iteration{}\\\".format(k + 1))\\r\\n print(self.multiplier_v)\\r\\n print(global_LB, global_UB)\\r\\n return optimal_solution_for_RSP,optimal_value_y,global_LB,global_UB\\r\\n\\r\\n if k == iteration_for_RSP - 1:\\r\\n print(\\\"iteration{}\\\".format(k + 1))\\r\\n print(self.multiplier_v)\\r\\n print(global_LB, global_UB)\\r\\n return optimal_solution_for_RSP,optimal_value_y,global_LB,global_UB\",\n \"def putxx(self,whichsol_,xx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if xx is None: raise TypeError(\\\"Invalid type for argument xx\\\")\\n if xx is None:\\n xx_ = None\\n else:\\n try:\\n xx_ = memoryview(xx)\\n except TypeError:\\n try:\\n _tmparr_xx = array.array(\\\"d\\\",xx)\\n except TypeError:\\n raise TypeError(\\\"Argument xx has wrong type\\\")\\n else:\\n xx_ = memoryview(_tmparr_xx)\\n \\n else:\\n if xx_.format != \\\"d\\\":\\n xx_ = memoryview(array.array(\\\"d\\\",xx))\\n \\n if xx_ is not None and len(xx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument xx has wrong length\\\")\\n res = self.__obj.putxx(whichsol_,xx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getdualobj(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getdualobj(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _dualobj_return_value = resargs\\n return _dualobj_return_value\",\n \"def y2(self, nx, ny, x_des):\\n\\n c_d, a1, output = self.component_dependency['y_2'], self.dependency_matrix, []\\n for i in range(ny):\\n [sum_i, row] = [[], a1[4 * ny + nx + i]]\\n sum_i.append(np.sum(row))\\n [assign, y] = [c_d[i], []]\\n # x_des = np.random.random_sample(4 * nx + 5 * ny) # this is an instance of the design vector\\n [y.append(self.aer_int.y2_int([x_des[k]], assign - 1)) for k in range(4 * nx + 5 * ny) if row[k] == 1]\\n output.append(np.sum(y) * 1 / sum_i)\\n\\n return output\",\n \"def LD_Vx_Vy(self, x, y):\\n\\t\\tself.V[x] = self.V[y]\",\n \"def putskx(self,whichsol_,skx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if skx is None: raise TypeError(\\\"Invalid type for argument skx\\\")\\n if skx is None:\\n skx_ = None\\n else:\\n try:\\n skx_ = memoryview(skx)\\n except TypeError:\\n try:\\n _tmparr_skx = array.array(\\\"i\\\",skx)\\n except TypeError:\\n raise TypeError(\\\"Argument skx has wrong type\\\")\\n else:\\n skx_ = memoryview(_tmparr_skx)\\n \\n else:\\n if skx_.format != \\\"i\\\":\\n skx_ = memoryview(array.array(\\\"i\\\",skx))\\n \\n if skx_ is not None and len(skx_) != self.getnumvar():\\n raise ValueError(\\\"Array argument skx has wrong length\\\")\\n res = self.__obj.putskx(whichsol_,skx_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def f(self,y,psi):\\r\\n raise NotImplementedError\",\n \"def getyslice(self,whichsol_,first_,last_,y): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n _copyback_y = False\\n if y is None:\\n y_ = None\\n else:\\n try:\\n y_ = memoryview(y)\\n except TypeError:\\n try:\\n _tmparr_y = array.array(\\\"d\\\",y)\\n except TypeError:\\n raise TypeError(\\\"Argument y has wrong type\\\")\\n else:\\n y_ = memoryview(_tmparr_y)\\n _copyback_y = True\\n else:\\n if y_.format != \\\"d\\\":\\n y_ = memoryview(array.array(\\\"d\\\",y))\\n _copyback_y = True\\n if y_ is not None and len(y_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument y has wrong length\\\")\\n res = self.__obj.getyslice(whichsol_,first_,last_,y_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_y:\\n y[:] = _tmparr_y\",\n \"def inv(self,yval,*args,**kwargs):\\n import scipy.optimize\\n\\n if not np.isscalar(yval):\\n raise ModelTypeError('generic 1D inverter can only accept scalar inputs')\\n\\n if len(args) == 0:\\n args=[0]\\n method = kwargs.pop('method','newton')\\n elif len(args) == 1:\\n method = kwargs.pop('method','newton')\\n else:\\n method = kwargs.pop('method','brentq')\\n\\n if isinstance(method,basestring):\\n f = getattr(scipy.optimize,method)\\n else:\\n f = method\\n\\n if kwargs.pop('abs',False):\\n g = lambda(x):np.abs(self(x)-yval)\\n else:\\n g = lambda(x):self(x)-yval\\n\\n return f(g,*args,**kwargs)\",\n \"def test_solve_quadratic(self):\\n iden1 = Identity()\\n iden2 = Identity()\\n iden3 = Identity()\\n iden1.x.fixed = False\\n iden2.x.fixed = False\\n iden3.x.fixed = False\\n term1 = LeastSquaresTerm(iden1.target, 1, 1)\\n term2 = LeastSquaresTerm(iden2.target, 2, 2)\\n term3 = LeastSquaresTerm(iden3.target, 3, 3)\\n prob = LeastSquaresProblem([term1, term2, term3])\\n prob.solve()\\n self.assertAlmostEqual(prob.objective, 0)\\n self.assertAlmostEqual(iden1.x.val, 1)\\n self.assertAlmostEqual(iden2.x.val, 2)\\n self.assertAlmostEqual(iden3.x.val, 3)\",\n \"def invert(self, solution, inverse_data):\\n status = solution['status']\\n\\n primal_vars = None\\n dual_vars = None\\n if status in s.SOLUTION_PRESENT:\\n opt_val = solution['value'] + inverse_data[s.OFFSET]\\n primal_vars = {inverse_data[self.VAR_ID]: solution['primal']}\\n return Solution(status, opt_val, primal_vars, dual_vars, {})\\n else:\\n return failure_solution(status)\",\n \"def y(self, number):\\n self.validate_int(\\\"y\\\", number)\\n if number < 0:\\n raise ValueError(\\\"y must be >= 0\\\")\\n self.__y = number\",\n \"def set_solver(self, solver):\\n self.solver = solver\\n solver.associate_with_model(self)\\n self.set_free_variable(0) # A reasonable initial assumption; can be overridden by simulations\",\n \"def solve(self, x_0, dual_x_0):\\n # Sanitize the inputs\\n if type(x_0) is not np.ndarray or type(dual_x_0) is not np.ndarray:\\n x_0 = np.array(x_0)\\n dual_x_0 = np.array(dual_x_0)\\n # Make sure that the arrays are column vectors\\n x_0 = x_0.reshape(-1, 1)\\n dual_x_0 = dual_x_0.reshape(-1, 1)\\n\\n print (\\\"Starting SQP minimization...\\\")\\n [x, dual_x, exit_info] = self.globalized_sqp(x_0, dual_x_0)\\n conv_criteria = exit_info['val']\\n\\n print (exit_info['msg'])\\n print (\\\"Exiting with ||grad[L]|| = {0:e}\\\".format(conv_criteria))\\n print (\\\"x = {0}\\\".format(x.reshape(-1)))\\n print (\\\"dual_x = {0}\\\".format(dual_x.reshape(-1)))\\n\\n return [x, dual_x]\",\n \"def Solve(self,iter_val=0):\\n\\n ### Save Files before solve ###\\n self.fprint(\\\"Saving Input Data\\\",special=\\\"header\\\")\\n if \\\"mesh\\\" in self.params.output:\\n self.problem.dom.Save(val=iter_val)\\n if \\\"initial_guess\\\" in self.params.output:\\n self.problem.bd.SaveInitialGuess(val=iter_val)\\n if \\\"height\\\" in self.params.output and self.problem.dom.dim == 3:\\n self.problem.bd.SaveHeight(val=iter_val)\\n if \\\"turbine_force\\\" in self.params.output:\\n self.problem.farm.SaveRotorDisks(val=iter_val)\\n self.fprint(\\\"Finished\\\",special=\\\"footer\\\")\\n\\n ####################################################################\\n ### This is the better way to define a nonlinear problem but it \\n ### doesn't play nice with dolfin_adjoint\\n # ### Define Jacobian ###\\n # dU = TrialFunction(self.problem.fs.W)\\n # J = derivative(self.problem.F, self.problem.up_next, dU)\\n\\n # ### Setup nonlinear solver ###\\n # nonlinear_problem = NonlinearVariationalProblem(self.problem.F, self.problem.up_next, self.problem.bd.bcs, J)\\n # nonlinear_solver = NonlinearVariationalSolver(nonlinear_problem)\\n\\n # ### Set some parameters ###\\n # solver_parameters = nonlinear_solver.parameters\\n # solver_parameters[\\\"nonlinear_solver\\\"] = \\\"snes\\\"\\n # solver_parameters[\\\"snes_solver\\\"][\\\"linear_solver\\\"] = \\\"mumps\\\"\\n # solver_parameters[\\\"snes_solver\\\"][\\\"maximum_iterations\\\"] = 50\\n # solver_parameters[\\\"snes_solver\\\"][\\\"error_on_nonconvergence\\\"] = False\\n # solver_parameters[\\\"snes_solver\\\"][\\\"line_search\\\"] = \\\"bt\\\" # Available: basic, bt, cp, l2, nleqerr\\n\\n ### Solve the problem ###\\n # self.fprint(\\\"Solving\\\",special=\\\"header\\\")\\n # start = time.time()\\n # iters, converged = nonlinear_solver.solve()\\n # stop = time.time()\\n # self.fprint(\\\"Total Nonlinear Iterations: {:d}\\\".format(iters))\\n # self.fprint(\\\"Converged Successfully: {0}\\\".format(converged))\\n ####################################################################\\n\\n\\n nonlinear_solver = self.params[\\\"solver\\\"].get(\\\"nonlinear_solver\\\", \\\"snes\\\")\\n relaxation = self.params[\\\"solver\\\"].get(\\\"newton_relaxation\\\", 1.0)\\n\\n self.fprint(\\\"Solving with {0}\\\".format(nonlinear_solver))\\n if nonlinear_solver == \\\"newton\\\":\\n self.fprint(\\\"Relaxation parameter = {: 1.2f}\\\".format(relaxation))\\n\\n newton_options = {\\\"relaxation_parameter\\\": relaxation,\\n \\\"maximum_iterations\\\": 40,\\n \\\"linear_solver\\\": \\\"mumps\\\",\\n \\\"absolute_tolerance\\\": 1e-6,\\n \\\"relative_tolerance\\\": 1e-5}\\n \\n solver_parameters = {\\\"nonlinear_solver\\\": \\\"newton\\\",\\n \\\"newton_solver\\\": newton_options}\\n\\n elif nonlinear_solver == \\\"snes\\\":\\n # ### Add some helper functions to solver options ###\\n solver_parameters = {\\\"nonlinear_solver\\\": \\\"snes\\\",\\n \\\"snes_solver\\\": {\\n \\\"linear_solver\\\": \\\"mumps\\\", \\n \\\"maximum_iterations\\\": 40,\\n \\\"error_on_nonconvergence\\\": True,\\n \\\"line_search\\\": \\\"bt\\\",\\n }}\\n \\n else:\\n raise ValueError(\\\"Unknown nonlinear solver type: {0}\\\".format(nonlinear_solver))\\n\\n ### Start the Solve Process ###\\n self.fprint(\\\"Solving\\\",special=\\\"header\\\")\\n start = time.time()\\n \\n # ### Solve the Baseline Problem ###\\n # solve(self.problem.F_sans_tf == 0, self.problem.up_next, self.problem.bd.bcs, solver_parameters=solver_parameters, **self.extra_kwarg)\\n\\n # ### Store the Baseline and Assign for the real solve ###\\n # self.up_baseline = self.problem.up_next.copy(deepcopy=True)\\n # self.problem.up_next.assign(self.up_baseline)\\n\\n ### Solve the real problem ###\\n solve(self.problem.F == 0, self.problem.up_next, self.problem.bd.bcs, solver_parameters=solver_parameters)\\n stop = time.time()\\n self.fprint(\\\"Solve Complete: {:1.2f} s\\\".format(stop-start),special=\\\"footer\\\")\\n # self.u_next,self.p_next = self.problem.up_next.split(True)\\n self.u_next,self.p_next = split(self.problem.up_next)\\n # self.nu_T = project(self.problem.nu_T,self.problem.fs.Q,solver_type='mumps',**self.extra_kwarg)\\n self.nu_T = None\\n\\n\\n ### Save solutions ###\\n if \\\"solution\\\" in self.params.output:\\n self.fprint(\\\"Saving Solution\\\",special=\\\"header\\\")\\n self.Save(val=iter_val)\\n self.fprint(\\\"Finished\\\",special=\\\"footer\\\")\\n\\n ### calculate the power for each turbine ###\\n ###################################\\n ### Fix how angle is transfered ###\\n ###################################\\n if self.optimizing or self.save_power:\\n self.J += -self.CalculatePowerFunctional((iter_val-self.problem.dom.init_wind)) \\n\\n # self.fprint(\\\"Speed Percent of Inflow Speed\\\")\\n # ps = []\\n # for i in range(6):\\n # HH = self.problem.farm.HH[0]\\n # RD = self.problem.farm.RD[0]\\n # x_val = (i+1)*RD\\n # vel = self.problem.up_next([x_val,0,HH])\\n # vel = vel[0:3]\\n # nom = np.linalg.norm(vel)\\n # perc = nom/self.problem.bd.HH_vel\\n # ps.append(perc)\\n # self.fprint(\\\"Speed Percent at (\\\"+repr(int(x_val))+\\\", 0, \\\"+repr(HH)+\\\"): \\\"+repr(perc))\\n # print(ps)\",\n \"def deletesolution(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res = self.__obj.deletesolution(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putsnx(self,whichsol_,sux): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n if sux is None: raise TypeError(\\\"Invalid type for argument sux\\\")\\n if sux is None:\\n sux_ = None\\n else:\\n try:\\n sux_ = memoryview(sux)\\n except TypeError:\\n try:\\n _tmparr_sux = array.array(\\\"d\\\",sux)\\n except TypeError:\\n raise TypeError(\\\"Argument sux has wrong type\\\")\\n else:\\n sux_ = memoryview(_tmparr_sux)\\n \\n else:\\n if sux_.format != \\\"d\\\":\\n sux_ = memoryview(array.array(\\\"d\\\",sux))\\n \\n if sux_ is not None and len(sux_) != self.getnumvar():\\n raise ValueError(\\\"Array argument sux has wrong length\\\")\\n res = self.__obj.putsnx(whichsol_,sux_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def y ( self ) :\\n return self.yvar\",\n \"def solve_nonlinear(self, params, unknowns, resids):\\n pass\",\n \"def solve_nonlinear(self, params, unknowns, resids):\\n pass\",\n \"def set_xyvxvy(self, y):\\n self.x = y[0]\\n self.y = y[1]\\n self.vx = y[2]\\n self.vy = y[3]\",\n \"def y(self, value):\\n self.data_validator(\\\"y\\\", value)\\n self.__y = value\",\n \"def solve_nonlinear(self, params, unknowns, resids):\\n\\n x = hash(params['x'])\\n y = params['y']\\n\\n unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0\",\n \"def y(self, value):\\n if not isinstance(value, int):\\n raise TypeError(\\\"y must be an integer\\\")\\n if value < 0:\\n raise ValueError(\\\"y must be >= 0\\\")\\n self.__y = value\",\n \"def solve_series(\\n self,\\n x0,\\n params,\\n varied_data,\\n varied_idx,\\n internal_x0=None,\\n solver=None,\\n propagate=True,\\n **kwargs\\n ):\\n if self.x_by_name and isinstance(x0, dict):\\n x0 = [x0[k] for k in self.names]\\n if self.par_by_name:\\n if isinstance(params, dict):\\n params = [params[k] for k in self.param_names]\\n if isinstance(varied_idx, str):\\n varied_idx = self.param_names.index(varied_idx)\\n\\n new_params = np.atleast_1d(np.array(params, dtype=np.float64))\\n xout = np.empty((len(varied_data), len(x0)))\\n self.internal_xout = np.empty_like(xout)\\n self.internal_params_out = np.empty((len(varied_data), len(new_params)))\\n info_dicts = []\\n new_x0 = np.array(x0, dtype=np.float64) # copy\\n conds = kwargs.get(\\\"initial_conditions\\\", None) # see ConditionalNeqSys\\n for idx, value in enumerate(varied_data):\\n try:\\n new_params[varied_idx] = value\\n except TypeError:\\n new_params = value # e.g. type(new_params) == int\\n if conds is not None:\\n kwargs[\\\"initial_conditions\\\"] = conds\\n x, info_dict = self.solve(new_x0, new_params, internal_x0, solver, **kwargs)\\n if propagate:\\n if info_dict[\\\"success\\\"]:\\n try:\\n # See ChainedNeqSys.solve\\n new_x0 = info_dict[\\\"x_vecs\\\"][0]\\n internal_x0 = info_dict[\\\"internal_x_vecs\\\"][0]\\n conds = info_dict[\\\"intermediate_info\\\"][0].get(\\n \\\"conditions\\\", None\\n )\\n except:\\n new_x0 = x\\n internal_x0 = None\\n conds = info_dict.get(\\\"conditions\\\", None)\\n xout[idx, :] = x\\n self.internal_xout[idx, :] = self.internal_x\\n self.internal_params_out[idx, :] = self.internal_params\\n info_dicts.append(info_dict)\\n return xout, info_dicts\",\n \"def y(self, value):\\n if isinstance(value, int) is False:\\n raise TypeError(\\\"y must be an integer\\\")\\n if value < 0:\\n raise ValueError(\\\"y must be >= 0\\\")\\n self.__y = value\",\n \"def lstsqnumpy(A,y):\\r\\n N=A.shape[1]\\r\\n b = np.linalg.lstsq(A,y)\\r\\n A = b[0][1::2]\\r\\n B = b[0][2::2]\\r\\n \\r\\n return A+1j*B, b[0][0::N]\",\n \"def solve_i():\\r\\n x = np.array([ -2.1, -1.45, -1.3, -0.2, 0.1, 0.15, 0.8, 1.1, 1.5, 2.8, 3.8 ])\\r\\n y = np.array([0.012155, 0.122151, 0.184520, 0.960789, 0.990050, 0.977751,\\r\\n 0.527292, 0.298197, 0.105399, 3.936690E-4, 5.355348E-7])\\r\\n # find and plot both interpolations and the oiginal points\\r\\n plt.figure(1)\\r\\n cubic_interpol(x,y)\\r\\n lin_interpol(x,y)\\r\\n plt.plot(x, y, 'rx', ms = 10, label = 'Points')\\r\\n # plot settings\\r\\n plt.title('Cubic & Linear Interpolation Given Points')\\r\\n plt.xlabel('x',fontsize = 14)\\r\\n plt.ylabel('y',fontsize = 14)\\r\\n plt.legend()\",\n \"def integrate(self, y0, t0, tend, N=None, abstol=1e-5, reltol=1e-5):\\n from ode45 import ode45\\n vfun = lambda t,y: self.rhs.Applyf(y)\\n vslot = (t0, tend)\\n vinit = y0\\n t,Y,stats = ode45(vfun,vslot,vinit,abstol=abstol,reltol=reltol,stats=True)\\n self.stats = stats\\n return t,Y\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.85774046","0.70034355","0.66922426","0.66091764","0.62563616","0.6108404","0.5998291","0.56189865","0.5569256","0.549024","0.5483207","0.5337774","0.52960414","0.52665466","0.5235813","0.52331036","0.52272004","0.51770955","0.51770955","0.5159293","0.5152492","0.513468","0.51193005","0.51014566","0.50550455","0.5054728","0.50455624","0.50371575","0.50365764","0.5031399","0.5031399","0.5012099","0.5006172","0.49865153","0.49828318","0.49595872","0.4952713","0.49454078","0.4944556","0.49420857","0.4936942","0.49366847","0.4926424","0.49244338","0.49138984","0.48980683","0.4894759","0.48912412","0.48899582","0.4881476","0.48716938","0.48619422","0.48455966","0.4843829","0.48328343","0.4827237","0.48089653","0.48082614","0.48046923","0.48032656","0.48000544","0.47988492","0.47977683","0.47916874","0.47845635","0.47827065","0.47821453","0.47814164","0.47809783","0.47787872","0.47739825","0.47719494","0.47692478","0.47601667","0.4755701","0.47500575","0.47457734","0.47333905","0.47291863","0.47278485","0.472478","0.47217914","0.4717703","0.47070485","0.4706816","0.47056773","0.47029918","0.46979305","0.46951145","0.4691841","0.4691841","0.46871603","0.46782285","0.46759096","0.4675371","0.4670071","0.4668563","0.46670526","0.46661115","0.4663861"],"string":"[\n \"0.85774046\",\n \"0.70034355\",\n \"0.66922426\",\n \"0.66091764\",\n \"0.62563616\",\n \"0.6108404\",\n \"0.5998291\",\n \"0.56189865\",\n \"0.5569256\",\n \"0.549024\",\n \"0.5483207\",\n \"0.5337774\",\n \"0.52960414\",\n \"0.52665466\",\n \"0.5235813\",\n \"0.52331036\",\n \"0.52272004\",\n \"0.51770955\",\n \"0.51770955\",\n \"0.5159293\",\n \"0.5152492\",\n \"0.513468\",\n \"0.51193005\",\n \"0.51014566\",\n \"0.50550455\",\n \"0.5054728\",\n \"0.50455624\",\n \"0.50371575\",\n \"0.50365764\",\n \"0.5031399\",\n \"0.5031399\",\n \"0.5012099\",\n \"0.5006172\",\n \"0.49865153\",\n \"0.49828318\",\n \"0.49595872\",\n \"0.4952713\",\n \"0.49454078\",\n \"0.4944556\",\n \"0.49420857\",\n \"0.4936942\",\n \"0.49366847\",\n \"0.4926424\",\n \"0.49244338\",\n \"0.49138984\",\n \"0.48980683\",\n \"0.4894759\",\n \"0.48912412\",\n \"0.48899582\",\n \"0.4881476\",\n \"0.48716938\",\n \"0.48619422\",\n \"0.48455966\",\n \"0.4843829\",\n \"0.48328343\",\n \"0.4827237\",\n \"0.48089653\",\n \"0.48082614\",\n \"0.48046923\",\n \"0.48032656\",\n \"0.48000544\",\n \"0.47988492\",\n \"0.47977683\",\n \"0.47916874\",\n \"0.47845635\",\n \"0.47827065\",\n \"0.47821453\",\n \"0.47814164\",\n \"0.47809783\",\n \"0.47787872\",\n \"0.47739825\",\n \"0.47719494\",\n \"0.47692478\",\n \"0.47601667\",\n \"0.4755701\",\n \"0.47500575\",\n \"0.47457734\",\n \"0.47333905\",\n \"0.47291863\",\n \"0.47278485\",\n \"0.472478\",\n \"0.47217914\",\n \"0.4717703\",\n \"0.47070485\",\n \"0.4706816\",\n \"0.47056773\",\n \"0.47029918\",\n \"0.46979305\",\n \"0.46951145\",\n \"0.4691841\",\n \"0.4691841\",\n \"0.46871603\",\n \"0.46782285\",\n \"0.46759096\",\n \"0.4675371\",\n \"0.4670071\",\n \"0.4668563\",\n \"0.46670526\",\n \"0.46661115\",\n \"0.4663861\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81092507"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11998,"cells":{"query":{"kind":"string","value":"Sets a string parameter. putstrparam(self,param_,parvalue_)"},"document":{"kind":"string","value":"def putstrparam(self,param_,parvalue_):\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def putstrparam(self,param_,parvalue_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putstrparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putnastrparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putnastrparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnastrparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def putparam(self,parname_,parvalue_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setString(self, name: unicode, value: unicode) -> None:\n ...","def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value","def set_param(self, name, value):\n name = str(name)\n value = str(value)\n cxnlib.CXNNetSetParam(self.handle,\n ctypes.c_char_p(name.encode('utf-8')),\n ctypes.c_char_p(value.encode('utf-8')))","def _set_string_value_pair(self, parameter, value=None):\n if type(parameter) is str:\n if value==None:\n raise Exception(\"Error: No value given in set() function for population parameter. Exiting.\")\n self.parameters[parameter] = value\n return\n if type(parameter) is not dict:\n raise Exception(\"Error: invalid parameter type for set() function for population parameter. Exiting.\")\n # Add a dictionary-structured set of new parameters to the current set:\n self.parameters.update(parameter)","def set_param(self, param, value):\n self._set_param_client(param, value)","def getstrparam(self,param_):\n maxlen_ = (1 + self.getstrparamlen((param_)))\n len_ = ctypes.c_int32()\n parvalue_ = (ctypes.c_char * (maxlen_))()\n res = __library__.MSK_XX_getstrparam(self.__nativep,param_,maxlen_,ctypes.byref(len_),parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n len_ = len_.value\n _len_return_value = len_\n _parvalue_retval = parvalue_.value.decode(\"utf-8\",errors=\"replace\")\n return (_len_return_value,_parvalue_retval)","def setParameter(self, name, value):","def isstrparname(self,parname_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n param_ = ctypes.c_int32()\n res = __library__.MSK_XX_isstrparname(self.__nativep,parname_,ctypes.byref(param_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = sparam(param_.value)\n return (_param_return_value)","def putnaintparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getstrparam(self,param_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n maxlen_ = (1 + self.getstrparamlen((param_)))\n arr_parvalue = array.array(\"b\",[0]*((maxlen_)))\n memview_arr_parvalue = memoryview(arr_parvalue)\n res,resargs = self.__obj.getstrparam(param_,maxlen_,memview_arr_parvalue)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _len_return_value,retarg_parvalue = resargs\n retarg_parvalue = arr_parvalue.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return _len_return_value,retarg_parvalue","def set_parameter(self, params, name, val):\n raise NotImplementedError()","def isstrparname(self,parname_): # 3\n res,resargs = self.__obj.isstrparname(parname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = resargs\n _param_return_value = sparam(_param_return_value)\n return _param_return_value","def set_param(self, param_key, value):\n return self._params.set_param_value(param_key, value)","def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_param(command):\n namespace = app.main(command)\n assert namespace.command == 'sp' or namespace.command == \"setparam\"\n assert namespace.name == \"test\"\n assert namespace.value == \"test\"","def _put_ssm_param(self, parameter, parameter_name):\n self.ssm_client.put_parameter(\n Name=parameter_name,\n Type=\"String\",\n Value=json.dumps(parameter),\n Overwrite=True,\n Tier=\"Intelligent-Tiering\",\n )","def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val","def param_name(self, value):\n self._param_name = value","def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError","def set_parameter(doc, o_path, param, value, mn_consts):\n if isinstance(value, str):\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\n elif isinstance(value, int):\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\n elif isinstance(value, list):\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)","def string_value(self, string_value):\n\n self._string_value = string_value","def put_param(self, attr_name, val):\n self._params[attr_name] = val","def set_param(self, param_value):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n\n setting[1] = param_value\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def set_parameter_value(self, parameter, value):\n pass","async def set_param(self, param: str, value: int) -> ArchonCommand:\n cmd = await self.send_command(f\"FASTLOADPARAM {param} {value}\")\n if not cmd.succeeded():\n raise ArchonError(\n f\"Failed setting parameter {param!r} ({cmd.status.name}).\"\n )\n return cmd","def setTemplateParameter(self,name,value):\n self.tplparam[name] = value","def string_check(param, name):\n\tif not isinstance(param, strcomp):\n\t\traise TypeError(\"Keyword arg '%s' must be of type string. Got: %s\" % (\n\t\t\tname, type(param)))\n\telse:\n\t\tpass","def getStrParam(self, paramkey, default=None):\n value = self.request.getParameter(paramkey)\n if value is None: return default\n return value","def set_param(self):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n setting[1] = str(self.param_value)\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")","def putintparam(self,param_,parvalue_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putintparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_param(param, value, verbose=False):\n set_param_raw(param, yaml.load(value), verbose=verbose)","def SetParameterValue(self, paramName, value):\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n if paramName == ISENTROPIC_PAR:\n if (self.ideal != None):\n self.ideal.SetParameterValue(paramName, value)","def set(self,name,val):\n matches = self.grep_param_names(name)\n if len(matches):\n x = self._get_params()\n x[matches] = val\n self._set_params(x)\n else:\n raise AttributeError, \"no parameter matches %s\"%name","def _build_set_command(self, cmd, param, val):\n try:\n str_val = self._param_dict.format(param, val)\n set_cmd = '%s=%s' % (param, str_val)\n set_cmd = set_cmd + SBE37_NEWLINE\n \n except KeyError:\n raise InstrumentParameterException('Unknown driver parameter %s' % param)\n \n return set_cmd","def _set_compiler_param(compiler_handle, params, value=None):\n if isinstance(params, collections.Mapping):\n params = params.items()\n elif isinstance(params, STRING_TYPES) and value is not None:\n params = [(params, value)]\n for key, val in params:\n _check_call(_LIB.TreeliteCompilerSetParam(compiler_handle, c_str(key),\n c_str(str(val))))","def addstr(self,name,string):\n\t\tself.windows[name].addstr(string)","def change_general_param(self, param, val):\n assert param in self.params, '%s is not recognized as a valid parameter' % param\n self.params[param].change_value(val)","def set_text( self, a_string ):\n self.a_string_var.set( a_string )","def set_text( self, a_string ):\n self.a_string_var.set( a_string )","def setFieldStringValue (self, fieldname, fieldstrvalue):\n if fieldname in self._table:\n pyval = self._table[fieldname].val_txt2py ( fieldstrvalue )\n self.setFieldValue (fieldname, pyval )","def __setattr__(self, name, value):\n if hasattr(self, name):\n super(JobSubmission, self).__setattr__(name, value)\n\n else:\n self.params[str(name)] = str(value) #TODO: resolve parameter cases","def _set_par(vid, par, value):\n traci.vehicle.setParameter(vid, \"carFollowModel.%s\" % par, str(value))","def setParameter(self,arg,value):\n self._params[arg] = value\n return self._params","def SetParameterValue(self, paramName, value):\n if paramName == NUMBTABLE_PAR:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n self.LookupTable.SetParameterValue(paramName, value)\n\n if paramName == IGNORECURVE_PAR: \n #...ignore the lookuptable and remove any specifications\n if value == 'None': value = None\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\n if value:\n port = self.GetPort(HEAD_PORT)\n port.SetValue(None, FIXED_V) \n else:\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)","def putintparam(self,param_,parvalue_):\n res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def gui_set_param(self,param_name,val):\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\n self._handle_gui_set(param_name,force=True)","def setPrm(self, key, val):\n if hasattr(key, \"encode\"):\n key = key.encode(\"utf-8\") # convert str to bytes\n if hasattr(val, \"encode\"):\n val = val.encode(\"utf-8\") # convert str to bytes\n return self.setVal(self.gbls, key, val)","def set(self, name, value=None):\n if isinstance(name, dict):\n for key, value in name.items():\n try:\n param, value = self.coerceParameter(key, value)\n self.params[param] = value\n except KeyError:\n pass\n elif isinstance(name, str):\n try:\n param, value = self.coerceParameter(name, value)\n self.params[param] = value\n except KeyError:\n pass","def log_param(self, name: str, value):\n self.params[name] = value\n\n self._sync_log_event()","def set_string(prop, value: typing.Union[str, None]) -> str:\n if (value is None) or (value == \"\"):\n raise ValueError(\"{} cannot be Null\".format(str(prop)))\n if not(isinstance(value, str)):\n raise TypeError(\"{} can only be a string\".format(str(prop)))\n return value.strip()","def check_param(param):\n return PREPEND_STR+param if keyword.iskeyword(param) else param","def set_param(param, num, set_val):\n param[0][num] = set_val","def set_params(self, params):","def store_string(self, string: str) -> None:","def do_set(self, arg):\n try:\n statement, param_name, val = arg.parsed.raw.split(None, 2)\n val = val.strip()\n param_name = param_name.strip().lower()\n if param_name not in self.settable:\n hits = [p for p in self.settable if p.startswith(param_name)]\n if len(hits) == 1:\n param_name = hits[0]\n else:\n return self.do_show(param_name)\n current_val = getattr(self, param_name)\n if (val[0] == val[-1]) and val[0] in (\"'\", '\"'):\n val = val[1:-1]\n else:\n val = cast(current_val, val)\n setattr(self, param_name, val)\n self.poutput('%s - was: %s\\nnow: %s\\n' % (param_name, current_val, val))\n if current_val != val:\n try:\n onchange_hook = getattr(self, '_onchange_%s' % param_name)\n onchange_hook(old=current_val, new=val)\n except AttributeError:\n pass\n except (ValueError, AttributeError):\n self.do_show(arg)","def param_str(self, pnames=None):\n l = self.get_params(pnames)\n s = \"\"\n for p in l:\n s += \"%s : %s\\n\" % (p.public_name, p.tostr(self))\n return s","def __setitem__(self, name: str, value):\n super(Parameter, self).__setitem__(name, value)","def 置项目文本(self, n, string): # real signature unknown; restored from __doc__\n self.SetString(n, string)","def __setitem__(self, key: str, value: typing.Any):\n self._params[key].value = value","def replace_param(string, param, value, param_format=None):\n\n if param_format == \"json\":\n return sub(r\"(?P<json_replacement>\\\"%s\\\"\\s*:\\s*)\\\"\\s*\\\"\" %\n escape(str(param)), \"\\\\1\\\"%s\\\"\" % value, string)\n elif param_format == \"header\":\n return sub(r\"%s=[^\\\\n]*\" % escape(str(param)), r\"%s=%s\" %\n (str(param).encode('string-escape'),\n str(value).encode('string-escape')), string)\n else:\n return sub(r\"%s=[^&]*\" % escape(str(param)), r\"%s=%s\" %\n (str(param).encode('string-escape'),\n str(value).encode('string-escape')), string)","def writeString(self, str):\n ret = libxml2mod.xmlOutputBufferWriteString(self._o, str)\n return ret","def setstring(self):\n self._str = 's '+' '.join([self.src, self.start, self.size,\n self.strand, self.srcSize, self.text])+'\\n'","def testParamsAreStrings(self):\n self.chart.display.extra_params['test'] = 32\n self.assertEqual(self.Param('test'), '32')","def value(self, p_str, p_str_1=None): # real signature unknown; restored from __doc__ with multiple overloads\n return \"\"","def putPrm(self, key, val):\n if hasattr(key, \"encode\"):\n key = key.encode(\"utf-8\") # convert str to bytes\n if hasattr(val, \"encode\"):\n val = val.encode(\"utf-8\") # convert str to bytes\n return self.putVal(self.gbls, key, val)","def setText(self, text):\n self._state[0] = str(text)\n self._param.update()","def set_parameter_value(self,name,val,parameterized_object=None):\n source = parameterized_object or self.get_source_po(name)\n object.__setattr__(source,name,val)\n\n # update the tkvar\n if name in self._tkvars:\n self._tkvars[name]._original_set(self._object2string(name,val))","def set_param(self, key='', value=None, dtype='str',\n wtype=None, name=None,\n options=None, option_index=0,\n description=None, hidden=False):\n if name is None:\n name = key\n if (value is None\n and options is not None\n and option_index is not None):\n try:\n value = options[option_index]\n except IndexError:\n pass\n # if not specified\n if wtype is None:\n if options is not None:\n wtype = 'combo_box'\n elif dtype == 'bool':\n wtype = 'check_box'\n else:\n wtype = 'text_box'\n if wtype == 'check_box':\n dtype = 'bool'\n if wtype == 'group':\n hidden = True\n pdict = {'value': value,\n 'dtype': dtype,\n 'wtype': wtype,\n 'name': name,\n 'options': options,\n 'option_index': option_index,\n 'description': description,\n 'hidden': hidden}\n OrderedDict.__setitem__(self, key, pdict)","def TeamCityParamSetter(keyName, value):\n print(\"##teamcity[setParameter name='{}' value='{}']\".format(keyName, value))","def _sanitize_param(self, param):\n if param:\n # Can't send unicode.\n param = str(param)\n return param","def SetParameterValue(self, paramName, value):\n super(LookupTable, self).SetParameterValue(paramName, value)\n if paramName == NUMBTABLE_PAR:\n self.SetTableCount()\n elif paramName == NUMBSERIES_PAR:\n self.SetSeriesCount()\n elif paramName == TABLETAGTYPE_PAR:\n self.SetTableTagType()\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\n idx = long(paramName[len(SERIESTYPE_PAR):])\n self.SetSeriesTypes(idx)","def set_param(params, pname, value=None, bounds=None):\n if value is not None:\n for p in params.flattened():\n if p.name == pname:\n p.value = value\n break\n\n if bounds is not None:\n for p in params.flattened():\n if p.name == pname:\n p.bounds = bounds\n p.vary = True\n break","def replace_param_occurrences(string, params):\n for k, v in params.items():\n string = string.replace(k, str(v))\n return string","def send_string(self, tosend, ypos=None, xpos=None):\n if xpos and ypos:\n self._check_limits(ypos, xpos)\n self.move_to(ypos, xpos)\n # escape double quotes in the data to send\n tosend = tosend.decode(\"utf-8\").replace('\"', '\"')\n self.exec_command('String(\"{0}\")'.format(tosend).encode(\"utf-8\"))","def setContentStr(_session, _segment, _el, _data):\n _session.set_content_str(_el, _data)\n _session.gen3_f_a_f(_segment, keynodes.ui.format_string, _el, sc.SC_A_CONST|sc.SC_POS)","def set_option(self, name, value):\n self._params[name] = value","def set_option(self, name, value):\n self._params[name] = value","def setText(self, t):\n #self._state[0] = str(t)\n self._param.update()","def param(self, param):\n if param is not None and len(param) < 1:\n raise ValueError(\"Invalid value for `param`, length must be greater than or equal to `1`\") # noqa: E501\n\n self._param = param","def set_param(\n self, param_name, *args,\n param_type=None, memo=None, **kwargs):\n # Cast param_name to str once, for convenience:\n # (This is needed because Parameter members are Enum objects,\n # which can't be used in place of string-valued indexes)\n param_name = str(param_name)\n param = self.build_param(\n param_name, *args, param_type=param_type, memo=memo, **kwargs)\n setattr(self, param_name, param)","def put_par(self, parname, value, sep=\".\"):\n pv = self.get_pvname(parname, sep=sep)\n return Pv.put(pv, value)","def set_component_parameter(self, key: str, value: Any):\n if not key:\n raise ValueError(\"Key is required\")\n\n if value is None:\n raise ValueError(\"Value is required\")\n\n self._node[\"app_data\"][\"component_parameters\"][key] = value","def __allowed_values_correct_string(self):\n strTestName = 'Values of a string (correct)'\n RxCSObject = _RxCSobject()\n\n RxCSObject.paramAddMan('parameter1', 'string')\n RxCSObject.paramType('parameter1', str)\n RxCSObject.paramAllowed('parameter1', ['Allowed string #1', 'Allowed string #2'])\n\n RxCSObject.parameter1 = 'Allowed string #2'\n\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)","def set_param(self, param_name, param, set_val = None, type_of_param = None):\n if (set_val is None):\n set_val = True\n param_index = self.index(param_name);\n \n if (type_of_param is None):\n type_of_param = self.type_param[param_index]\n if (param is None):\n param = copy.deepcopy(self._param_val[param_index])\n elif (type(param) is type_of_param):\n param = [param]\n elif (type(param) is not list):\n error_str = (\n \"\"\" \n Trying to set parameter %s to non-integer value (or string if instance name, float if eps+rayeps value), and not a list.\n Type of param: %s\n \"\"\" % (param_name, str(type(param)))\n )\n raise TypeError(error_str)\n\n if (set_val):\n self._param_val[param_index] = copy.deepcopy(param)\n return param","def set(self, key, param: Param):\n if not isinstance(param, Param):\n raise ValueError(\"Only accepts a Param instance.\")\n self._params[key] = param","def custom_string(self, custom_string):\n\n self._custom_string = custom_string","def set_param(self,set_dict):\n if self.query_running():\n self.params_pending = True\n self.pending_params = set_dict\n return \"Pending\"\n for param in set_dict:\n root={\"EXP\":self.app,\"DM\":self.appdoc}[param.split(\"_\")[0]]\n root.SetParam(win32com.client.constants.__dicts__[0][param],set_dict[param])\n rootd={\"EXP\":self.app_param,\"DM\":self.appdoc_param}[param.split(\"_\")[0]]\n rootd.update({param:root.GetParam(win32com.client.constants.__dicts__[0][param])[0]})\n return \"Updated\"","def test_putStrin_with_Stringlit(self):\n input = \"\"\"\n \n void main () {\n putStringLn(\"Tam nay thi ket thuc testcase 100 cho roi\");\n }\n \"\"\"\n expect = \"Tam nay thi ket thuc testcase 100 cho roi\\n\"\n self.assertTrue(TestCodeGen.test(input,expect,599))\n ##### dat ten trung nen chi chay ra 98 testcase","def setValue(self, *args):\n return _libsbml.Parameter_setValue(self, *args)","def putnadouparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def set_parameter(self, param, value, stripe = 0, location = 3):\n if param in self.fpga.cabac_top[0].params:\n self.fpga.set_cabac_value(param, value, stripe)\n time.sleep(0.1)\n self.fpga.send_cabac_config(stripe)\n\n elif param in [\"V_SL\", \"V_SH\", \"V_RGL\", \"V_RGH\", \"V_PL\", \"V_PH\"]:\n self.fpga.set_clock_voltages({param: value})\n\n elif param == \"I_OS\":\n self.fpga.set_current_source(value, stripe)\n\n else:\n print(\"Warning: unidentified parameter for the REB: %s\" % param)","def wString(self, string):\n self.w(string.encode('utf-8'))\n self.wByte(0x00)","def varStringMod(self, arg):\n\t\targ[0] = \"'\" + arg[0] + \"'\"\n\t\treturn arg","def test_str(self):\n \n from pystarlab.starlab import Option\n opt = Option(parameter=\"n\",\n long_name=\"specify number of particles\",\n is_required=True,\n default_value=None)\n self.assertEquals(str(opt), \" -n: specify number of particles [default: None] [required]\")","def HeaderParams(self, mstr, val):\n\n if len(val) > 0:\n if 'template' in mstr:\n self.templatename = val\n elif 'tenant' in mstr:\n self.tenantname = val\n elif 'policy' in mstr:\n self.policyname = val"],"string":"[\n \"def putstrparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,sparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putstrparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putnastrparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putnastrparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnastrparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def putparam(self,parname_,parvalue_):\\n if isinstance(parname_,unicode):\\n parname_ = parname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n if isinstance(parvalue_,unicode):\\n parvalue_ = parvalue_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setString(self, name: unicode, value: unicode) -> None:\\n ...\",\n \"def putparam(self,parname_,parvalue_): # 3\\n res = self.__obj.putparam(parname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def setParam(self,param,value):\\n if param in self.params.keys():\\n self.params[param] = value\",\n \"def set_param(self, name, value):\\n name = str(name)\\n value = str(value)\\n cxnlib.CXNNetSetParam(self.handle,\\n ctypes.c_char_p(name.encode('utf-8')),\\n ctypes.c_char_p(value.encode('utf-8')))\",\n \"def _set_string_value_pair(self, parameter, value=None):\\n if type(parameter) is str:\\n if value==None:\\n raise Exception(\\\"Error: No value given in set() function for population parameter. Exiting.\\\")\\n self.parameters[parameter] = value\\n return\\n if type(parameter) is not dict:\\n raise Exception(\\\"Error: invalid parameter type for set() function for population parameter. Exiting.\\\")\\n # Add a dictionary-structured set of new parameters to the current set:\\n self.parameters.update(parameter)\",\n \"def set_param(self, param, value):\\n self._set_param_client(param, value)\",\n \"def getstrparam(self,param_):\\n maxlen_ = (1 + self.getstrparamlen((param_)))\\n len_ = ctypes.c_int32()\\n parvalue_ = (ctypes.c_char * (maxlen_))()\\n res = __library__.MSK_XX_getstrparam(self.__nativep,param_,maxlen_,ctypes.byref(len_),parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n len_ = len_.value\\n _len_return_value = len_\\n _parvalue_retval = parvalue_.value.decode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n return (_len_return_value,_parvalue_retval)\",\n \"def setParameter(self, name, value):\",\n \"def isstrparname(self,parname_):\\n if isinstance(parname_,unicode):\\n parname_ = parname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n param_ = ctypes.c_int32()\\n res = __library__.MSK_XX_isstrparname(self.__nativep,parname_,ctypes.byref(param_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _param_return_value = sparam(param_.value)\\n return (_param_return_value)\",\n \"def putnaintparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getstrparam(self,param_): # 3\\n if not isinstance(param_,sparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n maxlen_ = (1 + self.getstrparamlen((param_)))\\n arr_parvalue = array.array(\\\"b\\\",[0]*((maxlen_)))\\n memview_arr_parvalue = memoryview(arr_parvalue)\\n res,resargs = self.__obj.getstrparam(param_,maxlen_,memview_arr_parvalue)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _len_return_value,retarg_parvalue = resargs\\n retarg_parvalue = arr_parvalue.tobytes()[:-1].decode(\\\"utf-8\\\",errors=\\\"ignore\\\")\\n return _len_return_value,retarg_parvalue\",\n \"def set_parameter(self, params, name, val):\\n raise NotImplementedError()\",\n \"def isstrparname(self,parname_): # 3\\n res,resargs = self.__obj.isstrparname(parname_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _param_return_value = resargs\\n _param_return_value = sparam(_param_return_value)\\n return _param_return_value\",\n \"def set_param(self, param_key, value):\\n return self._params.set_param_value(param_key, value)\",\n \"def putnaintparam(self,paramname_,parvalue_): # 3\\n res = self.__obj.putnaintparam(paramname_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_param(command):\\n namespace = app.main(command)\\n assert namespace.command == 'sp' or namespace.command == \\\"setparam\\\"\\n assert namespace.name == \\\"test\\\"\\n assert namespace.value == \\\"test\\\"\",\n \"def _put_ssm_param(self, parameter, parameter_name):\\n self.ssm_client.put_parameter(\\n Name=parameter_name,\\n Type=\\\"String\\\",\\n Value=json.dumps(parameter),\\n Overwrite=True,\\n Tier=\\\"Intelligent-Tiering\\\",\\n )\",\n \"def set_param(self, label, val):\\n assert type(label) is str, 'Parameter name \\\"%s\\\" is not string' % label\\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\\n self.params[label] = val\",\n \"def param_name(self, value):\\n self._param_name = value\",\n \"def set_param(self, name, value, *, distrib=None, ref=None):\\n raise NotImplementedError\",\n \"def set_parameter(doc, o_path, param, value, mn_consts):\\n if isinstance(value, str):\\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\\n elif isinstance(value, int):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\\n elif isinstance(value, list):\\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)\",\n \"def string_value(self, string_value):\\n\\n self._string_value = string_value\",\n \"def put_param(self, attr_name, val):\\n self._params[attr_name] = val\",\n \"def set_param(self, param_value):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n\\n setting[1] = param_value\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def set_parameter_value(self, parameter, value):\\n pass\",\n \"async def set_param(self, param: str, value: int) -> ArchonCommand:\\n cmd = await self.send_command(f\\\"FASTLOADPARAM {param} {value}\\\")\\n if not cmd.succeeded():\\n raise ArchonError(\\n f\\\"Failed setting parameter {param!r} ({cmd.status.name}).\\\"\\n )\\n return cmd\",\n \"def setTemplateParameter(self,name,value):\\n self.tplparam[name] = value\",\n \"def string_check(param, name):\\n\\tif not isinstance(param, strcomp):\\n\\t\\traise TypeError(\\\"Keyword arg '%s' must be of type string. Got: %s\\\" % (\\n\\t\\t\\tname, type(param)))\\n\\telse:\\n\\t\\tpass\",\n \"def getStrParam(self, paramkey, default=None):\\n value = self.request.getParameter(paramkey)\\n if value is None: return default\\n return value\",\n \"def set_param(self):\\n with open(\\\"settings.txt\\\", \\\"r\\\") as f:\\n filedata = f.read()\\n settings = [_.split(\\\"=\\\") for _ in filedata.split(\\\"\\\\n\\\")]\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n if setting[0] == self.param:\\n setting[1] = str(self.param_value)\\n\\n with open(\\\"settings.txt\\\", \\\"w\\\") as f:\\n for setting in settings:\\n if len(setting) < 2: # if blank line\\n continue\\n f.write(setting[0] + \\\"=\\\" + setting[1] + \\\"\\\\n\\\")\",\n \"def putintparam(self,param_,parvalue_): # 3\\n if not isinstance(param_,iparam): raise TypeError(\\\"Argument param has wrong type\\\")\\n res = self.__obj.putintparam(param_,parvalue_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_param(param, value, verbose=False):\\n set_param_raw(param, yaml.load(value), verbose=verbose)\",\n \"def SetParameterValue(self, paramName, value):\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n if paramName == ISENTROPIC_PAR:\\n if (self.ideal != None):\\n self.ideal.SetParameterValue(paramName, value)\",\n \"def set(self,name,val):\\n matches = self.grep_param_names(name)\\n if len(matches):\\n x = self._get_params()\\n x[matches] = val\\n self._set_params(x)\\n else:\\n raise AttributeError, \\\"no parameter matches %s\\\"%name\",\n \"def _build_set_command(self, cmd, param, val):\\n try:\\n str_val = self._param_dict.format(param, val)\\n set_cmd = '%s=%s' % (param, str_val)\\n set_cmd = set_cmd + SBE37_NEWLINE\\n \\n except KeyError:\\n raise InstrumentParameterException('Unknown driver parameter %s' % param)\\n \\n return set_cmd\",\n \"def _set_compiler_param(compiler_handle, params, value=None):\\n if isinstance(params, collections.Mapping):\\n params = params.items()\\n elif isinstance(params, STRING_TYPES) and value is not None:\\n params = [(params, value)]\\n for key, val in params:\\n _check_call(_LIB.TreeliteCompilerSetParam(compiler_handle, c_str(key),\\n c_str(str(val))))\",\n \"def addstr(self,name,string):\\n\\t\\tself.windows[name].addstr(string)\",\n \"def change_general_param(self, param, val):\\n assert param in self.params, '%s is not recognized as a valid parameter' % param\\n self.params[param].change_value(val)\",\n \"def set_text( self, a_string ):\\n self.a_string_var.set( a_string )\",\n \"def set_text( self, a_string ):\\n self.a_string_var.set( a_string )\",\n \"def setFieldStringValue (self, fieldname, fieldstrvalue):\\n if fieldname in self._table:\\n pyval = self._table[fieldname].val_txt2py ( fieldstrvalue )\\n self.setFieldValue (fieldname, pyval )\",\n \"def __setattr__(self, name, value):\\n if hasattr(self, name):\\n super(JobSubmission, self).__setattr__(name, value)\\n\\n else:\\n self.params[str(name)] = str(value) #TODO: resolve parameter cases\",\n \"def _set_par(vid, par, value):\\n traci.vehicle.setParameter(vid, \\\"carFollowModel.%s\\\" % par, str(value))\",\n \"def setParameter(self,arg,value):\\n self._params[arg] = value\\n return self._params\",\n \"def SetParameterValue(self, paramName, value):\\n if paramName == NUMBTABLE_PAR:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\\n self.LookupTable.SetParameterValue(paramName, value)\\n\\n if paramName == IGNORECURVE_PAR: \\n #...ignore the lookuptable and remove any specifications\\n if value == 'None': value = None\\n self.LookupTable.SetParameterValue(IGNORED_PAR, value)\\n if value:\\n port = self.GetPort(HEAD_PORT)\\n port.SetValue(None, FIXED_V) \\n else:\\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\",\n \"def putintparam(self,param_,parvalue_):\\n res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def gui_set_param(self,param_name,val):\\n self._tkvar_set(param_name,val) # ERROR: presumably calls trace stuff twice\\n self._handle_gui_set(param_name,force=True)\",\n \"def setPrm(self, key, val):\\n if hasattr(key, \\\"encode\\\"):\\n key = key.encode(\\\"utf-8\\\") # convert str to bytes\\n if hasattr(val, \\\"encode\\\"):\\n val = val.encode(\\\"utf-8\\\") # convert str to bytes\\n return self.setVal(self.gbls, key, val)\",\n \"def set(self, name, value=None):\\n if isinstance(name, dict):\\n for key, value in name.items():\\n try:\\n param, value = self.coerceParameter(key, value)\\n self.params[param] = value\\n except KeyError:\\n pass\\n elif isinstance(name, str):\\n try:\\n param, value = self.coerceParameter(name, value)\\n self.params[param] = value\\n except KeyError:\\n pass\",\n \"def log_param(self, name: str, value):\\n self.params[name] = value\\n\\n self._sync_log_event()\",\n \"def set_string(prop, value: typing.Union[str, None]) -> str:\\n if (value is None) or (value == \\\"\\\"):\\n raise ValueError(\\\"{} cannot be Null\\\".format(str(prop)))\\n if not(isinstance(value, str)):\\n raise TypeError(\\\"{} can only be a string\\\".format(str(prop)))\\n return value.strip()\",\n \"def check_param(param):\\n return PREPEND_STR+param if keyword.iskeyword(param) else param\",\n \"def set_param(param, num, set_val):\\n param[0][num] = set_val\",\n \"def set_params(self, params):\",\n \"def store_string(self, string: str) -> None:\",\n \"def do_set(self, arg):\\n try:\\n statement, param_name, val = arg.parsed.raw.split(None, 2)\\n val = val.strip()\\n param_name = param_name.strip().lower()\\n if param_name not in self.settable:\\n hits = [p for p in self.settable if p.startswith(param_name)]\\n if len(hits) == 1:\\n param_name = hits[0]\\n else:\\n return self.do_show(param_name)\\n current_val = getattr(self, param_name)\\n if (val[0] == val[-1]) and val[0] in (\\\"'\\\", '\\\"'):\\n val = val[1:-1]\\n else:\\n val = cast(current_val, val)\\n setattr(self, param_name, val)\\n self.poutput('%s - was: %s\\\\nnow: %s\\\\n' % (param_name, current_val, val))\\n if current_val != val:\\n try:\\n onchange_hook = getattr(self, '_onchange_%s' % param_name)\\n onchange_hook(old=current_val, new=val)\\n except AttributeError:\\n pass\\n except (ValueError, AttributeError):\\n self.do_show(arg)\",\n \"def param_str(self, pnames=None):\\n l = self.get_params(pnames)\\n s = \\\"\\\"\\n for p in l:\\n s += \\\"%s : %s\\\\n\\\" % (p.public_name, p.tostr(self))\\n return s\",\n \"def __setitem__(self, name: str, value):\\n super(Parameter, self).__setitem__(name, value)\",\n \"def 置项目文本(self, n, string): # real signature unknown; restored from __doc__\\n self.SetString(n, string)\",\n \"def __setitem__(self, key: str, value: typing.Any):\\n self._params[key].value = value\",\n \"def replace_param(string, param, value, param_format=None):\\n\\n if param_format == \\\"json\\\":\\n return sub(r\\\"(?P<json_replacement>\\\\\\\"%s\\\\\\\"\\\\s*:\\\\s*)\\\\\\\"\\\\s*\\\\\\\"\\\" %\\n escape(str(param)), \\\"\\\\\\\\1\\\\\\\"%s\\\\\\\"\\\" % value, string)\\n elif param_format == \\\"header\\\":\\n return sub(r\\\"%s=[^\\\\\\\\n]*\\\" % escape(str(param)), r\\\"%s=%s\\\" %\\n (str(param).encode('string-escape'),\\n str(value).encode('string-escape')), string)\\n else:\\n return sub(r\\\"%s=[^&]*\\\" % escape(str(param)), r\\\"%s=%s\\\" %\\n (str(param).encode('string-escape'),\\n str(value).encode('string-escape')), string)\",\n \"def writeString(self, str):\\n ret = libxml2mod.xmlOutputBufferWriteString(self._o, str)\\n return ret\",\n \"def setstring(self):\\n self._str = 's '+' '.join([self.src, self.start, self.size,\\n self.strand, self.srcSize, self.text])+'\\\\n'\",\n \"def testParamsAreStrings(self):\\n self.chart.display.extra_params['test'] = 32\\n self.assertEqual(self.Param('test'), '32')\",\n \"def value(self, p_str, p_str_1=None): # real signature unknown; restored from __doc__ with multiple overloads\\n return \\\"\\\"\",\n \"def putPrm(self, key, val):\\n if hasattr(key, \\\"encode\\\"):\\n key = key.encode(\\\"utf-8\\\") # convert str to bytes\\n if hasattr(val, \\\"encode\\\"):\\n val = val.encode(\\\"utf-8\\\") # convert str to bytes\\n return self.putVal(self.gbls, key, val)\",\n \"def setText(self, text):\\n self._state[0] = str(text)\\n self._param.update()\",\n \"def set_parameter_value(self,name,val,parameterized_object=None):\\n source = parameterized_object or self.get_source_po(name)\\n object.__setattr__(source,name,val)\\n\\n # update the tkvar\\n if name in self._tkvars:\\n self._tkvars[name]._original_set(self._object2string(name,val))\",\n \"def set_param(self, key='', value=None, dtype='str',\\n wtype=None, name=None,\\n options=None, option_index=0,\\n description=None, hidden=False):\\n if name is None:\\n name = key\\n if (value is None\\n and options is not None\\n and option_index is not None):\\n try:\\n value = options[option_index]\\n except IndexError:\\n pass\\n # if not specified\\n if wtype is None:\\n if options is not None:\\n wtype = 'combo_box'\\n elif dtype == 'bool':\\n wtype = 'check_box'\\n else:\\n wtype = 'text_box'\\n if wtype == 'check_box':\\n dtype = 'bool'\\n if wtype == 'group':\\n hidden = True\\n pdict = {'value': value,\\n 'dtype': dtype,\\n 'wtype': wtype,\\n 'name': name,\\n 'options': options,\\n 'option_index': option_index,\\n 'description': description,\\n 'hidden': hidden}\\n OrderedDict.__setitem__(self, key, pdict)\",\n \"def TeamCityParamSetter(keyName, value):\\n print(\\\"##teamcity[setParameter name='{}' value='{}']\\\".format(keyName, value))\",\n \"def _sanitize_param(self, param):\\n if param:\\n # Can't send unicode.\\n param = str(param)\\n return param\",\n \"def SetParameterValue(self, paramName, value):\\n super(LookupTable, self).SetParameterValue(paramName, value)\\n if paramName == NUMBTABLE_PAR:\\n self.SetTableCount()\\n elif paramName == NUMBSERIES_PAR:\\n self.SetSeriesCount()\\n elif paramName == TABLETAGTYPE_PAR:\\n self.SetTableTagType()\\n elif paramName[:len(SERIESTYPE_PAR)] == SERIESTYPE_PAR:\\n idx = long(paramName[len(SERIESTYPE_PAR):])\\n self.SetSeriesTypes(idx)\",\n \"def set_param(params, pname, value=None, bounds=None):\\n if value is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.value = value\\n break\\n\\n if bounds is not None:\\n for p in params.flattened():\\n if p.name == pname:\\n p.bounds = bounds\\n p.vary = True\\n break\",\n \"def replace_param_occurrences(string, params):\\n for k, v in params.items():\\n string = string.replace(k, str(v))\\n return string\",\n \"def send_string(self, tosend, ypos=None, xpos=None):\\n if xpos and ypos:\\n self._check_limits(ypos, xpos)\\n self.move_to(ypos, xpos)\\n # escape double quotes in the data to send\\n tosend = tosend.decode(\\\"utf-8\\\").replace('\\\"', '\\\"')\\n self.exec_command('String(\\\"{0}\\\")'.format(tosend).encode(\\\"utf-8\\\"))\",\n \"def setContentStr(_session, _segment, _el, _data):\\n _session.set_content_str(_el, _data)\\n _session.gen3_f_a_f(_segment, keynodes.ui.format_string, _el, sc.SC_A_CONST|sc.SC_POS)\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def set_option(self, name, value):\\n self._params[name] = value\",\n \"def setText(self, t):\\n #self._state[0] = str(t)\\n self._param.update()\",\n \"def param(self, param):\\n if param is not None and len(param) < 1:\\n raise ValueError(\\\"Invalid value for `param`, length must be greater than or equal to `1`\\\") # noqa: E501\\n\\n self._param = param\",\n \"def set_param(\\n self, param_name, *args,\\n param_type=None, memo=None, **kwargs):\\n # Cast param_name to str once, for convenience:\\n # (This is needed because Parameter members are Enum objects,\\n # which can't be used in place of string-valued indexes)\\n param_name = str(param_name)\\n param = self.build_param(\\n param_name, *args, param_type=param_type, memo=memo, **kwargs)\\n setattr(self, param_name, param)\",\n \"def put_par(self, parname, value, sep=\\\".\\\"):\\n pv = self.get_pvname(parname, sep=sep)\\n return Pv.put(pv, value)\",\n \"def set_component_parameter(self, key: str, value: Any):\\n if not key:\\n raise ValueError(\\\"Key is required\\\")\\n\\n if value is None:\\n raise ValueError(\\\"Value is required\\\")\\n\\n self._node[\\\"app_data\\\"][\\\"component_parameters\\\"][key] = value\",\n \"def __allowed_values_correct_string(self):\\n strTestName = 'Values of a string (correct)'\\n RxCSObject = _RxCSobject()\\n\\n RxCSObject.paramAddMan('parameter1', 'string')\\n RxCSObject.paramType('parameter1', str)\\n RxCSObject.paramAllowed('parameter1', ['Allowed string #1', 'Allowed string #2'])\\n\\n RxCSObject.parameter1 = 'Allowed string #2'\\n\\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)\",\n \"def set_param(self, param_name, param, set_val = None, type_of_param = None):\\n if (set_val is None):\\n set_val = True\\n param_index = self.index(param_name);\\n \\n if (type_of_param is None):\\n type_of_param = self.type_param[param_index]\\n if (param is None):\\n param = copy.deepcopy(self._param_val[param_index])\\n elif (type(param) is type_of_param):\\n param = [param]\\n elif (type(param) is not list):\\n error_str = (\\n \\\"\\\"\\\" \\n Trying to set parameter %s to non-integer value (or string if instance name, float if eps+rayeps value), and not a list.\\n Type of param: %s\\n \\\"\\\"\\\" % (param_name, str(type(param)))\\n )\\n raise TypeError(error_str)\\n\\n if (set_val):\\n self._param_val[param_index] = copy.deepcopy(param)\\n return param\",\n \"def set(self, key, param: Param):\\n if not isinstance(param, Param):\\n raise ValueError(\\\"Only accepts a Param instance.\\\")\\n self._params[key] = param\",\n \"def custom_string(self, custom_string):\\n\\n self._custom_string = custom_string\",\n \"def set_param(self,set_dict):\\n if self.query_running():\\n self.params_pending = True\\n self.pending_params = set_dict\\n return \\\"Pending\\\"\\n for param in set_dict:\\n root={\\\"EXP\\\":self.app,\\\"DM\\\":self.appdoc}[param.split(\\\"_\\\")[0]]\\n root.SetParam(win32com.client.constants.__dicts__[0][param],set_dict[param])\\n rootd={\\\"EXP\\\":self.app_param,\\\"DM\\\":self.appdoc_param}[param.split(\\\"_\\\")[0]]\\n rootd.update({param:root.GetParam(win32com.client.constants.__dicts__[0][param])[0]})\\n return \\\"Updated\\\"\",\n \"def test_putStrin_with_Stringlit(self):\\n input = \\\"\\\"\\\"\\n \\n void main () {\\n putStringLn(\\\"Tam nay thi ket thuc testcase 100 cho roi\\\");\\n }\\n \\\"\\\"\\\"\\n expect = \\\"Tam nay thi ket thuc testcase 100 cho roi\\\\n\\\"\\n self.assertTrue(TestCodeGen.test(input,expect,599))\\n ##### dat ten trung nen chi chay ra 98 testcase\",\n \"def setValue(self, *args):\\n return _libsbml.Parameter_setValue(self, *args)\",\n \"def putnadouparam(self,paramname_,parvalue_):\\n if isinstance(paramname_,unicode):\\n paramname_ = paramname_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def set_parameter(self, param, value, stripe = 0, location = 3):\\n if param in self.fpga.cabac_top[0].params:\\n self.fpga.set_cabac_value(param, value, stripe)\\n time.sleep(0.1)\\n self.fpga.send_cabac_config(stripe)\\n\\n elif param in [\\\"V_SL\\\", \\\"V_SH\\\", \\\"V_RGL\\\", \\\"V_RGH\\\", \\\"V_PL\\\", \\\"V_PH\\\"]:\\n self.fpga.set_clock_voltages({param: value})\\n\\n elif param == \\\"I_OS\\\":\\n self.fpga.set_current_source(value, stripe)\\n\\n else:\\n print(\\\"Warning: unidentified parameter for the REB: %s\\\" % param)\",\n \"def wString(self, string):\\n self.w(string.encode('utf-8'))\\n self.wByte(0x00)\",\n \"def varStringMod(self, arg):\\n\\t\\targ[0] = \\\"'\\\" + arg[0] + \\\"'\\\"\\n\\t\\treturn arg\",\n \"def test_str(self):\\n \\n from pystarlab.starlab import Option\\n opt = Option(parameter=\\\"n\\\",\\n long_name=\\\"specify number of particles\\\",\\n is_required=True,\\n default_value=None)\\n self.assertEquals(str(opt), \\\" -n: specify number of particles [default: None] [required]\\\")\",\n \"def HeaderParams(self, mstr, val):\\n\\n if len(val) > 0:\\n if 'template' in mstr:\\n self.templatename = val\\n elif 'tenant' in mstr:\\n self.tenantname = val\\n elif 'policy' in mstr:\\n self.policyname = val\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8903032","0.79385155","0.78946966","0.6862305","0.6671494","0.6618401","0.66161126","0.65867805","0.64877427","0.64677244","0.6323704","0.6307091","0.62468153","0.6246462","0.6167524","0.616714","0.61054486","0.60639423","0.59956676","0.5957143","0.59525156","0.59516406","0.5897451","0.5867103","0.5864356","0.5851361","0.5846874","0.58267576","0.58057153","0.5766778","0.576491","0.5763576","0.5743835","0.57398313","0.5703509","0.56748146","0.5663697","0.5655167","0.5620137","0.5603059","0.5601495","0.5592167","0.5574833","0.5574833","0.5569826","0.5561852","0.5542381","0.5518865","0.5511646","0.55085754","0.5505831","0.5500863","0.5499926","0.54977","0.5493993","0.5484424","0.5468391","0.5457867","0.5443236","0.5392846","0.53912467","0.5384664","0.5378524","0.5372588","0.5362847","0.53619343","0.535857","0.5358219","0.53537846","0.53470665","0.5344754","0.5341192","0.5336915","0.5332661","0.5308015","0.53070146","0.52890027","0.5263019","0.52491164","0.52484864","0.524042","0.524042","0.52391124","0.5223891","0.5221827","0.5189848","0.5187733","0.51763034","0.51748466","0.51718897","0.5163622","0.5159716","0.51529783","0.51517045","0.514654","0.5121712","0.51154345","0.51153725","0.5104374","0.5102674"],"string":"[\n \"0.8903032\",\n \"0.79385155\",\n \"0.78946966\",\n \"0.6862305\",\n \"0.6671494\",\n \"0.6618401\",\n \"0.66161126\",\n \"0.65867805\",\n \"0.64877427\",\n \"0.64677244\",\n \"0.6323704\",\n \"0.6307091\",\n \"0.62468153\",\n \"0.6246462\",\n \"0.6167524\",\n \"0.616714\",\n \"0.61054486\",\n \"0.60639423\",\n \"0.59956676\",\n \"0.5957143\",\n \"0.59525156\",\n \"0.59516406\",\n \"0.5897451\",\n \"0.5867103\",\n \"0.5864356\",\n \"0.5851361\",\n \"0.5846874\",\n \"0.58267576\",\n \"0.58057153\",\n \"0.5766778\",\n \"0.576491\",\n \"0.5763576\",\n \"0.5743835\",\n \"0.57398313\",\n \"0.5703509\",\n \"0.56748146\",\n \"0.5663697\",\n \"0.5655167\",\n \"0.5620137\",\n \"0.5603059\",\n \"0.5601495\",\n \"0.5592167\",\n \"0.5574833\",\n \"0.5574833\",\n \"0.5569826\",\n \"0.5561852\",\n \"0.5542381\",\n \"0.5518865\",\n \"0.5511646\",\n \"0.55085754\",\n \"0.5505831\",\n \"0.5500863\",\n \"0.5499926\",\n \"0.54977\",\n \"0.5493993\",\n \"0.5484424\",\n \"0.5468391\",\n \"0.5457867\",\n \"0.5443236\",\n \"0.5392846\",\n \"0.53912467\",\n \"0.5384664\",\n \"0.5378524\",\n \"0.5372588\",\n \"0.5362847\",\n \"0.53619343\",\n \"0.535857\",\n \"0.5358219\",\n \"0.53537846\",\n \"0.53470665\",\n \"0.5344754\",\n \"0.5341192\",\n \"0.5336915\",\n \"0.5332661\",\n \"0.5308015\",\n \"0.53070146\",\n \"0.52890027\",\n \"0.5263019\",\n \"0.52491164\",\n \"0.52484864\",\n \"0.524042\",\n \"0.524042\",\n \"0.52391124\",\n \"0.5223891\",\n \"0.5221827\",\n \"0.5189848\",\n \"0.5187733\",\n \"0.51763034\",\n \"0.51748466\",\n \"0.51718897\",\n \"0.5163622\",\n \"0.5159716\",\n \"0.51529783\",\n \"0.51517045\",\n \"0.514654\",\n \"0.5121712\",\n \"0.51154345\",\n \"0.51153725\",\n \"0.5104374\",\n \"0.5102674\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.85641146"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11999,"cells":{"query":{"kind":"string","value":"Assigns a new name to the task. puttaskname(self,taskname_)"},"document":{"kind":"string","value":"def puttaskname(self,taskname_):\n if isinstance(taskname_,unicode):\n taskname_ = taskname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_puttaskname(self.__nativep,taskname_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def puttaskname(self,taskname_): # 3\n res = self.__obj.puttaskname(taskname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def task_name(self, task_name):\n\n self._task_name = task_name","def _setTaskName(self, taskName):\n self._taskName = taskName\n self._progressChangedNotifier.notify(self)","def task_name(self, task_name: str):\n if task_name is None:\n raise ValueError(\"Invalid value for `task_name`, must not be `None`\") # noqa: E501\n\n self._task_name = task_name","def task(self, **task):\n task[\"name\"] = task[\"name\"].replace(\"=\", \"--\")\n return task","def task(self, name):\n pass","def edit_task_name(entry):\n entry.task_name = get_task_name()\n entry.save()\n input(\"Edit successful. \")\n return entry","def task_name(self):\n pass","def update_task(self, name, fields):\n pass","def setNextTask(self, taskname):\n self._NextTask = taskname\n return","def task_name(self) -> str:\n return self._task_name","def __setattr__(self, name, value):\n self.__curtask__.__dict__[name] = value","def __setattr__(self, name, value):\n container = getattr(self, \"__data\", None)\n if container and name in container: # Check if attribute is from task\n container[name] = value # Update task\n self.task_changed([name]) # Update task representation\n return\n self.__dict__[name] = value # Default action","def name(self, name: str):\n self.inst['targetname'] = name","def new_name(self,new_name):\n self.name = new_name","def set_name(self, name):\n\t\tself.name_ = name","def set_name(self, name):\n self.name = name # overwrite the existing name with the input name","def set_name(self, name):\n self.name = name # overwrite the existing name with the input name","def put(self, task):\n self.put_id += 1\n self.task_queue.put(task)","def task_id(self, task_id):\n self._task_id = task_id","def set_name(self,name):\r\n self._name = __name","def name(self, new_name: str) -> None:\n raise NotImplementedError()","def rename(self, new_name, sync_run=True):\n try:\n request = VI.Rename_TaskRequestMsg()\n _this = request.new__this(self._mor)\n _this.set_attribute_type(self._mor.get_attribute_type())\n request.set_element__this(_this)\n request.set_element_newName(new_name)\n\n task = self._server._proxy.Rename_Task(request)._returnval\n vi_task = VITask(task, self._server)\n if sync_run:\n status = vi_task.wait_for_state([vi_task.STATE_SUCCESS,\n vi_task.STATE_ERROR])\n if status == vi_task.STATE_ERROR:\n raise VIException(vi_task.get_error_message(),\n FaultTypes.TASK_ERROR)\n return\n\n return vi_task\n\n except (VI.ZSI.FaultException), e:\n raise VIApiException(e)","def _set_task(self, task_idx):\n self.task_idx = task_idx","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def name(self, name):\n self._name = name","def set_name(self, newname=\"\"):\n self.name = newname","def assign(self, task=None):\n if task is None:\n print(\"\\n*** Add Task ***\")\n name = input(\"Name of the task?: \")\n try:\n priority = int(input(\"Priority of the task (1-->5): \"))\n except ValueError:\n priority = 1\n steps = []\n while 1:\n step = input(\"Add step #\" + str(len(steps) + 1) + \" (Enter empty to finish): \")\n if step:\n steps.append(step)\n else:\n break\n self.tasks.append(Task(name, priority, steps))\n self.save()\n self.sort()\n print(\"*\"*16)\n else:\n self.tasks.append(task)\n self.save()\n self.sort()","def name(self, name) :\n\t\ttry :\n\t\t\tself._name = name\n\t\texcept Exception as e:\n\t\t\traise e","def create_task(self, name, value):\n pass","def assign_task(user_name, task_name, work_server_ip):\r\n\r\n database_handler.update_records(\"current_tasks\",\r\n {\"server_ip\": work_server_ip, \"Task_status\": TaskStatusNames.in_progress.value},\r\n condition=\"Task_name=$? and user_name=$?\", code_args=[task_name, user_name])","def schedule_task(self, name, date):\n pass","def task(self, callable, name=None):\n if name is None:\n name = callable.__name__\n if self.name:\n name = '%s.%s' % (self.name, name)\n if name in self.tasks:\n raise ValueError('task %r conflicts with existing task' % name)\n self.tasks[name] = callable\n return callable","def gen_task_name(app, name, module_name):\n ...","def name(self, name: str):\r\n self._name = name","def name(self, name: str):\r\n self._name = name","def name(self, name: str):\r\n self._name = name","def name(self, name: str):\r\n self._name = name","def schedule_name(self, schedule_name):\n\n self._schedule_name = schedule_name","def task_id(self, task_id):\n\n self._task_id = task_id","def name(self, name):\n \n self._name = name","def name(self, name):\n \n self._name = name","def name(self, name):\n \n self._name = name","def name(self, name):\n \n self._name = name","def setname(self, name):\n self.__name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name","def name(self, name):\n\n self._name = name"],"string":"[\n \"def puttaskname(self,taskname_): # 3\\n res = self.__obj.puttaskname(taskname_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def task_name(self, task_name):\\n\\n self._task_name = task_name\",\n \"def _setTaskName(self, taskName):\\n self._taskName = taskName\\n self._progressChangedNotifier.notify(self)\",\n \"def task_name(self, task_name: str):\\n if task_name is None:\\n raise ValueError(\\\"Invalid value for `task_name`, must not be `None`\\\") # noqa: E501\\n\\n self._task_name = task_name\",\n \"def task(self, **task):\\n task[\\\"name\\\"] = task[\\\"name\\\"].replace(\\\"=\\\", \\\"--\\\")\\n return task\",\n \"def task(self, name):\\n pass\",\n \"def edit_task_name(entry):\\n entry.task_name = get_task_name()\\n entry.save()\\n input(\\\"Edit successful. \\\")\\n return entry\",\n \"def task_name(self):\\n pass\",\n \"def update_task(self, name, fields):\\n pass\",\n \"def setNextTask(self, taskname):\\n self._NextTask = taskname\\n return\",\n \"def task_name(self) -> str:\\n return self._task_name\",\n \"def __setattr__(self, name, value):\\n self.__curtask__.__dict__[name] = value\",\n \"def __setattr__(self, name, value):\\n container = getattr(self, \\\"__data\\\", None)\\n if container and name in container: # Check if attribute is from task\\n container[name] = value # Update task\\n self.task_changed([name]) # Update task representation\\n return\\n self.__dict__[name] = value # Default action\",\n \"def name(self, name: str):\\n self.inst['targetname'] = name\",\n \"def new_name(self,new_name):\\n self.name = new_name\",\n \"def set_name(self, name):\\n\\t\\tself.name_ = name\",\n \"def set_name(self, name):\\n self.name = name # overwrite the existing name with the input name\",\n \"def set_name(self, name):\\n self.name = name # overwrite the existing name with the input name\",\n \"def put(self, task):\\n self.put_id += 1\\n self.task_queue.put(task)\",\n \"def task_id(self, task_id):\\n self._task_id = task_id\",\n \"def set_name(self,name):\\r\\n self._name = __name\",\n \"def name(self, new_name: str) -> None:\\n raise NotImplementedError()\",\n \"def rename(self, new_name, sync_run=True):\\n try:\\n request = VI.Rename_TaskRequestMsg()\\n _this = request.new__this(self._mor)\\n _this.set_attribute_type(self._mor.get_attribute_type())\\n request.set_element__this(_this)\\n request.set_element_newName(new_name)\\n\\n task = self._server._proxy.Rename_Task(request)._returnval\\n vi_task = VITask(task, self._server)\\n if sync_run:\\n status = vi_task.wait_for_state([vi_task.STATE_SUCCESS,\\n vi_task.STATE_ERROR])\\n if status == vi_task.STATE_ERROR:\\n raise VIException(vi_task.get_error_message(),\\n FaultTypes.TASK_ERROR)\\n return\\n\\n return vi_task\\n\\n except (VI.ZSI.FaultException), e:\\n raise VIApiException(e)\",\n \"def _set_task(self, task_idx):\\n self.task_idx = task_idx\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def name(self, name):\\n self._name = name\",\n \"def set_name(self, newname=\\\"\\\"):\\n self.name = newname\",\n \"def assign(self, task=None):\\n if task is None:\\n print(\\\"\\\\n*** Add Task ***\\\")\\n name = input(\\\"Name of the task?: \\\")\\n try:\\n priority = int(input(\\\"Priority of the task (1-->5): \\\"))\\n except ValueError:\\n priority = 1\\n steps = []\\n while 1:\\n step = input(\\\"Add step #\\\" + str(len(steps) + 1) + \\\" (Enter empty to finish): \\\")\\n if step:\\n steps.append(step)\\n else:\\n break\\n self.tasks.append(Task(name, priority, steps))\\n self.save()\\n self.sort()\\n print(\\\"*\\\"*16)\\n else:\\n self.tasks.append(task)\\n self.save()\\n self.sort()\",\n \"def name(self, name) :\\n\\t\\ttry :\\n\\t\\t\\tself._name = name\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def create_task(self, name, value):\\n pass\",\n \"def assign_task(user_name, task_name, work_server_ip):\\r\\n\\r\\n database_handler.update_records(\\\"current_tasks\\\",\\r\\n {\\\"server_ip\\\": work_server_ip, \\\"Task_status\\\": TaskStatusNames.in_progress.value},\\r\\n condition=\\\"Task_name=$? and user_name=$?\\\", code_args=[task_name, user_name])\",\n \"def schedule_task(self, name, date):\\n pass\",\n \"def task(self, callable, name=None):\\n if name is None:\\n name = callable.__name__\\n if self.name:\\n name = '%s.%s' % (self.name, name)\\n if name in self.tasks:\\n raise ValueError('task %r conflicts with existing task' % name)\\n self.tasks[name] = callable\\n return callable\",\n \"def gen_task_name(app, name, module_name):\\n ...\",\n \"def name(self, name: str):\\r\\n self._name = name\",\n \"def name(self, name: str):\\r\\n self._name = name\",\n \"def name(self, name: str):\\r\\n self._name = name\",\n \"def name(self, name: str):\\r\\n self._name = name\",\n \"def schedule_name(self, schedule_name):\\n\\n self._schedule_name = schedule_name\",\n \"def task_id(self, task_id):\\n\\n self._task_id = task_id\",\n \"def name(self, name):\\n \\n self._name = name\",\n \"def name(self, name):\\n \\n self._name = name\",\n \"def name(self, name):\\n \\n self._name = name\",\n \"def name(self, name):\\n \\n self._name = name\",\n \"def setname(self, name):\\n self.__name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\",\n \"def name(self, name):\\n\\n self._name = name\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.87137294","0.8589184","0.7855133","0.7552247","0.7284806","0.7256069","0.69043714","0.675982","0.6663191","0.6645687","0.6581016","0.6562904","0.65047216","0.648085","0.645912","0.64160436","0.63803774","0.63803774","0.6374819","0.63747865","0.63524747","0.633512","0.6324542","0.6323675","0.6319699","0.6319699","0.6319699","0.6319699","0.6319699","0.6319699","0.6319699","0.6319699","0.6319699","0.6319699","0.6319699","0.6319699","0.6310756","0.62938505","0.6293532","0.6291207","0.62894","0.6278316","0.6270881","0.6262943","0.6245557","0.6245557","0.6245557","0.6245557","0.6238909","0.6222399","0.6203642","0.6203642","0.6203642","0.6203642","0.6201881","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656","0.6201656"],"string":"[\n \"0.87137294\",\n \"0.8589184\",\n \"0.7855133\",\n \"0.7552247\",\n \"0.7284806\",\n \"0.7256069\",\n \"0.69043714\",\n \"0.675982\",\n \"0.6663191\",\n \"0.6645687\",\n \"0.6581016\",\n \"0.6562904\",\n \"0.65047216\",\n \"0.648085\",\n \"0.645912\",\n \"0.64160436\",\n \"0.63803774\",\n \"0.63803774\",\n \"0.6374819\",\n \"0.63747865\",\n \"0.63524747\",\n \"0.633512\",\n \"0.6324542\",\n \"0.6323675\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6319699\",\n \"0.6310756\",\n \"0.62938505\",\n \"0.6293532\",\n \"0.6291207\",\n \"0.62894\",\n \"0.6278316\",\n \"0.6270881\",\n \"0.6262943\",\n \"0.6245557\",\n \"0.6245557\",\n \"0.6245557\",\n \"0.6245557\",\n \"0.6238909\",\n \"0.6222399\",\n \"0.6203642\",\n \"0.6203642\",\n \"0.6203642\",\n \"0.6203642\",\n \"0.6201881\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\",\n \"0.6201656\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8126309"},"document_rank":{"kind":"string","value":"2"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":119,"numItemsPerPage":100,"numTotalItems":95772,"offset":11900,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTkzMTU5MCwianRpIjoiY2E4NTRmOTAtOGYyNC00ZWM2LTlhMDgtNTgzMzk5NmFjZDAzIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU5MzUxOTAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.sdyl7gJJJrOujdcuKcOhaiE9U5Q6MhWHpmMGnBFITJXwXpFcZ2WpeAlsjdXZvbHb0pb9irsdDV9HHZdLKlMmBw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Computes the violation of a dual solution associated with a set of scalar variables. getdviolvar(self,whichsol_,sub_,viol_) | def getdviolvar(self,whichsol_,sub_,viol_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if sub_ is None:
raise ValueError("Argument sub cannot be None")
if sub_ is None:
raise ValueError("Argument ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getdviolvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n i... | [
"0.842469",
"0.8020049",
"0.7901954",
"0.78885037",
"0.7700739",
"0.74876314",
"0.7316246",
"0.7308457",
"0.71310973",
"0.7126803",
"0.70612556",
"0.69538635",
"0.6839152",
"0.6820221",
"0.6764274",
"0.63441837",
"0.60243124",
"0.58630145",
"0.58565766",
"0.5746518",
"0.56699... | 0.8300813 | 1 |
Computes the violation of dual solution for a set of semidefinite variables. getdviolbarvar(self,whichsol_,sub_,viol_) | def getdviolbarvar(self,whichsol_,sub_,viol_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if sub_ is None:
raise ValueError("Argument sub cannot be None")
if sub_ is None:
raise ValueError("Argume... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getdviolbarvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n ... | [
"0.8365152",
"0.820791",
"0.80888903",
"0.80470765",
"0.8035463",
"0.79758537",
"0.7821625",
"0.71889246",
"0.7017734",
"0.69864213",
"0.69533056",
"0.6851415",
"0.68440586",
"0.6822642",
"0.6695411",
"0.5912819",
"0.5740153",
"0.5733863",
"0.56503063",
"0.55693674",
"0.54391... | 0.833021 | 1 |
Computes the violation of a solution for set of dual conic constraints. getdviolcones(self,whichsol_,sub_,viol_) | def getdviolcones(self,whichsol_,sub_,viol_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if sub_ is None:
raise ValueError("Argument sub cannot be None")
if sub_ is None:
raise ValueError("Argumen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getdviolcones(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n ... | [
"0.8349926",
"0.81372005",
"0.7943071",
"0.7794971",
"0.7692615",
"0.76759416",
"0.73636335",
"0.72565156",
"0.7177833",
"0.69880986",
"0.6783844",
"0.6722641",
"0.6663118",
"0.6470961",
"0.6186312",
"0.5971693",
"0.5965254",
"0.5513916",
"0.5476401",
"0.54692227",
"0.5461435... | 0.8095238 | 2 |
Obtains information about of a solution. getsolutioninfo(self,whichsol_) | def getsolutioninfo(self,whichsol_):
pobj_ = ctypes.c_double()
pviolcon_ = ctypes.c_double()
pviolvar_ = ctypes.c_double()
pviolbarvar_ = ctypes.c_double()
pviolcone_ = ctypes.c_double()
pviolitg_ = ctypes.c_double()
dobj_ = ctypes.c_double()
dviolcon_ = ctypes.c_double()
dviolvar_ =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getsolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _po... | [
"0.83259404",
"0.7097056",
"0.70560944",
"0.62482697",
"0.61821",
"0.6134684",
"0.5949938",
"0.5949938",
"0.58852124",
"0.5849498",
"0.58224726",
"0.5818088",
"0.5818062",
"0.58057815",
"0.5803957",
"0.57681364",
"0.5765166",
"0.57560706",
"0.5745533",
"0.57215154",
"0.570536... | 0.76058203 | 1 |
Compute norms of the dual solution. getdualsolutionnorms(self,whichsol_) | def getdualsolutionnorms(self,whichsol_):
nrmy_ = ctypes.c_double()
nrmslc_ = ctypes.c_double()
nrmsuc_ = ctypes.c_double()
nrmslx_ = ctypes.c_double()
nrmsux_ = ctypes.c_double()
nrmsnx_ = ctypes.c_double()
nrmbars_ = ctypes.c_double()
res = __library__.MSK_XX_getdualsolutionnorms(self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getdualsolutionnorms(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getdualsolutionnorms(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\... | [
"0.86213017",
"0.7799082",
"0.75975305",
"0.5843836",
"0.58425874",
"0.58296424",
"0.58143187",
"0.5741888",
"0.572548",
"0.5703944",
"0.56897783",
"0.5674844",
"0.56606346",
"0.5631683",
"0.5582",
"0.5572983",
"0.55595255",
"0.555448",
"0.5498853",
"0.54523396",
"0.544812",
... | 0.8386281 | 1 |
Compute norms of the primal solution. getprimalsolutionnorms(self,whichsol_) | def getprimalsolutionnorms(self,whichsol_):
nrmxc_ = ctypes.c_double()
nrmxx_ = ctypes.c_double()
nrmbarx_ = ctypes.c_double()
res = __library__.MSK_XX_getprimalsolutionnorms(self.__nativep,whichsol_,ctypes.byref(nrmxc_),ctypes.byref(nrmxx_),ctypes.byref(nrmbarx_))
if res != 0:
_,msg = self.__... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getprimalsolutionnorms(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getprimalsolutionnorms(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),m... | [
"0.8324788",
"0.7253409",
"0.7123311",
"0.5990859",
"0.5774027",
"0.5561484",
"0.5471691",
"0.5438935",
"0.5428926",
"0.5412315",
"0.54044545",
"0.5353224",
"0.5341129",
"0.5296491",
"0.5293551",
"0.5284404",
"0.52810794",
"0.52686256",
"0.52663827",
"0.52657825",
"0.52632594... | 0.8563136 | 0 |
Obtains a slice of the solution. getsolutionslice(self,whichsol_,solitem_,first_,last_,values_) | def getsolutionslice(self,whichsol_,solitem_,first_,last_,values_):
_values_minlength = ((last_) - (first_))
if ((last_) - (first_)) > 0 and values_ is not None and len(values_) != ((last_) - (first_)):
raise ValueError("Array argument values is not long enough: Is %d, expected %d" % (len(values_),((last_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getsolutionslice(self,whichsol_,solitem_,first_,last_,values): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if not isinstance(solitem_,solitem): raise TypeError(\"Argument solitem has wrong type\")\n _copyback_values = False\n if values... | [
"0.87597924",
"0.65897465",
"0.65460825",
"0.652142",
"0.64466506",
"0.6407843",
"0.6392543",
"0.6389086",
"0.638142",
"0.62045497",
"0.6178242",
"0.615228",
"0.614273",
"0.6135625",
"0.61208415",
"0.6096903",
"0.6052912",
"0.59870696",
"0.5830406",
"0.5768767",
"0.573988",
... | 0.8561782 | 1 |
Obtains the reduced costs for a sequence of variables. getreducedcosts(self,whichsol_,first_,last_,redcosts_) | def getreducedcosts(self,whichsol_,first_,last_,redcosts_):
_redcosts_minlength = ((last_) - (first_))
if ((last_) - (first_)) > 0 and redcosts_ is not None and len(redcosts_) != ((last_) - (first_)):
raise ValueError("Array argument redcosts is not long enough: Is %d, expected %d" % (len(redcosts_),((las... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getreducedcosts(self,whichsol_,first_,last_,redcosts): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_redcosts = False\n if redcosts is None:\n redcosts_ = None\n else:\n try:\n redcosts_ = memoryview(redc... | [
"0.8526416",
"0.5733916",
"0.5447237",
"0.535488",
"0.5351056",
"0.5340395",
"0.5331999",
"0.533112",
"0.523295",
"0.5223164",
"0.52220964",
"0.5167097",
"0.51390874",
"0.5138091",
"0.5131547",
"0.51277804",
"0.5109994",
"0.5080163",
"0.5077774",
"0.5056903",
"0.5050309",
"... | 0.8104121 | 1 |
Obtains the value of a string parameter. getstrparam(self,param_) | def getstrparam(self,param_):
maxlen_ = (1 + self.getstrparamlen((param_)))
len_ = ctypes.c_int32()
parvalue_ = (ctypes.c_char * (maxlen_))()
res = __library__.MSK_XX_getstrparam(self.__nativep,param_,maxlen_,ctypes.byref(len_),parvalue_)
if res != 0:
_,msg = self.__getlasterror(res)
rai... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getStrParam(self, paramkey, default=None):\n value = self.request.getParameter(paramkey)\n if value is None: return default\n return value",
"def getstrparam(self,param_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n maxlen_ = (1 ... | [
"0.7935466",
"0.7634089",
"0.7359659",
"0.720644",
"0.67880225",
"0.67430836",
"0.6694598",
"0.6593283",
"0.65884066",
"0.6575903",
"0.6563992",
"0.65217215",
"0.6457357",
"0.6379218",
"0.63673365",
"0.63578653",
"0.63423514",
"0.6282078",
"0.62551343",
"0.6241636",
"0.619703... | 0.80357796 | 0 |
Obtains the length of a string parameter. getstrparamlen(self,param_) | def getstrparamlen(self,param_):
len_ = ctypes.c_int32()
res = __library__.MSK_XX_getstrparamlen(self.__nativep,param_,ctypes.byref(len_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
len_ = len_.value
_len_return_value = len_
return (_len_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getstrparamlen(self,param_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n res,resargs = self.__obj.getstrparamlen(param_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _len_return_value ... | [
"0.9096072",
"0.71522003",
"0.6668345",
"0.65663975",
"0.6506962",
"0.64881116",
"0.6398439",
"0.614287",
"0.61132294",
"0.6104768",
"0.6094698",
"0.5968915",
"0.5955851",
"0.59100646",
"0.5908313",
"0.58431584",
"0.58408535",
"0.582819",
"0.5819093",
"0.5803778",
"0.5803778"... | 0.9009316 | 1 |
Obtains the length the task name. gettasknamelen(self) | def gettasknamelen(self):
len_ = ctypes.c_int32()
res = __library__.MSK_XX_gettasknamelen(self.__nativep,ctypes.byref(len_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
len_ = len_.value
_len_return_value = len_
return (_len_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gettasknamelen(self): # 3\n res,resargs = self.__obj.gettasknamelen()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _len_return_value = resargs\n return _len_return_value",
"def gettaskname(self):\n sizetaskname_ = (1 + self.gett... | [
"0.8965651",
"0.7397242",
"0.72126365",
"0.667672",
"0.6508015",
"0.6390483",
"0.6323408",
"0.62874895",
"0.61902267",
"0.61406875",
"0.61028075",
"0.61009693",
"0.61009693",
"0.60868466",
"0.58861375",
"0.58722395",
"0.58384854",
"0.5818338",
"0.5761852",
"0.5759502",
"0.575... | 0.88484406 | 1 |
Obtains the task name. gettaskname(self) | def gettaskname(self):
sizetaskname_ = (1 + self.gettasknamelen())
taskname_ = (ctypes.c_char * (sizetaskname_))()
res = __library__.MSK_XX_gettaskname(self.__nativep,sizetaskname_,taskname_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
_taskname_retval = t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getTaskName(self):\n return self._taskName",
"def task_name(self) -> str:\n return self._task_name",
"def task_name(self):\n pass",
"def task(self) -> str:\n return self._task",
"def gettaskname(self): # 3\n sizetaskname_ = (1 + self.gettasknamelen())\n arr_tasknam... | [
"0.9225546",
"0.9222605",
"0.8905019",
"0.80700994",
"0.7833355",
"0.7788377",
"0.7711659",
"0.7691786",
"0.7691786",
"0.7611951",
"0.7544766",
"0.7498057",
"0.74122566",
"0.73614204",
"0.7213984",
"0.7192169",
"0.71578455",
"0.7126896",
"0.7096741",
"0.70961493",
"0.7053906"... | 0.7600426 | 10 |
Gets the variable type of one variable. getvartype(self,j_) | def getvartype(self,j_):
vartype_ = ctypes.c_int32()
res = __library__.MSK_XX_getvartype(self.__nativep,j_,ctypes.byref(vartype_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
_vartype_return_value = variabletype(vartype_.value)
return (_vartype_return_valu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getvartype(self,j_): # 3\n res,resargs = self.__obj.getvartype(j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _vartype_return_value = resargs\n _vartype_return_value = variabletype(_vartype_return_value)\n return _vartype_retu... | [
"0.905106",
"0.68339425",
"0.6826129",
"0.6610592",
"0.64489937",
"0.6415037",
"0.63719475",
"0.6252331",
"0.61517644",
"0.61124295",
"0.60500795",
"0.6013344",
"0.5994384",
"0.59254044",
"0.5884005",
"0.5843133",
"0.5802532",
"0.5790517",
"0.5766481",
"0.56811124",
"0.566860... | 0.8521373 | 1 |
Obtains the variable type for one or more variables. getvartypelist(self,subj_,vartype_) | def getvartypelist(self,subj_,vartype_):
num_ = None
if num_ is None:
num_ = len(subj_)
elif num_ != len(subj_):
raise IndexError("Inconsistent length of array subj")
if subj_ is None:
raise ValueError("Argument subj cannot be None")
if subj_ is None:
raise ValueError("Argume... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getvartypelist(self,subj,vartype): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\"... | [
"0.79822195",
"0.6934229",
"0.6789694",
"0.65395796",
"0.61634475",
"0.6046211",
"0.5934622",
"0.58922654",
"0.5809417",
"0.5805788",
"0.56369525",
"0.55829936",
"0.5531917",
"0.5438396",
"0.5414235",
"0.53518057",
"0.53442925",
"0.5290569",
"0.52470815",
"0.52378434",
"0.522... | 0.7767394 | 1 |
Input the linear part of an optimization task in one function call. inputdata(self,maxnumcon_,maxnumvar_,c_,cfix_,aptrb_,aptre_,asub_,aval_,bkc_,blc_,buc_,bkx_,blx_,bux_) | def inputdata(self,maxnumcon_,maxnumvar_,c_,cfix_,aptrb_,aptre_,asub_,aval_,bkc_,blc_,buc_,bkx_,blx_,bux_):
numcon_ = None
if numcon_ is None:
numcon_ = len(buc_)
elif numcon_ != len(buc_):
raise IndexError("Inconsistent length of array buc")
if numcon_ is None:
numcon_ = len(blc_)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inputdata(self,maxnumcon_,maxnumvar_,c,cfix_,aptrb,aptre,asub,aval,bkc,blc,buc,bkx,blx,bux): # 3\n numcon_ = None\n if numcon_ is None:\n numcon_ = len(buc)\n elif numcon_ != len(buc):\n raise IndexError(\"Inconsistent length of array buc\")\n if numcon_ is None:\n numc... | [
"0.75954765",
"0.58628434",
"0.56659245",
"0.5474209",
"0.54651934",
"0.5464624",
"0.5457585",
"0.5383377",
"0.53758955",
"0.5368115",
"0.53530073",
"0.53413105",
"0.5320913",
"0.5311969",
"0.5309887",
"0.5296562",
"0.52821916",
"0.5266035",
"0.52494615",
"0.524528",
"0.52344... | 0.7667418 | 0 |
Checks a double parameter name. isdouparname(self,parname_) | def isdouparname(self,parname_):
if isinstance(parname_,unicode):
parname_ = parname_.encode("utf-8",errors="replace")
param_ = ctypes.c_int32()
res = __library__.MSK_XX_isdouparname(self.__nativep,parname_,ctypes.byref(param_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isdouparname(self,parname_): # 3\n res,resargs = self.__obj.isdouparname(parname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = resargs\n _param_return_value = dparam(_param_return_value)\n return _param_re... | [
"0.80806935",
"0.68700576",
"0.64967716",
"0.63709056",
"0.61381704",
"0.60840553",
"0.59852844",
"0.5982808",
"0.58434045",
"0.5807862",
"0.5747373",
"0.57192117",
"0.5711429",
"0.5684285",
"0.5672923",
"0.56625664",
"0.56414455",
"0.5623535",
"0.56149685",
"0.5614595",
"0.5... | 0.75950897 | 1 |
Checks an integer parameter name. isintparname(self,parname_) | def isintparname(self,parname_):
if isinstance(parname_,unicode):
parname_ = parname_.encode("utf-8",errors="replace")
param_ = ctypes.c_int32()
res = __library__.MSK_XX_isintparname(self.__nativep,parname_,ctypes.byref(param_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isintparname(self,parname_): # 3\n res,resargs = self.__obj.isintparname(parname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = resargs\n _param_return_value = iparam(_param_return_value)\n return _param_re... | [
"0.8943503",
"0.72626674",
"0.7105855",
"0.6482964",
"0.63203907",
"0.6214711",
"0.61602587",
"0.61551595",
"0.60910577",
"0.6028535",
"0.59252524",
"0.57617325",
"0.5749392",
"0.57139874",
"0.5683462",
"0.5636818",
"0.56267637",
"0.56267637",
"0.5624048",
"0.56225157",
"0.56... | 0.8926859 | 1 |
Checks a string parameter name. isstrparname(self,parname_) | def isstrparname(self,parname_):
if isinstance(parname_,unicode):
parname_ = parname_.encode("utf-8",errors="replace")
param_ = ctypes.c_int32()
res = __library__.MSK_XX_isstrparname(self.__nativep,parname_,ctypes.byref(param_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isstrparname(self,parname_): # 3\n res,resargs = self.__obj.isstrparname(parname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = resargs\n _param_return_value = sparam(_param_return_value)\n return _param_re... | [
"0.89850354",
"0.7278682",
"0.7277868",
"0.7031824",
"0.70296276",
"0.6844901",
"0.6804795",
"0.6768947",
"0.6753342",
"0.66259956",
"0.66259956",
"0.64886266",
"0.62590593",
"0.62335366",
"0.61893946",
"0.6130385",
"0.6129044",
"0.61108005",
"0.6060818",
"0.6014146",
"0.5991... | 0.8786404 | 1 |
Directs all output from a task stream to a file. linkfiletostream(self,whichstream_,filename_,append_) | def linkfiletostream(self,whichstream_,filename_,append_):
if isinstance(filename_,unicode):
filename_ = filename_.encode("utf-8",errors="replace")
res = __library__.MSK_XX_linkfiletotaskstream(self.__nativep,whichstream_,filename_,append_)
if res != 0:
_,msg = self.__getlasterror(res)
rai... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linkfiletostream(self,whichstream_,filename_,append_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.linkfiletotaskstream(whichstream_,filename_,append_)\n if res != 0:\n result,msg = self.__getlasterror(res)\... | [
"0.8318727",
"0.77448934",
"0.7373398",
"0.57945675",
"0.5791961",
"0.5715489",
"0.5558145",
"0.55282754",
"0.55161315",
"0.54058146",
"0.533981",
"0.5332803",
"0.5253724",
"0.5166495",
"0.51639307",
"0.51587784",
"0.5121433",
"0.50872654",
"0.50666916",
"0.50341964",
"0.5025... | 0.8256208 | 1 |
Repairs a primal infeasible optimization problem by adjusting the bounds on the constraints and variables. primalrepair(self,wlc_,wuc_,wlx_,wux_) | def primalrepair(self,wlc_,wuc_,wlx_,wux_):
_wlc_minlength = self.getnumcon()
if self.getnumcon() > 0 and wlc_ is not None and len(wlc_) != self.getnumcon():
raise ValueError("Array argument wlc is not long enough: Is %d, expected %d" % (len(wlc_),self.getnumcon()))
if isinstance(wlc_, numpy.ndarray) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def primalrepair(self,wlc,wuc,wlx,wux): # 3\n if wlc is None:\n wlc_ = None\n else:\n try:\n wlc_ = memoryview(wlc)\n except TypeError:\n try:\n _tmparr_wlc = array.array(\"d\",wlc)\n except TypeError:\n raise TypeError(\"Argument wlc ... | [
"0.80588853",
"0.57209605",
"0.56667006",
"0.56207",
"0.5570829",
"0.55265594",
"0.55035216",
"0.54500896",
"0.543349",
"0.5393113",
"0.53447133",
"0.533168",
"0.5316762",
"0.5287845",
"0.52589554",
"0.5221941",
"0.51770306",
"0.5143078",
"0.5087917",
"0.50838614",
"0.5065829... | 0.78439325 | 1 |
Inplace reformulation of a QCQP to a COP toconic(self) | def toconic(self):
res = __library__.MSK_XX_toconic(self.__nativep)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def qchannel_to_qiskit(representation):\n\n rep = representation.representation\n # Find what representation it is.\n # Then create the corresponding matrix and shape it like qiskit is expecting it.\n # Finally, create the qiskit representation from that matrix.\n if rep in (RepresentationType.PTM, ... | [
"0.5834786",
"0.5763762",
"0.5494851",
"0.54759103",
"0.54056937",
"0.52836055",
"0.5276868",
"0.52675694",
"0.523278",
"0.5229992",
"0.5162237",
"0.5157569",
"0.5150449",
"0.5148391",
"0.51430106",
"0.508227",
"0.50785273",
"0.50548977",
"0.5018255",
"0.49843046",
"0.4980547... | 0.0 | -1 |
Optimizes the problem. optimize(self) | def optimize(self):
trmcode_ = ctypes.c_int32()
res = __library__.MSK_XX_optimizetrm(self.__nativep,ctypes.byref(trmcode_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
_trmcode_return_value = rescode(trmcode_.value)
return (_trmcode_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def minimize(self):\n pass",
"def minimize(self):\n raise NotImplementedError",
"def Optimize(self):\n return _gmat_py.Optimizer_Optimize(self)",
"def _optimise(self):\n pass",
"def Optimize(self):\n return _gmat_py.ExternalOptimizer_Optimize(self)",
"def optimize(self)... | [
"0.77038383",
"0.7509769",
"0.735032",
"0.730405",
"0.7098665",
"0.7035441",
"0.6961135",
"0.68164253",
"0.6750857",
"0.67198616",
"0.6691278",
"0.66578263",
"0.6605731",
"0.6562218",
"0.6558255",
"0.65536994",
"0.6548704",
"0.65304416",
"0.65304416",
"0.65304416",
"0.6499936... | 0.0 | -1 |
Commits all cached problem changes. commitchanges(self) | def commitchanges(self):
res = __library__.MSK_XX_commitchanges(self.__nativep)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def commitchanges(self): # 3\n res = self.__obj.commitchanges()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def commit_changes(self):\n self.connection.commit()",
"def commit(self):\n pass",
"def commit(self):\n pass",... | [
"0.76084656",
"0.676833",
"0.6732544",
"0.6732544",
"0.6732544",
"0.6732544",
"0.6732544",
"0.6690966",
"0.66586244",
"0.6633915",
"0.6497338",
"0.64345556",
"0.6411314",
"0.6405037",
"0.63306653",
"0.6300718",
"0.6299956",
"0.62567204",
"0.62290084",
"0.6215825",
"0.615116",... | 0.70863163 | 1 |
Gets the current A matrix truncation threshold. getatruncatetol(self,tolzero_) | def getatruncatetol(self,tolzero_):
_tolzero_minlength = 1
if 1 > 0 and tolzero_ is not None and len(tolzero_) != 1:
raise ValueError("Array argument tolzero is not long enough: Is %d, expected %d" % (len(tolzero_),1))
if isinstance(tolzero_,numpy.ndarray) and not tolzero_.flags.writeable:
raise... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putatruncatetol(self,tolzero_):\n res = __library__.MSK_XX_putatruncatetol(self.__nativep,tolzero_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def root_threshold(self):\n return self._ll_tree.get_root_threshold()",
"def get_abs_tolerance(self... | [
"0.67641044",
"0.55815715",
"0.55104095",
"0.54922104",
"0.536327",
"0.53503454",
"0.5266048",
"0.52519375",
"0.5251207",
"0.51428974",
"0.50763685",
"0.5071689",
"0.505621",
"0.501474",
"0.50051814",
"0.49723387",
"0.49293035",
"0.49293035",
"0.48994562",
"0.48980278",
"0.48... | 0.7983391 | 0 |
Truncates all elements in A below a certain tolerance to zero. putatruncatetol(self,tolzero_) | def putatruncatetol(self,tolzero_):
res = __library__.MSK_XX_putatruncatetol(self.__nativep,tolzero_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getatruncatetol(self,tolzero_):\n _tolzero_minlength = 1\n if 1 > 0 and tolzero_ is not None and len(tolzero_) != 1:\n raise ValueError(\"Array argument tolzero is not long enough: Is %d, expected %d\" % (len(tolzero_),1))\n if isinstance(tolzero_,numpy.ndarray) and not tolzero_.flags.writeable:\... | [
"0.80036145",
"0.5751662",
"0.5640666",
"0.5628784",
"0.55079186",
"0.5492442",
"0.5491674",
"0.5307355",
"0.5290475",
"0.5286937",
"0.52232546",
"0.5209159",
"0.5189761",
"0.51801574",
"0.5154854",
"0.51260567",
"0.5098613",
"0.50700945",
"0.5041812",
"0.50397253",
"0.503684... | 0.7630597 | 1 |
Changes a single value in the linear coefficient matrix. putaij(self,i_,j_,aij_) | def putaij(self,i_,j_,aij_):
res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putaij(self,i_,j_,aij_): # 3\n res = self.__obj.putaij(i_,j_,aij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def getaij(self,i_,j_):\n aij_ = ctypes.c_double()\n res = __library__.MSK_XX_getaij(self.__nativep,i_,j_,ctypes.byref(aij... | [
"0.77300495",
"0.6668987",
"0.6660921",
"0.6471983",
"0.64646226",
"0.6461473",
"0.61172444",
"0.6107512",
"0.60523355",
"0.6006178",
"0.5970558",
"0.5945894",
"0.5909612",
"0.59052235",
"0.58720404",
"0.58607316",
"0.58551913",
"0.58178586",
"0.5790797",
"0.5705922",
"0.5656... | 0.783983 | 0 |
Changes one or more coefficients in the linear constraint matrix. putaijlist(self,subi_,subj_,valij_) | def putaijlist(self,subi_,subj_,valij_):
num_ = None
if num_ is None:
num_ = len(subi_)
elif num_ != len(subi_):
raise IndexError("Inconsistent length of array subi")
if num_ is None:
num_ = len(subj_)
elif num_ != len(subj_):
raise IndexError("Inconsistent length of array su... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putaijlist(self,subi,subj,valij): # 3\n num_ = None\n if num_ is None:\n num_ = len(subi)\n elif num_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\... | [
"0.76199657",
"0.67067486",
"0.6587105",
"0.6432624",
"0.6215005",
"0.61117065",
"0.5975101",
"0.59496117",
"0.5931895",
"0.5928728",
"0.5910426",
"0.5877166",
"0.56786174",
"0.5629414",
"0.5609968",
"0.5585486",
"0.5579588",
"0.55791116",
"0.5475792",
"0.54427344",
"0.536428... | 0.7752596 | 0 |
Replaces all elements in one column of the linear constraint matrix. putacol(self,j_,subj_,valj_) | def putacol(self,j_,subj_,valj_):
nzj_ = None
if nzj_ is None:
nzj_ = len(subj_)
elif nzj_ != len(subj_):
raise IndexError("Inconsistent length of array subj")
if nzj_ is None:
nzj_ = len(valj_)
elif nzj_ != len(valj_):
raise IndexError("Inconsistent length of array valj")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putacol(self,j_,subj,valj): # 3\n nzj_ = None\n if nzj_ is None:\n nzj_ = len(subj)\n elif nzj_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nzj_ is None:\n nzj_ = len(valj)\n elif nzj_ != len(valj):\n raise IndexError(\"Incon... | [
"0.7239256",
"0.66198826",
"0.6576874",
"0.6424902",
"0.5502499",
"0.54932886",
"0.5471949",
"0.546628",
"0.5395238",
"0.5250104",
"0.51579374",
"0.5112476",
"0.5112115",
"0.5078982",
"0.50694185",
"0.506477",
"0.5047945",
"0.50186586",
"0.5005424",
"0.49487793",
"0.49407876"... | 0.7026005 | 1 |
Replaces all elements in one row of the linear constraint matrix. putarow(self,i_,subi_,vali_) | def putarow(self,i_,subi_,vali_):
nzi_ = None
if nzi_ is None:
nzi_ = len(subi_)
elif nzi_ != len(subi_):
raise IndexError("Inconsistent length of array subi")
if nzi_ is None:
nzi_ = len(vali_)
elif nzi_ != len(vali_):
raise IndexError("Inconsistent length of array vali")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putarow(self,i_,subi,vali): # 3\n nzi_ = None\n if nzi_ is None:\n nzi_ = len(subi)\n elif nzi_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nzi_ is None:\n nzi_ = len(vali)\n elif nzi_ != len(vali):\n raise IndexError(\"Incon... | [
"0.67917335",
"0.60628814",
"0.5958818",
"0.5807271",
"0.5769178",
"0.56926376",
"0.5588199",
"0.5587641",
"0.5496159",
"0.54637206",
"0.5370489",
"0.5326177",
"0.5312469",
"0.5302563",
"0.52845985",
"0.52328",
"0.51951206",
"0.5188997",
"0.5179855",
"0.5082866",
"0.50648874"... | 0.6598026 | 1 |
Replaces all elements in several rows the linear constraint matrix. putarowslice(self,first_,last_,ptrb_,ptre_,asub_,aval_) | def putarowslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):
_ptrb_minlength = ((last_) - (first_))
if ((last_) - (first_)) > 0 and ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):
raise ValueError("Array argument ptrb is not long enough: Is %d, expected %d" % (len(ptrb_),((last_) - (first_))))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putacolslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb cannot be None\")\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb may not be None\")\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and pt... | [
"0.7404314",
"0.7322725",
"0.7104161",
"0.6849304",
"0.6482448",
"0.61469907",
"0.61213756",
"0.6089529",
"0.5984134",
"0.59686124",
"0.59560657",
"0.59348285",
"0.591947",
"0.5878643",
"0.58049715",
"0.5793094",
"0.574386",
"0.55775774",
"0.5560746",
"0.54988486",
"0.5478345... | 0.7555351 | 0 |
Replaces all elements in several rows of the linear constraint matrix. putarowlist(self,sub_,ptrb_,ptre_,asub_,aval_) | def putarowlist(self,sub_,ptrb_,ptre_,asub_,aval_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if num_ is None:
num_ = len(ptrb_)
elif num_ != len(ptrb_):
raise IndexError("Inconsistent length of ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putacollist(self,sub_,ptrb_,ptre_,asub_,aval_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(ptrb_)\n elif num_ != len(ptrb_):\n raise IndexError(\"Incons... | [
"0.7166984",
"0.70779383",
"0.7077155",
"0.7009495",
"0.6519843",
"0.61421853",
"0.6117272",
"0.608168",
"0.59091073",
"0.58321506",
"0.5795848",
"0.5780153",
"0.57748336",
"0.5751861",
"0.5738041",
"0.5662904",
"0.56070936",
"0.54496944",
"0.5404027",
"0.5375109",
"0.5352889... | 0.73380697 | 0 |
Replaces all elements in a sequence of columns the linear constraint matrix. putacolslice(self,first_,last_,ptrb_,ptre_,asub_,aval_) | def putacolslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):
if ptrb_ is None:
raise ValueError("Argument ptrb cannot be None")
if ptrb_ is None:
raise ValueError("Argument ptrb may not be None")
if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.cont... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putacolslice(self,first_,last_,ptrb,ptre,asub,aval): # 3\n if ptrb is None: raise TypeError(\"Invalid type for argument ptrb\")\n if ptrb is None:\n ptrb_ = None\n else:\n try:\n ptrb_ = memoryview(ptrb)\n except TypeError:\n try:\n _tmparr_ptrb ... | [
"0.7589353",
"0.7360616",
"0.6743218",
"0.64514834",
"0.6416378",
"0.64101464",
"0.63146544",
"0.6266777",
"0.6082965",
"0.58759755",
"0.57552385",
"0.5581388",
"0.55421555",
"0.54518884",
"0.53525954",
"0.53166443",
"0.52935916",
"0.52841985",
"0.5253678",
"0.5235242",
"0.52... | 0.79024863 | 0 |
Replaces all elements in several columns the linear constraint matrix. putacollist(self,sub_,ptrb_,ptre_,asub_,aval_) | def putacollist(self,sub_,ptrb_,ptre_,asub_,aval_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if num_ is None:
num_ = len(ptrb_)
elif num_ != len(ptrb_):
raise IndexError("Inconsistent length of ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putacollist(self,sub,ptrb,ptre,asub,aval): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(ptrb)\n elif num_ != len(ptrb):\n raise IndexE... | [
"0.74245644",
"0.6290077",
"0.6092594",
"0.60655177",
"0.5945023",
"0.5859933",
"0.5820203",
"0.58128494",
"0.57564044",
"0.56575847",
"0.5627294",
"0.55868393",
"0.5496758",
"0.5488544",
"0.5481107",
"0.5311536",
"0.49722016",
"0.49675912",
"0.4916937",
"0.49151236",
"0.4907... | 0.7473528 | 0 |
Inputs an element of barA. putbaraij(self,i_,j_,sub_,weights_) | def putbaraij(self,i_,j_,sub_,weights_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if num_ is None:
num_ = len(weights_)
elif num_ != len(weights_):
raise IndexError("Inconsistent length of array... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putbaraij(self,i_,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexErr... | [
"0.8622167",
"0.7611092",
"0.7276272",
"0.72428167",
"0.6288931",
"0.6111654",
"0.60395956",
"0.5904328",
"0.58659524",
"0.58246255",
"0.57645816",
"0.55474997",
"0.5455191",
"0.54046607",
"0.5398408",
"0.53886384",
"0.52858645",
"0.526097",
"0.5223889",
"0.5210424",
"0.52021... | 0.81481034 | 1 |
Inputs list of elements of barA. putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_) | def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):
num_ = None
if num_ is None:
num_ = len(subi_)
elif num_ != len(subi_):
raise IndexError("Inconsistent length of array subi")
if num_ is None:
num_ = len(subj_)
elif num_ != len(subj_):
raise IndexErr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putbaraij(self,i_,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexErr... | [
"0.72721004",
"0.6810426",
"0.671993",
"0.6488707",
"0.60943043",
"0.5819522",
"0.575237",
"0.54880625",
"0.52580094",
"0.51958394",
"0.5174712",
"0.5102174",
"0.50996226",
"0.5046311",
"0.50254816",
"0.49972755",
"0.49248773",
"0.49245527",
"0.48816815",
"0.48778692",
"0.487... | 0.822026 | 0 |
Replace a set of rows of barA putbararowlist(self,subi_,ptrb_,ptre_,subj_,nummat_,matidx_,weights_) | def putbararowlist(self,subi_,ptrb_,ptre_,subj_,nummat_,matidx_,weights_):
num_ = None
if num_ is None:
num_ = len(subi_)
elif num_ != len(subi_):
raise IndexError("Inconsistent length of array subi")
if num_ is None:
num_ = len(ptrb_)
elif num_ != len(ptrb_):
raise IndexErro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n ... | [
"0.69141024",
"0.57564807",
"0.56130505",
"0.55167717",
"0.5442003",
"0.5344216",
"0.5246333",
"0.5187387",
"0.5142179",
"0.49682668",
"0.48680598",
"0.48350987",
"0.4808861",
"0.4752955",
"0.4751432",
"0.47513345",
"0.47332814",
"0.46975785",
"0.467135",
"0.4622978",
"0.4565... | 0.7620331 | 0 |
Obtains the number of nonzero elements in barc. getnumbarcnz(self) | def getnumbarcnz(self):
nz_ = ctypes.c_int64()
res = __library__.MSK_XX_getnumbarcnz(self.__nativep,ctypes.byref(nz_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
nz_ = nz_.value
_nz_return_value = nz_
return (_nz_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getnumbarcnz(self): # 3\n res,resargs = self.__obj.getnumbarcnz()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nz_return_value = resargs\n return _nz_return_value",
"def getnumbaranz(self):\n nz_ = ctypes.c_int64()\n res = _... | [
"0.8414863",
"0.732054",
"0.7181858",
"0.6833535",
"0.6721521",
"0.66638815",
"0.65889084",
"0.64409876",
"0.64379865",
"0.64336276",
"0.63874006",
"0.6374218",
"0.6348899",
"0.6307783",
"0.6306807",
"0.62579656",
"0.62028146",
"0.6169162",
"0.61074764",
"0.60677207",
"0.6067... | 0.790851 | 1 |
Get the number of nonzero elements in barA. getnumbaranz(self) | def getnumbaranz(self):
nz_ = ctypes.c_int64()
res = __library__.MSK_XX_getnumbaranz(self.__nativep,ctypes.byref(nz_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
nz_ = nz_.value
_nz_return_value = nz_
return (_nz_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getnumbaranz(self): # 3\n res,resargs = self.__obj.getnumbaranz()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nz_return_value = resargs\n return _nz_return_value",
"def getnumbarcnz(self): # 3\n res,resargs = self.__obj.getn... | [
"0.73079044",
"0.695428",
"0.6405161",
"0.6346526",
"0.6294061",
"0.62621385",
"0.62515223",
"0.6134515",
"0.6053183",
"0.60438895",
"0.60046744",
"0.5997218",
"0.59886605",
"0.5983975",
"0.59795845",
"0.59599566",
"0.5944614",
"0.59325683",
"0.5888442",
"0.5874875",
"0.58482... | 0.71311706 | 1 |
Get the positions of the nonzero elements in barc. getbarcsparsity(self,idxj_) | def getbarcsparsity(self,idxj_):
maxnumnz_ = self.getnumbarcnz()
numnz_ = ctypes.c_int64()
_idxj_minlength = (maxnumnz_)
if (maxnumnz_) > 0 and idxj_ is not None and len(idxj_) != (maxnumnz_):
raise ValueError("Array argument idxj is not long enough: Is %d, expected %d" % (len(idxj_),(maxnumnz_)))... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getbarcsparsity(self,idxj): # 3\n maxnumnz_ = self.getnumbarcnz()\n if idxj is None: raise TypeError(\"Invalid type for argument idxj\")\n _copyback_idxj = False\n if idxj is None:\n idxj_ = None\n else:\n try:\n idxj_ = memoryview(idxj)\n except TypeError... | [
"0.7913995",
"0.7098691",
"0.70675564",
"0.6986523",
"0.6893359",
"0.6240509",
"0.62401795",
"0.5906834",
"0.5778606",
"0.57324004",
"0.5551815",
"0.5542871",
"0.54500717",
"0.52832943",
"0.52578807",
"0.52447194",
"0.5243626",
"0.5220802",
"0.5206901",
"0.51710534",
"0.51589... | 0.7981993 | 0 |
Obtains the sparsity pattern of the barA matrix. getbarasparsity(self,idxij_) | def getbarasparsity(self,idxij_):
maxnumnz_ = self.getnumbaranz()
numnz_ = ctypes.c_int64()
_idxij_minlength = (maxnumnz_)
if (maxnumnz_) > 0 and idxij_ is not None and len(idxij_) != (maxnumnz_):
raise ValueError("Array argument idxij is not long enough: Is %d, expected %d" % (len(idxij_),(maxnum... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getbarasparsity(self,idxij): # 3\n maxnumnz_ = self.getnumbaranz()\n if idxij is None: raise TypeError(\"Invalid type for argument idxij\")\n _copyback_idxij = False\n if idxij is None:\n idxij_ = None\n else:\n try:\n idxij_ = memoryview(idxij)\n except T... | [
"0.77351505",
"0.72915465",
"0.72167975",
"0.5404141",
"0.5340408",
"0.5338645",
"0.5234122",
"0.52128935",
"0.5133407",
"0.51011145",
"0.4957848",
"0.49559408",
"0.4946568",
"0.48629996",
"0.48176265",
"0.47592816",
"0.47335243",
"0.47119555",
"0.47081134",
"0.46808594",
"0.... | 0.7716057 | 1 |
Obtains information about an element in barc. getbarcidxinfo(self,idx_) | def getbarcidxinfo(self,idx_):
num_ = ctypes.c_int64()
res = __library__.MSK_XX_getbarcidxinfo(self.__nativep,idx_,ctypes.byref(num_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
num_ = num_.value
_num_return_value = num_
return (_num_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getbarcidxinfo(self,idx_): # 3\n res,resargs = self.__obj.getbarcidxinfo(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n return _num_return_value",
"def getbaraidxinfo(self,idx_):\n num_ = ctypes... | [
"0.86891156",
"0.71962595",
"0.70044905",
"0.69010836",
"0.6858001",
"0.5903754",
"0.5803983",
"0.5737491",
"0.56529295",
"0.56049544",
"0.55654025",
"0.5553507",
"0.54870677",
"0.5413116",
"0.5405953",
"0.53953177",
"0.5377202",
"0.53466",
"0.5336274",
"0.53325117",
"0.53131... | 0.84884477 | 1 |
Obtains the row index of an element in barc. getbarcidxj(self,idx_) | def getbarcidxj(self,idx_):
j_ = ctypes.c_int32()
res = __library__.MSK_XX_getbarcidxj(self.__nativep,idx_,ctypes.byref(j_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
j_ = j_.value
_j_return_value = j_
return (_j_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getbarcidxj(self,idx_): # 3\n res,resargs = self.__obj.getbarcidxj(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _j_return_value = resargs\n return _j_return_value",
"def getbaraidxij(self,idx_): # 3\n res,resargs = self.... | [
"0.8396391",
"0.71261173",
"0.70907575",
"0.67884517",
"0.6727334",
"0.6509022",
"0.6382574",
"0.62180233",
"0.61967444",
"0.6188288",
"0.6149179",
"0.61295444",
"0.6103931",
"0.6080982",
"0.6072824",
"0.60486424",
"0.6047505",
"0.6035643",
"0.6004207",
"0.5998882",
"0.594949... | 0.81668854 | 1 |
Obtains information about an element in barc. getbarcidx(self,idx_,sub_,weights_) | def getbarcidx(self,idx_,sub_,weights_):
maxnum_ = self.getbarcidxinfo((idx_))
j_ = ctypes.c_int32()
num_ = ctypes.c_int64()
_sub_minlength = (maxnum_)
if (maxnum_) > 0 and sub_ is not None and len(sub_) != (maxnum_):
raise ValueError("Array argument sub is not long enough: Is %d, expected %d"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getbarcidx(self,idx_,sub,weights): # 3\n maxnum_ = self.getbarcidxinfo((idx_))\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n _copyback_sub = False\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except Typ... | [
"0.81144726",
"0.72846395",
"0.7217906",
"0.665363",
"0.6640706",
"0.6635058",
"0.65106195",
"0.63355154",
"0.5838797",
"0.5818682",
"0.5737795",
"0.5736788",
"0.55965847",
"0.553447",
"0.5459193",
"0.5442391",
"0.5383529",
"0.5376213",
"0.5336467",
"0.5267637",
"0.5224516",
... | 0.80855787 | 1 |
Obtains the number of terms in the weighted sum that form a particular element in barA. getbaraidxinfo(self,idx_) | def getbaraidxinfo(self,idx_):
num_ = ctypes.c_int64()
res = __library__.MSK_XX_getbaraidxinfo(self.__nativep,idx_,ctypes.byref(num_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
num_ = num_.value
_num_return_value = num_
return (_num_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getbaraidxinfo(self,idx_): # 3\n res,resargs = self.__obj.getbaraidxinfo(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n return _num_return_value",
"def getbarcidxinfo(self,idx_): # 3\n res,res... | [
"0.7756773",
"0.6507387",
"0.64196265",
"0.6186367",
"0.6128352",
"0.5972996",
"0.5923507",
"0.59121716",
"0.5646623",
"0.5492446",
"0.5417949",
"0.5402761",
"0.53898597",
"0.53793544",
"0.5307793",
"0.5303424",
"0.5293265",
"0.5285976",
"0.5244835",
"0.523641",
"0.5207586",
... | 0.7473943 | 1 |
Obtains information about an element in barA. getbaraidxij(self,idx_) | def getbaraidxij(self,idx_):
i_ = ctypes.c_int32()
j_ = ctypes.c_int32()
res = __library__.MSK_XX_getbaraidxij(self.__nativep,idx_,ctypes.byref(i_),ctypes.byref(j_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
i_ = i_.value
_i_return_value = i_
j_ ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getbaraidxij(self,idx_): # 3\n res,resargs = self.__obj.getbaraidxij(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _i_return_value,_j_return_value = resargs\n return _i_return_value,_j_return_value",
"def getbaraidxinfo(self,i... | [
"0.8346773",
"0.77193934",
"0.754142",
"0.67876697",
"0.6486184",
"0.64195204",
"0.6404053",
"0.640032",
"0.6305876",
"0.59074754",
"0.5890297",
"0.58680964",
"0.58324194",
"0.5825518",
"0.5760624",
"0.5729917",
"0.565855",
"0.56582534",
"0.56447303",
"0.5631596",
"0.55810976... | 0.7791537 | 1 |
Obtains information about an element in barA. getbaraidx(self,idx_,sub_,weights_) | def getbaraidx(self,idx_,sub_,weights_):
maxnum_ = self.getbaraidxinfo((idx_))
i_ = ctypes.c_int32()
j_ = ctypes.c_int32()
num_ = ctypes.c_int64()
_sub_minlength = (maxnum_)
if (maxnum_) > 0 and sub_ is not None and len(sub_) != (maxnum_):
raise ValueError("Array argument sub is not long e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getbaraidx(self,idx_,sub,weights): # 3\n maxnum_ = self.getbaraidxinfo((idx_))\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n _copyback_sub = False\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except Typ... | [
"0.79349804",
"0.7220432",
"0.6909607",
"0.68911463",
"0.66678464",
"0.66436964",
"0.63469344",
"0.59986603",
"0.59368634",
"0.5867839",
"0.57814175",
"0.56003904",
"0.5393162",
"0.5353795",
"0.53340846",
"0.5328281",
"0.5303927",
"0.53004795",
"0.52901924",
"0.5209837",
"0.5... | 0.7813326 | 1 |
Obtains an upper bound on the number of elements in the block triplet form of barc. getnumbarcblocktriplets(self) | def getnumbarcblocktriplets(self):
num_ = ctypes.c_int64()
res = __library__.MSK_XX_getnumbarcblocktriplets(self.__nativep,ctypes.byref(num_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
num_ = num_.value
_num_return_value = num_
return (_num_return_va... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getnumbarcblocktriplets(self): # 3\n res,resargs = self.__obj.getnumbarcblocktriplets()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n return _num_return_value",
"def getnumbarablocktriplets(self): # 3\n... | [
"0.85420984",
"0.75696456",
"0.7015618",
"0.6847383",
"0.6627744",
"0.64907646",
"0.6451518",
"0.64438766",
"0.6355343",
"0.63185966",
"0.62884474",
"0.62712073",
"0.62185615",
"0.61999696",
"0.61999696",
"0.61999696",
"0.61999696",
"0.61817807",
"0.61790687",
"0.61690867",
"... | 0.7878391 | 1 |
Inputs barC in block triplet form. putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_) | def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):
_subj_minlength = (num_)
if (num_) > 0 and subj_ is not None and len(subj_) != (num_):
raise ValueError("Array argument subj is not long enough: Is %d, expected %d" % (len(subj_),(num_)))
if subj_ is None:
raise ValueError("Argument s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj... | [
"0.7797572",
"0.7431309",
"0.7382793",
"0.67077404",
"0.6499392",
"0.6443641",
"0.6285413",
"0.55697286",
"0.5277591",
"0.49592826",
"0.49534807",
"0.48163047",
"0.48131326",
"0.4768959",
"0.47384974",
"0.47332403",
"0.47224998",
"0.4697508",
"0.46888828",
"0.46643606",
"0.46... | 0.78210664 | 0 |
Obtains barC in block triplet form. getbarcblocktriplet(self,subj_,subk_,subl_,valjkl_) | def getbarcblocktriplet(self,subj_,subk_,subl_,valjkl_):
maxnum_ = self.getnumbarcblocktriplets()
num_ = ctypes.c_int64()
_subj_minlength = (maxnum_)
if (maxnum_) > 0 and subj_ is not None and len(subj_) != (maxnum_):
raise ValueError("Array argument subj is not long enough: Is %d, expected %d" % ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getbarcblocktriplet(self,subj,subk,subl,valjkl): # 3\n maxnum_ = self.getnumbarcblocktriplets()\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(s... | [
"0.8242245",
"0.73333",
"0.71987563",
"0.6874446",
"0.6724419",
"0.57594484",
"0.55948",
"0.52580005",
"0.51605344",
"0.5067685",
"0.5061451",
"0.5032754",
"0.50226194",
"0.49841416",
"0.49765313",
"0.4926271",
"0.49114424",
"0.4871388",
"0.48678747",
"0.48249632",
"0.4824963... | 0.8230452 | 1 |
Inputs barA in block triplet form. putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_) | def putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_):
_subi_minlength = (num_)
if (num_) > 0 and subi_ is not None and len(subi_) != (num_):
raise ValueError("Array argument subi is not long enough: Is %d, expected %d" % (len(subi_),(num_)))
if subi_ is None:
raise ValueError("Arg... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putbarablocktriplet(self,num_,subi,subj,subk,subl,valijkl): # 3\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmpar... | [
"0.7724536",
"0.76001674",
"0.7368044",
"0.7276849",
"0.70442146",
"0.6847936",
"0.66217136",
"0.6007909",
"0.5981407",
"0.5901117",
"0.5549072",
"0.5326024",
"0.53235453",
"0.51292914",
"0.5128834",
"0.50121427",
"0.5005257",
"0.4898183",
"0.48976943",
"0.48955905",
"0.48938... | 0.74522436 | 2 |
Obtains an upper bound on the number of scalar elements in the block triplet form of bara. getnumbarablocktriplets(self) | def getnumbarablocktriplets(self):
num_ = ctypes.c_int64()
res = __library__.MSK_XX_getnumbarablocktriplets(self.__nativep,ctypes.byref(num_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
num_ = num_.value
_num_return_value = num_
return (_num_return_va... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getnumbarablocktriplets(self): # 3\n res,resargs = self.__obj.getnumbarablocktriplets()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n return _num_return_value",
"def getnumbarcblocktriplets(self): # 3\n... | [
"0.82595944",
"0.8122216",
"0.7633738",
"0.6887998",
"0.6663699",
"0.6631301",
"0.64007086",
"0.6396754",
"0.63777614",
"0.632827",
"0.6285043",
"0.6273211",
"0.62282586",
"0.6213626",
"0.61684984",
"0.616576",
"0.61654574",
"0.61307305",
"0.608089",
"0.60745347",
"0.60691917... | 0.73850656 | 3 |
Obtains barA in block triplet form. getbarablocktriplet(self,subi_,subj_,subk_,subl_,valijkl_) | def getbarablocktriplet(self,subi_,subj_,subk_,subl_,valijkl_):
maxnum_ = self.getnumbarablocktriplets()
num_ = ctypes.c_int64()
_subi_minlength = (maxnum_)
if (maxnum_) > 0 and subi_ is not None and len(subi_) != (maxnum_):
raise ValueError("Array argument subi is not long enough: Is %d, expected... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getbarablocktriplet(self,subi,subj,subk,subl,valijkl): # 3\n maxnum_ = self.getnumbarablocktriplets()\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memory... | [
"0.80541843",
"0.7718639",
"0.7490929",
"0.70500946",
"0.69929564",
"0.692479",
"0.68191475",
"0.5320601",
"0.5103941",
"0.5083685",
"0.505206",
"0.5037894",
"0.49752373",
"0.49659145",
"0.49474913",
"0.48905304",
"0.48676184",
"0.4846214",
"0.48412463",
"0.4812279",
"0.47859... | 0.7856036 | 1 |
Changes the bound for one constraint. putconbound(self,i_,bkc_,blc_,buc_) | def putconbound(self,i_,bkc_,blc_,buc_):
res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putconbound(self,i_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putbound(self,accm... | [
"0.8431577",
"0.74671066",
"0.7019133",
"0.69383395",
"0.67582726",
"0.6713032",
"0.6695471",
"0.6479412",
"0.6461108",
"0.6328686",
"0.62583214",
"0.62065446",
"0.6160253",
"0.607104",
"0.60688263",
"0.6038663",
"0.6010842",
"0.5968073",
"0.59461397",
"0.59395623",
"0.587561... | 0.8324798 | 1 |
Changes the bounds of a list of constraints. putconboundlist(self,sub_,bkc_,blc_,buc_) | def putconboundlist(self,sub_,bkc_,blc_,buc_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if num_ is None:
num_ = len(bkc_)
elif num_ != len(bkc_):
raise IndexError("Inconsistent length of array b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n r... | [
"0.84011555",
"0.76973206",
"0.73664284",
"0.71282125",
"0.6763163",
"0.65985745",
"0.65650123",
"0.65250516",
"0.6457567",
"0.6309675",
"0.6039203",
"0.59684473",
"0.59512186",
"0.582025",
"0.5767994",
"0.5653089",
"0.56379414",
"0.5575884",
"0.55110615",
"0.54968846",
"0.54... | 0.80374026 | 1 |
Changes the bounds of a list of constraints. putconboundlistconst(self,sub_,bkc_,blc_,buc_) | def putconboundlistconst(self,sub_,bkc_,blc_,buc_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if sub_ is None:
raise ValueError("Argument sub cannot be None")
if sub_ is None:
raise ValueError("A... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putconboundlist(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkc_)\n elif num_ != len(bkc_):\n raise IndexError(\"Inconsistent ... | [
"0.78857696",
"0.78076875",
"0.7175673",
"0.7004306",
"0.67201793",
"0.6420127",
"0.6372808",
"0.63401717",
"0.623137",
"0.620073",
"0.60859454",
"0.59860647",
"0.58440953",
"0.58174753",
"0.5684682",
"0.56770265",
"0.56493884",
"0.56027967",
"0.5551834",
"0.551165",
"0.55048... | 0.870948 | 0 |
Changes the bounds for a slice of the constraints. putconboundslice(self,first_,last_,bkc_,blc_,buc_) | def putconboundslice(self,first_,last_,bkc_,blc_,buc_):
_bkc_minlength = ((last_) - (first_))
if ((last_) - (first_)) > 0 and bkc_ is not None and len(bkc_) != ((last_) - (first_)):
raise ValueError("Array argument bkc is not long enough: Is %d, expected %d" % (len(bkc_),((last_) - (first_))))
if bkc_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putconboundslice(self,first_,last_,bk,bl,bu): # 3\n ... | [
"0.8424689",
"0.7909946",
"0.77987653",
"0.7661096",
"0.7383701",
"0.72629064",
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"0.6054415",
"0.6003487",
"0.58678806",
"0.5852181",
"0.5829782",
"0.582154",
... | 0.79280216 | 1 |
Changes the bounds for a slice of the constraints. putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_) | def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):
res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putconboundslice(self,first_,last_,bkc_,blc_,buc_):\n ... | [
"0.7940694",
"0.7843107",
"0.7633073",
"0.7385376",
"0.7198701",
"0.7138386",
"0.7068631",
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"0.6657966",
"0.6564169",
"0.65478146",
"0.65182567",
"0.64068395",
"0.63733363",
"0.62952274",
"0.61070985",
"0.60120577",
"0.58785725",
"0.58716613",
"0.578715",
"0.57703... | 0.88294995 | 0 |
Changes the bounds for one variable. putvarbound(self,j_,bkx_,blx_,bux_) | def putvarbound(self,j_,bkx_,blx_,bux_):
res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putvarbound(self,j_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def chgvarbound(self,j... | [
"0.83352685",
"0.77197856",
"0.7631901",
"0.69681317",
"0.6884526",
"0.660315",
"0.65222496",
"0.6383775",
"0.63717395",
"0.62624204",
"0.62609136",
"0.62328184",
"0.6203693",
"0.6166688",
"0.6145063",
"0.5939188",
"0.5806258",
"0.57605875",
"0.57412565",
"0.5724231",
"0.5684... | 0.87297875 | 0 |
Changes the bounds of a list of variables. putvarboundlist(self,sub_,bkx_,blx_,bux_) | def putvarboundlist(self,sub_,bkx_,blx_,bux_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if num_ is None:
num_ = len(bkx_)
elif num_ != len(bkx_):
raise IndexError("Inconsistent length of array b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n r... | [
"0.8234259",
"0.7890434",
"0.70631224",
"0.6849752",
"0.6744828",
"0.6431723",
"0.6421552",
"0.64171445",
"0.641259",
"0.6356358",
"0.6287844",
"0.606733",
"0.60325545",
"0.5835947",
"0.5711706",
"0.56917834",
"0.5684577",
"0.5666441",
"0.56318825",
"0.5488267",
"0.54620206",... | 0.8044877 | 1 |
Changes the bounds of a list of variables. putvarboundlistconst(self,sub_,bkx_,blx_,bux_) | def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if sub_ is None:
raise ValueError("Argument sub cannot be None")
if sub_ is None:
raise ValueError("A... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putvarboundlist(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkx_)\n elif num_ != len(bkx_):\n raise IndexError(\"Inconsistent ... | [
"0.7802839",
"0.74736947",
"0.7351789",
"0.71760994",
"0.68241155",
"0.65913326",
"0.65883243",
"0.6351352",
"0.63493115",
"0.62824434",
"0.62661535",
"0.6100925",
"0.60384923",
"0.59712017",
"0.58503205",
"0.5785788",
"0.57782364",
"0.5726529",
"0.5715237",
"0.5681339",
"0.5... | 0.8611551 | 0 |
Changes the bounds for a slice of the variables. putvarboundslice(self,first_,last_,bkx_,blx_,bux_) | def putvarboundslice(self,first_,last_,bkx_,blx_,bux_):
_bkx_minlength = ((last_) - (first_))
if ((last_) - (first_)) > 0 and bkx_ is not None and len(bkx_) != ((last_) - (first_)):
raise ValueError("Array argument bkx is not long enough: Is %d, expected %d" % (len(bkx_),((last_) - (first_))))
if bkx_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\n ... | [
"0.85273284",
"0.8044646",
"0.7539585",
"0.7323293",
"0.67018676",
"0.66931504",
"0.659911",
"0.6440455",
"0.6379589",
"0.63711655",
"0.63274574",
"0.63001",
"0.62871003",
"0.6224053",
"0.6090941",
"0.6087178",
"0.6076162",
"0.60601884",
"0.6049202",
"0.596602",
"0.59273034",... | 0.7870629 | 2 |
Changes the bounds for a slice of the variables. putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_) | def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):
res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",b... | [
"0.78326833",
"0.7734105",
"0.7384246",
"0.73374903",
"0.7264578",
"0.676517",
"0.6706333",
"0.65771973",
"0.6273341",
"0.6261887",
"0.62256247",
"0.6213124",
"0.61776054",
"0.6171745",
"0.6162715",
"0.61598974",
"0.61103725",
"0.6071546",
"0.601926",
"0.6003408",
"0.58926064... | 0.89807767 | 0 |
Replaces the fixed term in the objective. putcfix(self,cfix_) | def putcfix(self,cfix_):
res = __library__.MSK_XX_putcfix(self.__nativep,cfix_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putcfix(self,cfix_): # 3\n res = self.__obj.putcfix(cfix_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def fixC(self,i,value):\n if self.coeffPattern[2] == None:\n m,n=self.m,self.n\n self.coeffPattern[2] = [No... | [
"0.708022",
"0.6352814",
"0.5975144",
"0.58999455",
"0.5676646",
"0.5464873",
"0.53494346",
"0.5349058",
"0.5343659",
"0.53432745",
"0.53168523",
"0.52869856",
"0.5279766",
"0.5264671",
"0.51587206",
"0.5142443",
"0.5114193",
"0.51119894",
"0.5060143",
"0.5042062",
"0.5015368... | 0.69024557 | 1 |
Modifies one linear coefficient in the objective. putcj(self,j_,cj_) | def putcj(self,j_,cj_):
res = __library__.MSK_XX_putcj(self.__nativep,j_,cj_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putcj(self,j_,cj_): # 3\n res = self.__obj.putcj(j_,cj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def jacobian(self, c):\n\n raise NotImplementedError",
"def getcj(self,j_): # 3\n res,resargs = self.__obj.getcj(j_)\n ... | [
"0.71400785",
"0.6213591",
"0.61696875",
"0.6164791",
"0.60605234",
"0.59805757",
"0.58748615",
"0.57106733",
"0.5628812",
"0.56003463",
"0.55685896",
"0.5553388",
"0.5516792",
"0.5434863",
"0.5434863",
"0.5425381",
"0.540043",
"0.5388786",
"0.5384671",
"0.53840244",
"0.53762... | 0.70735407 | 1 |
Sets the objective sense. putobjsense(self,sense_) | def putobjsense(self,sense_):
res = __library__.MSK_XX_putobjsense(self.__nativep,sense_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putobjsense(self,sense_): # 3\n if not isinstance(sense_,objsense): raise TypeError(\"Argument sense has wrong type\")\n res = self.__obj.putobjsense(sense_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def objective(self, objective):\... | [
"0.79471225",
"0.52841735",
"0.5171148",
"0.5096876",
"0.50680315",
"0.48795915",
"0.48363435",
"0.48355487",
"0.48335677",
"0.48176384",
"0.47935885",
"0.47814864",
"0.4768401",
"0.47334376",
"0.4723602",
"0.469889",
"0.46933207",
"0.46930823",
"0.4692844",
"0.46748367",
"0.... | 0.75217026 | 1 |
Gets the objective sense. getobjsense(self) | def getobjsense(self):
sense_ = ctypes.c_int32()
res = __library__.MSK_XX_getobjsense(self.__nativep,ctypes.byref(sense_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
_sense_return_value = objsense(sense_.value)
return (_sense_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getobjsense(self): # 3\n res,resargs = self.__obj.getobjsense()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sense_return_value = resargs\n _sense_return_value = objsense(_sense_return_value)\n return _sense_return_value",
"d... | [
"0.8077817",
"0.6475919",
"0.62005496",
"0.62005496",
"0.62005496",
"0.61305094",
"0.6124314",
"0.6089001",
"0.5931077",
"0.58792186",
"0.5827234",
"0.58187944",
"0.57927275",
"0.5724244",
"0.57183784",
"0.5676843",
"0.56714916",
"0.5586467",
"0.5586467",
"0.5586467",
"0.5568... | 0.73985374 | 1 |
Modifies a part of the linear objective coefficients. putclist(self,subj_,val_) | def putclist(self,subj_,val_):
num_ = None
if num_ is None:
num_ = len(subj_)
elif num_ != len(subj_):
raise IndexError("Inconsistent length of array subj")
if num_ is None:
num_ = len(val_)
elif num_ != len(val_):
raise IndexError("Inconsistent length of array val")
if s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putclist(self,subj,val): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(val)\n elif num_ != len(val):\n raise IndexError(\"Inconsiste... | [
"0.7019918",
"0.6498057",
"0.565561",
"0.55103093",
"0.5359275",
"0.53481793",
"0.5285191",
"0.5283986",
"0.5262921",
"0.5168434",
"0.5103253",
"0.50901055",
"0.50725174",
"0.5070623",
"0.50672764",
"0.5047127",
"0.49618518",
"0.49521777",
"0.49174896",
"0.4880588",
"0.487536... | 0.6732897 | 1 |
Modifies a slice of the linear objective coefficients. putcslice(self,first_,last_,slice_) | def putcslice(self,first_,last_,slice_):
_slice_minlength = ((last_) - (first_))
if ((last_) - (first_)) > 0 and slice_ is not None and len(slice_) != ((last_) - (first_)):
raise ValueError("Array argument slice is not long enough: Is %d, expected %d" % (len(slice_),((last_) - (first_))))
if slice_ is... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putcslice(self,first_,last_,slice): # 3\n if slice is None: raise TypeError(\"Invalid type for argument slice\")\n if slice is None:\n slice_ = None\n else:\n try:\n slice_ = memoryview(slice)\n except TypeError:\n try:\n _tmparr_slice = array.ar... | [
"0.7177398",
"0.64559644",
"0.64415175",
"0.6327401",
"0.63102835",
"0.62641084",
"0.6245864",
"0.6206491",
"0.61138916",
"0.60814995",
"0.597995",
"0.593315",
"0.5893596",
"0.58712506",
"0.5860539",
"0.5855395",
"0.58295006",
"0.58105403",
"0.5787265",
"0.5746839",
"0.570190... | 0.73769736 | 0 |
Changes one element in barc. putbarcj(self,j_,sub_,weights_) | def putbarcj(self,j_,sub_,weights_):
num_ = None
if num_ is None:
num_ = len(sub_)
elif num_ != len(sub_):
raise IndexError("Inconsistent length of array sub")
if num_ is None:
num_ = len(weights_)
elif num_ != len(weights_):
raise IndexError("Inconsistent length of array wei... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putbarcj(self,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexError(\... | [
"0.8175987",
"0.73748964",
"0.7216971",
"0.62823015",
"0.62727207",
"0.6010744",
"0.58721524",
"0.5819966",
"0.57759297",
"0.5744071",
"0.56815547",
"0.5676308",
"0.55539197",
"0.54821527",
"0.53647524",
"0.53490466",
"0.52245176",
"0.52084583",
"0.5198786",
"0.5183286",
"0.5... | 0.77634215 | 1 |
Replaces a conic constraint. putcone(self,k_,ct_,conepar_,submem_) | def putcone(self,k_,ct_,conepar_,submem_):
nummem_ = None
if nummem_ is None:
nummem_ = len(submem_)
elif nummem_ != len(submem_):
raise IndexError("Inconsistent length of array submem")
if submem_ is None:
raise ValueError("Argument submem cannot be None")
if submem_ is None:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putcone(self,k_,ct_,conepar_,submem): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem)\n elif nummem_ != len(submem):\n raise IndexError(\"Inconsistent length of array submem\"... | [
"0.79132587",
"0.65889823",
"0.6287175",
"0.60342395",
"0.58452046",
"0.584365",
"0.54893136",
"0.53622764",
"0.5344895",
"0.52238137",
"0.5210514",
"0.50818294",
"0.50687397",
"0.5066322",
"0.5015226",
"0.50098133",
"0.4991474",
"0.49618068",
"0.49253765",
"0.4921023",
"0.48... | 0.78817594 | 1 |
Appends a general sparse symmetric matrix to the storage of symmetric matrices. appendsparsesymmat(self,dim_,subi_,subj_,valij_) | def appendsparsesymmat(self,dim_,subi_,subj_,valij_):
nz_ = None
if nz_ is None:
nz_ = len(subi_)
elif nz_ != len(subi_):
raise IndexError("Inconsistent length of array subi")
if nz_ is None:
nz_ = len(subj_)
elif nz_ != len(subj_):
raise IndexError("Inconsistent length of ar... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexE... | [
"0.7391125",
"0.6855938",
"0.64830375",
"0.6458727",
"0.54127806",
"0.5294181",
"0.51218337",
"0.5078287",
"0.5072397",
"0.5031775",
"0.5025988",
"0.50101835",
"0.50026613",
"0.49249858",
"0.49118322",
"0.48946965",
"0.4886756",
"0.4860377",
"0.48591778",
"0.4854623",
"0.4850... | 0.7588713 | 0 |
Appends a general sparse symmetric matrix to the storage of symmetric matrices. appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_) | def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):
num_ = None
if num_ is None:
num_ = len(dims_)
elif num_ != len(dims_):
raise IndexError("Inconsistent length of array dims")
if num_ is None:
num_ = len(nz_)
elif num_ != len(nz_):
raise IndexError("Inconsis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsis... | [
"0.70590025",
"0.6795725",
"0.63318455",
"0.6023266",
"0.52935266",
"0.5206711",
"0.5191596",
"0.5079819",
"0.49437016",
"0.49408752",
"0.4925097",
"0.4889433",
"0.4782237",
"0.4736962",
"0.4685731",
"0.467853",
"0.46744376",
"0.46646318",
"0.46621722",
"0.46508092",
"0.46442... | 0.74942684 | 0 |
Obtains information about a matrix from the symmetric matrix storage. getsymmatinfo(self,idx_) | def getsymmatinfo(self,idx_):
dim_ = ctypes.c_int32()
nz_ = ctypes.c_int64()
type_ = ctypes.c_int32()
res = __library__.MSK_XX_getsymmatinfo(self.__nativep,idx_,ctypes.byref(dim_),ctypes.byref(nz_),ctypes.byref(type_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getsymmatinfo(self,idx_): # 3\n res,resargs = self.__obj.getsymmatinfo(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dim_return_value,_nz_return_value,_type_return_value = resargs\n _type_return_value = symmattype(_type_return_... | [
"0.8472209",
"0.61438686",
"0.60188043",
"0.59274673",
"0.5842971",
"0.57795507",
"0.57204145",
"0.57204145",
"0.57204145",
"0.57204145",
"0.5698095",
"0.5413046",
"0.5405503",
"0.5376912",
"0.5374786",
"0.531883",
"0.5297684",
"0.523657",
"0.51200414",
"0.51060134",
"0.50813... | 0.8566959 | 0 |
Obtains the number of symmetric matrices stored. getnumsymmat(self) | def getnumsymmat(self):
num_ = ctypes.c_int64()
res = __library__.MSK_XX_getnumsymmat(self.__nativep,ctypes.byref(num_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
num_ = num_.value
_num_return_value = num_
return (_num_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getnumsymmat(self): # 3\n res,resargs = self.__obj.getnumsymmat()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n return _num_return_value",
"def getsymmatinfo(self,idx_): # 3\n res,resargs = self.__... | [
"0.7524286",
"0.6255513",
"0.62463",
"0.6031631",
"0.5934353",
"0.58589876",
"0.5748166",
"0.5643437",
"0.5588444",
"0.5525005",
"0.551974",
"0.5482156",
"0.547971",
"0.54718024",
"0.54672223",
"0.5427831",
"0.54226774",
"0.5403771",
"0.5391538",
"0.5375537",
"0.53549564",
... | 0.7143468 | 1 |
Gets a single symmetric matrix from the matrix store. getsparsesymmat(self,idx_,subi_,subj_,valij_) | def getsparsesymmat(self,idx_,subi_,subj_,valij_):
maxlen_ = self.getsymmatinfo((idx_))[1]
_subi_minlength = (maxlen_)
if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):
raise ValueError("Array argument subi is not long enough: Is %d, expected %d" % (len(subi_),(maxlen_)))
if isin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.... | [
"0.75738734",
"0.6238482",
"0.6137533",
"0.56444985",
"0.5625718",
"0.558371",
"0.54747504",
"0.52915025",
"0.52799106",
"0.52322876",
"0.5228932",
"0.51636285",
"0.5140221",
"0.5121791",
"0.50425875",
"0.49852064",
"0.4978507",
"0.49779502",
"0.49698326",
"0.4951648",
"0.492... | 0.77381927 | 0 |
Sets a double parameter. putdouparam(self,param_,parvalue_) | def putdouparam(self,param_,parvalue_):
res = __library__.MSK_XX_putdouparam(self.__nativep,param_,parvalue_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putdouparam(self,param_,parvalue_): # 3\n if not isinstance(param_,dparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putdouparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def setDouble(... | [
"0.68033856",
"0.66483873",
"0.6322731",
"0.62533104",
"0.62401855",
"0.6139179",
"0.61077297",
"0.6105442",
"0.60806364",
"0.6070325",
"0.6022202",
"0.60140693",
"0.600733",
"0.5987992",
"0.5986172",
"0.5898448",
"0.5880513",
"0.5780829",
"0.57804143",
"0.5722894",
"0.567925... | 0.5916938 | 15 |
Sets an integer parameter. putintparam(self,param_,parvalue_) | def putintparam(self,param_,parvalue_):
res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putintparam(self,param_,parvalue_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putintparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def getintpara... | [
"0.8837676",
"0.71326786",
"0.6860121",
"0.68544596",
"0.6800701",
"0.6800701",
"0.6740805",
"0.6733184",
"0.6618874",
"0.658969",
"0.6509817",
"0.6441833",
"0.6363545",
"0.6344362",
"0.633649",
"0.6328604",
"0.63135433",
"0.62845325",
"0.6263861",
"0.62351775",
"0.6199873",
... | 0.85035557 | 1 |
Sets the number of preallocated constraints in the optimization task. putmaxnumcon(self,maxnumcon_) | def putmaxnumcon(self,maxnumcon_):
res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if... | [
"0.7620724",
"0.6740477",
"0.6646557",
"0.60926807",
"0.5997314",
"0.59877884",
"0.59074914",
"0.589972",
"0.5885956",
"0.57590777",
"0.5751454",
"0.5703682",
"0.5699466",
"0.56882566",
"0.5686642",
"0.568195",
"0.56570315",
"0.5625261",
"0.5607635",
"0.5532643",
"0.5513424",... | 0.73856825 | 1 |
Sets the number of preallocated conic constraints in the optimization task. putmaxnumcone(self,maxnumcone_) | def putmaxnumcone(self,maxnumcone_):
res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n ... | [
"0.7859737",
"0.687435",
"0.6576984",
"0.6516009",
"0.6250021",
"0.5742838",
"0.57224095",
"0.5664653",
"0.5662323",
"0.5644606",
"0.5565795",
"0.55509084",
"0.55272174",
"0.54718906",
"0.5445443",
"0.54382503",
"0.5431226",
"0.5375135",
"0.53488725",
"0.5343298",
"0.5334908"... | 0.76632214 | 1 |
Obtains the number of preallocated cones in the optimization task. getmaxnumcone(self) | def getmaxnumcone(self):
maxnumcone_ = ctypes.c_int32()
res = __library__.MSK_XX_getmaxnumcone(self.__nativep,ctypes.byref(maxnumcone_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
maxnumcone_ = maxnumcone_.value
_maxnumcone_return_value = maxnumcone_
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getmaxnumcone(self): # 3\n res,resargs = self.__obj.getmaxnumcone()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcone_return_value = resargs\n return _maxnumcone_return_value",
"def getnumcone(self): # 3\n res,resargs =... | [
"0.82559127",
"0.74485886",
"0.7001308",
"0.6936185",
"0.6920529",
"0.67586905",
"0.6735455",
"0.64610845",
"0.64500237",
"0.64104486",
"0.6385375",
"0.6377788",
"0.63512313",
"0.6300954",
"0.6270852",
"0.6235373",
"0.6222689",
"0.6206008",
"0.62018204",
"0.61789495",
"0.6172... | 0.7739373 | 1 |
Sets the number of preallocated variables in the optimization task. putmaxnumvar(self,maxnumvar_) | def putmaxnumvar(self,maxnumvar_):
res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res !=... | [
"0.80868816",
"0.7560464",
"0.73389435",
"0.65560055",
"0.65400666",
"0.6336814",
"0.63105714",
"0.62413776",
"0.61817116",
"0.6158278",
"0.60976154",
"0.60662615",
"0.6011411",
"0.5994649",
"0.5986585",
"0.5917469",
"0.58999217",
"0.58730257",
"0.58647376",
"0.58646524",
"0.... | 0.76906484 | 1 |
Sets the number of preallocated symmetric matrix variables. putmaxnumbarvar(self,maxnumbarvar_) | def putmaxnumbarvar(self,maxnumbarvar_):
res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res !=... | [
"0.7951648",
"0.6959753",
"0.6795598",
"0.65727705",
"0.6438065",
"0.6048308",
"0.5996996",
"0.5852882",
"0.5841968",
"0.5822205",
"0.5682764",
"0.5667983",
"0.5649398",
"0.5640302",
"0.5615086",
"0.56006753",
"0.55580956",
"0.5552524",
"0.5495012",
"0.54564613",
"0.5411751",... | 0.78251684 | 1 |
Sets the number of preallocated nonzero entries in the linear coefficient matrix. putmaxnumanz(self,maxnumanz_) | def putmaxnumanz(self,maxnumanz_):
res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res... | [
"0.7441124",
"0.68018794",
"0.66798395",
"0.6399272",
"0.6247705",
"0.61643946",
"0.6043132",
"0.59559965",
"0.57927316",
"0.57495356",
"0.57457256",
"0.56957024",
"0.5552648",
"0.5549431",
"0.5518782",
"0.55130196",
"0.55069184",
"0.54949814",
"0.53994",
"0.5381273",
"0.5353... | 0.7562487 | 0 |
Sets the number of preallocated nonzero entries in quadratic terms. putmaxnumqnz(self,maxnumqnz_) | def putmaxnumqnz(self,maxnumqnz_):
res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def getmaxnumqnz(self): # 3\n res,resargs = self.__obj.getmaxnumqnz64()\n if res != 0:\n result,msg... | [
"0.8385307",
"0.7157034",
"0.6808096",
"0.6252767",
"0.6003535",
"0.6001192",
"0.5939193",
"0.59272075",
"0.5830991",
"0.5783531",
"0.5719293",
"0.5708783",
"0.56221986",
"0.5621964",
"0.5618641",
"0.5608385",
"0.55962783",
"0.55303234",
"0.54971045",
"0.5450261",
"0.54395735... | 0.81725997 | 1 |
Obtains the number of preallocated nonzeros for all quadratic terms in objective and constraints. getmaxnumqnz(self) | def getmaxnumqnz(self):
maxnumqnz_ = ctypes.c_int64()
res = __library__.MSK_XX_getmaxnumqnz64(self.__nativep,ctypes.byref(maxnumqnz_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
maxnumqnz_ = maxnumqnz_.value
_maxnumqnz_return_value = maxnumqnz_
return... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getmaxnumqnz(self): # 3\n res,resargs = self.__obj.getmaxnumqnz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumqnz_return_value = resargs\n return _maxnumqnz_return_value",
"def getnumqobjnz(self): # 3\n res,resargs =... | [
"0.78014475",
"0.72534436",
"0.6734257",
"0.66473365",
"0.66222763",
"0.65740526",
"0.65578455",
"0.65316844",
"0.6484274",
"0.64780957",
"0.64710224",
"0.64367837",
"0.6432523",
"0.6382804",
"0.6381963",
"0.6368233",
"0.63451123",
"0.63023317",
"0.63014734",
"0.62077934",
"0... | 0.69867957 | 2 |
Sets a double parameter. putnadouparam(self,paramname_,parvalue_) | def putnadouparam(self,paramname_,parvalue_):
if isinstance(paramname_,unicode):
paramname_ = paramname_.encode("utf-8",errors="replace")
res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError",
"def setParameter(self, name, value):",
"def putnadouparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnadouparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(... | [
"0.67067724",
"0.66955644",
"0.66359127",
"0.65071666",
"0.63836837",
"0.63250875",
"0.6187464",
"0.61801934",
"0.61721647",
"0.6131051",
"0.6114965",
"0.6106153",
"0.6099241",
"0.60783494",
"0.6043893",
"0.6035908",
"0.5939651",
"0.5904762",
"0.5851977",
"0.584044",
"0.58300... | 0.59774894 | 16 |
Sets an integer parameter. putnaintparam(self,paramname_,parvalue_) | def putnaintparam(self,paramname_,parvalue_):
if isinstance(paramname_,unicode):
paramname_ = paramname_.encode("utf-8",errors="replace")
res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putintparam(self,param_,parvalue_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putintparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putintpara... | [
"0.8237327",
"0.8059725",
"0.76456845",
"0.73804134",
"0.7117238",
"0.710104",
"0.706935",
"0.70490587",
"0.69747484",
"0.6902738",
"0.6690662",
"0.6675924",
"0.6668182",
"0.65841776",
"0.6558105",
"0.65107626",
"0.6459708",
"0.6459708",
"0.6409349",
"0.6405313",
"0.63323426"... | 0.7418072 | 3 |
Sets a string parameter. putnastrparam(self,paramname_,parvalue_) | def putnastrparam(self,paramname_,parvalue_):
if isinstance(paramname_,unicode):
paramname_ = paramname_.encode("utf-8",errors="replace")
if isinstance(parvalue_,unicode):
parvalue_ = parvalue_.encode("utf-8",errors="replace")
res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parv... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putnastrparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnastrparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putstrparam(self,param_,parvalue_): # 3\n if not isinstance(param_,sparam): raise Typ... | [
"0.900503",
"0.8041969",
"0.7825734",
"0.6532771",
"0.64951867",
"0.64323235",
"0.63091147",
"0.62664294",
"0.62081355",
"0.6152447",
"0.61227375",
"0.5890109",
"0.58600485",
"0.58454996",
"0.5755116",
"0.5754159",
"0.575312",
"0.5688754",
"0.5685935",
"0.5649547",
"0.5641109... | 0.87847257 | 1 |
Assigns a new name to the objective. putobjname(self,objname_) | def putobjname(self,objname_):
if isinstance(objname_,unicode):
objname_ = objname_.encode("utf-8",errors="replace")
res = __library__.MSK_XX_putobjname(self.__nativep,objname_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putobjname(self,objname_): # 3\n res = self.__obj.putobjname(objname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def set_object_name(self, object_name = \"DefaultObject\"):\n self.obj_name = object_name",
"def set_object_name(se... | [
"0.8106175",
"0.71860224",
"0.70278543",
"0.6894341",
"0.68561333",
"0.66999173",
"0.663241",
"0.6498052",
"0.6482429",
"0.64018923",
"0.6332358",
"0.63283575",
"0.6323521",
"0.6323521",
"0.6271868",
"0.62309307",
"0.6210313",
"0.6210313",
"0.62092906",
"0.61988294",
"0.61915... | 0.7715799 | 1 |
Modifies the value of parameter. putparam(self,parname_,parvalue_) | def putparam(self,parname_,parvalue_):
if isinstance(parname_,unicode):
parname_ = parname_.encode("utf-8",errors="replace")
if isinstance(parvalue_,unicode):
parvalue_ = parvalue_.encode("utf-8",errors="replace")
res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)
if res !=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = v... | [
"0.89312124",
"0.8022478",
"0.7980035",
"0.7786411",
"0.77297336",
"0.75346786",
"0.746019",
"0.7395771",
"0.7331477",
"0.7268502",
"0.7261241",
"0.72502863",
"0.72311014",
"0.72027457",
"0.7178884",
"0.7141566",
"0.70621306",
"0.7020081",
"0.7006386",
"0.69946426",
"0.699218... | 0.8278058 | 1 |
Replaces all quadratic terms in constraints. putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_) | def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):
numqcnz_ = None
if numqcnz_ is None:
numqcnz_ = len(qcsubi_)
elif numqcnz_ != len(qcsubi_):
raise IndexError("Inconsistent length of array qcsubi")
if numqcnz_ is None:
numqcnz_ = len(qcsubj_)
elif numqcnz_ != len(qcsubj_):
ra... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_... | [
"0.7356916",
"0.7274804",
"0.7158734",
"0.66391945",
"0.6536458",
"0.5380898",
"0.5333184",
"0.5304116",
"0.52284986",
"0.52283263",
"0.5153612",
"0.5121988",
"0.50708705",
"0.50423145",
"0.4997849",
"0.49975076",
"0.49906054",
"0.49511495",
"0.4933693",
"0.4864164",
"0.48633... | 0.7487632 | 0 |
Replaces all quadratic terms in a single constraint. putqconk(self,k_,qcsubi_,qcsubj_,qcval_) | def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):
numqcnz_ = None
if numqcnz_ is None:
numqcnz_ = len(qcsubi_)
elif numqcnz_ != len(qcsubi_):
raise IndexError("Inconsistent length of array qcsubi")
if numqcnz_ is None:
numqcnz_ = len(qcsubj_)
elif numqcnz_ != len(qcsubj_):
raise ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ ... | [
"0.7407992",
"0.73707557",
"0.7119913",
"0.67798036",
"0.6661862",
"0.5547101",
"0.55400264",
"0.5332845",
"0.5304892",
"0.5226392",
"0.5216021",
"0.5155784",
"0.5152715",
"0.51131207",
"0.50926286",
"0.50507396",
"0.5048387",
"0.5019227",
"0.4992005",
"0.497944",
"0.49701524... | 0.76697636 | 0 |
Replaces all quadratic terms in the objective. putqobj(self,qosubi_,qosubj_,qoval_) | def putqobj(self,qosubi_,qosubj_,qoval_):
numqonz_ = None
if numqonz_ is None:
numqonz_ = len(qosubi_)
elif numqonz_ != len(qosubi_):
raise IndexError("Inconsistent length of array qosubi")
if numqonz_ is None:
numqonz_ = len(qosubj_)
elif numqonz_ != len(qosubj_):
raise Inde... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putqobj(self,qosubi,qosubj,qoval): # 3\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi)\n elif numqonz_ != len(qosubi):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj)\n elif numqonz_ != l... | [
"0.71988887",
"0.67377245",
"0.66842896",
"0.6546956",
"0.642287",
"0.5680763",
"0.565179",
"0.5649305",
"0.5616514",
"0.55763125",
"0.536642",
"0.5291595",
"0.5255819",
"0.51279503",
"0.51187503",
"0.51012385",
"0.50656164",
"0.50490975",
"0.5015812",
"0.5012745",
"0.5003809... | 0.7221554 | 0 |
Replaces one coefficient in the quadratic term in the objective. putqobjij(self,i_,j_,qoij_) | def putqobjij(self,i_,j_,qoij_):
res = __library__.MSK_XX_putqobjij(self.__nativep,i_,j_,qoij_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putqobjij(self,i_,j_,qoij_): # 3\n res = self.__obj.putqobjij(i_,j_,qoij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def getqobjij(self,i_,j_): # 3\n res,resargs = self.__obj.getqobjij(i_,j_)\n if res != 0:\n result,msg ... | [
"0.8022443",
"0.67813957",
"0.659472",
"0.62387276",
"0.6219436",
"0.61874425",
"0.6092477",
"0.6030711",
"0.6007447",
"0.58423615",
"0.58029187",
"0.5546479",
"0.5543163",
"0.54854363",
"0.54592514",
"0.54140776",
"0.5403207",
"0.54002947",
"0.537518",
"0.5340782",
"0.533449... | 0.78803056 | 1 |
Inserts a solution. putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_) | def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):
if skc_ is not None:
_skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)
else:
_skc_tmp = None
if skx_ is not None:
_skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)
else:
_skx_tmp = None
if s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n ... | [
"0.83637935",
"0.7895491",
"0.7831769",
"0.7328144",
"0.7040539",
"0.7033978",
"0.6997567",
"0.698759",
"0.66940373",
"0.6660308",
"0.66146725",
"0.6513136",
"0.65105414",
"0.6361307",
"0.621909",
"0.62079835",
"0.6107939",
"0.6081402",
"0.59675616",
"0.582417",
"0.57879984",... | 0.7681796 | 3 |
Sets the primal and dual solution information for a single constraint. putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_) | def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):
res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if not isinstance(sk_,stakey): raise TypeEr... | [
"0.7256664",
"0.7085556",
"0.70574284",
"0.67001754",
"0.66156536",
"0.6531177",
"0.6481275",
"0.63412815",
"0.63247937",
"0.6043454",
"0.5931599",
"0.5860473",
"0.58587015",
"0.57669955",
"0.57536566",
"0.5601438",
"0.55334353",
"0.55334353",
"0.55334353",
"0.5502079",
"0.54... | 0.8645917 | 0 |
Sets the primal and dual solution information for a single variable. putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_) | def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):
res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n ... | [
"0.6384697",
"0.59763306",
"0.5868142",
"0.58261186",
"0.58098394",
"0.5764213",
"0.5686661",
"0.56294215",
"0.55897075",
"0.555756",
"0.55094",
"0.54587734",
"0.5450689",
"0.5421204",
"0.5396719",
"0.5390872",
"0.53769356",
"0.53164923",
"0.52948296",
"0.52874386",
"0.524441... | 0.888349 | 0 |
Inputs the dual variable of a solution. putsolutionyi(self,i_,whichsol_,y_) | def putsolutionyi(self,i_,whichsol_,y_):
res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def p... | [
"0.85774046",
"0.70034355",
"0.66922426",
"0.66091764",
"0.62563616",
"0.6108404",
"0.5998291",
"0.56189865",
"0.5569256",
"0.549024",
"0.5483207",
"0.5337774",
"0.52960414",
"0.52665466",
"0.5235813",
"0.52331036",
"0.52272004",
"0.51770955",
"0.51770955",
"0.5159293",
"0.51... | 0.81092507 | 1 |
Sets a string parameter. putstrparam(self,param_,parvalue_) | def putstrparam(self,param_,parvalue_):
if isinstance(parvalue_,unicode):
parvalue_ = parvalue_.encode("utf-8",errors="replace")
res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putstrparam(self,param_,parvalue_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putstrparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def putnastrpa... | [
"0.8903032",
"0.79385155",
"0.78946966",
"0.6862305",
"0.6671494",
"0.6618401",
"0.66161126",
"0.65867805",
"0.64877427",
"0.64677244",
"0.6323704",
"0.6307091",
"0.62468153",
"0.6246462",
"0.6167524",
"0.616714",
"0.61054486",
"0.60639423",
"0.59956676",
"0.5957143",
"0.5952... | 0.85641146 | 1 |
Assigns a new name to the task. puttaskname(self,taskname_) | def puttaskname(self,taskname_):
if isinstance(taskname_,unicode):
taskname_ = taskname_.encode("utf-8",errors="replace")
res = __library__.MSK_XX_puttaskname(self.__nativep,taskname_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def puttaskname(self,taskname_): # 3\n res = self.__obj.puttaskname(taskname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def task_name(self, task_name):\n\n self._task_name = task_name",
"def _setTaskName(self, taskName):\n s... | [
"0.87137294",
"0.8589184",
"0.7855133",
"0.7552247",
"0.7284806",
"0.7256069",
"0.69043714",
"0.675982",
"0.6663191",
"0.6645687",
"0.6581016",
"0.6562904",
"0.65047216",
"0.648085",
"0.645912",
"0.64160436",
"0.63803774",
"0.63803774",
"0.6374819",
"0.63747865",
"0.63524747"... | 0.8126309 | 2 |
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