= 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 accls, 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 accl is defined.\\n ie = 0\\n # --- Find which element the new one goes before.\\n while (ie <= top.naccl and top.acclzs[ie] <= zs and\\n top.acclzs[ie] != top.acclze[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.naccl or top.acclzs[-1] != top.acclze[-1]:\\n top.naccl = top.naccl + 100\\n gchange(\\\"Lattice\\\")\\n if isinstance(et,(ndarray,collections.Sequence)) and len(et)-1 > top.ntaccl:\\n top.ntaccl = len(et) - 1\\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.acclzs,'ze':top.acclze,'ez':top.acclez,\\n 'ap':top.acclap,'ax':top.acclax,'ay':top.acclay,\\n 'ox':top.acclox,'oy':top.accloy,'xw':top.acclxw,'sw':top.acclsw,\\n 'et':top.acclet,'ts':top.acclts,'dt':top.accldt}\\n\\n # --- Shift the existing data in the arrays to open up a space for the\\n # --- new element.\\n if ie <= top.naccl:\\n for e in edict.itervalues():\\n if len(shape(e)) == 1:\\n e[ie+1:] = e[ie:-1] + 0\\n else:\\n # --- acclet is 2-D\\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 # --- acclet is 2-D\\n e[:,ie] = ldict[xx]\\n\\n # --- resetlat must be called before the data can be used\\n top.lresetlat = true\\n\\n if (time is not None and data is not None) or func is not None:\\n tdle = TimeDependentLatticeElement('acclez',ie,time,data,func)\\n return ie,tdle\\n\\n return ie\",\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 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 conj(self):\\n return np.conj(self)\",\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 placeConcentrationInto(self, region, conc):\\n self._sim.distributeConcentration(self, region, conc)\\n return self\",\n \"def conj(self):\\n out = empty((self._size, *self.shape[1:]), self.dtype)\\n\\n if self.fragmented:\\n k = self._capacity - self._begin # fragmentation index\\n np.conjugate(self[self._begin:], out[:k])\\n np.conjugate(self[:self._end], out[k:])\\n else:\\n if self._begin < self._end:\\n part = self[self._begin:self._end]\\n elif self._end == 0:\\n part = self[self._begin:]\\n\\n np.conjugate(part, out)\\n\\n return(out.view(ndarray))\",\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 append_constraints(parent_constraints, new_constraints):\\n new_con_dict = copy.deepcopy(parent_constraints)\\n for con in new_constraints:\\n new_con_dict[con[1]].append((con[0], con[2])) # Maps v -> (agent, time)\\n return new_con_dict\",\n \"def conj(self):\\n daskD.wait(self.client.map(_call_conj, self.vecDask, pure=False))\\n return self\",\n \"def add_compartment(self, Vp=1, Qp=1):\\n self.__n_compartments += 1\\n self.__compartments.append({'Vp': Vp, 'Qp': Qp})\",\n \"def append_circuit(self, lines: Tuple[int, int], circuit: ACircuit, content: str) -> None:\",\n \"def add_constraint(self, constraint, problem):\\n problem += constraint\",\n \"def make_prog(self):\\r\\n\\r\\n self.cnv.clear()\\r\\n cdf = self.df[self.df.L != 0]\\r\\n c0 = cdf['C0'].value_counts().idxmax()\\r\\n c1 = cdf['C1'].value_counts().idxmax()\\r\\n c2 = cdf['C2'].value_counts().idxmax()\\r\\n c3 = cdf['C3'].value_counts().idxmax()\\r\\n self.cnv.extend([c0, c1, c2, c3])\",\n \"def add_couche(self, pos):\\n if self.control == 0:\\n if pos >= 0 and pos < (self.couche):\\n self.couche.insert(pos, 0)\\n else:\\n print(\\\"Vous pouvez ajouter une couche dans l'intervale [0,\\\" + str(len(self.couche)) + \\\"]\\\")\\n else:\\n print(\\\"Le réseau est deja créé, vous en pouvez plus le modifier\\\")\",\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 conj(self, vecs):\\n raise NotImplementedError\",\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 cc(self):\\n return self.foreach(\\n lambda k,v: (k, numpy.conj(v)),\\n dimensions = self.dims,\\n shape = self.shape,\\n )\",\n \"def add_course(graph, course, parent, color):\\n if course[-1] == '*':\\n # Concurrent course\\n color = 'green'\\n course = course[:-1]\\n child = pydot.Node(course)\\n graph.add_node(child)\\n edge = pydot.Edge(parent, course, color=color)\\n graph.add_edge(edge)\",\n \"def complementary_seq(self):\\n if not self.data['DNAseq']:\\n self.complement_seq_var.set(0)\\n self.warning('No DNA sequence loaded','You have to load a DNA sequence first')\\n return\\n compl={'A':'T','T':'A','C':'G','G':'C'}\\n comDNA=''\\n for base in self.data['DNAseq']:\\n comDNA=comDNA+compl[base]\\n self.data['DNAseq']=comDNA\\n\\n # Update\\n self.update_sequence_window()\\n return\",\n \"def ccNew(self, cs, ta, qa, z, m):\\n\\t\\treturn self.cmNew(cs, ta, qa) + self.f_c(z, m)*self.C0\",\n \"def ccNew(self, cs, ta, qa, z, m):\\n\\t\\treturn self.cmNew(cs, ta, qa) + self.f_c(z, m)*self.C0\",\n \"def addCompartment(self, *args):\\n return _libsbml.Model_addCompartment(self, *args)\",\n \"def addConstraint(constraint, problem):\\n problem += constraint\",\n \"def cpos2codon(self, cpos):\\n self.ensure_seq()\\n cpos = int(cpos)\\n if self.strand == \\\"+\\\":\\n np = []\\n for beg, end in self.exons:\\n np += list(range(max(beg, self.cds_beg),\\n min(self.cds_end, end)+1))\\n assert len(np) == len(self.seq)\\n\\n ni = cpos*3\\n if ni <= len(np):\\n codon = Codon()\\n codon.index = cpos\\n codon.locs = tuple(np[ni-3:ni])\\n codon.gene = self.gene\\n codon.chrm = self.chrm\\n codon.strand = self.strand\\n codon.seq = self.seq[ni-3:ni]\\n return codon\\n else:\\n raise IncompatibleTranscriptError('invalid_cDNA_position_%d;expect_[0_%d]' % (ni, len(np)))\\n else:\\n np = []\\n for beg, end in reversed(self.exons):\\n np += list(range(min(self.cds_end, end),\\n max(beg, self.cds_beg)-1,-1))\\n assert len(np) == len(self.seq)\\n\\n ni = cpos*3\\n if ni <= len(np):\\n codon = Codon()\\n codon.index = cpos\\n codon.locs = tuple(reversed(np[ni-3:ni]))\\n codon.gene = self.gene\\n codon.chrm = self.chrm\\n codon.strand = self.strand\\n codon.seq = self.seq[ni-3:ni]\\n return codon\\n else:\\n raise IncompatibleTranscriptError('invalid_cDNA_position_%d;expect_[0_%d]' % (ni, len(np)))\",\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 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 addConstraint(self, conName, compIDs=None, lower=-1e20, upper=1e20, dvIndex=0):\\n if compIDs is not None:\\n # Make sure CompIDs is flat and get element numbers on each proc corresponding to specified compIDs\\n compIDs = self._flatten(compIDs)\\n else:\\n nComps = self.meshLoader.getNumComponents()\\n compIDs = list(range(nComps))\\n\\n constrObj = self._createConstraint(dvIndex, compIDs, lower, upper)\\n if constrObj.nCon > 0:\\n self.constraintList[conName] = constrObj\\n success = True\\n else:\\n self._TACSWarning(\\n f\\\"No adjacent components found in `compIDs`. Skipping {conName}.\\\"\\n )\\n success = False\\n\\n return success\",\n \"def concur_wait(self, node):\\n global BARRIER_CTR\\n\\n # This method will be destructive, unless we make a new\\n # copy of the AST tree first\\n #\\n node = deepcopy(node)\\n\\n seen_channels = set()\\n\\n # Channels in this with_concur\\n concur_channels = find_all_channels(node)\\n\\n # For creating the Barriers, we want QGL1 scoped variables that will be real channel instances.\\n # We basically have that already.\\n real_chans = set()\\n for chan in concur_channels:\\n real_chans.add(chan)\\n\\n start_barrier = BARRIER_CTR\\n end_barrier = start_barrier + 1\\n BARRIER_CTR += 2\\n\\n for stmnt in node.body:\\n if not is_seq(stmnt):\\n NodeError.error_msg(stmnt,\\n 'non-seq block inside concur block?')\\n return node\\n\\n seq_channels = find_all_channels(stmnt)\\n\\n if seq_channels.intersection(seen_channels):\\n NodeError.error_msg(stmnt,\\n 'seq blocks have overlapping channels')\\n return node\\n\\n seen_channels = seen_channels.union(seq_channels)\\n\\n chan_name = ','.join(seq_channels)\\n\\n # mark stmnt with chan_name or seq_channels in another way\\n if hasattr(stmnt, 'qgl_chan_list'):\\n oldChanSet = set(stmnt.qgl_chan_list)\\n newChanSet = seq_channels\\n oldMissing = newChanSet - oldChanSet\\n oldExtra = oldChanSet - newChanSet\\n if len(oldMissing) > 0:\\n NodeError.diag_msg(stmnt, 'marked chan list %s was missing %s' % (str(oldChanSet), str(oldMissing)))\\n if len(oldExtra) > 0:\\n NodeError.diag_msg(stmnt, 'marked chan list %s had extra %s' % (str(oldChanSet), str(oldExtra)))\\n NodeError.diag_msg(stmnt, 'Marking chan list %s' % (str(seq_channels)))\\n stmnt.qgl_chan_list = list(seq_channels)\\n\\n new_seq_body = list()\\n\\n # Helper to ensure the string we feed to AST doesn't put quotes around\\n # our Qubit variable names\\n def appendChans(bString, chans):\\n bString += '['\\n first = True\\n for chan in chans:\\n if first:\\n bString += str(chan)\\n first = False\\n else:\\n bString += \\\",\\\" + str(chan)\\n bString += ']'\\n return bString\\n\\n # Add global ctr, chanlist=concur_channels\\n # FIXME: Hold concur_channels as a string? List?\\n bstring = 'Barrier(\\\"%s\\\", ' % str(start_barrier)\\n bstring = appendChans(bstring, list(real_chans))\\n bstring += ')\\\\n'\\n barrier_ast = expr2ast(bstring)\\n # barrier_ast = expr2ast('Barrier(%s, %s)\\\\n' % (str(start_barrier), list(real_chans)))\\n copy_all_loc(barrier_ast, node)\\n barrier_ast.channels = concur_channels\\n # print(\\\"*****Start barrier: %s\\\" % pyqgl2.ast_util.ast2str(barrier_ast))\\n\\n new_seq_body.append(barrier_ast)\\n\\n new_seq_body += stmnt.body\\n\\n bstring = 'Barrier(\\\"%s\\\", ' % str(end_barrier)\\n bstring = appendChans(bstring, list(real_chans))\\n bstring += ')\\\\n'\\n end_barrier_ast = expr2ast(bstring)\\n #end_barrier_ast = expr2ast('Barrier(%s, %s)\\\\n' % (str(end_barrier), list(real_chans)))\\n copy_all_loc(end_barrier_ast, node)\\n # Add global ctr, chanlist=concur_channels\\n end_barrier_ast.channels = concur_channels\\n\\n # print('End AST: %s' % ast2str(end_barrier_ast))\\n\\n new_seq_body.append(end_barrier_ast)\\n\\n stmnt.body = new_seq_body\\n\\n # FIXME: In new thinking, is the proper unseen set the global one,\\n # Or only those local to this with concur. I think only local\\n for unseen_chan in concur_channels - seen_channels:\\n #print('DIAG %s' % ast2str(stmnt))\\n NodeError.diag_msg(stmnt,\\n 'channels unreferenced in concur: %s' % str(unseen_chan))\\n\\n bstring = 'with seq:\\\\n Barrier(\\\"%s\\\", ' % str(start_barrier)\\n bstring = appendChans(bstring, list(real_chans))\\n bstring += ')\\\\n Barrier(\\\"%s\\\",' % str(end_barrier)\\n bstring = appendChans(bstring, list(real_chans))\\n bstring += ')\\\\n'\\n empty_seq_ast = expr2ast(bstring)\\n # print('Empty AST: %s' % ast2str(empty_seq_ast))\\n # empty_seq_ast = expr2ast(\\n # 'with seq:\\\\n Barrier(%s, %s)\\\\n Barrier(%s, %s)' % (str(start_barrier), list(real_chans), str(end_barrier), list(real_chans)))\\n\\n # Mark empty_seq_ast with unseen_chan\\n empty_seq_ast.qgl_chan_list = [unseen_chan]\\n copy_all_loc(empty_seq_ast, node)\\n node.body.append(empty_seq_ast)\\n\\n return node\",\n \"def test03(self):\\n a = np.arange(1e4)\\n c = np.arange(2e5)\\n b = bcolz.carray(a, rootdir=self.rootdir)\\n b.append(c)\\n # print \\\"b->\\\", `b`\\n d = np.concatenate((a, c))\\n assert_array_equal(d, b[:], \\\"Arrays are not equal\\\")\",\n \"def conj(self):\\n \\n Out = self._CreateSameType()\\n \\n for Ind in self.IndList():\\n OutInd = tuple(-x for x in Ind)\\n \\n Out[OutInd]=self[Ind].conj().T\\n \\n return Out\",\n \"def _add_warp_ctc_loss(pred, seq_len, num_label, label):\\n label = mx.sym.Reshape(data=label, shape=(-1,))\\n label = mx.sym.Cast(data=label, dtype='int32')\\n return mx.sym.WarpCTC(data=pred, label=label, label_length=num_label, input_length=seq_len)\",\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 add_constraint(self, kind, hook, expr, queue=False,**kwargs):\\n\\n if isinstance(expr, GenericVariable):\\n # make sure we actually pass the optlang variable\\n expr = expr.variable\\n\\n # Initialisation links to the cobra_model\\n cons = kind(hook, expr, # problem = self.problem,\\n # lb=lower_bound if lower_bound != float('-inf') else None,\\n # ub=upper_bound if upper_bound != float('inf') else None,\\n queue=queue,\\n **kwargs)\\n self._cons_dict[cons.name] = cons\\n self.logger.debug('Added constraint: {}'.format(cons.name))\\n # self.add_cons_vars(cons.constraint)\\n\\n return cons\",\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 add_junction(ccTremb):\\n pass\",\n \"def conj(x, *args, **kwargs):\\n raise NotImplementedError\",\n \"def _constraints_task_contiguity(self):\\n # CONT_STRIDE=1 would give original implementation\\n triu = util.triu(self.num_timeslots, incr=self.cont_incr)\\n tril = util.tril(self.num_timeslots, incr=self.cont_incr)\\n\\n def rule(model, i, j):\\n \\\"\\\"\\\"\\n This rule is used to encourage early completion (in terms of\\n allocation) of a task.\\n\\n More precisely:\\n CTu[i,j] = whether task j is UNASSIGNED between slot i and the end\\n\\n Maximizing sum_i CTu[i,j] encourages early task completion.\\n Maximizing sum_i CTu[i,j]+CTl[i,j] encourages contiguous scheduling.\\n \\\"\\\"\\\"\\n active = 1-self.task_spread[j]\\n den = sum(triu[i, :])\\n ind = model.timeslots\\n # FIXME(cathywu) can/should be more precise with A,A2,A3 offsets\\n total = sum(triu[i, k] * (\\n 1 - model.A[k, j] - model.A2[k, j] - model.A3[k, j] - model.A4[\\n k, j]) for k in ind)\\n total /= den\\n total *= active\\n # CTu[i,j] = floor(total)\\n return -1 + EPS, model.CTu[i, j] - total, EPS + self.slack_cont\\n\\n self.model.constrain_contiguity_u = Constraint(self.model.contslots,\\n self.model.tasks,\\n rule=rule)\\n\\n def rule(model, i, j):\\n \\\"\\\"\\\"\\n This rule is used to encourage late start (in terms of\\n allocation) of a task.\\n\\n More precisely:\\n CTl[i,j] = whether task j is UNASSIGNED between slot 0 and slot i\\n\\n Maximizing sum_i CTl[i,j] encourages late starting.\\n Maximizing sum_i CTu[i,j]+CTl[i,j] encourages contiguous scheduling.\\n \\\"\\\"\\\"\\n active = 1-self.task_spread[j]\\n den = sum(tril[i, :])\\n ind = model.timeslots\\n total = sum(tril[i, k] * (\\n 1 - model.A[k, j] - model.A2[k, j] - model.A3[k, j] - model.A4[\\n k, j]) for k in ind)\\n total /= den\\n total *= active\\n return -1 + EPS, model.CTl[i, j] - total, EPS + self.slack_cont\\n\\n self.model.constrain_contiguity_l = Constraint(self.model.contslots,\\n self.model.tasks,\\n rule=rule)\\n\\n def rule(model):\\n den = self.num_tasks * self.cont_slots * (self.slack_cont + 1)\\n num = 0.25\\n total = summation(model.CTu) / den * num\\n return model.CTu_total == total\\n\\n self.model.constrain_contiguity_ut = Constraint(rule=rule)\\n\\n def rule(model):\\n den = self.num_tasks * self.cont_slots * (self.slack_cont + 1)\\n num = 0.25\\n total = summation(model.CTl) / den * num\\n return model.CTl_total == total\\n\\n self.model.constrain_contiguity_lt = Constraint(rule=rule)\",\n \"async def cadd(self, ctx, *args: Input):\\n await self.add(\\n MemoryInputSet(MemoryInputSetConfig(ctx=ctx, inputs=list(args)))\\n )\\n return ctx\",\n \"def mint_a_new_cid(self):\\n self.update({\\\"cid\\\": self.table.c.cid +1}, condition=None)\",\n \"def add_contig_ena_attrib(self, log_pfx, cs_name = \\\"contig\\\"):\\n sql = r'''insert ignore into seq_region_attrib (seq_region_id, attrib_type_id, value)\\n select\\n sr.seq_region_id, at.attrib_type_id, \\\"ENA\\\"\\n from\\n seq_region sr, coord_system cs, attrib_type at\\n where sr.coord_system_id = cs.coord_system_id\\n and cs.name = \\\"%s\\\"\\n and at.code = \\\"external_db\\\"\\n ;''' % (cs_name)\\n return self.run_sql_req(sql, log_pfx)\",\n \"def test02c(self):\\n\\n N = 1000-1\\n cn = bcolz.zeros(N, dtype=\\\"i1\\\", rootdir=self.rootdir)\\n self.assertTrue(len(cn) == N)\\n\\n cn = bcolz.carray(rootdir=self.rootdir, mode='a')\\n self.assertTrue(len(cn) == N)\\n\\n # Now check some accesses (no errors should be raised)\\n cn.append([1, 1])\\n self.assertTrue(len(cn) == N+2)\\n cn[1] = 2\\n self.assertTrue(cn[1] == 2)\\n cn[N+1] = 3\\n self.assertTrue(cn[N+1] == 3)\",\n \"def add_co_worker(self, employee):\\n self.co_worker_list.append(employee)\\n self.original_co_worker_list.append(employee)\",\n \"def MotcorCatenate(self, info, base, anat_tgt):\\n# First compute the transformation matrices due to epi-to-epi motion.\\n fmt = '3dvolreg -prefix NULL -1Dmatrix_save %s -twopass ' + \\\\\\n '-verbose -base %s+orig[%s] -dfile %s %s+orig'\\n cmd = fmt % (info['matfile_m'], info['basefile'], base, \\\\\\n info['mot_file'], info['imgfile_t'])\\n self.CheckExec(cmd, [info['matfile_m']])\\n\\n# Catenate with transformation from epi base image to the anatomical.\\n cmd = 'cat_matvec -ONELINE %s -P %s -P > %s' % \\\\\\n (self.info[anat_tgt]['matfile'], info['matfile_m'], \\\\\\n info['matfile_mcat'])\\n self.CheckExec(cmd, [info['matfile_mcat']])\\n\\n# Interpolate the data to the new grid.\\n fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s ' + \\\\\\n '-warp shift_rotate -base %s+orig[%s] %s+orig'\\n cmd = fmt % (info['imgfile_m'], info['matfile_mcat'], info['basefile'], \\\\\\n base, info['imgfile_t'])\\n self.CheckExec(cmd, ['%s+orig.BRIK'%info['imgfile_m'], \\\\\\n '%s+orig.HEAD'%info['imgfile_m']])\",\n \"def find_joint_extension(self, modelb, modelc, mace_time=10, prover_time=60):\\n n = modelb.cardinality\\n ne = ['b'+str(x)+'!=b'+str(y) for x in range(n) for y in range(x+1,n)]\\n n = modelc.cardinality\\n ne += ['c'+str(x)+'!=c'+str(y) for x in range(n) for y in range(x+1,n)]\\n return prover9(self.axioms+ne+modelb.positive_diagram('b') + \\n modelc.positive_diagram('c'), [], mace_time, prover_time)\",\n \"def add_cups(self, target_idx, cups_to_insert):\\n part_a = self.cups[0 : target_idx + 1]\\n part_b = self.cups[target_idx + 1 :]\\n print(f\\\"cups: {self.cups} part_a[{part_a}], part_b[{part_b}]\\\")\\n self.cups = part_a + cups_to_insert + part_b\",\n \"def addCompartmentGlyph(self, *args):\\n return _libsbml.Layout_addCompartmentGlyph(self, *args)\",\n \"def conj(self):\\n res = self._defer_unary_elementwise(np.conj)\\n res.dirs = list(map(opr.neg, res.dirs))\\n res.charge = -res.charge\\n if self.qodulus is not None:\\n res.charge %= res.qodulus\\n return res\",\n \"def test02c(self):\\n a = np.arange(1000*1000)\\n b = bcolz.carray(a, chunklen=100*1000-1, rootdir=self.rootdir)\\n b.append(a)\\n # print \\\"b->\\\", `b`\\n c = np.concatenate((a, a))\\n assert_array_equal(c, b[:], \\\"Arrays are not equal\\\")\",\n \"def add_constraint(self, constraint):\\n self._ckey += 1\\n self.constraints[self._ckey] = constraint\",\n \"def append_subcircuit(self, lines: Tuple[int, int], circuit: Circuit, content: str) -> None:\",\n \"def getCodonSeqs(self):\\r\\n combinations = list(self.codonTable[aa] for aa in self.peptide) # creates a list of possible codons based on AA\\r\\n self.allPepSeqs = list(''.join(codon) for codon in itertools.product(*combinations)) # creates list of peptides\\r\\n return\",\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 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 create_conserved_pos_list_otherclass(gpcr_pdb,gpcr_aa, i,my_pos, cons_pos_li, multiple_chains,chain_name,gnum_classes_rel,dict_class,current_class):\\n my_pos_bw=my_pos.split(\\\"x\\\")[0]\\n add_chain_name=\\\"\\\"\\n if multiple_chains:\\n add_chain_name=\\\":\\\"+chain_name\\n while i < len(cons_pos_li):\\n cons_pos_bw_ourclass = cons_pos_li[i][1]\\n if my_pos_bw==cons_pos_bw_ourclass:\\n pos_range=find_range_from_cons_pos(my_pos, gpcr_pdb)\\n if pos_range:\\n cons_pos_li[i][2]=pos_range + add_chain_name\\n cons_pos_li[i][1]=\\\"Correspods to \\\"+cons_pos_bw_ourclass + current_class.lower()\\n i+=1\",\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 adjoint(self) -> OperatorBase:\\n raise AquaError('Adjoint of a CVaR measurement not defined')\",\n \"def calc_conmat(self, jd=None, dt=20, write=False):\\n if not jd: jd = self.jd.min()\\n if not hasattr(self,'reg'): self.regvec_from_discs()\\n tmask1 = self.jd == jd\\n tmask2 = self.jd == jd + dt\\n try:\\n ntracmax = max(self.ntrac[tmask1].max(), self.ntrac[tmask2].max())\\n except ValueError:\\n return False\\n\\n convec = np.zeros((2, ntracmax+1))\\n convec[0,self.ntrac[tmask1]] = self.reg[tmask1]\\n convec[1,self.ntrac[tmask2]] = self.reg[tmask2]\\n convec = convec.astype(np.int)\\n\\n flat_coord = np.ravel_multi_index(convec, (self.nreg, self.nreg))\\n sums = np.bincount(flat_coord, minlength=self.nreg*self.nreg)\\n self.conmat = np.zeros((self.nreg,self.nreg))\\n self.conmat.flat[:len(sums)] = sums\\n\\n def write_hdf(jd, dt):\\n self.h5open()\\n jdpos = np.abs(self.h5f.root.jdvec[:] - jd).argmin()\\n self.h5f.root.conmat[jdpos,dt,:,:] = self.conmat.astype(np.uint32)\\n self.h5f.root.exist[jdpos,dt] = True\\n self.h5f.flush()\\n self.h5close()\\n\\n def write_npz(jd, dt):\\n conmatfile = (\\\"conmat_%s_%s_%06i_%04i.npz\\\" %\\n (self.projname, self.casename, jd, dt))\\n np.savez(os.path.join(self.conmatdir, conmatfile),\\n conmat=self.conmat.astype(np.uint32), jd=jd, dt=dt)\\n\\n if write is True: write_hdf(jd, dt)\",\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 _write_conne(parameters):\\n from ._common import connections\\n\\n # Reorder connections\\n if parameters[\\\"connections_order\\\"] is not None:\\n order = parameters[\\\"connections_order\\\"]\\n else:\\n order = parameters[\\\"connections\\\"].keys()\\n\\n # Format\\n label_length = len(max(parameters[\\\"connections\\\"], key=len)) // 2\\n fmt = block_to_format[\\\"CONNE\\\"]\\n fmt = str2format(fmt[label_length])\\n\\n out = []\\n for k in order:\\n data = deepcopy(connections)\\n data.update(parameters[\\\"connections\\\"][k])\\n\\n values = [\\n k,\\n data[\\\"nseq\\\"],\\n data[\\\"nadd\\\"][0] if data[\\\"nadd\\\"] is not None else None,\\n data[\\\"nadd\\\"][1] if data[\\\"nadd\\\"] is not None else None,\\n data[\\\"permeability_direction\\\"],\\n data[\\\"nodal_distances\\\"][0],\\n data[\\\"nodal_distances\\\"][1],\\n data[\\\"interface_area\\\"],\\n data[\\\"gravity_cosine_angle\\\"],\\n data[\\\"radiant_emittance_factor\\\"],\\n ]\\n out += write_record(values, fmt)\\n\\n return out\",\n \"def m_cliques(adj_list,labels,matrix,topology=\\\"clique\\\",cut=0):\\n num_clusters = list(np.unique(labels))\\n clusters = {i:[] for i in num_clusters}\\n print(\\\"m_cliques CUT:\\\", cut)\\n #Add nodes to clusters\\n for idx,n in enumerate(adj_list):\\n clusters[labels[idx]].append(n)\\n \\n if topology == \\\"sample_rand\\\":\\n G_cliques = sampled_clique(clusters,\\\"rand\\\")\\n print(\\\"Rand sampled clique size:\\\", G_cliques.number_of_nodes())\\n elif topology == \\\"sample_optim\\\":\\n G_cliques = sampled_clique(clusters,\\\"optim\\\")\\n print(\\\"Optim sampled clique size:\\\", G_cliques.number_of_nodes())\\n elif topology == \\\"pcc_rand\\\":\\n G_cliques = pcc_clique(clusters,\\\"rand\\\",labels,cut)\\n print(\\\"Rand sampled clique size:\\\", G_cliques.number_of_nodes())\\n elif topology == \\\"pcc_optim\\\":\\n G_cliques = pcc_clique(clusters,\\\"optim\\\",labels,cut)\\n print(\\\"Optim sampled clique size:\\\", G_cliques.number_of_nodes())\\n elif topology == \\\"pcc_greedy\\\":\\n G_cliques = pcc_clique(clusters,\\\"greedy\\\",labels,cut,matrix)\\n print(\\\"Greedy sampled clique size:\\\", G_cliques.number_of_nodes())\\n else:\\n G_cliques = nx.Graph()\\n cliques = []\\n while not is_empty(clusters):\\n clique = []\\n \\n #Add clique nodes\\n for _,cluster in clusters.items():\\n if len(cluster) > 0:\\n n = random.choice(cluster)\\n clique.append(n)\\n cluster.remove(n)\\n \\n #Cliqify\\n for n1 in clique:\\n for n2 in clique:\\n if n1 != n2:\\n G_cliques.add_edge(n1,n2)\\n \\n #Aggregate clique\\n cliques.append(clique)\\n\\n if topology == \\\"clique\\\":\\n clique_the_cliques(cliques,labels,G_cliques)\\n elif topology == \\\"ring\\\":\\n cliques_on_ring(cliques,labels,G_cliques)\\n \\n return G_cliques\",\n \"def pcc_clique(clusters,strategy, labels, cut=0, matrix=None):\\n G = nx.Graph()\\n cliques = []\\n size = len(clusters)\\n if strategy == \\\"rand\\\":\\n for _,cluster in clusters.items():\\n clique_size = len(cluster) if len(cluster) < size else size\\n cliques.append( random.sample(cluster,clique_size))\\n \\n elif strategy == \\\"optim\\\":\\n while len(cliques) < size:\\n clique = []\\n for _,cluster in clusters.items():\\n if len(cluster):\\n element = random.choice(cluster)\\n clique.append(element)\\n del cluster[cluster.index(element)]\\n if clique:\\n cliques.append(clique)\\n \\n elif strategy == \\\"greedy\\\":\\n cliques = greedy_cliques([],list(clusters.values()),matrix)[:len(clusters)]\\n \\n for clique in cliques:\\n if len(clique) == 1:\\n G.add_node(clique[0])\\n continue\\n for n1 in clique:\\n for n2 in clique:\\n if n1 != n2:\\n G.add_edge(n1,n2)\\n\\n print(\\\"pcc_clique CUT:\\\", cut)\\n cliques_on_ring(cliques,labels,G,cut)\\n return G\",\n \"def encode_conj(self, conj):\\n return self.conj(\\n tuple( self.fold_term(disj)\\n for disj in conj.disjs ))\",\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 BCC(self, value):\\n if not self.reg.C:\\n self.reg.PC += value\",\n \"def conj(self, init_repr, vecs):\\n rnnoutput = self.conjmodel(\\n torch.cat([\\n init_repr,\\n *vecs,\\n torch.zeros(self.hrr_size).detach()\\n ]).reshape((len(vecs)+2, 1, -1)),\\n (\\n torch.zeros((1, 1, self.hrr_size)).detach(),\\n torch.zeros((1, 1, self.hrr_size)).detach(),\\n )\\n )\\n\\n rnnoutput[1][0].detach_()\\n rnnoutput[1][1].detach_()\\n\\n return rnnoutput[0][-1,0,:]\",\n \"def concatenate(cls, char_matrices):\\n taxon_set = char_matrices[0].taxon_set\\n nseqs = len(char_matrices[0])\\n concatenated_chars = cls(taxon_set=taxon_set)\\n pos_start = 0\\n for cidx, cm in enumerate(char_matrices):\\n if cm.taxon_set is not taxon_set:\\n raise ValueError(\\\"Different `taxon_set` references in matrices to be merged\\\")\\n if len(cm) != len(taxon_set):\\n raise ValueError(\\\"Number of sequences not equal to the number of taxa\\\")\\n if len(cm) != nseqs:\\n raise ValueError(\\\"Different number of sequences across alignments: %d (expecting %d based on first matrix)\\\" % (len(cm), nseqs))\\n v1 = len(cm[0])\\n for t, s in cm.items():\\n if len(s) != v1:\\n raise ValueError(\\\"Unequal length sequences in character matrix %d\\\".format(cidx+1))\\n concatenated_chars.extend(cm,\\n extend_existing=True,\\n overwrite_existing=False)\\n if cm.label is None:\\n new_label = \\\"locus%03d\\\" % cidx\\n else:\\n new_label = cm.label\\n cs_label = new_label\\n i = 2\\n while cs_label in concatenated_chars.character_subsets:\\n label = \\\"%s_%03d\\\" % (new_label, i)\\n i += 1\\n character_indices = range(pos_start, pos_start + cm.vector_size)\\n pos_start += cm.vector_size\\n concatenated_chars.new_character_subset(character_indices=character_indices,\\n label=cs_label)\\n return concatenated_chars\",\n \"def add_rules(self, cliques):\\n self.cliques.extend(cliques)\",\n \"def _econ_append_pc(pp, cf_file, sites_per_worker=None):\\n multi_h5_res, hsds = check_res_file(cf_file)\\n if multi_h5_res:\\n res_cls = MultiFileResource\\n res_kwargs = {}\\n else:\\n res_cls = Resource\\n res_kwargs = {'hsds': hsds}\\n\\n with res_cls(cf_file, **res_kwargs) as f:\\n gid0 = f.meta['gid'].values[0]\\n gid1 = f.meta['gid'].values[-1]\\n\\n i0 = pp.index(gid0)\\n i1 = pp.index(gid1) + 1\\n pc = PointsControl.split(i0, i1, pp, sites_per_split=sites_per_worker)\\n\\n return pc\",\n \"def extend(network, coords=[], conns=[], labels=[], **kwargs):\\n if 'throat_conns' in kwargs.keys():\\n conns = kwargs['throat_conns']\\n if 'pore_coords' in kwargs.keys():\\n coords = kwargs['pore_coords']\\n coords = np.array(coords)\\n conns = np.array(conns)\\n Np_old = network.num_pores()\\n Nt_old = network.num_throats()\\n Np = Np_old + coords.shape[0]\\n Nt = Nt_old + conns.shape[0]\\n if np.any(conns > Np):\\n raise Exception('Some throat conns point to non-existent pores')\\n network.update({'pore.all': np.ones([Np, ], dtype=bool),\\n 'throat.all': np.ones([Nt, ], dtype=bool)})\\n # Add coords and conns\\n if np.size(coords) > 0:\\n coords = np.vstack((network['pore.coords'], coords))\\n network['pore.coords'] = coords\\n if np.size(conns) > 0:\\n conns = np.vstack((network['throat.conns'], conns))\\n network['throat.conns'] = conns\\n\\n # Increase size of any prop or label arrays already on network and phases\\n objs = list(network.project.phases().values())\\n objs.append(network)\\n for obj in objs:\\n obj.update({'pore.all': np.ones([Np, ], dtype=bool),\\n 'throat.all': np.ones([Nt, ], dtype=bool)})\\n for item in list(obj.keys()):\\n N = obj._count(element=item.split('.')[0])\\n if obj[item].shape[0] < N:\\n arr = obj.pop(item)\\n s = arr.shape\\n if arr.dtype == bool:\\n obj[item] = np.zeros(shape=(N, *s[1:]), dtype=bool)\\n else:\\n obj[item] = np.ones(shape=(N, *s[1:]), dtype=float)*np.nan\\n obj[item][:arr.shape[0]] = arr\\n\\n # Regenerate models on all objects to fill new elements\\n for obj in network.project.phases().values():\\n if hasattr(obj, 'models'):\\n obj.regenerate_models()\\n\\n # Apply labels, if supplied\\n if labels != []:\\n # Convert labels to list if necessary\\n if isinstance(labels, str):\\n labels = [labels]\\n for label in labels:\\n # Remove pore or throat from label, if present\\n label = label.split('.')[-1]\\n if np.size(coords) > 0:\\n Ps = np.r_[Np_old:Np]\\n if 'pore.'+label not in network.labels():\\n network['pore.'+label] = False\\n network['pore.'+label][Ps] = True\\n if np.size(conns) > 0:\\n Ts = np.r_[Nt_old:Nt]\\n if 'throat.'+label not in network.labels():\\n network['throat.'+label] = False\\n network['throat.'+label][Ts] = True\\n\\n # Clear adjacency and incidence matrices which will be out of date now\\n network._am.clear()\\n network._im.clear()\",\n \"def _tcoffee(exe, seq, outfile):\\n \\n wd = tempfile.mkdtemp(dir=os.path.dirname(seq))\\n args = [exe, '-in', seq, '-outfile', outfile, '-output', 'fasta_aln',\\n '-run_name', 't-coffee-alignment', '-quiet']\\n try:\\n process = Popen(args, stderr=PIPE, universal_newlines=True, cwd=wd)\\n code = process.wait()\\n if code:\\n if os.path.isfile(outfile):\\n os.remove(outfile)\\n msg = process.stderr.read() or process.stdout.read()\\n process.stderr.close()\\n error('Aligning sequence: {} via T-COFFEE failed due to:\\\\n{}.'\\n .format(seq, indent(msg, prefix='\\\\t')))\\n sys.exit(1)\\n finally:\\n shutil.rmtree(wd)\\n \\n return outfile\",\n \"def ccx(self, ctl1, ctl2, tgt):\\n return self.append(ToffoliGate(), [ctl1, ctl2, tgt], [])\",\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 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 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 addCollateral(self, _to: Address = None) -> None:\\n if self._distributing.get() == True:\\n self._total_stake.set(self._total_stake.get() + self._daily_reward.get())\\n self._distributing.set(False)\\n self._daily_reward.set(0)\\n if _to == None:\\n _to = self.tx.origin\\n self._reset_top_preps()\\n self._check_for_iscore()\\n self._check_unstake_result()\\n self._total_stake.set(self._total_stake.get()+self.msg.value)\\n amount = self._get_amount_to_mint()\\n self.sICX_score.mintTo(_to, amount)\\n self._stake(self._total_stake.get())\\n icx_to_distribute = self._evenly_distrubuted_amount()\\n remainder_icx = icx_to_distribute[1]\\n evenly_distributed_amount = icx_to_distribute[0]\\n self._delegations(evenly_distributed_amount,remainder_icx)\\n self._sICX_supply.set(self._sICX_supply.get() + amount)\\n self.TokenTransfer(_to, amount, f'{amount / DENOMINATOR} sICX minted to {_to}')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.86378384","0.75416327","0.7458773","0.7380015","0.73679143","0.646462","0.6225315","0.51709515","0.5064763","0.48974988","0.48840624","0.48815274","0.48780966","0.4864251","0.48559266","0.47979966","0.4756608","0.4728465","0.47282267","0.4699578","0.46717963","0.4667326","0.46460378","0.46260604","0.4619928","0.46079183","0.4607213","0.46067777","0.45833543","0.45797583","0.4569398","0.45478472","0.45051455","0.45025355","0.4494712","0.44931892","0.44771558","0.44734165","0.44589338","0.44508314","0.44418263","0.44391984","0.44388455","0.44309258","0.44307607","0.44307607","0.44263577","0.4420648","0.4418011","0.44141412","0.44067183","0.44041467","0.44036978","0.44031224","0.4393712","0.43927374","0.43924144","0.43783006","0.43529752","0.4344481","0.43433276","0.43344298","0.43150124","0.43144125","0.43061724","0.43043524","0.4302836","0.42998588","0.4290546","0.42859477","0.4268687","0.42681324","0.42609066","0.42584178","0.4251374","0.4246991","0.42461306","0.423299","0.4231269","0.4225405","0.42242217","0.42233524","0.42219105","0.421604","0.42103788","0.4205639","0.42008048","0.41979432","0.41940048","0.418634","0.41826677","0.41802084","0.41752222","0.41750607","0.41723973","0.4169797","0.4163972","0.41589397","0.41559872","0.41481608"],"string":"[\n \"0.86378384\",\n \"0.75416327\",\n \"0.7458773\",\n \"0.7380015\",\n \"0.73679143\",\n \"0.646462\",\n \"0.6225315\",\n \"0.51709515\",\n \"0.5064763\",\n \"0.48974988\",\n \"0.48840624\",\n \"0.48815274\",\n \"0.48780966\",\n \"0.4864251\",\n \"0.48559266\",\n \"0.47979966\",\n \"0.4756608\",\n \"0.4728465\",\n \"0.47282267\",\n \"0.4699578\",\n \"0.46717963\",\n \"0.4667326\",\n \"0.46460378\",\n \"0.46260604\",\n \"0.4619928\",\n \"0.46079183\",\n \"0.4607213\",\n \"0.46067777\",\n \"0.45833543\",\n \"0.45797583\",\n \"0.4569398\",\n \"0.45478472\",\n \"0.45051455\",\n \"0.45025355\",\n \"0.4494712\",\n \"0.44931892\",\n \"0.44771558\",\n \"0.44734165\",\n \"0.44589338\",\n \"0.44508314\",\n \"0.44418263\",\n \"0.44391984\",\n \"0.44388455\",\n \"0.44309258\",\n \"0.44307607\",\n \"0.44307607\",\n \"0.44263577\",\n \"0.4420648\",\n \"0.4418011\",\n \"0.44141412\",\n \"0.44067183\",\n \"0.44041467\",\n \"0.44036978\",\n \"0.44031224\",\n \"0.4393712\",\n \"0.43927374\",\n \"0.43924144\",\n \"0.43783006\",\n \"0.43529752\",\n \"0.4344481\",\n \"0.43433276\",\n \"0.43344298\",\n \"0.43150124\",\n \"0.43144125\",\n \"0.43061724\",\n \"0.43043524\",\n \"0.4302836\",\n \"0.42998588\",\n \"0.4290546\",\n \"0.42859477\",\n \"0.4268687\",\n \"0.42681324\",\n \"0.42609066\",\n \"0.42584178\",\n \"0.4251374\",\n \"0.4246991\",\n \"0.42461306\",\n \"0.423299\",\n \"0.4231269\",\n \"0.4225405\",\n \"0.42242217\",\n \"0.42233524\",\n \"0.42219105\",\n \"0.421604\",\n \"0.42103788\",\n \"0.4205639\",\n \"0.42008048\",\n \"0.41979432\",\n \"0.41940048\",\n \"0.418634\",\n \"0.41826677\",\n \"0.41802084\",\n \"0.41752222\",\n \"0.41750607\",\n \"0.41723973\",\n \"0.4169797\",\n \"0.4163972\",\n \"0.41589397\",\n \"0.41559872\",\n \"0.41481608\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.86945903"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11770,"cells":{"query":{"kind":"string","value":"Appends multiple conic constraints to the problem. appendconesseq(self,ct_,conepar_,nummem_,j_)"},"document":{"kind":"string","value":"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)"},"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 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 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 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 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 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 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 conj(self, vecs):\n raise NotImplementedError","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 set_conectividad(self, conec):\n self.add_conec_listoflists(conec) # calcula el ne y el je","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 conj(x, *args, **kwargs):\n raise NotImplementedError","def conj(traj):\r\n return Trajectory(np.conj(traj.modes))","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 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 addConstrs(self, constrs, name=''):\n ...","def _write_conne(parameters):\n from ._common import connections\n\n # Reorder connections\n if parameters[\"connections_order\"] is not None:\n order = parameters[\"connections_order\"]\n else:\n order = parameters[\"connections\"].keys()\n\n # Format\n label_length = len(max(parameters[\"connections\"], key=len)) // 2\n fmt = block_to_format[\"CONNE\"]\n fmt = str2format(fmt[label_length])\n\n out = []\n for k in order:\n data = deepcopy(connections)\n data.update(parameters[\"connections\"][k])\n\n values = [\n k,\n data[\"nseq\"],\n data[\"nadd\"][0] if data[\"nadd\"] is not None else None,\n data[\"nadd\"][1] if data[\"nadd\"] is not None else None,\n data[\"permeability_direction\"],\n data[\"nodal_distances\"][0],\n data[\"nodal_distances\"][1],\n data[\"interface_area\"],\n data[\"gravity_cosine_angle\"],\n data[\"radiant_emittance_factor\"],\n ]\n out += write_record(values, fmt)\n\n return out","def conj(self):\n \n Out = self._CreateSameType()\n \n for Ind in self.IndList():\n OutInd = tuple(-x for x in Ind)\n \n Out[OutInd]=self[Ind].conj().T\n \n return Out","def conj(self, vecs):\n mergedvecs = merge_comp(vecs)\n conjobj = associate_comp(mergedvecs, mergedvecs)\n return normalize_comp(\n self.conj_weights @\n torch.cat([\n conjobj,\n mergedvecs,\n ]).reshape(-1, 2 * self.hrr_size)).reshape(2, self.hrr_size)","def conj(self, vecs):\n mergedvecs = merge_comp(vecs)\n conjobj = associate_comp(mergedvecs, mergedvecs)\n return normalize_comp(\n self.conj_weights @\n torch.cat([\n conjobj,\n mergedvecs,\n ]).reshape(-1, 2 * self.hrr_size)).reshape(2, self.hrr_size)","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 conj(self, init_repr, vecs):\n rnnoutput = self.conjmodel(\n torch.cat([\n init_repr,\n *vecs,\n torch.zeros(self.hrr_size).detach()\n ]).reshape((len(vecs)+2, 1, -1)),\n (\n torch.zeros((1, 1, self.hrr_size)).detach(),\n torch.zeros((1, 1, self.hrr_size)).detach(),\n )\n )\n\n rnnoutput[1][0].detach_()\n rnnoutput[1][1].detach_()\n\n return rnnoutput[0][-1,0,:]","def conj(self):\n return np.conj(self)","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 append_constraints(parent_constraints, new_constraints):\n new_con_dict = copy.deepcopy(parent_constraints)\n for con in new_constraints:\n new_con_dict[con[1]].append((con[0], con[2])) # Maps v -> (agent, time)\n return new_con_dict","def conj(self):\n daskD.wait(self.client.map(_call_conj, self.vecDask, pure=False))\n return self","def conj(self):\n out = empty((self._size, *self.shape[1:]), self.dtype)\n\n if self.fragmented:\n k = self._capacity - self._begin # fragmentation index\n np.conjugate(self[self._begin:], out[:k])\n np.conjugate(self[:self._end], out[k:])\n else:\n if self._begin < self._end:\n part = self[self._begin:self._end]\n elif self._end == 0:\n part = self[self._begin:]\n\n np.conjugate(part, out)\n\n return(out.view(ndarray))","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 appendcons(self,num_): # 3\n res = self.__obj.appendcons(num_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def create_constraints(self, courses):\n for i, course1 in enumerate(courses):\n for j, course2 in enumerate(courses):\n if i <= j:\n continue\n self.p.add_constraint(section_constraint, [course1, course2])\n self.p.add_constraint(self.time_conflict, [course1])","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 conj(self, vecs):\n mergedvecs = merge(vecs)\n conjobj = associate(mergedvecs, mergedvecs)\n return normalize(\n self.conj_weights @\n torch.cat([\n conjobj,\n mergedvecs,\n ]).reshape(-1, self.hrr_size))","def make_prog(self):\r\n\r\n self.cnv.clear()\r\n cdf = self.df[self.df.L != 0]\r\n c0 = cdf['C0'].value_counts().idxmax()\r\n c1 = cdf['C1'].value_counts().idxmax()\r\n c2 = cdf['C2'].value_counts().idxmax()\r\n c3 = cdf['C3'].value_counts().idxmax()\r\n self.cnv.extend([c0, c1, c2, c3])","def c_code_contiguous(self, node, name, inp, out, sub):\r\n raise theano.gof.utils.MethodNotDefined()","def appendcons(self,num_):\n res = __library__.MSK_XX_appendcons(self.__nativep,num_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def _constraints_task_contiguity(self):\n # CONT_STRIDE=1 would give original implementation\n triu = util.triu(self.num_timeslots, incr=self.cont_incr)\n tril = util.tril(self.num_timeslots, incr=self.cont_incr)\n\n def rule(model, i, j):\n \"\"\"\n This rule is used to encourage early completion (in terms of\n allocation) of a task.\n\n More precisely:\n CTu[i,j] = whether task j is UNASSIGNED between slot i and the end\n\n Maximizing sum_i CTu[i,j] encourages early task completion.\n Maximizing sum_i CTu[i,j]+CTl[i,j] encourages contiguous scheduling.\n \"\"\"\n active = 1-self.task_spread[j]\n den = sum(triu[i, :])\n ind = model.timeslots\n # FIXME(cathywu) can/should be more precise with A,A2,A3 offsets\n total = sum(triu[i, k] * (\n 1 - model.A[k, j] - model.A2[k, j] - model.A3[k, j] - model.A4[\n k, j]) for k in ind)\n total /= den\n total *= active\n # CTu[i,j] = floor(total)\n return -1 + EPS, model.CTu[i, j] - total, EPS + self.slack_cont\n\n self.model.constrain_contiguity_u = Constraint(self.model.contslots,\n self.model.tasks,\n rule=rule)\n\n def rule(model, i, j):\n \"\"\"\n This rule is used to encourage late start (in terms of\n allocation) of a task.\n\n More precisely:\n CTl[i,j] = whether task j is UNASSIGNED between slot 0 and slot i\n\n Maximizing sum_i CTl[i,j] encourages late starting.\n Maximizing sum_i CTu[i,j]+CTl[i,j] encourages contiguous scheduling.\n \"\"\"\n active = 1-self.task_spread[j]\n den = sum(tril[i, :])\n ind = model.timeslots\n total = sum(tril[i, k] * (\n 1 - model.A[k, j] - model.A2[k, j] - model.A3[k, j] - model.A4[\n k, j]) for k in ind)\n total /= den\n total *= active\n return -1 + EPS, model.CTl[i, j] - total, EPS + self.slack_cont\n\n self.model.constrain_contiguity_l = Constraint(self.model.contslots,\n self.model.tasks,\n rule=rule)\n\n def rule(model):\n den = self.num_tasks * self.cont_slots * (self.slack_cont + 1)\n num = 0.25\n total = summation(model.CTu) / den * num\n return model.CTu_total == total\n\n self.model.constrain_contiguity_ut = Constraint(rule=rule)\n\n def rule(model):\n den = self.num_tasks * self.cont_slots * (self.slack_cont + 1)\n num = 0.25\n total = summation(model.CTl) / den * num\n return model.CTl_total == total\n\n self.model.constrain_contiguity_lt = Constraint(rule=rule)","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 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 cc(self):\n return self.foreach(\n lambda k,v: (k, numpy.conj(v)),\n dimensions = self.dims,\n shape = self.shape,\n )","def semcor2conc(args):\r\n input_files = list_files(*args.input_files)\r\n types = list(args.types)\r\n output_file = args.output_file or output_default / '{}_conc.csv'.format('_'.join(types))\r\n output_file = Path(output_file)\r\n left_context = args.left\r\n right_context = args.right\r\n separator = args.separator\r\n filter_pos = args.pos\r\n kind_id = args.kind_id\r\n with output_file.open('w') as file:\r\n x = 'last\\tnext\\tlemma' if args.add_closest else 'lemma'\r\n file.write('\\t'.join(['concordance', 'file', 'token_id', 'left', 'wordform', 'right', x, 'pos', 'sense_key\\n']))\r\n for input_file in input_files:\r\n corpus_file = CorpusFile(input_file)\r\n tokenlist = list(generate_tokenlist(corpus_file.text))\r\n chosen_words = [index for (index, token) in enumerate(tokenlist) if token.lemma in types]\r\n for word in chosen_words:\r\n node = tokenlist[word]\r\n pos = node.pos\r\n if filter_pos and not re.match(r'{}'.format([x for x in filter_pos]), pos):\r\n continue\r\n if kind_id == 'lemma_pos':\r\n wordtype = '/'.join([node.lemma, node.pos])\r\n elif kind_id == 'wordform':\r\n wordtype = node.wordform\r\n else:\r\n wordtype = node.lemma\r\n token_id = '/'.join([wordtype, corpus_file.shortname, str(word + 1)])\r\n left, right = generate_context(tokenlist, word, left_context, right_context, separator, len(tokenlist))\r\n if args.add_closest:\r\n last = tokenlist[word-1].wordform\r\n following = tokenlist[word+1].wordform\r\n line = [corpus_file.concordance, corpus_file.shortname, token_id, left, node.wordform, right, last, following, node.lemma, pos, node.sense_key or 'NA']\r\n else:\r\n line = [corpus_file.concordance, corpus_file.shortname, token_id, left, node.wordform, right, node.lemma, pos, node.sense_key or 'NA']\r\n file.write('\\t'.join(line) + '\\n')\r\n print('File \"{}\" processed.'.format(input_file.stem))","def encode_conj(self, conj):\n return self.conj(\n tuple( self.fold_term(disj)\n for disj in conj.disjs ))","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 add_conec_listoflists(self, conec):\n self.num = len(conec) # numero de elementos en conec\n self.ne = [] # numero de elem1 por cada elem0]\n self.je = [] # aca pongo la conectividad en un array chorizo\n self.len_je = 0\n for elem0 in conec:\n num_elem1 = 0 # numero de elem1 en elem0\n for item1 in elem0:\n num_elem1 += 1 # sumo un elem1 conectado a elem0\n self.je.append(item1) # lo agrego a la conectividad\n self.len_je += 1\n self.ne.append(num_elem1) # agrego el numero de elem1 de elem0 a la lista","def calc_conmat(self, jd=None, dt=20, write=False):\n if not jd: jd = self.jd.min()\n if not hasattr(self,'reg'): self.regvec_from_discs()\n tmask1 = self.jd == jd\n tmask2 = self.jd == jd + dt\n try:\n ntracmax = max(self.ntrac[tmask1].max(), self.ntrac[tmask2].max())\n except ValueError:\n return False\n\n convec = np.zeros((2, ntracmax+1))\n convec[0,self.ntrac[tmask1]] = self.reg[tmask1]\n convec[1,self.ntrac[tmask2]] = self.reg[tmask2]\n convec = convec.astype(np.int)\n\n flat_coord = np.ravel_multi_index(convec, (self.nreg, self.nreg))\n sums = np.bincount(flat_coord, minlength=self.nreg*self.nreg)\n self.conmat = np.zeros((self.nreg,self.nreg))\n self.conmat.flat[:len(sums)] = sums\n\n def write_hdf(jd, dt):\n self.h5open()\n jdpos = np.abs(self.h5f.root.jdvec[:] - jd).argmin()\n self.h5f.root.conmat[jdpos,dt,:,:] = self.conmat.astype(np.uint32)\n self.h5f.root.exist[jdpos,dt] = True\n self.h5f.flush()\n self.h5close()\n\n def write_npz(jd, dt):\n conmatfile = (\"conmat_%s_%s_%06i_%04i.npz\" %\n (self.projname, self.casename, jd, dt))\n np.savez(os.path.join(self.conmatdir, conmatfile),\n conmat=self.conmat.astype(np.uint32), jd=jd, dt=dt)\n\n if write is True: write_hdf(jd, dt)","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 getCodonSeqs(self):\r\n combinations = list(self.codonTable[aa] for aa in self.peptide) # creates a list of possible codons based on AA\r\n self.allPepSeqs = list(''.join(codon) for codon in itertools.product(*combinations)) # creates list of peptides\r\n return","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 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 generate_connectivity_constraint_all(problem):\n\n if problem.num_vars == None:\n problem.compute_num_var()\n\n ret = Constraint()\n\n # Iterator over all (v, t) subsets in the graph\n for b, b_r in enumerate(problem.src):\n # Convert each set in the iterator to (v,t) format\n add_S = map(\n lambda S: list(map(problem.get_time_augmented_n_t, S)),\n problem.powerset_exclude_agent(b_r),\n )\n ret &= generate_connectivity_constraint(problem, [b], add_S)\n\n return ret","def create_cont_constraint_mat_separable(H,v1s,v2s,nSides,nConstraints,nC,\n dim_domain,dim_range,tess):\n if dim_domain != 2:\n raise ValueError\n if dim_range not in [1,2]:\n raise ValueError\n nHomoCoo=dim_domain+1 \n length_Avee = dim_range*nHomoCoo\n L1 = np.zeros((nConstraints/2,nC*nHomoCoo))\n\n \n\n nPtsInSide = 2 # Since, in 2D, the side is always a line joining 2 pts.\n# if nSides != nConstraints/(nPtsInSide*dim_domain):\n# raise ValueError(nSides,nConstraints)\n \n if nSides != nConstraints/(nPtsInSide*dim_range):\n print \" print nSides , nConstraints/(nPtsInSide*dim_range):\"\n print nSides , nConstraints/(nPtsInSide*dim_range)\n ipshell('stop')\n raise ValueError( nSides , (nConstraints,nPtsInSide,dim_range))\n\n \n if nSides != H.shape[0]:\n raise ValueError(nSides,H.shape)\n\n\n# M = nPtsInSide*dim_range\n M = nPtsInSide\n if dim_range == 1:\n raise NotImplementedError\n for i in range(nSides): \n v1 = v1s[i]\n v2 = v2s[i]\n \n h = H[i]\n a,b = h.nonzero()[0] # idx for the relevant As \n # s stands for start\n # e stands for end \n s1 = a*length_Avee \n e1 = s1+nHomoCoo \n s2 = b*length_Avee\n e2 = s2+nHomoCoo \n \n # Constraint 1: \n L[i*M,s1:e1]= v1 \n L[i*M,s2:e2]= -v1 \n # Constraint 2: \n L[i*M+1,s1:e1]= v2 \n L[i*M+1,s2:e2]= -v2 \n \n \n elif dim_range==2:\n for i in range(nSides): \n v1 = v1s[i]\n v2 = v2s[i]\n\n if np.allclose(v1,v2):\n raise ValueError(v1,v2)\n\n\n \n \n \n h = H[i]\n a,b = h.nonzero()[0] # idx for the relevant As \n \n\n # L1 is acting on columns of the following form:\n # [ a_1 b_1 c_1 d_1 a_2 b_2 c_2 d_2 ... a_Nc b_Nc c_Nc d_Nc] \n # s stands for start\n # e stands for end \n s1 = a*nHomoCoo\n e1 = s1+nHomoCoo \n s2 = b*nHomoCoo\n e2 = s2+nHomoCoo \n \n \n try: \n # Constraint 1: \n row = np.zeros(L1.shape[1])\n row[s1:e1]=v1\n row[s2:e2]=-v1 \n # x component \n L1[i*M]=row \n except:\n ipshell('fail')\n raise \n\n # Constraint 2: \n row = np.zeros(L1.shape[1])\n row[s1:e1]=v2\n row[s2:e2]=-v2 \n # x component \n L1[i*M+1]=row\n \n\n \n \n \n \n \n else:\n raise ValueError(dim_range)\n\n \n return L1","def convex_conj(self):\n convex_conjs = [func.convex_conj for func in self.functionals]\n return SeparableSum(*convex_conjs)","def addConstraint(self, conName, compIDs=None, lower=-1e20, upper=1e20, dvIndex=0):\n if compIDs is not None:\n # Make sure CompIDs is flat and get element numbers on each proc corresponding to specified compIDs\n compIDs = self._flatten(compIDs)\n else:\n nComps = self.meshLoader.getNumComponents()\n compIDs = list(range(nComps))\n\n constrObj = self._createConstraint(dvIndex, compIDs, lower, upper)\n if constrObj.nCon > 0:\n self.constraintList[conName] = constrObj\n success = True\n else:\n self._TACSWarning(\n f\"No adjacent components found in `compIDs`. Skipping {conName}.\"\n )\n success = False\n\n return success","def calcConn(self,conMat):\n for i in range(len(conMat)):\n for j in range(i,len(conMat)):\n if conMat[i,j]==1:\n self.drawLine(i,j)","def add_c_mask(self, pre_offs, post_ons, post_offs=None, passive = False):\n\n pre_on = int(100/self.dt) # never check the first 100ms\n pre_offs = self.expand(pre_offs)\n post_ons = self.expand(post_ons)\n post_offs = self.expand(post_offs)# when to stop checking. default is the end of the trial\n\n if self.config['loss_type'] == 'lsq':\n c_mask = np.zeros((self.tdim, self.batch_size, self.n_output), dtype=self.float_type)\n for i in range(self.batch_size):\n # Post response periods usually have the same length across tasks\n if passive:\n c_mask[post_ons[i]:post_offs[i], i, :] = 0.\n else:\n c_mask[post_ons[i]:post_offs[i], i, :] = 5.\n # Pre-response periods usually have different lengths across tasks\n # To keep cost comparable across tasks\n # Scale the cost mask of the pre-response period by a factor\n c_mask[pre_on:pre_offs[i], i, :] = 1.\n\n # self.c_mask[:, :, 0] *= self.n_eachring # Fixation is important\n c_mask[:, :, 0] *= 2. # Fixation is important\n\n self.c_mask = c_mask.reshape((self.tdim*self.batch_size, self.n_output))\n else:\n c_mask = np.zeros((self.tdim, self.batch_size), dtype=self.float_type)\n for i in range(self.batch_size):\n # Post response periods usually have the same length across tasks\n # Having it larger than 1 encourages the network to achieve higher performance\n if passive:\n c_mask[post_ons[i]:, i] = 0.\n else:\n c_mask[post_ons[i]:, i] = 5.\n # Pre-response periods usually have different lengths across tasks\n # To keep cost comparable across tasks\n # Scale the cost mask of the pre-response period by a factor\n c_mask[pre_on:pre_offs[i], i] = 1.\n\n self.c_mask = c_mask.reshape((self.tdim*self.batch_size,))\n self.c_mask /= self.c_mask.mean()","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 connection(self, sampleseq, num):\n self.Adjmatrix = np.zeros((self.nodenum, self.nodenum), dtype = int)\n \n for i in range(self.supplynum):\n minindex = np.array(sf.minimumk(self.Dismatrix[self.supplyseries[i], self.trandemandseries], sampleseq[self.supplyseries[i]]))\n self.Adjmatrix[self.supplyseries[i], self.trandemandseries[minindex]] = 1\n# self.Adjmatrix[minindex, self.supplyseries[i]] = 1\n \n for i in range(self.trannum):\n if(np.sum(self.Adjmatrix[self.supplyseries, self.transeries[i]]) == 0):\n minindex = np.array(sf.minimumk(self.Dismatrix[self.supplyseries, self.transeries[i]], num))\n self.Adjmatrix[minindex, self.transeries[i]] = 1\n# self.Adjmatrix[self.transeries[i], minindex] = 1\n \n \n# for i in range(self.supplynum):\n# minindex = np.array(sf.minimumk(self.Dismatrix[self.supplyseries[i], self.supplyseries], num))\n# self.Adjmatrix[self.supplyseries[i], minindex] = 1\n# self.Adjmatrix[minindex, self.supplyseries[i]] = 1\n \n# for i in range(self.trannum):\n# if(np.sum(self.Adjmatrix[self.supplyseries, self.transeries[i]]) != 0):\n# continue\n# minindex = np.array(sf.minimumk(self.Dismatrix[self.supplyseries, self.transeries[i]], num))\n# self.Adjmatrix[minindex, self.transeries[i]] = 1\n## self.Adjmatrix[self.transeries[i], minindex] = 1\n# \n for i in range(self.trannum):\n minindex = np.array(sf.minimumk(self.Dismatrix[self.transeries[i], self.demandseries], min(sampleseq[self.transeries[i]], self.demandnum))) + self.supplynum + self.trannum\n self.Adjmatrix[self.transeries[i], minindex] = 1\n# self.Adjmatrix[minindex, self.transeries[i]] = 1\n \n# for i in range(self.demandnum):\n# if(np.sum(self.Adjmatrix[self.transeries, self.demandseries[i]]) == 0):\n# minindex = np.array(sf.minimumk(self.Dismatrix[self.transeries, self.demandseries[i]], 1)) + self.supplynum\n# self.Adjmatrix[minindex, self.demandseries[i]] = 1\n \n# for i in range(self.trannum):\n# minindex = np.array(sf.minimumk(self.Dismatrix[self.transeries[i], self.transeries], num)) + self.supplynum\n# self.Adjmatrix[self.transeries[i], minindex] = 1\n \n for i in range(self.demandnum):\n if(np.sum(self.Adjmatrix[self.transeries, self.demandseries[i]]) == 0):\n minindex = np.array(sf.minimumk(self.Dismatrix[self.transeries, self.demandseries[i]], num)) + self.supplynum\n self.Adjmatrix[minindex, self.demandseries[i]] = 1\n# self.Adjmatrix[self.demandseries[i], minindex] = 1\n \n for i in range(self.demandnum):\n minindex = np.array(sf.minimumk(self.Dismatrix[self.demandseries[i], self.demandseries], min(sampleseq[self.demandseries[i]] + 1, self.demandnum))) + self.supplynum + self.trannum\n minindex = minindex[1:-1]\n for j in range(len(minindex)):\n if(self.Adjmatrix[self.demandseries[i], minindex[j]] == 1 or self.Adjmatrix[minindex[j], self.demandseries[i]] == 1):\n continue\n self.Adjmatrix[self.demandseries[i], minindex[j]] = 1","def process_consequences(\n mt: Union[hl.MatrixTable, hl.Table],\n vep_root: str = \"vep\",\n penalize_flags: bool = True,\n) -> Union[hl.MatrixTable, hl.Table]:\n csqs = hl.literal(CSQ_ORDER)\n csq_dict = hl.literal(dict(zip(CSQ_ORDER, range(len(CSQ_ORDER)))))\n\n def find_worst_transcript_consequence(\n tcl: hl.expr.ArrayExpression,\n ) -> hl.expr.StructExpression:\n \"\"\"Get worst transcript_consequence from an array of em.\"\"\"\n flag_score = 500\n no_flag_score = flag_score * (1 + penalize_flags)\n\n def csq_score(tc):\n return csq_dict[csqs.find(lambda x: x == tc.most_severe_consequence)]\n\n tcl = tcl.map(\n lambda tc: tc.annotate(\n csq_score=hl.case(missing_false=True)\n .when(\n (tc.lof == \"HC\") & (tc.lof_flags == \"\"),\n csq_score(tc) - no_flag_score,\n )\n .when(\n (tc.lof == \"HC\") & (tc.lof_flags != \"\"), csq_score(tc) - flag_score\n )\n .when(tc.lof == \"OS\", csq_score(tc) - 20)\n .when(tc.lof == \"LC\", csq_score(tc) - 10)\n .when(\n tc.polyphen_prediction == \"probably_damaging\", csq_score(tc) - 0.5\n )\n .when(\n tc.polyphen_prediction == \"possibly_damaging\", csq_score(tc) - 0.25\n )\n .when(tc.polyphen_prediction == \"benign\", csq_score(tc) - 0.1)\n .default(csq_score(tc))\n )\n )\n return hl.or_missing(hl.len(tcl) > 0, hl.sorted(tcl, lambda x: x.csq_score)[0])\n\n transcript_csqs = mt[vep_root].transcript_consequences.map(\n add_most_severe_consequence_to_consequence\n )\n\n gene_dict = transcript_csqs.group_by(lambda tc: tc.gene_symbol)\n worst_csq_gene = gene_dict.map_values(find_worst_transcript_consequence).values()\n sorted_scores = hl.sorted(worst_csq_gene, key=lambda tc: tc.csq_score)\n\n canonical = transcript_csqs.filter(lambda csq: csq.canonical == 1)\n gene_canonical_dict = canonical.group_by(lambda tc: tc.gene_symbol)\n worst_csq_gene_canonical = gene_canonical_dict.map_values(\n find_worst_transcript_consequence\n ).values()\n sorted_canonical_scores = hl.sorted(\n worst_csq_gene_canonical, key=lambda tc: tc.csq_score\n )\n\n vep_data = mt[vep_root].annotate(\n transcript_consequences=transcript_csqs,\n worst_consequence_term=csqs.find(\n lambda c: transcript_csqs.map(\n lambda csq: csq.most_severe_consequence\n ).contains(c)\n ),\n worst_csq_by_gene=sorted_scores,\n worst_csq_for_variant=hl.or_missing(\n hl.len(sorted_scores) > 0, sorted_scores[0]\n ),\n worst_csq_by_gene_canonical=sorted_canonical_scores,\n worst_csq_for_variant_canonical=hl.or_missing(\n hl.len(sorted_canonical_scores) > 0, sorted_canonical_scores[0]\n ),\n )\n\n return (\n mt.annotate_rows(**{vep_root: vep_data})\n if isinstance(mt, hl.MatrixTable)\n else mt.annotate(**{vep_root: vep_data})\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","def create_conserved_motif_list_otherclass(gpcr_pdb,gpcr_aa,j,my_pos,motifs,multiple_chains,chain_name):\n my_pos_bw=my_pos.split(\"x\")[0]\n (my_aa,chain)=gpcr_aa[my_pos]\n add_chain_name=\"\"\n if multiple_chains:\n add_chain_name=\":\"+chain_name \n while j < len(motifs):\n cons_pos_bw = motifs[j][4]\n if my_pos_bw==cons_pos_bw:\n pos_range=find_range_from_cons_pos(my_pos, gpcr_pdb)\n if pos_range:\n motifs[j][2]=True\n motifs[j][3]=pos_range + add_chain_name\n j+=1","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 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 m_cliques(adj_list,labels,matrix,topology=\"clique\",cut=0):\n num_clusters = list(np.unique(labels))\n clusters = {i:[] for i in num_clusters}\n print(\"m_cliques CUT:\", cut)\n #Add nodes to clusters\n for idx,n in enumerate(adj_list):\n clusters[labels[idx]].append(n)\n \n if topology == \"sample_rand\":\n G_cliques = sampled_clique(clusters,\"rand\")\n print(\"Rand sampled clique size:\", G_cliques.number_of_nodes())\n elif topology == \"sample_optim\":\n G_cliques = sampled_clique(clusters,\"optim\")\n print(\"Optim sampled clique size:\", G_cliques.number_of_nodes())\n elif topology == \"pcc_rand\":\n G_cliques = pcc_clique(clusters,\"rand\",labels,cut)\n print(\"Rand sampled clique size:\", G_cliques.number_of_nodes())\n elif topology == \"pcc_optim\":\n G_cliques = pcc_clique(clusters,\"optim\",labels,cut)\n print(\"Optim sampled clique size:\", G_cliques.number_of_nodes())\n elif topology == \"pcc_greedy\":\n G_cliques = pcc_clique(clusters,\"greedy\",labels,cut,matrix)\n print(\"Greedy sampled clique size:\", G_cliques.number_of_nodes())\n else:\n G_cliques = nx.Graph()\n cliques = []\n while not is_empty(clusters):\n clique = []\n \n #Add clique nodes\n for _,cluster in clusters.items():\n if len(cluster) > 0:\n n = random.choice(cluster)\n clique.append(n)\n cluster.remove(n)\n \n #Cliqify\n for n1 in clique:\n for n2 in clique:\n if n1 != n2:\n G_cliques.add_edge(n1,n2)\n \n #Aggregate clique\n cliques.append(clique)\n\n if topology == \"clique\":\n clique_the_cliques(cliques,labels,G_cliques)\n elif topology == \"ring\":\n cliques_on_ring(cliques,labels,G_cliques)\n \n return G_cliques","def add_rules(self, cliques):\n self.cliques.extend(cliques)","def concatenate(cls, char_matrices):\n taxon_set = char_matrices[0].taxon_set\n nseqs = len(char_matrices[0])\n concatenated_chars = cls(taxon_set=taxon_set)\n pos_start = 0\n for cidx, cm in enumerate(char_matrices):\n if cm.taxon_set is not taxon_set:\n raise ValueError(\"Different `taxon_set` references in matrices to be merged\")\n if len(cm) != len(taxon_set):\n raise ValueError(\"Number of sequences not equal to the number of taxa\")\n if len(cm) != nseqs:\n raise ValueError(\"Different number of sequences across alignments: %d (expecting %d based on first matrix)\" % (len(cm), nseqs))\n v1 = len(cm[0])\n for t, s in cm.items():\n if len(s) != v1:\n raise ValueError(\"Unequal length sequences in character matrix %d\".format(cidx+1))\n concatenated_chars.extend(cm,\n extend_existing=True,\n overwrite_existing=False)\n if cm.label is None:\n new_label = \"locus%03d\" % cidx\n else:\n new_label = cm.label\n cs_label = new_label\n i = 2\n while cs_label in concatenated_chars.character_subsets:\n label = \"%s_%03d\" % (new_label, i)\n i += 1\n character_indices = range(pos_start, pos_start + cm.vector_size)\n pos_start += cm.vector_size\n concatenated_chars.new_character_subset(character_indices=character_indices,\n label=cs_label)\n return concatenated_chars","def allcJPDs(net,cond_node,cond_states):\r\n \r\n onodes = net.Outputnodes()\r\n onodes = sorted(onodes)\r\n combinations = [i for i in itertools.combinations(onodes,2)]\r\n JPDdatas = [] \r\n progress = 0\r\n for cs in cond_states:\r\n JPDdata = []\r\n for c in combinations:\r\n print 'Progress: ' +str(int(100*float(progress)/float(len(combinations)*len(cond_states))))+ ' %'\r\n priorA = getCondPrior(net,c[0],cond_node,cs)\r\n print priorA\r\n priorB = getCondPrior(net,c[1],cond_node,cs) \r\n print priorB\r\n JPD = cJPD(net,c,cond_node,cs)\r\n cov = Covariance (priorA,priorB,JPD)\r\n cor = Correlation (priorA,priorB,cov) \r\n JPDdata.append([c[0],c[1],JPD,cov,cor])\r\n progress+=1\r\n JPDdatas.append(JPDdata)\r\n return JPDdatas","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 find_joint_extension(self, modelb, modelc, mace_time=10, prover_time=60):\n n = modelb.cardinality\n ne = ['b'+str(x)+'!=b'+str(y) for x in range(n) for y in range(x+1,n)]\n n = modelc.cardinality\n ne += ['c'+str(x)+'!=c'+str(y) for x in range(n) for y in range(x+1,n)]\n return prover9(self.axioms+ne+modelb.positive_diagram('b') + \n modelc.positive_diagram('c'), [], mace_time, prover_time)","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 create_conserved_motif_list(dyn_id,gpcr_pdb,gpcr_aa,j,my_pos,motifs,multiple_chains,chain_name):\n my_pos_bw=my_pos.split(\"x\")[0]\n (my_aa,chain)=gpcr_aa[my_pos]\n add_chain_name=\"\"\n if multiple_chains:\n add_chain_name=\":\"+chain_name \n while j < len(motifs):\n cons_pos = motifs[j][1]\n cons_pos_bw=cons_pos[1:]\n cons_aa=cons_pos[0]\n if my_pos_bw==cons_pos_bw:\n pos_range=find_range_from_cons_pos(my_pos, gpcr_pdb)\n if pos_range:\n motifs[j][2]=True\n motifs[j][3]=pos_range + add_chain_name\n if cons_aa != \"x\" and my_aa != cons_aa:\n motifs[j][1]+=my_aa\n j+=1","def conj(self):\n res = self._defer_unary_elementwise(np.conj)\n res.dirs = list(map(opr.neg, res.dirs))\n res.charge = -res.charge\n if self.qodulus is not None:\n res.charge %= res.qodulus\n return res","def add_constraints(self, constraints):\n for const in constraints:\n self.add_constraint(const.type, const.column, const.check_clause)","def write_puzzle_constraints(name, constraints):\n with open(name, 'w') as f:\n for c in constraints:\n f.write('(')\n f.write(str(c[0]))\n f.write(',')\n f.write(str(c[1]))\n f.write(')\\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]","def addCanvases(self):\n self.canvasGrid = Grid(self.matrix.getHeight(),\n self.matrix.getWidth())\n for row in range(self.matrix.getHeight()):\n for column in range(self.matrix.getWidth()):\n canvas = MatrixCanvas(self, 20, 20,\n self.matrix[row][column])\n self.canvasGrid[row][column] = canvas\n self.matrixPanel.addCanvas(canvas = canvas,\n row = row,\n column = column)","def all_c(self):\n for i in range( self.N, 0, -1):\n self.C[i] = self.c(i)\n self.ok = True","def constraint(self, c):\n self.add_constraint(c)","def clauses_to_conjunct(clause_list):\n conj = Expr('&')\n conj.args = clause_list\n return conj\n #return ' & '.join(map(lambda(i): '{0}'.format(KB.clauses[i]), list))","def drawConns(self):\r\n for node in self.G.nodes():\r\n pX,pY = self.G.node[node][\"site\"].getCenter()\r\n for nb in self.G.neighbors(node):\r\n nbX,nbY = self.G.node[nb][\"site\"].getCenter()\r\n self.connLines.append(self.canvasCirkt.create_line(pX,pY,nbX,nbY))\r\n self.canvasCirkt.update()","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 cpos2codon(self, cpos):\n self.ensure_seq()\n cpos = int(cpos)\n if self.strand == \"+\":\n np = []\n for beg, end in self.exons:\n np += list(range(max(beg, self.cds_beg),\n min(self.cds_end, end)+1))\n assert len(np) == len(self.seq)\n\n ni = cpos*3\n if ni <= len(np):\n codon = Codon()\n codon.index = cpos\n codon.locs = tuple(np[ni-3:ni])\n codon.gene = self.gene\n codon.chrm = self.chrm\n codon.strand = self.strand\n codon.seq = self.seq[ni-3:ni]\n return codon\n else:\n raise IncompatibleTranscriptError('invalid_cDNA_position_%d;expect_[0_%d]' % (ni, len(np)))\n else:\n np = []\n for beg, end in reversed(self.exons):\n np += list(range(min(self.cds_end, end),\n max(beg, self.cds_beg)-1,-1))\n assert len(np) == len(self.seq)\n\n ni = cpos*3\n if ni <= len(np):\n codon = Codon()\n codon.index = cpos\n codon.locs = tuple(reversed(np[ni-3:ni]))\n codon.gene = self.gene\n codon.chrm = self.chrm\n codon.strand = self.strand\n codon.seq = self.seq[ni-3:ni]\n return codon\n else:\n raise IncompatibleTranscriptError('invalid_cDNA_position_%d;expect_[0_%d]' % (ni, len(np)))","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 conjugate(quats):\n res = np.zeros(quats.shape)\n res[:,0]=quats[:,0]\n res[:,1]=-quats[:,1]\n res[:,2]=-quats[:,2]\n res[:,3]=-quats[:,3]\n \n return res","def create_conserved_pos_list_otherclass(gpcr_pdb,gpcr_aa, i,my_pos, cons_pos_li, multiple_chains,chain_name,gnum_classes_rel,dict_class,current_class):\n my_pos_bw=my_pos.split(\"x\")[0]\n add_chain_name=\"\"\n if multiple_chains:\n add_chain_name=\":\"+chain_name\n while i < len(cons_pos_li):\n cons_pos_bw_ourclass = cons_pos_li[i][1]\n if my_pos_bw==cons_pos_bw_ourclass:\n pos_range=find_range_from_cons_pos(my_pos, gpcr_pdb)\n if pos_range:\n cons_pos_li[i][2]=pos_range + add_chain_name\n cons_pos_li[i][1]=\"Correspods to \"+cons_pos_bw_ourclass + current_class.lower()\n i+=1","def attach_CA(self):\n n = self.pC\n self.A[n] = self._mps_CA(self.C, self.A[n])","def conj(z):","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 molpbcs(strucC, cov_nblist, cov_nbindx ,verbose = False, debug = False ):\n \n debug2 = False\n latticevec = strucC.getLatVec()\n\n n_dim = 3\n\n if( debug ):\n print \"latticevec\",latticevec\n F = open(\"shift.rec\",\"w\")\n \n for mol_i in range( 1,strucC.ptclC.n_molecules()+1) :\n\n if( verbose ):\n print \" Checking molecule %d of %d \"%(mol_i,strucC.ptclC.n_molecules())\n\n searchD = {'chain':mol_i}\n mol_list = strucC.ptclC.getParticlesWithTags(searchD)\n\n if( len( mol_list) > 0 ):\n ptclObj = strucC.ptclC[1]\n r_o = ptclObj.position\n pid_o = 1\n\n part_shifted = [False]*len(strucC.ptclC)\n\n r_mol_mass = np.zeros(n_dim)\n shift = np.zeros(n_dim)\n total_mass = 0.0 \n\n # shift all atoms to be conected \n \n for pid_i in sorted(mol_list):\n ptclObj_i = strucC.ptclC[pid_i]\n a_mass_i = ptclObj_i.mass\n r_i = ptclObj_i.position\n\n r_io = np.array(r_o) - np.array(r_i)\n\n # sum center of mass\n total_mass += a_mass_i\n\n\n \n shifted = False \n for dim in range(n_dim):\n shift_dim = round( r_io[dim]/ latticevec[dim][dim] )\n r_i[dim] = r_i[dim] + latticevec[dim][dim] * shift_dim\n if( shift_dim != 0 ):\n shifted = True \n \n r_mol_mass[dim] = r_mol_mass[dim] + a_mass_i*r_i[dim] \n \n\n if( debug and shifted ):\n shift_line = \"\\n %d - %d r = %f %f %f dr = %f %f %f \"%(pid_i,pid_o,ptclObj_i.position[0],ptclObj_i.position[1],ptclObj_i.position[2],r_io[0],r_io[1],r_io[2])\n F.write(shift_line)\n \n ptclObj_i.position = r_i\n r_o = r_i\n pid_o = pid_i\n\n # Shift molecular center of mass into box \n for dim in range(n_dim):\n cent_mass_i = r_mol_mass[dim] /total_mass\n shift[dim] = latticevec[dim][dim] * round( cent_mass_i / latticevec[dim][dim] )\n\n \n for pid_i in sorted(mol_list):\n ptclObj_i = strucC.ptclC[pid_i]\n for dim in range(n_dim):\n ptclObj_i.position[dim] = ptclObj_i.position[dim] - shift[dim] \n \n \n\n if( debug ):\n F.close()","def extend(network, coords=[], conns=[], labels=[], **kwargs):\n if 'throat_conns' in kwargs.keys():\n conns = kwargs['throat_conns']\n if 'pore_coords' in kwargs.keys():\n coords = kwargs['pore_coords']\n coords = np.array(coords)\n conns = np.array(conns)\n Np_old = network.num_pores()\n Nt_old = network.num_throats()\n Np = Np_old + coords.shape[0]\n Nt = Nt_old + conns.shape[0]\n if np.any(conns > Np):\n raise Exception('Some throat conns point to non-existent pores')\n network.update({'pore.all': np.ones([Np, ], dtype=bool),\n 'throat.all': np.ones([Nt, ], dtype=bool)})\n # Add coords and conns\n if np.size(coords) > 0:\n coords = np.vstack((network['pore.coords'], coords))\n network['pore.coords'] = coords\n if np.size(conns) > 0:\n conns = np.vstack((network['throat.conns'], conns))\n network['throat.conns'] = conns\n\n # Increase size of any prop or label arrays already on network and phases\n objs = list(network.project.phases().values())\n objs.append(network)\n for obj in objs:\n obj.update({'pore.all': np.ones([Np, ], dtype=bool),\n 'throat.all': np.ones([Nt, ], dtype=bool)})\n for item in list(obj.keys()):\n N = obj._count(element=item.split('.')[0])\n if obj[item].shape[0] < N:\n arr = obj.pop(item)\n s = arr.shape\n if arr.dtype == bool:\n obj[item] = np.zeros(shape=(N, *s[1:]), dtype=bool)\n else:\n obj[item] = np.ones(shape=(N, *s[1:]), dtype=float)*np.nan\n obj[item][:arr.shape[0]] = arr\n\n # Regenerate models on all objects to fill new elements\n for obj in network.project.phases().values():\n if hasattr(obj, 'models'):\n obj.regenerate_models()\n\n # Apply labels, if supplied\n if labels != []:\n # Convert labels to list if necessary\n if isinstance(labels, str):\n labels = [labels]\n for label in labels:\n # Remove pore or throat from label, if present\n label = label.split('.')[-1]\n if np.size(coords) > 0:\n Ps = np.r_[Np_old:Np]\n if 'pore.'+label not in network.labels():\n network['pore.'+label] = False\n network['pore.'+label][Ps] = True\n if np.size(conns) > 0:\n Ts = np.r_[Nt_old:Nt]\n if 'throat.'+label not in network.labels():\n network['throat.'+label] = False\n network['throat.'+label][Ts] = True\n\n # Clear adjacency and incidence matrices which will be out of date now\n network._am.clear()\n network._im.clear()","def placeConcentrationInto(self, region, conc):\n self._sim.distributeConcentration(self, region, conc)\n return self","def constraint_matrix(self):\n\n con_filename = self.constraint_filename\n con_press, con_data = self._co_star_read(con_filename)\n return con_data","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 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 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 create_conserved_pos_list(gpcr_pdb,gpcr_aa, i,my_pos, cons_pos_li, multiple_chains,chain_name):\n my_pos_bw=my_pos.split(\"x\")[0]\n add_chain_name=\"\"\n if multiple_chains:\n add_chain_name=\":\"+chain_name\n while i < len(cons_pos_li):\n cons_pos = cons_pos_li[i][0]\n cons_pos_bw=cons_pos[1:]\n cons_aa=cons_pos[0]\n if my_pos_bw==cons_pos_bw:\n pos_range=find_range_from_cons_pos(my_pos, gpcr_pdb)\n if pos_range:\n cons_pos_li[i][2]=pos_range + add_chain_name\n (my_aa,chain)=gpcr_aa[my_pos]\n if my_aa != cons_aa:\n cons_pos_li[i][0]=cons_pos+my_aa\n i+=1","def example2_concatenated_RE(GeomCA_parameters):\n num_pts = 100\n GeomCA_parameters['experiment_filename_prefix'] = 'problematic_overlap_'\n subfolder = 'problematic_overlap'\n GeomCA_parameters['comp_consistency_threshold'] = 0.0\n GeomCA_parameters['comp_quality_threshold'] = 0.3\n R = np.concatenate([circle(n=num_pts, r=1.5), circle(n=int(num_pts/5), r=0.3)])\n E = np.concatenate([circle(n=num_pts, r=1.2), circle(n=int(num_pts/5), r=0.32)])\n return run_GeomCA_and_visualize(R, E, subfolder, GeomCA_parameters)","def express(self, *args, **kwargs) -> List[Union[LpConstraint, Constr]]:\n\n pass","def concur_wait(self, node):\n global BARRIER_CTR\n\n # This method will be destructive, unless we make a new\n # copy of the AST tree first\n #\n node = deepcopy(node)\n\n seen_channels = set()\n\n # Channels in this with_concur\n concur_channels = find_all_channels(node)\n\n # For creating the Barriers, we want QGL1 scoped variables that will be real channel instances.\n # We basically have that already.\n real_chans = set()\n for chan in concur_channels:\n real_chans.add(chan)\n\n start_barrier = BARRIER_CTR\n end_barrier = start_barrier + 1\n BARRIER_CTR += 2\n\n for stmnt in node.body:\n if not is_seq(stmnt):\n NodeError.error_msg(stmnt,\n 'non-seq block inside concur block?')\n return node\n\n seq_channels = find_all_channels(stmnt)\n\n if seq_channels.intersection(seen_channels):\n NodeError.error_msg(stmnt,\n 'seq blocks have overlapping channels')\n return node\n\n seen_channels = seen_channels.union(seq_channels)\n\n chan_name = ','.join(seq_channels)\n\n # mark stmnt with chan_name or seq_channels in another way\n if hasattr(stmnt, 'qgl_chan_list'):\n oldChanSet = set(stmnt.qgl_chan_list)\n newChanSet = seq_channels\n oldMissing = newChanSet - oldChanSet\n oldExtra = oldChanSet - newChanSet\n if len(oldMissing) > 0:\n NodeError.diag_msg(stmnt, 'marked chan list %s was missing %s' % (str(oldChanSet), str(oldMissing)))\n if len(oldExtra) > 0:\n NodeError.diag_msg(stmnt, 'marked chan list %s had extra %s' % (str(oldChanSet), str(oldExtra)))\n NodeError.diag_msg(stmnt, 'Marking chan list %s' % (str(seq_channels)))\n stmnt.qgl_chan_list = list(seq_channels)\n\n new_seq_body = list()\n\n # Helper to ensure the string we feed to AST doesn't put quotes around\n # our Qubit variable names\n def appendChans(bString, chans):\n bString += '['\n first = True\n for chan in chans:\n if first:\n bString += str(chan)\n first = False\n else:\n bString += \",\" + str(chan)\n bString += ']'\n return bString\n\n # Add global ctr, chanlist=concur_channels\n # FIXME: Hold concur_channels as a string? List?\n bstring = 'Barrier(\"%s\", ' % str(start_barrier)\n bstring = appendChans(bstring, list(real_chans))\n bstring += ')\\n'\n barrier_ast = expr2ast(bstring)\n # barrier_ast = expr2ast('Barrier(%s, %s)\\n' % (str(start_barrier), list(real_chans)))\n copy_all_loc(barrier_ast, node)\n barrier_ast.channels = concur_channels\n # print(\"*****Start barrier: %s\" % pyqgl2.ast_util.ast2str(barrier_ast))\n\n new_seq_body.append(barrier_ast)\n\n new_seq_body += stmnt.body\n\n bstring = 'Barrier(\"%s\", ' % str(end_barrier)\n bstring = appendChans(bstring, list(real_chans))\n bstring += ')\\n'\n end_barrier_ast = expr2ast(bstring)\n #end_barrier_ast = expr2ast('Barrier(%s, %s)\\n' % (str(end_barrier), list(real_chans)))\n copy_all_loc(end_barrier_ast, node)\n # Add global ctr, chanlist=concur_channels\n end_barrier_ast.channels = concur_channels\n\n # print('End AST: %s' % ast2str(end_barrier_ast))\n\n new_seq_body.append(end_barrier_ast)\n\n stmnt.body = new_seq_body\n\n # FIXME: In new thinking, is the proper unseen set the global one,\n # Or only those local to this with concur. I think only local\n for unseen_chan in concur_channels - seen_channels:\n #print('DIAG %s' % ast2str(stmnt))\n NodeError.diag_msg(stmnt,\n 'channels unreferenced in concur: %s' % str(unseen_chan))\n\n bstring = 'with seq:\\n Barrier(\"%s\", ' % str(start_barrier)\n bstring = appendChans(bstring, list(real_chans))\n bstring += ')\\n Barrier(\"%s\",' % str(end_barrier)\n bstring = appendChans(bstring, list(real_chans))\n bstring += ')\\n'\n empty_seq_ast = expr2ast(bstring)\n # print('Empty AST: %s' % ast2str(empty_seq_ast))\n # empty_seq_ast = expr2ast(\n # 'with seq:\\n Barrier(%s, %s)\\n Barrier(%s, %s)' % (str(start_barrier), list(real_chans), str(end_barrier), list(real_chans)))\n\n # Mark empty_seq_ast with unseen_chan\n empty_seq_ast.qgl_chan_list = [unseen_chan]\n copy_all_loc(empty_seq_ast, node)\n node.body.append(empty_seq_ast)\n\n return node","def add(self, inp, pos):\n self.pos = pos\n self.para = list()\n # Call backend for dependency parsing.\n cabo = CabochaClient()\n cabo.add(self.proc.query(inp), self.pos)\n pool = [cabo.root]\n plist = [cabo.root]\n self.vlist = dict()\n # Use BFS to get a list of nodes.\n while pool:\n pid = pool.pop(0)\n for cid in cabo.childrenList[pid]:\n pool.append(cid)\n plist.insert(0, cid)\n # Add nodes using plist(from leaves to roots).\n for i in range(len(plist)):\n pid = plist[i]\n self._addChildren(pid, cabo.chunks)\n self._processPara()\n\n # Return here if self.autosub is False.\n if not self.autosub:\n return\n # If root has no subject, add omitted subject node.\n if self.G.nodes[cabo.chunks[cabo.root].main]['sub'] == '':\n omitted = CaboChunk(-1, cabo.root)\n omitted.main = \"省略される主体[{0}@{1}]\".format(self.pos, 0)\n omitted.func = \"(省略)\"\n omitted.type = 0\n omitted.pro = 7\n omitted.surface = \"省略される主体\"\n omitted.yomi = \"ショウリャクサレルシュゴ\"\n self._addNode(omitted)\n self._addEdge(omitted.main, cabo.chunks[cabo.root].main, label=\"(省略)主体\", etype=\"sub\")\n self.G.nodes[cabo.chunks[cabo.root].main]['sub'] = omitted.main\n # Add autosub\n for i in range(len(plist)):\n pid = plist[i]\n if cabo.chunks[pid].type in [1, 2] and self.G.nodes[cabo.chunks[pid].main]['sub']== \"\":\n self._addEdge(self.G.nodes[cabo.chunks[cabo.root].main]['sub'], cabo.chunks[pid].main, label=\"主体候補\", etype=\"autosub\")\n self.G.nodes[cabo.chunks[pid].main]['sub'] = self.G.nodes[cabo.chunks[cabo.root].main]['sub']","def apply_joint_torques(self, actuator_nrs, axis_nrs, actuation_torques):\n raise NotImplementedError","def aveEx2CC(self):\n if getattr(self, '_aveEx2CC', None) is None:\n # The number of cell centers in each direction\n n = self.vnC\n if(self.dim == 1):\n self._aveEx2CC = speye(n[0])\n elif(self.dim == 2):\n self._aveEx2CC = sp.kron(av(n[1]), speye(n[0]))\n elif(self.dim == 3):\n self._aveEx2CC = kron3(av(n[2]), av(n[1]), speye(n[0]))\n return self._aveEx2CC"],"string":"[\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 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 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 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 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 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 conj(self, vecs):\\n raise NotImplementedError\",\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 set_conectividad(self, conec):\\n self.add_conec_listoflists(conec) # calcula el ne y el je\",\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 conj(x, *args, **kwargs):\\n raise NotImplementedError\",\n \"def conj(traj):\\r\\n return Trajectory(np.conj(traj.modes))\",\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 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 addConstrs(self, constrs, name=''):\\n ...\",\n \"def _write_conne(parameters):\\n from ._common import connections\\n\\n # Reorder connections\\n if parameters[\\\"connections_order\\\"] is not None:\\n order = parameters[\\\"connections_order\\\"]\\n else:\\n order = parameters[\\\"connections\\\"].keys()\\n\\n # Format\\n label_length = len(max(parameters[\\\"connections\\\"], key=len)) // 2\\n fmt = block_to_format[\\\"CONNE\\\"]\\n fmt = str2format(fmt[label_length])\\n\\n out = []\\n for k in order:\\n data = deepcopy(connections)\\n data.update(parameters[\\\"connections\\\"][k])\\n\\n values = [\\n k,\\n data[\\\"nseq\\\"],\\n data[\\\"nadd\\\"][0] if data[\\\"nadd\\\"] is not None else None,\\n data[\\\"nadd\\\"][1] if data[\\\"nadd\\\"] is not None else None,\\n data[\\\"permeability_direction\\\"],\\n data[\\\"nodal_distances\\\"][0],\\n data[\\\"nodal_distances\\\"][1],\\n data[\\\"interface_area\\\"],\\n data[\\\"gravity_cosine_angle\\\"],\\n data[\\\"radiant_emittance_factor\\\"],\\n ]\\n out += write_record(values, fmt)\\n\\n return out\",\n \"def conj(self):\\n \\n Out = self._CreateSameType()\\n \\n for Ind in self.IndList():\\n OutInd = tuple(-x for x in Ind)\\n \\n Out[OutInd]=self[Ind].conj().T\\n \\n return Out\",\n \"def conj(self, vecs):\\n mergedvecs = merge_comp(vecs)\\n conjobj = associate_comp(mergedvecs, mergedvecs)\\n return normalize_comp(\\n self.conj_weights @\\n torch.cat([\\n conjobj,\\n mergedvecs,\\n ]).reshape(-1, 2 * self.hrr_size)).reshape(2, self.hrr_size)\",\n \"def conj(self, vecs):\\n mergedvecs = merge_comp(vecs)\\n conjobj = associate_comp(mergedvecs, mergedvecs)\\n return normalize_comp(\\n self.conj_weights @\\n torch.cat([\\n conjobj,\\n mergedvecs,\\n ]).reshape(-1, 2 * self.hrr_size)).reshape(2, self.hrr_size)\",\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 conj(self, init_repr, vecs):\\n rnnoutput = self.conjmodel(\\n torch.cat([\\n init_repr,\\n *vecs,\\n torch.zeros(self.hrr_size).detach()\\n ]).reshape((len(vecs)+2, 1, -1)),\\n (\\n torch.zeros((1, 1, self.hrr_size)).detach(),\\n torch.zeros((1, 1, self.hrr_size)).detach(),\\n )\\n )\\n\\n rnnoutput[1][0].detach_()\\n rnnoutput[1][1].detach_()\\n\\n return rnnoutput[0][-1,0,:]\",\n \"def conj(self):\\n return np.conj(self)\",\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 append_constraints(parent_constraints, new_constraints):\\n new_con_dict = copy.deepcopy(parent_constraints)\\n for con in new_constraints:\\n new_con_dict[con[1]].append((con[0], con[2])) # Maps v -> (agent, time)\\n return new_con_dict\",\n \"def conj(self):\\n daskD.wait(self.client.map(_call_conj, self.vecDask, pure=False))\\n return self\",\n \"def conj(self):\\n out = empty((self._size, *self.shape[1:]), self.dtype)\\n\\n if self.fragmented:\\n k = self._capacity - self._begin # fragmentation index\\n np.conjugate(self[self._begin:], out[:k])\\n np.conjugate(self[:self._end], out[k:])\\n else:\\n if self._begin < self._end:\\n part = self[self._begin:self._end]\\n elif self._end == 0:\\n part = self[self._begin:]\\n\\n np.conjugate(part, out)\\n\\n return(out.view(ndarray))\",\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 appendcons(self,num_): # 3\\n res = self.__obj.appendcons(num_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def create_constraints(self, courses):\\n for i, course1 in enumerate(courses):\\n for j, course2 in enumerate(courses):\\n if i <= j:\\n continue\\n self.p.add_constraint(section_constraint, [course1, course2])\\n self.p.add_constraint(self.time_conflict, [course1])\",\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 conj(self, vecs):\\n mergedvecs = merge(vecs)\\n conjobj = associate(mergedvecs, mergedvecs)\\n return normalize(\\n self.conj_weights @\\n torch.cat([\\n conjobj,\\n mergedvecs,\\n ]).reshape(-1, self.hrr_size))\",\n \"def make_prog(self):\\r\\n\\r\\n self.cnv.clear()\\r\\n cdf = self.df[self.df.L != 0]\\r\\n c0 = cdf['C0'].value_counts().idxmax()\\r\\n c1 = cdf['C1'].value_counts().idxmax()\\r\\n c2 = cdf['C2'].value_counts().idxmax()\\r\\n c3 = cdf['C3'].value_counts().idxmax()\\r\\n self.cnv.extend([c0, c1, c2, c3])\",\n \"def c_code_contiguous(self, node, name, inp, out, sub):\\r\\n raise theano.gof.utils.MethodNotDefined()\",\n \"def appendcons(self,num_):\\n res = __library__.MSK_XX_appendcons(self.__nativep,num_)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def _constraints_task_contiguity(self):\\n # CONT_STRIDE=1 would give original implementation\\n triu = util.triu(self.num_timeslots, incr=self.cont_incr)\\n tril = util.tril(self.num_timeslots, incr=self.cont_incr)\\n\\n def rule(model, i, j):\\n \\\"\\\"\\\"\\n This rule is used to encourage early completion (in terms of\\n allocation) of a task.\\n\\n More precisely:\\n CTu[i,j] = whether task j is UNASSIGNED between slot i and the end\\n\\n Maximizing sum_i CTu[i,j] encourages early task completion.\\n Maximizing sum_i CTu[i,j]+CTl[i,j] encourages contiguous scheduling.\\n \\\"\\\"\\\"\\n active = 1-self.task_spread[j]\\n den = sum(triu[i, :])\\n ind = model.timeslots\\n # FIXME(cathywu) can/should be more precise with A,A2,A3 offsets\\n total = sum(triu[i, k] * (\\n 1 - model.A[k, j] - model.A2[k, j] - model.A3[k, j] - model.A4[\\n k, j]) for k in ind)\\n total /= den\\n total *= active\\n # CTu[i,j] = floor(total)\\n return -1 + EPS, model.CTu[i, j] - total, EPS + self.slack_cont\\n\\n self.model.constrain_contiguity_u = Constraint(self.model.contslots,\\n self.model.tasks,\\n rule=rule)\\n\\n def rule(model, i, j):\\n \\\"\\\"\\\"\\n This rule is used to encourage late start (in terms of\\n allocation) of a task.\\n\\n More precisely:\\n CTl[i,j] = whether task j is UNASSIGNED between slot 0 and slot i\\n\\n Maximizing sum_i CTl[i,j] encourages late starting.\\n Maximizing sum_i CTu[i,j]+CTl[i,j] encourages contiguous scheduling.\\n \\\"\\\"\\\"\\n active = 1-self.task_spread[j]\\n den = sum(tril[i, :])\\n ind = model.timeslots\\n total = sum(tril[i, k] * (\\n 1 - model.A[k, j] - model.A2[k, j] - model.A3[k, j] - model.A4[\\n k, j]) for k in ind)\\n total /= den\\n total *= active\\n return -1 + EPS, model.CTl[i, j] - total, EPS + self.slack_cont\\n\\n self.model.constrain_contiguity_l = Constraint(self.model.contslots,\\n self.model.tasks,\\n rule=rule)\\n\\n def rule(model):\\n den = self.num_tasks * self.cont_slots * (self.slack_cont + 1)\\n num = 0.25\\n total = summation(model.CTu) / den * num\\n return model.CTu_total == total\\n\\n self.model.constrain_contiguity_ut = Constraint(rule=rule)\\n\\n def rule(model):\\n den = self.num_tasks * self.cont_slots * (self.slack_cont + 1)\\n num = 0.25\\n total = summation(model.CTl) / den * num\\n return model.CTl_total == total\\n\\n self.model.constrain_contiguity_lt = Constraint(rule=rule)\",\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 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 cc(self):\\n return self.foreach(\\n lambda k,v: (k, numpy.conj(v)),\\n dimensions = self.dims,\\n shape = self.shape,\\n )\",\n \"def semcor2conc(args):\\r\\n input_files = list_files(*args.input_files)\\r\\n types = list(args.types)\\r\\n output_file = args.output_file or output_default / '{}_conc.csv'.format('_'.join(types))\\r\\n output_file = Path(output_file)\\r\\n left_context = args.left\\r\\n right_context = args.right\\r\\n separator = args.separator\\r\\n filter_pos = args.pos\\r\\n kind_id = args.kind_id\\r\\n with output_file.open('w') as file:\\r\\n x = 'last\\\\tnext\\\\tlemma' if args.add_closest else 'lemma'\\r\\n file.write('\\\\t'.join(['concordance', 'file', 'token_id', 'left', 'wordform', 'right', x, 'pos', 'sense_key\\\\n']))\\r\\n for input_file in input_files:\\r\\n corpus_file = CorpusFile(input_file)\\r\\n tokenlist = list(generate_tokenlist(corpus_file.text))\\r\\n chosen_words = [index for (index, token) in enumerate(tokenlist) if token.lemma in types]\\r\\n for word in chosen_words:\\r\\n node = tokenlist[word]\\r\\n pos = node.pos\\r\\n if filter_pos and not re.match(r'{}'.format([x for x in filter_pos]), pos):\\r\\n continue\\r\\n if kind_id == 'lemma_pos':\\r\\n wordtype = '/'.join([node.lemma, node.pos])\\r\\n elif kind_id == 'wordform':\\r\\n wordtype = node.wordform\\r\\n else:\\r\\n wordtype = node.lemma\\r\\n token_id = '/'.join([wordtype, corpus_file.shortname, str(word + 1)])\\r\\n left, right = generate_context(tokenlist, word, left_context, right_context, separator, len(tokenlist))\\r\\n if args.add_closest:\\r\\n last = tokenlist[word-1].wordform\\r\\n following = tokenlist[word+1].wordform\\r\\n line = [corpus_file.concordance, corpus_file.shortname, token_id, left, node.wordform, right, last, following, node.lemma, pos, node.sense_key or 'NA']\\r\\n else:\\r\\n line = [corpus_file.concordance, corpus_file.shortname, token_id, left, node.wordform, right, node.lemma, pos, node.sense_key or 'NA']\\r\\n file.write('\\\\t'.join(line) + '\\\\n')\\r\\n print('File \\\"{}\\\" processed.'.format(input_file.stem))\",\n \"def encode_conj(self, conj):\\n return self.conj(\\n tuple( self.fold_term(disj)\\n for disj in conj.disjs ))\",\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 add_conec_listoflists(self, conec):\\n self.num = len(conec) # numero de elementos en conec\\n self.ne = [] # numero de elem1 por cada elem0]\\n self.je = [] # aca pongo la conectividad en un array chorizo\\n self.len_je = 0\\n for elem0 in conec:\\n num_elem1 = 0 # numero de elem1 en elem0\\n for item1 in elem0:\\n num_elem1 += 1 # sumo un elem1 conectado a elem0\\n self.je.append(item1) # lo agrego a la conectividad\\n self.len_je += 1\\n self.ne.append(num_elem1) # agrego el numero de elem1 de elem0 a la lista\",\n \"def calc_conmat(self, jd=None, dt=20, write=False):\\n if not jd: jd = self.jd.min()\\n if not hasattr(self,'reg'): self.regvec_from_discs()\\n tmask1 = self.jd == jd\\n tmask2 = self.jd == jd + dt\\n try:\\n ntracmax = max(self.ntrac[tmask1].max(), self.ntrac[tmask2].max())\\n except ValueError:\\n return False\\n\\n convec = np.zeros((2, ntracmax+1))\\n convec[0,self.ntrac[tmask1]] = self.reg[tmask1]\\n convec[1,self.ntrac[tmask2]] = self.reg[tmask2]\\n convec = convec.astype(np.int)\\n\\n flat_coord = np.ravel_multi_index(convec, (self.nreg, self.nreg))\\n sums = np.bincount(flat_coord, minlength=self.nreg*self.nreg)\\n self.conmat = np.zeros((self.nreg,self.nreg))\\n self.conmat.flat[:len(sums)] = sums\\n\\n def write_hdf(jd, dt):\\n self.h5open()\\n jdpos = np.abs(self.h5f.root.jdvec[:] - jd).argmin()\\n self.h5f.root.conmat[jdpos,dt,:,:] = self.conmat.astype(np.uint32)\\n self.h5f.root.exist[jdpos,dt] = True\\n self.h5f.flush()\\n self.h5close()\\n\\n def write_npz(jd, dt):\\n conmatfile = (\\\"conmat_%s_%s_%06i_%04i.npz\\\" %\\n (self.projname, self.casename, jd, dt))\\n np.savez(os.path.join(self.conmatdir, conmatfile),\\n conmat=self.conmat.astype(np.uint32), jd=jd, dt=dt)\\n\\n if write is True: write_hdf(jd, dt)\",\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 getCodonSeqs(self):\\r\\n combinations = list(self.codonTable[aa] for aa in self.peptide) # creates a list of possible codons based on AA\\r\\n self.allPepSeqs = list(''.join(codon) for codon in itertools.product(*combinations)) # creates list of peptides\\r\\n return\",\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 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 generate_connectivity_constraint_all(problem):\\n\\n if problem.num_vars == None:\\n problem.compute_num_var()\\n\\n ret = Constraint()\\n\\n # Iterator over all (v, t) subsets in the graph\\n for b, b_r in enumerate(problem.src):\\n # Convert each set in the iterator to (v,t) format\\n add_S = map(\\n lambda S: list(map(problem.get_time_augmented_n_t, S)),\\n problem.powerset_exclude_agent(b_r),\\n )\\n ret &= generate_connectivity_constraint(problem, [b], add_S)\\n\\n return ret\",\n \"def create_cont_constraint_mat_separable(H,v1s,v2s,nSides,nConstraints,nC,\\n dim_domain,dim_range,tess):\\n if dim_domain != 2:\\n raise ValueError\\n if dim_range not in [1,2]:\\n raise ValueError\\n nHomoCoo=dim_domain+1 \\n length_Avee = dim_range*nHomoCoo\\n L1 = np.zeros((nConstraints/2,nC*nHomoCoo))\\n\\n \\n\\n nPtsInSide = 2 # Since, in 2D, the side is always a line joining 2 pts.\\n# if nSides != nConstraints/(nPtsInSide*dim_domain):\\n# raise ValueError(nSides,nConstraints)\\n \\n if nSides != nConstraints/(nPtsInSide*dim_range):\\n print \\\" print nSides , nConstraints/(nPtsInSide*dim_range):\\\"\\n print nSides , nConstraints/(nPtsInSide*dim_range)\\n ipshell('stop')\\n raise ValueError( nSides , (nConstraints,nPtsInSide,dim_range))\\n\\n \\n if nSides != H.shape[0]:\\n raise ValueError(nSides,H.shape)\\n\\n\\n# M = nPtsInSide*dim_range\\n M = nPtsInSide\\n if dim_range == 1:\\n raise NotImplementedError\\n for i in range(nSides): \\n v1 = v1s[i]\\n v2 = v2s[i]\\n \\n h = H[i]\\n a,b = h.nonzero()[0] # idx for the relevant As \\n # s stands for start\\n # e stands for end \\n s1 = a*length_Avee \\n e1 = s1+nHomoCoo \\n s2 = b*length_Avee\\n e2 = s2+nHomoCoo \\n \\n # Constraint 1: \\n L[i*M,s1:e1]= v1 \\n L[i*M,s2:e2]= -v1 \\n # Constraint 2: \\n L[i*M+1,s1:e1]= v2 \\n L[i*M+1,s2:e2]= -v2 \\n \\n \\n elif dim_range==2:\\n for i in range(nSides): \\n v1 = v1s[i]\\n v2 = v2s[i]\\n\\n if np.allclose(v1,v2):\\n raise ValueError(v1,v2)\\n\\n\\n \\n \\n \\n h = H[i]\\n a,b = h.nonzero()[0] # idx for the relevant As \\n \\n\\n # L1 is acting on columns of the following form:\\n # [ a_1 b_1 c_1 d_1 a_2 b_2 c_2 d_2 ... a_Nc b_Nc c_Nc d_Nc] \\n # s stands for start\\n # e stands for end \\n s1 = a*nHomoCoo\\n e1 = s1+nHomoCoo \\n s2 = b*nHomoCoo\\n e2 = s2+nHomoCoo \\n \\n \\n try: \\n # Constraint 1: \\n row = np.zeros(L1.shape[1])\\n row[s1:e1]=v1\\n row[s2:e2]=-v1 \\n # x component \\n L1[i*M]=row \\n except:\\n ipshell('fail')\\n raise \\n\\n # Constraint 2: \\n row = np.zeros(L1.shape[1])\\n row[s1:e1]=v2\\n row[s2:e2]=-v2 \\n # x component \\n L1[i*M+1]=row\\n \\n\\n \\n \\n \\n \\n \\n else:\\n raise ValueError(dim_range)\\n\\n \\n return L1\",\n \"def convex_conj(self):\\n convex_conjs = [func.convex_conj for func in self.functionals]\\n return SeparableSum(*convex_conjs)\",\n \"def addConstraint(self, conName, compIDs=None, lower=-1e20, upper=1e20, dvIndex=0):\\n if compIDs is not None:\\n # Make sure CompIDs is flat and get element numbers on each proc corresponding to specified compIDs\\n compIDs = self._flatten(compIDs)\\n else:\\n nComps = self.meshLoader.getNumComponents()\\n compIDs = list(range(nComps))\\n\\n constrObj = self._createConstraint(dvIndex, compIDs, lower, upper)\\n if constrObj.nCon > 0:\\n self.constraintList[conName] = constrObj\\n success = True\\n else:\\n self._TACSWarning(\\n f\\\"No adjacent components found in `compIDs`. Skipping {conName}.\\\"\\n )\\n success = False\\n\\n return success\",\n \"def calcConn(self,conMat):\\n for i in range(len(conMat)):\\n for j in range(i,len(conMat)):\\n if conMat[i,j]==1:\\n self.drawLine(i,j)\",\n \"def add_c_mask(self, pre_offs, post_ons, post_offs=None, passive = False):\\n\\n pre_on = int(100/self.dt) # never check the first 100ms\\n pre_offs = self.expand(pre_offs)\\n post_ons = self.expand(post_ons)\\n post_offs = self.expand(post_offs)# when to stop checking. default is the end of the trial\\n\\n if self.config['loss_type'] == 'lsq':\\n c_mask = np.zeros((self.tdim, self.batch_size, self.n_output), dtype=self.float_type)\\n for i in range(self.batch_size):\\n # Post response periods usually have the same length across tasks\\n if passive:\\n c_mask[post_ons[i]:post_offs[i], i, :] = 0.\\n else:\\n c_mask[post_ons[i]:post_offs[i], i, :] = 5.\\n # Pre-response periods usually have different lengths across tasks\\n # To keep cost comparable across tasks\\n # Scale the cost mask of the pre-response period by a factor\\n c_mask[pre_on:pre_offs[i], i, :] = 1.\\n\\n # self.c_mask[:, :, 0] *= self.n_eachring # Fixation is important\\n c_mask[:, :, 0] *= 2. # Fixation is important\\n\\n self.c_mask = c_mask.reshape((self.tdim*self.batch_size, self.n_output))\\n else:\\n c_mask = np.zeros((self.tdim, self.batch_size), dtype=self.float_type)\\n for i in range(self.batch_size):\\n # Post response periods usually have the same length across tasks\\n # Having it larger than 1 encourages the network to achieve higher performance\\n if passive:\\n c_mask[post_ons[i]:, i] = 0.\\n else:\\n c_mask[post_ons[i]:, i] = 5.\\n # Pre-response periods usually have different lengths across tasks\\n # To keep cost comparable across tasks\\n # Scale the cost mask of the pre-response period by a factor\\n c_mask[pre_on:pre_offs[i], i] = 1.\\n\\n self.c_mask = c_mask.reshape((self.tdim*self.batch_size,))\\n self.c_mask /= self.c_mask.mean()\",\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 connection(self, sampleseq, num):\\n self.Adjmatrix = np.zeros((self.nodenum, self.nodenum), dtype = int)\\n \\n for i in range(self.supplynum):\\n minindex = np.array(sf.minimumk(self.Dismatrix[self.supplyseries[i], self.trandemandseries], sampleseq[self.supplyseries[i]]))\\n self.Adjmatrix[self.supplyseries[i], self.trandemandseries[minindex]] = 1\\n# self.Adjmatrix[minindex, self.supplyseries[i]] = 1\\n \\n for i in range(self.trannum):\\n if(np.sum(self.Adjmatrix[self.supplyseries, self.transeries[i]]) == 0):\\n minindex = np.array(sf.minimumk(self.Dismatrix[self.supplyseries, self.transeries[i]], num))\\n self.Adjmatrix[minindex, self.transeries[i]] = 1\\n# self.Adjmatrix[self.transeries[i], minindex] = 1\\n \\n \\n# for i in range(self.supplynum):\\n# minindex = np.array(sf.minimumk(self.Dismatrix[self.supplyseries[i], self.supplyseries], num))\\n# self.Adjmatrix[self.supplyseries[i], minindex] = 1\\n# self.Adjmatrix[minindex, self.supplyseries[i]] = 1\\n \\n# for i in range(self.trannum):\\n# if(np.sum(self.Adjmatrix[self.supplyseries, self.transeries[i]]) != 0):\\n# continue\\n# minindex = np.array(sf.minimumk(self.Dismatrix[self.supplyseries, self.transeries[i]], num))\\n# self.Adjmatrix[minindex, self.transeries[i]] = 1\\n## self.Adjmatrix[self.transeries[i], minindex] = 1\\n# \\n for i in range(self.trannum):\\n minindex = np.array(sf.minimumk(self.Dismatrix[self.transeries[i], self.demandseries], min(sampleseq[self.transeries[i]], self.demandnum))) + self.supplynum + self.trannum\\n self.Adjmatrix[self.transeries[i], minindex] = 1\\n# self.Adjmatrix[minindex, self.transeries[i]] = 1\\n \\n# for i in range(self.demandnum):\\n# if(np.sum(self.Adjmatrix[self.transeries, self.demandseries[i]]) == 0):\\n# minindex = np.array(sf.minimumk(self.Dismatrix[self.transeries, self.demandseries[i]], 1)) + self.supplynum\\n# self.Adjmatrix[minindex, self.demandseries[i]] = 1\\n \\n# for i in range(self.trannum):\\n# minindex = np.array(sf.minimumk(self.Dismatrix[self.transeries[i], self.transeries], num)) + self.supplynum\\n# self.Adjmatrix[self.transeries[i], minindex] = 1\\n \\n for i in range(self.demandnum):\\n if(np.sum(self.Adjmatrix[self.transeries, self.demandseries[i]]) == 0):\\n minindex = np.array(sf.minimumk(self.Dismatrix[self.transeries, self.demandseries[i]], num)) + self.supplynum\\n self.Adjmatrix[minindex, self.demandseries[i]] = 1\\n# self.Adjmatrix[self.demandseries[i], minindex] = 1\\n \\n for i in range(self.demandnum):\\n minindex = np.array(sf.minimumk(self.Dismatrix[self.demandseries[i], self.demandseries], min(sampleseq[self.demandseries[i]] + 1, self.demandnum))) + self.supplynum + self.trannum\\n minindex = minindex[1:-1]\\n for j in range(len(minindex)):\\n if(self.Adjmatrix[self.demandseries[i], minindex[j]] == 1 or self.Adjmatrix[minindex[j], self.demandseries[i]] == 1):\\n continue\\n self.Adjmatrix[self.demandseries[i], minindex[j]] = 1\",\n \"def process_consequences(\\n mt: Union[hl.MatrixTable, hl.Table],\\n vep_root: str = \\\"vep\\\",\\n penalize_flags: bool = True,\\n) -> Union[hl.MatrixTable, hl.Table]:\\n csqs = hl.literal(CSQ_ORDER)\\n csq_dict = hl.literal(dict(zip(CSQ_ORDER, range(len(CSQ_ORDER)))))\\n\\n def find_worst_transcript_consequence(\\n tcl: hl.expr.ArrayExpression,\\n ) -> hl.expr.StructExpression:\\n \\\"\\\"\\\"Get worst transcript_consequence from an array of em.\\\"\\\"\\\"\\n flag_score = 500\\n no_flag_score = flag_score * (1 + penalize_flags)\\n\\n def csq_score(tc):\\n return csq_dict[csqs.find(lambda x: x == tc.most_severe_consequence)]\\n\\n tcl = tcl.map(\\n lambda tc: tc.annotate(\\n csq_score=hl.case(missing_false=True)\\n .when(\\n (tc.lof == \\\"HC\\\") & (tc.lof_flags == \\\"\\\"),\\n csq_score(tc) - no_flag_score,\\n )\\n .when(\\n (tc.lof == \\\"HC\\\") & (tc.lof_flags != \\\"\\\"), csq_score(tc) - flag_score\\n )\\n .when(tc.lof == \\\"OS\\\", csq_score(tc) - 20)\\n .when(tc.lof == \\\"LC\\\", csq_score(tc) - 10)\\n .when(\\n tc.polyphen_prediction == \\\"probably_damaging\\\", csq_score(tc) - 0.5\\n )\\n .when(\\n tc.polyphen_prediction == \\\"possibly_damaging\\\", csq_score(tc) - 0.25\\n )\\n .when(tc.polyphen_prediction == \\\"benign\\\", csq_score(tc) - 0.1)\\n .default(csq_score(tc))\\n )\\n )\\n return hl.or_missing(hl.len(tcl) > 0, hl.sorted(tcl, lambda x: x.csq_score)[0])\\n\\n transcript_csqs = mt[vep_root].transcript_consequences.map(\\n add_most_severe_consequence_to_consequence\\n )\\n\\n gene_dict = transcript_csqs.group_by(lambda tc: tc.gene_symbol)\\n worst_csq_gene = gene_dict.map_values(find_worst_transcript_consequence).values()\\n sorted_scores = hl.sorted(worst_csq_gene, key=lambda tc: tc.csq_score)\\n\\n canonical = transcript_csqs.filter(lambda csq: csq.canonical == 1)\\n gene_canonical_dict = canonical.group_by(lambda tc: tc.gene_symbol)\\n worst_csq_gene_canonical = gene_canonical_dict.map_values(\\n find_worst_transcript_consequence\\n ).values()\\n sorted_canonical_scores = hl.sorted(\\n worst_csq_gene_canonical, key=lambda tc: tc.csq_score\\n )\\n\\n vep_data = mt[vep_root].annotate(\\n transcript_consequences=transcript_csqs,\\n worst_consequence_term=csqs.find(\\n lambda c: transcript_csqs.map(\\n lambda csq: csq.most_severe_consequence\\n ).contains(c)\\n ),\\n worst_csq_by_gene=sorted_scores,\\n worst_csq_for_variant=hl.or_missing(\\n hl.len(sorted_scores) > 0, sorted_scores[0]\\n ),\\n worst_csq_by_gene_canonical=sorted_canonical_scores,\\n worst_csq_for_variant_canonical=hl.or_missing(\\n hl.len(sorted_canonical_scores) > 0, sorted_canonical_scores[0]\\n ),\\n )\\n\\n return (\\n mt.annotate_rows(**{vep_root: vep_data})\\n if isinstance(mt, hl.MatrixTable)\\n else mt.annotate(**{vep_root: vep_data})\\n )\",\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 create_conserved_motif_list_otherclass(gpcr_pdb,gpcr_aa,j,my_pos,motifs,multiple_chains,chain_name):\\n my_pos_bw=my_pos.split(\\\"x\\\")[0]\\n (my_aa,chain)=gpcr_aa[my_pos]\\n add_chain_name=\\\"\\\"\\n if multiple_chains:\\n add_chain_name=\\\":\\\"+chain_name \\n while j < len(motifs):\\n cons_pos_bw = motifs[j][4]\\n if my_pos_bw==cons_pos_bw:\\n pos_range=find_range_from_cons_pos(my_pos, gpcr_pdb)\\n if pos_range:\\n motifs[j][2]=True\\n motifs[j][3]=pos_range + add_chain_name\\n j+=1\",\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 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 m_cliques(adj_list,labels,matrix,topology=\\\"clique\\\",cut=0):\\n num_clusters = list(np.unique(labels))\\n clusters = {i:[] for i in num_clusters}\\n print(\\\"m_cliques CUT:\\\", cut)\\n #Add nodes to clusters\\n for idx,n in enumerate(adj_list):\\n clusters[labels[idx]].append(n)\\n \\n if topology == \\\"sample_rand\\\":\\n G_cliques = sampled_clique(clusters,\\\"rand\\\")\\n print(\\\"Rand sampled clique size:\\\", G_cliques.number_of_nodes())\\n elif topology == \\\"sample_optim\\\":\\n G_cliques = sampled_clique(clusters,\\\"optim\\\")\\n print(\\\"Optim sampled clique size:\\\", G_cliques.number_of_nodes())\\n elif topology == \\\"pcc_rand\\\":\\n G_cliques = pcc_clique(clusters,\\\"rand\\\",labels,cut)\\n print(\\\"Rand sampled clique size:\\\", G_cliques.number_of_nodes())\\n elif topology == \\\"pcc_optim\\\":\\n G_cliques = pcc_clique(clusters,\\\"optim\\\",labels,cut)\\n print(\\\"Optim sampled clique size:\\\", G_cliques.number_of_nodes())\\n elif topology == \\\"pcc_greedy\\\":\\n G_cliques = pcc_clique(clusters,\\\"greedy\\\",labels,cut,matrix)\\n print(\\\"Greedy sampled clique size:\\\", G_cliques.number_of_nodes())\\n else:\\n G_cliques = nx.Graph()\\n cliques = []\\n while not is_empty(clusters):\\n clique = []\\n \\n #Add clique nodes\\n for _,cluster in clusters.items():\\n if len(cluster) > 0:\\n n = random.choice(cluster)\\n clique.append(n)\\n cluster.remove(n)\\n \\n #Cliqify\\n for n1 in clique:\\n for n2 in clique:\\n if n1 != n2:\\n G_cliques.add_edge(n1,n2)\\n \\n #Aggregate clique\\n cliques.append(clique)\\n\\n if topology == \\\"clique\\\":\\n clique_the_cliques(cliques,labels,G_cliques)\\n elif topology == \\\"ring\\\":\\n cliques_on_ring(cliques,labels,G_cliques)\\n \\n return G_cliques\",\n \"def add_rules(self, cliques):\\n self.cliques.extend(cliques)\",\n \"def concatenate(cls, char_matrices):\\n taxon_set = char_matrices[0].taxon_set\\n nseqs = len(char_matrices[0])\\n concatenated_chars = cls(taxon_set=taxon_set)\\n pos_start = 0\\n for cidx, cm in enumerate(char_matrices):\\n if cm.taxon_set is not taxon_set:\\n raise ValueError(\\\"Different `taxon_set` references in matrices to be merged\\\")\\n if len(cm) != len(taxon_set):\\n raise ValueError(\\\"Number of sequences not equal to the number of taxa\\\")\\n if len(cm) != nseqs:\\n raise ValueError(\\\"Different number of sequences across alignments: %d (expecting %d based on first matrix)\\\" % (len(cm), nseqs))\\n v1 = len(cm[0])\\n for t, s in cm.items():\\n if len(s) != v1:\\n raise ValueError(\\\"Unequal length sequences in character matrix %d\\\".format(cidx+1))\\n concatenated_chars.extend(cm,\\n extend_existing=True,\\n overwrite_existing=False)\\n if cm.label is None:\\n new_label = \\\"locus%03d\\\" % cidx\\n else:\\n new_label = cm.label\\n cs_label = new_label\\n i = 2\\n while cs_label in concatenated_chars.character_subsets:\\n label = \\\"%s_%03d\\\" % (new_label, i)\\n i += 1\\n character_indices = range(pos_start, pos_start + cm.vector_size)\\n pos_start += cm.vector_size\\n concatenated_chars.new_character_subset(character_indices=character_indices,\\n label=cs_label)\\n return concatenated_chars\",\n \"def allcJPDs(net,cond_node,cond_states):\\r\\n \\r\\n onodes = net.Outputnodes()\\r\\n onodes = sorted(onodes)\\r\\n combinations = [i for i in itertools.combinations(onodes,2)]\\r\\n JPDdatas = [] \\r\\n progress = 0\\r\\n for cs in cond_states:\\r\\n JPDdata = []\\r\\n for c in combinations:\\r\\n print 'Progress: ' +str(int(100*float(progress)/float(len(combinations)*len(cond_states))))+ ' %'\\r\\n priorA = getCondPrior(net,c[0],cond_node,cs)\\r\\n print priorA\\r\\n priorB = getCondPrior(net,c[1],cond_node,cs) \\r\\n print priorB\\r\\n JPD = cJPD(net,c,cond_node,cs)\\r\\n cov = Covariance (priorA,priorB,JPD)\\r\\n cor = Correlation (priorA,priorB,cov) \\r\\n JPDdata.append([c[0],c[1],JPD,cov,cor])\\r\\n progress+=1\\r\\n JPDdatas.append(JPDdata)\\r\\n return JPDdatas\",\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 find_joint_extension(self, modelb, modelc, mace_time=10, prover_time=60):\\n n = modelb.cardinality\\n ne = ['b'+str(x)+'!=b'+str(y) for x in range(n) for y in range(x+1,n)]\\n n = modelc.cardinality\\n ne += ['c'+str(x)+'!=c'+str(y) for x in range(n) for y in range(x+1,n)]\\n return prover9(self.axioms+ne+modelb.positive_diagram('b') + \\n modelc.positive_diagram('c'), [], mace_time, prover_time)\",\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 create_conserved_motif_list(dyn_id,gpcr_pdb,gpcr_aa,j,my_pos,motifs,multiple_chains,chain_name):\\n my_pos_bw=my_pos.split(\\\"x\\\")[0]\\n (my_aa,chain)=gpcr_aa[my_pos]\\n add_chain_name=\\\"\\\"\\n if multiple_chains:\\n add_chain_name=\\\":\\\"+chain_name \\n while j < len(motifs):\\n cons_pos = motifs[j][1]\\n cons_pos_bw=cons_pos[1:]\\n cons_aa=cons_pos[0]\\n if my_pos_bw==cons_pos_bw:\\n pos_range=find_range_from_cons_pos(my_pos, gpcr_pdb)\\n if pos_range:\\n motifs[j][2]=True\\n motifs[j][3]=pos_range + add_chain_name\\n if cons_aa != \\\"x\\\" and my_aa != cons_aa:\\n motifs[j][1]+=my_aa\\n j+=1\",\n \"def conj(self):\\n res = self._defer_unary_elementwise(np.conj)\\n res.dirs = list(map(opr.neg, res.dirs))\\n res.charge = -res.charge\\n if self.qodulus is not None:\\n res.charge %= res.qodulus\\n return res\",\n \"def add_constraints(self, constraints):\\n for const in constraints:\\n self.add_constraint(const.type, const.column, const.check_clause)\",\n \"def write_puzzle_constraints(name, constraints):\\n with open(name, 'w') as f:\\n for c in constraints:\\n f.write('(')\\n f.write(str(c[0]))\\n f.write(',')\\n f.write(str(c[1]))\\n f.write(')\\\\n')\",\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 addCanvases(self):\\n self.canvasGrid = Grid(self.matrix.getHeight(),\\n self.matrix.getWidth())\\n for row in range(self.matrix.getHeight()):\\n for column in range(self.matrix.getWidth()):\\n canvas = MatrixCanvas(self, 20, 20,\\n self.matrix[row][column])\\n self.canvasGrid[row][column] = canvas\\n self.matrixPanel.addCanvas(canvas = canvas,\\n row = row,\\n column = column)\",\n \"def all_c(self):\\n for i in range( self.N, 0, -1):\\n self.C[i] = self.c(i)\\n self.ok = True\",\n \"def constraint(self, c):\\n self.add_constraint(c)\",\n \"def clauses_to_conjunct(clause_list):\\n conj = Expr('&')\\n conj.args = clause_list\\n return conj\\n #return ' & '.join(map(lambda(i): '{0}'.format(KB.clauses[i]), list))\",\n \"def drawConns(self):\\r\\n for node in self.G.nodes():\\r\\n pX,pY = self.G.node[node][\\\"site\\\"].getCenter()\\r\\n for nb in self.G.neighbors(node):\\r\\n nbX,nbY = self.G.node[nb][\\\"site\\\"].getCenter()\\r\\n self.connLines.append(self.canvasCirkt.create_line(pX,pY,nbX,nbY))\\r\\n self.canvasCirkt.update()\",\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 cpos2codon(self, cpos):\\n self.ensure_seq()\\n cpos = int(cpos)\\n if self.strand == \\\"+\\\":\\n np = []\\n for beg, end in self.exons:\\n np += list(range(max(beg, self.cds_beg),\\n min(self.cds_end, end)+1))\\n assert len(np) == len(self.seq)\\n\\n ni = cpos*3\\n if ni <= len(np):\\n codon = Codon()\\n codon.index = cpos\\n codon.locs = tuple(np[ni-3:ni])\\n codon.gene = self.gene\\n codon.chrm = self.chrm\\n codon.strand = self.strand\\n codon.seq = self.seq[ni-3:ni]\\n return codon\\n else:\\n raise IncompatibleTranscriptError('invalid_cDNA_position_%d;expect_[0_%d]' % (ni, len(np)))\\n else:\\n np = []\\n for beg, end in reversed(self.exons):\\n np += list(range(min(self.cds_end, end),\\n max(beg, self.cds_beg)-1,-1))\\n assert len(np) == len(self.seq)\\n\\n ni = cpos*3\\n if ni <= len(np):\\n codon = Codon()\\n codon.index = cpos\\n codon.locs = tuple(reversed(np[ni-3:ni]))\\n codon.gene = self.gene\\n codon.chrm = self.chrm\\n codon.strand = self.strand\\n codon.seq = self.seq[ni-3:ni]\\n return codon\\n else:\\n raise IncompatibleTranscriptError('invalid_cDNA_position_%d;expect_[0_%d]' % (ni, len(np)))\",\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 conjugate(quats):\\n res = np.zeros(quats.shape)\\n res[:,0]=quats[:,0]\\n res[:,1]=-quats[:,1]\\n res[:,2]=-quats[:,2]\\n res[:,3]=-quats[:,3]\\n \\n return res\",\n \"def create_conserved_pos_list_otherclass(gpcr_pdb,gpcr_aa, i,my_pos, cons_pos_li, multiple_chains,chain_name,gnum_classes_rel,dict_class,current_class):\\n my_pos_bw=my_pos.split(\\\"x\\\")[0]\\n add_chain_name=\\\"\\\"\\n if multiple_chains:\\n add_chain_name=\\\":\\\"+chain_name\\n while i < len(cons_pos_li):\\n cons_pos_bw_ourclass = cons_pos_li[i][1]\\n if my_pos_bw==cons_pos_bw_ourclass:\\n pos_range=find_range_from_cons_pos(my_pos, gpcr_pdb)\\n if pos_range:\\n cons_pos_li[i][2]=pos_range + add_chain_name\\n cons_pos_li[i][1]=\\\"Correspods to \\\"+cons_pos_bw_ourclass + current_class.lower()\\n i+=1\",\n \"def attach_CA(self):\\n n = self.pC\\n self.A[n] = self._mps_CA(self.C, self.A[n])\",\n \"def conj(z):\",\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 molpbcs(strucC, cov_nblist, cov_nbindx ,verbose = False, debug = False ):\\n \\n debug2 = False\\n latticevec = strucC.getLatVec()\\n\\n n_dim = 3\\n\\n if( debug ):\\n print \\\"latticevec\\\",latticevec\\n F = open(\\\"shift.rec\\\",\\\"w\\\")\\n \\n for mol_i in range( 1,strucC.ptclC.n_molecules()+1) :\\n\\n if( verbose ):\\n print \\\" Checking molecule %d of %d \\\"%(mol_i,strucC.ptclC.n_molecules())\\n\\n searchD = {'chain':mol_i}\\n mol_list = strucC.ptclC.getParticlesWithTags(searchD)\\n\\n if( len( mol_list) > 0 ):\\n ptclObj = strucC.ptclC[1]\\n r_o = ptclObj.position\\n pid_o = 1\\n\\n part_shifted = [False]*len(strucC.ptclC)\\n\\n r_mol_mass = np.zeros(n_dim)\\n shift = np.zeros(n_dim)\\n total_mass = 0.0 \\n\\n # shift all atoms to be conected \\n \\n for pid_i in sorted(mol_list):\\n ptclObj_i = strucC.ptclC[pid_i]\\n a_mass_i = ptclObj_i.mass\\n r_i = ptclObj_i.position\\n\\n r_io = np.array(r_o) - np.array(r_i)\\n\\n # sum center of mass\\n total_mass += a_mass_i\\n\\n\\n \\n shifted = False \\n for dim in range(n_dim):\\n shift_dim = round( r_io[dim]/ latticevec[dim][dim] )\\n r_i[dim] = r_i[dim] + latticevec[dim][dim] * shift_dim\\n if( shift_dim != 0 ):\\n shifted = True \\n \\n r_mol_mass[dim] = r_mol_mass[dim] + a_mass_i*r_i[dim] \\n \\n\\n if( debug and shifted ):\\n shift_line = \\\"\\\\n %d - %d r = %f %f %f dr = %f %f %f \\\"%(pid_i,pid_o,ptclObj_i.position[0],ptclObj_i.position[1],ptclObj_i.position[2],r_io[0],r_io[1],r_io[2])\\n F.write(shift_line)\\n \\n ptclObj_i.position = r_i\\n r_o = r_i\\n pid_o = pid_i\\n\\n # Shift molecular center of mass into box \\n for dim in range(n_dim):\\n cent_mass_i = r_mol_mass[dim] /total_mass\\n shift[dim] = latticevec[dim][dim] * round( cent_mass_i / latticevec[dim][dim] )\\n\\n \\n for pid_i in sorted(mol_list):\\n ptclObj_i = strucC.ptclC[pid_i]\\n for dim in range(n_dim):\\n ptclObj_i.position[dim] = ptclObj_i.position[dim] - shift[dim] \\n \\n \\n\\n if( debug ):\\n F.close()\",\n \"def extend(network, coords=[], conns=[], labels=[], **kwargs):\\n if 'throat_conns' in kwargs.keys():\\n conns = kwargs['throat_conns']\\n if 'pore_coords' in kwargs.keys():\\n coords = kwargs['pore_coords']\\n coords = np.array(coords)\\n conns = np.array(conns)\\n Np_old = network.num_pores()\\n Nt_old = network.num_throats()\\n Np = Np_old + coords.shape[0]\\n Nt = Nt_old + conns.shape[0]\\n if np.any(conns > Np):\\n raise Exception('Some throat conns point to non-existent pores')\\n network.update({'pore.all': np.ones([Np, ], dtype=bool),\\n 'throat.all': np.ones([Nt, ], dtype=bool)})\\n # Add coords and conns\\n if np.size(coords) > 0:\\n coords = np.vstack((network['pore.coords'], coords))\\n network['pore.coords'] = coords\\n if np.size(conns) > 0:\\n conns = np.vstack((network['throat.conns'], conns))\\n network['throat.conns'] = conns\\n\\n # Increase size of any prop or label arrays already on network and phases\\n objs = list(network.project.phases().values())\\n objs.append(network)\\n for obj in objs:\\n obj.update({'pore.all': np.ones([Np, ], dtype=bool),\\n 'throat.all': np.ones([Nt, ], dtype=bool)})\\n for item in list(obj.keys()):\\n N = obj._count(element=item.split('.')[0])\\n if obj[item].shape[0] < N:\\n arr = obj.pop(item)\\n s = arr.shape\\n if arr.dtype == bool:\\n obj[item] = np.zeros(shape=(N, *s[1:]), dtype=bool)\\n else:\\n obj[item] = np.ones(shape=(N, *s[1:]), dtype=float)*np.nan\\n obj[item][:arr.shape[0]] = arr\\n\\n # Regenerate models on all objects to fill new elements\\n for obj in network.project.phases().values():\\n if hasattr(obj, 'models'):\\n obj.regenerate_models()\\n\\n # Apply labels, if supplied\\n if labels != []:\\n # Convert labels to list if necessary\\n if isinstance(labels, str):\\n labels = [labels]\\n for label in labels:\\n # Remove pore or throat from label, if present\\n label = label.split('.')[-1]\\n if np.size(coords) > 0:\\n Ps = np.r_[Np_old:Np]\\n if 'pore.'+label not in network.labels():\\n network['pore.'+label] = False\\n network['pore.'+label][Ps] = True\\n if np.size(conns) > 0:\\n Ts = np.r_[Nt_old:Nt]\\n if 'throat.'+label not in network.labels():\\n network['throat.'+label] = False\\n network['throat.'+label][Ts] = True\\n\\n # Clear adjacency and incidence matrices which will be out of date now\\n network._am.clear()\\n network._im.clear()\",\n \"def placeConcentrationInto(self, region, conc):\\n self._sim.distributeConcentration(self, region, conc)\\n return self\",\n \"def constraint_matrix(self):\\n\\n con_filename = self.constraint_filename\\n con_press, con_data = self._co_star_read(con_filename)\\n return con_data\",\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 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 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 create_conserved_pos_list(gpcr_pdb,gpcr_aa, i,my_pos, cons_pos_li, multiple_chains,chain_name):\\n my_pos_bw=my_pos.split(\\\"x\\\")[0]\\n add_chain_name=\\\"\\\"\\n if multiple_chains:\\n add_chain_name=\\\":\\\"+chain_name\\n while i < len(cons_pos_li):\\n cons_pos = cons_pos_li[i][0]\\n cons_pos_bw=cons_pos[1:]\\n cons_aa=cons_pos[0]\\n if my_pos_bw==cons_pos_bw:\\n pos_range=find_range_from_cons_pos(my_pos, gpcr_pdb)\\n if pos_range:\\n cons_pos_li[i][2]=pos_range + add_chain_name\\n (my_aa,chain)=gpcr_aa[my_pos]\\n if my_aa != cons_aa:\\n cons_pos_li[i][0]=cons_pos+my_aa\\n i+=1\",\n \"def example2_concatenated_RE(GeomCA_parameters):\\n num_pts = 100\\n GeomCA_parameters['experiment_filename_prefix'] = 'problematic_overlap_'\\n subfolder = 'problematic_overlap'\\n GeomCA_parameters['comp_consistency_threshold'] = 0.0\\n GeomCA_parameters['comp_quality_threshold'] = 0.3\\n R = np.concatenate([circle(n=num_pts, r=1.5), circle(n=int(num_pts/5), r=0.3)])\\n E = np.concatenate([circle(n=num_pts, r=1.2), circle(n=int(num_pts/5), r=0.32)])\\n return run_GeomCA_and_visualize(R, E, subfolder, GeomCA_parameters)\",\n \"def express(self, *args, **kwargs) -> List[Union[LpConstraint, Constr]]:\\n\\n pass\",\n \"def concur_wait(self, node):\\n global BARRIER_CTR\\n\\n # This method will be destructive, unless we make a new\\n # copy of the AST tree first\\n #\\n node = deepcopy(node)\\n\\n seen_channels = set()\\n\\n # Channels in this with_concur\\n concur_channels = find_all_channels(node)\\n\\n # For creating the Barriers, we want QGL1 scoped variables that will be real channel instances.\\n # We basically have that already.\\n real_chans = set()\\n for chan in concur_channels:\\n real_chans.add(chan)\\n\\n start_barrier = BARRIER_CTR\\n end_barrier = start_barrier + 1\\n BARRIER_CTR += 2\\n\\n for stmnt in node.body:\\n if not is_seq(stmnt):\\n NodeError.error_msg(stmnt,\\n 'non-seq block inside concur block?')\\n return node\\n\\n seq_channels = find_all_channels(stmnt)\\n\\n if seq_channels.intersection(seen_channels):\\n NodeError.error_msg(stmnt,\\n 'seq blocks have overlapping channels')\\n return node\\n\\n seen_channels = seen_channels.union(seq_channels)\\n\\n chan_name = ','.join(seq_channels)\\n\\n # mark stmnt with chan_name or seq_channels in another way\\n if hasattr(stmnt, 'qgl_chan_list'):\\n oldChanSet = set(stmnt.qgl_chan_list)\\n newChanSet = seq_channels\\n oldMissing = newChanSet - oldChanSet\\n oldExtra = oldChanSet - newChanSet\\n if len(oldMissing) > 0:\\n NodeError.diag_msg(stmnt, 'marked chan list %s was missing %s' % (str(oldChanSet), str(oldMissing)))\\n if len(oldExtra) > 0:\\n NodeError.diag_msg(stmnt, 'marked chan list %s had extra %s' % (str(oldChanSet), str(oldExtra)))\\n NodeError.diag_msg(stmnt, 'Marking chan list %s' % (str(seq_channels)))\\n stmnt.qgl_chan_list = list(seq_channels)\\n\\n new_seq_body = list()\\n\\n # Helper to ensure the string we feed to AST doesn't put quotes around\\n # our Qubit variable names\\n def appendChans(bString, chans):\\n bString += '['\\n first = True\\n for chan in chans:\\n if first:\\n bString += str(chan)\\n first = False\\n else:\\n bString += \\\",\\\" + str(chan)\\n bString += ']'\\n return bString\\n\\n # Add global ctr, chanlist=concur_channels\\n # FIXME: Hold concur_channels as a string? List?\\n bstring = 'Barrier(\\\"%s\\\", ' % str(start_barrier)\\n bstring = appendChans(bstring, list(real_chans))\\n bstring += ')\\\\n'\\n barrier_ast = expr2ast(bstring)\\n # barrier_ast = expr2ast('Barrier(%s, %s)\\\\n' % (str(start_barrier), list(real_chans)))\\n copy_all_loc(barrier_ast, node)\\n barrier_ast.channels = concur_channels\\n # print(\\\"*****Start barrier: %s\\\" % pyqgl2.ast_util.ast2str(barrier_ast))\\n\\n new_seq_body.append(barrier_ast)\\n\\n new_seq_body += stmnt.body\\n\\n bstring = 'Barrier(\\\"%s\\\", ' % str(end_barrier)\\n bstring = appendChans(bstring, list(real_chans))\\n bstring += ')\\\\n'\\n end_barrier_ast = expr2ast(bstring)\\n #end_barrier_ast = expr2ast('Barrier(%s, %s)\\\\n' % (str(end_barrier), list(real_chans)))\\n copy_all_loc(end_barrier_ast, node)\\n # Add global ctr, chanlist=concur_channels\\n end_barrier_ast.channels = concur_channels\\n\\n # print('End AST: %s' % ast2str(end_barrier_ast))\\n\\n new_seq_body.append(end_barrier_ast)\\n\\n stmnt.body = new_seq_body\\n\\n # FIXME: In new thinking, is the proper unseen set the global one,\\n # Or only those local to this with concur. I think only local\\n for unseen_chan in concur_channels - seen_channels:\\n #print('DIAG %s' % ast2str(stmnt))\\n NodeError.diag_msg(stmnt,\\n 'channels unreferenced in concur: %s' % str(unseen_chan))\\n\\n bstring = 'with seq:\\\\n Barrier(\\\"%s\\\", ' % str(start_barrier)\\n bstring = appendChans(bstring, list(real_chans))\\n bstring += ')\\\\n Barrier(\\\"%s\\\",' % str(end_barrier)\\n bstring = appendChans(bstring, list(real_chans))\\n bstring += ')\\\\n'\\n empty_seq_ast = expr2ast(bstring)\\n # print('Empty AST: %s' % ast2str(empty_seq_ast))\\n # empty_seq_ast = expr2ast(\\n # 'with seq:\\\\n Barrier(%s, %s)\\\\n Barrier(%s, %s)' % (str(start_barrier), list(real_chans), str(end_barrier), list(real_chans)))\\n\\n # Mark empty_seq_ast with unseen_chan\\n empty_seq_ast.qgl_chan_list = [unseen_chan]\\n copy_all_loc(empty_seq_ast, node)\\n node.body.append(empty_seq_ast)\\n\\n return node\",\n \"def add(self, inp, pos):\\n self.pos = pos\\n self.para = list()\\n # Call backend for dependency parsing.\\n cabo = CabochaClient()\\n cabo.add(self.proc.query(inp), self.pos)\\n pool = [cabo.root]\\n plist = [cabo.root]\\n self.vlist = dict()\\n # Use BFS to get a list of nodes.\\n while pool:\\n pid = pool.pop(0)\\n for cid in cabo.childrenList[pid]:\\n pool.append(cid)\\n plist.insert(0, cid)\\n # Add nodes using plist(from leaves to roots).\\n for i in range(len(plist)):\\n pid = plist[i]\\n self._addChildren(pid, cabo.chunks)\\n self._processPara()\\n\\n # Return here if self.autosub is False.\\n if not self.autosub:\\n return\\n # If root has no subject, add omitted subject node.\\n if self.G.nodes[cabo.chunks[cabo.root].main]['sub'] == '':\\n omitted = CaboChunk(-1, cabo.root)\\n omitted.main = \\\"省略される主体[{0}@{1}]\\\".format(self.pos, 0)\\n omitted.func = \\\"(省略)\\\"\\n omitted.type = 0\\n omitted.pro = 7\\n omitted.surface = \\\"省略される主体\\\"\\n omitted.yomi = \\\"ショウリャクサレルシュゴ\\\"\\n self._addNode(omitted)\\n self._addEdge(omitted.main, cabo.chunks[cabo.root].main, label=\\\"(省略)主体\\\", etype=\\\"sub\\\")\\n self.G.nodes[cabo.chunks[cabo.root].main]['sub'] = omitted.main\\n # Add autosub\\n for i in range(len(plist)):\\n pid = plist[i]\\n if cabo.chunks[pid].type in [1, 2] and self.G.nodes[cabo.chunks[pid].main]['sub']== \\\"\\\":\\n self._addEdge(self.G.nodes[cabo.chunks[cabo.root].main]['sub'], cabo.chunks[pid].main, label=\\\"主体候補\\\", etype=\\\"autosub\\\")\\n self.G.nodes[cabo.chunks[pid].main]['sub'] = self.G.nodes[cabo.chunks[cabo.root].main]['sub']\",\n \"def apply_joint_torques(self, actuator_nrs, axis_nrs, actuation_torques):\\n raise NotImplementedError\",\n \"def aveEx2CC(self):\\n if getattr(self, '_aveEx2CC', None) is None:\\n # The number of cell centers in each direction\\n n = self.vnC\\n if(self.dim == 1):\\n self._aveEx2CC = speye(n[0])\\n elif(self.dim == 2):\\n self._aveEx2CC = sp.kron(av(n[1]), speye(n[0]))\\n elif(self.dim == 3):\\n self._aveEx2CC = kron3(av(n[2]), av(n[1]), speye(n[0]))\\n return self._aveEx2CC\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.804992","0.7942396","0.7373104","0.67235893","0.66512686","0.5923257","0.5694749","0.5581973","0.5278961","0.5160132","0.5134265","0.49960873","0.49393323","0.49215627","0.49015927","0.48836264","0.4847048","0.47998023","0.479868","0.479868","0.47911417","0.4780312","0.47359443","0.4705512","0.47028983","0.46969312","0.4675675","0.46709254","0.46695316","0.4662965","0.4660377","0.46488434","0.4639211","0.4632","0.46252692","0.46141618","0.4613822","0.46047276","0.4597965","0.45913583","0.45895103","0.45864892","0.45698306","0.45655409","0.45580238","0.45434558","0.4519388","0.45059854","0.44990155","0.4498853","0.44944263","0.44822362","0.44724822","0.44710454","0.44702414","0.44370362","0.4436355","0.4435582","0.44280875","0.44213274","0.4416525","0.44146508","0.44136396","0.44132626","0.44122994","0.44112492","0.4404108","0.44033873","0.44004124","0.43917415","0.4387594","0.43809348","0.43805698","0.4377793","0.4369545","0.43665037","0.43590137","0.43544903","0.43513983","0.43463466","0.43426257","0.43414155","0.43373796","0.4315789","0.43136457","0.43122613","0.43101496","0.4302715","0.42999515","0.42999306","0.42914614","0.4291119","0.42859375","0.42807725","0.42748943","0.42658558","0.42642537","0.42642468","0.4262291","0.42567733"],"string":"[\n \"0.804992\",\n \"0.7942396\",\n \"0.7373104\",\n \"0.67235893\",\n \"0.66512686\",\n \"0.5923257\",\n \"0.5694749\",\n \"0.5581973\",\n \"0.5278961\",\n \"0.5160132\",\n \"0.5134265\",\n \"0.49960873\",\n \"0.49393323\",\n \"0.49215627\",\n \"0.49015927\",\n \"0.48836264\",\n \"0.4847048\",\n \"0.47998023\",\n \"0.479868\",\n \"0.479868\",\n \"0.47911417\",\n \"0.4780312\",\n \"0.47359443\",\n \"0.4705512\",\n \"0.47028983\",\n \"0.46969312\",\n \"0.4675675\",\n \"0.46709254\",\n \"0.46695316\",\n \"0.4662965\",\n \"0.4660377\",\n \"0.46488434\",\n \"0.4639211\",\n \"0.4632\",\n \"0.46252692\",\n \"0.46141618\",\n \"0.4613822\",\n \"0.46047276\",\n \"0.4597965\",\n \"0.45913583\",\n \"0.45895103\",\n \"0.45864892\",\n \"0.45698306\",\n \"0.45655409\",\n \"0.45580238\",\n \"0.45434558\",\n \"0.4519388\",\n \"0.45059854\",\n \"0.44990155\",\n \"0.4498853\",\n \"0.44944263\",\n \"0.44822362\",\n \"0.44724822\",\n \"0.44710454\",\n \"0.44702414\",\n \"0.44370362\",\n \"0.4436355\",\n \"0.4435582\",\n \"0.44280875\",\n \"0.44213274\",\n \"0.4416525\",\n \"0.44146508\",\n \"0.44136396\",\n \"0.44132626\",\n \"0.44122994\",\n \"0.44112492\",\n \"0.4404108\",\n \"0.44033873\",\n \"0.44004124\",\n \"0.43917415\",\n \"0.4387594\",\n \"0.43809348\",\n \"0.43805698\",\n \"0.4377793\",\n \"0.4369545\",\n \"0.43665037\",\n \"0.43590137\",\n \"0.43544903\",\n \"0.43513983\",\n \"0.43463466\",\n \"0.43426257\",\n \"0.43414155\",\n \"0.43373796\",\n \"0.4315789\",\n \"0.43136457\",\n \"0.43122613\",\n \"0.43101496\",\n \"0.4302715\",\n \"0.42999515\",\n \"0.42999306\",\n \"0.42914614\",\n \"0.4291119\",\n \"0.42859375\",\n \"0.42807725\",\n \"0.42748943\",\n \"0.42658558\",\n \"0.42642537\",\n \"0.42642468\",\n \"0.4262291\",\n \"0.42567733\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74851453"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":11771,"cells":{"query":{"kind":"string","value":"Changes the bounds for one constraint. chgconbound(self,i_,lower_,finite_,value_)"},"document":{"kind":"string","value":"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)"},"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 chgconbound(self,i_,lower_,finite_,value_): # 3\n res = self.__obj.chgconbound(i_,lower_,finite_,value_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def chgbound(self,accmode_,i_,lower_,finite_,value_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n res = self.__obj.chgbound(accmode_,i_,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_): # 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 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 _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 _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 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 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 getconbound(self,i_):\n bk_ = ctypes.c_int32()\n bl_ = ctypes.c_double()\n bu_ = ctypes.c_double()\n res = __library__.MSK_XX_getconbound(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 set_concentration_boundaries(self, boundary_marker, concentration_function):\n self.concentration_boundaries[boundary_marker] = concentration_function","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 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 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 _process_individual_bound(self, val):\n if(val not in [True, False]):\n raise ValueError('For composition bounds expected are iether True' \n '(free function) or False (fixed function) not %s' % (str(val)))\n return val","def apply_bounds(self, column_name, lower_bound=-np.inf,\n upper_bound=np.inf):\n self.check_for_column(column_name)\n\n if lower_bound is None:\n lower_bound = -np.inf\n if upper_bound is None:\n upper_bound = np.inf\n column = self.data[column_name]\n self.data[column_name] = column.clip(lower_bound, upper_bound)","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 _set_constraint(self):\n pass","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 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 getconbound(self,i_): # 3\n res,resargs = self.__obj.getconbound(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 _setBound(self, value):\n if self._colormap is not None:\n if self._index == 0:\n min_ = value\n max_ = self._colormap.getVMax()\n else: # self._index == 1\n min_ = self._colormap.getVMin()\n max_ = value\n\n if max_ is not None and min_ is not None and min_ > max_:\n min_, max_ = max_, min_\n self._colormap.setVRange(min_, max_)","def bounds(self, new_bounds: devices.PrimaryBounds) -> None:\n self._assert_bounds_are_valid(new_bounds)\n self._bounds = list(new_bounds)","def _onSetParameterLower(self, value):\n self._parameters['lower'] = min(value, self._parameters['upper']) # Limit at upper\n self._logger.info(\"Parameter 'lower' of function '{}' changed to {}\".format(self._function, value))\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())","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 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 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 constraint(self, c):\n self.add_constraint(c)","def set_bounds(\n self: A,\n lower: BoundValue = None,\n upper: BoundValue = None,\n method: str = \"clipping\",\n full_range_sampling: bool = False,\n a_min: BoundValue = None,\n a_max: BoundValue = None,\n ) -> A: # TODO improve description of methods\n lower, upper = _a_min_max_deprecation(**locals())\n bounds = tuple(a if isinstance(a, np.ndarray) or a is None else np.array([a], dtype=float) for a in (lower, upper))\n both_bounds = all(b is not None for b in bounds)\n # preliminary checks\n if self.bound_transform is not None:\n raise RuntimeError(\"A bounding method has already been set\")\n if full_range_sampling and not both_bounds:\n raise ValueError(\"Cannot use full range sampling if both bounds are not set\")\n checker = BoundChecker(*bounds)\n if not checker(self.value):\n raise ValueError(\"Current value is not within bounds, please update it first\")\n if not (lower is None or upper is None):\n if (bounds[0] >= bounds[1]).any(): # type: ignore\n raise ValueError(f\"Lower bounds {lower} should be strictly smaller than upper bounds {upper}\")\n # update instance\n transforms = dict(clipping=trans.Clipping, arctan=trans.ArctanBound, tanh=trans.TanhBound)\n if method in transforms:\n if self.exponent is not None and method != \"clipping\":\n raise ValueError(f'Cannot use method \"{method}\" in logarithmic mode')\n self.bound_transform = transforms[method](*bounds)\n elif method == \"constraint\":\n self.register_cheap_constraint(checker)\n else:\n raise ValueError(f\"Unknown method {method}\")\n self.bounds = bounds # type: ignore\n self.full_range_sampling = full_range_sampling\n # warn if sigma is too large for range\n if both_bounds and method != \"tanh\": # tanh goes to infinity anyway\n std_bounds = tuple(self._to_reduced_space(b) for b in self.bounds) # type: ignore\n min_dist = np.min(np.abs(std_bounds[0] - std_bounds[1]).ravel())\n if min_dist < 3.0:\n warnings.warn(f\"Bounds are {min_dist} sigma away from each other at the closest, \"\n \"you should aim for at least 3 for better quality.\")\n return self","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 setLowerFrequencyBound(self, new_bound: int) -> None:\n self.lower_frequency_bound = new_bound","def __init__(self, lower_bound, upper_bound):\n\n if lower_bound > upper_bound:\n lower_bound, upper_bound = upper_bound, lower_bound\n\n self.lb = lower_bound\n self.ub = upper_bound\n\n super(ContinuousDomain, self).__init__(DomainType.continuous)","def simplebounds(cls, val, lower, upper):\n if val < lower:\n val = lower\n if val > upper:\n val = upper\n return val","def _setValidRange(self, contribs, valueRange):\n testfor(contribs.ndim == 2, ValueError)\n numContribs, numReps = contribs.shape\n self._validRange = np.zeros_like(contribs.T, dtype = bool)\n for ri in range(numReps):\n # the single set of R for this calculation\n rset = contribs[:, ri]\n self._validRange[ri] = ((rset > min(valueRange))\n * (rset < max(valueRange)))","def range(self, value: ArrayLike): # noqa: A003\n\n value = as_float_array(value, self.dtype)\n\n if not np.all(np.isfinite(value)):\n runtime_warning(\n f'\"{self.name}\" new \"range\" variable is not finite: {value}, '\n f\"unpredictable results may occur!\"\n )\n\n # Empty domain occurs during __init__ because range is set before domain\n attest(\n self._domain.size == 0 or value.size == self._domain.size,\n '\"domain\" and \"range\" variables must have same size!',\n )\n\n self._range = value\n self._function = None # Invalidate the underlying continuous function.","def min_values(self, lower, upper): \n if not self.lower_bounds is None:\n return self.lower_bounds\n\n minus = np.clip(self.coeffs,-math.inf,0)\n plus = np.clip(self.coeffs,0,math.inf)\n self.lower_bounds = plus.dot(lower) + minus.dot(upper) + self.const\n \n return self.lower_bounds","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 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 test_conf_interval_normal_method_with_bounds(data):\n df = data[\"df\"]\n new_df = data[\"new_df\"]\n # ``quantile_estimation_method = \"normal_fit\"``\n # with enforced lower limit (``min_admissible_value``)\n ci_model = conf_interval(\n df=df,\n value_col=\"y\",\n residual_col=\"residual\",\n conditional_cols=[\"x\"],\n quantiles=[0.005, 0.025, 0.975, 0.995],\n quantile_estimation_method=\"normal_fit\",\n sample_size_thresh=5,\n small_sample_size_method=\"std_quantiles\",\n small_sample_size_quantile=0.95,\n min_admissible_value=290.0,\n max_admissible_value=None)\n\n pred_df = predict_ci(\n new_df,\n ci_model)\n assert list(pred_df.columns) == [\"x\", \"y_quantile_summary\", ERR_STD_COL], (\n \"pred_df does not have the expected column names\")\n pred_df[\"y_quantile_summary\"] = pred_df[\"y_quantile_summary\"].apply(\n lambda x: tuple(round(e, 2) for e in x))\n assert pred_df[\"y_quantile_summary\"].values[5] == (290.0, 290.25, 292.54, 292.9), (\n \"quantiles are incorrect\")\n assert pred_df[\"y_quantile_summary\"].values[11] == (290.0, 290.0, 290.0, 290.0), (\n \"quantiles are incorrect\")","def constraint(self, constraint):\n\n self._constraint = constraint","def constraints(self, x):\n pass","def remove_invalid_values(self, lower_bound=float('-inf'), upper_bound=float('inf')) :\n valid_indices = (lower_bound < self.signal) * (self.signal < upper_bound)\n self.time_scale = self.time_scale[valid_indices]\n self.signal = self.signal[valid_indices]","def __scale_constraint(c, v):\n if c.equality:\n c.set_value((c.lower * v, c.body * v))\n else:\n c.set_value(\n (__none_left_mult(c.lower, v), c.body * v, __none_left_mult(c.upper, v))\n )","def _process_individual_bound(self, val):\n if(val == True):\n res = self._DEF_BOUNDS\n \n elif(val in [False, None]):\n res = val\n else:\n if(len(val) != 2):\n raise ValueError('Bound value is not recognized. '% (str(val)))\n if(val[0] > val[1]):\n raise ValueError('Bound values are inverted '% (str(val)))\n res = val\n return res","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 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 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 test_port_bounds(self):\n v1 = np.array(\n [1, 2, 3, 6, 5.5, -3, -2, -6.1, -10000, 10000],\n dtype=np.double\n )\n p1 = cn.Port()\n p1.value = v1\n\n self.assertEqual(\n np.allclose(\n p1.value,\n v1\n ),\n True\n )\n\n p1.bounds = 0, 1\n p1.bounded = True\n\n self.assertEqual(\n (p1.value <= 1).all(),\n True\n )\n self.assertEqual(\n (p1.value >= 0).all(),\n True\n )","def boundary_of_set(i):\n b = self.args[i].boundary\n for j, a in enumerate(self.args):\n if j != i:\n b = b - a.interior\n return b","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 _boundary_constraint_fence(\n self,\n x: np.ndarray,\n ) -> np.ndarray:\n # clip dimensions to fit within the boundary\n x_constrained = np.clip(\n x,\n self.boundary_fence['min'],\n self.boundary_fence['max'],\n )\n return x_constrained","def constraint_xml(self, constraint_xml):\n\n self._constraint_xml = constraint_xml","def fix_curvature(self) -> None:\n self.n1.fix = True\n self.n2.fix = True","def clip_by_bound(self) -> None:\n\n pass","def _fcn_minmax_roi(self):\n self.roi._update_cbar_minmax()\n self.cbqt.cbobjs._objs['roi']['clim'] = self.roi._clim\n kwargs = self.cbqt.cbobjs._objs['roi'].to_kwargs(True)\n self.roi.update_from_dict(kwargs)\n self.roi._update_cbar()","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 lower_bound(self) -> float:\n ...","def clip(self):\n if self._value < self.lowBound:\n self._value = self.lowBound\n elif self._value > self.upBound:\n self._value = self.upBound","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 add_constraint_one_way(self, i, j, filter_function):\n if not j in self.constraints[i]:\n # First, get a list of all possible pairs of values between variables i and j\n self.constraints[i][j] = self.get_all_possible_pairs(self.domains[i], self.domains[j])\n\n # Next, filter this list of value pairs through the function\n # 'filter_function', so that only the legal value pairs remain\n self.constraints[i][j] = filter(lambda value_pair: filter_function(*value_pair), self.constraints[i][j])","def _onSetParameterB(self, value):\n self._parameters['b'] = min(max(value, self._parameters['lower']), self._parameters['upper']) # Limit at upper and lower\n self._logger.info(\"Parameter ba' of function '{}' changed to {}\".format(self._function, value))\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())","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 put_weights_below_constraint(weights: np.ndarray, upper_constraints: np.ndarray, max_iter: int = 1000,\n epsilon: float = 0.00001) -> np.ndarray:\n assert upper_constraints.sum() >= 1\n\n new_weights = weights\n for _ in range(max_iter):\n new_weights, max_difference = _calculate_new_weights(new_weights, upper_constraints)\n\n if max_difference <= epsilon:\n break\n else:\n logger = qf_logger.getChild(__name__)\n logger.warning(\"put_weights_below_constraint: \\nIt was not possible to find weights within the constraints \"\n \"in {:d} iterations\".format(max_iter))\n\n return new_weights","def domain(self, value: ArrayLike):\n\n value = as_float_array(value, self.dtype)\n\n if not np.all(np.isfinite(value)):\n runtime_warning(\n f'\"{self.name}\" new \"domain\" variable is not finite: {value}, '\n f\"unpredictable results may occur!\"\n )\n else:\n attest(\n np.all(value[:-1] <= value[1:]),\n \"The new domain value is not monotonic! \",\n )\n\n if value.size != self._range.size:\n self._range = np.resize(self._range, value.shape)\n\n self._domain = value\n self._function = None # Invalidate the underlying continuous function.","def setUpperFrequencyBound(self, new_bound: int) -> None:\n self.upper_frequency_bound = new_bound","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 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 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 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 _bound(x, min_value, max_value):\n return np.maximum(min_value, np.minimum(x, max_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 set_bounds_atom(self,bounds):\n self.bounds = bounds # data bounds\n raise NotImplementedError\n self.bounds_atom = None\n return None","def _onSetParameterBIgnoreBounds(self, value):\n self._parameters['b'] = value\n self._logger.info(\"Parameter 'b' of function '{}' changed to {}\".format(self._function, value))\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())","def process_pain(x, lb, ub):\n x = x.abs()\n x.loc[(x > ub)] = 8\n x.loc[(x < lb) | (x > ub)] = np.nan\n return x","def soft_constraint ( self , var , value , name = '' , title = '' ) :\n \n assert isinstance ( var , ROOT.RooAbsReal ) ,\\\n \"Invalid ``v'': %s/%s\" % ( var , type ( var ) ) \n assert isinstance ( value , VE ),\\\n \"Invalid ``value'': %s/%s\" % ( value , type ( value ) )\n\n assert 0 < value.cov2() , 'Invalid error for %s' % value\n \n name = name if name else 'Gauss_%s_%s' % ( var.GetName() , self.name ) \n title = title if title else 'Gaussian Constraint(%s,%s) at %s' % ( var.GetName() , self.name , value )\n \n # value & error as RooFit objects: \n val = ROOT.RooFit.RooConst ( value.value () )\n err = ROOT.RooFit.RooConst ( value.error () )\n \n # Gaussian constrains \n gauss = ROOT.RooGaussian ( self.var_name ( name ) , title , var , val , err )\n \n # keep all the created technical stuff \n self.aux_keep.append ( val )\n self.aux_keep.append ( err )\n self.aux_keep.append ( gauss )\n\n self.info ('Constraint is created %s=%s' % ( var.name , value ) )\n return gauss","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 SetBoundaryCriterion(self, *args):\n return _ShapeUpgrade.ShapeUpgrade_ShapeDivideContinuity_SetBoundaryCriterion(self, *args)","def feasible(self):\n return self.lowBound <= self._value <= self.upBound","def constraints(self):\n ...","def _curve_constrain(self, x, idx, sign):\n x = x.reshape(1, -1)\n pdf = np.exp(self.ks_gaussian.score_samples(x))\n if self.band[0] < pdf < self.band[1]:\n value = sign * self.pca.inverse_transform(x)[0][idx]\n else:\n value = 1E6\n return value","def set_enzyme_constraint(model, reaction_kcat_mw, lowerbound, upperbound):\n coefficients = dict()\n for rxn in model.reactions:\n if rxn.id in reaction_kcat_mw.index:\n coefficients[rxn.forward_variable] = 1 / \\\n float(reaction_kcat_mw.loc[rxn.id, 'kcat_MW'])\n constraint = model.problem.Constraint(0, lb=lowerbound, ub=upperbound)\n model.add_cons_vars(constraint)\n model.solver.update()\n constraint.set_linear_coefficients(coefficients=coefficients)\n return model","def ctrl_limit_changed(self, which, new_limit):\n super(PyDMSpinbox, self).ctrl_limit_changed(which, new_limit)\n if not self.userDefinedLimits:\n if which == \"UPPER\":\n self.setMaximum(new_limit)\n else:\n self.setMinimum(new_limit)","def fill_bounds(x, upper, lower, axis, c = 'k', alpha = 0.2):\n axis.fill_between(\n x, \n upper[:, 0], \n lower[:, 0], \n facecolor = c,\n alpha = alpha\n )","def _discretize(self, constraints_object):\n pass","def mark_forced(self, i):\n assert 0 <= i < len(self.values)\n if self.__forced is None:\n self.__forced = set()\n self.__forced.add(i)","def testConstantBoundedField(self):\n photoCalib = lsst.afw.image.PhotoCalib(self.constantCalibration)\n self._testPhotoCalibCenter(photoCalib, 0)\n\n self.assertEqual(1, photoCalib.instFluxToMaggies(self.instFlux, self.pointYShift))\n self.assertEqual(0, photoCalib.instFluxToMagnitude(self.instFlux, self.pointYShift))\n self.assertFloatsAlmostEqual(1e-9, photoCalib.instFluxToMaggies(self.instFlux*1e-9, self.pointXShift))\n self.assertFloatsAlmostEqual(22.5, photoCalib.instFluxToMagnitude(\n self.instFlux*1e-9, self.pointXShift))\n\n photoCalib = lsst.afw.image.PhotoCalib(self.constantCalibration, self.calibrationErr)\n self._testPhotoCalibCenter(photoCalib, self.calibrationErr)","def constraints(self, constraints):\n\n self._constraints = constraints","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 update_constraints(self, target_row, target_col, value):\n for i in range(9):\n if value in self.possible_values[target_row][i]: # Update the column\n self.possible_values[target_row][i].remove(value)\n if value in self.possible_values[i][target_col]: # Update the row\n self.possible_values[i][target_col].remove(value)\n\n # Update the block:\n block_row = target_row - (target_row % 3)\n block_col = target_col - (target_col % 3)\n for row in range(3):\n for col in range(3):\n if value in self.possible_values[block_row + row][block_col + col]: # Remove possible value from block\n self.possible_values[block_row + row][block_col + col].remove(value)\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 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 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 setConstraints(self, boundsDict):\n for param in boundsDict.keys():\n try:\n if boundsDict[param][0] < boundsDict[param][1]:\n constraintString = \"{:4.4e} < {:s} < {:4.4e}\".format(boundsDict[param][0], param, boundsDict[param][1])\n self.addConstraints(constraintString)\n else:\n print('Setting constraints on mbvg; reversing bounds')\n self.addConstraints(\"{:4.4e} < A < {:4.4e}\".format(boundsDict[param][1], boundsDict[param][0]))\n except ValueError:\n print('Cannot set parameter {:s} for mbvg. Valid choices are', \\\n '(\\'A\\', \\'muX\\', \\'muY\\', \\'sigX\\', \\'sigY\\', \\'sigP\\', \\'bg\\')'.format(param))","def _convert_bound(value, lower_bound, upper_bound):\n # Converts value to 16 bit two's complement integer via bitwise.\n most_sig_bit = 0x8000\n\n # Gets the two least significant bits\n convert_val = value & _BYTE << _BYTE_SIZE | value & _BYTE\n # Extends the most significant bit if it is a 1. This is done by\n # carrying out the most significant bit.\n if bool(convert_val & most_sig_bit):\n convert_val |= ~(_BYTE << _BYTE_SIZE | _BYTE)\n\n # Bounds the converted value\n if convert_val > upper_bound:\n return upper_bound\n elif convert_val < lower_bound:\n return lower_bound\n return convert_val","def new_interval(self, epsilons):\n original = list()\n shrinked = list()\n\n for (i, j) in self.contingent_constraints:\n orig = (-self.stnu[j][i]['weight'], self.stnu[i][j]['weight'])\n original.append(orig)\n\n low = epsilons[(j, '-')].varValue\n high = epsilons[(j, '+')].varValue\n\n self.stnu.shrink_contingent_constraint(i, j, low, high)\n new = (-self.stnu[j][i]['weight'], self.stnu[i][j]['weight'])\n shrinked.append(new)\n\n return original, shrinked","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 update_old_branches(self, source, constraint):\n for dest in self.G_backup[source]:\n for source_con, dest_con_set in constraint.items():\n # Skip if branch already has constraints\n if (source_con in self.G_backup[source][dest]['constraint']):\n pass\n # Skip if constraint directly leads to the branching node\n elif (source in dest_con_set):\n pass\n else: # Otherwise, update new graph with new constraints\n self.G[source][dest]['constraint'].update({source_con: set(self.G_backup[source_con].keys())})","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 constrain(self, candidate_map, processed, to_process):\n for component in self._components:\n component.constrain(candidate_map, processed, to_process)"],"string":"[\n \"def chgconbound(self,i_,lower_,finite_,value_): # 3\\n res = self.__obj.chgconbound(i_,lower_,finite_,value_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def chgbound(self,accmode_,i_,lower_,finite_,value_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n res = self.__obj.chgbound(accmode_,i_,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_): # 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 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 _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 _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 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 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 getconbound(self,i_):\\n bk_ = ctypes.c_int32()\\n bl_ = ctypes.c_double()\\n bu_ = ctypes.c_double()\\n res = __library__.MSK_XX_getconbound(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 set_concentration_boundaries(self, boundary_marker, concentration_function):\\n self.concentration_boundaries[boundary_marker] = concentration_function\",\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 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 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 _process_individual_bound(self, val):\\n if(val not in [True, False]):\\n raise ValueError('For composition bounds expected are iether True' \\n '(free function) or False (fixed function) not %s' % (str(val)))\\n return val\",\n \"def apply_bounds(self, column_name, lower_bound=-np.inf,\\n upper_bound=np.inf):\\n self.check_for_column(column_name)\\n\\n if lower_bound is None:\\n lower_bound = -np.inf\\n if upper_bound is None:\\n upper_bound = np.inf\\n column = self.data[column_name]\\n self.data[column_name] = column.clip(lower_bound, upper_bound)\",\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 _set_constraint(self):\\n pass\",\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 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 getconbound(self,i_): # 3\\n res,resargs = self.__obj.getconbound(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 _setBound(self, value):\\n if self._colormap is not None:\\n if self._index == 0:\\n min_ = value\\n max_ = self._colormap.getVMax()\\n else: # self._index == 1\\n min_ = self._colormap.getVMin()\\n max_ = value\\n\\n if max_ is not None and min_ is not None and min_ > max_:\\n min_, max_ = max_, min_\\n self._colormap.setVRange(min_, max_)\",\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 _onSetParameterLower(self, value):\\n self._parameters['lower'] = min(value, self._parameters['upper']) # Limit at upper\\n self._logger.info(\\\"Parameter 'lower' of function '{}' changed to {}\\\".format(self._function, value))\\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())\",\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 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 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 constraint(self, c):\\n self.add_constraint(c)\",\n \"def set_bounds(\\n self: A,\\n lower: BoundValue = None,\\n upper: BoundValue = None,\\n method: str = \\\"clipping\\\",\\n full_range_sampling: bool = False,\\n a_min: BoundValue = None,\\n a_max: BoundValue = None,\\n ) -> A: # TODO improve description of methods\\n lower, upper = _a_min_max_deprecation(**locals())\\n bounds = tuple(a if isinstance(a, np.ndarray) or a is None else np.array([a], dtype=float) for a in (lower, upper))\\n both_bounds = all(b is not None for b in bounds)\\n # preliminary checks\\n if self.bound_transform is not None:\\n raise RuntimeError(\\\"A bounding method has already been set\\\")\\n if full_range_sampling and not both_bounds:\\n raise ValueError(\\\"Cannot use full range sampling if both bounds are not set\\\")\\n checker = BoundChecker(*bounds)\\n if not checker(self.value):\\n raise ValueError(\\\"Current value is not within bounds, please update it first\\\")\\n if not (lower is None or upper is None):\\n if (bounds[0] >= bounds[1]).any(): # type: ignore\\n raise ValueError(f\\\"Lower bounds {lower} should be strictly smaller than upper bounds {upper}\\\")\\n # update instance\\n transforms = dict(clipping=trans.Clipping, arctan=trans.ArctanBound, tanh=trans.TanhBound)\\n if method in transforms:\\n if self.exponent is not None and method != \\\"clipping\\\":\\n raise ValueError(f'Cannot use method \\\"{method}\\\" in logarithmic mode')\\n self.bound_transform = transforms[method](*bounds)\\n elif method == \\\"constraint\\\":\\n self.register_cheap_constraint(checker)\\n else:\\n raise ValueError(f\\\"Unknown method {method}\\\")\\n self.bounds = bounds # type: ignore\\n self.full_range_sampling = full_range_sampling\\n # warn if sigma is too large for range\\n if both_bounds and method != \\\"tanh\\\": # tanh goes to infinity anyway\\n std_bounds = tuple(self._to_reduced_space(b) for b in self.bounds) # type: ignore\\n min_dist = np.min(np.abs(std_bounds[0] - std_bounds[1]).ravel())\\n if min_dist < 3.0:\\n warnings.warn(f\\\"Bounds are {min_dist} sigma away from each other at the closest, \\\"\\n \\\"you should aim for at least 3 for better quality.\\\")\\n return self\",\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 setLowerFrequencyBound(self, new_bound: int) -> None:\\n self.lower_frequency_bound = new_bound\",\n \"def __init__(self, lower_bound, upper_bound):\\n\\n if lower_bound > upper_bound:\\n lower_bound, upper_bound = upper_bound, lower_bound\\n\\n self.lb = lower_bound\\n self.ub = upper_bound\\n\\n super(ContinuousDomain, self).__init__(DomainType.continuous)\",\n \"def simplebounds(cls, val, lower, upper):\\n if val < lower:\\n val = lower\\n if val > upper:\\n val = upper\\n return val\",\n \"def _setValidRange(self, contribs, valueRange):\\n testfor(contribs.ndim == 2, ValueError)\\n numContribs, numReps = contribs.shape\\n self._validRange = np.zeros_like(contribs.T, dtype = bool)\\n for ri in range(numReps):\\n # the single set of R for this calculation\\n rset = contribs[:, ri]\\n self._validRange[ri] = ((rset > min(valueRange))\\n * (rset < max(valueRange)))\",\n \"def range(self, value: ArrayLike): # noqa: A003\\n\\n value = as_float_array(value, self.dtype)\\n\\n if not np.all(np.isfinite(value)):\\n runtime_warning(\\n f'\\\"{self.name}\\\" new \\\"range\\\" variable is not finite: {value}, '\\n f\\\"unpredictable results may occur!\\\"\\n )\\n\\n # Empty domain occurs during __init__ because range is set before domain\\n attest(\\n self._domain.size == 0 or value.size == self._domain.size,\\n '\\\"domain\\\" and \\\"range\\\" variables must have same size!',\\n )\\n\\n self._range = value\\n self._function = None # Invalidate the underlying continuous function.\",\n \"def min_values(self, lower, upper): \\n if not self.lower_bounds is None:\\n return self.lower_bounds\\n\\n minus = np.clip(self.coeffs,-math.inf,0)\\n plus = np.clip(self.coeffs,0,math.inf)\\n self.lower_bounds = plus.dot(lower) + minus.dot(upper) + self.const\\n \\n return self.lower_bounds\",\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 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 test_conf_interval_normal_method_with_bounds(data):\\n df = data[\\\"df\\\"]\\n new_df = data[\\\"new_df\\\"]\\n # ``quantile_estimation_method = \\\"normal_fit\\\"``\\n # with enforced lower limit (``min_admissible_value``)\\n ci_model = conf_interval(\\n df=df,\\n value_col=\\\"y\\\",\\n residual_col=\\\"residual\\\",\\n conditional_cols=[\\\"x\\\"],\\n quantiles=[0.005, 0.025, 0.975, 0.995],\\n quantile_estimation_method=\\\"normal_fit\\\",\\n sample_size_thresh=5,\\n small_sample_size_method=\\\"std_quantiles\\\",\\n small_sample_size_quantile=0.95,\\n min_admissible_value=290.0,\\n max_admissible_value=None)\\n\\n pred_df = predict_ci(\\n new_df,\\n ci_model)\\n assert list(pred_df.columns) == [\\\"x\\\", \\\"y_quantile_summary\\\", ERR_STD_COL], (\\n \\\"pred_df does not have the expected column names\\\")\\n pred_df[\\\"y_quantile_summary\\\"] = pred_df[\\\"y_quantile_summary\\\"].apply(\\n lambda x: tuple(round(e, 2) for e in x))\\n assert pred_df[\\\"y_quantile_summary\\\"].values[5] == (290.0, 290.25, 292.54, 292.9), (\\n \\\"quantiles are incorrect\\\")\\n assert pred_df[\\\"y_quantile_summary\\\"].values[11] == (290.0, 290.0, 290.0, 290.0), (\\n \\\"quantiles are incorrect\\\")\",\n \"def constraint(self, constraint):\\n\\n self._constraint = constraint\",\n \"def constraints(self, x):\\n pass\",\n \"def remove_invalid_values(self, lower_bound=float('-inf'), upper_bound=float('inf')) :\\n valid_indices = (lower_bound < self.signal) * (self.signal < upper_bound)\\n self.time_scale = self.time_scale[valid_indices]\\n self.signal = self.signal[valid_indices]\",\n \"def __scale_constraint(c, v):\\n if c.equality:\\n c.set_value((c.lower * v, c.body * v))\\n else:\\n c.set_value(\\n (__none_left_mult(c.lower, v), c.body * v, __none_left_mult(c.upper, v))\\n )\",\n \"def _process_individual_bound(self, val):\\n if(val == True):\\n res = self._DEF_BOUNDS\\n \\n elif(val in [False, None]):\\n res = val\\n else:\\n if(len(val) != 2):\\n raise ValueError('Bound value is not recognized. '% (str(val)))\\n if(val[0] > val[1]):\\n raise ValueError('Bound values are inverted '% (str(val)))\\n res = val\\n return res\",\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 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 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 test_port_bounds(self):\\n v1 = np.array(\\n [1, 2, 3, 6, 5.5, -3, -2, -6.1, -10000, 10000],\\n dtype=np.double\\n )\\n p1 = cn.Port()\\n p1.value = v1\\n\\n self.assertEqual(\\n np.allclose(\\n p1.value,\\n v1\\n ),\\n True\\n )\\n\\n p1.bounds = 0, 1\\n p1.bounded = True\\n\\n self.assertEqual(\\n (p1.value <= 1).all(),\\n True\\n )\\n self.assertEqual(\\n (p1.value >= 0).all(),\\n True\\n )\",\n \"def boundary_of_set(i):\\n b = self.args[i].boundary\\n for j, a in enumerate(self.args):\\n if j != i:\\n b = b - a.interior\\n return b\",\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 _boundary_constraint_fence(\\n self,\\n x: np.ndarray,\\n ) -> np.ndarray:\\n # clip dimensions to fit within the boundary\\n x_constrained = np.clip(\\n x,\\n self.boundary_fence['min'],\\n self.boundary_fence['max'],\\n )\\n return x_constrained\",\n \"def constraint_xml(self, constraint_xml):\\n\\n self._constraint_xml = constraint_xml\",\n \"def fix_curvature(self) -> None:\\n self.n1.fix = True\\n self.n2.fix = True\",\n \"def clip_by_bound(self) -> None:\\n\\n pass\",\n \"def _fcn_minmax_roi(self):\\n self.roi._update_cbar_minmax()\\n self.cbqt.cbobjs._objs['roi']['clim'] = self.roi._clim\\n kwargs = self.cbqt.cbobjs._objs['roi'].to_kwargs(True)\\n self.roi.update_from_dict(kwargs)\\n self.roi._update_cbar()\",\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 lower_bound(self) -> float:\\n ...\",\n \"def clip(self):\\n if self._value < self.lowBound:\\n self._value = self.lowBound\\n elif self._value > self.upBound:\\n self._value = self.upBound\",\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 add_constraint_one_way(self, i, j, filter_function):\\n if not j in self.constraints[i]:\\n # First, get a list of all possible pairs of values between variables i and j\\n self.constraints[i][j] = self.get_all_possible_pairs(self.domains[i], self.domains[j])\\n\\n # Next, filter this list of value pairs through the function\\n # 'filter_function', so that only the legal value pairs remain\\n self.constraints[i][j] = filter(lambda value_pair: filter_function(*value_pair), self.constraints[i][j])\",\n \"def _onSetParameterB(self, value):\\n self._parameters['b'] = min(max(value, self._parameters['lower']), self._parameters['upper']) # Limit at upper and lower\\n self._logger.info(\\\"Parameter ba' of function '{}' changed to {}\\\".format(self._function, value))\\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())\",\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 put_weights_below_constraint(weights: np.ndarray, upper_constraints: np.ndarray, max_iter: int = 1000,\\n epsilon: float = 0.00001) -> np.ndarray:\\n assert upper_constraints.sum() >= 1\\n\\n new_weights = weights\\n for _ in range(max_iter):\\n new_weights, max_difference = _calculate_new_weights(new_weights, upper_constraints)\\n\\n if max_difference <= epsilon:\\n break\\n else:\\n logger = qf_logger.getChild(__name__)\\n logger.warning(\\\"put_weights_below_constraint: \\\\nIt was not possible to find weights within the constraints \\\"\\n \\\"in {:d} iterations\\\".format(max_iter))\\n\\n return new_weights\",\n \"def domain(self, value: ArrayLike):\\n\\n value = as_float_array(value, self.dtype)\\n\\n if not np.all(np.isfinite(value)):\\n runtime_warning(\\n f'\\\"{self.name}\\\" new \\\"domain\\\" variable is not finite: {value}, '\\n f\\\"unpredictable results may occur!\\\"\\n )\\n else:\\n attest(\\n np.all(value[:-1] <= value[1:]),\\n \\\"The new domain value is not monotonic! \\\",\\n )\\n\\n if value.size != self._range.size:\\n self._range = np.resize(self._range, value.shape)\\n\\n self._domain = value\\n self._function = None # Invalidate the underlying continuous function.\",\n \"def setUpperFrequencyBound(self, new_bound: int) -> None:\\n self.upper_frequency_bound = new_bound\",\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 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 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 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 _bound(x, min_value, max_value):\\n return np.maximum(min_value, np.minimum(x, max_value))\",\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 set_bounds_atom(self,bounds):\\n self.bounds = bounds # data bounds\\n raise NotImplementedError\\n self.bounds_atom = None\\n return None\",\n \"def _onSetParameterBIgnoreBounds(self, value):\\n self._parameters['b'] = value\\n self._logger.info(\\\"Parameter 'b' of function '{}' changed to {}\\\".format(self._function, value))\\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())\",\n \"def process_pain(x, lb, ub):\\n x = x.abs()\\n x.loc[(x > ub)] = 8\\n x.loc[(x < lb) | (x > ub)] = np.nan\\n return x\",\n \"def soft_constraint ( self , var , value , name = '' , title = '' ) :\\n \\n assert isinstance ( var , ROOT.RooAbsReal ) ,\\\\\\n \\\"Invalid ``v'': %s/%s\\\" % ( var , type ( var ) ) \\n assert isinstance ( value , VE ),\\\\\\n \\\"Invalid ``value'': %s/%s\\\" % ( value , type ( value ) )\\n\\n assert 0 < value.cov2() , 'Invalid error for %s' % value\\n \\n name = name if name else 'Gauss_%s_%s' % ( var.GetName() , self.name ) \\n title = title if title else 'Gaussian Constraint(%s,%s) at %s' % ( var.GetName() , self.name , value )\\n \\n # value & error as RooFit objects: \\n val = ROOT.RooFit.RooConst ( value.value () )\\n err = ROOT.RooFit.RooConst ( value.error () )\\n \\n # Gaussian constrains \\n gauss = ROOT.RooGaussian ( self.var_name ( name ) , title , var , val , err )\\n \\n # keep all the created technical stuff \\n self.aux_keep.append ( val )\\n self.aux_keep.append ( err )\\n self.aux_keep.append ( gauss )\\n\\n self.info ('Constraint is created %s=%s' % ( var.name , value ) )\\n return gauss\",\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 SetBoundaryCriterion(self, *args):\\n return _ShapeUpgrade.ShapeUpgrade_ShapeDivideContinuity_SetBoundaryCriterion(self, *args)\",\n \"def feasible(self):\\n return self.lowBound <= self._value <= self.upBound\",\n \"def constraints(self):\\n ...\",\n \"def _curve_constrain(self, x, idx, sign):\\n x = x.reshape(1, -1)\\n pdf = np.exp(self.ks_gaussian.score_samples(x))\\n if self.band[0] < pdf < self.band[1]:\\n value = sign * self.pca.inverse_transform(x)[0][idx]\\n else:\\n value = 1E6\\n return value\",\n \"def set_enzyme_constraint(model, reaction_kcat_mw, lowerbound, upperbound):\\n coefficients = dict()\\n for rxn in model.reactions:\\n if rxn.id in reaction_kcat_mw.index:\\n coefficients[rxn.forward_variable] = 1 / \\\\\\n float(reaction_kcat_mw.loc[rxn.id, 'kcat_MW'])\\n constraint = model.problem.Constraint(0, lb=lowerbound, ub=upperbound)\\n model.add_cons_vars(constraint)\\n model.solver.update()\\n constraint.set_linear_coefficients(coefficients=coefficients)\\n return model\",\n \"def ctrl_limit_changed(self, which, new_limit):\\n super(PyDMSpinbox, self).ctrl_limit_changed(which, new_limit)\\n if not self.userDefinedLimits:\\n if which == \\\"UPPER\\\":\\n self.setMaximum(new_limit)\\n else:\\n self.setMinimum(new_limit)\",\n \"def fill_bounds(x, upper, lower, axis, c = 'k', alpha = 0.2):\\n axis.fill_between(\\n x, \\n upper[:, 0], \\n lower[:, 0], \\n facecolor = c,\\n alpha = alpha\\n )\",\n \"def _discretize(self, constraints_object):\\n pass\",\n \"def mark_forced(self, i):\\n assert 0 <= i < len(self.values)\\n if self.__forced is None:\\n self.__forced = set()\\n self.__forced.add(i)\",\n \"def testConstantBoundedField(self):\\n photoCalib = lsst.afw.image.PhotoCalib(self.constantCalibration)\\n self._testPhotoCalibCenter(photoCalib, 0)\\n\\n self.assertEqual(1, photoCalib.instFluxToMaggies(self.instFlux, self.pointYShift))\\n self.assertEqual(0, photoCalib.instFluxToMagnitude(self.instFlux, self.pointYShift))\\n self.assertFloatsAlmostEqual(1e-9, photoCalib.instFluxToMaggies(self.instFlux*1e-9, self.pointXShift))\\n self.assertFloatsAlmostEqual(22.5, photoCalib.instFluxToMagnitude(\\n self.instFlux*1e-9, self.pointXShift))\\n\\n photoCalib = lsst.afw.image.PhotoCalib(self.constantCalibration, self.calibrationErr)\\n self._testPhotoCalibCenter(photoCalib, self.calibrationErr)\",\n \"def constraints(self, constraints):\\n\\n self._constraints = constraints\",\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 update_constraints(self, target_row, target_col, value):\\n for i in range(9):\\n if value in self.possible_values[target_row][i]: # Update the column\\n self.possible_values[target_row][i].remove(value)\\n if value in self.possible_values[i][target_col]: # Update the row\\n self.possible_values[i][target_col].remove(value)\\n\\n # Update the block:\\n block_row = target_row - (target_row % 3)\\n block_col = target_col - (target_col % 3)\\n for row in range(3):\\n for col in range(3):\\n if value in self.possible_values[block_row + row][block_col + col]: # Remove possible value from block\\n self.possible_values[block_row + row][block_col + col].remove(value)\\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 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 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 setConstraints(self, boundsDict):\\n for param in boundsDict.keys():\\n try:\\n if boundsDict[param][0] < boundsDict[param][1]:\\n constraintString = \\\"{:4.4e} < {:s} < {:4.4e}\\\".format(boundsDict[param][0], param, boundsDict[param][1])\\n self.addConstraints(constraintString)\\n else:\\n print('Setting constraints on mbvg; reversing bounds')\\n self.addConstraints(\\\"{:4.4e} < A < {:4.4e}\\\".format(boundsDict[param][1], boundsDict[param][0]))\\n except ValueError:\\n print('Cannot set parameter {:s} for mbvg. Valid choices are', \\\\\\n '(\\\\'A\\\\', \\\\'muX\\\\', \\\\'muY\\\\', \\\\'sigX\\\\', \\\\'sigY\\\\', \\\\'sigP\\\\', \\\\'bg\\\\')'.format(param))\",\n \"def _convert_bound(value, lower_bound, upper_bound):\\n # Converts value to 16 bit two's complement integer via bitwise.\\n most_sig_bit = 0x8000\\n\\n # Gets the two least significant bits\\n convert_val = value & _BYTE << _BYTE_SIZE | value & _BYTE\\n # Extends the most significant bit if it is a 1. This is done by\\n # carrying out the most significant bit.\\n if bool(convert_val & most_sig_bit):\\n convert_val |= ~(_BYTE << _BYTE_SIZE | _BYTE)\\n\\n # Bounds the converted value\\n if convert_val > upper_bound:\\n return upper_bound\\n elif convert_val < lower_bound:\\n return lower_bound\\n return convert_val\",\n \"def new_interval(self, epsilons):\\n original = list()\\n shrinked = list()\\n\\n for (i, j) in self.contingent_constraints:\\n orig = (-self.stnu[j][i]['weight'], self.stnu[i][j]['weight'])\\n original.append(orig)\\n\\n low = epsilons[(j, '-')].varValue\\n high = epsilons[(j, '+')].varValue\\n\\n self.stnu.shrink_contingent_constraint(i, j, low, high)\\n new = (-self.stnu[j][i]['weight'], self.stnu[i][j]['weight'])\\n shrinked.append(new)\\n\\n return original, shrinked\",\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 update_old_branches(self, source, constraint):\\n for dest in self.G_backup[source]:\\n for source_con, dest_con_set in constraint.items():\\n # Skip if branch already has constraints\\n if (source_con in self.G_backup[source][dest]['constraint']):\\n pass\\n # Skip if constraint directly leads to the branching node\\n elif (source in dest_con_set):\\n pass\\n else: # Otherwise, update new graph with new constraints\\n self.G[source][dest]['constraint'].update({source_con: set(self.G_backup[source_con].keys())})\",\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 constrain(self, candidate_map, processed, to_process):\\n for component in self._components:\\n component.constrain(candidate_map, processed, to_process)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.9179256","0.7916872","0.7376792","0.7195413","0.6016417","0.6004763","0.5817389","0.5724509","0.5707932","0.56736284","0.5642968","0.5613745","0.5549648","0.5543372","0.55391896","0.5501184","0.5480884","0.5466385","0.5455747","0.54376006","0.543686","0.5429047","0.53966075","0.5349046","0.5345001","0.5336194","0.531211","0.53000784","0.52504855","0.5246634","0.5245515","0.5245066","0.52189416","0.5186586","0.5183232","0.5179955","0.5169101","0.5154618","0.51475984","0.513207","0.5112581","0.5073659","0.50563186","0.50485694","0.50423133","0.5030229","0.5025372","0.5024538","0.50006896","0.49952418","0.4993318","0.49915087","0.49825892","0.49722886","0.49722496","0.4958935","0.49569622","0.4956467","0.49537972","0.49291486","0.49183175","0.4911505","0.4911505","0.49014685","0.48960385","0.48909718","0.4890867","0.48766202","0.48704708","0.4869734","0.48681858","0.48662442","0.48645616","0.48578867","0.48560643","0.48452082","0.48411393","0.48305312","0.48283932","0.4820212","0.4815376","0.48071364","0.48027566","0.48009756","0.4796201","0.479577","0.47887552","0.47851458","0.47831735","0.4763118","0.47565448","0.47561663","0.475263","0.47500512","0.47488484","0.47466636","0.4731436","0.47217187","0.47143728","0.47117448"],"string":"[\n \"0.9179256\",\n \"0.7916872\",\n \"0.7376792\",\n \"0.7195413\",\n \"0.6016417\",\n \"0.6004763\",\n \"0.5817389\",\n \"0.5724509\",\n \"0.5707932\",\n \"0.56736284\",\n \"0.5642968\",\n \"0.5613745\",\n \"0.5549648\",\n \"0.5543372\",\n \"0.55391896\",\n \"0.5501184\",\n \"0.5480884\",\n \"0.5466385\",\n \"0.5455747\",\n \"0.54376006\",\n \"0.543686\",\n \"0.5429047\",\n \"0.53966075\",\n \"0.5349046\",\n \"0.5345001\",\n \"0.5336194\",\n \"0.531211\",\n \"0.53000784\",\n \"0.52504855\",\n \"0.5246634\",\n \"0.5245515\",\n \"0.5245066\",\n \"0.52189416\",\n \"0.5186586\",\n \"0.5183232\",\n \"0.5179955\",\n \"0.5169101\",\n \"0.5154618\",\n \"0.51475984\",\n \"0.513207\",\n \"0.5112581\",\n \"0.5073659\",\n \"0.50563186\",\n \"0.50485694\",\n \"0.50423133\",\n \"0.5030229\",\n \"0.5025372\",\n \"0.5024538\",\n \"0.50006896\",\n \"0.49952418\",\n \"0.4993318\",\n \"0.49915087\",\n \"0.49825892\",\n \"0.49722886\",\n \"0.49722496\",\n \"0.4958935\",\n \"0.49569622\",\n \"0.4956467\",\n \"0.49537972\",\n \"0.49291486\",\n \"0.49183175\",\n \"0.4911505\",\n \"0.4911505\",\n \"0.49014685\",\n \"0.48960385\",\n \"0.48909718\",\n \"0.4890867\",\n \"0.48766202\",\n \"0.48704708\",\n \"0.4869734\",\n \"0.48681858\",\n \"0.48662442\",\n \"0.48645616\",\n \"0.48578867\",\n \"0.48560643\",\n \"0.48452082\",\n \"0.48411393\",\n \"0.48305312\",\n \"0.48283932\",\n \"0.4820212\",\n \"0.4815376\",\n \"0.48071364\",\n \"0.48027566\",\n \"0.48009756\",\n \"0.4796201\",\n \"0.479577\",\n \"0.47887552\",\n \"0.47851458\",\n \"0.47831735\",\n \"0.4763118\",\n \"0.47565448\",\n \"0.47561663\",\n \"0.475263\",\n \"0.47500512\",\n \"0.47488484\",\n \"0.47466636\",\n \"0.4731436\",\n \"0.47217187\",\n \"0.47143728\",\n \"0.47117448\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8795833"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11772,"cells":{"query":{"kind":"string","value":"Changes the bounds for one variable. chgvarbound(self,j_,lower_,finite_,value_)"},"document":{"kind":"string","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)"},"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 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 chgconbound(self,i_,lower_,finite_,value_): # 3\n res = self.__obj.chgconbound(i_,lower_,finite_,value_)\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 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 chgbound(self,accmode_,i_,lower_,finite_,value_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n res = self.__obj.chgbound(accmode_,i_,lower_,finite_,value_)\n if res != 0:\n result,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 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 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(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 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 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 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 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 _setBound(self, value):\n if self._colormap is not None:\n if self._index == 0:\n min_ = value\n max_ = self._colormap.getVMax()\n else: # self._index == 1\n min_ = self._colormap.getVMin()\n max_ = value\n\n if max_ is not None and min_ is not None and min_ > max_:\n min_, max_ = max_, min_\n self._colormap.setVRange(min_, max_)","def test_basic_handling_range_with_variable_bounds(spark_ctx):\n\n dr = Drudge(spark_ctx)\n\n j1, j2 = symbols('j1 j2')\n m1, m2 = symbols('m1, m2')\n j_max = symbols('j_max')\n j = Range('j', 0, j_max)\n m = Range('m')\n dr.set_dumms(j, [j1, j2])\n dr.set_dumms(m, [m1, m2])\n\n v = Vec('v')\n x = IndexedBase('x')\n tensor = dr.sum((j2, j), (m2, m[0, j2]), x[j2, m2] * v[j2, m2])\n\n reset = tensor.reset_dumms()\n assert reset.n_terms == 1\n term = reset.local_terms[0]\n assert len(term.sums) == 2\n if term.sums[0][1].label == 'j':\n j_sum, m_sum = term.sums\n else:\n m_sum, j_sum = term.sums\n assert j_sum[0] == j1\n assert j_sum[1].args == j.args\n assert m_sum[0] == m1\n assert m_sum[1].label == 'm'\n assert m_sum[1].lower == 0\n assert m_sum[1].upper == j1 # Important!\n assert term.amp == x[j1, m1]\n assert term.vecs == (v[j1, m1],)\n\n # Test that functions can be mapped to the bounds.\n repled = reset.map2scalars(\n lambda x: x.xreplace({j_max: 10}), skip_ranges=False\n )\n assert repled.n_terms == 1\n term = repled.local_terms[0]\n checked = False\n for _, i in term.sums:\n if i.label == 'j':\n assert i.lower == 0\n assert i.upper == 10\n checked = True\n continue\n assert checked","def set_bounds_atom(self,bounds):\n self.bounds = bounds # data bounds\n raise NotImplementedError\n self.bounds_atom = None\n return None","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 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 _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 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 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 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 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 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 atvar(a,limits=None,inclusive=(1,1)):\r\n a = a.astype(N.float_)\r\n if limits == None or limits == [None,None]:\r\n return avar(a)\r\n assert type(limits) in [ListType,TupleType,N.ndarray], \"Wrong type for limits in atvar\"\r\n if inclusive[0]: lowerfcn = N.greater_equal\r\n else: lowerfcn = N.greater\r\n if inclusive[1]: upperfcn = N.less_equal\r\n else: upperfcn = N.less\r\n if limits[0] > N.maximum.reduce(N.ravel(a)) or limits[1] < N.minimum.reduce(N.ravel(a)):\r\n raise ValueError, \"No array values within given limits (atvar).\"\r\n elif limits[0]==None and limits[1]<>None:\r\n mask = upperfcn(a,limits[1])\r\n elif limits[0]<>None and limits[1]==None:\r\n mask = lowerfcn(a,limits[0])\r\n elif limits[0]<>None and limits[1]<>None:\r\n mask = lowerfcn(a,limits[0])*upperfcn(a,limits[1])\r\n\r\n a = N.compress(mask,a) # squish out excluded values\r\n return avar(a)","def _onSetParameterBIgnoreBounds(self, value):\n self._parameters['b'] = value\n self._logger.info(\"Parameter 'b' of function '{}' changed to {}\".format(self._function, value))\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())","def _process_individual_bound(self, val):\n if(val not in [True, False]):\n raise ValueError('For composition bounds expected are iether True' \n '(free function) or False (fixed function) not %s' % (str(val)))\n return val","def _onSetParameterB(self, value):\n self._parameters['b'] = min(max(value, self._parameters['lower']), self._parameters['upper']) # Limit at upper and lower\n self._logger.info(\"Parameter ba' of function '{}' changed to {}\".format(self._function, value))\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())","def _onSetParameterUpper(self, value):\n self._parameters['upper'] = max(value, self._parameters['lower']) # Limit at lower\n self._logger.info(\"Parameter 'upper' of function '{}' changed to {}\".format(self._function, value))\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())","def update(self, function_values, es, bounds=None):\r\n if bounds is None:\r\n bounds = self.bounds\r\n if bounds is None or (bounds[0] is None and bounds[1] is None): # no bounds ==> no penalty\r\n return self # len(function_values) * [0.0] # case without voilations\r\n\r\n N = es.N\r\n ### prepare\r\n # compute varis = sigma**2 * C_ii\r\n varis = es.sigma**2 * array(N * [es.C] if np.isscalar(es.C) else ( # scalar case\r\n es.C if np.isscalar(es.C[0]) else # diagonal matrix case\r\n [es.C[i][i] for i in xrange(N)])) # full matrix case\r\n\r\n # dmean = (es.mean - es.gp.into_bounds(es.mean)) / varis**0.5\r\n dmean = (es.mean - es.gp.geno(es.gp.into_bounds(es.gp.pheno(es.mean)))) / varis**0.5\r\n\r\n ### Store/update a history of delta fitness value\r\n fvals = sorted(function_values)\r\n l = 1 + len(fvals)\r\n val = fvals[3*l // 4] - fvals[l // 4] # exact interquartile range apart interpolation\r\n val = val / np.mean(varis) # new: val is normalized with sigma of the same iteration\r\n # insert val in history\r\n if np.isfinite(val) and val > 0:\r\n self.hist.insert(0, val)\r\n elif val == inf and len(self.hist) > 1:\r\n self.hist.insert(0, max(self.hist))\r\n else:\r\n pass # ignore 0 or nan values\r\n if len(self.hist) > 20 + (3*N) / es.popsize:\r\n self.hist.pop()\r\n\r\n ### prepare\r\n dfit = np.median(self.hist) # median interquartile range\r\n damp = min(1, es.sp.mueff/10./N)\r\n\r\n ### set/update weights\r\n # Throw initialization error\r\n if len(self.hist) == 0:\r\n raise _Error('wrongful initialization, no feasible solution sampled. ' +\r\n 'Reasons can be mistakenly set bounds (lower bound not smaller than upper bound) or a too large initial sigma0 or... ' +\r\n 'See description of argument func in help(cma.fmin) or an example handling infeasible solutions in help(cma.CMAEvolutionStrategy). ')\r\n # initialize weights\r\n if (dmean.any() and (not self.weights_initialized or es.countiter == 2)): # TODO\r\n self.gamma = array(N * [2*dfit])\r\n self.weights_initialized = True\r\n # update weights gamma\r\n if self.weights_initialized:\r\n edist = array(abs(dmean) - 3 * max(1, N**0.5/es.sp.mueff))\r\n if 1 < 3: # this is better, around a factor of two\r\n # increase single weights possibly with a faster rate than they can decrease\r\n # value unit of edst is std dev, 3==random walk of 9 steps\r\n self.gamma *= exp((edist>0) * np.tanh(edist/3) / 2.)**damp\r\n # decrease all weights up to the same level to avoid single extremely small weights\r\n # use a constant factor for pseudo-keeping invariance\r\n self.gamma[self.gamma > 5 * dfit] *= exp(-1./3)**damp\r\n # self.gamma[idx] *= exp(5*dfit/self.gamma[idx] - 1)**(damp/3)\r\n elif 1 < 3 and (edist>0).any(): # previous method\r\n # CAVE: min was max in TEC 2009\r\n self.gamma[edist>0] *= 1.1**min(1, es.sp.mueff/10./N)\r\n # max fails on cigtab(N=12,bounds=[0.1,None]):\r\n # self.gamma[edist>0] *= 1.1**max(1, es.sp.mueff/10./N) # this was a bug!?\r\n # self.gamma *= exp((edist>0) * np.tanh(edist))**min(1, es.sp.mueff/10./N)\r\n else: # alternative version, but not better\r\n solutions = es.pop # this has not been checked\r\n r = self.feasible_ratio(solutions) # has to be the averaged over N iterations\r\n self.gamma *= exp(np.max([N*[0], 0.3 - r], axis=0))**min(1, es.sp.mueff/10/N)\r\n es.more_to_write += list(self.gamma) if self.weights_initialized else N * [1.0]\r\n ### return penalty\r\n # es.more_to_write = self.gamma if not np.isscalar(self.gamma) else N*[1]\r\n return self # bound penalty values\r","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 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 variable_integrate(self, variable, *bounds):\n try:\n index = list(self.variables).index(variable)\n except ValueError:\n index = None\n if index is not None:\n indef_integral = self.head.integrate_indefinite_index(type(self), self.data, self, index)\n if bounds:\n low, high = bounds\n return indef_integral.variable_subs(variable, high) - indef_integral.variable_subs(variable, low)\n return indef_integral\n raise NotImplementedError(`self.variables, variable, index`)","def _onSetParameterLower(self, value):\n self._parameters['lower'] = min(value, self._parameters['upper']) # Limit at upper\n self._logger.info(\"Parameter 'lower' of function '{}' changed to {}\".format(self._function, value))\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())","def range(self, value: ArrayLike): # noqa: A003\n\n value = as_float_array(value, self.dtype)\n\n if not np.all(np.isfinite(value)):\n runtime_warning(\n f'\"{self.name}\" new \"range\" variable is not finite: {value}, '\n f\"unpredictable results may occur!\"\n )\n\n # Empty domain occurs during __init__ because range is set before domain\n attest(\n self._domain.size == 0 or value.size == self._domain.size,\n '\"domain\" and \"range\" variables must have same size!',\n )\n\n self._range = value\n self._function = None # Invalidate the underlying continuous function.","def vcjamged(self, whichval, newvalue):\n\n if self.performingupdate or whichval >= self.numcols or type(newvalue) != float:\n return\n\n diff = newvalue - self.currentvalues[whichval]\n if abs(diff) < 0.01:\n return\n\n incr = 0.01\n if diff < 0.0:\n incr = -.01\n\n while newvalue in self.currentvalues:\n newvalue = round(newvalue + incr, 2)\n\n # If we've run off either end, we'll have to go back to where we were\n\n if newvalue < self.minvalue or newvalue > self.maxvalue:\n self.performingupdate = True\n self.vspins[whichval].setValue(self.currentvalues[whichval])\n self.performingupdate = False\n return\n\n self.performingupdate = True\n self.currentvalues[whichval] = newvalue\n self.currentvalues.sort()\n self.createrest()\n self.fillingrid()\n self.performingupdate = False\n self.plotmap()","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 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 boundgreed(N,j,U,P,f,fu,ub,lb,prob):\n U[j]=np.minimum(U[j], ub)\n U[j]=np.maximum(U[j], lb)\n ##\n fu[j]=prob(U[j])\n N = N+1\n if fu[j] < f[j]:\n P[j]= U[j]\n f[j]=fu[j]\n return N,f,P","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 __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 apply_bounds(self, column_name, lower_bound=-np.inf,\n upper_bound=np.inf):\n self.check_for_column(column_name)\n\n if lower_bound is None:\n lower_bound = -np.inf\n if upper_bound is None:\n upper_bound = np.inf\n column = self.data[column_name]\n self.data[column_name] = column.clip(lower_bound, upper_bound)","def compute_bounds(self, field):\n # Call general-purpose bound computation.\n super(VertexBasedP1DGLimiter, self).compute_bounds(field)\n\n # Add the average of lateral boundary facets to min/max fields\n # NOTE this just computes the arithmetic mean of nodal values on the facet,\n # which in general is not equivalent to the mean of the field over the bnd facet.\n # This is OK for P1DG triangles, but not exact for the extruded case (quad facets)\n from finat.finiteelementbase import entity_support_dofs\n\n if self.extruded:\n entity_dim = (self.dim-2, 1) # get vertical facets\n else:\n entity_dim = self.dim-1\n boundary_dofs = entity_support_dofs(self.P1DG.finat_element, entity_dim)\n local_facet_nodes = np.array([boundary_dofs[e] for e in sorted(boundary_dofs.keys())])\n n_bnd_nodes = local_facet_nodes.shape[1]\n local_facet_idx = op2.Global(local_facet_nodes.shape, local_facet_nodes, dtype=np.int32, name='local_facet_idx')\n code = \"\"\"\n void my_kernel(double *qmax, double *qmin, double *field, unsigned int *facet, unsigned int *local_facet_idx)\n {\n double face_mean = 0.0;\n for (int i = 0; i < %(nnodes)d; i++) {\n unsigned int idx = local_facet_idx[facet[0]*%(nnodes)d + i];\n face_mean += field[idx];\n }\n face_mean /= %(nnodes)d;\n for (int i = 0; i < %(nnodes)d; i++) {\n unsigned int idx = local_facet_idx[facet[0]*%(nnodes)d + i];\n qmax[idx] = fmax(qmax[idx], face_mean);\n qmin[idx] = fmin(qmin[idx], face_mean);\n }\n }\"\"\"\n bnd_kernel = op2.Kernel(code % {'nnodes': n_bnd_nodes}, 'my_kernel')\n op2.par_loop(bnd_kernel,\n self.P1DG.mesh().exterior_facets.set,\n self.max_field.dat(op2.MAX, self.max_field.exterior_facet_node_map()),\n self.min_field.dat(op2.MIN, self.min_field.exterior_facet_node_map()),\n field.dat(op2.READ, field.exterior_facet_node_map()),\n self.P1DG.mesh().exterior_facets.local_facet_dat(op2.READ),\n local_facet_idx(op2.READ))\n if self.extruded:\n # Add nodal values from surface/bottom boundaries\n # NOTE calling firedrake par_loop with measure=ds_t raises an error\n bottom_nodes = get_facet_mask(self.P1CG, 'bottom')\n top_nodes = get_facet_mask(self.P1CG, 'top')\n bottom_idx = op2.Global(len(bottom_nodes), bottom_nodes, dtype=np.int32, name='node_idx')\n top_idx = op2.Global(len(top_nodes), top_nodes, dtype=np.int32, name='node_idx')\n code = \"\"\"\n void my_kernel(double *qmax, double *qmin, double *field, int *idx) {\n double face_mean = 0;\n for (int i=0; i<%(nnodes)d; i++) {\n face_mean += field[idx[i]];\n }\n face_mean /= %(nnodes)d;\n for (int i=0; i<%(nnodes)d; i++) {\n qmax[idx[i]] = fmax(qmax[idx[i]], face_mean);\n qmin[idx[i]] = fmin(qmin[idx[i]], face_mean);\n }\n }\"\"\"\n kernel = op2.Kernel(code % {'nnodes': len(bottom_nodes)}, 'my_kernel')\n\n op2.par_loop(kernel, self.mesh.cell_set,\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\n field.dat(op2.READ, field.function_space().cell_node_map()),\n bottom_idx(op2.READ),\n iteration_region=op2.ON_BOTTOM)\n\n op2.par_loop(kernel, self.mesh.cell_set,\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\n field.dat(op2.READ, field.function_space().cell_node_map()),\n top_idx(op2.READ),\n iteration_region=op2.ON_TOP)\n if self.squeezed_triangles:\n code = \"\"\"\n void my_kernel(double *qmax, double *qmin, double *marker) {\n float min_val, max_val;\n for (int i=0; i<%(nnodes)d; i++) {\n if (marker[i] > 0) {\n max_val = qmax[i];\n min_val = qmin[i];\n break;\n }\n }\n for (int i=i+1; i<%(nnodes)d; i++) {\n if (marker[i] > 0) {\n max_val = fmax(qmax[i], max_val);\n min_val = fmin(qmin[i], min_val);\n }\n }\n for (int i=0; i<%(nnodes)d; i++) {\n if (marker[i] > 0) {\n qmax[i] = max_val;\n qmin[i] = min_val;\n }\n }\n }\"\"\"\n cnode_map = self.min_field.function_space().cell_node_map()\n kernel = op2.Kernel(code % {'nnodes': cnode_map.shape[1]}, 'my_kernel')\n\n marker = self.squeezed_filter.marker\n\n # NOTE: for multiple squeezed triangle on top (e.g. ice front!) this currently only\n # works at the top, under the assumption that cells are iterated\n # over in each column bottom to top:\n op2.par_loop(kernel, self.mesh.cell_set,\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\n marker.dat(op2.READ, marker.function_space().cell_node_map()))","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 bounds(self, new_bounds: devices.PrimaryBounds) -> None:\n self._assert_bounds_are_valid(new_bounds)\n self._bounds = list(new_bounds)","def set_boundary(self, t):\n gw_level = self.gwhead[t]\n if np.isfinite(gw_level):\n self.gw.potential = gw_level","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 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 set_value ( var , value , ok = lambda a , b : True ) :\n\n ## must be roofit variable! \n assert isinstance ( var , ROOT.RooAbsReal ) , 'Invalid type of ``var'' %s' % type ( var )\n \n if not hasattr ( var , 'setVal' ) :\n raise ValueError ( \"No value can be set for %s/%s\" % ( var , type ( var ) ) ) \n\n ## convert to float \n value = float ( value )\n\n ## check for the range, if defined \n minmax = var.minmax ()\n if minmax :\n mn , mx = minmax\n if not ( mn <= value <= mx or isequal ( mn , value ) or isequal ( mx , value ) ) :\n raise ValueError ( \"Value %s is outside of the [%s,%s] region\" % ( value , mn , mx ) ) \n \n ## check for external conditions, if specified \n if not ok ( var , value ) :\n raise ValueError ( \"Value %s is not OK\" % value ) \n\n ## finally set the value \n var.setVal ( value )\n\n return isequal ( value , var.getVal () )","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 _update_local_solution(self, x: np.ndarray, x_neigh: dict, stepsize: float, **kwargs):\r\n for j, x_j in x_neigh.items():\r\n self.lambd[j] += stepsize * (x - x_j)\r\n \r\n self.x = x","def finalize_variable(self):\n # variables for which there has been a constraint\n constrained_values = []\n for constraint_type in self.constraint:\n for constraint in self.constraint[constraint_type]:\n if constraint_type in ['threshold', 'count', 'only_one']:\n constraint_value = constraint[-1]\n constrained_values.append(constraint_value)\n elif constraint_type == 'time':\n constraint_values = constraint[-2:]\n constrained_values += constraint_values\n # compare constrained values to all populated values\n unconstrained_values = [value for value in self.value if value not in constrained_values]\n\n # TODO: make sure constraint interpreter knows 1,0,0 is a special case of just making sure a matching value is seen\n for value in unconstrained_values:\n if 'count' in self.constraint.keys():\n self.constraint['count'].append([[1, 0, 0], value])\n else:\n self.constraint['count'] = [[1, 0, 0], value]\n # default is a single variable count if not otherswise stated\n for value in unconstrained_values:\n self.constraint\n\n ##TODO: if variable is seen in multiple constraints, link those constraints to create a special super constraint of some sort","def test_port_bounds(self):\n v1 = np.array(\n [1, 2, 3, 6, 5.5, -3, -2, -6.1, -10000, 10000],\n dtype=np.double\n )\n p1 = cn.Port()\n p1.value = v1\n\n self.assertEqual(\n np.allclose(\n p1.value,\n v1\n ),\n True\n )\n\n p1.bounds = 0, 1\n p1.bounded = True\n\n self.assertEqual(\n (p1.value <= 1).all(),\n True\n )\n self.assertEqual(\n (p1.value >= 0).all(),\n True\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)","def _bound(x, min_value, max_value):\n return np.maximum(min_value, np.minimum(x, max_value))","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_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 simplebounds(cls, val, lower, upper):\n if val < lower:\n val = lower\n if val > upper:\n val = upper\n return val","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 checkbox_changed(self, i, j, var):\n def f():\n self.model[i][j] = var.get()\n return f","def test_v_bounds(self):\n n = 50\n t_max = 100.0\n dt = 0.1\n\n G = StudentLayer(n)\n G.i_ext_init = np.linspace(-1.0, 1.0, n)\n\n class BoundsChecker(object):\n def __init__(self, target):\n self.target = target\n self.small = None\n self.large = None\n self.order = 1\n\n def evolve(self, t, dt):\n small = np.min(self.target.v)\n large = np.max(self.target.v)\n if self.small is None or self.small > small:\n self.small = small\n if self.large is None or self.large < large:\n self.large = large\n \n M = BoundsChecker(G)\n\n sim = simulation.Simulation(G, M, dt=dt)\n sim.run(t_max)\n\n self.assertLess(M.large, G.v_th)","def solveForQuantity(self, pointDict, quantity, target, bounds=None,\n function=(lambda x, q: q),\n pointAsFunctionOfSolveVar=lambda x: None,\n tol=1.e-6):\n assert isinstance(pointDict, dict)\n\n assert len(pointDict) < 3, \"Can't solve anything if you've specified more than 2 indVars!\"\n assert len(pointDict) > 1, \"Solve is under-determined with less than 2 indVars!\"\n\n solveRoot = scipyOptimize.brentq\n #solveRoot = solveRootBisect\n solveVar = [indVar for indVar in self.indVars if indVar not in pointDict][0]\n\n #todo: add some good asserts for bounds\n #NOTE BOUNDS MUST BE IN LOGVAR!!!\n if bounds is not None:\n boundMin = bounds[0]\n boundMax = bounds[1]\n else:\n boundMin = self.h5file[solveVar][0]\n boundMax = self.h5file[solveVar][-1]\n\n #todo Fix this hack for BetaEq\n setBetaEqInSolve = False\n if 'ye' in pointDict and pointDict['ye'] == 'BetaEq':\n self.clearState()\n setBetaEqInSolve = True\n pointDict['ye'] = 0.1 # do not like this hack; necessary to pass pointDict validation\n\n self.validatePointDict(pointDict)\n\n #TODO FIX THIS HARD CODING FUCK FUKC FUCK\n if pointAsFunctionOfSolveVar(14.0) is None:\n val = pointDict['logtemp']\n pointAsFunctionOfSolveVar = lambda x: val\n\n indVarsTable = self.getIndVarsTable()\n\n if setBetaEqInSolve:\n if self.cachedBetaEqYeVsRhos is not None:\n cachedBetaEqYeVsRhos = self.cachedBetaEqYeVsRhos\n else:\n cachedBetaEqYeVsRhos = self.getBetaEqYeVsRhobTable(pointAsFunctionOfSolveVar,\n boundMin, boundMax)\n\n def quantityOfSolveVar(x):\n #Here we construct the point to interpolate at, but we\n # must do it carefully since we don't know apriori what\n # solveVar is\n point = []\n #todo factor this for out of quantityOfSolveVar\n for indVar in self.indVars:\n if indVar not in pointDict:\n #print \"NOT\", indVar\n value = x\n else:\n value = pointDict[indVar]\n if indVar == 'logtemp':\n value = pointAsFunctionOfSolveVar(x)\n #print indVar, value\n point.append(value)\n point = tuple(point)\n if setBetaEqInSolve:\n# tempPointDict = {self.indVars[i]: point[i]\n# for i in range(len(self.indVars)) if not self.indVars[i] == 'ye'}\n for i in range(len(self.indVars)):\n print self.indVars[i]\n tempPointDict = []\n print \"Should not have gotten to this point; debug me!\"\n sys.exit()\n yeForSolve = linInterp(tempPointDict['logrho'],\n cachedBetaEqYeVsRhos[0],\n cachedBetaEqYeVsRhos[1])\n tempPointDict.update({'ye': yeForSolve})\n point = self.pointFromDict(tempPointDict)\n del tempPointDict\n answer = function(x, multidimInterp(point, indVarsTable,\n self.h5file[quantity][...],\n linInterp, 2)\n ) - target\n return answer\n\n try:\n answer = solveRoot(quantityOfSolveVar, boundMin, boundMax, (), tol)\n except ValueError as err:\n #todo: note this is slightly incorrect if pointAsFunctionOfSolveVar is specified\n print \"Error in root solver solving for %s: \" % solveVar, str(err)\n answer = self.findIndVarOfMinAbsQuantity(solveVar,\n self.pointFromDict(pointDict),\n quantity,\n function,\n target)\n print \"Recovering with findIndVarOfMinAbsQuantity, answer: %s\" % answer\n\n return answer","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 _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_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 _process_individual_bound(self, val):\n if(val == True):\n res = self._DEF_BOUNDS\n \n elif(val in [False, None]):\n res = val\n else:\n if(len(val) != 2):\n raise ValueError('Bound value is not recognized. '% (str(val)))\n if(val[0] > val[1]):\n raise ValueError('Bound values are inverted '% (str(val)))\n res = val\n return res","def change_var(self, var):\n return _coconut_tail_call(self.__class__, var, self.elem.substitute({self.var: var}))","def update_bins_to_view(self, *args):\n with delay_callback(self, 'hist_x_min', 'hist_x_max'):\n if self.x_max > self.x_min:\n self.hist_x_min = self.x_min\n self.hist_x_max = self.x_max\n else:\n self.hist_x_min = self.x_max\n self.hist_x_max = self.x_min","def update_bins_to_view(self, *args):\n with delay_callback(self, 'hist_x_min', 'hist_x_max'):\n if self.x_max > self.x_min:\n self.hist_x_min = self.x_min\n self.hist_x_max = self.x_max\n else:\n self.hist_x_min = self.x_max\n self.hist_x_max = self.x_min","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 _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 process_pain(x, lb, ub):\n x = x.abs()\n x.loc[(x > ub)] = 8\n x.loc[(x < lb) | (x > ub)] = np.nan\n return x","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 bounds(self, pos):","def domain(self, value: ArrayLike):\n\n value = as_float_array(value, self.dtype)\n\n if not np.all(np.isfinite(value)):\n runtime_warning(\n f'\"{self.name}\" new \"domain\" variable is not finite: {value}, '\n f\"unpredictable results may occur!\"\n )\n else:\n attest(\n np.all(value[:-1] <= value[1:]),\n \"The new domain value is not monotonic! \",\n )\n\n if value.size != self._range.size:\n self._range = np.resize(self._range, value.shape)\n\n self._domain = value\n self._function = None # Invalidate the underlying continuous function.","def variational_update(self):\n with self.elbo_check('update_p_allele_swap'):\n self.model.update_p_allele_swap()\n\n with self.elbo_check('p_cn'):\n self.model.update_p_cn()\n\n with self.elbo_check('p_breakpoint'):\n self.model.update_p_breakpoint()\n\n with self.elbo_check('p_outlier_total'):\n self.model.update_p_outlier_total()\n\n with self.elbo_check('p_outlier_allele'):\n self.model.update_p_outlier_allele()","def update_binding(self, variable, value):\n old_value = self.lookup(variable).value\n if old_value is None:\n raise BaseException(\n \"Tried to update a variable that's not in scope!\")\n var_x, var_y = self.lookup(variable).variable.pos\n self.lookup(variable).value = value\n if old_value.moves_with_binding:\n old_value.set_pos(0, 0) # Or better yet, somehow remove it\n if value.moves_with_binding:\n value.set_pos(var_x + 130, var_y)\n self.update()","def soft_constraint ( self , var , value , name = '' , title = '' ) :\n \n assert isinstance ( var , ROOT.RooAbsReal ) ,\\\n \"Invalid ``v'': %s/%s\" % ( var , type ( var ) ) \n assert isinstance ( value , VE ),\\\n \"Invalid ``value'': %s/%s\" % ( value , type ( value ) )\n\n assert 0 < value.cov2() , 'Invalid error for %s' % value\n \n name = name if name else 'Gauss_%s_%s' % ( var.GetName() , self.name ) \n title = title if title else 'Gaussian Constraint(%s,%s) at %s' % ( var.GetName() , self.name , value )\n \n # value & error as RooFit objects: \n val = ROOT.RooFit.RooConst ( value.value () )\n err = ROOT.RooFit.RooConst ( value.error () )\n \n # Gaussian constrains \n gauss = ROOT.RooGaussian ( self.var_name ( name ) , title , var , val , err )\n \n # keep all the created technical stuff \n self.aux_keep.append ( val )\n self.aux_keep.append ( err )\n self.aux_keep.append ( gauss )\n\n self.info ('Constraint is created %s=%s' % ( var.name , value ) )\n return gauss","def _localSetState(self,pdict):\n #self.lowerBound = pdict.pop('lowerBound' )\n #self.upperBound = pdict.pop('upperBound' )\n self.range = pdict.pop('range')","def ctrl_limit_changed(self, which, new_limit):\n super(PyDMSpinbox, self).ctrl_limit_changed(which, new_limit)\n if not self.userDefinedLimits:\n if which == \"UPPER\":\n self.setMaximum(new_limit)\n else:\n self.setMinimum(new_limit)","def set_variable_values(self, vars_values):\n raise NotImplementedError()","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 _tkvar_changed(self,name):\n self.debug(\"_tkvar_changed(%s)\"%name)\n displayed_value = self._string2object(name,self._tkvars[name]._original_get())\n object_value = self.get_parameter_value(name) #getattr(self._extraPO,name)\n\n # use equality check then identity check because e.g. val\n # starts at 0.5, type 0.8, then type 0.5, need that to be\n # changed is False, but some types cannot be equality compared\n # (can be identity compared).\n # CEBALERT: need to add a unit test to ensure this keeps working.\n # Plus, I need to think about this, because while the above is\n # true for floats, identity tests make more sense for many types\n # (i.e. you want to know the object is the same).\n try:\n if displayed_value != object_value:\n changed = True\n else:\n changed = False\n except:\n if displayed_value is not object_value:\n changed = True\n else:\n changed = False\n\n self.debug(\"..._v_c return %s\"%changed)\n return changed","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 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 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_update_boundary_opt(self):\n self.set_up()\n boundary_opt_new = np.hstack((self.vmecOptimization.boundary_opt,\n self.vmecOptimization.boundary_opt))\n self.assertRaises(ValueError,self.vmecOptimization.update_boundary_opt,\n boundary_opt_new)\n boundary_opt_new = np.copy(self.vmecOptimization.boundary_opt)\n boundary_opt_new[0] = 1\n self.vmecOptimization.update_boundary_opt(boundary_opt_new)\n self.assertEqual(1,self.vmecOptimization.boundary_opt[0])\n self.tear_down()","def change_dimensions(self, xmin: float, xmax: float,\n ymin: float, ymax: float) -> None:\n self.xmin = xmin\n self.xmax = xmax\n self.ymin = ymin\n self.ymax = ymax\n self.input_equation(self.equation)","def set_variable(self, name, value):\n if self._scalamagic and (not name.startswith(\"_i\")):\n self.scala_interpreter.bind(name, value)\n else:\n self.log.debug('Not setting variable %s', name)","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 max_values(self, lower, upper):\n if not self.upper_bounds is None:\n return self.upper_bounds\n\n minus = np.clip(self.coeffs,-math.inf,0)\n plus = np.clip(self.coeffs,0,math.inf)\n self.upper_bounds = plus.dot(upper) + minus.dot(lower) + self.const\n \n return self.upper_bounds","def update_view_to_bins(self, *args):\n with delay_callback(self, 'x_min', 'x_max'):\n self.x_min = self.hist_x_min\n self.x_max = self.hist_x_max","def update_view_to_bins(self, *args):\n with delay_callback(self, 'x_min', 'x_max'):\n self.x_min = self.hist_x_min\n self.x_max = self.hist_x_max","def _try_to_set_slider(self):\n tagvar_val = self.variable.get()\n try:\n val = float(tagvar_val)\n self.set_slider_bounds(min(self.bounds[0],val),\n max(self.bounds[1],val))\n self.slider.set(val)\n except ValueError:\n pass","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 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 is_bounded_continuous_variable(self):\n for rv in self.unique_variables:\n if not is_bounded_continuous_variable(rv):\n return False\n return True","def set_variables(self, new_variables: np.array):\n pass","def test_lagrangian_bound(self):\n from mpisppy.extensions.xhatlooper import XhatLooper\n PHoptions = self._copy_of_base_options()\n PHoptions[\"PHIterLimit\"] = 1\n PHoptions[\"xhat_looper_options\"] = {\"xhat_solver_options\":\\\n PHoptions[\"iterk_solver_options\"],\n \"scen_limit\": 3}\n ph = mpisppy.opt.ph.PH(PHoptions, self.all3_scenario_names,\n scenario_creator, scenario_denouement,\n cb_data=3, PH_extensions=XhatLooper)\n conv, basic_obj, tbound = ph.ph_main()\n xhatobj = ph.extobject._xhat_looper_obj_final\n dopts = sputils.option_string_to_dict(\"mipgap=0.0001\")\n objbound = ph.post_solve_bound(solver_options=dopts, verbose=False)\n self.assertGreaterEqual(xhatobj, objbound)"],"string":"[\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 chgconbound(self,i_,lower_,finite_,value_): # 3\\n res = self.__obj.chgconbound(i_,lower_,finite_,value_)\\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 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 chgbound(self,accmode_,i_,lower_,finite_,value_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n res = self.__obj.chgbound(accmode_,i_,lower_,finite_,value_)\\n if res != 0:\\n result,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 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 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(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 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 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 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 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 _setBound(self, value):\\n if self._colormap is not None:\\n if self._index == 0:\\n min_ = value\\n max_ = self._colormap.getVMax()\\n else: # self._index == 1\\n min_ = self._colormap.getVMin()\\n max_ = value\\n\\n if max_ is not None and min_ is not None and min_ > max_:\\n min_, max_ = max_, min_\\n self._colormap.setVRange(min_, max_)\",\n \"def test_basic_handling_range_with_variable_bounds(spark_ctx):\\n\\n dr = Drudge(spark_ctx)\\n\\n j1, j2 = symbols('j1 j2')\\n m1, m2 = symbols('m1, m2')\\n j_max = symbols('j_max')\\n j = Range('j', 0, j_max)\\n m = Range('m')\\n dr.set_dumms(j, [j1, j2])\\n dr.set_dumms(m, [m1, m2])\\n\\n v = Vec('v')\\n x = IndexedBase('x')\\n tensor = dr.sum((j2, j), (m2, m[0, j2]), x[j2, m2] * v[j2, m2])\\n\\n reset = tensor.reset_dumms()\\n assert reset.n_terms == 1\\n term = reset.local_terms[0]\\n assert len(term.sums) == 2\\n if term.sums[0][1].label == 'j':\\n j_sum, m_sum = term.sums\\n else:\\n m_sum, j_sum = term.sums\\n assert j_sum[0] == j1\\n assert j_sum[1].args == j.args\\n assert m_sum[0] == m1\\n assert m_sum[1].label == 'm'\\n assert m_sum[1].lower == 0\\n assert m_sum[1].upper == j1 # Important!\\n assert term.amp == x[j1, m1]\\n assert term.vecs == (v[j1, m1],)\\n\\n # Test that functions can be mapped to the bounds.\\n repled = reset.map2scalars(\\n lambda x: x.xreplace({j_max: 10}), skip_ranges=False\\n )\\n assert repled.n_terms == 1\\n term = repled.local_terms[0]\\n checked = False\\n for _, i in term.sums:\\n if i.label == 'j':\\n assert i.lower == 0\\n assert i.upper == 10\\n checked = True\\n continue\\n assert checked\",\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 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 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 _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 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 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 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 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 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 atvar(a,limits=None,inclusive=(1,1)):\\r\\n a = a.astype(N.float_)\\r\\n if limits == None or limits == [None,None]:\\r\\n return avar(a)\\r\\n assert type(limits) in [ListType,TupleType,N.ndarray], \\\"Wrong type for limits in atvar\\\"\\r\\n if inclusive[0]: lowerfcn = N.greater_equal\\r\\n else: lowerfcn = N.greater\\r\\n if inclusive[1]: upperfcn = N.less_equal\\r\\n else: upperfcn = N.less\\r\\n if limits[0] > N.maximum.reduce(N.ravel(a)) or limits[1] < N.minimum.reduce(N.ravel(a)):\\r\\n raise ValueError, \\\"No array values within given limits (atvar).\\\"\\r\\n elif limits[0]==None and limits[1]<>None:\\r\\n mask = upperfcn(a,limits[1])\\r\\n elif limits[0]<>None and limits[1]==None:\\r\\n mask = lowerfcn(a,limits[0])\\r\\n elif limits[0]<>None and limits[1]<>None:\\r\\n mask = lowerfcn(a,limits[0])*upperfcn(a,limits[1])\\r\\n\\r\\n a = N.compress(mask,a) # squish out excluded values\\r\\n return avar(a)\",\n \"def _onSetParameterBIgnoreBounds(self, value):\\n self._parameters['b'] = value\\n self._logger.info(\\\"Parameter 'b' of function '{}' changed to {}\\\".format(self._function, value))\\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())\",\n \"def _process_individual_bound(self, val):\\n if(val not in [True, False]):\\n raise ValueError('For composition bounds expected are iether True' \\n '(free function) or False (fixed function) not %s' % (str(val)))\\n return val\",\n \"def _onSetParameterB(self, value):\\n self._parameters['b'] = min(max(value, self._parameters['lower']), self._parameters['upper']) # Limit at upper and lower\\n self._logger.info(\\\"Parameter ba' of function '{}' changed to {}\\\".format(self._function, value))\\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())\",\n \"def _onSetParameterUpper(self, value):\\n self._parameters['upper'] = max(value, self._parameters['lower']) # Limit at lower\\n self._logger.info(\\\"Parameter 'upper' of function '{}' changed to {}\\\".format(self._function, value))\\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())\",\n \"def update(self, function_values, es, bounds=None):\\r\\n if bounds is None:\\r\\n bounds = self.bounds\\r\\n if bounds is None or (bounds[0] is None and bounds[1] is None): # no bounds ==> no penalty\\r\\n return self # len(function_values) * [0.0] # case without voilations\\r\\n\\r\\n N = es.N\\r\\n ### prepare\\r\\n # compute varis = sigma**2 * C_ii\\r\\n varis = es.sigma**2 * array(N * [es.C] if np.isscalar(es.C) else ( # scalar case\\r\\n es.C if np.isscalar(es.C[0]) else # diagonal matrix case\\r\\n [es.C[i][i] for i in xrange(N)])) # full matrix case\\r\\n\\r\\n # dmean = (es.mean - es.gp.into_bounds(es.mean)) / varis**0.5\\r\\n dmean = (es.mean - es.gp.geno(es.gp.into_bounds(es.gp.pheno(es.mean)))) / varis**0.5\\r\\n\\r\\n ### Store/update a history of delta fitness value\\r\\n fvals = sorted(function_values)\\r\\n l = 1 + len(fvals)\\r\\n val = fvals[3*l // 4] - fvals[l // 4] # exact interquartile range apart interpolation\\r\\n val = val / np.mean(varis) # new: val is normalized with sigma of the same iteration\\r\\n # insert val in history\\r\\n if np.isfinite(val) and val > 0:\\r\\n self.hist.insert(0, val)\\r\\n elif val == inf and len(self.hist) > 1:\\r\\n self.hist.insert(0, max(self.hist))\\r\\n else:\\r\\n pass # ignore 0 or nan values\\r\\n if len(self.hist) > 20 + (3*N) / es.popsize:\\r\\n self.hist.pop()\\r\\n\\r\\n ### prepare\\r\\n dfit = np.median(self.hist) # median interquartile range\\r\\n damp = min(1, es.sp.mueff/10./N)\\r\\n\\r\\n ### set/update weights\\r\\n # Throw initialization error\\r\\n if len(self.hist) == 0:\\r\\n raise _Error('wrongful initialization, no feasible solution sampled. ' +\\r\\n 'Reasons can be mistakenly set bounds (lower bound not smaller than upper bound) or a too large initial sigma0 or... ' +\\r\\n 'See description of argument func in help(cma.fmin) or an example handling infeasible solutions in help(cma.CMAEvolutionStrategy). ')\\r\\n # initialize weights\\r\\n if (dmean.any() and (not self.weights_initialized or es.countiter == 2)): # TODO\\r\\n self.gamma = array(N * [2*dfit])\\r\\n self.weights_initialized = True\\r\\n # update weights gamma\\r\\n if self.weights_initialized:\\r\\n edist = array(abs(dmean) - 3 * max(1, N**0.5/es.sp.mueff))\\r\\n if 1 < 3: # this is better, around a factor of two\\r\\n # increase single weights possibly with a faster rate than they can decrease\\r\\n # value unit of edst is std dev, 3==random walk of 9 steps\\r\\n self.gamma *= exp((edist>0) * np.tanh(edist/3) / 2.)**damp\\r\\n # decrease all weights up to the same level to avoid single extremely small weights\\r\\n # use a constant factor for pseudo-keeping invariance\\r\\n self.gamma[self.gamma > 5 * dfit] *= exp(-1./3)**damp\\r\\n # self.gamma[idx] *= exp(5*dfit/self.gamma[idx] - 1)**(damp/3)\\r\\n elif 1 < 3 and (edist>0).any(): # previous method\\r\\n # CAVE: min was max in TEC 2009\\r\\n self.gamma[edist>0] *= 1.1**min(1, es.sp.mueff/10./N)\\r\\n # max fails on cigtab(N=12,bounds=[0.1,None]):\\r\\n # self.gamma[edist>0] *= 1.1**max(1, es.sp.mueff/10./N) # this was a bug!?\\r\\n # self.gamma *= exp((edist>0) * np.tanh(edist))**min(1, es.sp.mueff/10./N)\\r\\n else: # alternative version, but not better\\r\\n solutions = es.pop # this has not been checked\\r\\n r = self.feasible_ratio(solutions) # has to be the averaged over N iterations\\r\\n self.gamma *= exp(np.max([N*[0], 0.3 - r], axis=0))**min(1, es.sp.mueff/10/N)\\r\\n es.more_to_write += list(self.gamma) if self.weights_initialized else N * [1.0]\\r\\n ### return penalty\\r\\n # es.more_to_write = self.gamma if not np.isscalar(self.gamma) else N*[1]\\r\\n return self # bound penalty values\\r\",\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 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 variable_integrate(self, variable, *bounds):\\n try:\\n index = list(self.variables).index(variable)\\n except ValueError:\\n index = None\\n if index is not None:\\n indef_integral = self.head.integrate_indefinite_index(type(self), self.data, self, index)\\n if bounds:\\n low, high = bounds\\n return indef_integral.variable_subs(variable, high) - indef_integral.variable_subs(variable, low)\\n return indef_integral\\n raise NotImplementedError(`self.variables, variable, index`)\",\n \"def _onSetParameterLower(self, value):\\n self._parameters['lower'] = min(value, self._parameters['upper']) # Limit at upper\\n self._logger.info(\\\"Parameter 'lower' of function '{}' changed to {}\\\".format(self._function, value))\\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())\",\n \"def range(self, value: ArrayLike): # noqa: A003\\n\\n value = as_float_array(value, self.dtype)\\n\\n if not np.all(np.isfinite(value)):\\n runtime_warning(\\n f'\\\"{self.name}\\\" new \\\"range\\\" variable is not finite: {value}, '\\n f\\\"unpredictable results may occur!\\\"\\n )\\n\\n # Empty domain occurs during __init__ because range is set before domain\\n attest(\\n self._domain.size == 0 or value.size == self._domain.size,\\n '\\\"domain\\\" and \\\"range\\\" variables must have same size!',\\n )\\n\\n self._range = value\\n self._function = None # Invalidate the underlying continuous function.\",\n \"def vcjamged(self, whichval, newvalue):\\n\\n if self.performingupdate or whichval >= self.numcols or type(newvalue) != float:\\n return\\n\\n diff = newvalue - self.currentvalues[whichval]\\n if abs(diff) < 0.01:\\n return\\n\\n incr = 0.01\\n if diff < 0.0:\\n incr = -.01\\n\\n while newvalue in self.currentvalues:\\n newvalue = round(newvalue + incr, 2)\\n\\n # If we've run off either end, we'll have to go back to where we were\\n\\n if newvalue < self.minvalue or newvalue > self.maxvalue:\\n self.performingupdate = True\\n self.vspins[whichval].setValue(self.currentvalues[whichval])\\n self.performingupdate = False\\n return\\n\\n self.performingupdate = True\\n self.currentvalues[whichval] = newvalue\\n self.currentvalues.sort()\\n self.createrest()\\n self.fillingrid()\\n self.performingupdate = False\\n self.plotmap()\",\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 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 boundgreed(N,j,U,P,f,fu,ub,lb,prob):\\n U[j]=np.minimum(U[j], ub)\\n U[j]=np.maximum(U[j], lb)\\n ##\\n fu[j]=prob(U[j])\\n N = N+1\\n if fu[j] < f[j]:\\n P[j]= U[j]\\n f[j]=fu[j]\\n return N,f,P\",\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 __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 apply_bounds(self, column_name, lower_bound=-np.inf,\\n upper_bound=np.inf):\\n self.check_for_column(column_name)\\n\\n if lower_bound is None:\\n lower_bound = -np.inf\\n if upper_bound is None:\\n upper_bound = np.inf\\n column = self.data[column_name]\\n self.data[column_name] = column.clip(lower_bound, upper_bound)\",\n \"def compute_bounds(self, field):\\n # Call general-purpose bound computation.\\n super(VertexBasedP1DGLimiter, self).compute_bounds(field)\\n\\n # Add the average of lateral boundary facets to min/max fields\\n # NOTE this just computes the arithmetic mean of nodal values on the facet,\\n # which in general is not equivalent to the mean of the field over the bnd facet.\\n # This is OK for P1DG triangles, but not exact for the extruded case (quad facets)\\n from finat.finiteelementbase import entity_support_dofs\\n\\n if self.extruded:\\n entity_dim = (self.dim-2, 1) # get vertical facets\\n else:\\n entity_dim = self.dim-1\\n boundary_dofs = entity_support_dofs(self.P1DG.finat_element, entity_dim)\\n local_facet_nodes = np.array([boundary_dofs[e] for e in sorted(boundary_dofs.keys())])\\n n_bnd_nodes = local_facet_nodes.shape[1]\\n local_facet_idx = op2.Global(local_facet_nodes.shape, local_facet_nodes, dtype=np.int32, name='local_facet_idx')\\n code = \\\"\\\"\\\"\\n void my_kernel(double *qmax, double *qmin, double *field, unsigned int *facet, unsigned int *local_facet_idx)\\n {\\n double face_mean = 0.0;\\n for (int i = 0; i < %(nnodes)d; i++) {\\n unsigned int idx = local_facet_idx[facet[0]*%(nnodes)d + i];\\n face_mean += field[idx];\\n }\\n face_mean /= %(nnodes)d;\\n for (int i = 0; i < %(nnodes)d; i++) {\\n unsigned int idx = local_facet_idx[facet[0]*%(nnodes)d + i];\\n qmax[idx] = fmax(qmax[idx], face_mean);\\n qmin[idx] = fmin(qmin[idx], face_mean);\\n }\\n }\\\"\\\"\\\"\\n bnd_kernel = op2.Kernel(code % {'nnodes': n_bnd_nodes}, 'my_kernel')\\n op2.par_loop(bnd_kernel,\\n self.P1DG.mesh().exterior_facets.set,\\n self.max_field.dat(op2.MAX, self.max_field.exterior_facet_node_map()),\\n self.min_field.dat(op2.MIN, self.min_field.exterior_facet_node_map()),\\n field.dat(op2.READ, field.exterior_facet_node_map()),\\n self.P1DG.mesh().exterior_facets.local_facet_dat(op2.READ),\\n local_facet_idx(op2.READ))\\n if self.extruded:\\n # Add nodal values from surface/bottom boundaries\\n # NOTE calling firedrake par_loop with measure=ds_t raises an error\\n bottom_nodes = get_facet_mask(self.P1CG, 'bottom')\\n top_nodes = get_facet_mask(self.P1CG, 'top')\\n bottom_idx = op2.Global(len(bottom_nodes), bottom_nodes, dtype=np.int32, name='node_idx')\\n top_idx = op2.Global(len(top_nodes), top_nodes, dtype=np.int32, name='node_idx')\\n code = \\\"\\\"\\\"\\n void my_kernel(double *qmax, double *qmin, double *field, int *idx) {\\n double face_mean = 0;\\n for (int i=0; i<%(nnodes)d; i++) {\\n face_mean += field[idx[i]];\\n }\\n face_mean /= %(nnodes)d;\\n for (int i=0; i<%(nnodes)d; i++) {\\n qmax[idx[i]] = fmax(qmax[idx[i]], face_mean);\\n qmin[idx[i]] = fmin(qmin[idx[i]], face_mean);\\n }\\n }\\\"\\\"\\\"\\n kernel = op2.Kernel(code % {'nnodes': len(bottom_nodes)}, 'my_kernel')\\n\\n op2.par_loop(kernel, self.mesh.cell_set,\\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\\n field.dat(op2.READ, field.function_space().cell_node_map()),\\n bottom_idx(op2.READ),\\n iteration_region=op2.ON_BOTTOM)\\n\\n op2.par_loop(kernel, self.mesh.cell_set,\\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\\n field.dat(op2.READ, field.function_space().cell_node_map()),\\n top_idx(op2.READ),\\n iteration_region=op2.ON_TOP)\\n if self.squeezed_triangles:\\n code = \\\"\\\"\\\"\\n void my_kernel(double *qmax, double *qmin, double *marker) {\\n float min_val, max_val;\\n for (int i=0; i<%(nnodes)d; i++) {\\n if (marker[i] > 0) {\\n max_val = qmax[i];\\n min_val = qmin[i];\\n break;\\n }\\n }\\n for (int i=i+1; i<%(nnodes)d; i++) {\\n if (marker[i] > 0) {\\n max_val = fmax(qmax[i], max_val);\\n min_val = fmin(qmin[i], min_val);\\n }\\n }\\n for (int i=0; i<%(nnodes)d; i++) {\\n if (marker[i] > 0) {\\n qmax[i] = max_val;\\n qmin[i] = min_val;\\n }\\n }\\n }\\\"\\\"\\\"\\n cnode_map = self.min_field.function_space().cell_node_map()\\n kernel = op2.Kernel(code % {'nnodes': cnode_map.shape[1]}, 'my_kernel')\\n\\n marker = self.squeezed_filter.marker\\n\\n # NOTE: for multiple squeezed triangle on top (e.g. ice front!) this currently only\\n # works at the top, under the assumption that cells are iterated\\n # over in each column bottom to top:\\n op2.par_loop(kernel, self.mesh.cell_set,\\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\\n marker.dat(op2.READ, marker.function_space().cell_node_map()))\",\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 bounds(self, new_bounds: devices.PrimaryBounds) -> None:\\n self._assert_bounds_are_valid(new_bounds)\\n self._bounds = list(new_bounds)\",\n \"def set_boundary(self, t):\\n gw_level = self.gwhead[t]\\n if np.isfinite(gw_level):\\n self.gw.potential = gw_level\",\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 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 set_value ( var , value , ok = lambda a , b : True ) :\\n\\n ## must be roofit variable! \\n assert isinstance ( var , ROOT.RooAbsReal ) , 'Invalid type of ``var'' %s' % type ( var )\\n \\n if not hasattr ( var , 'setVal' ) :\\n raise ValueError ( \\\"No value can be set for %s/%s\\\" % ( var , type ( var ) ) ) \\n\\n ## convert to float \\n value = float ( value )\\n\\n ## check for the range, if defined \\n minmax = var.minmax ()\\n if minmax :\\n mn , mx = minmax\\n if not ( mn <= value <= mx or isequal ( mn , value ) or isequal ( mx , value ) ) :\\n raise ValueError ( \\\"Value %s is outside of the [%s,%s] region\\\" % ( value , mn , mx ) ) \\n \\n ## check for external conditions, if specified \\n if not ok ( var , value ) :\\n raise ValueError ( \\\"Value %s is not OK\\\" % value ) \\n\\n ## finally set the value \\n var.setVal ( value )\\n\\n return isequal ( value , var.getVal () )\",\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 _update_local_solution(self, x: np.ndarray, x_neigh: dict, stepsize: float, **kwargs):\\r\\n for j, x_j in x_neigh.items():\\r\\n self.lambd[j] += stepsize * (x - x_j)\\r\\n \\r\\n self.x = x\",\n \"def finalize_variable(self):\\n # variables for which there has been a constraint\\n constrained_values = []\\n for constraint_type in self.constraint:\\n for constraint in self.constraint[constraint_type]:\\n if constraint_type in ['threshold', 'count', 'only_one']:\\n constraint_value = constraint[-1]\\n constrained_values.append(constraint_value)\\n elif constraint_type == 'time':\\n constraint_values = constraint[-2:]\\n constrained_values += constraint_values\\n # compare constrained values to all populated values\\n unconstrained_values = [value for value in self.value if value not in constrained_values]\\n\\n # TODO: make sure constraint interpreter knows 1,0,0 is a special case of just making sure a matching value is seen\\n for value in unconstrained_values:\\n if 'count' in self.constraint.keys():\\n self.constraint['count'].append([[1, 0, 0], value])\\n else:\\n self.constraint['count'] = [[1, 0, 0], value]\\n # default is a single variable count if not otherswise stated\\n for value in unconstrained_values:\\n self.constraint\\n\\n ##TODO: if variable is seen in multiple constraints, link those constraints to create a special super constraint of some sort\",\n \"def test_port_bounds(self):\\n v1 = np.array(\\n [1, 2, 3, 6, 5.5, -3, -2, -6.1, -10000, 10000],\\n dtype=np.double\\n )\\n p1 = cn.Port()\\n p1.value = v1\\n\\n self.assertEqual(\\n np.allclose(\\n p1.value,\\n v1\\n ),\\n True\\n )\\n\\n p1.bounds = 0, 1\\n p1.bounded = True\\n\\n self.assertEqual(\\n (p1.value <= 1).all(),\\n True\\n )\\n self.assertEqual(\\n (p1.value >= 0).all(),\\n True\\n )\",\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 _bound(x, min_value, max_value):\\n return np.maximum(min_value, np.minimum(x, max_value))\",\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_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 simplebounds(cls, val, lower, upper):\\n if val < lower:\\n val = lower\\n if val > upper:\\n val = upper\\n return val\",\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 checkbox_changed(self, i, j, var):\\n def f():\\n self.model[i][j] = var.get()\\n return f\",\n \"def test_v_bounds(self):\\n n = 50\\n t_max = 100.0\\n dt = 0.1\\n\\n G = StudentLayer(n)\\n G.i_ext_init = np.linspace(-1.0, 1.0, n)\\n\\n class BoundsChecker(object):\\n def __init__(self, target):\\n self.target = target\\n self.small = None\\n self.large = None\\n self.order = 1\\n\\n def evolve(self, t, dt):\\n small = np.min(self.target.v)\\n large = np.max(self.target.v)\\n if self.small is None or self.small > small:\\n self.small = small\\n if self.large is None or self.large < large:\\n self.large = large\\n \\n M = BoundsChecker(G)\\n\\n sim = simulation.Simulation(G, M, dt=dt)\\n sim.run(t_max)\\n\\n self.assertLess(M.large, G.v_th)\",\n \"def solveForQuantity(self, pointDict, quantity, target, bounds=None,\\n function=(lambda x, q: q),\\n pointAsFunctionOfSolveVar=lambda x: None,\\n tol=1.e-6):\\n assert isinstance(pointDict, dict)\\n\\n assert len(pointDict) < 3, \\\"Can't solve anything if you've specified more than 2 indVars!\\\"\\n assert len(pointDict) > 1, \\\"Solve is under-determined with less than 2 indVars!\\\"\\n\\n solveRoot = scipyOptimize.brentq\\n #solveRoot = solveRootBisect\\n solveVar = [indVar for indVar in self.indVars if indVar not in pointDict][0]\\n\\n #todo: add some good asserts for bounds\\n #NOTE BOUNDS MUST BE IN LOGVAR!!!\\n if bounds is not None:\\n boundMin = bounds[0]\\n boundMax = bounds[1]\\n else:\\n boundMin = self.h5file[solveVar][0]\\n boundMax = self.h5file[solveVar][-1]\\n\\n #todo Fix this hack for BetaEq\\n setBetaEqInSolve = False\\n if 'ye' in pointDict and pointDict['ye'] == 'BetaEq':\\n self.clearState()\\n setBetaEqInSolve = True\\n pointDict['ye'] = 0.1 # do not like this hack; necessary to pass pointDict validation\\n\\n self.validatePointDict(pointDict)\\n\\n #TODO FIX THIS HARD CODING FUCK FUKC FUCK\\n if pointAsFunctionOfSolveVar(14.0) is None:\\n val = pointDict['logtemp']\\n pointAsFunctionOfSolveVar = lambda x: val\\n\\n indVarsTable = self.getIndVarsTable()\\n\\n if setBetaEqInSolve:\\n if self.cachedBetaEqYeVsRhos is not None:\\n cachedBetaEqYeVsRhos = self.cachedBetaEqYeVsRhos\\n else:\\n cachedBetaEqYeVsRhos = self.getBetaEqYeVsRhobTable(pointAsFunctionOfSolveVar,\\n boundMin, boundMax)\\n\\n def quantityOfSolveVar(x):\\n #Here we construct the point to interpolate at, but we\\n # must do it carefully since we don't know apriori what\\n # solveVar is\\n point = []\\n #todo factor this for out of quantityOfSolveVar\\n for indVar in self.indVars:\\n if indVar not in pointDict:\\n #print \\\"NOT\\\", indVar\\n value = x\\n else:\\n value = pointDict[indVar]\\n if indVar == 'logtemp':\\n value = pointAsFunctionOfSolveVar(x)\\n #print indVar, value\\n point.append(value)\\n point = tuple(point)\\n if setBetaEqInSolve:\\n# tempPointDict = {self.indVars[i]: point[i]\\n# for i in range(len(self.indVars)) if not self.indVars[i] == 'ye'}\\n for i in range(len(self.indVars)):\\n print self.indVars[i]\\n tempPointDict = []\\n print \\\"Should not have gotten to this point; debug me!\\\"\\n sys.exit()\\n yeForSolve = linInterp(tempPointDict['logrho'],\\n cachedBetaEqYeVsRhos[0],\\n cachedBetaEqYeVsRhos[1])\\n tempPointDict.update({'ye': yeForSolve})\\n point = self.pointFromDict(tempPointDict)\\n del tempPointDict\\n answer = function(x, multidimInterp(point, indVarsTable,\\n self.h5file[quantity][...],\\n linInterp, 2)\\n ) - target\\n return answer\\n\\n try:\\n answer = solveRoot(quantityOfSolveVar, boundMin, boundMax, (), tol)\\n except ValueError as err:\\n #todo: note this is slightly incorrect if pointAsFunctionOfSolveVar is specified\\n print \\\"Error in root solver solving for %s: \\\" % solveVar, str(err)\\n answer = self.findIndVarOfMinAbsQuantity(solveVar,\\n self.pointFromDict(pointDict),\\n quantity,\\n function,\\n target)\\n print \\\"Recovering with findIndVarOfMinAbsQuantity, answer: %s\\\" % answer\\n\\n return answer\",\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 _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_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 _process_individual_bound(self, val):\\n if(val == True):\\n res = self._DEF_BOUNDS\\n \\n elif(val in [False, None]):\\n res = val\\n else:\\n if(len(val) != 2):\\n raise ValueError('Bound value is not recognized. '% (str(val)))\\n if(val[0] > val[1]):\\n raise ValueError('Bound values are inverted '% (str(val)))\\n res = val\\n return res\",\n \"def change_var(self, var):\\n return _coconut_tail_call(self.__class__, var, self.elem.substitute({self.var: var}))\",\n \"def update_bins_to_view(self, *args):\\n with delay_callback(self, 'hist_x_min', 'hist_x_max'):\\n if self.x_max > self.x_min:\\n self.hist_x_min = self.x_min\\n self.hist_x_max = self.x_max\\n else:\\n self.hist_x_min = self.x_max\\n self.hist_x_max = self.x_min\",\n \"def update_bins_to_view(self, *args):\\n with delay_callback(self, 'hist_x_min', 'hist_x_max'):\\n if self.x_max > self.x_min:\\n self.hist_x_min = self.x_min\\n self.hist_x_max = self.x_max\\n else:\\n self.hist_x_min = self.x_max\\n self.hist_x_max = self.x_min\",\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 _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 process_pain(x, lb, ub):\\n x = x.abs()\\n x.loc[(x > ub)] = 8\\n x.loc[(x < lb) | (x > ub)] = np.nan\\n return x\",\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 bounds(self, pos):\",\n \"def domain(self, value: ArrayLike):\\n\\n value = as_float_array(value, self.dtype)\\n\\n if not np.all(np.isfinite(value)):\\n runtime_warning(\\n f'\\\"{self.name}\\\" new \\\"domain\\\" variable is not finite: {value}, '\\n f\\\"unpredictable results may occur!\\\"\\n )\\n else:\\n attest(\\n np.all(value[:-1] <= value[1:]),\\n \\\"The new domain value is not monotonic! \\\",\\n )\\n\\n if value.size != self._range.size:\\n self._range = np.resize(self._range, value.shape)\\n\\n self._domain = value\\n self._function = None # Invalidate the underlying continuous function.\",\n \"def variational_update(self):\\n with self.elbo_check('update_p_allele_swap'):\\n self.model.update_p_allele_swap()\\n\\n with self.elbo_check('p_cn'):\\n self.model.update_p_cn()\\n\\n with self.elbo_check('p_breakpoint'):\\n self.model.update_p_breakpoint()\\n\\n with self.elbo_check('p_outlier_total'):\\n self.model.update_p_outlier_total()\\n\\n with self.elbo_check('p_outlier_allele'):\\n self.model.update_p_outlier_allele()\",\n \"def update_binding(self, variable, value):\\n old_value = self.lookup(variable).value\\n if old_value is None:\\n raise BaseException(\\n \\\"Tried to update a variable that's not in scope!\\\")\\n var_x, var_y = self.lookup(variable).variable.pos\\n self.lookup(variable).value = value\\n if old_value.moves_with_binding:\\n old_value.set_pos(0, 0) # Or better yet, somehow remove it\\n if value.moves_with_binding:\\n value.set_pos(var_x + 130, var_y)\\n self.update()\",\n \"def soft_constraint ( self , var , value , name = '' , title = '' ) :\\n \\n assert isinstance ( var , ROOT.RooAbsReal ) ,\\\\\\n \\\"Invalid ``v'': %s/%s\\\" % ( var , type ( var ) ) \\n assert isinstance ( value , VE ),\\\\\\n \\\"Invalid ``value'': %s/%s\\\" % ( value , type ( value ) )\\n\\n assert 0 < value.cov2() , 'Invalid error for %s' % value\\n \\n name = name if name else 'Gauss_%s_%s' % ( var.GetName() , self.name ) \\n title = title if title else 'Gaussian Constraint(%s,%s) at %s' % ( var.GetName() , self.name , value )\\n \\n # value & error as RooFit objects: \\n val = ROOT.RooFit.RooConst ( value.value () )\\n err = ROOT.RooFit.RooConst ( value.error () )\\n \\n # Gaussian constrains \\n gauss = ROOT.RooGaussian ( self.var_name ( name ) , title , var , val , err )\\n \\n # keep all the created technical stuff \\n self.aux_keep.append ( val )\\n self.aux_keep.append ( err )\\n self.aux_keep.append ( gauss )\\n\\n self.info ('Constraint is created %s=%s' % ( var.name , value ) )\\n return gauss\",\n \"def _localSetState(self,pdict):\\n #self.lowerBound = pdict.pop('lowerBound' )\\n #self.upperBound = pdict.pop('upperBound' )\\n self.range = pdict.pop('range')\",\n \"def ctrl_limit_changed(self, which, new_limit):\\n super(PyDMSpinbox, self).ctrl_limit_changed(which, new_limit)\\n if not self.userDefinedLimits:\\n if which == \\\"UPPER\\\":\\n self.setMaximum(new_limit)\\n else:\\n self.setMinimum(new_limit)\",\n \"def set_variable_values(self, vars_values):\\n raise NotImplementedError()\",\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 _tkvar_changed(self,name):\\n self.debug(\\\"_tkvar_changed(%s)\\\"%name)\\n displayed_value = self._string2object(name,self._tkvars[name]._original_get())\\n object_value = self.get_parameter_value(name) #getattr(self._extraPO,name)\\n\\n # use equality check then identity check because e.g. val\\n # starts at 0.5, type 0.8, then type 0.5, need that to be\\n # changed is False, but some types cannot be equality compared\\n # (can be identity compared).\\n # CEBALERT: need to add a unit test to ensure this keeps working.\\n # Plus, I need to think about this, because while the above is\\n # true for floats, identity tests make more sense for many types\\n # (i.e. you want to know the object is the same).\\n try:\\n if displayed_value != object_value:\\n changed = True\\n else:\\n changed = False\\n except:\\n if displayed_value is not object_value:\\n changed = True\\n else:\\n changed = False\\n\\n self.debug(\\\"..._v_c return %s\\\"%changed)\\n return changed\",\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 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 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_update_boundary_opt(self):\\n self.set_up()\\n boundary_opt_new = np.hstack((self.vmecOptimization.boundary_opt,\\n self.vmecOptimization.boundary_opt))\\n self.assertRaises(ValueError,self.vmecOptimization.update_boundary_opt,\\n boundary_opt_new)\\n boundary_opt_new = np.copy(self.vmecOptimization.boundary_opt)\\n boundary_opt_new[0] = 1\\n self.vmecOptimization.update_boundary_opt(boundary_opt_new)\\n self.assertEqual(1,self.vmecOptimization.boundary_opt[0])\\n self.tear_down()\",\n \"def change_dimensions(self, xmin: float, xmax: float,\\n ymin: float, ymax: float) -> None:\\n self.xmin = xmin\\n self.xmax = xmax\\n self.ymin = ymin\\n self.ymax = ymax\\n self.input_equation(self.equation)\",\n \"def set_variable(self, name, value):\\n if self._scalamagic and (not name.startswith(\\\"_i\\\")):\\n self.scala_interpreter.bind(name, value)\\n else:\\n self.log.debug('Not setting variable %s', name)\",\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 max_values(self, lower, upper):\\n if not self.upper_bounds is None:\\n return self.upper_bounds\\n\\n minus = np.clip(self.coeffs,-math.inf,0)\\n plus = np.clip(self.coeffs,0,math.inf)\\n self.upper_bounds = plus.dot(upper) + minus.dot(lower) + self.const\\n \\n return self.upper_bounds\",\n \"def update_view_to_bins(self, *args):\\n with delay_callback(self, 'x_min', 'x_max'):\\n self.x_min = self.hist_x_min\\n self.x_max = self.hist_x_max\",\n \"def update_view_to_bins(self, *args):\\n with delay_callback(self, 'x_min', 'x_max'):\\n self.x_min = self.hist_x_min\\n self.x_max = self.hist_x_max\",\n \"def _try_to_set_slider(self):\\n tagvar_val = self.variable.get()\\n try:\\n val = float(tagvar_val)\\n self.set_slider_bounds(min(self.bounds[0],val),\\n max(self.bounds[1],val))\\n self.slider.set(val)\\n except ValueError:\\n pass\",\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 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 is_bounded_continuous_variable(self):\\n for rv in self.unique_variables:\\n if not is_bounded_continuous_variable(rv):\\n return False\\n return True\",\n \"def set_variables(self, new_variables: np.array):\\n pass\",\n \"def test_lagrangian_bound(self):\\n from mpisppy.extensions.xhatlooper import XhatLooper\\n PHoptions = self._copy_of_base_options()\\n PHoptions[\\\"PHIterLimit\\\"] = 1\\n PHoptions[\\\"xhat_looper_options\\\"] = {\\\"xhat_solver_options\\\":\\\\\\n PHoptions[\\\"iterk_solver_options\\\"],\\n \\\"scen_limit\\\": 3}\\n ph = mpisppy.opt.ph.PH(PHoptions, self.all3_scenario_names,\\n scenario_creator, scenario_denouement,\\n cb_data=3, PH_extensions=XhatLooper)\\n conv, basic_obj, tbound = ph.ph_main()\\n xhatobj = ph.extobject._xhat_looper_obj_final\\n dopts = sputils.option_string_to_dict(\\\"mipgap=0.0001\\\")\\n objbound = ph.post_solve_bound(solver_options=dopts, verbose=False)\\n self.assertGreaterEqual(xhatobj, objbound)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.92657965","0.6645805","0.66179705","0.63044393","0.6285839","0.6257946","0.62345994","0.61521727","0.57639956","0.56784153","0.5577765","0.5573649","0.54211104","0.5403837","0.53660214","0.52223015","0.52110463","0.5164623","0.51575583","0.51482254","0.51427233","0.513306","0.5117852","0.51147217","0.511384","0.5095138","0.50924027","0.50913715","0.5057961","0.50448924","0.5024978","0.50172347","0.50172347","0.49989143","0.49811092","0.49786556","0.4963631","0.4962184","0.49447832","0.49428454","0.4918468","0.49155557","0.49026173","0.4897824","0.4894957","0.48860484","0.48593923","0.4838011","0.48199543","0.4808971","0.48082474","0.48078305","0.4805811","0.48048034","0.4789217","0.47874168","0.47873598","0.47793055","0.47792566","0.47747514","0.47614858","0.47608283","0.47555506","0.47512692","0.4748861","0.47372475","0.4735277","0.47191572","0.47076562","0.47076562","0.46991462","0.4692452","0.4681719","0.46782607","0.46705112","0.46683893","0.46578965","0.4657367","0.46565905","0.465129","0.46453166","0.46415734","0.4629623","0.46199575","0.46070042","0.46011385","0.4599529","0.4591783","0.45882958","0.4588253","0.4584531","0.45760256","0.45693958","0.45693958","0.45537534","0.4552739","0.45526537","0.4552025","0.4549712","0.4546758"],"string":"[\n \"0.92657965\",\n \"0.6645805\",\n \"0.66179705\",\n \"0.63044393\",\n \"0.6285839\",\n \"0.6257946\",\n \"0.62345994\",\n \"0.61521727\",\n \"0.57639956\",\n \"0.56784153\",\n \"0.5577765\",\n \"0.5573649\",\n \"0.54211104\",\n \"0.5403837\",\n \"0.53660214\",\n \"0.52223015\",\n \"0.52110463\",\n \"0.5164623\",\n \"0.51575583\",\n \"0.51482254\",\n \"0.51427233\",\n \"0.513306\",\n \"0.5117852\",\n \"0.51147217\",\n \"0.511384\",\n \"0.5095138\",\n \"0.50924027\",\n \"0.50913715\",\n \"0.5057961\",\n \"0.50448924\",\n \"0.5024978\",\n \"0.50172347\",\n \"0.50172347\",\n \"0.49989143\",\n \"0.49811092\",\n \"0.49786556\",\n \"0.4963631\",\n \"0.4962184\",\n \"0.49447832\",\n \"0.49428454\",\n \"0.4918468\",\n \"0.49155557\",\n \"0.49026173\",\n \"0.4897824\",\n \"0.4894957\",\n \"0.48860484\",\n \"0.48593923\",\n \"0.4838011\",\n \"0.48199543\",\n \"0.4808971\",\n \"0.48082474\",\n \"0.48078305\",\n \"0.4805811\",\n \"0.48048034\",\n \"0.4789217\",\n \"0.47874168\",\n \"0.47873598\",\n \"0.47793055\",\n \"0.47792566\",\n \"0.47747514\",\n \"0.47614858\",\n \"0.47608283\",\n \"0.47555506\",\n \"0.47512692\",\n \"0.4748861\",\n \"0.47372475\",\n \"0.4735277\",\n \"0.47191572\",\n \"0.47076562\",\n \"0.47076562\",\n \"0.46991462\",\n \"0.4692452\",\n \"0.4681719\",\n \"0.46782607\",\n \"0.46705112\",\n \"0.46683893\",\n \"0.46578965\",\n \"0.4657367\",\n \"0.46565905\",\n \"0.465129\",\n \"0.46453166\",\n \"0.46415734\",\n \"0.4629623\",\n \"0.46199575\",\n \"0.46070042\",\n \"0.46011385\",\n \"0.4599529\",\n \"0.4591783\",\n \"0.45882958\",\n \"0.4588253\",\n \"0.4584531\",\n \"0.45760256\",\n \"0.45693958\",\n \"0.45693958\",\n \"0.45537534\",\n \"0.4552739\",\n \"0.45526537\",\n \"0.4552025\",\n \"0.4549712\",\n \"0.4546758\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8982534"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11773,"cells":{"query":{"kind":"string","value":"Obtains a single coefficient in linear constraint matrix. getaij(self,i_,j_)"},"document":{"kind":"string","value":"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)"},"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 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 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 _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 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 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 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 jacobian_i(self, x):\n return np.matrix([-x**3, -x**2, -x, -1])","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 jmat(ind: int):\n return _jm[ind - 1]","def jac(self):\n return self.unit_jac if self._jac is None else self._jac","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 __get_p_i_j():\n try:\n lambdas_r = self.lambdas_r\n lambdas_c = self.lambdas_c\n r_indexer = self.r_indexer\n c_indexer = self.c_indexer\n value = self.data.adjacencymat[i, j]\n sum_value = (lambdas_r[r_indexer[i]] + lambdas_c[c_indexer[j]])\n except AttributeError:\n self.lambdas_r, self.lambdas_c, self.r_indexer, self.c_indexer, _, _ = self.compute_lambdas_in_a_cooler_way(**kwargs)\n value = self.data.adjacencymat[i, j]\n sum_value = self.lambdas_r[self.r_indexer[i]] + self.lambdas_c[self.c_indexer[j]]\n except IndexError:\n raise\n return value, sum_value","def __getslice__(self,i,j):\n nv=_Matr()\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\n nv._Matr__maj()\n return nv","def get_cell(self, i, j):\n return self._env[i][j]","def __getslice__(self,i,j):\n nv=_Matr()\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\n nv.__maj()\n return nv","def GetJ(self, *args):\n return _table.Table_GetJ(self, *args)","def c(self, i):\n value = self.b(i)\n if i == self.N:\n return value\n else:\n for j in range(i+1, self.N+1):\n value = value - self.a(i,j) * self.C[j]\n return value","def getGradient(self,j):\n i = int(self.indicator['term'][j])\n r = int(self.indicator['row'][j])\n c = int(self.indicator['col'][j])\n rv = -np.kron(self.Fstar()[i][:,[r]],self.Astar()[i][[c],:])\n return rv","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 rule(model, i, j):\n return 1, model.T0_end[i, j] + (1-model.A[i, j]), None","def get_elem (A, i, j):\n\treturn A[j][i]","def c(self,j,i_j):\n \"\"\" The index j of the chains goes from 0 to k-1 (where k is the \n number of chains in our decomposition \"\"\"\n assert j < len(self.lcd), \"j must be the index of a chain\"\n \"\"\" The index i_j goes from 0 to len(lcd[j]) this range is one longer\n than the length of the chain because we go from {} to the full chain. \"\"\"\n assert i_j <= self.lcd_dims[j], \"i_j = {}, dims[j] = {}\".format(i_j, self.lcd_dims[j])\n if i_j == 0:\n return None\n else:\n return self.lcd[j][i_j-1]","def acoeff(self):\n return np.dot(self.mmi,np.dot(self.mmatrix.T,self.bvec))","def ij(ij, pol, ant) :\n s.ij(pol, ij, ant)","def _jac_im_getter(self, component: str, surface_shape: tuple, periodic: bool, *im_args, **im_kwargs):\n\n inf_comp = self.influence_matrix(component, *im_args, **im_kwargs)[component]\n influence_martix_span = inf_comp.shape\n if periodic:\n # check that the surface shape is odd in both dimensions\n if not all([el % 2 for el in surface_shape]):\n raise ValueError(\"Surface shape must be odd in both dimensions for periodic surfaces\")\n # trim the influence matrix if necessary\n dif = [int((ims - ss) / 2) for ims, ss in zip(influence_martix_span, surface_shape)]\n if dif[0] > 0:\n inf_comp = inf_comp[dif[0]:-1 * dif[0], :]\n if dif[1] > 0:\n inf_comp = inf_comp[:, dif[1]:-1 * dif[1]]\n trimmed_ims = inf_comp.shape\n # pad to the same shape as the surface (this is why it has to be odd size)\n inf_mat = np.pad(inf_comp, ((0, surface_shape[0] - trimmed_ims[0]),\n (0, surface_shape[1] - trimmed_ims[1])), mode='constant')\n inf_mat = np.roll(inf_mat, (-1 * int(trimmed_ims[0] / 2), -1 * int(trimmed_ims[1] / 2)),\n axis=[0, 1]).flatten()\n jac_comp = []\n roll_num = 0\n # roll the influence matrix to fill in rows of the jacobian\n for n in range(surface_shape[0]):\n for m in range(surface_shape[1]):\n jac_comp.append(np.roll(inf_mat, roll_num))\n roll_num += 1\n jac_comp = np.asarray(jac_comp)\n\n else: # not periodic\n pad_0 = int(surface_shape[0] - np.floor(influence_martix_span[0] / 2))\n pad_1 = int(surface_shape[1] - np.floor(influence_martix_span[1] / 2))\n if pad_0 < 0:\n inf_comp = inf_comp[-1 * pad_0:pad_0, :]\n pad_0 = 0\n if pad_1 < 0:\n inf_comp = inf_comp[:, -1 * pad_1:pad_1]\n pad_1 = 0\n inf_mat = np.pad(inf_comp, ((pad_0, pad_0), (pad_1, pad_1)), mode='constant')\n jac_comp = []\n idx_0 = 0\n for n in range(surface_shape[0]):\n idx_1 = 0\n for m in range(surface_shape[1]):\n jac_comp.append(inf_mat[surface_shape[0] - idx_0:2 * surface_shape[0] - idx_0,\n surface_shape[1] - idx_1:2 * surface_shape[1] - idx_1].copy().flatten())\n idx_1 += 1\n idx_0 += 1\n jac_comp = np.asarray(jac_comp)\n\n return jac_comp","def Read_IJMatrix(self, comm, fname):\n return _hypre.HypreParMatrix_Read_IJMatrix(self, comm, fname)","def jacobian(self, x):\n pass","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 jacobian(self, c):\n\n raise NotImplementedError","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 _jz(j, *, dtype=None):\n dtype = dtype or settings.core[\"default_dtype\"] or _data.CSR\n N = int(2*j + 1)\n data = np.array([j-k for k in range(N)], dtype=complex)\n return _data.diag[dtype](data, 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 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 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 jacobian_c(self, x, out=None, **kwargs):\n return empty_matrix(0, self.nx)","def get_con_elem0(self, j0):\n return self.je[ self.ie[j0] : self.ie[j0+1] ]","def correlation_fn(i, j, bJ):\n\treturn np.exp(np.abs(i - j)*np.log(np.tanh(bJ)))","def _get_minibatch_jacobian(y, x):\n assert y.shape[0] == x.shape[0]\n y = y.view(y.shape[0], -1)\n\n # Compute Jacobian row by row.\n jac = []\n for j in range(y.shape[1]):\n dy_j_dx = torch.autograd.grad(y[:, j], x, torch.ones_like(y[:, j]), retain_graph=True,\n create_graph=True)[0].view(x.shape[0], -1)\n jac.append(torch.unsqueeze(dy_j_dx, 1))\n jac = torch.cat(jac, 1)\n return jac","def calculate_parameter_covariance_ij(self, i, j):\n\n # Initialise\n\n # 1. Input data\n dataset_paths = self.dataset_paths\n sensor_data_path = self.sensor_data_path\n\n # 2. Output Data\n temp_directory = self.temp_directory\n\n ################################################################################################################\n # 1.\tRead Matchup Data and harmonisation output data\n ################################################################################################################\n\n # Input data\n HData = self.data_reader(dataset_paths, sensor_data_path,\n open_uncertainty=True, open_additional_values=False)\n\n # Re-open final solver\n GNOp = GNAlgo(HData)\n GNOp.open(temp_directory)\n\n ################################################################################################################\n # 2.\tPerform harmonisation parameter covariance matrix element\n ################################################################################################################\n\n parameter_covariance_ij = GNOp.calculate_parameter_covariance_matrix_ij([i, j])\n\n return parameter_covariance_ij","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 _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 J(self):\n return self._J","def get_rating(self, i, j):\n prediction = self.P[i, :].dot(self.Q[j, :].T)\n return prediction","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 C_ret(img):\r\n return solveJ(img, 0, tc(img, ignore_ch=2))","def get_ijk( self, active_index = None , global_index = None):\n i = ctypes.c_int()\n j = ctypes.c_int()\n k = ctypes.c_int()\n\n gi = self.__global_index( active_index = active_index , global_index = global_index)\n self._get_ijk1( gi , ctypes.byref(i) , ctypes.byref(j) , ctypes.byref(k))\n\n return (i.value , j.value , k.value)","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 getbaraidxij(self,idx_):\n i_ = ctypes.c_int32()\n j_ = ctypes.c_int32()\n res = __library__.MSK_XX_getbaraidxij(self.__nativep,idx_,ctypes.byref(i_),ctypes.byref(j_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n i_ = i_.value\n _i_return_value = i_\n j_ = j_.value\n _j_return_value = j_\n return (_i_return_value,_j_return_value)","def __getitem__(self, i_j):\n\t\t\n\t\ttry:\n\t\t\tif i_j != Ellipsis:\n\t\t\t\treturn self.item_cache[i_j]\n\t\texcept TypeError:\n\t\t\t'''\n\t\t\tc_i_j = []\n\t\t\tfor n in (0, 1):\n\t\t\t\ttry:\n\t\t\t\t\tc_i_j.append((i_j[n].start, i_j[n].stop, i_j[n].step))\n\t\t\t\texcept AttributeError:\n\t\t\t\t\tc_i_j.append(i_j[n])\n\t\t\tc_i_j = tuple(c_i_j)\n\t\t\ttry:\n\t\t\t\treturn self.item_cache[c_i_j]\n\t\t\texcept KeyError:\n\t\t\t\tpass\n\t\t\t'''\n\t\t\tpass\n\t\t\tc_i_j = i_j\n\t\texcept (AttributeError, KeyError):\n\t\t\tc_i_j = i_j\n\t\t\n\t\tdef getitem(direction, indices_i, indices_j):\n\t\t\tif direction == self.__direction.scalar:\n\t\t\t\treturn self.value[self.row_dimension * indices_i + indices_j]\n\t\t\telif direction == self.__direction.row:\n\t\t\t\tj = indices_j\n\t\t\t\treturn self.algebra.vector_algebra(self.value[self.row_dimension * _i + j] for _i in indices_i)\n\t\t\telif direction == self.__direction.column:\n\t\t\t\ti = indices_i\n\t\t\t\treturn self.algebra.vector_algebra(self.value[self.row_dimension * i + _j] for _j in indices_j)\n\t\t\telif direction == self.__direction.matrix:\n\t\t\t\tselection = {}\n\t\t\t\tfor (m, i), (n, j) in zip(enumerate(indices_i), enumerate(indices_j)):\n\t\t\t\t\tselection[m, n] = i, j\n\t\t\t\treturn self.algebra((lambda _m, _n: self.value[self.row_dimension * selection[_m, _n][0] + selection[_m, _n][1]]), row_dimension=len(indices_i), column_dimension=len(indices_j))\n\t\t\telif direction == self.__direction.copy:\n\t\t\t\t#return self.algebra(self.value, row_dimension=self.row_dimension, column_dimension=self.column_dimension)\n\t\t\t\treturn self.algebra(self)\n\t\t\telse:\n\t\t\t\traise RuntimeError(\"Unknown direction value: `{}`\".format(repr(direction)))\n\t\t\n\t\tresult = self.__analyze_indices(i_j, getitem)\n\t\t\n\t\tif __debug__:\n\t\t\ttry:\n\t\t\t\tif i_j != Ellipsis:\n\t\t\t\t\tassert self.item_cache[c_i_j] == result, f\"{repr(c_i_j)}, {id(self)}\"\n\t\t\texcept (AttributeError, KeyError, TypeError):\n\t\t\t\tpass\n\t\t\n\t\ttry:\n\t\t\tif i_j != Ellipsis:\n\t\t\t\tself.item_cache[c_i_j] = result\n\t\texcept (AttributeError, TypeError):\n\t\t\tpass\n\t\t\n\t\treturn result","def get_coefficient(self, line, column):\n return self.coefficients[line][column]","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 dij(self, i, j):\n if self.dipole is None:\n return 1 if i != j else 0\n\n # <s_j|D|s_i>\n return (self.s[j:j+1] @ self.dipole @ self.s[i:i+1].T.conj())[0, 0]","def edge_operator_aij(edge_matrix_indices: numpy.ndarray, i: int,\n j: int) -> QubitOperator:\n a_ij = QubitOperator()\n operator = tuple()\n position_ij = -1\n qubit_position_i = numpy.array(numpy.where(edge_matrix_indices == i))\n for edge_index in range(numpy.size(edge_matrix_indices[0, :])):\n if set((i, j)) == set(edge_matrix_indices[:, edge_index]):\n position_ij = edge_index\n operator += ((int(position_ij), 'X'),)\n\n for edge_index in range(numpy.size(qubit_position_i[0, :])):\n if edge_matrix_indices[int(not (qubit_position_i[0, edge_index]))][\n qubit_position_i[1, edge_index]] < j:\n operator += ((int(qubit_position_i[1, edge_index]), 'Z'),)\n qubit_position_j = numpy.array(numpy.where(edge_matrix_indices == j))\n for edge_index in range(numpy.size(qubit_position_j[0, :])):\n if edge_matrix_indices[int(not (qubit_position_j[0, edge_index]))][\n qubit_position_j[1, edge_index]] < i:\n operator += ((int(qubit_position_j[1, edge_index]), 'Z'),)\n a_ij += QubitOperator(operator, 1)\n if j < i:\n a_ij = -1 * a_ij\n return a_ij","def J(t,y):\n return A","def substitute_cost(self, i, j):\n raise NotImplementedError","def one_dim_index(self, i, j):\n return int(i + j * self.nx)","def _get_minibatch_jacobian(y, x, create_graph=False):\n assert y.shape[0] == x.shape[0]\n y = y.view(y.shape[0], -1)\n\n # Compute Jacobian row by row.\n jac = []\n for j in range(y.shape[1]):\n dy_j_dx = torch.autograd.grad(y[:, j], x, torch.ones_like(y[:, j]), retain_graph=True,\n create_graph=True)[0].view(x.shape[0], -1)\n jac.append(torch.unsqueeze(dy_j_dx, 1))\n jac = torch.cat(jac, 1)\n return jac","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 ckm(i,j):\n if i >= 1 and i <= 3 and j >= 1 and j <= 3:\n return _ckm_abs[i-1, j-1]\n else:\n raise(ValueError('Wrong generation index in CKM matrix: ({},{}).'.format(i,j)))","def _get_jacobian(tris_pts):\n a = np.array(tris_pts[:, 1, :] - tris_pts[:, 0, :])\n b = np.array(tris_pts[:, 2, :] - tris_pts[:, 0, :])\n J = _to_matrix_vectorized([[a[:, 0], a[:, 1]],\n [b[:, 0], b[:, 1]]])\n return J","def getCoefficient(self):\n return _libsbml.FluxObjective_getCoefficient(self)","def get_Amn_one_k(self, ik):\n\n if self.use_proj:\n psi = self.get_psi_k(ik)[self.cols, :] * (self.occ[ik] *\n self.projs[ik])[None, :]\n else:\n psi = self.get_psi_k(ik)[self.cols, :] * self.occ[ik][None, :]\n U, _S, VT = svd(psi.T.conj(), full_matrices=False)\n Amn_k = U @ VT\n return Amn_k","def getItem(self, i, j):\n if i < 0:\n raise IndexError('Row index must be nonnegative.')\n if j < 0:\n raise IndexError('Column index must be nonnegative.')\n\n return self.__m[i - 1][j - 1]","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 get_stain_matrix(I):","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 ij2xy(self, i=None, j=None):\n pass","def cost_fun(x, problem):\n j = 0\n if problem['use_log_bar']:\n c = ineqconstr(x, problem)\n j += np.sum(logbarrierfunc(0.1, c, problem['use_sigma']))\n\n x, t_final = matrify(x, problem)\n if problem['T']!=0:\n j += np.sum([problem['cost_fun_single'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])\n else:\n j = t_final\n return j","def vectorized_result(j):\n\te = np.zeros((3, 1))\n\te[j] = 1.0\n\treturn e","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 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 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 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 jacobi(self, lattice):\n kernel = np.array([[[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]],\n [[0.0,1.0,0.0],[1.0,0.0,1.0],[0.0,1.0,0.0]],\n [[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]])\n return ((signal.fftconvolve(lattice, kernel, mode='same') + self.J)/ 6.0)","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 jacobian(self, dt):\n return self._F_cache","def vectorized_result(j):\n i = int(j)\n e = np.zeros((2, 1))\n e[i] = 1.0\n return e","def _index(self, i, j):\n assert self._isvalid(i), 'i term (%s) is out of bounds' % i\n assert self._isvalid(j), 'j term (%s) is out of bounds' % j\n\n return self.n * (i - 1) + (j - 1)","def calc_j(basis):\n b1 = basis[0]\n b2 = basis[1]\n T = b1.shape[1]\n integrand11 = zeros(T)\n integrand12 = zeros(T)\n integrand22 = zeros(T)\n\n for i in range(0, T):\n a = b1[:, i].T\n b = b2[:, i].T\n integrand11[i] = a.dot(b1[:, i])\n integrand12[i] = a.dot(b2[:, i])\n integrand22[i] = b.dot(b2[:, i])\n\n j = zeros((2, 2))\n j[0, 0] = trapz(integrand11, linspace(0, 1, T))\n j[0, 1] = trapz(integrand12, linspace(0, 1, T))\n j[1, 1] = trapz(integrand22, linspace(0, 1, T))\n j[1, 0] = j[0, 1]\n\n return (j)","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 ij_to_latlon(self, i, j):\n if self.top_level:\n lon, lat = pyproj.transform(self.lambert_grid, self.latlon_sphere,\n i * self.cell_size[0] + self.offset_i,\n j * self.cell_size[1] + self.offset_j)\n return lat, lon\n else:\n pcsr, ps = self.parent_cell_size_ratio, self.parent_start\n return self.parent.ij_to_latlon((i+.5)/pcsr+ps[0]-1.5, (j+.5)/pcsr+ps[1]-1.5)","def jacobian_c(self, x, out=None, **kwargs):\n return self._base_nlp.jacobian_c(x, out=out, **kwargs)","def get_jacobian_spatial(self, qs=None) -> np.ndarray:\n if qs is None:\n qs = self.get_current_joint_position()\n return self.robot.jacob0(qs)","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 _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 value_comp(self, x: float, j: int) -> complex:\n return np.interp([x], self.x, self.components[j])[0]","def mod_agreement_index(self, j=1) -> float:\n a = (np.abs(self.predicted - self.true)) ** j\n b = np.abs(self.predicted - np.mean(self.true))\n c = np.abs(self.true - np.mean(self.true))\n e = (b + c) ** j\n return float(1 - (np.sum(a) / np.sum(e)))","def J(self) -> float:\n return self._J","def jac_pos(self):\n J = self.sim.data.get_body_jacp(self.end_effector)\n J = J.reshape(3, -1)[:, 0:7].T\n return J","def M(self,i,j):\n node_i = self.internal[i]\n node_j = self.internal[j]\n adj_i = self.adj_triangles(node_i)\n adj_j = self.adj_triangles(node_j)\n shared_tri = [] \n for tri_i in adj_i:\n for tri_j in adj_j:\n if sorted(tri_i) == sorted(tri_j):\n shared_tri.append(tri_i)\n break \n\n # print(len(shared_tri))\n\n\n total_integral = 0.0\n for tri_h in shared_tri:\n beta_i, gamma_i = self.calc_param(tri_h,node_i)\n beta_j, gamma_j = self.calc_param(tri_h,node_j)\n total_integral += ((beta_i*beta_j)+(gamma_i*gamma_j))*self.area \n \n return total_integral","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 get_j1(mol, atom, coord):\n\n omega = np.identity(2)\n spin_j = np.einsum(\"ij,kl->ikjl\", omega, omega)\n\n twoe = -mol.intor(\"int2e_ip1\")[coord] #minus sign due to pyscf definition\n\n j1_spatial = np.zeros((twoe.shape[0],twoe.shape[0],twoe.shape[0],\n twoe.shape[0]))\n\n for i in range(twoe.shape[0]):\n\n lambda_i = int(i in range(mol.aoslice_by_atom()[atom][2],\n mol.aoslice_by_atom()[atom][3]))\n\n for j in range(twoe.shape[0]):\n\n lambda_j = int(j in range(mol.aoslice_by_atom()[atom][2],\n mol.aoslice_by_atom()[atom][3]))\n\n for k in range(twoe.shape[0]):\n\n lambda_k = int(k in range(mol.aoslice_by_atom()[atom][2],\n mol.aoslice_by_atom()[atom][3]))\n\n for l in range(twoe.shape[0]):\n\n lambda_l = int(l in range(mol.aoslice_by_atom()[atom][2],\n mol.aoslice_by_atom()[atom][3]))\n\n j1_spatial[i][j][k][l] += (twoe[i][j][k][l] * lambda_i\n + twoe[j][i][k][l] * lambda_j\n + twoe[k][l][i][j] * lambda_k\n + twoe[l][k][i][j] * lambda_l)\n\n j1_spatial = np.einsum(\"abcd->acbd\", j1_spatial,\n optimize='optimal') #convert to physicists\n j1 = np.kron(spin_j, j1_spatial)\n\n return j1","def ssc.inverse_jacob (x_ij):\n return diff.numerical_jacobian (ssc.inverse, x_ij)","def _get_ij_(lats, lons, lat, lon):\n _ij_ = (np.argmin(np.abs(lats-lat)), np.argmin(np.abs(lons-lon)))\n return _ij_","def getConstraint(self, *args):\n return _libsbml.Model_getConstraint(self, *args)","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 _jac_mat_prod(self, module, g_inp, g_out, mat):\n raise NotImplementedError","def J (self, n):","def jacobian_cost(self, joint_angles: dict, ee_goals) -> np.ndarray:\n kinematic_map = self.kinematic_map[\"p0\"] # get map to all nodes from root\n end_effector_nodes = ee_goals.keys()\n J = np.zeros(self.n)\n for (\n ee\n ) in end_effector_nodes: # iterate through end-effector nodes, assumes sorted\n ee_path = kinematic_map[ee][\n 1:\n ] # [:-1] # no last node, only phys. joint locations\n t_ee = self.get_pose(joint_angles, ee).trans\n dg_ee_x = t_ee[0] - ee_goals[ee].trans[0]\n dg_ee_y = t_ee[1] - ee_goals[ee].trans[1]\n for (pdx, joint_p) in enumerate(ee_path): # algorithm fills Jac per column\n p_idx = int(joint_p[1:]) - 1\n for jdx in range(pdx, len(ee_path)):\n node_jdx = ee_path[jdx]\n theta_jdx = sum([joint_angles[key] for key in ee_path[0 : jdx + 1]])\n J[p_idx] += (\n 2.0\n * self.a[node_jdx]\n * (-dg_ee_x * np.sin(theta_jdx) + dg_ee_y * np.cos(theta_jdx))\n )\n\n return J","def select_ijentry(self, matrices = '', i = '' , j = ''):\n lista = [ m[i, j] for m in matrices ]\n return lista"],"string":"[\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 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 _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 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 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 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 jacobian_i(self, x):\\n return np.matrix([-x**3, -x**2, -x, -1])\",\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 jmat(ind: int):\\n return _jm[ind - 1]\",\n \"def jac(self):\\n return self.unit_jac if self._jac is None else self._jac\",\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 __get_p_i_j():\\n try:\\n lambdas_r = self.lambdas_r\\n lambdas_c = self.lambdas_c\\n r_indexer = self.r_indexer\\n c_indexer = self.c_indexer\\n value = self.data.adjacencymat[i, j]\\n sum_value = (lambdas_r[r_indexer[i]] + lambdas_c[c_indexer[j]])\\n except AttributeError:\\n self.lambdas_r, self.lambdas_c, self.r_indexer, self.c_indexer, _, _ = self.compute_lambdas_in_a_cooler_way(**kwargs)\\n value = self.data.adjacencymat[i, j]\\n sum_value = self.lambdas_r[self.r_indexer[i]] + self.lambdas_c[self.c_indexer[j]]\\n except IndexError:\\n raise\\n return value, sum_value\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\\n nv._Matr__maj()\\n return nv\",\n \"def get_cell(self, i, j):\\n return self._env[i][j]\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\\n nv.__maj()\\n return nv\",\n \"def GetJ(self, *args):\\n return _table.Table_GetJ(self, *args)\",\n \"def c(self, i):\\n value = self.b(i)\\n if i == self.N:\\n return value\\n else:\\n for j in range(i+1, self.N+1):\\n value = value - self.a(i,j) * self.C[j]\\n return value\",\n \"def getGradient(self,j):\\n i = int(self.indicator['term'][j])\\n r = int(self.indicator['row'][j])\\n c = int(self.indicator['col'][j])\\n rv = -np.kron(self.Fstar()[i][:,[r]],self.Astar()[i][[c],:])\\n return rv\",\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 rule(model, i, j):\\n return 1, model.T0_end[i, j] + (1-model.A[i, j]), None\",\n \"def get_elem (A, i, j):\\n\\treturn A[j][i]\",\n \"def c(self,j,i_j):\\n \\\"\\\"\\\" The index j of the chains goes from 0 to k-1 (where k is the \\n number of chains in our decomposition \\\"\\\"\\\"\\n assert j < len(self.lcd), \\\"j must be the index of a chain\\\"\\n \\\"\\\"\\\" The index i_j goes from 0 to len(lcd[j]) this range is one longer\\n than the length of the chain because we go from {} to the full chain. \\\"\\\"\\\"\\n assert i_j <= self.lcd_dims[j], \\\"i_j = {}, dims[j] = {}\\\".format(i_j, self.lcd_dims[j])\\n if i_j == 0:\\n return None\\n else:\\n return self.lcd[j][i_j-1]\",\n \"def acoeff(self):\\n return np.dot(self.mmi,np.dot(self.mmatrix.T,self.bvec))\",\n \"def ij(ij, pol, ant) :\\n s.ij(pol, ij, ant)\",\n \"def _jac_im_getter(self, component: str, surface_shape: tuple, periodic: bool, *im_args, **im_kwargs):\\n\\n inf_comp = self.influence_matrix(component, *im_args, **im_kwargs)[component]\\n influence_martix_span = inf_comp.shape\\n if periodic:\\n # check that the surface shape is odd in both dimensions\\n if not all([el % 2 for el in surface_shape]):\\n raise ValueError(\\\"Surface shape must be odd in both dimensions for periodic surfaces\\\")\\n # trim the influence matrix if necessary\\n dif = [int((ims - ss) / 2) for ims, ss in zip(influence_martix_span, surface_shape)]\\n if dif[0] > 0:\\n inf_comp = inf_comp[dif[0]:-1 * dif[0], :]\\n if dif[1] > 0:\\n inf_comp = inf_comp[:, dif[1]:-1 * dif[1]]\\n trimmed_ims = inf_comp.shape\\n # pad to the same shape as the surface (this is why it has to be odd size)\\n inf_mat = np.pad(inf_comp, ((0, surface_shape[0] - trimmed_ims[0]),\\n (0, surface_shape[1] - trimmed_ims[1])), mode='constant')\\n inf_mat = np.roll(inf_mat, (-1 * int(trimmed_ims[0] / 2), -1 * int(trimmed_ims[1] / 2)),\\n axis=[0, 1]).flatten()\\n jac_comp = []\\n roll_num = 0\\n # roll the influence matrix to fill in rows of the jacobian\\n for n in range(surface_shape[0]):\\n for m in range(surface_shape[1]):\\n jac_comp.append(np.roll(inf_mat, roll_num))\\n roll_num += 1\\n jac_comp = np.asarray(jac_comp)\\n\\n else: # not periodic\\n pad_0 = int(surface_shape[0] - np.floor(influence_martix_span[0] / 2))\\n pad_1 = int(surface_shape[1] - np.floor(influence_martix_span[1] / 2))\\n if pad_0 < 0:\\n inf_comp = inf_comp[-1 * pad_0:pad_0, :]\\n pad_0 = 0\\n if pad_1 < 0:\\n inf_comp = inf_comp[:, -1 * pad_1:pad_1]\\n pad_1 = 0\\n inf_mat = np.pad(inf_comp, ((pad_0, pad_0), (pad_1, pad_1)), mode='constant')\\n jac_comp = []\\n idx_0 = 0\\n for n in range(surface_shape[0]):\\n idx_1 = 0\\n for m in range(surface_shape[1]):\\n jac_comp.append(inf_mat[surface_shape[0] - idx_0:2 * surface_shape[0] - idx_0,\\n surface_shape[1] - idx_1:2 * surface_shape[1] - idx_1].copy().flatten())\\n idx_1 += 1\\n idx_0 += 1\\n jac_comp = np.asarray(jac_comp)\\n\\n return jac_comp\",\n \"def Read_IJMatrix(self, comm, fname):\\n return _hypre.HypreParMatrix_Read_IJMatrix(self, comm, fname)\",\n \"def jacobian(self, x):\\n pass\",\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 jacobian(self, c):\\n\\n raise NotImplementedError\",\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 _jz(j, *, dtype=None):\\n dtype = dtype or settings.core[\\\"default_dtype\\\"] or _data.CSR\\n N = int(2*j + 1)\\n data = np.array([j-k for k in range(N)], dtype=complex)\\n return _data.diag[dtype](data, 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 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 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 jacobian_c(self, x, out=None, **kwargs):\\n return empty_matrix(0, self.nx)\",\n \"def get_con_elem0(self, j0):\\n return self.je[ self.ie[j0] : self.ie[j0+1] ]\",\n \"def correlation_fn(i, j, bJ):\\n\\treturn np.exp(np.abs(i - j)*np.log(np.tanh(bJ)))\",\n \"def _get_minibatch_jacobian(y, x):\\n assert y.shape[0] == x.shape[0]\\n y = y.view(y.shape[0], -1)\\n\\n # Compute Jacobian row by row.\\n jac = []\\n for j in range(y.shape[1]):\\n dy_j_dx = torch.autograd.grad(y[:, j], x, torch.ones_like(y[:, j]), retain_graph=True,\\n create_graph=True)[0].view(x.shape[0], -1)\\n jac.append(torch.unsqueeze(dy_j_dx, 1))\\n jac = torch.cat(jac, 1)\\n return jac\",\n \"def calculate_parameter_covariance_ij(self, i, j):\\n\\n # Initialise\\n\\n # 1. Input data\\n dataset_paths = self.dataset_paths\\n sensor_data_path = self.sensor_data_path\\n\\n # 2. Output Data\\n temp_directory = self.temp_directory\\n\\n ################################################################################################################\\n # 1.\\tRead Matchup Data and harmonisation output data\\n ################################################################################################################\\n\\n # Input data\\n HData = self.data_reader(dataset_paths, sensor_data_path,\\n open_uncertainty=True, open_additional_values=False)\\n\\n # Re-open final solver\\n GNOp = GNAlgo(HData)\\n GNOp.open(temp_directory)\\n\\n ################################################################################################################\\n # 2.\\tPerform harmonisation parameter covariance matrix element\\n ################################################################################################################\\n\\n parameter_covariance_ij = GNOp.calculate_parameter_covariance_matrix_ij([i, j])\\n\\n return parameter_covariance_ij\",\n \"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\",\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 J(self):\\n return self._J\",\n \"def get_rating(self, i, j):\\n prediction = self.P[i, :].dot(self.Q[j, :].T)\\n return prediction\",\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 C_ret(img):\\r\\n return solveJ(img, 0, tc(img, ignore_ch=2))\",\n \"def get_ijk( self, active_index = None , global_index = None):\\n i = ctypes.c_int()\\n j = ctypes.c_int()\\n k = ctypes.c_int()\\n\\n gi = self.__global_index( active_index = active_index , global_index = global_index)\\n self._get_ijk1( gi , ctypes.byref(i) , ctypes.byref(j) , ctypes.byref(k))\\n\\n return (i.value , j.value , k.value)\",\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 getbaraidxij(self,idx_):\\n i_ = ctypes.c_int32()\\n j_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getbaraidxij(self.__nativep,idx_,ctypes.byref(i_),ctypes.byref(j_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n i_ = i_.value\\n _i_return_value = i_\\n j_ = j_.value\\n _j_return_value = j_\\n return (_i_return_value,_j_return_value)\",\n \"def __getitem__(self, i_j):\\n\\t\\t\\n\\t\\ttry:\\n\\t\\t\\tif i_j != Ellipsis:\\n\\t\\t\\t\\treturn self.item_cache[i_j]\\n\\t\\texcept TypeError:\\n\\t\\t\\t'''\\n\\t\\t\\tc_i_j = []\\n\\t\\t\\tfor n in (0, 1):\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tc_i_j.append((i_j[n].start, i_j[n].stop, i_j[n].step))\\n\\t\\t\\t\\texcept AttributeError:\\n\\t\\t\\t\\t\\tc_i_j.append(i_j[n])\\n\\t\\t\\tc_i_j = tuple(c_i_j)\\n\\t\\t\\ttry:\\n\\t\\t\\t\\treturn self.item_cache[c_i_j]\\n\\t\\t\\texcept KeyError:\\n\\t\\t\\t\\tpass\\n\\t\\t\\t'''\\n\\t\\t\\tpass\\n\\t\\t\\tc_i_j = i_j\\n\\t\\texcept (AttributeError, KeyError):\\n\\t\\t\\tc_i_j = i_j\\n\\t\\t\\n\\t\\tdef getitem(direction, indices_i, indices_j):\\n\\t\\t\\tif direction == self.__direction.scalar:\\n\\t\\t\\t\\treturn self.value[self.row_dimension * indices_i + indices_j]\\n\\t\\t\\telif direction == self.__direction.row:\\n\\t\\t\\t\\tj = indices_j\\n\\t\\t\\t\\treturn self.algebra.vector_algebra(self.value[self.row_dimension * _i + j] for _i in indices_i)\\n\\t\\t\\telif direction == self.__direction.column:\\n\\t\\t\\t\\ti = indices_i\\n\\t\\t\\t\\treturn self.algebra.vector_algebra(self.value[self.row_dimension * i + _j] for _j in indices_j)\\n\\t\\t\\telif direction == self.__direction.matrix:\\n\\t\\t\\t\\tselection = {}\\n\\t\\t\\t\\tfor (m, i), (n, j) in zip(enumerate(indices_i), enumerate(indices_j)):\\n\\t\\t\\t\\t\\tselection[m, n] = i, j\\n\\t\\t\\t\\treturn self.algebra((lambda _m, _n: self.value[self.row_dimension * selection[_m, _n][0] + selection[_m, _n][1]]), row_dimension=len(indices_i), column_dimension=len(indices_j))\\n\\t\\t\\telif direction == self.__direction.copy:\\n\\t\\t\\t\\t#return self.algebra(self.value, row_dimension=self.row_dimension, column_dimension=self.column_dimension)\\n\\t\\t\\t\\treturn self.algebra(self)\\n\\t\\t\\telse:\\n\\t\\t\\t\\traise RuntimeError(\\\"Unknown direction value: `{}`\\\".format(repr(direction)))\\n\\t\\t\\n\\t\\tresult = self.__analyze_indices(i_j, getitem)\\n\\t\\t\\n\\t\\tif __debug__:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tif i_j != Ellipsis:\\n\\t\\t\\t\\t\\tassert self.item_cache[c_i_j] == result, f\\\"{repr(c_i_j)}, {id(self)}\\\"\\n\\t\\t\\texcept (AttributeError, KeyError, TypeError):\\n\\t\\t\\t\\tpass\\n\\t\\t\\n\\t\\ttry:\\n\\t\\t\\tif i_j != Ellipsis:\\n\\t\\t\\t\\tself.item_cache[c_i_j] = result\\n\\t\\texcept (AttributeError, TypeError):\\n\\t\\t\\tpass\\n\\t\\t\\n\\t\\treturn result\",\n \"def get_coefficient(self, line, column):\\n return self.coefficients[line][column]\",\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 dij(self, i, j):\\n if self.dipole is None:\\n return 1 if i != j else 0\\n\\n # <s_j|D|s_i>\\n return (self.s[j:j+1] @ self.dipole @ self.s[i:i+1].T.conj())[0, 0]\",\n \"def edge_operator_aij(edge_matrix_indices: numpy.ndarray, i: int,\\n j: int) -> QubitOperator:\\n a_ij = QubitOperator()\\n operator = tuple()\\n position_ij = -1\\n qubit_position_i = numpy.array(numpy.where(edge_matrix_indices == i))\\n for edge_index in range(numpy.size(edge_matrix_indices[0, :])):\\n if set((i, j)) == set(edge_matrix_indices[:, edge_index]):\\n position_ij = edge_index\\n operator += ((int(position_ij), 'X'),)\\n\\n for edge_index in range(numpy.size(qubit_position_i[0, :])):\\n if edge_matrix_indices[int(not (qubit_position_i[0, edge_index]))][\\n qubit_position_i[1, edge_index]] < j:\\n operator += ((int(qubit_position_i[1, edge_index]), 'Z'),)\\n qubit_position_j = numpy.array(numpy.where(edge_matrix_indices == j))\\n for edge_index in range(numpy.size(qubit_position_j[0, :])):\\n if edge_matrix_indices[int(not (qubit_position_j[0, edge_index]))][\\n qubit_position_j[1, edge_index]] < i:\\n operator += ((int(qubit_position_j[1, edge_index]), 'Z'),)\\n a_ij += QubitOperator(operator, 1)\\n if j < i:\\n a_ij = -1 * a_ij\\n return a_ij\",\n \"def J(t,y):\\n return A\",\n \"def substitute_cost(self, i, j):\\n raise NotImplementedError\",\n \"def one_dim_index(self, i, j):\\n return int(i + j * self.nx)\",\n \"def _get_minibatch_jacobian(y, x, create_graph=False):\\n assert y.shape[0] == x.shape[0]\\n y = y.view(y.shape[0], -1)\\n\\n # Compute Jacobian row by row.\\n jac = []\\n for j in range(y.shape[1]):\\n dy_j_dx = torch.autograd.grad(y[:, j], x, torch.ones_like(y[:, j]), retain_graph=True,\\n create_graph=True)[0].view(x.shape[0], -1)\\n jac.append(torch.unsqueeze(dy_j_dx, 1))\\n jac = torch.cat(jac, 1)\\n return jac\",\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 ckm(i,j):\\n if i >= 1 and i <= 3 and j >= 1 and j <= 3:\\n return _ckm_abs[i-1, j-1]\\n else:\\n raise(ValueError('Wrong generation index in CKM matrix: ({},{}).'.format(i,j)))\",\n \"def _get_jacobian(tris_pts):\\n a = np.array(tris_pts[:, 1, :] - tris_pts[:, 0, :])\\n b = np.array(tris_pts[:, 2, :] - tris_pts[:, 0, :])\\n J = _to_matrix_vectorized([[a[:, 0], a[:, 1]],\\n [b[:, 0], b[:, 1]]])\\n return J\",\n \"def getCoefficient(self):\\n return _libsbml.FluxObjective_getCoefficient(self)\",\n \"def get_Amn_one_k(self, ik):\\n\\n if self.use_proj:\\n psi = self.get_psi_k(ik)[self.cols, :] * (self.occ[ik] *\\n self.projs[ik])[None, :]\\n else:\\n psi = self.get_psi_k(ik)[self.cols, :] * self.occ[ik][None, :]\\n U, _S, VT = svd(psi.T.conj(), full_matrices=False)\\n Amn_k = U @ VT\\n return Amn_k\",\n \"def getItem(self, i, j):\\n if i < 0:\\n raise IndexError('Row index must be nonnegative.')\\n if j < 0:\\n raise IndexError('Column index must be nonnegative.')\\n\\n return self.__m[i - 1][j - 1]\",\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 get_stain_matrix(I):\",\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 ij2xy(self, i=None, j=None):\\n pass\",\n \"def cost_fun(x, problem):\\n j = 0\\n if problem['use_log_bar']:\\n c = ineqconstr(x, problem)\\n j += np.sum(logbarrierfunc(0.1, c, problem['use_sigma']))\\n\\n x, t_final = matrify(x, problem)\\n if problem['T']!=0:\\n j += np.sum([problem['cost_fun_single'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])\\n else:\\n j = t_final\\n return j\",\n \"def vectorized_result(j):\\n\\te = np.zeros((3, 1))\\n\\te[j] = 1.0\\n\\treturn e\",\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 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 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 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 jacobi(self, lattice):\\n kernel = np.array([[[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]],\\n [[0.0,1.0,0.0],[1.0,0.0,1.0],[0.0,1.0,0.0]],\\n [[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]])\\n return ((signal.fftconvolve(lattice, kernel, mode='same') + self.J)/ 6.0)\",\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 jacobian(self, dt):\\n return self._F_cache\",\n \"def vectorized_result(j):\\n i = int(j)\\n e = np.zeros((2, 1))\\n e[i] = 1.0\\n return e\",\n \"def _index(self, i, j):\\n assert self._isvalid(i), 'i term (%s) is out of bounds' % i\\n assert self._isvalid(j), 'j term (%s) is out of bounds' % j\\n\\n return self.n * (i - 1) + (j - 1)\",\n \"def calc_j(basis):\\n b1 = basis[0]\\n b2 = basis[1]\\n T = b1.shape[1]\\n integrand11 = zeros(T)\\n integrand12 = zeros(T)\\n integrand22 = zeros(T)\\n\\n for i in range(0, T):\\n a = b1[:, i].T\\n b = b2[:, i].T\\n integrand11[i] = a.dot(b1[:, i])\\n integrand12[i] = a.dot(b2[:, i])\\n integrand22[i] = b.dot(b2[:, i])\\n\\n j = zeros((2, 2))\\n j[0, 0] = trapz(integrand11, linspace(0, 1, T))\\n j[0, 1] = trapz(integrand12, linspace(0, 1, T))\\n j[1, 1] = trapz(integrand22, linspace(0, 1, T))\\n j[1, 0] = j[0, 1]\\n\\n return (j)\",\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 ij_to_latlon(self, i, j):\\n if self.top_level:\\n lon, lat = pyproj.transform(self.lambert_grid, self.latlon_sphere,\\n i * self.cell_size[0] + self.offset_i,\\n j * self.cell_size[1] + self.offset_j)\\n return lat, lon\\n else:\\n pcsr, ps = self.parent_cell_size_ratio, self.parent_start\\n return self.parent.ij_to_latlon((i+.5)/pcsr+ps[0]-1.5, (j+.5)/pcsr+ps[1]-1.5)\",\n \"def jacobian_c(self, x, out=None, **kwargs):\\n return self._base_nlp.jacobian_c(x, out=out, **kwargs)\",\n \"def get_jacobian_spatial(self, qs=None) -> np.ndarray:\\n if qs is None:\\n qs = self.get_current_joint_position()\\n return self.robot.jacob0(qs)\",\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 _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 value_comp(self, x: float, j: int) -> complex:\\n return np.interp([x], self.x, self.components[j])[0]\",\n \"def mod_agreement_index(self, j=1) -> float:\\n a = (np.abs(self.predicted - self.true)) ** j\\n b = np.abs(self.predicted - np.mean(self.true))\\n c = np.abs(self.true - np.mean(self.true))\\n e = (b + c) ** j\\n return float(1 - (np.sum(a) / np.sum(e)))\",\n \"def J(self) -> float:\\n return self._J\",\n \"def jac_pos(self):\\n J = self.sim.data.get_body_jacp(self.end_effector)\\n J = J.reshape(3, -1)[:, 0:7].T\\n return J\",\n \"def M(self,i,j):\\n node_i = self.internal[i]\\n node_j = self.internal[j]\\n adj_i = self.adj_triangles(node_i)\\n adj_j = self.adj_triangles(node_j)\\n shared_tri = [] \\n for tri_i in adj_i:\\n for tri_j in adj_j:\\n if sorted(tri_i) == sorted(tri_j):\\n shared_tri.append(tri_i)\\n break \\n\\n # print(len(shared_tri))\\n\\n\\n total_integral = 0.0\\n for tri_h in shared_tri:\\n beta_i, gamma_i = self.calc_param(tri_h,node_i)\\n beta_j, gamma_j = self.calc_param(tri_h,node_j)\\n total_integral += ((beta_i*beta_j)+(gamma_i*gamma_j))*self.area \\n \\n return total_integral\",\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 get_j1(mol, atom, coord):\\n\\n omega = np.identity(2)\\n spin_j = np.einsum(\\\"ij,kl->ikjl\\\", omega, omega)\\n\\n twoe = -mol.intor(\\\"int2e_ip1\\\")[coord] #minus sign due to pyscf definition\\n\\n j1_spatial = np.zeros((twoe.shape[0],twoe.shape[0],twoe.shape[0],\\n twoe.shape[0]))\\n\\n for i in range(twoe.shape[0]):\\n\\n lambda_i = int(i in range(mol.aoslice_by_atom()[atom][2],\\n mol.aoslice_by_atom()[atom][3]))\\n\\n for j in range(twoe.shape[0]):\\n\\n lambda_j = int(j in range(mol.aoslice_by_atom()[atom][2],\\n mol.aoslice_by_atom()[atom][3]))\\n\\n for k in range(twoe.shape[0]):\\n\\n lambda_k = int(k in range(mol.aoslice_by_atom()[atom][2],\\n mol.aoslice_by_atom()[atom][3]))\\n\\n for l in range(twoe.shape[0]):\\n\\n lambda_l = int(l in range(mol.aoslice_by_atom()[atom][2],\\n mol.aoslice_by_atom()[atom][3]))\\n\\n j1_spatial[i][j][k][l] += (twoe[i][j][k][l] * lambda_i\\n + twoe[j][i][k][l] * lambda_j\\n + twoe[k][l][i][j] * lambda_k\\n + twoe[l][k][i][j] * lambda_l)\\n\\n j1_spatial = np.einsum(\\\"abcd->acbd\\\", j1_spatial,\\n optimize='optimal') #convert to physicists\\n j1 = np.kron(spin_j, j1_spatial)\\n\\n return j1\",\n \"def ssc.inverse_jacob (x_ij):\\n return diff.numerical_jacobian (ssc.inverse, x_ij)\",\n \"def _get_ij_(lats, lons, lat, lon):\\n _ij_ = (np.argmin(np.abs(lats-lat)), np.argmin(np.abs(lons-lon)))\\n return _ij_\",\n \"def getConstraint(self, *args):\\n return _libsbml.Model_getConstraint(self, *args)\",\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 _jac_mat_prod(self, module, g_inp, g_out, mat):\\n raise NotImplementedError\",\n \"def J (self, n):\",\n \"def jacobian_cost(self, joint_angles: dict, ee_goals) -> np.ndarray:\\n kinematic_map = self.kinematic_map[\\\"p0\\\"] # get map to all nodes from root\\n end_effector_nodes = ee_goals.keys()\\n J = np.zeros(self.n)\\n for (\\n ee\\n ) in end_effector_nodes: # iterate through end-effector nodes, assumes sorted\\n ee_path = kinematic_map[ee][\\n 1:\\n ] # [:-1] # no last node, only phys. joint locations\\n t_ee = self.get_pose(joint_angles, ee).trans\\n dg_ee_x = t_ee[0] - ee_goals[ee].trans[0]\\n dg_ee_y = t_ee[1] - ee_goals[ee].trans[1]\\n for (pdx, joint_p) in enumerate(ee_path): # algorithm fills Jac per column\\n p_idx = int(joint_p[1:]) - 1\\n for jdx in range(pdx, len(ee_path)):\\n node_jdx = ee_path[jdx]\\n theta_jdx = sum([joint_angles[key] for key in ee_path[0 : jdx + 1]])\\n J[p_idx] += (\\n 2.0\\n * self.a[node_jdx]\\n * (-dg_ee_x * np.sin(theta_jdx) + dg_ee_y * np.cos(theta_jdx))\\n )\\n\\n return J\",\n \"def select_ijentry(self, matrices = '', i = '' , j = ''):\\n lista = [ m[i, j] for m in matrices ]\\n return lista\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7493986","0.64376247","0.6392298","0.6329551","0.6308506","0.6269593","0.6261952","0.6139124","0.61024755","0.61000454","0.60522926","0.5945203","0.5922942","0.59190106","0.5905818","0.5891535","0.5806236","0.578032","0.57758945","0.5755988","0.572439","0.5722308","0.57164514","0.5714595","0.56900984","0.5689677","0.5682416","0.5678144","0.56766146","0.5655368","0.5652944","0.56472874","0.56419885","0.5627241","0.5627031","0.56233436","0.5617997","0.56099933","0.5587849","0.55634135","0.556103","0.5547829","0.5544907","0.5536335","0.5536323","0.5534191","0.5530354","0.5504757","0.5502489","0.5496008","0.54956263","0.5488448","0.54739547","0.54685754","0.54659975","0.54613894","0.5459805","0.5456622","0.54553396","0.54468715","0.543625","0.5434901","0.54268324","0.54202247","0.5399657","0.53868175","0.5376673","0.5373869","0.5361735","0.5347369","0.5343833","0.53352636","0.5331729","0.53259546","0.5325934","0.5316587","0.53116435","0.5305622","0.5297066","0.5294709","0.52914864","0.5287045","0.5285725","0.52769005","0.52756864","0.52713025","0.5264244","0.5261914","0.5256385","0.5252959","0.52529454","0.5250865","0.524609","0.52433014","0.52268887","0.52212334","0.522012","0.52154166","0.521323","0.52115566"],"string":"[\n \"0.7493986\",\n \"0.64376247\",\n \"0.6392298\",\n \"0.6329551\",\n \"0.6308506\",\n \"0.6269593\",\n \"0.6261952\",\n \"0.6139124\",\n \"0.61024755\",\n \"0.61000454\",\n \"0.60522926\",\n \"0.5945203\",\n \"0.5922942\",\n \"0.59190106\",\n \"0.5905818\",\n \"0.5891535\",\n \"0.5806236\",\n \"0.578032\",\n \"0.57758945\",\n \"0.5755988\",\n \"0.572439\",\n \"0.5722308\",\n \"0.57164514\",\n \"0.5714595\",\n \"0.56900984\",\n \"0.5689677\",\n \"0.5682416\",\n \"0.5678144\",\n \"0.56766146\",\n \"0.5655368\",\n \"0.5652944\",\n \"0.56472874\",\n \"0.56419885\",\n \"0.5627241\",\n \"0.5627031\",\n \"0.56233436\",\n \"0.5617997\",\n \"0.56099933\",\n \"0.5587849\",\n \"0.55634135\",\n \"0.556103\",\n \"0.5547829\",\n \"0.5544907\",\n \"0.5536335\",\n \"0.5536323\",\n \"0.5534191\",\n \"0.5530354\",\n \"0.5504757\",\n \"0.5502489\",\n \"0.5496008\",\n \"0.54956263\",\n \"0.5488448\",\n \"0.54739547\",\n \"0.54685754\",\n \"0.54659975\",\n \"0.54613894\",\n \"0.5459805\",\n \"0.5456622\",\n \"0.54553396\",\n \"0.54468715\",\n \"0.543625\",\n \"0.5434901\",\n \"0.54268324\",\n \"0.54202247\",\n \"0.5399657\",\n \"0.53868175\",\n \"0.5376673\",\n \"0.5373869\",\n \"0.5361735\",\n \"0.5347369\",\n \"0.5343833\",\n \"0.53352636\",\n \"0.5331729\",\n \"0.53259546\",\n \"0.5325934\",\n \"0.5316587\",\n \"0.53116435\",\n \"0.5305622\",\n \"0.5297066\",\n \"0.5294709\",\n \"0.52914864\",\n \"0.5287045\",\n \"0.5285725\",\n \"0.52769005\",\n \"0.52756864\",\n \"0.52713025\",\n \"0.5264244\",\n \"0.5261914\",\n \"0.5256385\",\n \"0.5252959\",\n \"0.52529454\",\n \"0.5250865\",\n \"0.524609\",\n \"0.52433014\",\n \"0.52268887\",\n \"0.52212334\",\n \"0.522012\",\n \"0.52154166\",\n \"0.521323\",\n \"0.52115566\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7658511"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11774,"cells":{"query":{"kind":"string","value":"Obtains the number nonzeros in a rectangular piece of the linear constraint matrix. getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_)"},"document":{"kind":"string","value":"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\n numnz_ = ctypes.c_int32()\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\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 return (_numnz_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 getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","def getacolnumnz(self,i_): # 3\n res,resargs = self.__obj.getacolnumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n return _nzj_return_value","def getacolnumnz(self,i_):\n nzj_ = ctypes.c_int32()\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n return (_nzj_return_value)","def getarownumnz(self,i_): # 3\n res,resargs = self.__obj.getarownumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzi_return_value = resargs\n return _nzi_return_value","def getacolslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def getarownumnz(self,i_):\n nzi_ = ctypes.c_int32()\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzi_ = nzi_.value\n _nzi_return_value = nzi_\n return (_nzi_return_value)","def getarowslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def getaslicenumnz(self,accmode_,first_,last_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n res,resargs = self.__obj.getaslicenumnz64(accmode_,first_,last_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","def nNz(self):\n if self.dim < 3:\n return None\n return self.nCz + 1","def nz(self):\n return self._dim[2]","def nCz(self):\n if self.dim < 3:\n return None\n return int(self._n[2])","def nnz(self):","def N_Col(self,val):\n\t\tif val in self.colums:\n\t\t\treturn self.colums.index(val)\n\t\telse:\n\t\t\treturn False","def nnz(self):\n return self.rep.nnz()","def innulo(self):\n for i in range(self.n):\n if not comozero(self[i]):\n return i\n return None","def nnz(self):\n return self.to_ddm().nnz()","def build_collocation(nr, nz):\n\n rootsr, _, Br, Wr = recur_colloc_symm(nr, 3)\n rootsz, Az, _ = recur_colloc(nz)\n \n return rootsz, Az, rootsr, Br, Wr","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 get_cells(self):\r\n return \\\r\n (self.nx-1 if self.nx>1 else 1)* \\\r\n (self.ny-1 if self.ny>1 else 1)* \\\r\n (self.nz-1 if self.nz>1 else 1)","def zenith_nadir(x, y):\n if y == 'm':\n bb = []\n cc = []\n for i in range(x.shape[1]):\n bb.append(amax(x[:, i:i + 1]))\n b = array(bb)\n cc.append(amin(x[:, i:i + 1]))\n c = array(cc)\n return (b, c)\n else:\n b = ones(x.shape[1])\n c = zeros(x.shape[1])\n return (b, c)","def test_get_date_column_index_first_col(self, one_row_worksheet):\n\n actual_result = one_row_worksheet.get_date_column_index()\n assert actual_result == 0","def __column_height(self, x):\n\t\tcolumn = self.board[:, x]\n\t\treturn np.count_nonzero(column)","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 first_zombie_col(self, row_num):\n row = self.board[row_num]\n for col_num, square in enumerate(row):\n if any(self.is_zombie([row_num, col_num])):\n return col_num","def nnz(self):\n t = self.get_MSC()\n return len(np.unique(t['masks']))","def nnz(self):\n\t\treturn self.st.size()","def _non_zero_columns_search(array):\n col_num = array.shape[1]\n non_zero_col = CArray([], dtype=int)\n for c in range(col_num):\n col = array[:, c]\n if col.any() == True:\n non_zero_col = non_zero_col.append(c)\n\n return non_zero_col","def GetNumCols(self):\n return _hypre.HypreParMatrix_GetNumCols(self)","def vnEz(self):\n if self.dim < 3:\n return None\n return np.array(\n [x for x in [self.nNx, self.nNy, self.nCz] if x is not None],\n dtype=int\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","def matrix_dim(CT):\r\n if CT[0]==0 and CT[-1]==0:\r\n return 2\r\n elif CT[0]!=0 and CT[-1]!=0:\r\n return 4","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 fn(i, j):\n if i < 0 or j < 0 or matrix[i][j] == \"0\": return 0\n return 1 + min(fn(i-1, j-1), fn(i-1, j), fn(i, j-1))","def NNZ(self):\n return _hypre.HypreParMatrix_NNZ(self)","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 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 getNZ(self):\n return self._get_nz( )","def fn(i, j):\n if i < 0 or j < 0 or obstacleGrid[i][j]: return 0\n if i == 0 and j == 0: return 1 \n return fn(i-1, j) + fn(i, j-1)","def nnz(self):\n return len(self.value)","def vnFz(self):\n if self.dim < 3:\n return None\n return np.array(\n [x for x in [self.nCx, self.nCy, self.nNz] if x is not None],\n dtype=int\n )","def nnz(self):\n return len(self.data)","def get_odd_collatz(n):\n # Please print out which line of the above program contains an error. E.g. if the bug is on line 4 then print 4\n # END OF CONTEXT\n print(\"12\")\n # END OF SOLUTION","def get_tile(self, row, col):\n # replace with your code\n return 0","def calc_px_column(root_i, roots):\n \n idxs = (roots - root_i).nonzero() # exclude root_i\n \n # initial derivative values for recurrence\n p1 = 1.0\n p2 = 0.0\n p3 = 0.0\n \n pn = np.array([p1, p2, p3])\n \n for jj in idxs[0]: # do recurrence, root by root, skipping root_i\n\n H = root_i - roots[jj]\n pn = recur(pn, H)\n \n return pn # return column for px_mat","def GetColStarts(self):\n return _hypre.HypreParMatrix_GetColStarts(self)","def test00(self):\n\n cn = bcolz.zeros(5e9, dtype=\"i1\")\n self.assertTrue(len(cn) == long(5e9))\n\n # Now check some accesses\n cn[1] = 1\n self.assertTrue(cn[1] == 1)\n cn[int(2e9)] = 2\n self.assertTrue(cn[int(2e9)] == 2)\n cn[long(3e9)] = 3\n self.assertTrue(cn[long(3e9)] == 3)\n cn[-1] = 4\n self.assertTrue(cn[-1] == 4)\n\n self.assertTrue(cn.sum() == 10)","def test_get_column_with_negative_index(self):\n self.assertRaises(ValueError, self.sudoku.get_column, -1)","def nNy(self):\n if self.dim < 2:\n return None\n return self.nCy + 1","def M_z(self) -> int:\n return self.params.M_z","def cpp_getInColCount(self, patchNo, colsInPatch):\n return _patchExtractor.patchExtractor_cpp_getInColCount(self, patchNo, colsInPatch)","def get_vector_span(self, i, j):\n from sage.rings.infinity import Infinity\n from sage.matrix.constructor import matrix\n data = self[i,j]\n if not data:\n return None\n elif len(data) == 1:\n return FreeModule(ZZ, self._nvars).submodule([])\n else:\n return matrix([x-data[0] for x in data]).row_space()","def test_find_row_col_indices(self):\r\n obs = self.mc._find_row_col_indices(0)\r\n self.assertEqual(obs, (1, 0))\r\n\r\n obs = self.mc._find_row_col_indices(1)\r\n self.assertEqual(obs, (2, 0))\r\n\r\n obs = self.mc._find_row_col_indices(2)\r\n self.assertEqual(obs, (2, 1))\r\n\r\n obs = self.mc._find_row_col_indices(3)\r\n self.assertEqual(obs, (3, 0))\r\n\r\n obs = self.mc._find_row_col_indices(4)\r\n self.assertEqual(obs, (3, 1))\r\n\r\n obs = self.mc._find_row_col_indices(5)\r\n self.assertEqual(obs, (3, 2))\r\n\r\n obs = self.mc._find_row_col_indices(6)\r\n self.assertEqual(obs, (4, 0))\r\n\r\n self.assertRaises(IndexError, self.mc._find_row_col_indices, -1)","def N_z(self) -> int:\n return self.params.N_z","def colnum(self):\n \n colnum = 0\n for table in self.columnlabels:\n table = np.asarray(table)\n if np.ndim(table) <= 1:\n table = np.reshape(table, (1, -1))\n colnum += table.shape[1]\n return colnum","def _CalculateColumn(self, record):\n if not record:\n return 0\n if isinstance(record, Table):\n add_width = len(record.separator)\n if record.skip_empty:\n if not any(v for _, v in record):\n return 0\n ret = max(len(k) for k, v in record if v) + add_width\n ret = max(ret, 2 + max(self._CalculateColumn(v) for _, v in record))\n return min(ret, self.MAX_MAP_WIDTH)\n elif isinstance(record, Lines):\n return max(self._CalculateColumn(l) for l in record)\n else:\n return 0","def test_get_column(self):\n expected = [8, 9, 5, 2, 4, 7, 1, 3, 6]\n self.assertEqual(expected, self.sudoku.get_column(6))","def test01(self):\n\n cn = bcolz.zeros(5e9, dtype=\"i1\", rootdir=self.rootdir)\n self.assertTrue(len(cn) == long(5e9))\n\n # Now check some accesses\n cn[1] = 1\n self.assertTrue(cn[1] == 1)\n cn[int(2e9)] = 2\n self.assertTrue(cn[int(2e9)] == 2)\n cn[long(3e9)] = 3\n self.assertTrue(cn[long(3e9)] == 3)\n cn[-1] = 4\n self.assertTrue(cn[-1] == 4)\n\n self.assertTrue(cn.sum() == 10)","def find_last_valid_col(row):\n last_el = len(row) - 1\n while last_el > 0:\n col = row[last_el]\n if col is None or col == \"\":\n last_el -= 1\n continue\n else:\n return last_el\n return last_el","def _fix_uniq_col(self):\n # subgradient; for two boolean arrays, multiplication seems to be the best way \n # (equivalent to logical_and)\n n_covered_col = self.a_csr.dot(np.ones(self.ncols)) \n ifix = np.zeros(self.ncols, dtype=bool)\n if (np.count_nonzero(n_covered_col) != self.mrows):\n raise ValueError(\"There are uncovered rows! Please check your input!\")\n if (np.any(n_covered_col==1)):\n inonzero = self.a_csr[n_covered_col==1,:].nonzero()\n ifix[inonzero[1]] = True\n\n return ifix","def get_stain_matrix(I):","def test_get_date_column_index_no_date(self, no_date_col_name_worksheet):\n\n actual_result = no_date_col_name_worksheet.get_date_column_index()\n assert actual_result == -1","def test07(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 cr = c[\"a + 2 * d - 3 > 0\"]\n nr = a[(a + 2 * b - 3) > 0]\n # print \"ca[expr] ->\", cr\n # print \"numpy ->\", nr\n assert_array_equal(cr[:], nr, \"carray[expr] does not work correctly\")","def cpp_getInColExtrema(self, patchNo, colsInPatch, colsInPatch2):\n return _patchExtractor.patchExtractor_cpp_getInColExtrema(self, patchNo, colsInPatch, colsInPatch2)","def sudoku_getcol(A, j):\r\n n = len(A)\r\n B = [0 for i in range(n)]\r\n for i in range(n):\r\n B[i] = A[i][j]\r\n return B","def get_colnumber(self, header):\n for i in range(0, len(self.data)):\n if self.data[i][0] == header:\n return i\n return None","def z(self):\r\n return self.unif[2]","def ny(self):\n return self._dim[1]","def columnCount(self, parent):\r\n if len(self.arraydata) > 0:\r\n return len(self.arraydata[0]) - 2\r\n return 0","def strict_max(x, minicolumns):\n\n x = np.reshape(x, (x.size // minicolumns, minicolumns))\n z = np.zeros_like(x)\n maxes = np.argmax(x, axis=1)\n for max_index, max_aux in enumerate(maxes):\n z[max_index, max_aux] = 1\n\n return z.reshape(x.size)","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)","def cells_z(self):\n if self.is_depth:\n return list(reversed(self._cells[2]))\n return self._cells[2]","def cpp_getInColDist(self, patchNo, colsInPatch):\n return _patchExtractor.patchExtractor_cpp_getInColDist(self, patchNo, colsInPatch)","def GetGlobalNumCols(self):\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)","def idx_z(self, zval):\r\n iz = np.around((zval - self.oz) / self.dz)\r\n return int(iz)","def colfct(self, x):\n for i in xrange(self.anz_seg):\n # find interval which contains x\n if self.xmin[i]<=x<=self.xmax[i]:\n # normalize to [0, 1]\n x = (x-self.xmin[i])/(self.xmax[i]-self.xmin[i])\n return self.colmap[i].colfct(x)\n print \"no interval found for x=%e - should not happen\" % x\n return 0.0","def num_cols(self):\n return (len(self.rows[0]))","def _find_zero(board):\n for r_index, row in enumerate(board):\n for c_index, num in enumerate(row):\n if num == 0:\n return r_index, c_index","def NNZ(self):\n return len(self.__IndList)","def find_empty_col(slots):\n index = 0\n for i in list(zip(*list(slots.values())[::])):\n if sum([1 for j in list(i) if j]) == 0:\n return index\n index += 1\n return 6","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 count_lead_zs(self,x):\n display_mask = 1 << 31\n cnt = 0\n for c in xrange(1,33):\n if((x & display_mask) == 0):\n cnt += 1\n else:\n return cnt\n x <<= 1\n return cnt","def vnFy(self):\n if self.dim < 2:\n return None\n return np.array(\n [x for x in [self.nCx, self.nNy, self.nCz] if x is not None],\n dtype=int\n )","def fun(self, x):\n if np.any(x[self._z] != 0):\n return np.inf\n else:\n return 0","def check_cols(self):\r\n for i in range(3):\r\n if self.grid[i][-1] != ' ' and self.grid[i][-1] == self.grid[i+3][-1] and self.grid[i+3][-1] == self.grid[i+6][-1]:\r\n return (i, (self.grid[i], self.grid[i+6]))\r\n return (-1, None)","def getRowColumn(N):\n N += 1\n y = int((np.sqrt(1 + 8 * N) - 1) / 2)\n b = int(N - (y**2 + y) / 2)\n if b == 0:\n return (y - 1, y - 1)\n else:\n return (y, b - 1)","def ColPart(self, *args):\n return _hypre.HypreParMatrix_ColPart(self, *args)","def build_upper_zeros(self):\r\n for row in range(0, self.SIZE - 1):\r\n self.__obtain_zero(row, self.SIZE - 1, self.SIZE - 1)\r\n\r\n for col in range(self.SIZE - 2, 0, -1):\r\n for row in range(0, col):\r\n self.__obtain_zero(row, col, row + 1)","def test_get_date_column_index_right_trailing_spaces(self, date_trailing_right_spaces_worksheet):\n\n actual_result = date_trailing_right_spaces_worksheet.get_date_column_index()\n assert actual_result == 0","def columns(self) -> int:\n return self.__squares[0].__len__()","def firstEmptyCell(board):\r\n for i in range(9):\r\n for j in range(9):\r\n if board[i][j] == 0:\r\n return (i, j) # row, col\r\n return None","def getNumCols(self):\n return self.__cols","def _get_col(self, idx):\n return self.line[self._fwf.column_slices[idx]]","def __find_start(puzzle):\n for i in range(len(puzzle)):\n for j in range(len(puzzle[0])):\n if puzzle[i][j] == 0:\n return i\n return 0","def find_cell( self , x , y , z , start_ijk = None):\n\n if start_ijk:\n start_index = self.__global_index( ijk = start_ijk )\n else:\n start_index = 0\n global_index = self._get_ijk_xyz( x , y , z , start_index)\n if global_index >= 0:\n i = ctypes.c_int()\n j = ctypes.c_int()\n k = ctypes.c_int()\n self._get_ijk1( global_index , ctypes.byref(i) , ctypes.byref(j) , ctypes.byref(k)) \n return (i.value , j.value , k.value)\n else:\n return None","def collatz(n):\n if n%2==0: return n/2\n else: return 3*n+1","def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","def test_get_cell_with_negative_column(self):\n self.assertRaises(ValueError, self.sudoku.get_cell, (0, -1))"],"string":"[\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\n \"def getacolnumnz(self,i_): # 3\\n res,resargs = self.__obj.getacolnumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n return _nzj_return_value\",\n \"def getacolnumnz(self,i_):\\n nzj_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzj_ = nzj_.value\\n _nzj_return_value = nzj_\\n return (_nzj_return_value)\",\n \"def getarownumnz(self,i_): # 3\\n res,resargs = self.__obj.getarownumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzi_return_value = resargs\\n return _nzi_return_value\",\n \"def getacolslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def getarownumnz(self,i_):\\n nzi_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzi_ = nzi_.value\\n _nzi_return_value = nzi_\\n return (_nzi_return_value)\",\n \"def getarowslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def getaslicenumnz(self,accmode_,first_,last_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n res,resargs = self.__obj.getaslicenumnz64(accmode_,first_,last_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\n \"def nNz(self):\\n if self.dim < 3:\\n return None\\n return self.nCz + 1\",\n \"def nz(self):\\n return self._dim[2]\",\n \"def nCz(self):\\n if self.dim < 3:\\n return None\\n return int(self._n[2])\",\n \"def nnz(self):\",\n \"def N_Col(self,val):\\n\\t\\tif val in self.colums:\\n\\t\\t\\treturn self.colums.index(val)\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def nnz(self):\\n return self.rep.nnz()\",\n \"def innulo(self):\\n for i in range(self.n):\\n if not comozero(self[i]):\\n return i\\n return None\",\n \"def nnz(self):\\n return self.to_ddm().nnz()\",\n \"def build_collocation(nr, nz):\\n\\n rootsr, _, Br, Wr = recur_colloc_symm(nr, 3)\\n rootsz, Az, _ = recur_colloc(nz)\\n \\n return rootsz, Az, rootsr, Br, Wr\",\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 get_cells(self):\\r\\n return \\\\\\r\\n (self.nx-1 if self.nx>1 else 1)* \\\\\\r\\n (self.ny-1 if self.ny>1 else 1)* \\\\\\r\\n (self.nz-1 if self.nz>1 else 1)\",\n \"def zenith_nadir(x, y):\\n if y == 'm':\\n bb = []\\n cc = []\\n for i in range(x.shape[1]):\\n bb.append(amax(x[:, i:i + 1]))\\n b = array(bb)\\n cc.append(amin(x[:, i:i + 1]))\\n c = array(cc)\\n return (b, c)\\n else:\\n b = ones(x.shape[1])\\n c = zeros(x.shape[1])\\n return (b, c)\",\n \"def test_get_date_column_index_first_col(self, one_row_worksheet):\\n\\n actual_result = one_row_worksheet.get_date_column_index()\\n assert actual_result == 0\",\n \"def __column_height(self, x):\\n\\t\\tcolumn = self.board[:, x]\\n\\t\\treturn np.count_nonzero(column)\",\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 first_zombie_col(self, row_num):\\n row = self.board[row_num]\\n for col_num, square in enumerate(row):\\n if any(self.is_zombie([row_num, col_num])):\\n return col_num\",\n \"def nnz(self):\\n t = self.get_MSC()\\n return len(np.unique(t['masks']))\",\n \"def nnz(self):\\n\\t\\treturn self.st.size()\",\n \"def _non_zero_columns_search(array):\\n col_num = array.shape[1]\\n non_zero_col = CArray([], dtype=int)\\n for c in range(col_num):\\n col = array[:, c]\\n if col.any() == True:\\n non_zero_col = non_zero_col.append(c)\\n\\n return non_zero_col\",\n \"def GetNumCols(self):\\n return _hypre.HypreParMatrix_GetNumCols(self)\",\n \"def vnEz(self):\\n if self.dim < 3:\\n return None\\n return np.array(\\n [x for x in [self.nNx, self.nNy, self.nCz] if x is not None],\\n dtype=int\\n )\",\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 matrix_dim(CT):\\r\\n if CT[0]==0 and CT[-1]==0:\\r\\n return 2\\r\\n elif CT[0]!=0 and CT[-1]!=0:\\r\\n return 4\",\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 fn(i, j):\\n if i < 0 or j < 0 or matrix[i][j] == \\\"0\\\": return 0\\n return 1 + min(fn(i-1, j-1), fn(i-1, j), fn(i, j-1))\",\n \"def NNZ(self):\\n return _hypre.HypreParMatrix_NNZ(self)\",\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 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 getNZ(self):\\n return self._get_nz( )\",\n \"def fn(i, j):\\n if i < 0 or j < 0 or obstacleGrid[i][j]: return 0\\n if i == 0 and j == 0: return 1 \\n return fn(i-1, j) + fn(i, j-1)\",\n \"def nnz(self):\\n return len(self.value)\",\n \"def vnFz(self):\\n if self.dim < 3:\\n return None\\n return np.array(\\n [x for x in [self.nCx, self.nCy, self.nNz] if x is not None],\\n dtype=int\\n )\",\n \"def nnz(self):\\n return len(self.data)\",\n \"def get_odd_collatz(n):\\n # Please print out which line of the above program contains an error. E.g. if the bug is on line 4 then print 4\\n # END OF CONTEXT\\n print(\\\"12\\\")\\n # END OF SOLUTION\",\n \"def get_tile(self, row, col):\\n # replace with your code\\n return 0\",\n \"def calc_px_column(root_i, roots):\\n \\n idxs = (roots - root_i).nonzero() # exclude root_i\\n \\n # initial derivative values for recurrence\\n p1 = 1.0\\n p2 = 0.0\\n p3 = 0.0\\n \\n pn = np.array([p1, p2, p3])\\n \\n for jj in idxs[0]: # do recurrence, root by root, skipping root_i\\n\\n H = root_i - roots[jj]\\n pn = recur(pn, H)\\n \\n return pn # return column for px_mat\",\n \"def GetColStarts(self):\\n return _hypre.HypreParMatrix_GetColStarts(self)\",\n \"def test00(self):\\n\\n cn = bcolz.zeros(5e9, dtype=\\\"i1\\\")\\n self.assertTrue(len(cn) == long(5e9))\\n\\n # Now check some accesses\\n cn[1] = 1\\n self.assertTrue(cn[1] == 1)\\n cn[int(2e9)] = 2\\n self.assertTrue(cn[int(2e9)] == 2)\\n cn[long(3e9)] = 3\\n self.assertTrue(cn[long(3e9)] == 3)\\n cn[-1] = 4\\n self.assertTrue(cn[-1] == 4)\\n\\n self.assertTrue(cn.sum() == 10)\",\n \"def test_get_column_with_negative_index(self):\\n self.assertRaises(ValueError, self.sudoku.get_column, -1)\",\n \"def nNy(self):\\n if self.dim < 2:\\n return None\\n return self.nCy + 1\",\n \"def M_z(self) -> int:\\n return self.params.M_z\",\n \"def cpp_getInColCount(self, patchNo, colsInPatch):\\n return _patchExtractor.patchExtractor_cpp_getInColCount(self, patchNo, colsInPatch)\",\n \"def get_vector_span(self, i, j):\\n from sage.rings.infinity import Infinity\\n from sage.matrix.constructor import matrix\\n data = self[i,j]\\n if not data:\\n return None\\n elif len(data) == 1:\\n return FreeModule(ZZ, self._nvars).submodule([])\\n else:\\n return matrix([x-data[0] for x in data]).row_space()\",\n \"def test_find_row_col_indices(self):\\r\\n obs = self.mc._find_row_col_indices(0)\\r\\n self.assertEqual(obs, (1, 0))\\r\\n\\r\\n obs = self.mc._find_row_col_indices(1)\\r\\n self.assertEqual(obs, (2, 0))\\r\\n\\r\\n obs = self.mc._find_row_col_indices(2)\\r\\n self.assertEqual(obs, (2, 1))\\r\\n\\r\\n obs = self.mc._find_row_col_indices(3)\\r\\n self.assertEqual(obs, (3, 0))\\r\\n\\r\\n obs = self.mc._find_row_col_indices(4)\\r\\n self.assertEqual(obs, (3, 1))\\r\\n\\r\\n obs = self.mc._find_row_col_indices(5)\\r\\n self.assertEqual(obs, (3, 2))\\r\\n\\r\\n obs = self.mc._find_row_col_indices(6)\\r\\n self.assertEqual(obs, (4, 0))\\r\\n\\r\\n self.assertRaises(IndexError, self.mc._find_row_col_indices, -1)\",\n \"def N_z(self) -> int:\\n return self.params.N_z\",\n \"def colnum(self):\\n \\n colnum = 0\\n for table in self.columnlabels:\\n table = np.asarray(table)\\n if np.ndim(table) <= 1:\\n table = np.reshape(table, (1, -1))\\n colnum += table.shape[1]\\n return colnum\",\n \"def _CalculateColumn(self, record):\\n if not record:\\n return 0\\n if isinstance(record, Table):\\n add_width = len(record.separator)\\n if record.skip_empty:\\n if not any(v for _, v in record):\\n return 0\\n ret = max(len(k) for k, v in record if v) + add_width\\n ret = max(ret, 2 + max(self._CalculateColumn(v) for _, v in record))\\n return min(ret, self.MAX_MAP_WIDTH)\\n elif isinstance(record, Lines):\\n return max(self._CalculateColumn(l) for l in record)\\n else:\\n return 0\",\n \"def test_get_column(self):\\n expected = [8, 9, 5, 2, 4, 7, 1, 3, 6]\\n self.assertEqual(expected, self.sudoku.get_column(6))\",\n \"def test01(self):\\n\\n cn = bcolz.zeros(5e9, dtype=\\\"i1\\\", rootdir=self.rootdir)\\n self.assertTrue(len(cn) == long(5e9))\\n\\n # Now check some accesses\\n cn[1] = 1\\n self.assertTrue(cn[1] == 1)\\n cn[int(2e9)] = 2\\n self.assertTrue(cn[int(2e9)] == 2)\\n cn[long(3e9)] = 3\\n self.assertTrue(cn[long(3e9)] == 3)\\n cn[-1] = 4\\n self.assertTrue(cn[-1] == 4)\\n\\n self.assertTrue(cn.sum() == 10)\",\n \"def find_last_valid_col(row):\\n last_el = len(row) - 1\\n while last_el > 0:\\n col = row[last_el]\\n if col is None or col == \\\"\\\":\\n last_el -= 1\\n continue\\n else:\\n return last_el\\n return last_el\",\n \"def _fix_uniq_col(self):\\n # subgradient; for two boolean arrays, multiplication seems to be the best way \\n # (equivalent to logical_and)\\n n_covered_col = self.a_csr.dot(np.ones(self.ncols)) \\n ifix = np.zeros(self.ncols, dtype=bool)\\n if (np.count_nonzero(n_covered_col) != self.mrows):\\n raise ValueError(\\\"There are uncovered rows! Please check your input!\\\")\\n if (np.any(n_covered_col==1)):\\n inonzero = self.a_csr[n_covered_col==1,:].nonzero()\\n ifix[inonzero[1]] = True\\n\\n return ifix\",\n \"def get_stain_matrix(I):\",\n \"def test_get_date_column_index_no_date(self, no_date_col_name_worksheet):\\n\\n actual_result = no_date_col_name_worksheet.get_date_column_index()\\n assert actual_result == -1\",\n \"def test07(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 cr = c[\\\"a + 2 * d - 3 > 0\\\"]\\n nr = a[(a + 2 * b - 3) > 0]\\n # print \\\"ca[expr] ->\\\", cr\\n # print \\\"numpy ->\\\", nr\\n assert_array_equal(cr[:], nr, \\\"carray[expr] does not work correctly\\\")\",\n \"def cpp_getInColExtrema(self, patchNo, colsInPatch, colsInPatch2):\\n return _patchExtractor.patchExtractor_cpp_getInColExtrema(self, patchNo, colsInPatch, colsInPatch2)\",\n \"def sudoku_getcol(A, j):\\r\\n n = len(A)\\r\\n B = [0 for i in range(n)]\\r\\n for i in range(n):\\r\\n B[i] = A[i][j]\\r\\n return B\",\n \"def get_colnumber(self, header):\\n for i in range(0, len(self.data)):\\n if self.data[i][0] == header:\\n return i\\n return None\",\n \"def z(self):\\r\\n return self.unif[2]\",\n \"def ny(self):\\n return self._dim[1]\",\n \"def columnCount(self, parent):\\r\\n if len(self.arraydata) > 0:\\r\\n return len(self.arraydata[0]) - 2\\r\\n return 0\",\n \"def strict_max(x, minicolumns):\\n\\n x = np.reshape(x, (x.size // minicolumns, minicolumns))\\n z = np.zeros_like(x)\\n maxes = np.argmax(x, axis=1)\\n for max_index, max_aux in enumerate(maxes):\\n z[max_index, max_aux] = 1\\n\\n return z.reshape(x.size)\",\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 \"def cells_z(self):\\n if self.is_depth:\\n return list(reversed(self._cells[2]))\\n return self._cells[2]\",\n \"def cpp_getInColDist(self, patchNo, colsInPatch):\\n return _patchExtractor.patchExtractor_cpp_getInColDist(self, patchNo, colsInPatch)\",\n \"def GetGlobalNumCols(self):\\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)\",\n \"def idx_z(self, zval):\\r\\n iz = np.around((zval - self.oz) / self.dz)\\r\\n return int(iz)\",\n \"def colfct(self, x):\\n for i in xrange(self.anz_seg):\\n # find interval which contains x\\n if self.xmin[i]<=x<=self.xmax[i]:\\n # normalize to [0, 1]\\n x = (x-self.xmin[i])/(self.xmax[i]-self.xmin[i])\\n return self.colmap[i].colfct(x)\\n print \\\"no interval found for x=%e - should not happen\\\" % x\\n return 0.0\",\n \"def num_cols(self):\\n return (len(self.rows[0]))\",\n \"def _find_zero(board):\\n for r_index, row in enumerate(board):\\n for c_index, num in enumerate(row):\\n if num == 0:\\n return r_index, c_index\",\n \"def NNZ(self):\\n return len(self.__IndList)\",\n \"def find_empty_col(slots):\\n index = 0\\n for i in list(zip(*list(slots.values())[::])):\\n if sum([1 for j in list(i) if j]) == 0:\\n return index\\n index += 1\\n return 6\",\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 count_lead_zs(self,x):\\n display_mask = 1 << 31\\n cnt = 0\\n for c in xrange(1,33):\\n if((x & display_mask) == 0):\\n cnt += 1\\n else:\\n return cnt\\n x <<= 1\\n return cnt\",\n \"def vnFy(self):\\n if self.dim < 2:\\n return None\\n return np.array(\\n [x for x in [self.nCx, self.nNy, self.nCz] if x is not None],\\n dtype=int\\n )\",\n \"def fun(self, x):\\n if np.any(x[self._z] != 0):\\n return np.inf\\n else:\\n return 0\",\n \"def check_cols(self):\\r\\n for i in range(3):\\r\\n if self.grid[i][-1] != ' ' and self.grid[i][-1] == self.grid[i+3][-1] and self.grid[i+3][-1] == self.grid[i+6][-1]:\\r\\n return (i, (self.grid[i], self.grid[i+6]))\\r\\n return (-1, None)\",\n \"def getRowColumn(N):\\n N += 1\\n y = int((np.sqrt(1 + 8 * N) - 1) / 2)\\n b = int(N - (y**2 + y) / 2)\\n if b == 0:\\n return (y - 1, y - 1)\\n else:\\n return (y, b - 1)\",\n \"def ColPart(self, *args):\\n return _hypre.HypreParMatrix_ColPart(self, *args)\",\n \"def build_upper_zeros(self):\\r\\n for row in range(0, self.SIZE - 1):\\r\\n self.__obtain_zero(row, self.SIZE - 1, self.SIZE - 1)\\r\\n\\r\\n for col in range(self.SIZE - 2, 0, -1):\\r\\n for row in range(0, col):\\r\\n self.__obtain_zero(row, col, row + 1)\",\n \"def test_get_date_column_index_right_trailing_spaces(self, date_trailing_right_spaces_worksheet):\\n\\n actual_result = date_trailing_right_spaces_worksheet.get_date_column_index()\\n assert actual_result == 0\",\n \"def columns(self) -> int:\\n return self.__squares[0].__len__()\",\n \"def firstEmptyCell(board):\\r\\n for i in range(9):\\r\\n for j in range(9):\\r\\n if board[i][j] == 0:\\r\\n return (i, j) # row, col\\r\\n return None\",\n \"def getNumCols(self):\\n return self.__cols\",\n \"def _get_col(self, idx):\\n return self.line[self._fwf.column_slices[idx]]\",\n \"def __find_start(puzzle):\\n for i in range(len(puzzle)):\\n for j in range(len(puzzle[0])):\\n if puzzle[i][j] == 0:\\n return i\\n return 0\",\n \"def find_cell( self , x , y , z , start_ijk = None):\\n\\n if start_ijk:\\n start_index = self.__global_index( ijk = start_ijk )\\n else:\\n start_index = 0\\n global_index = self._get_ijk_xyz( x , y , z , start_index)\\n if global_index >= 0:\\n i = ctypes.c_int()\\n j = ctypes.c_int()\\n k = ctypes.c_int()\\n self._get_ijk1( global_index , ctypes.byref(i) , ctypes.byref(j) , ctypes.byref(k)) \\n return (i.value , j.value , k.value)\\n else:\\n return None\",\n \"def collatz(n):\\n if n%2==0: return n/2\\n else: return 3*n+1\",\n \"def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def test_get_cell_with_negative_column(self):\\n self.assertRaises(ValueError, self.sudoku.get_cell, (0, -1))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.81828666","0.7616695","0.75478375","0.7345593","0.7094305","0.69862217","0.6438003","0.6430326","0.61437994","0.5996406","0.57730126","0.5638592","0.55720025","0.5547465","0.5504438","0.54538846","0.54515296","0.5447022","0.5447022","0.5447022","0.5447022","0.5428881","0.53894436","0.53851813","0.53596854","0.53377306","0.5321408","0.52835464","0.5254005","0.52440995","0.5195953","0.51915276","0.51483184","0.51423424","0.51415116","0.5139631","0.5131931","0.51136327","0.5109081","0.5106624","0.509919","0.50976443","0.5079343","0.5077347","0.5047185","0.5042629","0.50281596","0.5018732","0.5010749","0.5010604","0.4999717","0.4999211","0.4997174","0.49861822","0.49805126","0.4972116","0.49598","0.49593684","0.49571437","0.49561936","0.49528095","0.49522775","0.49487895","0.4943859","0.4932793","0.4928512","0.49241838","0.49220544","0.4920708","0.49167186","0.49118075","0.49080932","0.490626","0.49059314","0.4892764","0.4889226","0.4888973","0.4886455","0.48818484","0.48800367","0.4873414","0.48720503","0.4858917","0.48558757","0.48543376","0.48526564","0.48518804","0.4851852","0.48504758","0.4850392","0.4843703","0.4842827","0.48405746","0.48382792","0.48359686","0.482928","0.48162124","0.48071882","0.48035794","0.48018402"],"string":"[\n \"0.81828666\",\n \"0.7616695\",\n \"0.75478375\",\n \"0.7345593\",\n \"0.7094305\",\n \"0.69862217\",\n \"0.6438003\",\n \"0.6430326\",\n \"0.61437994\",\n \"0.5996406\",\n \"0.57730126\",\n \"0.5638592\",\n \"0.55720025\",\n \"0.5547465\",\n \"0.5504438\",\n \"0.54538846\",\n \"0.54515296\",\n \"0.5447022\",\n \"0.5447022\",\n \"0.5447022\",\n \"0.5447022\",\n \"0.5428881\",\n \"0.53894436\",\n \"0.53851813\",\n \"0.53596854\",\n \"0.53377306\",\n \"0.5321408\",\n \"0.52835464\",\n \"0.5254005\",\n \"0.52440995\",\n \"0.5195953\",\n \"0.51915276\",\n \"0.51483184\",\n \"0.51423424\",\n \"0.51415116\",\n \"0.5139631\",\n \"0.5131931\",\n \"0.51136327\",\n \"0.5109081\",\n \"0.5106624\",\n \"0.509919\",\n \"0.50976443\",\n \"0.5079343\",\n \"0.5077347\",\n \"0.5047185\",\n \"0.5042629\",\n \"0.50281596\",\n \"0.5018732\",\n \"0.5010749\",\n \"0.5010604\",\n \"0.4999717\",\n \"0.4999211\",\n \"0.4997174\",\n \"0.49861822\",\n \"0.49805126\",\n \"0.4972116\",\n \"0.49598\",\n \"0.49593684\",\n \"0.49571437\",\n \"0.49561936\",\n \"0.49528095\",\n \"0.49522775\",\n \"0.49487895\",\n \"0.4943859\",\n \"0.4932793\",\n \"0.4928512\",\n \"0.49241838\",\n \"0.49220544\",\n \"0.4920708\",\n \"0.49167186\",\n \"0.49118075\",\n \"0.49080932\",\n \"0.490626\",\n \"0.49059314\",\n \"0.4892764\",\n \"0.4889226\",\n \"0.4888973\",\n \"0.4886455\",\n \"0.48818484\",\n \"0.48800367\",\n \"0.4873414\",\n \"0.48720503\",\n \"0.4858917\",\n \"0.48558757\",\n \"0.48543376\",\n \"0.48526564\",\n \"0.48518804\",\n \"0.4851852\",\n \"0.48504758\",\n \"0.4850392\",\n \"0.4843703\",\n \"0.4842827\",\n \"0.48405746\",\n \"0.48382792\",\n \"0.48359686\",\n \"0.482928\",\n \"0.48162124\",\n \"0.48071882\",\n \"0.48035794\",\n \"0.48018402\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79388636"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11775,"cells":{"query":{"kind":"string","value":"Obtains the number of nonzero elements in one column of the linear constraint matrix getacolnumnz(self,i_)"},"document":{"kind":"string","value":"def getacolnumnz(self,i_):\n nzj_ = ctypes.c_int32()\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n return (_nzj_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 getacolnumnz(self,i_): # 3\n res,resargs = self.__obj.getacolnumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n return _nzj_return_value","def getarownumnz(self,i_): # 3\n res,resargs = self.__obj.getarownumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzi_return_value = resargs\n return _nzi_return_value","def getarownumnz(self,i_):\n nzi_ = ctypes.c_int32()\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzi_ = nzi_.value\n _nzi_return_value = nzi_\n return (_nzi_return_value)","def colnum(self):\n \n colnum = 0\n for table in self.columnlabels:\n table = np.asarray(table)\n if np.ndim(table) <= 1:\n table = np.reshape(table, (1, -1))\n colnum += table.shape[1]\n return colnum","def __column_height(self, x):\n\t\tcolumn = self.board[:, x]\n\t\treturn np.count_nonzero(column)","def N_Col(self,val):\n\t\tif val in self.colums:\n\t\t\treturn self.colums.index(val)\n\t\telse:\n\t\t\treturn False","def GetNumCols(self):\n return _hypre.HypreParMatrix_GetNumCols(self)","def getNumCols(self):\n return self.__cols","def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","def nNz(self):\n if self.dim < 3:\n return None\n return self.nCz + 1","def nCz(self):\n if self.dim < 3:\n return None\n return int(self._n[2])","def n_cols(self):\n ch = self.children\n return 1 if not ch else sum([c.n_cols for c in ch])","def columnCount(self, parent_midx):\n return self._cols_nb","def columnCount(self, parent_midx):\n return self._cols_nb","def _non_zero_columns_search(array):\n col_num = array.shape[1]\n non_zero_col = CArray([], dtype=int)\n for c in range(col_num):\n col = array[:, c]\n if col.any() == True:\n non_zero_col = non_zero_col.append(c)\n\n return non_zero_col","def getNbColumns(self):\n return self.data.shape[0]","def ncolumns(self):\n return self.__ncols","def num_cols(self):\n return (len(self.rows[0]))","def columnCount(self):\n return abs(self.minCol) + abs(self.maxCol)","def GetGlobalNumCols(self):\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)","def nz(self):\n return self._dim[2]","def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\n numnz_ = ctypes.c_int32()\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def GetNumberCols(self):\n return len(self.__colsKey)","def columns(self) -> int:\n return self.__squares[0].__len__()","def GetColumnCount(self):\r\n\r\n return len(self._columns)","def columnCount(self, parent):\r\n if len(self.arraydata) > 0:\r\n return len(self.arraydata[0]) - 2\r\n return 0","def ncolumns(self):\n return len(self.__column_list)","def nnz(self):\n return self.to_ddm().nnz()","def num_cols(self):\n return len(self.rows[0])","def columns(self):\n try:\n return self._data.shape[1] or 1\n except (AttributeError, IndexError):\n return 1","def nnz(self):\n return self.rep.nnz()","def GetNumColumns(self):\n return len(self.columns)","def get_num_cols(self):\n return self._num_cols","def n_cols(self):\n\n return len(self.plaincolumns)","def getNoOfCols(self):\n return _patchExtractor.patchExtractor_getNoOfCols(self)","def ncol(arr):\n assert isinstance(arr, ndarray)\n assert len(arr.shape) == 2\n return arr.shape[1]","def num_cols(self):\n return len(self.column_names())","def getacolslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def TzscorrCols(Xn):\n C_X = T.dot(Xn.T, Xn)-T.eye(Xn.shape[1])\n return 0.5*T.log((1+C_X)/(1-C_X))","def col_count(self):\n return self.__col_count","def constraint_col_coef(n_col, n_row):\n\n all_rows = []\n for i in range(n_col):\n matrix_values = np.zeros((n_row, n_col), dtype=int)\n col_offer = np.ones(n_row, dtype=int)\n matrix_values[:, i] = col_offer\n all_rows.append(matrix_values.flatten())\n\n cols_constraints = np.stack(all_rows)\n\n return cols_constraints","def columnCount( self ):\n if not self.o_data is None:\n if self.isItemMultiDimensional():\n return len(self.o_data)\n else:\n return 1\n else:\n return 1","def nnz(self):\n return len(self.data)","def sudoku_getcol(A, j):\r\n n = len(A)\r\n B = [0 for i in range(n)]\r\n for i in range(n):\r\n B[i] = A[i][j]\r\n return B","def first_zombie_col(self, row_num):\n row = self.board[row_num]\n for col_num, square in enumerate(row):\n if any(self.is_zombie([row_num, col_num])):\n return col_num","def nnz(self):\n return len(self.value)","def columns_count(self):\n if self.value.count != 0:\n return len(self.value[0])\n else:\n return 0","def dof(self):\n if self._dof is None:\n self._dof = self.tri.nbr_cells - (self.tri.columns.size - 1) + self.tri.index.size\n return(self._dof)","def is_column_unique(self, i: int) -> bool:\n #print(\"enter bartpy/bartpy/data.py CovariateMatrix is_column_unique\")\n \n if self._unique_columns[i] is None:\n self._unique_columns[i] = len(np.unique(self.get_column(i))) == self._n_obsv\n output = self._unique_columns[i]\n #print(\"-exit bartpy/bartpy/data.py CovariateMatrix is_column_unique\")\n return output","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 col_count(self):\n if isinstance(self.data, pd.DataFrame) is False:\n return None\n else:\n return self.data.shape[1]","def cols(self) -> int:\n if self.is_empty():\n raise ValueError(\"Empty DataView contains no columns\")\n\n return len(self[0])","def get_num_columns(table):\n\n\treturn max((len(row) for row in table))","def matrix_dim(CT):\r\n if CT[0]==0 and CT[-1]==0:\r\n return 2\r\n elif CT[0]!=0 and CT[-1]!=0:\r\n return 4","def _CalculateColumn(self, record):\n if not record:\n return 0\n if isinstance(record, Table):\n add_width = len(record.separator)\n if record.skip_empty:\n if not any(v for _, v in record):\n return 0\n ret = max(len(k) for k, v in record if v) + add_width\n ret = max(ret, 2 + max(self._CalculateColumn(v) for _, v in record))\n return min(ret, self.MAX_MAP_WIDTH)\n elif isinstance(record, Lines):\n return max(self._CalculateColumn(l) for l in record)\n else:\n return 0","def max_width_col(table, col_idx):\n return max(len(row[col_idx]) for row in table)","def get_colnumber(self, header):\n for i in range(0, len(self.data)):\n if self.data[i][0] == header:\n return i\n return None","def nnz(self):\n t = self.get_MSC()\n return len(np.unique(t['masks']))","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 columnCount(self, index):\n return 1","def numRowsCols(array):\n return len(array),len(array[0])","def getColumnCount(self) -> int:\n ...","def N_z(self) -> int:\n return self.params.N_z","def column_count(self):\n return self.column_length","def col(self, i):\n return Vector([row[i] for row in self.data])","def columnCount(self, _parent=None):\n return len(self._attr_cols)","def columnCount(self, index):\n return 4","def nnz(self):","def find_empty_col(slots):\n index = 0\n for i in list(zip(*list(slots.values())[::])):\n if sum([1 for j in list(i) if j]) == 0:\n return index\n index += 1\n return 6","def nnz(self):\n\t\treturn self.st.size()","def get_columns(self) -> int:\r\n return 1 + self.display.get_columns() + 1","def build_collocation(nr, nz):\n\n rootsr, _, Br, Wr = recur_colloc_symm(nr, 3)\n rootsz, Az, _ = recur_colloc(nz)\n \n return rootsz, Az, rootsr, Br, Wr","def column (self, i):\n return Vector(tuple(zip(*self._m))[i])","def get_cols(self):\n return np.transpose(self.board).tolist()","def fast_get_col(self,j):\n col = self.col_view[:,j].copy()\n col.data = self.X.data[col.data]\n return col","def no_of_columns(self): \n return len(self.columns) + (1 if self.serialize else 0)","def _get_col(self, idx):\n return self.line[self._fwf.column_slices[idx]]","def GetColumnCount(self):\r\n\r\n return self._owner.GetHeaderWindow().GetColumnCount()","def vnFz(self):\n if self.dim < 3:\n return None\n return np.array(\n [x for x in [self.nCx, self.nCy, self.nNz] if x is not None],\n dtype=int\n )","def columnCount(self,\n parent=QtCore.QModelIndex()) -> int:\n return len(self.Column);","def GetColStarts(self):\n return _hypre.HypreParMatrix_GetColStarts(self)","def getaslicenumnz(self,accmode_,first_,last_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n res,resargs = self.__obj.getaslicenumnz64(accmode_,first_,last_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","def table_column(self, i):\n return self.__column_list[i]","def _num_of_cols(self):\n if not self.connection:\n self.close()\n noc = C_SHORT()\n ret = ODBC_API.SQLNumResultCols(self.stmt_h, ADDR(noc))\n if ret != SQL_SUCCESS:\n check_success(self, ret)\n return noc.value","def ColPart(self, *args):\n return _hypre.HypreParMatrix_ColPart(self, *args)","def collatz(n):\n if n%2==0: return n/2\n else: return 3*n+1","def columnCount(self, index=QtCore.QModelIndex()):\n # speed comparison:\n # In [23]: %timeit len(df.columns)\n # 10000000 loops, best of 3: 108 ns per loop\n\n # In [24]: %timeit df.shape[1]\n # 1000000 loops, best of 3: 440 ns per loop\n return len(self._dataFrame.columns)","def calc_col_len(self): # Calculates length of each column\n print('\\nColumn Lengths\\n' +\n '--------------')\n results.append('\\nColumn Lengths\\n' +\n '--------------')\n for x in range(0, self.tot_col):\n blank = 0\n for y in range(1, self.tot_rows + 1):\n if self.file_list[y][x] == '':\n blank += 1\n column_count = self.tot_rows - blank\n\n results.append('Column \\'' + self.file_list[0][x] + '\\' length: ' + str(column_count))\n print('Column \\'' + self.file_list[0][x] + '\\' length: ' + str(column_count))","def nNy(self):\n if self.dim < 2:\n return None\n return self.nCy + 1","def count_level(self):\r\n count = 0\r\n\r\n for i in range(0, len(self.matrix)):\r\n for j in range(0,len(self.matrix[i])):\r\n if self.matrix[i][j] == \"0\":\r\n count += 1\r\n\r\n # We substract 1 to count level from 0\r\n return count - 1","def get_zdim(self):\n return self.decoder.get_input_info_dict()['latent_vector'].get_shape()[1]","def cols(self) -> int:\n return self.train.cols()","def print_col(input, iy=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, y = %d col)\" % (iz, iy)\n\tline = []\n\tfor ix in xrange(nx):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((ix + 1) % 5 == 0): line.append(\"\\n \")\n\tline.append(\"\\n\")\n\tprint \"\".join(line)","def get_ncols(self):\n return self.ncols","def get_ncols(self):\n return self.ncols","def getRowColumn(N):\n N += 1\n y = int((np.sqrt(1 + 8 * N) - 1) / 2)\n b = int(N - (y**2 + y) / 2)\n if b == 0:\n return (y - 1, y - 1)\n else:\n return (y, b - 1)","def getCol(self, n, offset=0):\n return self._c[(n*self.__height + offset):((n+1) * self.__height)]"],"string":"[\n \"def getacolnumnz(self,i_): # 3\\n res,resargs = self.__obj.getacolnumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n return _nzj_return_value\",\n \"def getarownumnz(self,i_): # 3\\n res,resargs = self.__obj.getarownumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzi_return_value = resargs\\n return _nzi_return_value\",\n \"def getarownumnz(self,i_):\\n nzi_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzi_ = nzi_.value\\n _nzi_return_value = nzi_\\n return (_nzi_return_value)\",\n \"def colnum(self):\\n \\n colnum = 0\\n for table in self.columnlabels:\\n table = np.asarray(table)\\n if np.ndim(table) <= 1:\\n table = np.reshape(table, (1, -1))\\n colnum += table.shape[1]\\n return colnum\",\n \"def __column_height(self, x):\\n\\t\\tcolumn = self.board[:, x]\\n\\t\\treturn np.count_nonzero(column)\",\n \"def N_Col(self,val):\\n\\t\\tif val in self.colums:\\n\\t\\t\\treturn self.colums.index(val)\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def GetNumCols(self):\\n return _hypre.HypreParMatrix_GetNumCols(self)\",\n \"def getNumCols(self):\\n return self.__cols\",\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\n \"def nNz(self):\\n if self.dim < 3:\\n return None\\n return self.nCz + 1\",\n \"def nCz(self):\\n if self.dim < 3:\\n return None\\n return int(self._n[2])\",\n \"def n_cols(self):\\n ch = self.children\\n return 1 if not ch else sum([c.n_cols for c in ch])\",\n \"def columnCount(self, parent_midx):\\n return self._cols_nb\",\n \"def columnCount(self, parent_midx):\\n return self._cols_nb\",\n \"def _non_zero_columns_search(array):\\n col_num = array.shape[1]\\n non_zero_col = CArray([], dtype=int)\\n for c in range(col_num):\\n col = array[:, c]\\n if col.any() == True:\\n non_zero_col = non_zero_col.append(c)\\n\\n return non_zero_col\",\n \"def getNbColumns(self):\\n return self.data.shape[0]\",\n \"def ncolumns(self):\\n return self.__ncols\",\n \"def num_cols(self):\\n return (len(self.rows[0]))\",\n \"def columnCount(self):\\n return abs(self.minCol) + abs(self.maxCol)\",\n \"def GetGlobalNumCols(self):\\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)\",\n \"def nz(self):\\n return self._dim[2]\",\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\\n numnz_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def GetNumberCols(self):\\n return len(self.__colsKey)\",\n \"def columns(self) -> int:\\n return self.__squares[0].__len__()\",\n \"def GetColumnCount(self):\\r\\n\\r\\n return len(self._columns)\",\n \"def columnCount(self, parent):\\r\\n if len(self.arraydata) > 0:\\r\\n return len(self.arraydata[0]) - 2\\r\\n return 0\",\n \"def ncolumns(self):\\n return len(self.__column_list)\",\n \"def nnz(self):\\n return self.to_ddm().nnz()\",\n \"def num_cols(self):\\n return len(self.rows[0])\",\n \"def columns(self):\\n try:\\n return self._data.shape[1] or 1\\n except (AttributeError, IndexError):\\n return 1\",\n \"def nnz(self):\\n return self.rep.nnz()\",\n \"def GetNumColumns(self):\\n return len(self.columns)\",\n \"def get_num_cols(self):\\n return self._num_cols\",\n \"def n_cols(self):\\n\\n return len(self.plaincolumns)\",\n \"def getNoOfCols(self):\\n return _patchExtractor.patchExtractor_getNoOfCols(self)\",\n \"def ncol(arr):\\n assert isinstance(arr, ndarray)\\n assert len(arr.shape) == 2\\n return arr.shape[1]\",\n \"def num_cols(self):\\n return len(self.column_names())\",\n \"def getacolslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def TzscorrCols(Xn):\\n C_X = T.dot(Xn.T, Xn)-T.eye(Xn.shape[1])\\n return 0.5*T.log((1+C_X)/(1-C_X))\",\n \"def col_count(self):\\n return self.__col_count\",\n \"def constraint_col_coef(n_col, n_row):\\n\\n all_rows = []\\n for i in range(n_col):\\n matrix_values = np.zeros((n_row, n_col), dtype=int)\\n col_offer = np.ones(n_row, dtype=int)\\n matrix_values[:, i] = col_offer\\n all_rows.append(matrix_values.flatten())\\n\\n cols_constraints = np.stack(all_rows)\\n\\n return cols_constraints\",\n \"def columnCount( self ):\\n if not self.o_data is None:\\n if self.isItemMultiDimensional():\\n return len(self.o_data)\\n else:\\n return 1\\n else:\\n return 1\",\n \"def nnz(self):\\n return len(self.data)\",\n \"def sudoku_getcol(A, j):\\r\\n n = len(A)\\r\\n B = [0 for i in range(n)]\\r\\n for i in range(n):\\r\\n B[i] = A[i][j]\\r\\n return B\",\n \"def first_zombie_col(self, row_num):\\n row = self.board[row_num]\\n for col_num, square in enumerate(row):\\n if any(self.is_zombie([row_num, col_num])):\\n return col_num\",\n \"def nnz(self):\\n return len(self.value)\",\n \"def columns_count(self):\\n if self.value.count != 0:\\n return len(self.value[0])\\n else:\\n return 0\",\n \"def dof(self):\\n if self._dof is None:\\n self._dof = self.tri.nbr_cells - (self.tri.columns.size - 1) + self.tri.index.size\\n return(self._dof)\",\n \"def is_column_unique(self, i: int) -> bool:\\n #print(\\\"enter bartpy/bartpy/data.py CovariateMatrix is_column_unique\\\")\\n \\n if self._unique_columns[i] is None:\\n self._unique_columns[i] = len(np.unique(self.get_column(i))) == self._n_obsv\\n output = self._unique_columns[i]\\n #print(\\\"-exit bartpy/bartpy/data.py CovariateMatrix is_column_unique\\\")\\n return output\",\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 col_count(self):\\n if isinstance(self.data, pd.DataFrame) is False:\\n return None\\n else:\\n return self.data.shape[1]\",\n \"def cols(self) -> int:\\n if self.is_empty():\\n raise ValueError(\\\"Empty DataView contains no columns\\\")\\n\\n return len(self[0])\",\n \"def get_num_columns(table):\\n\\n\\treturn max((len(row) for row in table))\",\n \"def matrix_dim(CT):\\r\\n if CT[0]==0 and CT[-1]==0:\\r\\n return 2\\r\\n elif CT[0]!=0 and CT[-1]!=0:\\r\\n return 4\",\n \"def _CalculateColumn(self, record):\\n if not record:\\n return 0\\n if isinstance(record, Table):\\n add_width = len(record.separator)\\n if record.skip_empty:\\n if not any(v for _, v in record):\\n return 0\\n ret = max(len(k) for k, v in record if v) + add_width\\n ret = max(ret, 2 + max(self._CalculateColumn(v) for _, v in record))\\n return min(ret, self.MAX_MAP_WIDTH)\\n elif isinstance(record, Lines):\\n return max(self._CalculateColumn(l) for l in record)\\n else:\\n return 0\",\n \"def max_width_col(table, col_idx):\\n return max(len(row[col_idx]) for row in table)\",\n \"def get_colnumber(self, header):\\n for i in range(0, len(self.data)):\\n if self.data[i][0] == header:\\n return i\\n return None\",\n \"def nnz(self):\\n t = self.get_MSC()\\n return len(np.unique(t['masks']))\",\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 columnCount(self, index):\\n return 1\",\n \"def numRowsCols(array):\\n return len(array),len(array[0])\",\n \"def getColumnCount(self) -> int:\\n ...\",\n \"def N_z(self) -> int:\\n return self.params.N_z\",\n \"def column_count(self):\\n return self.column_length\",\n \"def col(self, i):\\n return Vector([row[i] for row in self.data])\",\n \"def columnCount(self, _parent=None):\\n return len(self._attr_cols)\",\n \"def columnCount(self, index):\\n return 4\",\n \"def nnz(self):\",\n \"def find_empty_col(slots):\\n index = 0\\n for i in list(zip(*list(slots.values())[::])):\\n if sum([1 for j in list(i) if j]) == 0:\\n return index\\n index += 1\\n return 6\",\n \"def nnz(self):\\n\\t\\treturn self.st.size()\",\n \"def get_columns(self) -> int:\\r\\n return 1 + self.display.get_columns() + 1\",\n \"def build_collocation(nr, nz):\\n\\n rootsr, _, Br, Wr = recur_colloc_symm(nr, 3)\\n rootsz, Az, _ = recur_colloc(nz)\\n \\n return rootsz, Az, rootsr, Br, Wr\",\n \"def column (self, i):\\n return Vector(tuple(zip(*self._m))[i])\",\n \"def get_cols(self):\\n return np.transpose(self.board).tolist()\",\n \"def fast_get_col(self,j):\\n col = self.col_view[:,j].copy()\\n col.data = self.X.data[col.data]\\n return col\",\n \"def no_of_columns(self): \\n return len(self.columns) + (1 if self.serialize else 0)\",\n \"def _get_col(self, idx):\\n return self.line[self._fwf.column_slices[idx]]\",\n \"def GetColumnCount(self):\\r\\n\\r\\n return self._owner.GetHeaderWindow().GetColumnCount()\",\n \"def vnFz(self):\\n if self.dim < 3:\\n return None\\n return np.array(\\n [x for x in [self.nCx, self.nCy, self.nNz] if x is not None],\\n dtype=int\\n )\",\n \"def columnCount(self,\\n parent=QtCore.QModelIndex()) -> int:\\n return len(self.Column);\",\n \"def GetColStarts(self):\\n return _hypre.HypreParMatrix_GetColStarts(self)\",\n \"def getaslicenumnz(self,accmode_,first_,last_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n res,resargs = self.__obj.getaslicenumnz64(accmode_,first_,last_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\n \"def table_column(self, i):\\n return self.__column_list[i]\",\n \"def _num_of_cols(self):\\n if not self.connection:\\n self.close()\\n noc = C_SHORT()\\n ret = ODBC_API.SQLNumResultCols(self.stmt_h, ADDR(noc))\\n if ret != SQL_SUCCESS:\\n check_success(self, ret)\\n return noc.value\",\n \"def ColPart(self, *args):\\n return _hypre.HypreParMatrix_ColPart(self, *args)\",\n \"def collatz(n):\\n if n%2==0: return n/2\\n else: return 3*n+1\",\n \"def columnCount(self, index=QtCore.QModelIndex()):\\n # speed comparison:\\n # In [23]: %timeit len(df.columns)\\n # 10000000 loops, best of 3: 108 ns per loop\\n\\n # In [24]: %timeit df.shape[1]\\n # 1000000 loops, best of 3: 440 ns per loop\\n return len(self._dataFrame.columns)\",\n \"def calc_col_len(self): # Calculates length of each column\\n print('\\\\nColumn Lengths\\\\n' +\\n '--------------')\\n results.append('\\\\nColumn Lengths\\\\n' +\\n '--------------')\\n for x in range(0, self.tot_col):\\n blank = 0\\n for y in range(1, self.tot_rows + 1):\\n if self.file_list[y][x] == '':\\n blank += 1\\n column_count = self.tot_rows - blank\\n\\n results.append('Column \\\\'' + self.file_list[0][x] + '\\\\' length: ' + str(column_count))\\n print('Column \\\\'' + self.file_list[0][x] + '\\\\' length: ' + str(column_count))\",\n \"def nNy(self):\\n if self.dim < 2:\\n return None\\n return self.nCy + 1\",\n \"def count_level(self):\\r\\n count = 0\\r\\n\\r\\n for i in range(0, len(self.matrix)):\\r\\n for j in range(0,len(self.matrix[i])):\\r\\n if self.matrix[i][j] == \\\"0\\\":\\r\\n count += 1\\r\\n\\r\\n # We substract 1 to count level from 0\\r\\n return count - 1\",\n \"def get_zdim(self):\\n return self.decoder.get_input_info_dict()['latent_vector'].get_shape()[1]\",\n \"def cols(self) -> int:\\n return self.train.cols()\",\n \"def print_col(input, iy=0, iz=0):\\n\\timage=get_image(input)\\n\\tnx = image.get_xsize()\\n\\tny = image.get_ysize()\\n\\tnz = image.get_zsize()\\n\\tprint \\\"(z = %d slice, y = %d col)\\\" % (iz, iy)\\n\\tline = []\\n\\tfor ix in xrange(nx):\\n\\t\\tline.append(\\\"%12.5g \\\" % (image.get_value_at(ix,iy,iz)))\\n\\t\\tif ((ix + 1) % 5 == 0): line.append(\\\"\\\\n \\\")\\n\\tline.append(\\\"\\\\n\\\")\\n\\tprint \\\"\\\".join(line)\",\n \"def get_ncols(self):\\n return self.ncols\",\n \"def get_ncols(self):\\n return self.ncols\",\n \"def getRowColumn(N):\\n N += 1\\n y = int((np.sqrt(1 + 8 * N) - 1) / 2)\\n b = int(N - (y**2 + y) / 2)\\n if b == 0:\\n return (y - 1, y - 1)\\n else:\\n return (y, b - 1)\",\n \"def getCol(self, n, offset=0):\\n return self._c[(n*self.__height + offset):((n+1) * self.__height)]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78955895","0.6964569","0.66872597","0.6391909","0.63877714","0.632295","0.63077927","0.6225606","0.6197037","0.61326903","0.61255556","0.60932916","0.60414433","0.60414433","0.5994617","0.5992448","0.59544677","0.5947154","0.5945124","0.5940501","0.5913796","0.5907808","0.5896922","0.58867306","0.5871345","0.58662325","0.5861006","0.5858896","0.58293295","0.57936364","0.5792636","0.57816446","0.57778645","0.576669","0.57577765","0.5754957","0.5749165","0.5733951","0.57160646","0.566274","0.56367266","0.5636548","0.56299114","0.5615894","0.56007266","0.5585369","0.55836","0.55831003","0.5582092","0.55819535","0.55819535","0.55819535","0.55819535","0.5575077","0.5569048","0.5553538","0.55525386","0.55327076","0.55208343","0.5518601","0.54994136","0.54931515","0.54620457","0.5440413","0.5438497","0.5435828","0.54281783","0.5419454","0.5405043","0.54046977","0.5390971","0.53717786","0.5369746","0.5350108","0.53452015","0.5325349","0.5318956","0.5305354","0.5303435","0.5291917","0.52792835","0.52729255","0.52674365","0.5257636","0.5257477","0.5256195","0.52416676","0.5213925","0.5194019","0.5189049","0.51862967","0.51767087","0.5174023","0.51691884","0.5169043","0.51627624","0.5146773","0.5146773","0.51458293","0.5144067"],"string":"[\n \"0.78955895\",\n \"0.6964569\",\n \"0.66872597\",\n \"0.6391909\",\n \"0.63877714\",\n \"0.632295\",\n \"0.63077927\",\n \"0.6225606\",\n \"0.6197037\",\n \"0.61326903\",\n \"0.61255556\",\n \"0.60932916\",\n \"0.60414433\",\n \"0.60414433\",\n \"0.5994617\",\n \"0.5992448\",\n \"0.59544677\",\n \"0.5947154\",\n \"0.5945124\",\n \"0.5940501\",\n \"0.5913796\",\n \"0.5907808\",\n \"0.5896922\",\n \"0.58867306\",\n \"0.5871345\",\n \"0.58662325\",\n \"0.5861006\",\n \"0.5858896\",\n \"0.58293295\",\n \"0.57936364\",\n \"0.5792636\",\n \"0.57816446\",\n \"0.57778645\",\n \"0.576669\",\n \"0.57577765\",\n \"0.5754957\",\n \"0.5749165\",\n \"0.5733951\",\n \"0.57160646\",\n \"0.566274\",\n \"0.56367266\",\n \"0.5636548\",\n \"0.56299114\",\n \"0.5615894\",\n \"0.56007266\",\n \"0.5585369\",\n \"0.55836\",\n \"0.55831003\",\n \"0.5582092\",\n \"0.55819535\",\n \"0.55819535\",\n \"0.55819535\",\n \"0.55819535\",\n \"0.5575077\",\n \"0.5569048\",\n \"0.5553538\",\n \"0.55525386\",\n \"0.55327076\",\n \"0.55208343\",\n \"0.5518601\",\n \"0.54994136\",\n \"0.54931515\",\n \"0.54620457\",\n \"0.5440413\",\n \"0.5438497\",\n \"0.5435828\",\n \"0.54281783\",\n \"0.5419454\",\n \"0.5405043\",\n \"0.54046977\",\n \"0.5390971\",\n \"0.53717786\",\n \"0.5369746\",\n \"0.5350108\",\n \"0.53452015\",\n \"0.5325349\",\n \"0.5318956\",\n \"0.5305354\",\n \"0.5303435\",\n \"0.5291917\",\n \"0.52792835\",\n \"0.52729255\",\n \"0.52674365\",\n \"0.5257636\",\n \"0.5257477\",\n \"0.5256195\",\n \"0.52416676\",\n \"0.5213925\",\n \"0.5194019\",\n \"0.5189049\",\n \"0.51862967\",\n \"0.51767087\",\n \"0.5174023\",\n \"0.51691884\",\n \"0.5169043\",\n \"0.51627624\",\n \"0.5146773\",\n \"0.5146773\",\n \"0.51458293\",\n \"0.5144067\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7779385"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11776,"cells":{"query":{"kind":"string","value":"Obtains one column of the linear constraint matrix. getacol(self,j_,subj_,valj_)"},"document":{"kind":"string","value":"def getacol(self,j_,subj_,valj_):\n nzj_ = ctypes.c_int32()\n _subj_minlength = self.getacolnumnz((j_))\n if self.getacolnumnz((j_)) > 0 and subj_ is not None and len(subj_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),self.getacolnumnz((j_))))\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 _valj_minlength = self.getacolnumnz((j_))\n if self.getacolnumnz((j_)) > 0 and valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument valj is not long enough: Is %d, expected %d\" % (len(valj_),self.getacolnumnz((j_))))\n if isinstance(valj_,numpy.ndarray) and not valj_.flags.writeable:\n raise ValueError(\"Argument valj must be writable\")\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_getacol(self.__nativep,j_,ctypes.byref(nzj_),_subj_tmp,_valj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valj_copyarray:\n valj_[:] = _valj_np_tmp\n return (_nzj_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 getacol(self,j_,subj,valj): # 3\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_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument subj has wrong length\")\n if valj is None: raise TypeError(\"Invalid type for argument valj\")\n _copyback_valj = False\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 _copyback_valj = True\n else:\n if valj_.format != \"d\":\n valj_ = memoryview(array.array(\"d\",valj))\n _copyback_valj = True\n if valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument valj has wrong length\")\n res,resargs = self.__obj.getacol(j_,subj_,valj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n if _copyback_valj:\n valj[:] = _tmparr_valj\n if _copyback_subj:\n subj[:] = _tmparr_subj\n return _nzj_return_value","def fast_get_col(self,j):\n col = self.col_view[:,j].copy()\n col.data = self.X.data[col.data]\n return col","def sudoku_getcol(A, j):\r\n n = len(A)\r\n B = [0 for i in range(n)]\r\n for i in range(n):\r\n B[i] = A[i][j]\r\n return B","def get_column(A: Matrix, j: int) -> Vector:\n return [A_i[j] for A_i in A]","def get_column(A: Matrix, j: int) -> Vector:\n return [A_i[j] for A_i in A]","def get_column(A: Matrix, j: int) -> Vector:\n return [A_i[j] for A_i in A]","def get_column(A: Matrix, j: int) -> Vector:\n return [A_i[j] # jth element of row A_i\n for A_i in A] # for each row A_i","def _get_col(self, idx):\n return self.line[self._fwf.column_slices[idx]]","def getacolnumnz(self,i_): # 3\n res,resargs = self.__obj.getacolnumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n return _nzj_return_value","def _get_col(self, idx):\n return self.text[self._fwf.column_slices[idx]]","def get_col(b, ci):\r\n return [b[0][ci], b[1][ci], b[2][ci]]","def get_col(A,r=0):\n\treturn list(A[r])","def col(self, i):\n return Vector([row[i] for row in self.data])","def getacolnumnz(self,i_):\n nzj_ = ctypes.c_int32()\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n return (_nzj_return_value)","def ColPart(self, *args):\n return _hypre.HypreParMatrix_ColPart(self, *args)","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 col(self):\n\t\treturn self.__col","def GetColPartArray(self):\n return _hypre.HypreParMatrix_GetColPartArray(self)","def get_cell(self, i, j):\n return self._env[i][j]","def collocation(lhs, rhs, basis, nterms, domain=(0, 1), x_col=None):\n x0, x1 = domain\n if x_col is None:\n dx = S(x1 - x0)/(nterms - 2)\n x_col = [dx + dx*cont for cont in range(nterms)]\n x = symbols(\"x\")\n A_mat = zeros(nterms, nterms)\n b_vec = zeros(nterms, 1)\n for row in range(nterms):\n b_vec[row] = rhs(x_col[row])\n for col in range(nterms):\n phi_j = basis(x, col)\n A_mat[row, col] = lhs(phi_j, x).subs(x, x_col[row])\n return A_mat, b_vec","def _colvec(x):\n x = np.atleast_1d(x)\n return x[:, None]","def jmat(ind: int):\n return _jm[ind - 1]","def _get_column(self, index):\n left, right = self._get_columns()\n return left if index < left.count else right","def __getslice__(self,i,j):\n nv=_Matr()\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\n nv.__maj()\n return nv","def column(self, index):\n return [row[index - 1] for row in self.matrix_list]","def column_fast(self, key):\n return self._matrix[:, self.dataframe.columns.get_loc(key)]","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 get_real_col(self, index):\n\n return self.col2virt.get(index, index)","def getCol(self, column_name):\n idx = self.colnames.index(column_name)\n return self.getColByIdx(idx)","def __getslice__(self,i,j):\n nv=_Matr()\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\n nv._Matr__maj()\n return nv","def getColumn(self, idx):\r\n m=self.mlist\r\n if idx==0: return _vec4(m[0], m[4], m[8], m[12])\r\n elif idx==1: return _vec4(m[1], m[5], m[9], m[13])\r\n elif idx==2: return _vec4(m[2], m[6], m[10], m[14])\r\n elif idx==3: return _vec4(m[3], m[7], m[11], m[15])\r\n else:\r\n raise IndexError,\"index out of range\"","def _value_column(self, field):\n for field_dict in self.schema['idx_fields']:\n if field_dict['field_name'] == field:\n return field_dict['column']\n return 0","def get_elem (A, i, j):\n\treturn A[j][i]","def column (self, i):\n return Vector(tuple(zip(*self._m))[i])","def get_value(self, col, i) :\n\n if col not in self.cols :\n raise Exception('Column %s not in data' % col)\n if len(self.rows) <= i :\n raise Exception('Table has fewer than %d rows' % i)\n return self.rows[i][self.col_indices[col]]","def _get_column(self, name):\r\n return self.column(name)","def N_Col(self,val):\n\t\tif val in self.colums:\n\t\t\treturn self.colums.index(val)\n\t\telse:\n\t\t\treturn False","def __getattr__(self, col):\n return self._obj[col].to_numpy()","def get_cols(self) :\n\n return list(self.cols)[1:]","def get(self, row, col):\n return self._varvals[row][col]","def get_virt_col(self, index):\n\n return self.virt2col.get(index, index)","def _get_column(cls, name):\r\n return cls._columns[name]","def getItem(self, i, j):\n if i < 0:\n raise IndexError('Row index must be nonnegative.')\n if j < 0:\n raise IndexError('Column index must be nonnegative.')\n\n return self.__m[i - 1][j - 1]","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 col_for_cell(self, coords, include_self=False):\n row, col = coords\n return ((r, col) for r in self.rows if include_self or r != row)","def table_column(self, i):\n return self.__column_list[i]","def column(self, label):\n dis = []\n for x in self.rows:\n dis = dis + [x[self.column_labels.index(label)]]\n return dis\n # return self.rows[self.column_labels.index(label)]","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 column(self, index: int) -> List[int]:\n return [x[index - 1] for x in self.matrix]","def getColumn(self):\n return _libsbml.SBase_getColumn(self)","def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","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 get_column(self, key):\n return self._get_column(key)","def jac_pos(self):\n J = self.sim.data.get_body_jacp(self.end_effector)\n J = J.reshape(3, -1)[:, 0:7].T\n return J","def get(self,row,col):\r\n return self.puzzle[row][col]","def _get_column(self, column_or_label):\n c = column_or_label\n if isinstance(c, collections.Hashable) and c in self.column_labels:\n return self[c]\n else:\n assert len(c) == self.num_rows, 'column length mismatch'\n return c","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 getConstraint(self, *args):\n return _libsbml.Model_getConstraint(self, *args)","def column(self):\n return self.reshape((self.size, 1))","def constraint_col_coef(n_col, n_row):\n\n all_rows = []\n for i in range(n_col):\n matrix_values = np.zeros((n_row, n_col), dtype=int)\n col_offer = np.ones(n_row, dtype=int)\n matrix_values[:, i] = col_offer\n all_rows.append(matrix_values.flatten())\n\n cols_constraints = np.stack(all_rows)\n\n return cols_constraints","def matGet(mat, r, c):\n return mat[r][c]","def get_column(self):\n return self._column_number","def col(self):\n return self.address.col","def column(self, row, column):\n if type(row) is not int or type(column) is not int:\n raise TypeError(self._ERRORS[4])\n if row > 2 or row < 0 or column > 2 or column < 0:\n raise ValueError(self._ERRORS[5])\n return self._matrix_data[row][column]","def __getitem__(self, col_name):\n return self.data[self.cols[col_name]]","def cols(self):\n return self.col","def return_col(self, num_col):\n result = []\n for i in range(self.num_rows):\n result.append(self.board[i][num_col])\n return result","def get_dual_val(self, var_name, pos):\n val = self.get_other_value(self.dual_var, var_name, pos)\n if not self.pdv_to_csv: # if not saved to csv file\n return val\n else: # otherwise, we should get the file path and read from the file to array or mat\n f_path = os.path.join(self.root_dir, 'dual_vars', var_name, str(val) + '.csv')\n df = pd.read_csv(f_path, header = None) # first read csv file into a pandas data frame and then transform\n return np.asmatrix(df.values)","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 getColumn(self, x):\n i = _getIndex(x, self.columnNames)\n return self.data[i]","def column(self): \r\n\r\n return self._column","def retrievematrixelement(self, coord):\n currentelement = self.matrix\n for u in coord:\n currentelement = currentelement[u]\n\n return currentelement","def getGradient(self,j):\n i = int(self.indicator['term'][j])\n r = int(self.indicator['row'][j])\n c = int(self.indicator['col'][j])\n rv = -np.kron(self.Fstar()[i][:,[r]],self.Astar()[i][[c],:])\n return rv","def _get_column_values(self, col: str) -> ndarray:\n dtype, loc = self._get_col_dtype_loc(col) # type: str, int\n return self._data[dtype][:, loc]","def jac(self):\n return self.unit_jac if self._jac is None else self._jac","def __getitem__(self, colname):\n colname = colname.lower()\n\n if colname in self.data:\n return self.data[colname]\n else:\n if colname in self.required:\n raise ColumnNameError(\n 'A required column, \"' + colname + '\", was missing.',\n self\n )\n else:\n # If self.optional == [0], it means that all non-required\n # columns are optional (i.e., will not trigger a warning).\n if (self.optional != [0]) and (colname not in self.optional):\n logger.warning(\n 'The column \"' + colname\n + '\" was missing in the table row.'\n )\n\n if colname in self.defaults:\n return self.defaults[colname]\n else:\n return ''","def index_col(self, i0, i1, j0, j1):\n edges = self.h5['indexes']['bin1_offset'][i0:i1 + 1]\n index = []\n for lo1, hi1 in zip(edges[:-1], edges[1:]):\n if hi1 - lo1 > 0:\n bin2 = self.h5['pixels']['bin2_id'][lo1:hi1]\n mask = (bin2 >= j0) & (bin2 < j1)\n index.append(lo1 + np.flatnonzero(mask))\n if not index:\n return np.array([], dtype=int)\n else:\n return np.concatenate(index, axis=0)","def entry(self, i, j):\n return self.data[self.columns * (i - 1) + j - 1]","def get_columns(self, *args, **kw):\n\n v = self.namespace[self._name]\n while True:\n if callable(v):\n if isinstance(v, (Term, Formula)):\n v = copy.copy(v)\n v.namespace = self.namespace\n v = v(*args, **kw)\n else: break\n\n n = len(v)\n\n if self.ordinal:\n col = [float(self.keys.index(v[i])) for i in range(n)]\n return np.array(col)\n\n else:\n value = []\n for key in self.keys:\n col = [float((v[i] == key)) for i in range(n)]\n value.append(col)\n return np.array(value)","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 get_column(poll: str, adjustment: str, ba: str = \"\"):\n assert poll in POLLUTANTS\n assert adjustment in ADJUSTMENTS\n if ba == \"\": # no BA, looking for output file column\n column = poll.lower() + \"_mass_lb_\" + adjustment\n assert column in EMISSION_COLS\n else:\n column = ba + \"_\" + poll.lower() + \"_mass_lb_\" + adjustment\n return column","def max_score_column(self, scores):\r\n m = max(scores)\r\n lc = []\r\n for i in range(len(scores)):\r\n if scores[i] == m:\r\n lc += [i]\r\n if len(lc) > 1:\r\n if self.tiebreak == \"LEFT\":\r\n column = lc[0]\r\n elif self.tiebreak == \"RIGHT\":\r\n column = lc[-1]\r\n else:\r\n column = random.choice(lc)\r\n return column\r\n else:\r\n return lc[0]","def column(self, index):\n result = []\n for col in self.data:\n result.append(col[index])\n return result","def get_column(row, cname):\n\n try:\n return row[_colmap[cname]]\n\n except KeyError:\n raise ValueError(\"Invalid column name: {0}!\".format(cname))","def get_compartment_soln(self, label):\n\n assert self.soln_array is not None, 'calculate_diagnostics has not been run'\n i_label = self.labels.index(label)\n return self.soln_array[:, i_label]","def getColumn(self):\n return _libsbml.SBasePlugin_getColumn(self)","def get_column(self, pos, **opts):\n lnum, cnum = self._get_column(pos, **opts)\n return lnum + self.LINE_NUM_BASE, cnum + self.COLUMN_NUM_BASE","def jacobian_cost(self, joint_angles: dict, ee_goals) -> np.ndarray:\n kinematic_map = self.kinematic_map[\"p0\"] # get map to all nodes from root\n end_effector_nodes = ee_goals.keys()\n J = np.zeros(self.n)\n for (\n ee\n ) in end_effector_nodes: # iterate through end-effector nodes, assumes sorted\n ee_path = kinematic_map[ee][\n 1:\n ] # [:-1] # no last node, only phys. joint locations\n t_ee = self.get_pose(joint_angles, ee).trans\n dg_ee_x = t_ee[0] - ee_goals[ee].trans[0]\n dg_ee_y = t_ee[1] - ee_goals[ee].trans[1]\n for (pdx, joint_p) in enumerate(ee_path): # algorithm fills Jac per column\n p_idx = int(joint_p[1:]) - 1\n for jdx in range(pdx, len(ee_path)):\n node_jdx = ee_path[jdx]\n theta_jdx = sum([joint_angles[key] for key in ee_path[0 : jdx + 1]])\n J[p_idx] += (\n 2.0\n * self.a[node_jdx]\n * (-dg_ee_x * np.sin(theta_jdx) + dg_ee_y * np.cos(theta_jdx))\n )\n\n return J","def _get_column(self, pos, **opts):\n lnum, cpos = self._get_linepos(pos)\n start, end = self._get_linespan(lnum)\n return lnum, self._pos2col(start, cpos, **opts)","def column(self):\n return self[\"column\"]","def getCol(self, n, offset=0):\n return self._c[(n*self.__height + offset):((n+1) * self.__height)]","def to_col_vec(x):\n assert x.ndim == 1\n return jnp.expand_dims(x, 1)","def GetColumn(self, column):\r\n \r\n return self._header_win.GetColumn(column)","def GetJ(self, *args):\n return _table.Table_GetJ(self, *args)","def get_column(puzzle, col_num):\n\n puzzle_list = puzzle.strip().split('\\n')\n column = ''\n for row in puzzle_list:\n column += row[col_num]\n\n return column","def get_element(self,mat,row,column):\n result = mat[row-1][column-1]\n self.element = result\n return self.element","def fast_update_col(self,j,vals):\n dataptr = self.col_view[:,j].data\n self.X.data[dataptr] = vals","def get_col(grid, n):\n\t\n if grid == []:\n return grid\n \n return [grid[0][n]] + get_col(grid[1:],n)","def rowcol(self, file_name):\n return make_rowcol(self.raw_content(file_name))"],"string":"[\n \"def getacol(self,j_,subj,valj): # 3\\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_) != self.getacolnumnz((j_)):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if valj is None: raise TypeError(\\\"Invalid type for argument valj\\\")\\n _copyback_valj = False\\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 _copyback_valj = True\\n else:\\n if valj_.format != \\\"d\\\":\\n valj_ = memoryview(array.array(\\\"d\\\",valj))\\n _copyback_valj = True\\n if valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\\n raise ValueError(\\\"Array argument valj has wrong length\\\")\\n res,resargs = self.__obj.getacol(j_,subj_,valj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n if _copyback_valj:\\n valj[:] = _tmparr_valj\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n return _nzj_return_value\",\n \"def fast_get_col(self,j):\\n col = self.col_view[:,j].copy()\\n col.data = self.X.data[col.data]\\n return col\",\n \"def sudoku_getcol(A, j):\\r\\n n = len(A)\\r\\n B = [0 for i in range(n)]\\r\\n for i in range(n):\\r\\n B[i] = A[i][j]\\r\\n return B\",\n \"def get_column(A: Matrix, j: int) -> Vector:\\n return [A_i[j] for A_i in A]\",\n \"def get_column(A: Matrix, j: int) -> Vector:\\n return [A_i[j] for A_i in A]\",\n \"def get_column(A: Matrix, j: int) -> Vector:\\n return [A_i[j] for A_i in A]\",\n \"def get_column(A: Matrix, j: int) -> Vector:\\n return [A_i[j] # jth element of row A_i\\n for A_i in A] # for each row A_i\",\n \"def _get_col(self, idx):\\n return self.line[self._fwf.column_slices[idx]]\",\n \"def getacolnumnz(self,i_): # 3\\n res,resargs = self.__obj.getacolnumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n return _nzj_return_value\",\n \"def _get_col(self, idx):\\n return self.text[self._fwf.column_slices[idx]]\",\n \"def get_col(b, ci):\\r\\n return [b[0][ci], b[1][ci], b[2][ci]]\",\n \"def get_col(A,r=0):\\n\\treturn list(A[r])\",\n \"def col(self, i):\\n return Vector([row[i] for row in self.data])\",\n \"def getacolnumnz(self,i_):\\n nzj_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzj_ = nzj_.value\\n _nzj_return_value = nzj_\\n return (_nzj_return_value)\",\n \"def ColPart(self, *args):\\n return _hypre.HypreParMatrix_ColPart(self, *args)\",\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 col(self):\\n\\t\\treturn self.__col\",\n \"def GetColPartArray(self):\\n return _hypre.HypreParMatrix_GetColPartArray(self)\",\n \"def get_cell(self, i, j):\\n return self._env[i][j]\",\n \"def collocation(lhs, rhs, basis, nterms, domain=(0, 1), x_col=None):\\n x0, x1 = domain\\n if x_col is None:\\n dx = S(x1 - x0)/(nterms - 2)\\n x_col = [dx + dx*cont for cont in range(nterms)]\\n x = symbols(\\\"x\\\")\\n A_mat = zeros(nterms, nterms)\\n b_vec = zeros(nterms, 1)\\n for row in range(nterms):\\n b_vec[row] = rhs(x_col[row])\\n for col in range(nterms):\\n phi_j = basis(x, col)\\n A_mat[row, col] = lhs(phi_j, x).subs(x, x_col[row])\\n return A_mat, b_vec\",\n \"def _colvec(x):\\n x = np.atleast_1d(x)\\n return x[:, None]\",\n \"def jmat(ind: int):\\n return _jm[ind - 1]\",\n \"def _get_column(self, index):\\n left, right = self._get_columns()\\n return left if index < left.count else right\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\\n nv.__maj()\\n return nv\",\n \"def column(self, index):\\n return [row[index - 1] for row in self.matrix_list]\",\n \"def column_fast(self, key):\\n return self._matrix[:, self.dataframe.columns.get_loc(key)]\",\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 get_real_col(self, index):\\n\\n return self.col2virt.get(index, index)\",\n \"def getCol(self, column_name):\\n idx = self.colnames.index(column_name)\\n return self.getColByIdx(idx)\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\\n nv._Matr__maj()\\n return nv\",\n \"def getColumn(self, idx):\\r\\n m=self.mlist\\r\\n if idx==0: return _vec4(m[0], m[4], m[8], m[12])\\r\\n elif idx==1: return _vec4(m[1], m[5], m[9], m[13])\\r\\n elif idx==2: return _vec4(m[2], m[6], m[10], m[14])\\r\\n elif idx==3: return _vec4(m[3], m[7], m[11], m[15])\\r\\n else:\\r\\n raise IndexError,\\\"index out of range\\\"\",\n \"def _value_column(self, field):\\n for field_dict in self.schema['idx_fields']:\\n if field_dict['field_name'] == field:\\n return field_dict['column']\\n return 0\",\n \"def get_elem (A, i, j):\\n\\treturn A[j][i]\",\n \"def column (self, i):\\n return Vector(tuple(zip(*self._m))[i])\",\n \"def get_value(self, col, i) :\\n\\n if col not in self.cols :\\n raise Exception('Column %s not in data' % col)\\n if len(self.rows) <= i :\\n raise Exception('Table has fewer than %d rows' % i)\\n return self.rows[i][self.col_indices[col]]\",\n \"def _get_column(self, name):\\r\\n return self.column(name)\",\n \"def N_Col(self,val):\\n\\t\\tif val in self.colums:\\n\\t\\t\\treturn self.colums.index(val)\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def __getattr__(self, col):\\n return self._obj[col].to_numpy()\",\n \"def get_cols(self) :\\n\\n return list(self.cols)[1:]\",\n \"def get(self, row, col):\\n return self._varvals[row][col]\",\n \"def get_virt_col(self, index):\\n\\n return self.virt2col.get(index, index)\",\n \"def _get_column(cls, name):\\r\\n return cls._columns[name]\",\n \"def getItem(self, i, j):\\n if i < 0:\\n raise IndexError('Row index must be nonnegative.')\\n if j < 0:\\n raise IndexError('Column index must be nonnegative.')\\n\\n return self.__m[i - 1][j - 1]\",\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 col_for_cell(self, coords, include_self=False):\\n row, col = coords\\n return ((r, col) for r in self.rows if include_self or r != row)\",\n \"def table_column(self, i):\\n return self.__column_list[i]\",\n \"def column(self, label):\\n dis = []\\n for x in self.rows:\\n dis = dis + [x[self.column_labels.index(label)]]\\n return dis\\n # return self.rows[self.column_labels.index(label)]\",\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 column(self, index: int) -> List[int]:\\n return [x[index - 1] for x in self.matrix]\",\n \"def getColumn(self):\\n return _libsbml.SBase_getColumn(self)\",\n \"def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\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 get_column(self, key):\\n return self._get_column(key)\",\n \"def jac_pos(self):\\n J = self.sim.data.get_body_jacp(self.end_effector)\\n J = J.reshape(3, -1)[:, 0:7].T\\n return J\",\n \"def get(self,row,col):\\r\\n return self.puzzle[row][col]\",\n \"def _get_column(self, column_or_label):\\n c = column_or_label\\n if isinstance(c, collections.Hashable) and c in self.column_labels:\\n return self[c]\\n else:\\n assert len(c) == self.num_rows, 'column length mismatch'\\n return c\",\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 getConstraint(self, *args):\\n return _libsbml.Model_getConstraint(self, *args)\",\n \"def column(self):\\n return self.reshape((self.size, 1))\",\n \"def constraint_col_coef(n_col, n_row):\\n\\n all_rows = []\\n for i in range(n_col):\\n matrix_values = np.zeros((n_row, n_col), dtype=int)\\n col_offer = np.ones(n_row, dtype=int)\\n matrix_values[:, i] = col_offer\\n all_rows.append(matrix_values.flatten())\\n\\n cols_constraints = np.stack(all_rows)\\n\\n return cols_constraints\",\n \"def matGet(mat, r, c):\\n return mat[r][c]\",\n \"def get_column(self):\\n return self._column_number\",\n \"def col(self):\\n return self.address.col\",\n \"def column(self, row, column):\\n if type(row) is not int or type(column) is not int:\\n raise TypeError(self._ERRORS[4])\\n if row > 2 or row < 0 or column > 2 or column < 0:\\n raise ValueError(self._ERRORS[5])\\n return self._matrix_data[row][column]\",\n \"def __getitem__(self, col_name):\\n return self.data[self.cols[col_name]]\",\n \"def cols(self):\\n return self.col\",\n \"def return_col(self, num_col):\\n result = []\\n for i in range(self.num_rows):\\n result.append(self.board[i][num_col])\\n return result\",\n \"def get_dual_val(self, var_name, pos):\\n val = self.get_other_value(self.dual_var, var_name, pos)\\n if not self.pdv_to_csv: # if not saved to csv file\\n return val\\n else: # otherwise, we should get the file path and read from the file to array or mat\\n f_path = os.path.join(self.root_dir, 'dual_vars', var_name, str(val) + '.csv')\\n df = pd.read_csv(f_path, header = None) # first read csv file into a pandas data frame and then transform\\n return np.asmatrix(df.values)\",\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 getColumn(self, x):\\n i = _getIndex(x, self.columnNames)\\n return self.data[i]\",\n \"def column(self): \\r\\n\\r\\n return self._column\",\n \"def retrievematrixelement(self, coord):\\n currentelement = self.matrix\\n for u in coord:\\n currentelement = currentelement[u]\\n\\n return currentelement\",\n \"def getGradient(self,j):\\n i = int(self.indicator['term'][j])\\n r = int(self.indicator['row'][j])\\n c = int(self.indicator['col'][j])\\n rv = -np.kron(self.Fstar()[i][:,[r]],self.Astar()[i][[c],:])\\n return rv\",\n \"def _get_column_values(self, col: str) -> ndarray:\\n dtype, loc = self._get_col_dtype_loc(col) # type: str, int\\n return self._data[dtype][:, loc]\",\n \"def jac(self):\\n return self.unit_jac if self._jac is None else self._jac\",\n \"def __getitem__(self, colname):\\n colname = colname.lower()\\n\\n if colname in self.data:\\n return self.data[colname]\\n else:\\n if colname in self.required:\\n raise ColumnNameError(\\n 'A required column, \\\"' + colname + '\\\", was missing.',\\n self\\n )\\n else:\\n # If self.optional == [0], it means that all non-required\\n # columns are optional (i.e., will not trigger a warning).\\n if (self.optional != [0]) and (colname not in self.optional):\\n logger.warning(\\n 'The column \\\"' + colname\\n + '\\\" was missing in the table row.'\\n )\\n\\n if colname in self.defaults:\\n return self.defaults[colname]\\n else:\\n return ''\",\n \"def index_col(self, i0, i1, j0, j1):\\n edges = self.h5['indexes']['bin1_offset'][i0:i1 + 1]\\n index = []\\n for lo1, hi1 in zip(edges[:-1], edges[1:]):\\n if hi1 - lo1 > 0:\\n bin2 = self.h5['pixels']['bin2_id'][lo1:hi1]\\n mask = (bin2 >= j0) & (bin2 < j1)\\n index.append(lo1 + np.flatnonzero(mask))\\n if not index:\\n return np.array([], dtype=int)\\n else:\\n return np.concatenate(index, axis=0)\",\n \"def entry(self, i, j):\\n return self.data[self.columns * (i - 1) + j - 1]\",\n \"def get_columns(self, *args, **kw):\\n\\n v = self.namespace[self._name]\\n while True:\\n if callable(v):\\n if isinstance(v, (Term, Formula)):\\n v = copy.copy(v)\\n v.namespace = self.namespace\\n v = v(*args, **kw)\\n else: break\\n\\n n = len(v)\\n\\n if self.ordinal:\\n col = [float(self.keys.index(v[i])) for i in range(n)]\\n return np.array(col)\\n\\n else:\\n value = []\\n for key in self.keys:\\n col = [float((v[i] == key)) for i in range(n)]\\n value.append(col)\\n return np.array(value)\",\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 get_column(poll: str, adjustment: str, ba: str = \\\"\\\"):\\n assert poll in POLLUTANTS\\n assert adjustment in ADJUSTMENTS\\n if ba == \\\"\\\": # no BA, looking for output file column\\n column = poll.lower() + \\\"_mass_lb_\\\" + adjustment\\n assert column in EMISSION_COLS\\n else:\\n column = ba + \\\"_\\\" + poll.lower() + \\\"_mass_lb_\\\" + adjustment\\n return column\",\n \"def max_score_column(self, scores):\\r\\n m = max(scores)\\r\\n lc = []\\r\\n for i in range(len(scores)):\\r\\n if scores[i] == m:\\r\\n lc += [i]\\r\\n if len(lc) > 1:\\r\\n if self.tiebreak == \\\"LEFT\\\":\\r\\n column = lc[0]\\r\\n elif self.tiebreak == \\\"RIGHT\\\":\\r\\n column = lc[-1]\\r\\n else:\\r\\n column = random.choice(lc)\\r\\n return column\\r\\n else:\\r\\n return lc[0]\",\n \"def column(self, index):\\n result = []\\n for col in self.data:\\n result.append(col[index])\\n return result\",\n \"def get_column(row, cname):\\n\\n try:\\n return row[_colmap[cname]]\\n\\n except KeyError:\\n raise ValueError(\\\"Invalid column name: {0}!\\\".format(cname))\",\n \"def get_compartment_soln(self, label):\\n\\n assert self.soln_array is not None, 'calculate_diagnostics has not been run'\\n i_label = self.labels.index(label)\\n return self.soln_array[:, i_label]\",\n \"def getColumn(self):\\n return _libsbml.SBasePlugin_getColumn(self)\",\n \"def get_column(self, pos, **opts):\\n lnum, cnum = self._get_column(pos, **opts)\\n return lnum + self.LINE_NUM_BASE, cnum + self.COLUMN_NUM_BASE\",\n \"def jacobian_cost(self, joint_angles: dict, ee_goals) -> np.ndarray:\\n kinematic_map = self.kinematic_map[\\\"p0\\\"] # get map to all nodes from root\\n end_effector_nodes = ee_goals.keys()\\n J = np.zeros(self.n)\\n for (\\n ee\\n ) in end_effector_nodes: # iterate through end-effector nodes, assumes sorted\\n ee_path = kinematic_map[ee][\\n 1:\\n ] # [:-1] # no last node, only phys. joint locations\\n t_ee = self.get_pose(joint_angles, ee).trans\\n dg_ee_x = t_ee[0] - ee_goals[ee].trans[0]\\n dg_ee_y = t_ee[1] - ee_goals[ee].trans[1]\\n for (pdx, joint_p) in enumerate(ee_path): # algorithm fills Jac per column\\n p_idx = int(joint_p[1:]) - 1\\n for jdx in range(pdx, len(ee_path)):\\n node_jdx = ee_path[jdx]\\n theta_jdx = sum([joint_angles[key] for key in ee_path[0 : jdx + 1]])\\n J[p_idx] += (\\n 2.0\\n * self.a[node_jdx]\\n * (-dg_ee_x * np.sin(theta_jdx) + dg_ee_y * np.cos(theta_jdx))\\n )\\n\\n return J\",\n \"def _get_column(self, pos, **opts):\\n lnum, cpos = self._get_linepos(pos)\\n start, end = self._get_linespan(lnum)\\n return lnum, self._pos2col(start, cpos, **opts)\",\n \"def column(self):\\n return self[\\\"column\\\"]\",\n \"def getCol(self, n, offset=0):\\n return self._c[(n*self.__height + offset):((n+1) * self.__height)]\",\n \"def to_col_vec(x):\\n assert x.ndim == 1\\n return jnp.expand_dims(x, 1)\",\n \"def GetColumn(self, column):\\r\\n \\r\\n return self._header_win.GetColumn(column)\",\n \"def GetJ(self, *args):\\n return _table.Table_GetJ(self, *args)\",\n \"def get_column(puzzle, col_num):\\n\\n puzzle_list = puzzle.strip().split('\\\\n')\\n column = ''\\n for row in puzzle_list:\\n column += row[col_num]\\n\\n return column\",\n \"def get_element(self,mat,row,column):\\n result = mat[row-1][column-1]\\n self.element = result\\n return self.element\",\n \"def fast_update_col(self,j,vals):\\n dataptr = self.col_view[:,j].data\\n self.X.data[dataptr] = vals\",\n \"def get_col(grid, n):\\n\\t\\n if grid == []:\\n return grid\\n \\n return [grid[0][n]] + get_col(grid[1:],n)\",\n \"def rowcol(self, file_name):\\n return make_rowcol(self.raw_content(file_name))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7543346","0.6976331","0.6404439","0.6330695","0.6330695","0.6330695","0.6279653","0.6225708","0.6189424","0.61752653","0.61163116","0.5897423","0.5880596","0.5877866","0.5869485","0.58650994","0.58379793","0.57342744","0.56699353","0.564279","0.5636988","0.5570075","0.5565305","0.55593073","0.5546852","0.5543069","0.5525753","0.549953","0.5475881","0.5465472","0.5427042","0.53937197","0.5384185","0.5379425","0.53779095","0.53715557","0.5369766","0.5364034","0.534669","0.53376603","0.53166026","0.53102344","0.5309186","0.528803","0.52871263","0.5282061","0.525821","0.52579916","0.523331","0.5219756","0.5218414","0.52145624","0.52132046","0.5194678","0.51819193","0.5159535","0.51423323","0.5120467","0.5115807","0.5111166","0.51058143","0.51033264","0.5096371","0.5091039","0.5074187","0.5066283","0.50649387","0.5061745","0.50593543","0.5058336","0.5043854","0.5041422","0.5034291","0.5023155","0.5019962","0.5015265","0.49940848","0.4987235","0.4983062","0.49802634","0.4973417","0.49694955","0.49658114","0.49589613","0.4957355","0.49507934","0.49492392","0.49432516","0.49202386","0.4910649","0.49082512","0.4907258","0.48922756","0.48817787","0.4876901","0.48741665","0.48729253","0.4865685","0.48472244","0.48407316"],"string":"[\n \"0.7543346\",\n \"0.6976331\",\n \"0.6404439\",\n \"0.6330695\",\n \"0.6330695\",\n \"0.6330695\",\n \"0.6279653\",\n \"0.6225708\",\n \"0.6189424\",\n \"0.61752653\",\n \"0.61163116\",\n \"0.5897423\",\n \"0.5880596\",\n \"0.5877866\",\n \"0.5869485\",\n \"0.58650994\",\n \"0.58379793\",\n \"0.57342744\",\n \"0.56699353\",\n \"0.564279\",\n \"0.5636988\",\n \"0.5570075\",\n \"0.5565305\",\n \"0.55593073\",\n \"0.5546852\",\n \"0.5543069\",\n \"0.5525753\",\n \"0.549953\",\n \"0.5475881\",\n \"0.5465472\",\n \"0.5427042\",\n \"0.53937197\",\n \"0.5384185\",\n \"0.5379425\",\n \"0.53779095\",\n \"0.53715557\",\n \"0.5369766\",\n \"0.5364034\",\n \"0.534669\",\n \"0.53376603\",\n \"0.53166026\",\n \"0.53102344\",\n \"0.5309186\",\n \"0.528803\",\n \"0.52871263\",\n \"0.5282061\",\n \"0.525821\",\n \"0.52579916\",\n \"0.523331\",\n \"0.5219756\",\n \"0.5218414\",\n \"0.52145624\",\n \"0.52132046\",\n \"0.5194678\",\n \"0.51819193\",\n \"0.5159535\",\n \"0.51423323\",\n \"0.5120467\",\n \"0.5115807\",\n \"0.5111166\",\n \"0.51058143\",\n \"0.51033264\",\n \"0.5096371\",\n \"0.5091039\",\n \"0.5074187\",\n \"0.5066283\",\n \"0.50649387\",\n \"0.5061745\",\n \"0.50593543\",\n \"0.5058336\",\n \"0.5043854\",\n \"0.5041422\",\n \"0.5034291\",\n \"0.5023155\",\n \"0.5019962\",\n \"0.5015265\",\n \"0.49940848\",\n \"0.4987235\",\n \"0.4983062\",\n \"0.49802634\",\n \"0.4973417\",\n \"0.49694955\",\n \"0.49658114\",\n \"0.49589613\",\n \"0.4957355\",\n \"0.49507934\",\n \"0.49492392\",\n \"0.49432516\",\n \"0.49202386\",\n \"0.4910649\",\n \"0.49082512\",\n \"0.4907258\",\n \"0.48922756\",\n \"0.48817787\",\n \"0.4876901\",\n \"0.48741665\",\n \"0.48729253\",\n \"0.4865685\",\n \"0.48472244\",\n \"0.48407316\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74751633"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11777,"cells":{"query":{"kind":"string","value":"Obtains a sequence of columns from the coefficient matrix. getacolslice(self,first_,last_,ptrb_,ptre_,sub_,val_)"},"document":{"kind":"string","value":"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"},"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 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 getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\n maxnumnz_ = self.getarowslicenumnz((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_getarowslice64(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 getacolslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","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 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 getaslice(self,accmode_,first_,last_,ptrb,ptre,sub,val): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n maxnumnz_ = self.getaslicenumnz((accmode_),(first_),(last_))\n _copyback_ptrb = False\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 _copyback_ptrb = True\n else:\n if ptrb_.format != \"q\":\n ptrb_ = memoryview(array.array(\"q\",ptrb))\n _copyback_ptrb = True\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptrb has wrong length\")\n _copyback_ptre = False\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 _copyback_ptre = True\n else:\n if ptre_.format != \"q\":\n ptre_ = memoryview(array.array(\"q\",ptre))\n _copyback_ptre = True\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptre has wrong length\")\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(\"i\",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 != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n _copyback_sub = True\n if sub_ is not None and len(sub_) != (maxnumnz_):\n raise ValueError(\"Array argument sub 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.getaslice64(accmode_,first_,last_,maxnumnz_,len(sub),ptrb_,ptre_,sub_,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_sub:\n sub[:] = _tmparr_sub\n if _copyback_ptre:\n ptre[:] = _tmparr_ptre\n if _copyback_ptrb:\n ptrb[:] = _tmparr_ptrb","def __getslice__(self, i, j):\n return self.dtrs[i:j]","def ColPart(self, *args):\n return _hypre.HypreParMatrix_ColPart(self, *args)","def __getslice__(self,i,j):\n return self.x[i:j]","def slice(A,rowrange,colrange):\n\n\treturn [[get_elem(A,j,i) for j in rowrange] for i in colrange]","def get_col(b, ci):\r\n return [b[0][ci], b[1][ci], b[2][ci]]","def getacolslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def getarowslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def __getslice__(self,i,j):\n nv=_Matr()\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\n nv.__maj()\n return nv","def __getslice__(self,i,j):\n nv=_Matr()\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\n nv._Matr__maj()\n return nv","def get_cols(self) :\n\n return list(self.cols)[1:]","def get_col(A,r=0):\n\treturn list(A[r])","def sub_columns(arr, sub_size):\n return sub_rows(arr.T, sub_size)","def slice_matrix(m,i,j):\n return np.take(np.take(m,i,0),j,1)","def GetColPartArray(self):\n return _hypre.HypreParMatrix_GetColPartArray(self)","def slice2(self, cvars=None,ctuple=None):\n return self.condition2(cvars,ctuple)","def test05(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(None, None, -3)\n # print \"b[sl]->\", `b[sl]`\n self.assertRaises(NotImplementedError, b.__getitem__, sl)","def test02b(self):\n a = np.arange(1e2)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(-3)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def test02d(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(-3, -1)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def test02c(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(1, -3)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def relay_array_getitem(c, a, start, stop, strides):\n assert start.is_constant(tuple)\n assert stop.is_constant(tuple)\n assert strides.is_constant(tuple)\n return relay.op.transform.strided_slice(c.ref(a), start.value, stop.value,\n strides.value)","def slice_pdb(self, start, stop):\n return","def test03b(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(1, 80, 30)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def test03(self):\n a = np.arange(1, 101)\n b = bcolz.carray(a)\n c = b[[1.1, 3.3]]\n r = a[[1, 3]]\n assert_array_equal(c, r, \"fancy indexing does not work correctly\")","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 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 test01b(self):\n a = np.arange(1e2)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(-1)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def test07(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 cr = c[\"a + 2 * d - 3 > 0\"]\n nr = a[(a + 2 * b - 3) > 0]\n # print \"ca[expr] ->\", cr\n # print \"numpy ->\", nr\n assert_array_equal(cr[:], nr, \"carray[expr] does not work correctly\")","def __getslice__(self, i, j):\n return self.__getitem__(slice(i,j))","def getarowslicetrip(self,first_,last_,subi,subj,val): # 3\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 __getslice__(self, start, stop):\n return self.__getitem__(slice(start, stop, None))","def putarowslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\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 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 _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 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_putarowslice64(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 test02b(self):\n a = np.arange(101)\n b = bcolz.carray(a, chunklen=2, rootdir=self.rootdir)\n # print \"sum iter->\", sum(b.iter(-24, -3))\n self.assertTrue(sum(a[-24:-3]) == sum(b.iter(-24, -3)),\n \"Sums are not equal\")","def test02a(self):\n a = np.arange(1e2)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(1, 3)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def __getslice__(self, i, j):\n return self.__getitem__(slice(i, j))","def matPart(mat, rs, re, cs, ce):\n return [[matGet(mat,x,y) for y in range(cs,ce)] \\\n for x in range(rs,re)]","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 test04b(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=100, rootdir=self.rootdir)\n sl = slice(None, 8000)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def test04d(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=100, rootdir=self.rootdir)\n sl = slice(None, None, 2)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def slice2(self, vs=None,xs=None):\n return self.condition2(vs,xs)","def test02e(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(4, 3, 30)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def getarowslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","def slice( mat, n, pt ):\n \n lc, uc = TemplateMatcher.hinge( pt.x, n ) ## column limits\n lr, ur = TemplateMatcher.hinge( pt.y, n )\n nr, nc = np.shape( mat )\n nr = nr - 1\n nc = nc - 1\n \n alc, auc = lc, uc \n alr, aur = lr, ur \n rpb, rpa, cpl, cpr = [0,0,0,0]\n\n if lc < 0:\n alc = 0\n cpl = -lc \n\n if uc > nc:\n auc = nc \n cpr = uc - auc \n\n if lr < 0:\n alr = 0\n rpb = -lr\n\n if ur > nr:\n aur = nr \n rpa = ur - aur \n\n return np.pad( mat[ alr : aur + 1 , alc : auc + 1 ], (( rpb, rpa ),( cpl, cpr )), mode ='constant' )","def iter_col(self, inclusive=False, **kwargs):\n ret = self.sudoku.col(self.col, **kwargs)\n if not inclusive:\n ret -= self\n return ret","def test01b(self):\n a = np.arange(101)\n b = bcolz.carray(a, chunklen=2, rootdir=self.rootdir)\n # print \"sum iter->\", sum(b.iter(-3))\n self.assertTrue(sum(a[-3:]) == sum(b.iter(-3)), \"Sums are not equal\")","def test08b(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, rootdir=self.rootdir)\n u = b.iter(3, 30, 3)\n w = b.iter(2, 20, 2)\n self.assertEqual(a.tolist(), list(b))\n self.assertEqual(sum(a[3:30:3]), sum(u))\n self.assertEqual(sum(a[2:20:2]), sum(w))","def test00(self):\n a = np.arange(1, 111)\n b = bcolz.carray(a)\n c = b[[3, 1]]\n r = a[[3, 1]]\n assert_array_equal(c, r, \"fancy indexing does not work correctly\")","def test02(self):\n a = np.arange(101)\n b = bcolz.carray(a)\n c = b[[]]\n r = a[[]]\n assert_array_equal(c, r, \"fancy indexing does not work correctly\")","def test03c(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(990, 998, 2)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def separate_array_by_last_dim(adata,bx,atype):\n\n if(len(atype)==1):\n cx=adata\n else:\n cx=tuple([adata[...,slice(bx[i],bx[i+1])].astype(atype[i]) for i in np.arange(0,len(bx)-1)])\n\n return cx","def GetColStarts(self):\n return _hypre.HypreParMatrix_GetColStarts(self)","def test02(self):\n a = np.arange(0, 1000)\n b = bcolz.carray(a, chunklen=10)\n sl = np.random.randint(0, 1000, size=3*30)\n vals = np.random.randint(1, 1000, size=3*30)\n b[sl] = vals\n a[sl] = vals\n # print \"b[%s] -> %r\" % (sl, b)\n assert_array_equal(b[:], a, \"fancy indexing does not work correctly\")","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 getcols(d,l,*args):\n ret = []\n for i in range(len(args)):\n ret = ret + [getcol(d[args[i]],l)]\n return ret","def as_slice(self):\n # slice for accessing arrays of values\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)","def test05(self):\n a = np.arange(1e2)\n b = bcolz.carray(a, chunklen=1, rootdir=self.rootdir)\n sl = slice(2, 99, -30)\n self.assertRaises(NotImplementedError, b.__setitem__, sl, 3.)","def test04a(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=16, rootdir=self.rootdir)\n sl = slice(1, 2)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","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 test03a(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(1, 80, 3)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def slice0(A,rowrange,colrange):\n\treturn [[A[i][j] for j in range(rowrange[0],rowrange[1])] for i in range(colrange[0],colrange[1])]","def test03b(self):\n a = np.arange(101)\n b = bcolz.carray(a, chunklen=2, rootdir=self.rootdir)\n # print \"sum iter->\", sum(b.iter(3, 24, 4))\n self.assertTrue(sum(a[3:24:4]) == sum(b.iter(3, 24, 4)),\n \"Sums are not equal\")","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 fast_get_col(self,j):\n col = self.col_view[:,j].copy()\n col.data = self.X.data[col.data]\n return col","def _slice(tensor, size, i):\n return tensor[:, i * size : (i + 1) * size]","def __getslice__( self, *args):\n return array.array.__getslice__(self, *args).tostring()","def last_col_filtered(self):\n data = []\n for row in range(self.proxyModel3.rowCount()):\n data.append([])\n for column in range(self.proxyModel3.columnCount()):\n index = self.proxyModel3.index(row, column)\n data[row].append(str(self.proxyModel3.data(index)))\n data2 = [col[6] for col in data]\n # print(data)\n # print(data2)\n return data2","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 test04ab(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=100, rootdir=self.rootdir)\n sl = slice(1, 8000)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def slice(tensor):\n out = tensor[:, 444:524, :]\n return out","def get_slice(self, start: Optional[Index] = None, end: Optional[Index] = None):\n index = self._slice_index(slice(start, end))\n return self.get_table(index, self._columns)","def test02(self):\n a = np.arange(self.N, dtype='uint64')\n b = bcolz.carray(a, rootdir=self.rootdir)\n c = iter(b.view())\n u = c.iter(3)\n w = b.iter(2)\n self.assertEqual(sum(a[3:]), sum(u))\n self.assertEqual(sum(a[2:]), sum(w))","def to_slice(self):\n return np.index_exp[self.start[2]:self.end[2], #\n self.start[1]:self.end[1], #\n self.start[0]:self.end[0]]","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 test02c(self):\n a = np.arange(101)\n b = bcolz.carray(a, chunklen=2, rootdir=self.rootdir)\n # print \"sum iter->\", sum(b.iter(24, -3))\n self.assertTrue(sum(a[24:-3]) == sum(b.iter(24, -3)),\n \"Sums are not equal\")","def getacolnumnz(self,i_): # 3\n res,resargs = self.__obj.getacolnumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n return _nzj_return_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)","def getCol(self, n, offset=0):\n return self._c[(n*self.__height + offset):((n+1) * self.__height)]","def test04c(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=100, rootdir=self.rootdir)\n sl = slice(8000, None)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def col(self, i):\n return Vector([row[i] for row in self.data])","def row_slice(self, xt, nproc):\n if nproc is None: nproc = self.nproc\n cs = xt.shape[0]//nproc #chuncksize\n tmp = [xt[i*cs:cs*i+cs,:] for i in range(nproc)]\n if nproc*cs != xt.shape[0]:\n tmp[-1] = np.concatenate((tmp[-1],xt[nproc*cs:xt.shape[0],:]),axis=0)\n return tmp","def columnIndexes(a):\n nrows = (a.size-2)+1\n return a[1*np.arange(nrows)[:,None] + np.arange(2)]","def _slice(self, start, stop, step=None):\n\n slices = [slice(None)] * self.data.ndim\n slices[self.axis] = slice(start, stop, step)\n return tuple(slices)","def cfdGetBoundaryElementsSubArrayForBoundaryPatch(self):\r\n\r\n for iBPatch, theBCInfo in self.cfdBoundaryPatchesArray.items():\r\n \r\n startBElement=self.numberOfElements+self.cfdBoundaryPatchesArray[iBPatch]['startFaceIndex']-self.numberOfInteriorFaces\r\n endBElement=startBElement+self.cfdBoundaryPatchesArray[iBPatch]['numberOfBFaces']\r\n \r\n self.cfdBoundaryPatchesArray[iBPatch]['iBElements']=list(range(int(startBElement),int(endBElement)))","def _colvec(x):\n x = np.atleast_1d(x)\n return x[:, None]","def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\n numnz_ = ctypes.c_int32()\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def getVisibleCols(self, x1, x2):\n start = self.getColPosition(x1)\n end = self.getColPosition(x2) + 1\n if end > self.cols:\n end = self.cols\n return start, end","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 get_slice(self):\n return self.locs[tuple(self.indices), :]","def __substr(self, begin, length):\n col = _VirtualCategoricalColumn(\n df_name=self.thisptr[\"df_name_\"],\n operator=\"substr\",\n operand1=self,\n operand2=None\n )\n col.thisptr[\"begin_\"] = begin\n col.thisptr[\"len_\"] = length\n return col","def collocation(lhs, rhs, basis, nterms, domain=(0, 1), x_col=None):\n x0, x1 = domain\n if x_col is None:\n dx = S(x1 - x0)/(nterms - 2)\n x_col = [dx + dx*cont for cont in range(nterms)]\n x = symbols(\"x\")\n A_mat = zeros(nterms, nterms)\n b_vec = zeros(nterms, 1)\n for row in range(nterms):\n b_vec[row] = rhs(x_col[row])\n for col in range(nterms):\n phi_j = basis(x, col)\n A_mat[row, col] = lhs(phi_j, x).subs(x, x_col[row])\n return A_mat, b_vec","def _get_col(self, idx):\n return self.line[self._fwf.column_slices[idx]]","def get_cols(self, *dims):\n dims_not_found = [d for d in dims if not d in self.dims]\n if dims_not_found:\n raise Exception('Some dims were not found.\\n Dims not found: %s\\n Dims in table: %s' % (str(dims_not_found), str(self.dims)))\n \n return self.get_points(*dims).T","def test00(self):\n a = np.arange(1, 111)\n b = bcolz.carray(a, chunklen=10)\n sl = [3, 1]\n b[sl] = (10, 20)\n a[sl] = (10, 20)\n # print \"b[%s] -> %r\" % (sl, b)\n assert_array_equal(b[:], a, \"fancy indexing does not work correctly\")","def test03d(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(4, 80, 3000)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def get_column(self, from_int=None, to_int=None):\n\n try:\n if from_int is None and to_int is None: \n return list(self.df_input)\n else:\n get_col_arr = list(self.df_input)\n column_arr = []\n while from_int < to_int:\n column_arr.append(get_col_arr[from_int])\n from_int += 1\n return column_arr\n except Exception as e:\n print(e)"],"string":"[\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 getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\\n maxnumnz_ = self.getarowslicenumnz((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_getarowslice64(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 getacolslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\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 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 getaslice(self,accmode_,first_,last_,ptrb,ptre,sub,val): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n maxnumnz_ = self.getaslicenumnz((accmode_),(first_),(last_))\\n _copyback_ptrb = False\\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 _copyback_ptrb = True\\n else:\\n if ptrb_.format != \\\"q\\\":\\n ptrb_ = memoryview(array.array(\\\"q\\\",ptrb))\\n _copyback_ptrb = True\\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptrb has wrong length\\\")\\n _copyback_ptre = False\\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 _copyback_ptre = True\\n else:\\n if ptre_.format != \\\"q\\\":\\n ptre_ = memoryview(array.array(\\\"q\\\",ptre))\\n _copyback_ptre = True\\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptre has wrong length\\\")\\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(\\\"i\\\",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 != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n _copyback_sub = True\\n if sub_ is not None and len(sub_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument sub 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.getaslice64(accmode_,first_,last_,maxnumnz_,len(sub),ptrb_,ptre_,sub_,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_sub:\\n sub[:] = _tmparr_sub\\n if _copyback_ptre:\\n ptre[:] = _tmparr_ptre\\n if _copyback_ptrb:\\n ptrb[:] = _tmparr_ptrb\",\n \"def __getslice__(self, i, j):\\n return self.dtrs[i:j]\",\n \"def ColPart(self, *args):\\n return _hypre.HypreParMatrix_ColPart(self, *args)\",\n \"def __getslice__(self,i,j):\\n return self.x[i:j]\",\n \"def slice(A,rowrange,colrange):\\n\\n\\treturn [[get_elem(A,j,i) for j in rowrange] for i in colrange]\",\n \"def get_col(b, ci):\\r\\n return [b[0][ci], b[1][ci], b[2][ci]]\",\n \"def getacolslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def getarowslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\\n nv.__maj()\\n return nv\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\\n nv._Matr__maj()\\n return nv\",\n \"def get_cols(self) :\\n\\n return list(self.cols)[1:]\",\n \"def get_col(A,r=0):\\n\\treturn list(A[r])\",\n \"def sub_columns(arr, sub_size):\\n return sub_rows(arr.T, sub_size)\",\n \"def slice_matrix(m,i,j):\\n return np.take(np.take(m,i,0),j,1)\",\n \"def GetColPartArray(self):\\n return _hypre.HypreParMatrix_GetColPartArray(self)\",\n \"def slice2(self, cvars=None,ctuple=None):\\n return self.condition2(cvars,ctuple)\",\n \"def test05(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(None, None, -3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n self.assertRaises(NotImplementedError, b.__getitem__, sl)\",\n \"def test02b(self):\\n a = np.arange(1e2)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(-3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def test02d(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(-3, -1)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def test02c(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(1, -3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def relay_array_getitem(c, a, start, stop, strides):\\n assert start.is_constant(tuple)\\n assert stop.is_constant(tuple)\\n assert strides.is_constant(tuple)\\n return relay.op.transform.strided_slice(c.ref(a), start.value, stop.value,\\n strides.value)\",\n \"def slice_pdb(self, start, stop):\\n return\",\n \"def test03b(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(1, 80, 30)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def test03(self):\\n a = np.arange(1, 101)\\n b = bcolz.carray(a)\\n c = b[[1.1, 3.3]]\\n r = a[[1, 3]]\\n assert_array_equal(c, r, \\\"fancy indexing does not work correctly\\\")\",\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 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 test01b(self):\\n a = np.arange(1e2)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(-1)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def test07(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 cr = c[\\\"a + 2 * d - 3 > 0\\\"]\\n nr = a[(a + 2 * b - 3) > 0]\\n # print \\\"ca[expr] ->\\\", cr\\n # print \\\"numpy ->\\\", nr\\n assert_array_equal(cr[:], nr, \\\"carray[expr] does not work correctly\\\")\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i,j))\",\n \"def getarowslicetrip(self,first_,last_,subi,subj,val): # 3\\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 __getslice__(self, start, stop):\\n return self.__getitem__(slice(start, stop, None))\",\n \"def putarowslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\\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 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 _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 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_putarowslice64(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 test02b(self):\\n a = np.arange(101)\\n b = bcolz.carray(a, chunklen=2, rootdir=self.rootdir)\\n # print \\\"sum iter->\\\", sum(b.iter(-24, -3))\\n self.assertTrue(sum(a[-24:-3]) == sum(b.iter(-24, -3)),\\n \\\"Sums are not equal\\\")\",\n \"def test02a(self):\\n a = np.arange(1e2)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(1, 3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i, j))\",\n \"def matPart(mat, rs, re, cs, ce):\\n return [[matGet(mat,x,y) for y in range(cs,ce)] \\\\\\n for x in range(rs,re)]\",\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 test04b(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=100, rootdir=self.rootdir)\\n sl = slice(None, 8000)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def test04d(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=100, rootdir=self.rootdir)\\n sl = slice(None, None, 2)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def slice2(self, vs=None,xs=None):\\n return self.condition2(vs,xs)\",\n \"def test02e(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(4, 3, 30)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def getarowslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def slice( mat, n, pt ):\\n \\n lc, uc = TemplateMatcher.hinge( pt.x, n ) ## column limits\\n lr, ur = TemplateMatcher.hinge( pt.y, n )\\n nr, nc = np.shape( mat )\\n nr = nr - 1\\n nc = nc - 1\\n \\n alc, auc = lc, uc \\n alr, aur = lr, ur \\n rpb, rpa, cpl, cpr = [0,0,0,0]\\n\\n if lc < 0:\\n alc = 0\\n cpl = -lc \\n\\n if uc > nc:\\n auc = nc \\n cpr = uc - auc \\n\\n if lr < 0:\\n alr = 0\\n rpb = -lr\\n\\n if ur > nr:\\n aur = nr \\n rpa = ur - aur \\n\\n return np.pad( mat[ alr : aur + 1 , alc : auc + 1 ], (( rpb, rpa ),( cpl, cpr )), mode ='constant' )\",\n \"def iter_col(self, inclusive=False, **kwargs):\\n ret = self.sudoku.col(self.col, **kwargs)\\n if not inclusive:\\n ret -= self\\n return ret\",\n \"def test01b(self):\\n a = np.arange(101)\\n b = bcolz.carray(a, chunklen=2, rootdir=self.rootdir)\\n # print \\\"sum iter->\\\", sum(b.iter(-3))\\n self.assertTrue(sum(a[-3:]) == sum(b.iter(-3)), \\\"Sums are not equal\\\")\",\n \"def test08b(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, rootdir=self.rootdir)\\n u = b.iter(3, 30, 3)\\n w = b.iter(2, 20, 2)\\n self.assertEqual(a.tolist(), list(b))\\n self.assertEqual(sum(a[3:30:3]), sum(u))\\n self.assertEqual(sum(a[2:20:2]), sum(w))\",\n \"def test00(self):\\n a = np.arange(1, 111)\\n b = bcolz.carray(a)\\n c = b[[3, 1]]\\n r = a[[3, 1]]\\n assert_array_equal(c, r, \\\"fancy indexing does not work correctly\\\")\",\n \"def test02(self):\\n a = np.arange(101)\\n b = bcolz.carray(a)\\n c = b[[]]\\n r = a[[]]\\n assert_array_equal(c, r, \\\"fancy indexing does not work correctly\\\")\",\n \"def test03c(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(990, 998, 2)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def separate_array_by_last_dim(adata,bx,atype):\\n\\n if(len(atype)==1):\\n cx=adata\\n else:\\n cx=tuple([adata[...,slice(bx[i],bx[i+1])].astype(atype[i]) for i in np.arange(0,len(bx)-1)])\\n\\n return cx\",\n \"def GetColStarts(self):\\n return _hypre.HypreParMatrix_GetColStarts(self)\",\n \"def test02(self):\\n a = np.arange(0, 1000)\\n b = bcolz.carray(a, chunklen=10)\\n sl = np.random.randint(0, 1000, size=3*30)\\n vals = np.random.randint(1, 1000, size=3*30)\\n b[sl] = vals\\n a[sl] = vals\\n # print \\\"b[%s] -> %r\\\" % (sl, b)\\n assert_array_equal(b[:], a, \\\"fancy indexing does not work correctly\\\")\",\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 getcols(d,l,*args):\\n ret = []\\n for i in range(len(args)):\\n ret = ret + [getcol(d[args[i]],l)]\\n return ret\",\n \"def as_slice(self):\\n # slice for accessing arrays of values\\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)\",\n \"def test05(self):\\n a = np.arange(1e2)\\n b = bcolz.carray(a, chunklen=1, rootdir=self.rootdir)\\n sl = slice(2, 99, -30)\\n self.assertRaises(NotImplementedError, b.__setitem__, sl, 3.)\",\n \"def test04a(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=16, rootdir=self.rootdir)\\n sl = slice(1, 2)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\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 test03a(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(1, 80, 3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def slice0(A,rowrange,colrange):\\n\\treturn [[A[i][j] for j in range(rowrange[0],rowrange[1])] for i in range(colrange[0],colrange[1])]\",\n \"def test03b(self):\\n a = np.arange(101)\\n b = bcolz.carray(a, chunklen=2, rootdir=self.rootdir)\\n # print \\\"sum iter->\\\", sum(b.iter(3, 24, 4))\\n self.assertTrue(sum(a[3:24:4]) == sum(b.iter(3, 24, 4)),\\n \\\"Sums are not equal\\\")\",\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 fast_get_col(self,j):\\n col = self.col_view[:,j].copy()\\n col.data = self.X.data[col.data]\\n return col\",\n \"def _slice(tensor, size, i):\\n return tensor[:, i * size : (i + 1) * size]\",\n \"def __getslice__( self, *args):\\n return array.array.__getslice__(self, *args).tostring()\",\n \"def last_col_filtered(self):\\n data = []\\n for row in range(self.proxyModel3.rowCount()):\\n data.append([])\\n for column in range(self.proxyModel3.columnCount()):\\n index = self.proxyModel3.index(row, column)\\n data[row].append(str(self.proxyModel3.data(index)))\\n data2 = [col[6] for col in data]\\n # print(data)\\n # print(data2)\\n return data2\",\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 test04ab(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=100, rootdir=self.rootdir)\\n sl = slice(1, 8000)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def slice(tensor):\\n out = tensor[:, 444:524, :]\\n return out\",\n \"def get_slice(self, start: Optional[Index] = None, end: Optional[Index] = None):\\n index = self._slice_index(slice(start, end))\\n return self.get_table(index, self._columns)\",\n \"def test02(self):\\n a = np.arange(self.N, dtype='uint64')\\n b = bcolz.carray(a, rootdir=self.rootdir)\\n c = iter(b.view())\\n u = c.iter(3)\\n w = b.iter(2)\\n self.assertEqual(sum(a[3:]), sum(u))\\n self.assertEqual(sum(a[2:]), sum(w))\",\n \"def to_slice(self):\\n return np.index_exp[self.start[2]:self.end[2], #\\n self.start[1]:self.end[1], #\\n self.start[0]:self.end[0]]\",\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 test02c(self):\\n a = np.arange(101)\\n b = bcolz.carray(a, chunklen=2, rootdir=self.rootdir)\\n # print \\\"sum iter->\\\", sum(b.iter(24, -3))\\n self.assertTrue(sum(a[24:-3]) == sum(b.iter(24, -3)),\\n \\\"Sums are not equal\\\")\",\n \"def getacolnumnz(self,i_): # 3\\n res,resargs = self.__obj.getacolnumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n return _nzj_return_value\",\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 getCol(self, n, offset=0):\\n return self._c[(n*self.__height + offset):((n+1) * self.__height)]\",\n \"def test04c(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=100, rootdir=self.rootdir)\\n sl = slice(8000, None)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def col(self, i):\\n return Vector([row[i] for row in self.data])\",\n \"def row_slice(self, xt, nproc):\\n if nproc is None: nproc = self.nproc\\n cs = xt.shape[0]//nproc #chuncksize\\n tmp = [xt[i*cs:cs*i+cs,:] for i in range(nproc)]\\n if nproc*cs != xt.shape[0]:\\n tmp[-1] = np.concatenate((tmp[-1],xt[nproc*cs:xt.shape[0],:]),axis=0)\\n return tmp\",\n \"def columnIndexes(a):\\n nrows = (a.size-2)+1\\n return a[1*np.arange(nrows)[:,None] + np.arange(2)]\",\n \"def _slice(self, start, stop, step=None):\\n\\n slices = [slice(None)] * self.data.ndim\\n slices[self.axis] = slice(start, stop, step)\\n return tuple(slices)\",\n \"def cfdGetBoundaryElementsSubArrayForBoundaryPatch(self):\\r\\n\\r\\n for iBPatch, theBCInfo in self.cfdBoundaryPatchesArray.items():\\r\\n \\r\\n startBElement=self.numberOfElements+self.cfdBoundaryPatchesArray[iBPatch]['startFaceIndex']-self.numberOfInteriorFaces\\r\\n endBElement=startBElement+self.cfdBoundaryPatchesArray[iBPatch]['numberOfBFaces']\\r\\n \\r\\n self.cfdBoundaryPatchesArray[iBPatch]['iBElements']=list(range(int(startBElement),int(endBElement)))\",\n \"def _colvec(x):\\n x = np.atleast_1d(x)\\n return x[:, None]\",\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\\n numnz_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def getVisibleCols(self, x1, x2):\\n start = self.getColPosition(x1)\\n end = self.getColPosition(x2) + 1\\n if end > self.cols:\\n end = self.cols\\n return start, end\",\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 get_slice(self):\\n return self.locs[tuple(self.indices), :]\",\n \"def __substr(self, begin, length):\\n col = _VirtualCategoricalColumn(\\n df_name=self.thisptr[\\\"df_name_\\\"],\\n operator=\\\"substr\\\",\\n operand1=self,\\n operand2=None\\n )\\n col.thisptr[\\\"begin_\\\"] = begin\\n col.thisptr[\\\"len_\\\"] = length\\n return col\",\n \"def collocation(lhs, rhs, basis, nterms, domain=(0, 1), x_col=None):\\n x0, x1 = domain\\n if x_col is None:\\n dx = S(x1 - x0)/(nterms - 2)\\n x_col = [dx + dx*cont for cont in range(nterms)]\\n x = symbols(\\\"x\\\")\\n A_mat = zeros(nterms, nterms)\\n b_vec = zeros(nterms, 1)\\n for row in range(nterms):\\n b_vec[row] = rhs(x_col[row])\\n for col in range(nterms):\\n phi_j = basis(x, col)\\n A_mat[row, col] = lhs(phi_j, x).subs(x, x_col[row])\\n return A_mat, b_vec\",\n \"def _get_col(self, idx):\\n return self.line[self._fwf.column_slices[idx]]\",\n \"def get_cols(self, *dims):\\n dims_not_found = [d for d in dims if not d in self.dims]\\n if dims_not_found:\\n raise Exception('Some dims were not found.\\\\n Dims not found: %s\\\\n Dims in table: %s' % (str(dims_not_found), str(self.dims)))\\n \\n return self.get_points(*dims).T\",\n \"def test00(self):\\n a = np.arange(1, 111)\\n b = bcolz.carray(a, chunklen=10)\\n sl = [3, 1]\\n b[sl] = (10, 20)\\n a[sl] = (10, 20)\\n # print \\\"b[%s] -> %r\\\" % (sl, b)\\n assert_array_equal(b[:], a, \\\"fancy indexing does not work correctly\\\")\",\n \"def test03d(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(4, 80, 3000)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def get_column(self, from_int=None, to_int=None):\\n\\n try:\\n if from_int is None and to_int is None: \\n return list(self.df_input)\\n else:\\n get_col_arr = list(self.df_input)\\n column_arr = []\\n while from_int < to_int:\\n column_arr.append(get_col_arr[from_int])\\n from_int += 1\\n return column_arr\\n except Exception as e:\\n print(e)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68298435","0.68189096","0.6633351","0.6410343","0.635861","0.62143683","0.59577066","0.58845204","0.5850808","0.5840428","0.5830795","0.5818751","0.5787353","0.5729822","0.57094395","0.5652637","0.5626832","0.5602785","0.557522","0.55677515","0.5536629","0.55353665","0.5508671","0.54177946","0.53788126","0.5366509","0.5364556","0.53594303","0.5341699","0.5337989","0.5330868","0.5312625","0.5306566","0.53038096","0.5302301","0.5296313","0.52644277","0.5256803","0.524407","0.5242737","0.5214247","0.5210205","0.5209233","0.52057576","0.5201481","0.5179186","0.5173908","0.5164348","0.51607144","0.51606387","0.5156799","0.51554185","0.51532835","0.5149097","0.51351947","0.5130021","0.5127955","0.5113938","0.50927573","0.5090291","0.5081135","0.50739604","0.506498","0.50639105","0.50628966","0.5039461","0.5036261","0.5028162","0.5023357","0.50179875","0.50116044","0.501029","0.50073415","0.49990648","0.4992293","0.49903628","0.49898764","0.49754736","0.49717283","0.49697882","0.49594384","0.49522","0.49511263","0.49442714","0.49426055","0.49423116","0.49320996","0.49271378","0.49240655","0.49191725","0.49172235","0.4909352","0.49031612","0.4900648","0.48946851","0.48925987","0.48884344","0.488804","0.48861665","0.48778751"],"string":"[\n \"0.68298435\",\n \"0.68189096\",\n \"0.6633351\",\n \"0.6410343\",\n \"0.635861\",\n \"0.62143683\",\n \"0.59577066\",\n \"0.58845204\",\n \"0.5850808\",\n \"0.5840428\",\n \"0.5830795\",\n \"0.5818751\",\n \"0.5787353\",\n \"0.5729822\",\n \"0.57094395\",\n \"0.5652637\",\n \"0.5626832\",\n \"0.5602785\",\n \"0.557522\",\n \"0.55677515\",\n \"0.5536629\",\n \"0.55353665\",\n \"0.5508671\",\n \"0.54177946\",\n \"0.53788126\",\n \"0.5366509\",\n \"0.5364556\",\n \"0.53594303\",\n \"0.5341699\",\n \"0.5337989\",\n \"0.5330868\",\n \"0.5312625\",\n \"0.5306566\",\n \"0.53038096\",\n \"0.5302301\",\n \"0.5296313\",\n \"0.52644277\",\n \"0.5256803\",\n \"0.524407\",\n \"0.5242737\",\n \"0.5214247\",\n \"0.5210205\",\n \"0.5209233\",\n \"0.52057576\",\n \"0.5201481\",\n \"0.5179186\",\n \"0.5173908\",\n \"0.5164348\",\n \"0.51607144\",\n \"0.51606387\",\n \"0.5156799\",\n \"0.51554185\",\n \"0.51532835\",\n \"0.5149097\",\n \"0.51351947\",\n \"0.5130021\",\n \"0.5127955\",\n \"0.5113938\",\n \"0.50927573\",\n \"0.5090291\",\n \"0.5081135\",\n \"0.50739604\",\n \"0.506498\",\n \"0.50639105\",\n \"0.50628966\",\n \"0.5039461\",\n \"0.5036261\",\n \"0.5028162\",\n \"0.5023357\",\n \"0.50179875\",\n \"0.50116044\",\n \"0.501029\",\n \"0.50073415\",\n \"0.49990648\",\n \"0.4992293\",\n \"0.49903628\",\n \"0.49898764\",\n \"0.49754736\",\n \"0.49717283\",\n \"0.49697882\",\n \"0.49594384\",\n \"0.49522\",\n \"0.49511263\",\n \"0.49442714\",\n \"0.49426055\",\n \"0.49423116\",\n \"0.49320996\",\n \"0.49271378\",\n \"0.49240655\",\n \"0.49191725\",\n \"0.49172235\",\n \"0.4909352\",\n \"0.49031612\",\n \"0.4900648\",\n \"0.48946851\",\n \"0.48925987\",\n \"0.48884344\",\n \"0.488804\",\n \"0.48861665\",\n \"0.48778751\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8195238"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11778,"cells":{"query":{"kind":"string","value":"Obtains the number of nonzero elements in one row of the linear constraint matrix getarownumnz(self,i_)"},"document":{"kind":"string","value":"def getarownumnz(self,i_):\n nzi_ = ctypes.c_int32()\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzi_ = nzi_.value\n _nzi_return_value = nzi_\n return (_nzi_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 getacolnumnz(self,i_):\n nzj_ = ctypes.c_int32()\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n return (_nzj_return_value)","def getacolnumnz(self,i_): # 3\n res,resargs = self.__obj.getacolnumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n return _nzj_return_value","def getarownumnz(self,i_): # 3\n res,resargs = self.__obj.getarownumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzi_return_value = resargs\n return _nzi_return_value","def nNz(self):\n if self.dim < 3:\n return None\n return self.nCz + 1","def nnz(self):\n return self.rep.nnz()","def nnz(self):\n return self.to_ddm().nnz()","def nnz(self):\n return len(self.data)","def nCz(self):\n if self.dim < 3:\n return None\n return int(self._n[2])","def nnz(self):\n return len(self.value)","def nz(self):\n return self._dim[2]","def nnz(self):","def nnz(self):\n t = self.get_MSC()\n return len(np.unique(t['masks']))","def nnz(self):\n\t\treturn self.st.size()","def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","def N_z(self) -> int:\n return self.params.N_z","def GetNumCols(self):\n return _hypre.HypreParMatrix_GetNumCols(self)","def __column_height(self, x):\n\t\tcolumn = self.board[:, x]\n\t\treturn np.count_nonzero(column)","def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\n numnz_ = ctypes.c_int32()\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def NNZ(self):\n return _hypre.HypreParMatrix_NNZ(self)","def n_cols(self):\n ch = self.children\n return 1 if not ch else sum([c.n_cols for c in ch])","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 columnCount(self, parent):\r\n if len(self.arraydata) > 0:\r\n return len(self.arraydata[0]) - 2\r\n return 0","def colnum(self):\n \n colnum = 0\n for table in self.columnlabels:\n table = np.asarray(table)\n if np.ndim(table) <= 1:\n table = np.reshape(table, (1, -1))\n colnum += table.shape[1]\n return colnum","def n(self):\n return nrow(self._array)","def matrix_dim(CT):\r\n if CT[0]==0 and CT[-1]==0:\r\n return 2\r\n elif CT[0]!=0 and CT[-1]!=0:\r\n return 4","def NNZ(self):\n return len(self.__IndList)","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 dof(self):\n if self._dof is None:\n self._dof = self.tri.nbr_cells - (self.tri.columns.size - 1) + self.tri.index.size\n return(self._dof)","def vnFz(self):\n if self.dim < 3:\n return None\n return np.array(\n [x for x in [self.nCx, self.nCy, self.nNz] if x is not None],\n dtype=int\n )","def N_Col(self,val):\n\t\tif val in self.colums:\n\t\t\treturn self.colums.index(val)\n\t\telse:\n\t\t\treturn False","def M_z(self) -> int:\n return self.params.M_z","def count_level(self):\r\n count = 0\r\n\r\n for i in range(0, len(self.matrix)):\r\n for j in range(0,len(self.matrix[i])):\r\n if self.matrix[i][j] == \"0\":\r\n count += 1\r\n\r\n # We substract 1 to count level from 0\r\n return count - 1","def columnCount(self, parent_midx):\n return self._cols_nb","def columnCount(self, parent_midx):\n return self._cols_nb","def num_cols(self):\n return (len(self.rows[0]))","def get_zdim(self):\n return self.decoder.get_input_info_dict()['latent_vector'].get_shape()[1]","def getlen(self):\n if self.onlydiag():\n return self.lendiag()\n else:\n return len(self)","def getNumCols(self):\n return self.__cols","def GetGlobalNumCols(self):\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)","def fn(i, j):\n if i < 0 or j < 0 or matrix[i][j] == \"0\": return 0\n return 1 + min(fn(i-1, j-1), fn(i-1, j), fn(i, j-1))","def columns(self) -> int:\n return self.__squares[0].__len__()","def ncolumns(self):\n return self.__ncols","def get_local_nnz(self):\n return _hypre.HypreParMatrix_get_local_nnz(self)","def num_cols(self):\n return len(self.rows[0])","def innulo(self):\n for i in range(self.n):\n if not comozero(self[i]):\n return i\n return None","def dim(self):\n if '_dim' in self.__dict__:\n return self._dim\n\n if len(self._Vrepresentation)==0:\n self._dim = -1\n return self._dim\n\n origin = vector(self._Vrepresentation[0])\n v_list = [ vector(v)-origin for v in self._Vrepresentation ]\n self._dim = matrix(v_list).rank()\n return self._dim","def __len__(self):\n return len(np.where(np.logical_not(self.data.mask))[0])","def __len__(self):\n return np.size(self.A,0)","def get_cells(self):\r\n return \\\r\n (self.nx-1 if self.nx>1 else 1)* \\\r\n (self.ny-1 if self.ny>1 else 1)* \\\r\n (self.nz-1 if self.nz>1 else 1)","def GetNumColumns(self):\n return len(self.columns)","def getacolslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def getNbColumns(self):\n return self.data.shape[0]","def ncolumns(self):\n return len(self.__column_list)","def _non_zero_columns_search(array):\n col_num = array.shape[1]\n non_zero_col = CArray([], dtype=int)\n for c in range(col_num):\n col = array[:, c]\n if col.any() == True:\n non_zero_col = non_zero_col.append(c)\n\n return non_zero_col","def columnCount(self):\n return abs(self.minCol) + abs(self.maxCol)","def __len__(self):\n return self.xyz.shape[0]","def __len__(self):\n return self.xyz.shape[0]","def columnCount( self ):\n if not self.o_data is None:\n if self.isItemMultiDimensional():\n return len(self.o_data)\n else:\n return 1\n else:\n return 1","def GetColumnCount(self):\r\n\r\n return len(self._columns)","def ncells(self):\n return self.izone.size","def GetNumberCols(self):\n return len(self.__colsKey)","def nNy(self):\n if self.dim < 2:\n return None\n return self.nCy + 1","def no_of_columns(self): \n return len(self.columns) + (1 if self.serialize else 0)","def vnEz(self):\n if self.dim < 3:\n return None\n return np.array(\n [x for x in [self.nNx, self.nNy, self.nCz] if x is not None],\n dtype=int\n )","def noOfElem(classObj, index):\r\n return len(classObj.dataSet[:, index])","def nvar(self):\n return self.h.shape[0]","def TzscorrCols(Xn):\n C_X = T.dot(Xn.T, Xn)-T.eye(Xn.shape[1])\n return 0.5*T.log((1+C_X)/(1-C_X))","def constraint_col_coef(n_col, n_row):\n\n all_rows = []\n for i in range(n_col):\n matrix_values = np.zeros((n_row, n_col), dtype=int)\n col_offer = np.ones(n_row, dtype=int)\n matrix_values[:, i] = col_offer\n all_rows.append(matrix_values.flatten())\n\n cols_constraints = np.stack(all_rows)\n\n return cols_constraints","def getNoOfCols(self):\n return _patchExtractor.patchExtractor_getNoOfCols(self)","def n_cols(self):\n\n return len(self.plaincolumns)","def __len__(self):\n return self.N.shape[0]","def ny(self):\n return self._dim[1]","def count_nonzero(tensor):\n raise NotImplementedError","def m(self):\n\t\tn = 0\n\t\ti = self.k0\n\t\twhile 1:\n\t\t\tif i > self.j:\n\t\t\t\treturn n\n\t\t\tif not self.cons(i):\n\t\t\t\tbreak\n\t\t\ti = i + 1\n\t\ti = i + 1\n\t\twhile 1:\n\t\t\twhile 1:\n\t\t\t\tif i > self.j:\n\t\t\t\t\treturn n\n\t\t\t\tif self.cons(i):\n\t\t\t\t\tbreak\n\t\t\t\ti = i + 1\n\t\t\ti = i + 1\n\t\t\tn = n + 1\n\t\t\twhile 1:\n\t\t\t\tif i > self.j:\n\t\t\t\t\treturn n\n\t\t\t\tif not self.cons(i):\n\t\t\t\t\tbreak\n\t\t\t\ti = i + 1\n\t\t\ti = i + 1","def columns_count(self):\n if self.value.count != 0:\n return len(self.value[0])\n else:\n return 0","def _CalculateColumn(self, record):\n if not record:\n return 0\n if isinstance(record, Table):\n add_width = len(record.separator)\n if record.skip_empty:\n if not any(v for _, v in record):\n return 0\n ret = max(len(k) for k, v in record if v) + add_width\n ret = max(ret, 2 + max(self._CalculateColumn(v) for _, v in record))\n return min(ret, self.MAX_MAP_WIDTH)\n elif isinstance(record, Lines):\n return max(self._CalculateColumn(l) for l in record)\n else:\n return 0","def first_zombie_col(self, row_num):\n row = self.board[row_num]\n for col_num, square in enumerate(row):\n if any(self.is_zombie([row_num, col_num])):\n return col_num","def numel(self):\n return self.t.size","def get_num_columns(table):\n\n\treturn max((len(row) for row in table))","def num_cells_down(self):\n if self.dim == 0:\n return None\n if hasattr(self, '__num_cells_down__'):\n return self.__num_cells_down__\n if self.lower_index is None:\n return 0\n raise ValueError('Cannot infer the number of cells in the cochain below.')","def get_num_cols(self):\n return self._num_cols","def getaslicenumnz(self,accmode_,first_,last_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n res,resargs = self.__obj.getaslicenumnz64(accmode_,first_,last_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","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 find_empty_col(slots):\n index = 0\n for i in list(zip(*list(slots.values())[::])):\n if sum([1 for j in list(i) if j]) == 0:\n return index\n index += 1\n return 6","def get_location_count(self):\n return len(self.matrix)","def is_column_unique(self, i: int) -> bool:\n #print(\"enter bartpy/bartpy/data.py CovariateMatrix is_column_unique\")\n \n if self._unique_columns[i] is None:\n self._unique_columns[i] = len(np.unique(self.get_column(i))) == self._n_obsv\n output = self._unique_columns[i]\n #print(\"-exit bartpy/bartpy/data.py CovariateMatrix is_column_unique\")\n return output","def n_elem(self) -> u.m**-3:\n return self._n_elem.to(u.m**-3)","def columns(self):\n try:\n return self._data.shape[1] or 1\n except (AttributeError, IndexError):\n return 1","def Nx(self):\n return self.shape[-1]","def nrows(self):\n if self.ncolumns() == 0:\n return 0\n nrows = self.table_column(0).nrows()\n for i in range(1, self.ncolumns()):\n nrows = min(self.table_column(i).nrows(), nrows)\n return nrows","def rank(self):\n return self.matrix().rank()","def get_local_true_nnz(self):\n return _hypre.HypreParMatrix_get_local_true_nnz(self)","def count_neighbor_mines(self, i, j):\n n_neighbor_mines = -1\n if not self.mines[i, j]:\n n_neighbor_mines = np.count_nonzero(\n self.mines[(i-1 if i > 0 else 0):i+2, (j-1 if j > 0 else 0):j+2])\n return n_neighbor_mines","def cols(self) -> int:\n if self.is_empty():\n raise ValueError(\"Empty DataView contains no columns\")\n\n return len(self[0])","def z_operator_matrix(self):\n n, r_1, r_2, k = self.n, self.r_1, self.r_2, self.k\n\n # Use the row vector [ A2^T 0 I ], which commutes with the check matrix.\n check_mat = np.zeros((k, n), dtype='int')\n check_mat[:, 0:r_1] = np.transpose(self.parity_check_c1[:, (r_1 + r_2):n])\n check_mat[:, (r_1 + r_2):n] = np.identity(k)\n return check_mat","def ncol(arr):\n assert isinstance(arr, ndarray)\n assert len(arr.shape) == 2\n return arr.shape[1]","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"],"string":"[\n \"def getacolnumnz(self,i_):\\n nzj_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzj_ = nzj_.value\\n _nzj_return_value = nzj_\\n return (_nzj_return_value)\",\n \"def getacolnumnz(self,i_): # 3\\n res,resargs = self.__obj.getacolnumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n return _nzj_return_value\",\n \"def getarownumnz(self,i_): # 3\\n res,resargs = self.__obj.getarownumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzi_return_value = resargs\\n return _nzi_return_value\",\n \"def nNz(self):\\n if self.dim < 3:\\n return None\\n return self.nCz + 1\",\n \"def nnz(self):\\n return self.rep.nnz()\",\n \"def nnz(self):\\n return self.to_ddm().nnz()\",\n \"def nnz(self):\\n return len(self.data)\",\n \"def nCz(self):\\n if self.dim < 3:\\n return None\\n return int(self._n[2])\",\n \"def nnz(self):\\n return len(self.value)\",\n \"def nz(self):\\n return self._dim[2]\",\n \"def nnz(self):\",\n \"def nnz(self):\\n t = self.get_MSC()\\n return len(np.unique(t['masks']))\",\n \"def nnz(self):\\n\\t\\treturn self.st.size()\",\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\n \"def N_z(self) -> int:\\n return self.params.N_z\",\n \"def GetNumCols(self):\\n return _hypre.HypreParMatrix_GetNumCols(self)\",\n \"def __column_height(self, x):\\n\\t\\tcolumn = self.board[:, x]\\n\\t\\treturn np.count_nonzero(column)\",\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\\n numnz_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def NNZ(self):\\n return _hypre.HypreParMatrix_NNZ(self)\",\n \"def n_cols(self):\\n ch = self.children\\n return 1 if not ch else sum([c.n_cols for c in ch])\",\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 columnCount(self, parent):\\r\\n if len(self.arraydata) > 0:\\r\\n return len(self.arraydata[0]) - 2\\r\\n return 0\",\n \"def colnum(self):\\n \\n colnum = 0\\n for table in self.columnlabels:\\n table = np.asarray(table)\\n if np.ndim(table) <= 1:\\n table = np.reshape(table, (1, -1))\\n colnum += table.shape[1]\\n return colnum\",\n \"def n(self):\\n return nrow(self._array)\",\n \"def matrix_dim(CT):\\r\\n if CT[0]==0 and CT[-1]==0:\\r\\n return 2\\r\\n elif CT[0]!=0 and CT[-1]!=0:\\r\\n return 4\",\n \"def NNZ(self):\\n return len(self.__IndList)\",\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 dof(self):\\n if self._dof is None:\\n self._dof = self.tri.nbr_cells - (self.tri.columns.size - 1) + self.tri.index.size\\n return(self._dof)\",\n \"def vnFz(self):\\n if self.dim < 3:\\n return None\\n return np.array(\\n [x for x in [self.nCx, self.nCy, self.nNz] if x is not None],\\n dtype=int\\n )\",\n \"def N_Col(self,val):\\n\\t\\tif val in self.colums:\\n\\t\\t\\treturn self.colums.index(val)\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def M_z(self) -> int:\\n return self.params.M_z\",\n \"def count_level(self):\\r\\n count = 0\\r\\n\\r\\n for i in range(0, len(self.matrix)):\\r\\n for j in range(0,len(self.matrix[i])):\\r\\n if self.matrix[i][j] == \\\"0\\\":\\r\\n count += 1\\r\\n\\r\\n # We substract 1 to count level from 0\\r\\n return count - 1\",\n \"def columnCount(self, parent_midx):\\n return self._cols_nb\",\n \"def columnCount(self, parent_midx):\\n return self._cols_nb\",\n \"def num_cols(self):\\n return (len(self.rows[0]))\",\n \"def get_zdim(self):\\n return self.decoder.get_input_info_dict()['latent_vector'].get_shape()[1]\",\n \"def getlen(self):\\n if self.onlydiag():\\n return self.lendiag()\\n else:\\n return len(self)\",\n \"def getNumCols(self):\\n return self.__cols\",\n \"def GetGlobalNumCols(self):\\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)\",\n \"def fn(i, j):\\n if i < 0 or j < 0 or matrix[i][j] == \\\"0\\\": return 0\\n return 1 + min(fn(i-1, j-1), fn(i-1, j), fn(i, j-1))\",\n \"def columns(self) -> int:\\n return self.__squares[0].__len__()\",\n \"def ncolumns(self):\\n return self.__ncols\",\n \"def get_local_nnz(self):\\n return _hypre.HypreParMatrix_get_local_nnz(self)\",\n \"def num_cols(self):\\n return len(self.rows[0])\",\n \"def innulo(self):\\n for i in range(self.n):\\n if not comozero(self[i]):\\n return i\\n return None\",\n \"def dim(self):\\n if '_dim' in self.__dict__:\\n return self._dim\\n\\n if len(self._Vrepresentation)==0:\\n self._dim = -1\\n return self._dim\\n\\n origin = vector(self._Vrepresentation[0])\\n v_list = [ vector(v)-origin for v in self._Vrepresentation ]\\n self._dim = matrix(v_list).rank()\\n return self._dim\",\n \"def __len__(self):\\n return len(np.where(np.logical_not(self.data.mask))[0])\",\n \"def __len__(self):\\n return np.size(self.A,0)\",\n \"def get_cells(self):\\r\\n return \\\\\\r\\n (self.nx-1 if self.nx>1 else 1)* \\\\\\r\\n (self.ny-1 if self.ny>1 else 1)* \\\\\\r\\n (self.nz-1 if self.nz>1 else 1)\",\n \"def GetNumColumns(self):\\n return len(self.columns)\",\n \"def getacolslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def getNbColumns(self):\\n return self.data.shape[0]\",\n \"def ncolumns(self):\\n return len(self.__column_list)\",\n \"def _non_zero_columns_search(array):\\n col_num = array.shape[1]\\n non_zero_col = CArray([], dtype=int)\\n for c in range(col_num):\\n col = array[:, c]\\n if col.any() == True:\\n non_zero_col = non_zero_col.append(c)\\n\\n return non_zero_col\",\n \"def columnCount(self):\\n return abs(self.minCol) + abs(self.maxCol)\",\n \"def __len__(self):\\n return self.xyz.shape[0]\",\n \"def __len__(self):\\n return self.xyz.shape[0]\",\n \"def columnCount( self ):\\n if not self.o_data is None:\\n if self.isItemMultiDimensional():\\n return len(self.o_data)\\n else:\\n return 1\\n else:\\n return 1\",\n \"def GetColumnCount(self):\\r\\n\\r\\n return len(self._columns)\",\n \"def ncells(self):\\n return self.izone.size\",\n \"def GetNumberCols(self):\\n return len(self.__colsKey)\",\n \"def nNy(self):\\n if self.dim < 2:\\n return None\\n return self.nCy + 1\",\n \"def no_of_columns(self): \\n return len(self.columns) + (1 if self.serialize else 0)\",\n \"def vnEz(self):\\n if self.dim < 3:\\n return None\\n return np.array(\\n [x for x in [self.nNx, self.nNy, self.nCz] if x is not None],\\n dtype=int\\n )\",\n \"def noOfElem(classObj, index):\\r\\n return len(classObj.dataSet[:, index])\",\n \"def nvar(self):\\n return self.h.shape[0]\",\n \"def TzscorrCols(Xn):\\n C_X = T.dot(Xn.T, Xn)-T.eye(Xn.shape[1])\\n return 0.5*T.log((1+C_X)/(1-C_X))\",\n \"def constraint_col_coef(n_col, n_row):\\n\\n all_rows = []\\n for i in range(n_col):\\n matrix_values = np.zeros((n_row, n_col), dtype=int)\\n col_offer = np.ones(n_row, dtype=int)\\n matrix_values[:, i] = col_offer\\n all_rows.append(matrix_values.flatten())\\n\\n cols_constraints = np.stack(all_rows)\\n\\n return cols_constraints\",\n \"def getNoOfCols(self):\\n return _patchExtractor.patchExtractor_getNoOfCols(self)\",\n \"def n_cols(self):\\n\\n return len(self.plaincolumns)\",\n \"def __len__(self):\\n return self.N.shape[0]\",\n \"def ny(self):\\n return self._dim[1]\",\n \"def count_nonzero(tensor):\\n raise NotImplementedError\",\n \"def m(self):\\n\\t\\tn = 0\\n\\t\\ti = self.k0\\n\\t\\twhile 1:\\n\\t\\t\\tif i > self.j:\\n\\t\\t\\t\\treturn n\\n\\t\\t\\tif not self.cons(i):\\n\\t\\t\\t\\tbreak\\n\\t\\t\\ti = i + 1\\n\\t\\ti = i + 1\\n\\t\\twhile 1:\\n\\t\\t\\twhile 1:\\n\\t\\t\\t\\tif i > self.j:\\n\\t\\t\\t\\t\\treturn n\\n\\t\\t\\t\\tif self.cons(i):\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\ti = i + 1\\n\\t\\t\\ti = i + 1\\n\\t\\t\\tn = n + 1\\n\\t\\t\\twhile 1:\\n\\t\\t\\t\\tif i > self.j:\\n\\t\\t\\t\\t\\treturn n\\n\\t\\t\\t\\tif not self.cons(i):\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\ti = i + 1\\n\\t\\t\\ti = i + 1\",\n \"def columns_count(self):\\n if self.value.count != 0:\\n return len(self.value[0])\\n else:\\n return 0\",\n \"def _CalculateColumn(self, record):\\n if not record:\\n return 0\\n if isinstance(record, Table):\\n add_width = len(record.separator)\\n if record.skip_empty:\\n if not any(v for _, v in record):\\n return 0\\n ret = max(len(k) for k, v in record if v) + add_width\\n ret = max(ret, 2 + max(self._CalculateColumn(v) for _, v in record))\\n return min(ret, self.MAX_MAP_WIDTH)\\n elif isinstance(record, Lines):\\n return max(self._CalculateColumn(l) for l in record)\\n else:\\n return 0\",\n \"def first_zombie_col(self, row_num):\\n row = self.board[row_num]\\n for col_num, square in enumerate(row):\\n if any(self.is_zombie([row_num, col_num])):\\n return col_num\",\n \"def numel(self):\\n return self.t.size\",\n \"def get_num_columns(table):\\n\\n\\treturn max((len(row) for row in table))\",\n \"def num_cells_down(self):\\n if self.dim == 0:\\n return None\\n if hasattr(self, '__num_cells_down__'):\\n return self.__num_cells_down__\\n if self.lower_index is None:\\n return 0\\n raise ValueError('Cannot infer the number of cells in the cochain below.')\",\n \"def get_num_cols(self):\\n return self._num_cols\",\n \"def getaslicenumnz(self,accmode_,first_,last_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n res,resargs = self.__obj.getaslicenumnz64(accmode_,first_,last_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\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 find_empty_col(slots):\\n index = 0\\n for i in list(zip(*list(slots.values())[::])):\\n if sum([1 for j in list(i) if j]) == 0:\\n return index\\n index += 1\\n return 6\",\n \"def get_location_count(self):\\n return len(self.matrix)\",\n \"def is_column_unique(self, i: int) -> bool:\\n #print(\\\"enter bartpy/bartpy/data.py CovariateMatrix is_column_unique\\\")\\n \\n if self._unique_columns[i] is None:\\n self._unique_columns[i] = len(np.unique(self.get_column(i))) == self._n_obsv\\n output = self._unique_columns[i]\\n #print(\\\"-exit bartpy/bartpy/data.py CovariateMatrix is_column_unique\\\")\\n return output\",\n \"def n_elem(self) -> u.m**-3:\\n return self._n_elem.to(u.m**-3)\",\n \"def columns(self):\\n try:\\n return self._data.shape[1] or 1\\n except (AttributeError, IndexError):\\n return 1\",\n \"def Nx(self):\\n return self.shape[-1]\",\n \"def nrows(self):\\n if self.ncolumns() == 0:\\n return 0\\n nrows = self.table_column(0).nrows()\\n for i in range(1, self.ncolumns()):\\n nrows = min(self.table_column(i).nrows(), nrows)\\n return nrows\",\n \"def rank(self):\\n return self.matrix().rank()\",\n \"def get_local_true_nnz(self):\\n return _hypre.HypreParMatrix_get_local_true_nnz(self)\",\n \"def count_neighbor_mines(self, i, j):\\n n_neighbor_mines = -1\\n if not self.mines[i, j]:\\n n_neighbor_mines = np.count_nonzero(\\n self.mines[(i-1 if i > 0 else 0):i+2, (j-1 if j > 0 else 0):j+2])\\n return n_neighbor_mines\",\n \"def cols(self) -> int:\\n if self.is_empty():\\n raise ValueError(\\\"Empty DataView contains no columns\\\")\\n\\n return len(self[0])\",\n \"def z_operator_matrix(self):\\n n, r_1, r_2, k = self.n, self.r_1, self.r_2, self.k\\n\\n # Use the row vector [ A2^T 0 I ], which commutes with the check matrix.\\n check_mat = np.zeros((k, n), dtype='int')\\n check_mat[:, 0:r_1] = np.transpose(self.parity_check_c1[:, (r_1 + r_2):n])\\n check_mat[:, (r_1 + r_2):n] = np.identity(k)\\n return check_mat\",\n \"def ncol(arr):\\n assert isinstance(arr, ndarray)\\n assert len(arr.shape) == 2\\n return arr.shape[1]\",\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]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7470606","0.7418583","0.72705734","0.6671628","0.65264225","0.64747626","0.6341131","0.62474984","0.62369037","0.6191386","0.61299306","0.60664266","0.60327625","0.60295826","0.59809643","0.58457065","0.5787058","0.57626253","0.567574","0.56754756","0.56733835","0.5656158","0.5651212","0.56307113","0.56154794","0.55964875","0.5584938","0.5584938","0.5584938","0.5584938","0.5565058","0.55512744","0.55204827","0.55203605","0.5507856","0.55006063","0.55006063","0.54920685","0.54727054","0.54288447","0.54270756","0.5420866","0.5416743","0.5404818","0.5401382","0.5397451","0.53858614","0.5375532","0.53681713","0.5357479","0.5352358","0.5313637","0.53019243","0.52973217","0.52918166","0.52849776","0.5282649","0.52823985","0.52735347","0.52735347","0.52617085","0.5253219","0.52520937","0.5248461","0.52465534","0.52328014","0.52319974","0.5217612","0.5210491","0.520851","0.52058536","0.51994646","0.51974213","0.5196526","0.519181","0.51810014","0.51763153","0.51737815","0.5161067","0.51497847","0.51487494","0.51457816","0.51379114","0.51353633","0.5128455","0.5109519","0.5100279","0.50951326","0.50948834","0.5088874","0.508745","0.507415","0.50733256","0.50705934","0.5068537","0.50677776","0.5057339","0.50554585","0.5053294","0.5052422"],"string":"[\n \"0.7470606\",\n \"0.7418583\",\n \"0.72705734\",\n \"0.6671628\",\n \"0.65264225\",\n \"0.64747626\",\n \"0.6341131\",\n \"0.62474984\",\n \"0.62369037\",\n \"0.6191386\",\n \"0.61299306\",\n \"0.60664266\",\n \"0.60327625\",\n \"0.60295826\",\n \"0.59809643\",\n \"0.58457065\",\n \"0.5787058\",\n \"0.57626253\",\n \"0.567574\",\n \"0.56754756\",\n \"0.56733835\",\n \"0.5656158\",\n \"0.5651212\",\n \"0.56307113\",\n \"0.56154794\",\n \"0.55964875\",\n \"0.5584938\",\n \"0.5584938\",\n \"0.5584938\",\n \"0.5584938\",\n \"0.5565058\",\n \"0.55512744\",\n \"0.55204827\",\n \"0.55203605\",\n \"0.5507856\",\n \"0.55006063\",\n \"0.55006063\",\n \"0.54920685\",\n \"0.54727054\",\n \"0.54288447\",\n \"0.54270756\",\n \"0.5420866\",\n \"0.5416743\",\n \"0.5404818\",\n \"0.5401382\",\n \"0.5397451\",\n \"0.53858614\",\n \"0.5375532\",\n \"0.53681713\",\n \"0.5357479\",\n \"0.5352358\",\n \"0.5313637\",\n \"0.53019243\",\n \"0.52973217\",\n \"0.52918166\",\n \"0.52849776\",\n \"0.5282649\",\n \"0.52823985\",\n \"0.52735347\",\n \"0.52735347\",\n \"0.52617085\",\n \"0.5253219\",\n \"0.52520937\",\n \"0.5248461\",\n \"0.52465534\",\n \"0.52328014\",\n \"0.52319974\",\n \"0.5217612\",\n \"0.5210491\",\n \"0.520851\",\n \"0.52058536\",\n \"0.51994646\",\n \"0.51974213\",\n \"0.5196526\",\n \"0.519181\",\n \"0.51810014\",\n \"0.51763153\",\n \"0.51737815\",\n \"0.5161067\",\n \"0.51497847\",\n \"0.51487494\",\n \"0.51457816\",\n \"0.51379114\",\n \"0.51353633\",\n \"0.5128455\",\n \"0.5109519\",\n \"0.5100279\",\n \"0.50951326\",\n \"0.50948834\",\n \"0.5088874\",\n \"0.508745\",\n \"0.507415\",\n \"0.50733256\",\n \"0.50705934\",\n \"0.5068537\",\n \"0.50677776\",\n \"0.5057339\",\n \"0.50554585\",\n \"0.5053294\",\n \"0.5052422\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70268023"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":11779,"cells":{"query":{"kind":"string","value":"Obtains one row of the linear constraint matrix. getarow(self,i_,subi_,vali_)"},"document":{"kind":"string","value":"def getarow(self,i_,subi_,vali_):\n nzi_ = ctypes.c_int32()\n _subi_minlength = self.getarownumnz((i_))\n if self.getarownumnz((i_)) > 0 and subi_ is not None and len(subi_) != self.getarownumnz((i_)):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),self.getarownumnz((i_))))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\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 _vali_minlength = self.getarownumnz((i_))\n if self.getarownumnz((i_)) > 0 and vali_ is not None and len(vali_) != self.getarownumnz((i_)):\n raise ValueError(\"Array argument vali is not long enough: Is %d, expected %d\" % (len(vali_),self.getarownumnz((i_))))\n if isinstance(vali_,numpy.ndarray) and not vali_.flags.writeable:\n raise ValueError(\"Argument vali must be writable\")\n if vali_ is None:\n raise ValueError(\"Argument vali may not be None\")\n if isinstance(vali_, numpy.ndarray) and vali_.dtype is numpy.dtype(numpy.float64) and vali_.flags.contiguous:\n _vali_copyarray = False\n _vali_tmp = ctypes.cast(vali_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif vali_ is not None:\n _vali_copyarray = True\n _vali_np_tmp = numpy.zeros(len(vali_),numpy.dtype(numpy.float64))\n _vali_np_tmp[:] = vali_\n assert _vali_np_tmp.flags.contiguous\n _vali_tmp = ctypes.cast(_vali_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _vali_copyarray = False\n _vali_tmp = None\n \n res = __library__.MSK_XX_getarow(self.__nativep,i_,ctypes.byref(nzi_),_subi_tmp,_vali_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzi_ = nzi_.value\n _nzi_return_value = nzi_\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _vali_copyarray:\n vali_[:] = _vali_np_tmp\n return (_nzi_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 getarow(self,i_,subi,vali): # 3\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_) != self.getarownumnz((i_)):\n raise ValueError(\"Array argument subi has wrong length\")\n if vali is None: raise TypeError(\"Invalid type for argument vali\")\n _copyback_vali = False\n if vali is None:\n vali_ = None\n else:\n try:\n vali_ = memoryview(vali)\n except TypeError:\n try:\n _tmparr_vali = array.array(\"d\",vali)\n except TypeError:\n raise TypeError(\"Argument vali has wrong type\")\n else:\n vali_ = memoryview(_tmparr_vali)\n _copyback_vali = True\n else:\n if vali_.format != \"d\":\n vali_ = memoryview(array.array(\"d\",vali))\n _copyback_vali = True\n if vali_ is not None and len(vali_) != self.getarownumnz((i_)):\n raise ValueError(\"Array argument vali has wrong length\")\n res,resargs = self.__obj.getarow(i_,subi_,vali_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzi_return_value = resargs\n if _copyback_vali:\n vali[:] = _tmparr_vali\n if _copyback_subi:\n subi[:] = _tmparr_subi\n return _nzi_return_value","def getrow(self, i):\n new = lil_matrix((1, self.shape[1]), dtype=self.dtype)\n new.rows[0] = self.rows[i][:]\n new.data[0] = self.data[i][:]\n return new","def get_row(A: Matrix, i: int) -> Vector:\n return A[i]","def get_row(A: Matrix, i: int) -> Vector:\n return A[i]","def get_row(A: Matrix, i: int) -> Vector:\n return A[i]","def get_row(A: Matrix, i: int) -> Vector:\n return A[i] # A[i] is already the ith row","def getrowview(self, i):\n new = lil_matrix((1, self.shape[1]), dtype=self.dtype)\n new.rows[0] = self.rows[i]\n new.data[0] = self.data[i]\n return new","def est_row_2_base_row(i):\n row = np.zeros(n)\n for j in range(k):\n row[est_inx_2_base_inx[j]] = est_T[i,j]\n return row","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 getRow(self, i):\n return self.data[:,i]","def row (self, i):\n return Vector(self._m[i])","def __getslice__(self,i,j):\n nv=_Matr()\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\n nv.__maj()\n return nv","def putarow(self,i_,subi_,vali_):\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(\"Inconsistent length of array vali\")\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 vali_ is None:\n raise ValueError(\"Argument vali cannot be None\")\n if vali_ is None:\n raise ValueError(\"Argument vali may not be None\")\n if isinstance(vali_, numpy.ndarray) and vali_.dtype is numpy.dtype(numpy.float64) and vali_.flags.contiguous:\n _vali_copyarray = False\n _vali_tmp = ctypes.cast(vali_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif vali_ is not None:\n _vali_copyarray = True\n _vali_np_tmp = numpy.zeros(len(vali_),numpy.dtype(numpy.float64))\n _vali_np_tmp[:] = vali_\n assert _vali_np_tmp.flags.contiguous\n _vali_tmp = ctypes.cast(_vali_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _vali_copyarray = False\n _vali_tmp = None\n \n res = __library__.MSK_XX_putarow(self.__nativep,i_,nzi_,_subi_tmp,_vali_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def row(self, i):\n return Vector(self.data[i], False)","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(\"Inconsistent length of array vali\")\n if nzi_ is None: nzi_ = 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 vali is None: raise TypeError(\"Invalid type for argument vali\")\n if vali is None:\n vali_ = None\n else:\n try:\n vali_ = memoryview(vali)\n except TypeError:\n try:\n _tmparr_vali = array.array(\"d\",vali)\n except TypeError:\n raise TypeError(\"Argument vali has wrong type\")\n else:\n vali_ = memoryview(_tmparr_vali)\n \n else:\n if vali_.format != \"d\":\n vali_ = memoryview(array.array(\"d\",vali))\n \n res = self.__obj.putarow(i_,nzi_,subi_,vali_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def adj(self):\n\t\tres = SquareMatrix(self._rows)\n\t\tfor i in range(self._rows):\n\t\t\tfor j in range(self._rows):\n\t\t\t\tres[i][j] = ((-1) ** (i + j)) * self.minor(j, i)\n\t\treturn res","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 __getslice__(self,i,j):\n nv=_Matr()\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\n nv._Matr__maj()\n return nv","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 row(self, index):\n return self.matrix_list[index - 1]","def get_weight_row(self, i):\n return self.weights[i]","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 getarownumnz(self,i_): # 3\n res,resargs = self.__obj.getarownumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzi_return_value = resargs\n return _nzi_return_value","def get_stain_matrix(I):","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 get_element(self,mat,row,column):\n result = mat[row-1][column-1]\n self.element = result\n return self.element","def get_elem (A, i, j):\n\treturn A[j][i]","def rule(model, i, j):\n return 1, model.T0_end[i, j] + (1-model.A[i, j]), None","def getarowslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","def right(self, A, i):\n if 2*i + 1 +1 < len(A):\n return 2*i + 1 +1\n return None","def right(self, A, i):\n if 2*i + 1 +1 < len(A):\n return 2*i + 1 +1\n return None","def getacol(self,j_,subj,valj): # 3\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_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument subj has wrong length\")\n if valj is None: raise TypeError(\"Invalid type for argument valj\")\n _copyback_valj = False\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 _copyback_valj = True\n else:\n if valj_.format != \"d\":\n valj_ = memoryview(array.array(\"d\",valj))\n _copyback_valj = True\n if valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument valj has wrong length\")\n res,resargs = self.__obj.getacol(j_,subj_,valj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n if _copyback_valj:\n valj[:] = _tmparr_valj\n if _copyback_subj:\n subj[:] = _tmparr_subj\n return _nzj_return_value","def getacol(self,j_,subj_,valj_):\n nzj_ = ctypes.c_int32()\n _subj_minlength = self.getacolnumnz((j_))\n if self.getacolnumnz((j_)) > 0 and subj_ is not None and len(subj_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),self.getacolnumnz((j_))))\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 _valj_minlength = self.getacolnumnz((j_))\n if self.getacolnumnz((j_)) > 0 and valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument valj is not long enough: Is %d, expected %d\" % (len(valj_),self.getacolnumnz((j_))))\n if isinstance(valj_,numpy.ndarray) and not valj_.flags.writeable:\n raise ValueError(\"Argument valj must be writable\")\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_getacol(self.__nativep,j_,ctypes.byref(nzj_),_subj_tmp,_valj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valj_copyarray:\n valj_[:] = _valj_np_tmp\n return (_nzj_return_value)","def row_reduce(self):\n res = self.row_echelon()\n for i in range(1, res.m):\n for j in range(res.n):\n if res[i, j] == 1:\n for k in range(i):\n constant = res[k, j]\n res.data[k] = [elem_k - elem_i * constant\n for elem_i, elem_k in\n zip(res.data[i], res.data[k])]\n break\n return res","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 get_value(self, col, i) :\n\n if col not in self.cols :\n raise Exception('Column %s not in data' % col)\n if len(self.rows) <= i :\n raise Exception('Table has fewer than %d rows' % i)\n return self.rows[i][self.col_indices[col]]","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 getarowslicetrip(self,first_,last_,subi,subj,val): # 3\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 entry(self, i, j):\n return self.data[self.columns * (i - 1) + j - 1]","def getbarablocktriplet(self,subi_,subj_,subk_,subl_,valijkl_):\n maxnum_ = self.getnumbarablocktriplets()\n num_ = ctypes.c_int64()\n _subi_minlength = (maxnum_)\n if (maxnum_) > 0 and subi_ is not None and len(subi_) != (maxnum_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnum_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\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 = (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 _valijkl_minlength = (maxnum_)\n if (maxnum_) > 0 and valijkl_ is not None and len(valijkl_) != (maxnum_):\n raise ValueError(\"Array argument valijkl is not long enough: Is %d, expected %d\" % (len(valijkl_),(maxnum_)))\n if isinstance(valijkl_,numpy.ndarray) and not valijkl_.flags.writeable:\n raise ValueError(\"Argument valijkl must be writable\")\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_getbarablocktriplet(self.__nativep,maxnum_,ctypes.byref(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 num_ = num_.value\n _num_return_value = num_\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\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 _valijkl_copyarray:\n valijkl_[:] = _valijkl_np_tmp\n return (_num_return_value)","def getacolnumnz(self,i_):\n nzj_ = ctypes.c_int32()\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n return (_nzj_return_value)","def sudoku_getline(A, i):\r\n return A[i]","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 get_row( self, rowIndex ):\n return self.data[rowIndex,:]","def row(self, index: int) -> List[int]:\n return self.matrix[index - 1]","def getacolnumnz(self,i_): # 3\n res,resargs = self.__obj.getacolnumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n return _nzj_return_value","def generate_ineq_constraints_matrix(n_col, n_row):\n offers_constraints = constraint_col_coef(n_col, n_row)\n customers_constraints = constraints_row_coef(n_col, n_row)\n offer_per_cust_constraints = constraints_max_offer_per_cust(n_col, n_row)\n mat_a = np.append(offers_constraints, customers_constraints, axis=0)\n mat_a = np.append(mat_a, offer_per_cust_constraints, axis=0)\n return mat_a","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 row_echelon(self):\n # TODO: This can be refactored for better efficiency\n if all([all([self[i, j] == 0 for j in range(self.n)])\n for i in range(self.m)]):\n return Matrix.makeZero(self.m, self.n)\n res = deepcopy(self)\n i, j = 0, 0\n while i < res.m and j < res.n:\n # Use R2 to make pivot non-zero\n if res[i, j] == 0:\n found_non_zero = False\n for k in range(i, res.m):\n if res[k, j] != 0:\n found_non_zero = True\n break\n if not found_non_zero:\n j += 1\n continue\n res.data[i], res.data[k] = res.data[k], res.data[i]\n # Use R3 to make pivot one\n if res[i, j] != 1:\n res.data[i] = [elem / res[i, j] for elem in res.data[i]]\n # Use R1 to eliminate entries below the pivot\n for k in range(i + 1, res.m):\n if res[k, j] != 0:\n constant = res[k, j] / res[i, j]\n res.data[k] = [elem_k - elem_i * constant\n for elem_i, elem_k in\n zip(res.data[i], res.data[k])]\n i, j = i + 1, j + 1\n return res","def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","def GetRow(a: numpy.ndarray, index: int) -> numpy.ndarray:\n return a[index, numpy.newaxis]","def ROW(x):\n return (x >> 3)","def ROW(x):\n return (x >> 3)","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 row(self):\n return self.reshape((1, self.size))","def lap_mat(self):","def get_row(A,r=0):\n\tA = zip(*A)\n\treturn list(A[r])","def jmat(ind: int):\n return _jm[ind - 1]","def matI(a):\n shape=matShape(a)\n if shape[0]!=shape[1]: raise ValueError\n n=shape[0]\n ret=matZeros((n,n*2))\n for i in range(n):\n for j in range(n):\n matSet(ret,i,j,matGet(a,i,j))\n for i in range(n):\n matSet(ret,i,i+n,1)\n for row in range(n):\n rm=row\n ap=abs(matGet(ret,rm,row))\n for rint in range(row+1,n):\n p=abs(matGet(ret,rint,row))\n if ap<p:\n ap=p\n rm=rint\n if 0.000000001 > ap:\n return matCopy(a) # Not invertible\n di=matGet(ret,rm,row)\n if rm!=row:\n for i in range(n*2):\n t=matGet(ret,rm,i)\n matSet(ret,rm,i,matGet(ret,row,i))\n matSet(ret,row,i,t)\n idi=1.0/di\n for rint in range(row+1,n):\n f=idi*matGet(ret,rint,row)\n if f!=0:\n for co in range(row,n*2):\n matSet(ret,rint,co,matGet(ret,rint,co)-f*matGet(ret,row,co))\n row=n-1\n while row>=0:\n ic=1.0/matGet(ret,row,row)\n for rint in range(row):\n icx=ic*matGet(ret,rint,row)\n if icx!=0:\n for co in range(row, n*2):\n matSet(ret,rint,co,matGet(ret,rint,co)-icx*matGet(ret,row,co))\n matSet(ret,row,row,ic*matGet(ret,row,row))\n for co in range(n,n*2):\n matSet(ret,row,co,ic*matGet(ret,row,co))\n row-=1\n return matPart(ret,0,n,n,n*2)","def get_diagonal_subtensor_view(x, i0, i1):\r\n # We have to cast i0 and i0 to int because python 2.4 (and maybe later)\r\n # do not support indexing with 0-dim, 'int*' ndarrays.\r\n i0 = int(i0)\r\n i1 = int(i1)\r\n if x.shape[i0] < x.shape[i1]:\r\n raise NotImplementedError('is this allowed?')\r\n idx = [slice(None)] * x.ndim\r\n idx[i0] = slice(x.shape[i1] - 1, None, None)\r\n xview = x.__getitem__(tuple(idx))\r\n strides = list(xview.strides)\r\n strides[i1] -= strides[i0]\r\n xview.strides = strides\r\n return xview","def row_echelon(self):\n # TODO: This can be refactored for better efficiency\n if all([all([self[i, j] == 0 for j in range(self.n)])\n for i in range(self.m)]):\n return Matrix.makeZero(self.m, self.n)\n res = deepcopy(self)\n i, j = 0, 0\n while i < res.m and j < res.n:\n # Use R2 to make pivot non-zero\n if res[i, j] == 0:\n found_non_zero = False\n for k in range(i, res.m):\n if res[k, j] != 0:\n found_non_zero = True\n break\n if not found_non_zero:\n j += 1\n continue\n res.data[i], res.data[k] = res.data[k], res.data[i]\n # Use R3 to make pivot one\n if res[i, j] != 1:\n if any([elem % res[i, j] != 0 for elem in res.data[i]]):\n raise ValueError\n res.data[i] = [elem / res[i, j] for elem in res.data[i]]\n # Use R1 to eliminate entries below the pivot\n for k in range(i + 1, res.m):\n if res[k, j] != 0:\n constant = res[k, j] / res[i, j]\n res.data[k] = [elem_k - elem_i * constant\n for elem_i, elem_k in\n zip(res.data[i], res.data[k])]\n i, j = i + 1, j + 1\n return res","def __getitem__(self, index_tuple):\n assert len(index_tuple) == 2, \"Invalid number of array subscripts.\"\n row, col = index_tuple\n assert 0 <= row < self.num_rows() and 0 <= col < self.num_cols(), \\\n \"Array subscript out of range.\"\n array_1d = self.rows[row]\n return array_1d[col]","def matGet(mat, r, c):\n return mat[r][c]","def getarownumnz(self,i_):\n nzi_ = ctypes.c_int32()\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzi_ = nzi_.value\n _nzi_return_value = nzi_\n return (_nzi_return_value)","def constraints_row_coef(n_row, n_col):\n identities = [np.identity(n_col) for _ in range(n_row)]\n cust_constraints = np.concatenate(identities, axis=1)\n permutation = np.transpose(np.reshape(np.arange(n_col * n_row), (n_col, n_row))).flatten()\n idx = np.empty_like(permutation)\n idx[permutation] = np.arange(len(permutation))\n cust_constraints = cust_constraints[:, idx]\n\n return cust_constraints","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 col(self, i):\n return Vector([row[i] for row in self.data])","def get_column(A: Matrix, j: int) -> Vector:\n return [A_i[j] # jth element of row A_i\n for A_i in A] # for each row A_i","def pop(self, i):\n row = self._table.pop(i); return row[self._j]","def _jplus(j, *, dtype=None):\n m = np.arange(j, -j - 1, -1, dtype=complex)\n data = np.sqrt(j * (j + 1) - m * (m + 1))[1:]\n return _data.diag[dtype](data, 1)","def right(self):\n n = len(self)-1\n fr = np.zeros(n+1)\n for ii in range(n+1):\n for jj in range(n+1-ii):\n for kk in range(n+1-ii-jj):\n fr[ii+kk] += self[ii,jj,kk]\n return fr","def lambda_i(self, i):\n return self._eig_val[i]","def matrix_element(self, op=None):\n ans = 0.0\n for _ in self.term_visitor(op=op):\n ans += self.root.matrix_element()\n return ans","def __call__(self, i, j):\n return _table.Table___call__(self, i, j)","def Matrix(row, col, val=0):\n\treturn [[val for _j in xrange(col)] for _i in xrange(row)]","def getItem(self, i, j):\n if i < 0:\n raise IndexError('Row index must be nonnegative.')\n if j < 0:\n raise IndexError('Column index must be nonnegative.')\n\n return self.__m[i - 1][j - 1]","def restriction(n, I, axis=0):\n k = len(I)\n times1d = lambda x: x[I]\n trans1d = lambda x: jnp.zeros((n,), dtype=x.dtype).at[I].set(x)\n\n def times(x):\n if x.ndim == 1:\n return times1d(x)\n if x.ndim == 2:\n if axis == 0:\n # we apply column wise\n return x[I, :]\n # we apply row-wise\n return x[:, I]\n # general case\n return jnp.apply_along_axis(times1d, axis, x)\n\n def trans(x):\n if x.ndim == 1:\n return trans1d(x)\n # general case\n return jnp.apply_along_axis(trans1d, axis, x)\n\n return Operator(times=times, trans=trans, shape=(k,n))","def red_matrix(A, i, j):\n row = [0, 1, 2]\n col = [0, 1, 2]\n row.remove(i-1)\n col.remove(j-1)\n return A[row, col]","def sub_matrix(self, line, column):\n\n mat = []\n for i, row in enumerate(self.coefficients):\n if i != line:\n new_row = []\n for j, value in enumerate(row):\n if j != column:\n new_row.append(value)\n mat.append(new_row)\n\n return Matrix(mat)","def aind(x):\n\treturn tuple(x.T)","def trace(self):\n if self.rows != self.columns:\n raise ValueError(\"Trace undefined for non-square matrices\")\n\n return reduce(lambda x,y: x + y, [self.data[self.columns * i + i] for i in range(self.rows)])","def getRow(self, idx):\r\n m=self.mlist\r\n if idx==0: return _vec4(m[0], m[1], m[2], m[3])\r\n elif idx==1: return _vec4(m[4], m[5], m[6], m[7])\r\n elif idx==2: return _vec4(m[8], m[9], m[10], m[11])\r\n elif idx==3: return _vec4(m[12], m[13], m[14], m[15])\r\n else:\r\n raise IndexError,\"index out of range\"","def ij(ij, pol, ant) :\n s.ij(pol, ij, ant)","def _getMatrixRow(self):\n item = self._item()\n if item is not None:\n matrix = item.getMatrix()\n return qt.QVector3D(*matrix[self._index, :])\n else:\n return None","def constraint_col_coef(n_col, n_row):\n\n all_rows = []\n for i in range(n_col):\n matrix_values = np.zeros((n_row, n_col), dtype=int)\n col_offer = np.ones(n_row, dtype=int)\n matrix_values[:, i] = col_offer\n all_rows.append(matrix_values.flatten())\n\n cols_constraints = np.stack(all_rows)\n\n return cols_constraints","def get_main_array_right(self):\n return self.bitcell_array_inst.rx()","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 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 get_cell(self, i, j):\n return self._env[i][j]","def column (self, i):\n return Vector(tuple(zip(*self._m))[i])","def getGradient(self,j):\n i = int(self.indicator['term'][j])\n r = int(self.indicator['row'][j])\n c = int(self.indicator['col'][j])\n rv = -np.kron(self.Fstar()[i][:,[r]],self.Astar()[i][[c],:])\n return rv","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 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 getbaraidxij(self,idx_):\n i_ = ctypes.c_int32()\n j_ = ctypes.c_int32()\n res = __library__.MSK_XX_getbaraidxij(self.__nativep,idx_,ctypes.byref(i_),ctypes.byref(j_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n i_ = i_.value\n _i_return_value = i_\n j_ = j_.value\n _j_return_value = j_\n return (_i_return_value,_j_return_value)","def RowPart(self, *args):\n return _hypre.HypreParMatrix_RowPart(self, *args)","def matIxs( n ):\n rows, cols = np.indices( (n,n) )\n row = rows.flatten()\n col = cols.flatten()\n \n return map( lambda x: Vector( x[0], x[1] ), zip( col, row ) )","def c(self, i):\n value = self.b(i)\n if i == self.N:\n return value\n else:\n for j in range(i+1, self.N+1):\n value = value - self.a(i,j) * self.C[j]\n return value","def get_row(self, idx):\n return '{offset}{idx}|{field}'.format(\n offset=' ' * (self.OFFSET - 1),\n idx=str(idx + 1).rjust(2, ' '),\n field=self._get_field_row(idx),\n )","def get_l(GW_glitch,i,j):\n\t\t \n\ttemp = np.einsum('nmk,nmk->k', GW_glitch.r_outer_r[:,:,i,j,:], GW_glitch.Hij[:,:,i,j,:])\n\t\t \n\treturn temp","def get_matrix(self):\n return self._matrix[:3, :]"],"string":"[\n \"def getarow(self,i_,subi,vali): # 3\\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_) != self.getarownumnz((i_)):\\n raise ValueError(\\\"Array argument subi has wrong length\\\")\\n if vali is None: raise TypeError(\\\"Invalid type for argument vali\\\")\\n _copyback_vali = False\\n if vali is None:\\n vali_ = None\\n else:\\n try:\\n vali_ = memoryview(vali)\\n except TypeError:\\n try:\\n _tmparr_vali = array.array(\\\"d\\\",vali)\\n except TypeError:\\n raise TypeError(\\\"Argument vali has wrong type\\\")\\n else:\\n vali_ = memoryview(_tmparr_vali)\\n _copyback_vali = True\\n else:\\n if vali_.format != \\\"d\\\":\\n vali_ = memoryview(array.array(\\\"d\\\",vali))\\n _copyback_vali = True\\n if vali_ is not None and len(vali_) != self.getarownumnz((i_)):\\n raise ValueError(\\\"Array argument vali has wrong length\\\")\\n res,resargs = self.__obj.getarow(i_,subi_,vali_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzi_return_value = resargs\\n if _copyback_vali:\\n vali[:] = _tmparr_vali\\n if _copyback_subi:\\n subi[:] = _tmparr_subi\\n return _nzi_return_value\",\n \"def getrow(self, i):\\n new = lil_matrix((1, self.shape[1]), dtype=self.dtype)\\n new.rows[0] = self.rows[i][:]\\n new.data[0] = self.data[i][:]\\n return new\",\n \"def get_row(A: Matrix, i: int) -> Vector:\\n return A[i]\",\n \"def get_row(A: Matrix, i: int) -> Vector:\\n return A[i]\",\n \"def get_row(A: Matrix, i: int) -> Vector:\\n return A[i]\",\n \"def get_row(A: Matrix, i: int) -> Vector:\\n return A[i] # A[i] is already the ith row\",\n \"def getrowview(self, i):\\n new = lil_matrix((1, self.shape[1]), dtype=self.dtype)\\n new.rows[0] = self.rows[i]\\n new.data[0] = self.data[i]\\n return new\",\n \"def est_row_2_base_row(i):\\n row = np.zeros(n)\\n for j in range(k):\\n row[est_inx_2_base_inx[j]] = est_T[i,j]\\n return row\",\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 getRow(self, i):\\n return self.data[:,i]\",\n \"def row (self, i):\\n return Vector(self._m[i])\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\\n nv.__maj()\\n return nv\",\n \"def putarow(self,i_,subi_,vali_):\\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(\\\"Inconsistent length of array vali\\\")\\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 vali_ is None:\\n raise ValueError(\\\"Argument vali cannot be None\\\")\\n if vali_ is None:\\n raise ValueError(\\\"Argument vali may not be None\\\")\\n if isinstance(vali_, numpy.ndarray) and vali_.dtype is numpy.dtype(numpy.float64) and vali_.flags.contiguous:\\n _vali_copyarray = False\\n _vali_tmp = ctypes.cast(vali_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif vali_ is not None:\\n _vali_copyarray = True\\n _vali_np_tmp = numpy.zeros(len(vali_),numpy.dtype(numpy.float64))\\n _vali_np_tmp[:] = vali_\\n assert _vali_np_tmp.flags.contiguous\\n _vali_tmp = ctypes.cast(_vali_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _vali_copyarray = False\\n _vali_tmp = None\\n \\n res = __library__.MSK_XX_putarow(self.__nativep,i_,nzi_,_subi_tmp,_vali_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def row(self, i):\\n return Vector(self.data[i], False)\",\n \"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(\\\"Inconsistent length of array vali\\\")\\n if nzi_ is None: nzi_ = 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 vali is None: raise TypeError(\\\"Invalid type for argument vali\\\")\\n if vali is None:\\n vali_ = None\\n else:\\n try:\\n vali_ = memoryview(vali)\\n except TypeError:\\n try:\\n _tmparr_vali = array.array(\\\"d\\\",vali)\\n except TypeError:\\n raise TypeError(\\\"Argument vali has wrong type\\\")\\n else:\\n vali_ = memoryview(_tmparr_vali)\\n \\n else:\\n if vali_.format != \\\"d\\\":\\n vali_ = memoryview(array.array(\\\"d\\\",vali))\\n \\n res = self.__obj.putarow(i_,nzi_,subi_,vali_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def adj(self):\\n\\t\\tres = SquareMatrix(self._rows)\\n\\t\\tfor i in range(self._rows):\\n\\t\\t\\tfor j in range(self._rows):\\n\\t\\t\\t\\tres[i][j] = ((-1) ** (i + j)) * self.minor(j, i)\\n\\t\\treturn res\",\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 __getslice__(self,i,j):\\n nv=_Matr()\\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\\n nv._Matr__maj()\\n return nv\",\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 row(self, index):\\n return self.matrix_list[index - 1]\",\n \"def get_weight_row(self, i):\\n return self.weights[i]\",\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 getarownumnz(self,i_): # 3\\n res,resargs = self.__obj.getarownumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzi_return_value = resargs\\n return _nzi_return_value\",\n \"def get_stain_matrix(I):\",\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 get_element(self,mat,row,column):\\n result = mat[row-1][column-1]\\n self.element = result\\n return self.element\",\n \"def get_elem (A, i, j):\\n\\treturn A[j][i]\",\n \"def rule(model, i, j):\\n return 1, model.T0_end[i, j] + (1-model.A[i, j]), None\",\n \"def getarowslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def right(self, A, i):\\n if 2*i + 1 +1 < len(A):\\n return 2*i + 1 +1\\n return None\",\n \"def right(self, A, i):\\n if 2*i + 1 +1 < len(A):\\n return 2*i + 1 +1\\n return None\",\n \"def getacol(self,j_,subj,valj): # 3\\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_) != self.getacolnumnz((j_)):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if valj is None: raise TypeError(\\\"Invalid type for argument valj\\\")\\n _copyback_valj = False\\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 _copyback_valj = True\\n else:\\n if valj_.format != \\\"d\\\":\\n valj_ = memoryview(array.array(\\\"d\\\",valj))\\n _copyback_valj = True\\n if valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\\n raise ValueError(\\\"Array argument valj has wrong length\\\")\\n res,resargs = self.__obj.getacol(j_,subj_,valj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n if _copyback_valj:\\n valj[:] = _tmparr_valj\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n return _nzj_return_value\",\n \"def getacol(self,j_,subj_,valj_):\\n nzj_ = ctypes.c_int32()\\n _subj_minlength = self.getacolnumnz((j_))\\n if self.getacolnumnz((j_)) > 0 and subj_ is not None and len(subj_) != self.getacolnumnz((j_)):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),self.getacolnumnz((j_))))\\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 _valj_minlength = self.getacolnumnz((j_))\\n if self.getacolnumnz((j_)) > 0 and valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\\n raise ValueError(\\\"Array argument valj is not long enough: Is %d, expected %d\\\" % (len(valj_),self.getacolnumnz((j_))))\\n if isinstance(valj_,numpy.ndarray) and not valj_.flags.writeable:\\n raise ValueError(\\\"Argument valj must be writable\\\")\\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_getacol(self.__nativep,j_,ctypes.byref(nzj_),_subj_tmp,_valj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzj_ = nzj_.value\\n _nzj_return_value = nzj_\\n if _subj_copyarray:\\n subj_[:] = _subj_np_tmp\\n if _valj_copyarray:\\n valj_[:] = _valj_np_tmp\\n return (_nzj_return_value)\",\n \"def row_reduce(self):\\n res = self.row_echelon()\\n for i in range(1, res.m):\\n for j in range(res.n):\\n if res[i, j] == 1:\\n for k in range(i):\\n constant = res[k, j]\\n res.data[k] = [elem_k - elem_i * constant\\n for elem_i, elem_k in\\n zip(res.data[i], res.data[k])]\\n break\\n return res\",\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 get_value(self, col, i) :\\n\\n if col not in self.cols :\\n raise Exception('Column %s not in data' % col)\\n if len(self.rows) <= i :\\n raise Exception('Table has fewer than %d rows' % i)\\n return self.rows[i][self.col_indices[col]]\",\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 getarowslicetrip(self,first_,last_,subi,subj,val): # 3\\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 entry(self, i, j):\\n return self.data[self.columns * (i - 1) + j - 1]\",\n \"def getbarablocktriplet(self,subi_,subj_,subk_,subl_,valijkl_):\\n maxnum_ = self.getnumbarablocktriplets()\\n num_ = ctypes.c_int64()\\n _subi_minlength = (maxnum_)\\n if (maxnum_) > 0 and subi_ is not None and len(subi_) != (maxnum_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnum_)))\\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\\n raise ValueError(\\\"Argument subi must be writable\\\")\\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 = (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 _valijkl_minlength = (maxnum_)\\n if (maxnum_) > 0 and valijkl_ is not None and len(valijkl_) != (maxnum_):\\n raise ValueError(\\\"Array argument valijkl is not long enough: Is %d, expected %d\\\" % (len(valijkl_),(maxnum_)))\\n if isinstance(valijkl_,numpy.ndarray) and not valijkl_.flags.writeable:\\n raise ValueError(\\\"Argument valijkl must be writable\\\")\\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_getbarablocktriplet(self.__nativep,maxnum_,ctypes.byref(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 num_ = num_.value\\n _num_return_value = num_\\n if _subi_copyarray:\\n subi_[:] = _subi_np_tmp\\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 _valijkl_copyarray:\\n valijkl_[:] = _valijkl_np_tmp\\n return (_num_return_value)\",\n \"def getacolnumnz(self,i_):\\n nzj_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzj_ = nzj_.value\\n _nzj_return_value = nzj_\\n return (_nzj_return_value)\",\n \"def sudoku_getline(A, i):\\r\\n return A[i]\",\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 get_row( self, rowIndex ):\\n return self.data[rowIndex,:]\",\n \"def row(self, index: int) -> List[int]:\\n return self.matrix[index - 1]\",\n \"def getacolnumnz(self,i_): # 3\\n res,resargs = self.__obj.getacolnumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n return _nzj_return_value\",\n \"def generate_ineq_constraints_matrix(n_col, n_row):\\n offers_constraints = constraint_col_coef(n_col, n_row)\\n customers_constraints = constraints_row_coef(n_col, n_row)\\n offer_per_cust_constraints = constraints_max_offer_per_cust(n_col, n_row)\\n mat_a = np.append(offers_constraints, customers_constraints, axis=0)\\n mat_a = np.append(mat_a, offer_per_cust_constraints, axis=0)\\n return mat_a\",\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 row_echelon(self):\\n # TODO: This can be refactored for better efficiency\\n if all([all([self[i, j] == 0 for j in range(self.n)])\\n for i in range(self.m)]):\\n return Matrix.makeZero(self.m, self.n)\\n res = deepcopy(self)\\n i, j = 0, 0\\n while i < res.m and j < res.n:\\n # Use R2 to make pivot non-zero\\n if res[i, j] == 0:\\n found_non_zero = False\\n for k in range(i, res.m):\\n if res[k, j] != 0:\\n found_non_zero = True\\n break\\n if not found_non_zero:\\n j += 1\\n continue\\n res.data[i], res.data[k] = res.data[k], res.data[i]\\n # Use R3 to make pivot one\\n if res[i, j] != 1:\\n res.data[i] = [elem / res[i, j] for elem in res.data[i]]\\n # Use R1 to eliminate entries below the pivot\\n for k in range(i + 1, res.m):\\n if res[k, j] != 0:\\n constant = res[k, j] / res[i, j]\\n res.data[k] = [elem_k - elem_i * constant\\n for elem_i, elem_k in\\n zip(res.data[i], res.data[k])]\\n i, j = i + 1, j + 1\\n return res\",\n \"def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def GetRow(a: numpy.ndarray, index: int) -> numpy.ndarray:\\n return a[index, numpy.newaxis]\",\n \"def ROW(x):\\n return (x >> 3)\",\n \"def ROW(x):\\n return (x >> 3)\",\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 row(self):\\n return self.reshape((1, self.size))\",\n \"def lap_mat(self):\",\n \"def get_row(A,r=0):\\n\\tA = zip(*A)\\n\\treturn list(A[r])\",\n \"def jmat(ind: int):\\n return _jm[ind - 1]\",\n \"def matI(a):\\n shape=matShape(a)\\n if shape[0]!=shape[1]: raise ValueError\\n n=shape[0]\\n ret=matZeros((n,n*2))\\n for i in range(n):\\n for j in range(n):\\n matSet(ret,i,j,matGet(a,i,j))\\n for i in range(n):\\n matSet(ret,i,i+n,1)\\n for row in range(n):\\n rm=row\\n ap=abs(matGet(ret,rm,row))\\n for rint in range(row+1,n):\\n p=abs(matGet(ret,rint,row))\\n if ap<p:\\n ap=p\\n rm=rint\\n if 0.000000001 > ap:\\n return matCopy(a) # Not invertible\\n di=matGet(ret,rm,row)\\n if rm!=row:\\n for i in range(n*2):\\n t=matGet(ret,rm,i)\\n matSet(ret,rm,i,matGet(ret,row,i))\\n matSet(ret,row,i,t)\\n idi=1.0/di\\n for rint in range(row+1,n):\\n f=idi*matGet(ret,rint,row)\\n if f!=0:\\n for co in range(row,n*2):\\n matSet(ret,rint,co,matGet(ret,rint,co)-f*matGet(ret,row,co))\\n row=n-1\\n while row>=0:\\n ic=1.0/matGet(ret,row,row)\\n for rint in range(row):\\n icx=ic*matGet(ret,rint,row)\\n if icx!=0:\\n for co in range(row, n*2):\\n matSet(ret,rint,co,matGet(ret,rint,co)-icx*matGet(ret,row,co))\\n matSet(ret,row,row,ic*matGet(ret,row,row))\\n for co in range(n,n*2):\\n matSet(ret,row,co,ic*matGet(ret,row,co))\\n row-=1\\n return matPart(ret,0,n,n,n*2)\",\n \"def get_diagonal_subtensor_view(x, i0, i1):\\r\\n # We have to cast i0 and i0 to int because python 2.4 (and maybe later)\\r\\n # do not support indexing with 0-dim, 'int*' ndarrays.\\r\\n i0 = int(i0)\\r\\n i1 = int(i1)\\r\\n if x.shape[i0] < x.shape[i1]:\\r\\n raise NotImplementedError('is this allowed?')\\r\\n idx = [slice(None)] * x.ndim\\r\\n idx[i0] = slice(x.shape[i1] - 1, None, None)\\r\\n xview = x.__getitem__(tuple(idx))\\r\\n strides = list(xview.strides)\\r\\n strides[i1] -= strides[i0]\\r\\n xview.strides = strides\\r\\n return xview\",\n \"def row_echelon(self):\\n # TODO: This can be refactored for better efficiency\\n if all([all([self[i, j] == 0 for j in range(self.n)])\\n for i in range(self.m)]):\\n return Matrix.makeZero(self.m, self.n)\\n res = deepcopy(self)\\n i, j = 0, 0\\n while i < res.m and j < res.n:\\n # Use R2 to make pivot non-zero\\n if res[i, j] == 0:\\n found_non_zero = False\\n for k in range(i, res.m):\\n if res[k, j] != 0:\\n found_non_zero = True\\n break\\n if not found_non_zero:\\n j += 1\\n continue\\n res.data[i], res.data[k] = res.data[k], res.data[i]\\n # Use R3 to make pivot one\\n if res[i, j] != 1:\\n if any([elem % res[i, j] != 0 for elem in res.data[i]]):\\n raise ValueError\\n res.data[i] = [elem / res[i, j] for elem in res.data[i]]\\n # Use R1 to eliminate entries below the pivot\\n for k in range(i + 1, res.m):\\n if res[k, j] != 0:\\n constant = res[k, j] / res[i, j]\\n res.data[k] = [elem_k - elem_i * constant\\n for elem_i, elem_k in\\n zip(res.data[i], res.data[k])]\\n i, j = i + 1, j + 1\\n return res\",\n \"def __getitem__(self, index_tuple):\\n assert len(index_tuple) == 2, \\\"Invalid number of array subscripts.\\\"\\n row, col = index_tuple\\n assert 0 <= row < self.num_rows() and 0 <= col < self.num_cols(), \\\\\\n \\\"Array subscript out of range.\\\"\\n array_1d = self.rows[row]\\n return array_1d[col]\",\n \"def matGet(mat, r, c):\\n return mat[r][c]\",\n \"def getarownumnz(self,i_):\\n nzi_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzi_ = nzi_.value\\n _nzi_return_value = nzi_\\n return (_nzi_return_value)\",\n \"def constraints_row_coef(n_row, n_col):\\n identities = [np.identity(n_col) for _ in range(n_row)]\\n cust_constraints = np.concatenate(identities, axis=1)\\n permutation = np.transpose(np.reshape(np.arange(n_col * n_row), (n_col, n_row))).flatten()\\n idx = np.empty_like(permutation)\\n idx[permutation] = np.arange(len(permutation))\\n cust_constraints = cust_constraints[:, idx]\\n\\n return cust_constraints\",\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 col(self, i):\\n return Vector([row[i] for row in self.data])\",\n \"def get_column(A: Matrix, j: int) -> Vector:\\n return [A_i[j] # jth element of row A_i\\n for A_i in A] # for each row A_i\",\n \"def pop(self, i):\\n row = self._table.pop(i); return row[self._j]\",\n \"def _jplus(j, *, dtype=None):\\n m = np.arange(j, -j - 1, -1, dtype=complex)\\n data = np.sqrt(j * (j + 1) - m * (m + 1))[1:]\\n return _data.diag[dtype](data, 1)\",\n \"def right(self):\\n n = len(self)-1\\n fr = np.zeros(n+1)\\n for ii in range(n+1):\\n for jj in range(n+1-ii):\\n for kk in range(n+1-ii-jj):\\n fr[ii+kk] += self[ii,jj,kk]\\n return fr\",\n \"def lambda_i(self, i):\\n return self._eig_val[i]\",\n \"def matrix_element(self, op=None):\\n ans = 0.0\\n for _ in self.term_visitor(op=op):\\n ans += self.root.matrix_element()\\n return ans\",\n \"def __call__(self, i, j):\\n return _table.Table___call__(self, i, j)\",\n \"def Matrix(row, col, val=0):\\n\\treturn [[val for _j in xrange(col)] for _i in xrange(row)]\",\n \"def getItem(self, i, j):\\n if i < 0:\\n raise IndexError('Row index must be nonnegative.')\\n if j < 0:\\n raise IndexError('Column index must be nonnegative.')\\n\\n return self.__m[i - 1][j - 1]\",\n \"def restriction(n, I, axis=0):\\n k = len(I)\\n times1d = lambda x: x[I]\\n trans1d = lambda x: jnp.zeros((n,), dtype=x.dtype).at[I].set(x)\\n\\n def times(x):\\n if x.ndim == 1:\\n return times1d(x)\\n if x.ndim == 2:\\n if axis == 0:\\n # we apply column wise\\n return x[I, :]\\n # we apply row-wise\\n return x[:, I]\\n # general case\\n return jnp.apply_along_axis(times1d, axis, x)\\n\\n def trans(x):\\n if x.ndim == 1:\\n return trans1d(x)\\n # general case\\n return jnp.apply_along_axis(trans1d, axis, x)\\n\\n return Operator(times=times, trans=trans, shape=(k,n))\",\n \"def red_matrix(A, i, j):\\n row = [0, 1, 2]\\n col = [0, 1, 2]\\n row.remove(i-1)\\n col.remove(j-1)\\n return A[row, col]\",\n \"def sub_matrix(self, line, column):\\n\\n mat = []\\n for i, row in enumerate(self.coefficients):\\n if i != line:\\n new_row = []\\n for j, value in enumerate(row):\\n if j != column:\\n new_row.append(value)\\n mat.append(new_row)\\n\\n return Matrix(mat)\",\n \"def aind(x):\\n\\treturn tuple(x.T)\",\n \"def trace(self):\\n if self.rows != self.columns:\\n raise ValueError(\\\"Trace undefined for non-square matrices\\\")\\n\\n return reduce(lambda x,y: x + y, [self.data[self.columns * i + i] for i in range(self.rows)])\",\n \"def getRow(self, idx):\\r\\n m=self.mlist\\r\\n if idx==0: return _vec4(m[0], m[1], m[2], m[3])\\r\\n elif idx==1: return _vec4(m[4], m[5], m[6], m[7])\\r\\n elif idx==2: return _vec4(m[8], m[9], m[10], m[11])\\r\\n elif idx==3: return _vec4(m[12], m[13], m[14], m[15])\\r\\n else:\\r\\n raise IndexError,\\\"index out of range\\\"\",\n \"def ij(ij, pol, ant) :\\n s.ij(pol, ij, ant)\",\n \"def _getMatrixRow(self):\\n item = self._item()\\n if item is not None:\\n matrix = item.getMatrix()\\n return qt.QVector3D(*matrix[self._index, :])\\n else:\\n return None\",\n \"def constraint_col_coef(n_col, n_row):\\n\\n all_rows = []\\n for i in range(n_col):\\n matrix_values = np.zeros((n_row, n_col), dtype=int)\\n col_offer = np.ones(n_row, dtype=int)\\n matrix_values[:, i] = col_offer\\n all_rows.append(matrix_values.flatten())\\n\\n cols_constraints = np.stack(all_rows)\\n\\n return cols_constraints\",\n \"def get_main_array_right(self):\\n return self.bitcell_array_inst.rx()\",\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 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 get_cell(self, i, j):\\n return self._env[i][j]\",\n \"def column (self, i):\\n return Vector(tuple(zip(*self._m))[i])\",\n \"def getGradient(self,j):\\n i = int(self.indicator['term'][j])\\n r = int(self.indicator['row'][j])\\n c = int(self.indicator['col'][j])\\n rv = -np.kron(self.Fstar()[i][:,[r]],self.Astar()[i][[c],:])\\n return rv\",\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 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\",\n \"def getbaraidxij(self,idx_):\\n i_ = ctypes.c_int32()\\n j_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getbaraidxij(self.__nativep,idx_,ctypes.byref(i_),ctypes.byref(j_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n i_ = i_.value\\n _i_return_value = i_\\n j_ = j_.value\\n _j_return_value = j_\\n return (_i_return_value,_j_return_value)\",\n \"def RowPart(self, *args):\\n return _hypre.HypreParMatrix_RowPart(self, *args)\",\n \"def matIxs( n ):\\n rows, cols = np.indices( (n,n) )\\n row = rows.flatten()\\n col = cols.flatten()\\n \\n return map( lambda x: Vector( x[0], x[1] ), zip( col, row ) )\",\n \"def c(self, i):\\n value = self.b(i)\\n if i == self.N:\\n return value\\n else:\\n for j in range(i+1, self.N+1):\\n value = value - self.a(i,j) * self.C[j]\\n return value\",\n \"def get_row(self, idx):\\n return '{offset}{idx}|{field}'.format(\\n offset=' ' * (self.OFFSET - 1),\\n idx=str(idx + 1).rjust(2, ' '),\\n field=self._get_field_row(idx),\\n )\",\n \"def get_l(GW_glitch,i,j):\\n\\t\\t \\n\\ttemp = np.einsum('nmk,nmk->k', GW_glitch.r_outer_r[:,:,i,j,:], GW_glitch.Hij[:,:,i,j,:])\\n\\t\\t \\n\\treturn temp\",\n \"def get_matrix(self):\\n return self._matrix[:3, :]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6920191","0.6274343","0.62173253","0.62173253","0.62173253","0.6196485","0.5856732","0.5815419","0.5781744","0.5771555","0.5736372","0.56483686","0.5642731","0.5605737","0.55912125","0.5473553","0.5381739","0.53433347","0.5286959","0.52848995","0.52556133","0.5220344","0.51907104","0.5190614","0.5186549","0.5185217","0.5182906","0.5165823","0.51297396","0.51153654","0.51153654","0.5104119","0.5103812","0.5085461","0.5057107","0.5035985","0.50174314","0.5011988","0.5002106","0.5001334","0.5001249","0.4977972","0.4968806","0.49636108","0.49545652","0.49507934","0.4950508","0.49478218","0.49465308","0.49315745","0.49293035","0.4891781","0.4891781","0.4874262","0.48600447","0.4853134","0.48416284","0.48393267","0.48315158","0.48292062","0.48259908","0.48246765","0.48137206","0.4800471","0.4799048","0.47950748","0.47893527","0.4774344","0.47689354","0.4760635","0.47603446","0.47564018","0.475019","0.47385356","0.47349235","0.47119823","0.47061527","0.4698213","0.4698102","0.46914274","0.46831617","0.46806166","0.4680171","0.46784762","0.467838","0.46756968","0.46716544","0.46691483","0.46579698","0.46466383","0.46433505","0.46399212","0.46371266","0.46324158","0.46304375","0.46251053","0.4624625","0.46242058","0.4623321","0.4616245"],"string":"[\n \"0.6920191\",\n \"0.6274343\",\n \"0.62173253\",\n \"0.62173253\",\n \"0.62173253\",\n \"0.6196485\",\n \"0.5856732\",\n \"0.5815419\",\n \"0.5781744\",\n \"0.5771555\",\n \"0.5736372\",\n \"0.56483686\",\n \"0.5642731\",\n \"0.5605737\",\n \"0.55912125\",\n \"0.5473553\",\n \"0.5381739\",\n \"0.53433347\",\n \"0.5286959\",\n \"0.52848995\",\n \"0.52556133\",\n \"0.5220344\",\n \"0.51907104\",\n \"0.5190614\",\n \"0.5186549\",\n \"0.5185217\",\n \"0.5182906\",\n \"0.5165823\",\n \"0.51297396\",\n \"0.51153654\",\n \"0.51153654\",\n \"0.5104119\",\n \"0.5103812\",\n \"0.5085461\",\n \"0.5057107\",\n \"0.5035985\",\n \"0.50174314\",\n \"0.5011988\",\n \"0.5002106\",\n \"0.5001334\",\n \"0.5001249\",\n \"0.4977972\",\n \"0.4968806\",\n \"0.49636108\",\n \"0.49545652\",\n \"0.49507934\",\n \"0.4950508\",\n \"0.49478218\",\n \"0.49465308\",\n \"0.49315745\",\n \"0.49293035\",\n \"0.4891781\",\n \"0.4891781\",\n \"0.4874262\",\n \"0.48600447\",\n \"0.4853134\",\n \"0.48416284\",\n \"0.48393267\",\n \"0.48315158\",\n \"0.48292062\",\n \"0.48259908\",\n \"0.48246765\",\n \"0.48137206\",\n \"0.4800471\",\n \"0.4799048\",\n \"0.47950748\",\n \"0.47893527\",\n \"0.4774344\",\n \"0.47689354\",\n \"0.4760635\",\n \"0.47603446\",\n \"0.47564018\",\n \"0.475019\",\n \"0.47385356\",\n \"0.47349235\",\n \"0.47119823\",\n \"0.47061527\",\n \"0.4698213\",\n \"0.4698102\",\n \"0.46914274\",\n \"0.46831617\",\n \"0.46806166\",\n \"0.4680171\",\n \"0.46784762\",\n \"0.467838\",\n \"0.46756968\",\n \"0.46716544\",\n \"0.46691483\",\n \"0.46579698\",\n \"0.46466383\",\n \"0.46433505\",\n \"0.46399212\",\n \"0.46371266\",\n \"0.46324158\",\n \"0.46304375\",\n \"0.46251053\",\n \"0.4624625\",\n \"0.46242058\",\n \"0.4623321\",\n \"0.4616245\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6652325"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11780,"cells":{"query":{"kind":"string","value":"Obtains the number of nonzeros in a slice of columns of the coefficient matrix. getacolslicenumnz(self,first_,last_)"},"document":{"kind":"string","value":"def getacolslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_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 getarowslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def getacolnumnz(self,i_): # 3\n res,resargs = self.__obj.getacolnumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n return _nzj_return_value","def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","def getaslicenumnz(self,accmode_,first_,last_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n res,resargs = self.__obj.getaslicenumnz64(accmode_,first_,last_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","def getacolnumnz(self,i_):\n nzj_ = ctypes.c_int32()\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n return (_nzj_return_value)","def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\n numnz_ = ctypes.c_int32()\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def getarownumnz(self,i_): # 3\n res,resargs = self.__obj.getarownumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzi_return_value = resargs\n return _nzi_return_value","def getNumCols(self):\n return self.__cols","def GetNumCols(self):\n return _hypre.HypreParMatrix_GetNumCols(self)","def colnum(self):\n \n colnum = 0\n for table in self.columnlabels:\n table = np.asarray(table)\n if np.ndim(table) <= 1:\n table = np.reshape(table, (1, -1))\n colnum += table.shape[1]\n return colnum","def columnCount(self):\n return abs(self.minCol) + abs(self.maxCol)","def getNoOfCols(self):\n return _patchExtractor.patchExtractor_getNoOfCols(self)","def getarownumnz(self,i_):\n nzi_ = ctypes.c_int32()\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzi_ = nzi_.value\n _nzi_return_value = nzi_\n return (_nzi_return_value)","def nNz(self):\n if self.dim < 3:\n return None\n return self.nCz + 1","def nCz(self):\n if self.dim < 3:\n return None\n return int(self._n[2])","def GetNumberCols(self):\n return len(self.__colsKey)","def num_cols(self):\n return (len(self.rows[0]))","def getNbColumns(self):\n return self.data.shape[0]","def ncolumns(self):\n return self.__ncols","def get_num_cols(self):\n return self._num_cols","def GetNumColumns(self):\n return len(self.columns)","def get_cols_dummy():","def columns(self) -> int:\n return self.__squares[0].__len__()","def num_cols(self):\n return len(self.rows[0])","def GetColStarts(self):\n return _hypre.HypreParMatrix_GetColStarts(self)","def get_cols(self) :\n\n return list(self.cols)[1:]","def ncolumns(self):\n return len(self.__column_list)","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 __column_height(self, x):\n\t\tcolumn = self.board[:, x]\n\t\treturn np.count_nonzero(column)","def N_Col(self,val):\n\t\tif val in self.colums:\n\t\t\treturn self.colums.index(val)\n\t\telse:\n\t\t\treturn False","def nnz(self):\n return self.to_ddm().nnz()","def matrix_dim(CT):\r\n if CT[0]==0 and CT[-1]==0:\r\n return 2\r\n elif CT[0]!=0 and CT[-1]!=0:\r\n return 4","def nz(self):\n return self._dim[2]","def n_cols(self):\n ch = self.children\n return 1 if not ch else sum([c.n_cols for c in ch])","def num_cols(self):\n return len(self.column_names())","def columns(self):\n try:\n return self._data.shape[1] or 1\n except (AttributeError, IndexError):\n return 1","def n_cols(self):\n\n return len(self.plaincolumns)","def cpp_getInColCount(self, patchNo, colsInPatch):\n return _patchExtractor.patchExtractor_cpp_getInColCount(self, patchNo, colsInPatch)","def nnz(self):\n t = self.get_MSC()\n return len(np.unique(t['masks']))","def _non_zero_columns_search(array):\n col_num = array.shape[1]\n non_zero_col = CArray([], dtype=int)\n for c in range(col_num):\n col = array[:, c]\n if col.any() == True:\n non_zero_col = non_zero_col.append(c)\n\n return non_zero_col","def nnz(self):\n return self.rep.nnz()","def columns_count(self):\n if self.value.count != 0:\n return len(self.value[0])\n else:\n return 0","def cols(self) -> int:\n if self.is_empty():\n raise ValueError(\"Empty DataView contains no columns\")\n\n return len(self[0])","def get_columns(self) -> int:\r\n return 1 + self.display.get_columns() + 1","def GetColumnCount(self):\r\n\r\n return len(self._columns)","def numRowsCols(array):\n return len(array),len(array[0])","def columnCount(self, parent_midx):\n return self._cols_nb","def columnCount(self, parent_midx):\n return self._cols_nb","def TzscorrCols(Xn):\n C_X = T.dot(Xn.T, Xn)-T.eye(Xn.shape[1])\n return 0.5*T.log((1+C_X)/(1-C_X))","def columnCount(self, parent):\r\n if len(self.arraydata) > 0:\r\n return len(self.arraydata[0]) - 2\r\n return 0","def cpp_getInColExtrema(self, patchNo, colsInPatch, colsInPatch2):\n return _patchExtractor.patchExtractor_cpp_getInColExtrema(self, patchNo, colsInPatch, colsInPatch2)","def GetGlobalNumCols(self):\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)","def ColPart(self, *args):\n return _hypre.HypreParMatrix_ColPart(self, *args)","def collatz(start):\n counter = 1\n n = start\n while n != 2:\n if n % 2 == 0:\n n /= 2\n else:\n n = (n * 3) + 1\n counter += 1\n\n counter += 1\n return counter","def test_get_date_column_index_first_col(self, one_row_worksheet):\n\n actual_result = one_row_worksheet.get_date_column_index()\n assert actual_result == 0","def col_count(self):\n return self.__col_count","def nnz(self):","def count_lead_zs(self,x):\n display_mask = 1 << 31\n cnt = 0\n for c in xrange(1,33):\n if((x & display_mask) == 0):\n cnt += 1\n else:\n return cnt\n x <<= 1\n return cnt","def build_collocation(nr, nz):\n\n rootsr, _, Br, Wr = recur_colloc_symm(nr, 3)\n rootsz, Az, _ = recur_colloc(nz)\n \n return rootsz, Az, rootsr, Br, Wr","def cols(self) -> int:\n return self.train.cols()","def col_count(self):\n if isinstance(self.data, pd.DataFrame) is False:\n return None\n else:\n return self.data.shape[1]","def columnCount( self ):\n if not self.o_data is None:\n if self.isItemMultiDimensional():\n return len(self.o_data)\n else:\n return 1\n else:\n return 1","def collatz(n):\n if n%2==0: return n/2\n else: return 3*n+1","def no_of_columns(self): \n return len(self.columns) + (1 if self.serialize else 0)","def get_num_columns(table):\n\n\treturn max((len(row) for row in table))","def getColumnCount(self) -> int:\n ...","def column_count(self):\n return self.column_length","def _num_of_cols(self):\n if not self.connection:\n self.close()\n noc = C_SHORT()\n ret = ODBC_API.SQLNumResultCols(self.stmt_h, ADDR(noc))\n if ret != SQL_SUCCESS:\n check_success(self, ret)\n return noc.value","def nnz(self):\n return len(self.value)","def nnz(self):\n return len(self.data)","def calc_col_len(self): # Calculates length of each column\n print('\\nColumn Lengths\\n' +\n '--------------')\n results.append('\\nColumn Lengths\\n' +\n '--------------')\n for x in range(0, self.tot_col):\n blank = 0\n for y in range(1, self.tot_rows + 1):\n if self.file_list[y][x] == '':\n blank += 1\n column_count = self.tot_rows - blank\n\n results.append('Column \\'' + self.file_list[0][x] + '\\' length: ' + str(column_count))\n print('Column \\'' + self.file_list[0][x] + '\\' length: ' + str(column_count))","def test_get_date_column_index_left_trailing_spaces(self, date_trailing_left_spaces_worksheet):\n\n actual_result = date_trailing_left_spaces_worksheet.get_date_column_index()\n assert actual_result == 0","def get_columns(self) -> int:\r\n boader_len = len(self.boader_char)\r\n return boader_len * 2 + self.display.get_columns()","def columnCount(self, index):\n return 4","def collatz(n):\n iterCount = 0\n while(n != 1):\n if(n & 1):\n n = 3 * n + 1\n else:\n n //= 2\n iterCount += 1\n return iterCount","def get_cols_drop():","def get_cols(self):\n return np.transpose(self.board).tolist()","def _compute_cols(hooks, verbose):\n if hooks:\n name_len = max(len(_hook_msg_start(hook, verbose)) for hook in hooks)\n else:\n name_len = 0\n\n cols = name_len + 3 + len(NO_FILES) + 1 + len(SKIPPED)\n return max(cols, 80)","def ncol(arr):\n assert isinstance(arr, ndarray)\n assert len(arr.shape) == 2\n return arr.shape[1]","def test_get_date_column_index_right_trailing_spaces(self, date_trailing_right_spaces_worksheet):\n\n actual_result = date_trailing_right_spaces_worksheet.get_date_column_index()\n assert actual_result == 0","def cpp_getInColDist(self, patchNo, colsInPatch):\n return _patchExtractor.patchExtractor_cpp_getInColDist(self, patchNo, colsInPatch)","def get_column_offsets(self):\n offsets = [x + self.bitcell_array_inst.lx() for x in self.bitcell_array.get_column_offsets()]\n return offsets","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 N_z(self) -> int:\n return self.params.N_z","def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","def get_zeros(self):\n zero_values = self.df[self.col_name].isin([0]).sum(axis=0)\n return zero_values","def iter_col(self, inclusive=False, **kwargs):\n ret = self.sudoku.col(self.col, **kwargs)\n if not inclusive:\n ret -= self\n return ret","def get_attr_cols(self):\n all_cols = np.arange(self.col_count)\n attr_cols = np.setdiff1d(all_cols, self.time_cols)\n return attr_cols","def GetColumnCount(self):\r\n\r\n return self._owner.GetHeaderWindow().GetColumnCount()","def get_table_total_cols(table_name):\n return table_spec[table_name]['number_of_columns']","def test07(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 cr = c[\"a + 2 * d - 3 > 0\"]\n nr = a[(a + 2 * b - 3) > 0]\n # print \"ca[expr] ->\", cr\n # print \"numpy ->\", nr\n assert_array_equal(cr[:], nr, \"carray[expr] does not work correctly\")","def _CalculateColumn(self, record):\n if not record:\n return 0\n if isinstance(record, Table):\n add_width = len(record.separator)\n if record.skip_empty:\n if not any(v for _, v in record):\n return 0\n ret = max(len(k) for k, v in record if v) + add_width\n ret = max(ret, 2 + max(self._CalculateColumn(v) for _, v in record))\n return min(ret, self.MAX_MAP_WIDTH)\n elif isinstance(record, Lines):\n return max(self._CalculateColumn(l) for l in record)\n else:\n return 0","def nnz(self):\n\t\treturn self.st.size()","def get_odd_collatz(n):\n # Please print out which line of the above program contains an error. E.g. if the bug is on line 4 then print 4\n # END OF CONTEXT\n print(\"12\")\n # END OF SOLUTION","def test_get_last_column(workbook):\n assert workbook.get_last_column() == 'F'","def get_ncols(self):\n return self.ncols","def get_ncols(self):\n return self.ncols"],"string":"[\n \"def getarowslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def getacolnumnz(self,i_): # 3\\n res,resargs = self.__obj.getacolnumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n return _nzj_return_value\",\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\n \"def getaslicenumnz(self,accmode_,first_,last_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n res,resargs = self.__obj.getaslicenumnz64(accmode_,first_,last_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\n \"def getacolnumnz(self,i_):\\n nzj_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzj_ = nzj_.value\\n _nzj_return_value = nzj_\\n return (_nzj_return_value)\",\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\\n numnz_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def getarownumnz(self,i_): # 3\\n res,resargs = self.__obj.getarownumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzi_return_value = resargs\\n return _nzi_return_value\",\n \"def getNumCols(self):\\n return self.__cols\",\n \"def GetNumCols(self):\\n return _hypre.HypreParMatrix_GetNumCols(self)\",\n \"def colnum(self):\\n \\n colnum = 0\\n for table in self.columnlabels:\\n table = np.asarray(table)\\n if np.ndim(table) <= 1:\\n table = np.reshape(table, (1, -1))\\n colnum += table.shape[1]\\n return colnum\",\n \"def columnCount(self):\\n return abs(self.minCol) + abs(self.maxCol)\",\n \"def getNoOfCols(self):\\n return _patchExtractor.patchExtractor_getNoOfCols(self)\",\n \"def getarownumnz(self,i_):\\n nzi_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzi_ = nzi_.value\\n _nzi_return_value = nzi_\\n return (_nzi_return_value)\",\n \"def nNz(self):\\n if self.dim < 3:\\n return None\\n return self.nCz + 1\",\n \"def nCz(self):\\n if self.dim < 3:\\n return None\\n return int(self._n[2])\",\n \"def GetNumberCols(self):\\n return len(self.__colsKey)\",\n \"def num_cols(self):\\n return (len(self.rows[0]))\",\n \"def getNbColumns(self):\\n return self.data.shape[0]\",\n \"def ncolumns(self):\\n return self.__ncols\",\n \"def get_num_cols(self):\\n return self._num_cols\",\n \"def GetNumColumns(self):\\n return len(self.columns)\",\n \"def get_cols_dummy():\",\n \"def columns(self) -> int:\\n return self.__squares[0].__len__()\",\n \"def num_cols(self):\\n return len(self.rows[0])\",\n \"def GetColStarts(self):\\n return _hypre.HypreParMatrix_GetColStarts(self)\",\n \"def get_cols(self) :\\n\\n return list(self.cols)[1:]\",\n \"def ncolumns(self):\\n return len(self.__column_list)\",\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 __column_height(self, x):\\n\\t\\tcolumn = self.board[:, x]\\n\\t\\treturn np.count_nonzero(column)\",\n \"def N_Col(self,val):\\n\\t\\tif val in self.colums:\\n\\t\\t\\treturn self.colums.index(val)\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def nnz(self):\\n return self.to_ddm().nnz()\",\n \"def matrix_dim(CT):\\r\\n if CT[0]==0 and CT[-1]==0:\\r\\n return 2\\r\\n elif CT[0]!=0 and CT[-1]!=0:\\r\\n return 4\",\n \"def nz(self):\\n return self._dim[2]\",\n \"def n_cols(self):\\n ch = self.children\\n return 1 if not ch else sum([c.n_cols for c in ch])\",\n \"def num_cols(self):\\n return len(self.column_names())\",\n \"def columns(self):\\n try:\\n return self._data.shape[1] or 1\\n except (AttributeError, IndexError):\\n return 1\",\n \"def n_cols(self):\\n\\n return len(self.plaincolumns)\",\n \"def cpp_getInColCount(self, patchNo, colsInPatch):\\n return _patchExtractor.patchExtractor_cpp_getInColCount(self, patchNo, colsInPatch)\",\n \"def nnz(self):\\n t = self.get_MSC()\\n return len(np.unique(t['masks']))\",\n \"def _non_zero_columns_search(array):\\n col_num = array.shape[1]\\n non_zero_col = CArray([], dtype=int)\\n for c in range(col_num):\\n col = array[:, c]\\n if col.any() == True:\\n non_zero_col = non_zero_col.append(c)\\n\\n return non_zero_col\",\n \"def nnz(self):\\n return self.rep.nnz()\",\n \"def columns_count(self):\\n if self.value.count != 0:\\n return len(self.value[0])\\n else:\\n return 0\",\n \"def cols(self) -> int:\\n if self.is_empty():\\n raise ValueError(\\\"Empty DataView contains no columns\\\")\\n\\n return len(self[0])\",\n \"def get_columns(self) -> int:\\r\\n return 1 + self.display.get_columns() + 1\",\n \"def GetColumnCount(self):\\r\\n\\r\\n return len(self._columns)\",\n \"def numRowsCols(array):\\n return len(array),len(array[0])\",\n \"def columnCount(self, parent_midx):\\n return self._cols_nb\",\n \"def columnCount(self, parent_midx):\\n return self._cols_nb\",\n \"def TzscorrCols(Xn):\\n C_X = T.dot(Xn.T, Xn)-T.eye(Xn.shape[1])\\n return 0.5*T.log((1+C_X)/(1-C_X))\",\n \"def columnCount(self, parent):\\r\\n if len(self.arraydata) > 0:\\r\\n return len(self.arraydata[0]) - 2\\r\\n return 0\",\n \"def cpp_getInColExtrema(self, patchNo, colsInPatch, colsInPatch2):\\n return _patchExtractor.patchExtractor_cpp_getInColExtrema(self, patchNo, colsInPatch, colsInPatch2)\",\n \"def GetGlobalNumCols(self):\\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)\",\n \"def ColPart(self, *args):\\n return _hypre.HypreParMatrix_ColPart(self, *args)\",\n \"def collatz(start):\\n counter = 1\\n n = start\\n while n != 2:\\n if n % 2 == 0:\\n n /= 2\\n else:\\n n = (n * 3) + 1\\n counter += 1\\n\\n counter += 1\\n return counter\",\n \"def test_get_date_column_index_first_col(self, one_row_worksheet):\\n\\n actual_result = one_row_worksheet.get_date_column_index()\\n assert actual_result == 0\",\n \"def col_count(self):\\n return self.__col_count\",\n \"def nnz(self):\",\n \"def count_lead_zs(self,x):\\n display_mask = 1 << 31\\n cnt = 0\\n for c in xrange(1,33):\\n if((x & display_mask) == 0):\\n cnt += 1\\n else:\\n return cnt\\n x <<= 1\\n return cnt\",\n \"def build_collocation(nr, nz):\\n\\n rootsr, _, Br, Wr = recur_colloc_symm(nr, 3)\\n rootsz, Az, _ = recur_colloc(nz)\\n \\n return rootsz, Az, rootsr, Br, Wr\",\n \"def cols(self) -> int:\\n return self.train.cols()\",\n \"def col_count(self):\\n if isinstance(self.data, pd.DataFrame) is False:\\n return None\\n else:\\n return self.data.shape[1]\",\n \"def columnCount( self ):\\n if not self.o_data is None:\\n if self.isItemMultiDimensional():\\n return len(self.o_data)\\n else:\\n return 1\\n else:\\n return 1\",\n \"def collatz(n):\\n if n%2==0: return n/2\\n else: return 3*n+1\",\n \"def no_of_columns(self): \\n return len(self.columns) + (1 if self.serialize else 0)\",\n \"def get_num_columns(table):\\n\\n\\treturn max((len(row) for row in table))\",\n \"def getColumnCount(self) -> int:\\n ...\",\n \"def column_count(self):\\n return self.column_length\",\n \"def _num_of_cols(self):\\n if not self.connection:\\n self.close()\\n noc = C_SHORT()\\n ret = ODBC_API.SQLNumResultCols(self.stmt_h, ADDR(noc))\\n if ret != SQL_SUCCESS:\\n check_success(self, ret)\\n return noc.value\",\n \"def nnz(self):\\n return len(self.value)\",\n \"def nnz(self):\\n return len(self.data)\",\n \"def calc_col_len(self): # Calculates length of each column\\n print('\\\\nColumn Lengths\\\\n' +\\n '--------------')\\n results.append('\\\\nColumn Lengths\\\\n' +\\n '--------------')\\n for x in range(0, self.tot_col):\\n blank = 0\\n for y in range(1, self.tot_rows + 1):\\n if self.file_list[y][x] == '':\\n blank += 1\\n column_count = self.tot_rows - blank\\n\\n results.append('Column \\\\'' + self.file_list[0][x] + '\\\\' length: ' + str(column_count))\\n print('Column \\\\'' + self.file_list[0][x] + '\\\\' length: ' + str(column_count))\",\n \"def test_get_date_column_index_left_trailing_spaces(self, date_trailing_left_spaces_worksheet):\\n\\n actual_result = date_trailing_left_spaces_worksheet.get_date_column_index()\\n assert actual_result == 0\",\n \"def get_columns(self) -> int:\\r\\n boader_len = len(self.boader_char)\\r\\n return boader_len * 2 + self.display.get_columns()\",\n \"def columnCount(self, index):\\n return 4\",\n \"def collatz(n):\\n iterCount = 0\\n while(n != 1):\\n if(n & 1):\\n n = 3 * n + 1\\n else:\\n n //= 2\\n iterCount += 1\\n return iterCount\",\n \"def get_cols_drop():\",\n \"def get_cols(self):\\n return np.transpose(self.board).tolist()\",\n \"def _compute_cols(hooks, verbose):\\n if hooks:\\n name_len = max(len(_hook_msg_start(hook, verbose)) for hook in hooks)\\n else:\\n name_len = 0\\n\\n cols = name_len + 3 + len(NO_FILES) + 1 + len(SKIPPED)\\n return max(cols, 80)\",\n \"def ncol(arr):\\n assert isinstance(arr, ndarray)\\n assert len(arr.shape) == 2\\n return arr.shape[1]\",\n \"def test_get_date_column_index_right_trailing_spaces(self, date_trailing_right_spaces_worksheet):\\n\\n actual_result = date_trailing_right_spaces_worksheet.get_date_column_index()\\n assert actual_result == 0\",\n \"def cpp_getInColDist(self, patchNo, colsInPatch):\\n return _patchExtractor.patchExtractor_cpp_getInColDist(self, patchNo, colsInPatch)\",\n \"def get_column_offsets(self):\\n offsets = [x + self.bitcell_array_inst.lx() for x in self.bitcell_array.get_column_offsets()]\\n return offsets\",\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 N_z(self) -> int:\\n return self.params.N_z\",\n \"def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def get_zeros(self):\\n zero_values = self.df[self.col_name].isin([0]).sum(axis=0)\\n return zero_values\",\n \"def iter_col(self, inclusive=False, **kwargs):\\n ret = self.sudoku.col(self.col, **kwargs)\\n if not inclusive:\\n ret -= self\\n return ret\",\n \"def get_attr_cols(self):\\n all_cols = np.arange(self.col_count)\\n attr_cols = np.setdiff1d(all_cols, self.time_cols)\\n return attr_cols\",\n \"def GetColumnCount(self):\\r\\n\\r\\n return self._owner.GetHeaderWindow().GetColumnCount()\",\n \"def get_table_total_cols(table_name):\\n return table_spec[table_name]['number_of_columns']\",\n \"def test07(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 cr = c[\\\"a + 2 * d - 3 > 0\\\"]\\n nr = a[(a + 2 * b - 3) > 0]\\n # print \\\"ca[expr] ->\\\", cr\\n # print \\\"numpy ->\\\", nr\\n assert_array_equal(cr[:], nr, \\\"carray[expr] does not work correctly\\\")\",\n \"def _CalculateColumn(self, record):\\n if not record:\\n return 0\\n if isinstance(record, Table):\\n add_width = len(record.separator)\\n if record.skip_empty:\\n if not any(v for _, v in record):\\n return 0\\n ret = max(len(k) for k, v in record if v) + add_width\\n ret = max(ret, 2 + max(self._CalculateColumn(v) for _, v in record))\\n return min(ret, self.MAX_MAP_WIDTH)\\n elif isinstance(record, Lines):\\n return max(self._CalculateColumn(l) for l in record)\\n else:\\n return 0\",\n \"def nnz(self):\\n\\t\\treturn self.st.size()\",\n \"def get_odd_collatz(n):\\n # Please print out which line of the above program contains an error. E.g. if the bug is on line 4 then print 4\\n # END OF CONTEXT\\n print(\\\"12\\\")\\n # END OF SOLUTION\",\n \"def test_get_last_column(workbook):\\n assert workbook.get_last_column() == 'F'\",\n \"def get_ncols(self):\\n return self.ncols\",\n \"def get_ncols(self):\\n return self.ncols\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7030425","0.6908309","0.69077706","0.68798804","0.67562634","0.6697987","0.60632735","0.60440516","0.59911394","0.5967844","0.59044164","0.5890069","0.58208543","0.57897","0.5725127","0.5724517","0.57188284","0.5676727","0.5675189","0.5605064","0.5597147","0.5590894","0.5590643","0.5586166","0.5583682","0.5570443","0.55701524","0.5567639","0.5559105","0.5553589","0.55534434","0.55463374","0.55345786","0.55295193","0.55219674","0.55170137","0.55108434","0.5495844","0.54742986","0.54724693","0.5471681","0.5462101","0.5440803","0.5429683","0.5388258","0.53844917","0.5380238","0.5380238","0.53598803","0.53411937","0.5323606","0.52958506","0.52945167","0.5284278","0.5260019","0.5255862","0.5239435","0.5233814","0.5230994","0.5230702","0.52190286","0.519922","0.519901","0.5194213","0.5188695","0.5187551","0.51834106","0.5178041","0.5175298","0.51581013","0.5153666","0.51435155","0.5128932","0.5109425","0.50907034","0.50880784","0.50799876","0.5071551","0.50669974","0.50486773","0.50454026","0.5044902","0.5042254","0.5042254","0.5042254","0.5042254","0.50414324","0.5035869","0.5033896","0.5028099","0.50179785","0.50095636","0.49793696","0.49672744","0.4962643","0.49518573","0.49435836","0.49434978","0.49342582","0.49342582"],"string":"[\n \"0.7030425\",\n \"0.6908309\",\n \"0.69077706\",\n \"0.68798804\",\n \"0.67562634\",\n \"0.6697987\",\n \"0.60632735\",\n \"0.60440516\",\n \"0.59911394\",\n \"0.5967844\",\n \"0.59044164\",\n \"0.5890069\",\n \"0.58208543\",\n \"0.57897\",\n \"0.5725127\",\n \"0.5724517\",\n \"0.57188284\",\n \"0.5676727\",\n \"0.5675189\",\n \"0.5605064\",\n \"0.5597147\",\n \"0.5590894\",\n \"0.5590643\",\n \"0.5586166\",\n \"0.5583682\",\n \"0.5570443\",\n \"0.55701524\",\n \"0.5567639\",\n \"0.5559105\",\n \"0.5553589\",\n \"0.55534434\",\n \"0.55463374\",\n \"0.55345786\",\n \"0.55295193\",\n \"0.55219674\",\n \"0.55170137\",\n \"0.55108434\",\n \"0.5495844\",\n \"0.54742986\",\n \"0.54724693\",\n \"0.5471681\",\n \"0.5462101\",\n \"0.5440803\",\n \"0.5429683\",\n \"0.5388258\",\n \"0.53844917\",\n \"0.5380238\",\n \"0.5380238\",\n \"0.53598803\",\n \"0.53411937\",\n \"0.5323606\",\n \"0.52958506\",\n \"0.52945167\",\n \"0.5284278\",\n \"0.5260019\",\n \"0.5255862\",\n \"0.5239435\",\n \"0.5233814\",\n \"0.5230994\",\n \"0.5230702\",\n \"0.52190286\",\n \"0.519922\",\n \"0.519901\",\n \"0.5194213\",\n \"0.5188695\",\n \"0.5187551\",\n \"0.51834106\",\n \"0.5178041\",\n \"0.5175298\",\n \"0.51581013\",\n \"0.5153666\",\n \"0.51435155\",\n \"0.5128932\",\n \"0.5109425\",\n \"0.50907034\",\n \"0.50880784\",\n \"0.50799876\",\n \"0.5071551\",\n \"0.50669974\",\n \"0.50486773\",\n \"0.50454026\",\n \"0.5044902\",\n \"0.5042254\",\n \"0.5042254\",\n \"0.5042254\",\n \"0.5042254\",\n \"0.50414324\",\n \"0.5035869\",\n \"0.5033896\",\n \"0.5028099\",\n \"0.50179785\",\n \"0.50095636\",\n \"0.49793696\",\n \"0.49672744\",\n \"0.4962643\",\n \"0.49518573\",\n \"0.49435836\",\n \"0.49434978\",\n \"0.49342582\",\n \"0.49342582\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7720494"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11781,"cells":{"query":{"kind":"string","value":"Obtains the number of nonzeros in a slice of rows of the coefficient matrix. getarowslicenumnz(self,first_,last_)"},"document":{"kind":"string","value":"def getarowslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_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 getacolslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\n numnz_ = ctypes.c_int32()\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def getaslicenumnz(self,accmode_,first_,last_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n res,resargs = self.__obj.getaslicenumnz64(accmode_,first_,last_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","def getacolnumnz(self,i_):\n nzj_ = ctypes.c_int32()\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n return (_nzj_return_value)","def nNz(self):\n if self.dim < 3:\n return None\n return self.nCz + 1","def getacolnumnz(self,i_): # 3\n res,resargs = self.__obj.getacolnumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n return _nzj_return_value","def getarownumnz(self,i_):\n nzi_ = ctypes.c_int32()\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzi_ = nzi_.value\n _nzi_return_value = nzi_\n return (_nzi_return_value)","def nnz(self):\n return self.rep.nnz()","def nnz(self):\n return self.to_ddm().nnz()","def getarownumnz(self,i_): # 3\n res,resargs = self.__obj.getarownumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzi_return_value = resargs\n return _nzi_return_value","def nnz(self):\n return len(self.data)","def nCz(self):\n if self.dim < 3:\n return None\n return int(self._n[2])","def numRowsCols(array):\n return len(array),len(array[0])","def nnz(self):","def nnz(self):\n return len(self.value)","def nnz(self):\n t = self.get_MSC()\n return len(np.unique(t['masks']))","def count_lead_zs(self,x):\n display_mask = 1 << 31\n cnt = 0\n for c in xrange(1,33):\n if((x & display_mask) == 0):\n cnt += 1\n else:\n return cnt\n x <<= 1\n return cnt","def matrix_dim(CT):\r\n if CT[0]==0 and CT[-1]==0:\r\n return 2\r\n elif CT[0]!=0 and CT[-1]!=0:\r\n return 4","def nz(self):\n return self._dim[2]","def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","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 getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\n maxnumnz_ = self.getarowslicenumnz((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_getarowslice64(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 nnz(self):\n\t\treturn self.st.size()","def GetNumCols(self):\n return _hypre.HypreParMatrix_GetNumCols(self)","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 N_z(self) -> int:\n return self.params.N_z","def getarowslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","def NNZ(self):\n return _hypre.HypreParMatrix_NNZ(self)","def nrows(self):\n if self.ncolumns() == 0:\n return 0\n nrows = self.table_column(0).nrows()\n for i in range(1, self.ncolumns()):\n nrows = min(self.table_column(i).nrows(), nrows)\n return nrows","def NNZ(self):\n return len(self.__IndList)","def ncol(arr):\n assert isinstance(arr, ndarray)\n assert len(arr.shape) == 2\n return arr.shape[1]","def trailingZeroes(self, n):\n cnt = 0\n while n:\n n /= 5\n cnt += n\n\n return cnt","def count_nonzero(tensor):\n raise NotImplementedError","def num_cols(self):\n return (len(self.rows[0]))","def collatz(start):\n counter = 1\n n = start\n while n != 2:\n if n % 2 == 0:\n n /= 2\n else:\n n = (n * 3) + 1\n counter += 1\n\n counter += 1\n return counter","def zscore_brain_slice(X):\n X[X == 0] = np.nan\n X = (X - np.nanmean(X)) / np.nanstd(X)\n X[np.isnan(X)] = 0\n\n return(X)","def columns(self) -> int:\n return self.__squares[0].__len__()","def test07(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 cr = c[\"a + 2 * d - 3 > 0\"]\n nr = a[(a + 2 * b - 3) > 0]\n # print \"ca[expr] ->\", cr\n # print \"numpy ->\", nr\n assert_array_equal(cr[:], nr, \"carray[expr] does not work correctly\")","def noOfElem(classObj, index):\r\n return len(classObj.dataSet[:, index])","def part_one():\n return len(numpy.where(grid > 1)[0])","def colnum(self):\n \n colnum = 0\n for table in self.columnlabels:\n table = np.asarray(table)\n if np.ndim(table) <= 1:\n table = np.reshape(table, (1, -1))\n colnum += table.shape[1]\n return colnum","def scalar_countleading0(self, dst, src):\n return self._scalar_single_func('clz', dst, src)","def n(self):\n return nrow(self._array)","def collatz(n):\n if n%2==0: return n/2\n else: return 3*n+1","def getNsubint(self):\n return self.shape(squeeze=False)[0]","def num_cols(self):\n return len(self.rows[0])","def num_zeros_end(num):\r\n \r\n iszero = True\r\n num_zeros = 0\r\n i = len(num)-1\r\n \r\n while (iszero == True) and (i != 0):\r\n \r\n if num[i] == \"0\":\r\n num_zeros += 1\r\n \r\n elif num[i] != \"0\":\r\n iszero = False\r\n \r\n i -= 1 \r\n \r\n return num_zeros","def columnCount(self, parent):\r\n if len(self.arraydata) > 0:\r\n return len(self.arraydata[0]) - 2\r\n return 0","def __len__(self):\n return self.xyz.shape[0]","def __len__(self):\n return self.xyz.shape[0]","def __len__(self):\n return len(np.where(np.logical_not(self.data.mask))[0])","def zenith_nadir(x, y):\n if y == 'm':\n bb = []\n cc = []\n for i in range(x.shape[1]):\n bb.append(amax(x[:, i:i + 1]))\n b = array(bb)\n cc.append(amin(x[:, i:i + 1]))\n c = array(cc)\n return (b, c)\n else:\n b = ones(x.shape[1])\n c = zeros(x.shape[1])\n return (b, c)","def countZeroes(arr):\n counter = 0\n #sort the array\n arr.sort(reverse=True)\n print(arr)\n n = len(arr)\n print(n)\n\n # Find index of first zero in given array\n first = firstZero(arr, 0, n - 1)\n \n # If 0 is not present at all, return 0\n if (first == -1):\n return 0\n\n for i in range(first,len(arr)):\n if (arr[i] == 0):\n counter += 1\n else:\n break\n\n return counter","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 test_get_date_column_index_first_col(self, one_row_worksheet):\n\n actual_result = one_row_worksheet.get_date_column_index()\n assert actual_result == 0","def _get_tr_dataset_size_from_z0(z0):\n return int(np.round(np.exp(z0)))","def numel(self):\n return self.t.size","def getNode_zSlice(self, nodeIdx):\n\t\treturn self.nodeDictList[nodeIdx]['zSlice']","def GetColStarts(self):\n return _hypre.HypreParMatrix_GetColStarts(self)","def columnCount(self):\n return abs(self.minCol) + abs(self.maxCol)","def count_level(self):\r\n count = 0\r\n\r\n for i in range(0, len(self.matrix)):\r\n for j in range(0,len(self.matrix[i])):\r\n if self.matrix[i][j] == \"0\":\r\n count += 1\r\n\r\n # We substract 1 to count level from 0\r\n return count - 1","def __len__(self):\n return np.size(self.A,0)","def nonzero_last(arr, *, axis):\n def nonzero_last_1d(arr):\n try:\n return np.nonzero(arr)[0][-1]\n except IndexError:\n return -1\n return np.apply_along_axis(nonzero_last_1d, axis, arr)","def getNbColumns(self):\n return self.data.shape[0]","def __column_height(self, x):\n\t\tcolumn = self.board[:, x]\n\t\treturn np.count_nonzero(column)","def getNumCols(self):\n return self.__cols","def getlen(self):\n if self.onlydiag():\n return self.lendiag()\n else:\n return len(self)","def num_cells_down(self):\n if self.dim == 0:\n return None\n if hasattr(self, '__num_cells_down__'):\n return self.__num_cells_down__\n if self.lower_index is None:\n return 0\n raise ValueError('Cannot infer the number of cells in the cochain below.')","def collatz(n):\n iterCount = 0\n while(n != 1):\n if(n & 1):\n n = 3 * n + 1\n else:\n n //= 2\n iterCount += 1\n return iterCount","def Nx(self):\n return self.shape[-1]","def n_z(self, level):\n resolution = self.resolution(level)\n return (self.z_extent // resolution + 63) // 64","def test_get_date_column_index_left_trailing_spaces(self, date_trailing_left_spaces_worksheet):\n\n actual_result = date_trailing_left_spaces_worksheet.get_date_column_index()\n assert actual_result == 0","def getarowslicetrip(self,first_,last_,subi,subj,val): # 3\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 dim(self) -> int:","def test02(self):\n a = np.arange(self.N, dtype='uint64')\n b = bcolz.carray(a, rootdir=self.rootdir)\n c = iter(b.view())\n u = c.iter(3)\n w = b.iter(2)\n self.assertEqual(sum(a[3:]), sum(u))\n self.assertEqual(sum(a[2:]), sum(w))","def n_cols(self):\n ch = self.children\n return 1 if not ch else sum([c.n_cols for c in ch])","def TzscorrCols(Xn):\n C_X = T.dot(Xn.T, Xn)-T.eye(Xn.shape[1])\n return 0.5*T.log((1+C_X)/(1-C_X))","def GetNumColumns(self):\n return len(self.columns)","def no_of_columns(self): \n return len(self.columns) + (1 if self.serialize else 0)","def ncolumns(self):\n return self.__ncols","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 columns_count(self):\n if self.value.count != 0:\n return len(self.value[0])\n else:\n return 0","def test01b(self):\n a = np.arange(101)\n b = bcolz.carray(a, chunklen=2, rootdir=self.rootdir)\n # print \"sum iter->\", sum(b.iter(-3))\n self.assertTrue(sum(a[-3:]) == sum(b.iter(-3)), \"Sums are not equal\")","def getNoOfCols(self):\n return _patchExtractor.patchExtractor_getNoOfCols(self)","def get_zeros(self):\n zero_values = self.df[self.col_name].isin([0]).sum(axis=0)\n return zero_values","def __len__(self):\n return self.last - self.first + 1","def z(self):\n return self[:, 2]","def get_nb_element_per_dimension(recipe):\n return len(recipe[\"r\"]), len(recipe[\"c\"]), len(recipe[\"z\"])","def test03(self):\n a = np.arange(0.)\n b = bcolz.arange(1e4, rootdir=self.rootdir)\n b.trim(1e4)\n # print \"b->\", `b`\n self.assertTrue(len(a) == len(b), \"Lengths are not equal\")","def get_cells(self):\r\n return \\\r\n (self.nx-1 if self.nx>1 else 1)* \\\r\n (self.ny-1 if self.ny>1 else 1)* \\\r\n (self.nz-1 if self.nz>1 else 1)","def test06(self):\n a = np.arange(1e4, dtype='f8')\n b = bcolz.carray(a, chunklen=100, rootdir=self.rootdir)\n c = bcolz.fromiter((v for v in b.iter(skip=1010)), dtype='f8',\n count=10000-1010)\n # print \"c ->\", repr(c)\n assert_allclose(a[1010:], c, err_msg=\"iterator fails on zeros\")","def count_placeholders(series):\n count = 0\n\n for i in range(series.size-1, -1, -1):\n if pd.isnull(series[i]) or series[i] == 0:\n count += 1\n else:\n break\n\n return count","def __len__(self):\n return self._nrows","def __len__(self):\n return self.N.shape[0]","def countTrailingZeros(num):\n if num == 0:\n return 32 # Assumes 32 bit integer inputs!\n p = 0\n while (num >> p) & 1 == 0:\n p += 1\n return p","def nNy(self):\n if self.dim < 2:\n return None\n return self.nCy + 1"],"string":"[\n \"def getacolslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):\\n numnz_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def getaslicenumnz(self,accmode_,first_,last_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n res,resargs = self.__obj.getaslicenumnz64(accmode_,first_,last_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\n \"def getacolnumnz(self,i_):\\n nzj_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzj_ = nzj_.value\\n _nzj_return_value = nzj_\\n return (_nzj_return_value)\",\n \"def nNz(self):\\n if self.dim < 3:\\n return None\\n return self.nCz + 1\",\n \"def getacolnumnz(self,i_): # 3\\n res,resargs = self.__obj.getacolnumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n return _nzj_return_value\",\n \"def getarownumnz(self,i_):\\n nzi_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzi_ = nzi_.value\\n _nzi_return_value = nzi_\\n return (_nzi_return_value)\",\n \"def nnz(self):\\n return self.rep.nnz()\",\n \"def nnz(self):\\n return self.to_ddm().nnz()\",\n \"def getarownumnz(self,i_): # 3\\n res,resargs = self.__obj.getarownumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzi_return_value = resargs\\n return _nzi_return_value\",\n \"def nnz(self):\\n return len(self.data)\",\n \"def nCz(self):\\n if self.dim < 3:\\n return None\\n return int(self._n[2])\",\n \"def numRowsCols(array):\\n return len(array),len(array[0])\",\n \"def nnz(self):\",\n \"def nnz(self):\\n return len(self.value)\",\n \"def nnz(self):\\n t = self.get_MSC()\\n return len(np.unique(t['masks']))\",\n \"def count_lead_zs(self,x):\\n display_mask = 1 << 31\\n cnt = 0\\n for c in xrange(1,33):\\n if((x & display_mask) == 0):\\n cnt += 1\\n else:\\n return cnt\\n x <<= 1\\n return cnt\",\n \"def matrix_dim(CT):\\r\\n if CT[0]==0 and CT[-1]==0:\\r\\n return 2\\r\\n elif CT[0]!=0 and CT[-1]!=0:\\r\\n return 4\",\n \"def nz(self):\\n return self._dim[2]\",\n \"def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\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 getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\\n maxnumnz_ = self.getarowslicenumnz((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_getarowslice64(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 nnz(self):\\n\\t\\treturn self.st.size()\",\n \"def GetNumCols(self):\\n return _hypre.HypreParMatrix_GetNumCols(self)\",\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 N_z(self) -> int:\\n return self.params.N_z\",\n \"def getarowslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def NNZ(self):\\n return _hypre.HypreParMatrix_NNZ(self)\",\n \"def nrows(self):\\n if self.ncolumns() == 0:\\n return 0\\n nrows = self.table_column(0).nrows()\\n for i in range(1, self.ncolumns()):\\n nrows = min(self.table_column(i).nrows(), nrows)\\n return nrows\",\n \"def NNZ(self):\\n return len(self.__IndList)\",\n \"def ncol(arr):\\n assert isinstance(arr, ndarray)\\n assert len(arr.shape) == 2\\n return arr.shape[1]\",\n \"def trailingZeroes(self, n):\\n cnt = 0\\n while n:\\n n /= 5\\n cnt += n\\n\\n return cnt\",\n \"def count_nonzero(tensor):\\n raise NotImplementedError\",\n \"def num_cols(self):\\n return (len(self.rows[0]))\",\n \"def collatz(start):\\n counter = 1\\n n = start\\n while n != 2:\\n if n % 2 == 0:\\n n /= 2\\n else:\\n n = (n * 3) + 1\\n counter += 1\\n\\n counter += 1\\n return counter\",\n \"def zscore_brain_slice(X):\\n X[X == 0] = np.nan\\n X = (X - np.nanmean(X)) / np.nanstd(X)\\n X[np.isnan(X)] = 0\\n\\n return(X)\",\n \"def columns(self) -> int:\\n return self.__squares[0].__len__()\",\n \"def test07(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 cr = c[\\\"a + 2 * d - 3 > 0\\\"]\\n nr = a[(a + 2 * b - 3) > 0]\\n # print \\\"ca[expr] ->\\\", cr\\n # print \\\"numpy ->\\\", nr\\n assert_array_equal(cr[:], nr, \\\"carray[expr] does not work correctly\\\")\",\n \"def noOfElem(classObj, index):\\r\\n return len(classObj.dataSet[:, index])\",\n \"def part_one():\\n return len(numpy.where(grid > 1)[0])\",\n \"def colnum(self):\\n \\n colnum = 0\\n for table in self.columnlabels:\\n table = np.asarray(table)\\n if np.ndim(table) <= 1:\\n table = np.reshape(table, (1, -1))\\n colnum += table.shape[1]\\n return colnum\",\n \"def scalar_countleading0(self, dst, src):\\n return self._scalar_single_func('clz', dst, src)\",\n \"def n(self):\\n return nrow(self._array)\",\n \"def collatz(n):\\n if n%2==0: return n/2\\n else: return 3*n+1\",\n \"def getNsubint(self):\\n return self.shape(squeeze=False)[0]\",\n \"def num_cols(self):\\n return len(self.rows[0])\",\n \"def num_zeros_end(num):\\r\\n \\r\\n iszero = True\\r\\n num_zeros = 0\\r\\n i = len(num)-1\\r\\n \\r\\n while (iszero == True) and (i != 0):\\r\\n \\r\\n if num[i] == \\\"0\\\":\\r\\n num_zeros += 1\\r\\n \\r\\n elif num[i] != \\\"0\\\":\\r\\n iszero = False\\r\\n \\r\\n i -= 1 \\r\\n \\r\\n return num_zeros\",\n \"def columnCount(self, parent):\\r\\n if len(self.arraydata) > 0:\\r\\n return len(self.arraydata[0]) - 2\\r\\n return 0\",\n \"def __len__(self):\\n return self.xyz.shape[0]\",\n \"def __len__(self):\\n return self.xyz.shape[0]\",\n \"def __len__(self):\\n return len(np.where(np.logical_not(self.data.mask))[0])\",\n \"def zenith_nadir(x, y):\\n if y == 'm':\\n bb = []\\n cc = []\\n for i in range(x.shape[1]):\\n bb.append(amax(x[:, i:i + 1]))\\n b = array(bb)\\n cc.append(amin(x[:, i:i + 1]))\\n c = array(cc)\\n return (b, c)\\n else:\\n b = ones(x.shape[1])\\n c = zeros(x.shape[1])\\n return (b, c)\",\n \"def countZeroes(arr):\\n counter = 0\\n #sort the array\\n arr.sort(reverse=True)\\n print(arr)\\n n = len(arr)\\n print(n)\\n\\n # Find index of first zero in given array\\n first = firstZero(arr, 0, n - 1)\\n \\n # If 0 is not present at all, return 0\\n if (first == -1):\\n return 0\\n\\n for i in range(first,len(arr)):\\n if (arr[i] == 0):\\n counter += 1\\n else:\\n break\\n\\n return counter\",\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 test_get_date_column_index_first_col(self, one_row_worksheet):\\n\\n actual_result = one_row_worksheet.get_date_column_index()\\n assert actual_result == 0\",\n \"def _get_tr_dataset_size_from_z0(z0):\\n return int(np.round(np.exp(z0)))\",\n \"def numel(self):\\n return self.t.size\",\n \"def getNode_zSlice(self, nodeIdx):\\n\\t\\treturn self.nodeDictList[nodeIdx]['zSlice']\",\n \"def GetColStarts(self):\\n return _hypre.HypreParMatrix_GetColStarts(self)\",\n \"def columnCount(self):\\n return abs(self.minCol) + abs(self.maxCol)\",\n \"def count_level(self):\\r\\n count = 0\\r\\n\\r\\n for i in range(0, len(self.matrix)):\\r\\n for j in range(0,len(self.matrix[i])):\\r\\n if self.matrix[i][j] == \\\"0\\\":\\r\\n count += 1\\r\\n\\r\\n # We substract 1 to count level from 0\\r\\n return count - 1\",\n \"def __len__(self):\\n return np.size(self.A,0)\",\n \"def nonzero_last(arr, *, axis):\\n def nonzero_last_1d(arr):\\n try:\\n return np.nonzero(arr)[0][-1]\\n except IndexError:\\n return -1\\n return np.apply_along_axis(nonzero_last_1d, axis, arr)\",\n \"def getNbColumns(self):\\n return self.data.shape[0]\",\n \"def __column_height(self, x):\\n\\t\\tcolumn = self.board[:, x]\\n\\t\\treturn np.count_nonzero(column)\",\n \"def getNumCols(self):\\n return self.__cols\",\n \"def getlen(self):\\n if self.onlydiag():\\n return self.lendiag()\\n else:\\n return len(self)\",\n \"def num_cells_down(self):\\n if self.dim == 0:\\n return None\\n if hasattr(self, '__num_cells_down__'):\\n return self.__num_cells_down__\\n if self.lower_index is None:\\n return 0\\n raise ValueError('Cannot infer the number of cells in the cochain below.')\",\n \"def collatz(n):\\n iterCount = 0\\n while(n != 1):\\n if(n & 1):\\n n = 3 * n + 1\\n else:\\n n //= 2\\n iterCount += 1\\n return iterCount\",\n \"def Nx(self):\\n return self.shape[-1]\",\n \"def n_z(self, level):\\n resolution = self.resolution(level)\\n return (self.z_extent // resolution + 63) // 64\",\n \"def test_get_date_column_index_left_trailing_spaces(self, date_trailing_left_spaces_worksheet):\\n\\n actual_result = date_trailing_left_spaces_worksheet.get_date_column_index()\\n assert actual_result == 0\",\n \"def getarowslicetrip(self,first_,last_,subi,subj,val): # 3\\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 dim(self) -> int:\",\n \"def test02(self):\\n a = np.arange(self.N, dtype='uint64')\\n b = bcolz.carray(a, rootdir=self.rootdir)\\n c = iter(b.view())\\n u = c.iter(3)\\n w = b.iter(2)\\n self.assertEqual(sum(a[3:]), sum(u))\\n self.assertEqual(sum(a[2:]), sum(w))\",\n \"def n_cols(self):\\n ch = self.children\\n return 1 if not ch else sum([c.n_cols for c in ch])\",\n \"def TzscorrCols(Xn):\\n C_X = T.dot(Xn.T, Xn)-T.eye(Xn.shape[1])\\n return 0.5*T.log((1+C_X)/(1-C_X))\",\n \"def GetNumColumns(self):\\n return len(self.columns)\",\n \"def no_of_columns(self): \\n return len(self.columns) + (1 if self.serialize else 0)\",\n \"def ncolumns(self):\\n return self.__ncols\",\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 columns_count(self):\\n if self.value.count != 0:\\n return len(self.value[0])\\n else:\\n return 0\",\n \"def test01b(self):\\n a = np.arange(101)\\n b = bcolz.carray(a, chunklen=2, rootdir=self.rootdir)\\n # print \\\"sum iter->\\\", sum(b.iter(-3))\\n self.assertTrue(sum(a[-3:]) == sum(b.iter(-3)), \\\"Sums are not equal\\\")\",\n \"def getNoOfCols(self):\\n return _patchExtractor.patchExtractor_getNoOfCols(self)\",\n \"def get_zeros(self):\\n zero_values = self.df[self.col_name].isin([0]).sum(axis=0)\\n return zero_values\",\n \"def __len__(self):\\n return self.last - self.first + 1\",\n \"def z(self):\\n return self[:, 2]\",\n \"def get_nb_element_per_dimension(recipe):\\n return len(recipe[\\\"r\\\"]), len(recipe[\\\"c\\\"]), len(recipe[\\\"z\\\"])\",\n \"def test03(self):\\n a = np.arange(0.)\\n b = bcolz.arange(1e4, rootdir=self.rootdir)\\n b.trim(1e4)\\n # print \\\"b->\\\", `b`\\n self.assertTrue(len(a) == len(b), \\\"Lengths are not equal\\\")\",\n \"def get_cells(self):\\r\\n return \\\\\\r\\n (self.nx-1 if self.nx>1 else 1)* \\\\\\r\\n (self.ny-1 if self.ny>1 else 1)* \\\\\\r\\n (self.nz-1 if self.nz>1 else 1)\",\n \"def test06(self):\\n a = np.arange(1e4, dtype='f8')\\n b = bcolz.carray(a, chunklen=100, rootdir=self.rootdir)\\n c = bcolz.fromiter((v for v in b.iter(skip=1010)), dtype='f8',\\n count=10000-1010)\\n # print \\\"c ->\\\", repr(c)\\n assert_allclose(a[1010:], c, err_msg=\\\"iterator fails on zeros\\\")\",\n \"def count_placeholders(series):\\n count = 0\\n\\n for i in range(series.size-1, -1, -1):\\n if pd.isnull(series[i]) or series[i] == 0:\\n count += 1\\n else:\\n break\\n\\n return count\",\n \"def __len__(self):\\n return self._nrows\",\n \"def __len__(self):\\n return self.N.shape[0]\",\n \"def countTrailingZeros(num):\\n if num == 0:\\n return 32 # Assumes 32 bit integer inputs!\\n p = 0\\n while (num >> p) & 1 == 0:\\n p += 1\\n return p\",\n \"def nNy(self):\\n if self.dim < 2:\\n return None\\n return self.nCy + 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6821424","0.6395617","0.63196206","0.6160555","0.6055939","0.5976274","0.5861332","0.57293874","0.57270485","0.5675843","0.5640828","0.5561198","0.5522415","0.5509788","0.5488033","0.5468512","0.5459757","0.54302907","0.5399739","0.5388458","0.5382514","0.5348088","0.530128","0.5296612","0.51692855","0.50978416","0.50846","0.5081954","0.5080517","0.5080003","0.5077511","0.50485593","0.5021734","0.5020248","0.5011321","0.5011043","0.5005687","0.5001721","0.49781078","0.4976853","0.49711913","0.49615544","0.49544755","0.49446443","0.4939621","0.49215093","0.49181747","0.49151942","0.49110487","0.4901192","0.4901192","0.48758623","0.48669064","0.48474398","0.48472536","0.48390225","0.48375067","0.48306182","0.48283395","0.48165223","0.48116252","0.48033315","0.47997534","0.47962338","0.47857016","0.47831032","0.47752756","0.47749788","0.47736296","0.4772557","0.47692788","0.47691217","0.47626528","0.4761656","0.47515705","0.47410277","0.47387198","0.473403","0.47275788","0.47219548","0.47210297","0.4716367","0.4716367","0.4716367","0.4716367","0.4711346","0.47093692","0.47078493","0.47044513","0.47015232","0.46976617","0.4688688","0.4685905","0.46856713","0.46788332","0.4673877","0.46708304","0.46647826","0.4651293","0.46490005"],"string":"[\n \"0.6821424\",\n \"0.6395617\",\n \"0.63196206\",\n \"0.6160555\",\n \"0.6055939\",\n \"0.5976274\",\n \"0.5861332\",\n \"0.57293874\",\n \"0.57270485\",\n \"0.5675843\",\n \"0.5640828\",\n \"0.5561198\",\n \"0.5522415\",\n \"0.5509788\",\n \"0.5488033\",\n \"0.5468512\",\n \"0.5459757\",\n \"0.54302907\",\n \"0.5399739\",\n \"0.5388458\",\n \"0.5382514\",\n \"0.5348088\",\n \"0.530128\",\n \"0.5296612\",\n \"0.51692855\",\n \"0.50978416\",\n \"0.50846\",\n \"0.5081954\",\n \"0.5080517\",\n \"0.5080003\",\n \"0.5077511\",\n \"0.50485593\",\n \"0.5021734\",\n \"0.5020248\",\n \"0.5011321\",\n \"0.5011043\",\n \"0.5005687\",\n \"0.5001721\",\n \"0.49781078\",\n \"0.4976853\",\n \"0.49711913\",\n \"0.49615544\",\n \"0.49544755\",\n \"0.49446443\",\n \"0.4939621\",\n \"0.49215093\",\n \"0.49181747\",\n \"0.49151942\",\n \"0.49110487\",\n \"0.4901192\",\n \"0.4901192\",\n \"0.48758623\",\n \"0.48669064\",\n \"0.48474398\",\n \"0.48472536\",\n \"0.48390225\",\n \"0.48375067\",\n \"0.48306182\",\n \"0.48283395\",\n \"0.48165223\",\n \"0.48116252\",\n \"0.48033315\",\n \"0.47997534\",\n \"0.47962338\",\n \"0.47857016\",\n \"0.47831032\",\n \"0.47752756\",\n \"0.47749788\",\n \"0.47736296\",\n \"0.4772557\",\n \"0.47692788\",\n \"0.47691217\",\n \"0.47626528\",\n \"0.4761656\",\n \"0.47515705\",\n \"0.47410277\",\n \"0.47387198\",\n \"0.473403\",\n \"0.47275788\",\n \"0.47219548\",\n \"0.47210297\",\n \"0.4716367\",\n \"0.4716367\",\n \"0.4716367\",\n \"0.4716367\",\n \"0.4711346\",\n \"0.47093692\",\n \"0.47078493\",\n \"0.47044513\",\n \"0.47015232\",\n \"0.46976617\",\n \"0.4688688\",\n \"0.4685905\",\n \"0.46856713\",\n \"0.46788332\",\n \"0.4673877\",\n \"0.46708304\",\n \"0.46647826\",\n \"0.4651293\",\n \"0.46490005\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7653431"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11782,"cells":{"query":{"kind":"string","value":"Obtains a sequence of rows from the coefficient matrix. getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_)"},"document":{"kind":"string","value":"def getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\n maxnumnz_ = self.getarowslicenumnz((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_getarowslice64(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"},"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 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 getaslice(self,accmode_,first_,last_,ptrb,ptre,sub,val): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n maxnumnz_ = self.getaslicenumnz((accmode_),(first_),(last_))\n _copyback_ptrb = False\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 _copyback_ptrb = True\n else:\n if ptrb_.format != \"q\":\n ptrb_ = memoryview(array.array(\"q\",ptrb))\n _copyback_ptrb = True\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptrb has wrong length\")\n _copyback_ptre = False\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 _copyback_ptre = True\n else:\n if ptre_.format != \"q\":\n ptre_ = memoryview(array.array(\"q\",ptre))\n _copyback_ptre = True\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptre has wrong length\")\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(\"i\",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 != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n _copyback_sub = True\n if sub_ is not None and len(sub_) != (maxnumnz_):\n raise ValueError(\"Array argument sub 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.getaslice64(accmode_,first_,last_,maxnumnz_,len(sub),ptrb_,ptre_,sub_,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_sub:\n sub[:] = _tmparr_sub\n if _copyback_ptre:\n ptre[:] = _tmparr_ptre\n if _copyback_ptrb:\n ptrb[:] = _tmparr_ptrb","def __getslice__(self, i, j):\n return self.dtrs[i:j]","def getarowslicetrip(self,first_,last_,subi,subj,val): # 3\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 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 getacolslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","def getarowslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","def putarowslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\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 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 _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 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_putarowslice64(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 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 __getslice__(self,i,j):\n return self.x[i:j]","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 getarowslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","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 row_slice(self, xt, nproc):\n if nproc is None: nproc = self.nproc\n cs = xt.shape[0]//nproc #chuncksize\n tmp = [xt[i*cs:cs*i+cs,:] for i in range(nproc)]\n if nproc*cs != xt.shape[0]:\n tmp[-1] = np.concatenate((tmp[-1],xt[nproc*cs:xt.shape[0],:]),axis=0)\n return tmp","def slice(A,rowrange,colrange):\n\n\treturn [[get_elem(A,j,i) for j in rowrange] for i in colrange]","def __getslice__(self,i,j):\n nv=_Matr()\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\n nv.__maj()\n return nv","def slice_matrix(m,i,j):\n return np.take(np.take(m,i,0),j,1)","def __getslice__(self,i,j):\n nv=_Matr()\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\n nv._Matr__maj()\n return nv","def __getslice__(self, i, j):\n return self.__getitem__(slice(i,j))","def relay_array_getitem(c, a, start, stop, strides):\n assert start.is_constant(tuple)\n assert stop.is_constant(tuple)\n assert strides.is_constant(tuple)\n return relay.op.transform.strided_slice(c.ref(a), start.value, stop.value,\n strides.value)","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 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 __getslice__(self, i, j):\n return self.__getitem__(slice(i, j))","def __getslice__(self, start, stop):\n return self.__getitem__(slice(start, stop, None))","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 slice2(self, vs=None,xs=None):\n return self.condition2(vs,xs)","def as_slice(self):\n # slice for accessing arrays of values\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)","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 slice2(self, cvars=None,ctuple=None):\n return self.condition2(cvars,ctuple)","def __getslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___getslice__(self, *args)","def slice( mat, n, pt ):\n \n lc, uc = TemplateMatcher.hinge( pt.x, n ) ## column limits\n lr, ur = TemplateMatcher.hinge( pt.y, n )\n nr, nc = np.shape( mat )\n nr = nr - 1\n nc = nc - 1\n \n alc, auc = lc, uc \n alr, aur = lr, ur \n rpb, rpa, cpl, cpr = [0,0,0,0]\n\n if lc < 0:\n alc = 0\n cpl = -lc \n\n if uc > nc:\n auc = nc \n cpr = uc - auc \n\n if lr < 0:\n alr = 0\n rpb = -lr\n\n if ur > nr:\n aur = nr \n rpa = ur - aur \n\n return np.pad( mat[ alr : aur + 1 , alc : auc + 1 ], (( rpb, rpa ),( cpl, cpr )), mode ='constant' )","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 get(self):\n return np.hstack((self.data[:, self.cur:], self.data[:, :self.cur])) #Concatena los datos en horizontal","def to_slice(self):\n return np.index_exp[self.start[2]:self.end[2], #\n self.start[1]:self.end[1], #\n self.start[0]:self.end[0]]","def tail(self, rows, as_list=False):\n indexes = self.index[-int(rows) :]\n return self.get_table(indexes, as_list=as_list)","def subset(arr, start, end):\n return [[row_data for row_data in row[start[1]:end[1]]] for row in arr[start[0]:end[0]]]","def slice(self, i, j, n, m):\n return Table(rows=[list.__getitem__(self, i)[j:j+m] for i in range(i, i+n)])","def slice(tensor):\n out = tensor[:, 444:524, :]\n return out","def __getslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___getslice__(self, *args)","def _slice(tensor, size, i):\n return tensor[:, i * size : (i + 1) * size]","def slice(self, pred):\n return NotImplemented","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 get_row(A,r=0):\n\tA = zip(*A)\n\treturn list(A[r])","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 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 __getslice__( self, *args):\n return array.array.__getslice__(self, *args).tostring()","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 __getitem__(self, index: Union[int, slice]) -> Union[D2TXTRow, List[D2TXTRow]]:\n return self._rows[index]","def slice0(A,rowrange,colrange):\n\treturn [[A[i][j] for j in range(rowrange[0],rowrange[1])] for i in range(colrange[0],colrange[1])]","def getRow(self, i):\n return self.data[:,i]","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 _get_slice(series, start, length):\n return [ int(s) for s in series[start:start+length] ]","def get_slice(self, start: Optional[Index] = None, end: Optional[Index] = None):\n index = self._slice_index(slice(start, end))\n return self.get_table(index, self._columns)","def __getitem__(self, ind):\n try:\n\n if isinstance(ind, slice):\n if ind.start is None:\n start = self.increments\n else:\n start = ind.start + self.increments\n\n if ind.stop is not None:\n stop = ind.stop + self.increments\n\n ind = slice(start, stop)\n else:\n ind += self.increments\n\n return self.data.iloc[ind,:]\n\n except IndexError:\n warning('DataEngine: Index out of bounds')\n return None","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 genomeslice(input_array, strand, left, right, wrt = '5_to_3'):\n if left > right: # empty slice case\n return np.asarray([])\n elif (strand == 0) or (wrt is 'genome'):\n return input_array[strand,left:right+1]\n elif (strand == 1) and (wrt is '5_to_3'):\n return np.flip(input_array[strand,left:right+1],axis=0)\n else:\n raise ValueError(\"Unhandled strand {0 or 1} or wrt {'genome' or '5_to_3'} value.\")","def RowPart(self, *args):\n return _hypre.HypreParMatrix_RowPart(self, *args)","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 slice_pdb(self, start, stop):\n return","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 __getslice__(self, i, j):\n return OutputGroup(list.__getslice__(self, i, j))","def test05(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(None, None, -3)\n # print \"b[sl]->\", `b[sl]`\n self.assertRaises(NotImplementedError, b.__getitem__, sl)","def get_table_slice(\n self, table: Table, start: Optional[Index] = None, end: Optional[Index] = None\n ) -> Union[Table, List[List]]:\n self._requires_table(table)\n return table.get_slice(start, end)","def _slice(self, start, stop, step=None):\n\n slices = [slice(None)] * self.data.ndim\n slices[self.axis] = slice(start, stop, step)\n return tuple(slices)","def cqt_slices(record, t_len, shuffle=True, auto_restart=True,\n add_noise=True, random_seed=None,\n slice_logger=None):\n def cqt_slicer(cqt, idx, counter, t_len):\n obs = utils.slice_ndarray(cqt, idx[counter], length=t_len, axis=1)\n return obs[np.newaxis, ...]\n\n for cqt_slice in base_slicer(\n record, t_len, cqt_slicer,\n shuffle=shuffle, auto_restart=auto_restart,\n add_noise=add_noise, random_seed=random_seed,\n npz_data_key='cqt',\n slice_logger=slice_logger):\n yield cqt_slice","def slicer(seq, start=None, stop=None, step=None):\n return seq[start:stop:step]","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 sub_matrix(self, line, column):\n\n mat = []\n for i, row in enumerate(self.coefficients):\n if i != line:\n new_row = []\n for j, value in enumerate(row):\n if j != column:\n new_row.append(value)\n mat.append(new_row)\n\n return Matrix(mat)","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 test07(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 cr = c[\"a + 2 * d - 3 > 0\"]\n nr = a[(a + 2 * b - 3) > 0]\n # print \"ca[expr] ->\", cr\n # print \"numpy ->\", nr\n assert_array_equal(cr[:], nr, \"carray[expr] does not work correctly\")","def alternatingSlice(self,geom,polyLayer,targetArea,granularity,direction,method):\r\n global recurs\r\n recurs+=1\r\n if self.debug: print \"******************************\"\r\n if self.debug: print \"Slicing, No of part: \",str(recurs)\r\n if self.debug: print \"Slicing, Granularity remaining: \", str(granularity)\r\n bbox=[geom.boundingBox().xMinimum(),geom.boundingBox().yMinimum(),geom.boundingBox().xMaximum(),geom.boundingBox().yMaximum()]\r\n if direction==\"h\":\r\n step=(bbox[2]-bbox[0])/granularity\r\n pointer=bbox[0]\r\n else:\r\n step=(bbox[3]-bbox[1])/granularity\r\n pointer=bbox[1]\r\n totalArea=0\r\n slices=0\r\n #save the original geom\r\n tempGeom=QgsGeometry(geom)\r\n #start slicing until targetArea is reached\r\n while totalArea<targetArea*0.999:\r\n pointer+=step\r\n if direction==\"h\":\r\n startPt=QgsPoint(pointer,bbox[1])\r\n endPt=QgsPoint(pointer,bbox[3])\r\n (multiGeom,tempGeom)=self.cutPoly(tempGeom,startPt,endPt)\r\n else:\r\n startPt=QgsPoint(bbox[0],pointer)\r\n endPt=QgsPoint(bbox[2],pointer)\r\n (tempGeom,multiGeom)=self.cutPoly(tempGeom,startPt,endPt)\r\n if multiGeom!=None:\r\n totalArea+=multiGeom.area();\r\n slices+=1\r\n if self.debug: print \"Slicing, Slices: \", str(slices)\r\n #do the real cutting when reached targetArea and add \"left\" feature to layer\r\n if self.debug: print \"Cutting with line, Cutline:\", startPt,\",\",endPt\r\n if direction==\"h\":\r\n (multiGeom,geom)=self.cutPoly(geom,startPt,endPt,True)\r\n if multiGeom:\r\n if self.debug: print \"After split, Parts to the left:\",str(len(multiGeom.asGeometryCollection()))\r\n if geom:\r\n if self.debug: print \"After split, Parts to the right:\",str(len(geom.asGeometryCollection()))\r\n else:\r\n (geom,multiGeom)=self.cutPoly(geom,startPt,endPt,True)\r\n if geom:\r\n if self.debug: print \"After split, Parts above:\",str(len(geom.asGeometryCollection()))\r\n if multiGeom:\r\n if self.debug: print \"After split, Parts under:\",str(len(multiGeom.asGeometryCollection()))\r\n self.addGeomToLayer(multiGeom,polyLayer)\r\n #self.addGeomToLayer(QgsGeometry.fromPolyline([startPt,endPt]),lineLayer)\r\n if geom:\r\n if geom.area()>targetArea:\r\n if (method==\"v\") or ((method==\"a\") and (direction==\"h\")):\r\n self.alternatingSlice(geom,polyLayer,targetArea,granularity-slices,\"v\",method)\r\n else:\r\n self.alternatingSlice(geom,polyLayer,targetArea,granularity-slices,\"h\",method)\r\n else:\r\n self.addGeomToLayer(geom,polyLayer)","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 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 get_rows(self):\n raise NotImplementedError('Subclass this to make the rows')","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 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 test02b(self):\n a = np.arange(1e2)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(-3)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def gather_rows_1(tt_mat, inds):\n cores = tt_mat.tt_cores\n slices = []\n batch_size = int(inds[0].shape[0])\n\n\n ranks = [int(tt_core.shape[0]) for tt_core in tt_mat.tt_cores] + [1, ]\n\n\n for k, core in enumerate(cores):\n i = inds[k]\n #core = core.permute(1, 0, 2, 3).to(inds.device)\n\n cur_slice = torch.index_select(core, 1, i)\n\n if k == 0:\n res = cur_slice\n\n else:\n res = res.view(batch_size, -1, ranks[k])\n curr_core = cur_slice.view(ranks[k], batch_size, -1)\n res = torch.einsum('oqb,bow->oqw', (res, curr_core))\n\n return res\n\n #slices.append(torch.index_select(core, 1, i).permute(1, 0, 2, 3))","def get_row_slice(xlrd_sheet, start_row):\r\n num_rows = xlrd_sheet.nrows\r\n\r\n for _ in range(num_rows):\r\n # print start_row\r\n yield xlrd_sheet.row_slice(rowx=start_row, start_colx=0, end_colx=3)\r\n start_row += 1","def getSlice(properties=None, **kw):","def test_slice_second_last(self):\n self.table.append(['Tom', 26])\n self.table.append(['Chantelle', 24])\n self.assertEqual(self.table[-2], ['Tom', 26])","def get_slice(self):\n return self.locs[tuple(self.indices), :]","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 __getslice__(self, *args):\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___getslice__(self, *args)","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 test02(self):\n a = np.arange(0, 1000)\n b = bcolz.carray(a, chunklen=10)\n sl = np.random.randint(0, 1000, size=3*30)\n vals = np.random.randint(1, 1000, size=3*30)\n b[sl] = vals\n a[sl] = vals\n # print \"b[%s] -> %r\" % (sl, b)\n assert_array_equal(b[:], a, \"fancy indexing does not work correctly\")","def wcqt_slices(record, t_len, shuffle=True, auto_restart=True, add_noise=True,\n p_len=54, p_stride=36, random_seed=None, slice_logger=None):\n def wcqt_slicer(cqt, idx, counter, t_len):\n # Grab the obs\n obs = utils.slice_ndarray(cqt, idx[counter], length=t_len, axis=1)\n # Convert it to WCQT\n wcqt = utils.fold_array(obs[0], length=p_len, stride=p_stride)\n # Fix the shape.s\n return wcqt[np.newaxis, ...]\n\n for wcqt_slice in base_slicer(\n record, t_len, wcqt_slicer,\n shuffle=shuffle, auto_restart=auto_restart,\n add_noise=add_noise, random_seed=random_seed,\n npz_data_key='cqt',\n slice_logger=slice_logger):\n yield wcqt_slice","def test02a(self):\n a = np.arange(1e2)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(1, 3)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","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 Rt(X):\n return X[:2,:2], X[:2, 2]","def _fprop_slice_np(h, stride, H, roi_offset):\n hstart = int(np.floor(float(h) * stride))\n hend = int(np.ceil(float(h + 1) * stride))\n\n hstart = min(max(hstart + roi_offset, 0), H)\n hend = min(max(hend + roi_offset, 0), H)\n\n return slice(hstart, hend), hend - hstart","def GetRowPartArray(self):\n return _hypre.HypreParMatrix_GetRowPartArray(self)","def subArray(self,left,rightplus):\n lst=self.boolVals[:]\n \n sm=\"\"\n for i in range(len(lst)):\n if lst[i]:\n sm+=\"1\"\n else:\n sm+=\"0\"\n newlst=sm[left:rightplus]\n newlst=newlst[::-1]\n final=hex(int(newlst,2))\n final=final[2:]\n\n return BoolArray(final,rightplus-left)","def test03(self):\n a = np.arange(1, 101)\n b = bcolz.carray(a)\n c = b[[1.1, 3.3]]\n r = a[[1, 3]]\n assert_array_equal(c, r, \"fancy indexing does not work correctly\")","def rows(self):\r\n raise NotImplementedError","def get_row( self, rowIndex ):\n return self.data[rowIndex,:]","def __getslice__(self, *args):\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint2___getslice__(self, *args)","def sub_columns(arr, sub_size):\n return sub_rows(arr.T, sub_size)","def matPart(mat, rs, re, cs, ce):\n return [[matGet(mat,x,y) for y in range(cs,ce)] \\\n for x in range(rs,re)]","def block(a, r=3, cs=3, row_order=True):\n lenr = a.shape[0]//rs\n lenc = a.shape[1]//cs\n if row_order:\n iter = [(i, j) for (i, j) in np.ndindex(lenr, lenc)]\n else:\n iter = [(j, i) for (i, j) in np.ndindex(lenr, lenc)]\n b = np.array([a[i*rs:(i+1)*rs, j*cs:(j+1)*cs] for (i,j) in iter])\n #b = np.array([a[i*rs:(i+1)*rs, j*cs:(j+1)*cs] \n # for (i, j) in np.ndindex(lenr, lenc)])\n return b"],"string":"[\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 getaslice(self,accmode_,first_,last_,ptrb,ptre,sub,val): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n maxnumnz_ = self.getaslicenumnz((accmode_),(first_),(last_))\\n _copyback_ptrb = False\\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 _copyback_ptrb = True\\n else:\\n if ptrb_.format != \\\"q\\\":\\n ptrb_ = memoryview(array.array(\\\"q\\\",ptrb))\\n _copyback_ptrb = True\\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptrb has wrong length\\\")\\n _copyback_ptre = False\\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 _copyback_ptre = True\\n else:\\n if ptre_.format != \\\"q\\\":\\n ptre_ = memoryview(array.array(\\\"q\\\",ptre))\\n _copyback_ptre = True\\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptre has wrong length\\\")\\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(\\\"i\\\",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 != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n _copyback_sub = True\\n if sub_ is not None and len(sub_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument sub 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.getaslice64(accmode_,first_,last_,maxnumnz_,len(sub),ptrb_,ptre_,sub_,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_sub:\\n sub[:] = _tmparr_sub\\n if _copyback_ptre:\\n ptre[:] = _tmparr_ptre\\n if _copyback_ptrb:\\n ptrb[:] = _tmparr_ptrb\",\n \"def __getslice__(self, i, j):\\n return self.dtrs[i:j]\",\n \"def getarowslicetrip(self,first_,last_,subi,subj,val): # 3\\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 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 getacolslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def getarowslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def putarowslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\\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 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 _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 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_putarowslice64(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 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 __getslice__(self,i,j):\\n return self.x[i:j]\",\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 getarowslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\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 row_slice(self, xt, nproc):\\n if nproc is None: nproc = self.nproc\\n cs = xt.shape[0]//nproc #chuncksize\\n tmp = [xt[i*cs:cs*i+cs,:] for i in range(nproc)]\\n if nproc*cs != xt.shape[0]:\\n tmp[-1] = np.concatenate((tmp[-1],xt[nproc*cs:xt.shape[0],:]),axis=0)\\n return tmp\",\n \"def slice(A,rowrange,colrange):\\n\\n\\treturn [[get_elem(A,j,i) for j in rowrange] for i in colrange]\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\\n nv.__maj()\\n return nv\",\n \"def slice_matrix(m,i,j):\\n return np.take(np.take(m,i,0),j,1)\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\\n nv._Matr__maj()\\n return nv\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i,j))\",\n \"def relay_array_getitem(c, a, start, stop, strides):\\n assert start.is_constant(tuple)\\n assert stop.is_constant(tuple)\\n assert strides.is_constant(tuple)\\n return relay.op.transform.strided_slice(c.ref(a), start.value, stop.value,\\n strides.value)\",\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 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 __getslice__(self, i, j):\\n return self.__getitem__(slice(i, j))\",\n \"def __getslice__(self, start, stop):\\n return self.__getitem__(slice(start, stop, None))\",\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 slice2(self, vs=None,xs=None):\\n return self.condition2(vs,xs)\",\n \"def as_slice(self):\\n # slice for accessing arrays of values\\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)\",\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 slice2(self, cvars=None,ctuple=None):\\n return self.condition2(cvars,ctuple)\",\n \"def __getslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___getslice__(self, *args)\",\n \"def slice( mat, n, pt ):\\n \\n lc, uc = TemplateMatcher.hinge( pt.x, n ) ## column limits\\n lr, ur = TemplateMatcher.hinge( pt.y, n )\\n nr, nc = np.shape( mat )\\n nr = nr - 1\\n nc = nc - 1\\n \\n alc, auc = lc, uc \\n alr, aur = lr, ur \\n rpb, rpa, cpl, cpr = [0,0,0,0]\\n\\n if lc < 0:\\n alc = 0\\n cpl = -lc \\n\\n if uc > nc:\\n auc = nc \\n cpr = uc - auc \\n\\n if lr < 0:\\n alr = 0\\n rpb = -lr\\n\\n if ur > nr:\\n aur = nr \\n rpa = ur - aur \\n\\n return np.pad( mat[ alr : aur + 1 , alc : auc + 1 ], (( rpb, rpa ),( cpl, cpr )), mode ='constant' )\",\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 get(self):\\n return np.hstack((self.data[:, self.cur:], self.data[:, :self.cur])) #Concatena los datos en horizontal\",\n \"def to_slice(self):\\n return np.index_exp[self.start[2]:self.end[2], #\\n self.start[1]:self.end[1], #\\n self.start[0]:self.end[0]]\",\n \"def tail(self, rows, as_list=False):\\n indexes = self.index[-int(rows) :]\\n return self.get_table(indexes, as_list=as_list)\",\n \"def subset(arr, start, end):\\n return [[row_data for row_data in row[start[1]:end[1]]] for row in arr[start[0]:end[0]]]\",\n \"def slice(self, i, j, n, m):\\n return Table(rows=[list.__getitem__(self, i)[j:j+m] for i in range(i, i+n)])\",\n \"def slice(tensor):\\n out = tensor[:, 444:524, :]\\n return out\",\n \"def __getslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___getslice__(self, *args)\",\n \"def _slice(tensor, size, i):\\n return tensor[:, i * size : (i + 1) * size]\",\n \"def slice(self, pred):\\n return NotImplemented\",\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 get_row(A,r=0):\\n\\tA = zip(*A)\\n\\treturn list(A[r])\",\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 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 __getslice__( self, *args):\\n return array.array.__getslice__(self, *args).tostring()\",\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 __getitem__(self, index: Union[int, slice]) -> Union[D2TXTRow, List[D2TXTRow]]:\\n return self._rows[index]\",\n \"def slice0(A,rowrange,colrange):\\n\\treturn [[A[i][j] for j in range(rowrange[0],rowrange[1])] for i in range(colrange[0],colrange[1])]\",\n \"def getRow(self, i):\\n return self.data[:,i]\",\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 _get_slice(series, start, length):\\n return [ int(s) for s in series[start:start+length] ]\",\n \"def get_slice(self, start: Optional[Index] = None, end: Optional[Index] = None):\\n index = self._slice_index(slice(start, end))\\n return self.get_table(index, self._columns)\",\n \"def __getitem__(self, ind):\\n try:\\n\\n if isinstance(ind, slice):\\n if ind.start is None:\\n start = self.increments\\n else:\\n start = ind.start + self.increments\\n\\n if ind.stop is not None:\\n stop = ind.stop + self.increments\\n\\n ind = slice(start, stop)\\n else:\\n ind += self.increments\\n\\n return self.data.iloc[ind,:]\\n\\n except IndexError:\\n warning('DataEngine: Index out of bounds')\\n return None\",\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 genomeslice(input_array, strand, left, right, wrt = '5_to_3'):\\n if left > right: # empty slice case\\n return np.asarray([])\\n elif (strand == 0) or (wrt is 'genome'):\\n return input_array[strand,left:right+1]\\n elif (strand == 1) and (wrt is '5_to_3'):\\n return np.flip(input_array[strand,left:right+1],axis=0)\\n else:\\n raise ValueError(\\\"Unhandled strand {0 or 1} or wrt {'genome' or '5_to_3'} value.\\\")\",\n \"def RowPart(self, *args):\\n return _hypre.HypreParMatrix_RowPart(self, *args)\",\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 slice_pdb(self, start, stop):\\n return\",\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 __getslice__(self, i, j):\\n return OutputGroup(list.__getslice__(self, i, j))\",\n \"def test05(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(None, None, -3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n self.assertRaises(NotImplementedError, b.__getitem__, sl)\",\n \"def get_table_slice(\\n self, table: Table, start: Optional[Index] = None, end: Optional[Index] = None\\n ) -> Union[Table, List[List]]:\\n self._requires_table(table)\\n return table.get_slice(start, end)\",\n \"def _slice(self, start, stop, step=None):\\n\\n slices = [slice(None)] * self.data.ndim\\n slices[self.axis] = slice(start, stop, step)\\n return tuple(slices)\",\n \"def cqt_slices(record, t_len, shuffle=True, auto_restart=True,\\n add_noise=True, random_seed=None,\\n slice_logger=None):\\n def cqt_slicer(cqt, idx, counter, t_len):\\n obs = utils.slice_ndarray(cqt, idx[counter], length=t_len, axis=1)\\n return obs[np.newaxis, ...]\\n\\n for cqt_slice in base_slicer(\\n record, t_len, cqt_slicer,\\n shuffle=shuffle, auto_restart=auto_restart,\\n add_noise=add_noise, random_seed=random_seed,\\n npz_data_key='cqt',\\n slice_logger=slice_logger):\\n yield cqt_slice\",\n \"def slicer(seq, start=None, stop=None, step=None):\\n return seq[start:stop:step]\",\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 sub_matrix(self, line, column):\\n\\n mat = []\\n for i, row in enumerate(self.coefficients):\\n if i != line:\\n new_row = []\\n for j, value in enumerate(row):\\n if j != column:\\n new_row.append(value)\\n mat.append(new_row)\\n\\n return Matrix(mat)\",\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 test07(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 cr = c[\\\"a + 2 * d - 3 > 0\\\"]\\n nr = a[(a + 2 * b - 3) > 0]\\n # print \\\"ca[expr] ->\\\", cr\\n # print \\\"numpy ->\\\", nr\\n assert_array_equal(cr[:], nr, \\\"carray[expr] does not work correctly\\\")\",\n \"def alternatingSlice(self,geom,polyLayer,targetArea,granularity,direction,method):\\r\\n global recurs\\r\\n recurs+=1\\r\\n if self.debug: print \\\"******************************\\\"\\r\\n if self.debug: print \\\"Slicing, No of part: \\\",str(recurs)\\r\\n if self.debug: print \\\"Slicing, Granularity remaining: \\\", str(granularity)\\r\\n bbox=[geom.boundingBox().xMinimum(),geom.boundingBox().yMinimum(),geom.boundingBox().xMaximum(),geom.boundingBox().yMaximum()]\\r\\n if direction==\\\"h\\\":\\r\\n step=(bbox[2]-bbox[0])/granularity\\r\\n pointer=bbox[0]\\r\\n else:\\r\\n step=(bbox[3]-bbox[1])/granularity\\r\\n pointer=bbox[1]\\r\\n totalArea=0\\r\\n slices=0\\r\\n #save the original geom\\r\\n tempGeom=QgsGeometry(geom)\\r\\n #start slicing until targetArea is reached\\r\\n while totalArea<targetArea*0.999:\\r\\n pointer+=step\\r\\n if direction==\\\"h\\\":\\r\\n startPt=QgsPoint(pointer,bbox[1])\\r\\n endPt=QgsPoint(pointer,bbox[3])\\r\\n (multiGeom,tempGeom)=self.cutPoly(tempGeom,startPt,endPt)\\r\\n else:\\r\\n startPt=QgsPoint(bbox[0],pointer)\\r\\n endPt=QgsPoint(bbox[2],pointer)\\r\\n (tempGeom,multiGeom)=self.cutPoly(tempGeom,startPt,endPt)\\r\\n if multiGeom!=None:\\r\\n totalArea+=multiGeom.area();\\r\\n slices+=1\\r\\n if self.debug: print \\\"Slicing, Slices: \\\", str(slices)\\r\\n #do the real cutting when reached targetArea and add \\\"left\\\" feature to layer\\r\\n if self.debug: print \\\"Cutting with line, Cutline:\\\", startPt,\\\",\\\",endPt\\r\\n if direction==\\\"h\\\":\\r\\n (multiGeom,geom)=self.cutPoly(geom,startPt,endPt,True)\\r\\n if multiGeom:\\r\\n if self.debug: print \\\"After split, Parts to the left:\\\",str(len(multiGeom.asGeometryCollection()))\\r\\n if geom:\\r\\n if self.debug: print \\\"After split, Parts to the right:\\\",str(len(geom.asGeometryCollection()))\\r\\n else:\\r\\n (geom,multiGeom)=self.cutPoly(geom,startPt,endPt,True)\\r\\n if geom:\\r\\n if self.debug: print \\\"After split, Parts above:\\\",str(len(geom.asGeometryCollection()))\\r\\n if multiGeom:\\r\\n if self.debug: print \\\"After split, Parts under:\\\",str(len(multiGeom.asGeometryCollection()))\\r\\n self.addGeomToLayer(multiGeom,polyLayer)\\r\\n #self.addGeomToLayer(QgsGeometry.fromPolyline([startPt,endPt]),lineLayer)\\r\\n if geom:\\r\\n if geom.area()>targetArea:\\r\\n if (method==\\\"v\\\") or ((method==\\\"a\\\") and (direction==\\\"h\\\")):\\r\\n self.alternatingSlice(geom,polyLayer,targetArea,granularity-slices,\\\"v\\\",method)\\r\\n else:\\r\\n self.alternatingSlice(geom,polyLayer,targetArea,granularity-slices,\\\"h\\\",method)\\r\\n else:\\r\\n self.addGeomToLayer(geom,polyLayer)\",\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 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 get_rows(self):\\n raise NotImplementedError('Subclass this to make the rows')\",\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 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 test02b(self):\\n a = np.arange(1e2)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(-3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def gather_rows_1(tt_mat, inds):\\n cores = tt_mat.tt_cores\\n slices = []\\n batch_size = int(inds[0].shape[0])\\n\\n\\n ranks = [int(tt_core.shape[0]) for tt_core in tt_mat.tt_cores] + [1, ]\\n\\n\\n for k, core in enumerate(cores):\\n i = inds[k]\\n #core = core.permute(1, 0, 2, 3).to(inds.device)\\n\\n cur_slice = torch.index_select(core, 1, i)\\n\\n if k == 0:\\n res = cur_slice\\n\\n else:\\n res = res.view(batch_size, -1, ranks[k])\\n curr_core = cur_slice.view(ranks[k], batch_size, -1)\\n res = torch.einsum('oqb,bow->oqw', (res, curr_core))\\n\\n return res\\n\\n #slices.append(torch.index_select(core, 1, i).permute(1, 0, 2, 3))\",\n \"def get_row_slice(xlrd_sheet, start_row):\\r\\n num_rows = xlrd_sheet.nrows\\r\\n\\r\\n for _ in range(num_rows):\\r\\n # print start_row\\r\\n yield xlrd_sheet.row_slice(rowx=start_row, start_colx=0, end_colx=3)\\r\\n start_row += 1\",\n \"def getSlice(properties=None, **kw):\",\n \"def test_slice_second_last(self):\\n self.table.append(['Tom', 26])\\n self.table.append(['Chantelle', 24])\\n self.assertEqual(self.table[-2], ['Tom', 26])\",\n \"def get_slice(self):\\n return self.locs[tuple(self.indices), :]\",\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 __getslice__(self, *args):\\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___getslice__(self, *args)\",\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 test02(self):\\n a = np.arange(0, 1000)\\n b = bcolz.carray(a, chunklen=10)\\n sl = np.random.randint(0, 1000, size=3*30)\\n vals = np.random.randint(1, 1000, size=3*30)\\n b[sl] = vals\\n a[sl] = vals\\n # print \\\"b[%s] -> %r\\\" % (sl, b)\\n assert_array_equal(b[:], a, \\\"fancy indexing does not work correctly\\\")\",\n \"def wcqt_slices(record, t_len, shuffle=True, auto_restart=True, add_noise=True,\\n p_len=54, p_stride=36, random_seed=None, slice_logger=None):\\n def wcqt_slicer(cqt, idx, counter, t_len):\\n # Grab the obs\\n obs = utils.slice_ndarray(cqt, idx[counter], length=t_len, axis=1)\\n # Convert it to WCQT\\n wcqt = utils.fold_array(obs[0], length=p_len, stride=p_stride)\\n # Fix the shape.s\\n return wcqt[np.newaxis, ...]\\n\\n for wcqt_slice in base_slicer(\\n record, t_len, wcqt_slicer,\\n shuffle=shuffle, auto_restart=auto_restart,\\n add_noise=add_noise, random_seed=random_seed,\\n npz_data_key='cqt',\\n slice_logger=slice_logger):\\n yield wcqt_slice\",\n \"def test02a(self):\\n a = np.arange(1e2)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(1, 3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\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 Rt(X):\\n return X[:2,:2], X[:2, 2]\",\n \"def _fprop_slice_np(h, stride, H, roi_offset):\\n hstart = int(np.floor(float(h) * stride))\\n hend = int(np.ceil(float(h + 1) * stride))\\n\\n hstart = min(max(hstart + roi_offset, 0), H)\\n hend = min(max(hend + roi_offset, 0), H)\\n\\n return slice(hstart, hend), hend - hstart\",\n \"def GetRowPartArray(self):\\n return _hypre.HypreParMatrix_GetRowPartArray(self)\",\n \"def subArray(self,left,rightplus):\\n lst=self.boolVals[:]\\n \\n sm=\\\"\\\"\\n for i in range(len(lst)):\\n if lst[i]:\\n sm+=\\\"1\\\"\\n else:\\n sm+=\\\"0\\\"\\n newlst=sm[left:rightplus]\\n newlst=newlst[::-1]\\n final=hex(int(newlst,2))\\n final=final[2:]\\n\\n return BoolArray(final,rightplus-left)\",\n \"def test03(self):\\n a = np.arange(1, 101)\\n b = bcolz.carray(a)\\n c = b[[1.1, 3.3]]\\n r = a[[1, 3]]\\n assert_array_equal(c, r, \\\"fancy indexing does not work correctly\\\")\",\n \"def rows(self):\\r\\n raise NotImplementedError\",\n \"def get_row( self, rowIndex ):\\n return self.data[rowIndex,:]\",\n \"def __getslice__(self, *args):\\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint2___getslice__(self, *args)\",\n \"def sub_columns(arr, sub_size):\\n return sub_rows(arr.T, sub_size)\",\n \"def matPart(mat, rs, re, cs, ce):\\n return [[matGet(mat,x,y) for y in range(cs,ce)] \\\\\\n for x in range(rs,re)]\",\n \"def block(a, r=3, cs=3, row_order=True):\\n lenr = a.shape[0]//rs\\n lenc = a.shape[1]//cs\\n if row_order:\\n iter = [(i, j) for (i, j) in np.ndindex(lenr, lenc)]\\n else:\\n iter = [(j, i) for (i, j) in np.ndindex(lenr, lenc)]\\n b = np.array([a[i*rs:(i+1)*rs, j*cs:(j+1)*cs] for (i,j) in iter])\\n #b = np.array([a[i*rs:(i+1)*rs, j*cs:(j+1)*cs] \\n # for (i, j) in np.ndindex(lenr, lenc)])\\n return b\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72424006","0.7017984","0.6397862","0.6392683","0.6295936","0.62946856","0.62616587","0.6202416","0.6158898","0.60779953","0.5999932","0.59529024","0.5874609","0.5798113","0.57943004","0.5755523","0.56610376","0.5640397","0.5582677","0.55730736","0.5569688","0.5535197","0.5525514","0.551946","0.5454037","0.5440375","0.5414295","0.53724456","0.5327867","0.5305298","0.5298006","0.52820367","0.5265003","0.52584136","0.525321","0.5246065","0.5230291","0.52270246","0.5214388","0.5187147","0.5130512","0.5099331","0.50985104","0.5092332","0.50838596","0.5076242","0.5065706","0.50629324","0.5062075","0.50402427","0.50356245","0.5008236","0.4997074","0.4987978","0.4986138","0.4985667","0.49790362","0.4974492","0.49615982","0.49455583","0.49434","0.49399355","0.49288315","0.4917927","0.491423","0.49070853","0.48913264","0.48851478","0.4866479","0.48626688","0.48610938","0.48609728","0.48602855","0.48554674","0.48527542","0.48474294","0.48395285","0.48374873","0.48365036","0.48344576","0.48327783","0.48306268","0.4827815","0.48253673","0.48163384","0.47826645","0.4763669","0.47569114","0.4754894","0.47530338","0.47491363","0.47472987","0.4746375","0.47424126","0.4740368","0.4738886","0.47382462","0.47326967","0.47326776","0.47316495"],"string":"[\n \"0.72424006\",\n \"0.7017984\",\n \"0.6397862\",\n \"0.6392683\",\n \"0.6295936\",\n \"0.62946856\",\n \"0.62616587\",\n \"0.6202416\",\n \"0.6158898\",\n \"0.60779953\",\n \"0.5999932\",\n \"0.59529024\",\n \"0.5874609\",\n \"0.5798113\",\n \"0.57943004\",\n \"0.5755523\",\n \"0.56610376\",\n \"0.5640397\",\n \"0.5582677\",\n \"0.55730736\",\n \"0.5569688\",\n \"0.5535197\",\n \"0.5525514\",\n \"0.551946\",\n \"0.5454037\",\n \"0.5440375\",\n \"0.5414295\",\n \"0.53724456\",\n \"0.5327867\",\n \"0.5305298\",\n \"0.5298006\",\n \"0.52820367\",\n \"0.5265003\",\n \"0.52584136\",\n \"0.525321\",\n \"0.5246065\",\n \"0.5230291\",\n \"0.52270246\",\n \"0.5214388\",\n \"0.5187147\",\n \"0.5130512\",\n \"0.5099331\",\n \"0.50985104\",\n \"0.5092332\",\n \"0.50838596\",\n \"0.5076242\",\n \"0.5065706\",\n \"0.50629324\",\n \"0.5062075\",\n \"0.50402427\",\n \"0.50356245\",\n \"0.5008236\",\n \"0.4997074\",\n \"0.4987978\",\n \"0.4986138\",\n \"0.4985667\",\n \"0.49790362\",\n \"0.4974492\",\n \"0.49615982\",\n \"0.49455583\",\n \"0.49434\",\n \"0.49399355\",\n \"0.49288315\",\n \"0.4917927\",\n \"0.491423\",\n \"0.49070853\",\n \"0.48913264\",\n \"0.48851478\",\n \"0.4866479\",\n \"0.48626688\",\n \"0.48610938\",\n \"0.48609728\",\n \"0.48602855\",\n \"0.48554674\",\n \"0.48527542\",\n \"0.48474294\",\n \"0.48395285\",\n \"0.48374873\",\n \"0.48365036\",\n \"0.48344576\",\n \"0.48327783\",\n \"0.48306268\",\n \"0.4827815\",\n \"0.48253673\",\n \"0.48163384\",\n \"0.47826645\",\n \"0.4763669\",\n \"0.47569114\",\n \"0.4754894\",\n \"0.47530338\",\n \"0.47491363\",\n \"0.47472987\",\n \"0.4746375\",\n \"0.47424126\",\n \"0.4740368\",\n \"0.4738886\",\n \"0.47382462\",\n \"0.47326967\",\n \"0.47326776\",\n \"0.47316495\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7663158"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11783,"cells":{"query":{"kind":"string","value":"Obtains a sequence of rows from the coefficient matrix in sparse triplet format. getarowslicetrip(self,first_,last_,subi_,subj_,val_)"},"document":{"kind":"string","value":"def getarowslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_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 getarowslicetrip(self,first_,last_,subi,subj,val): # 3\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 getacolslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","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 __getslice__(self, i, j):\n return self.dtrs[i:j]","def getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\n maxnumnz_ = self.getarowslicenumnz((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_getarowslice64(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 getaslice(self,accmode_,first_,last_,ptrb,ptre,sub,val): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n maxnumnz_ = self.getaslicenumnz((accmode_),(first_),(last_))\n _copyback_ptrb = False\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 _copyback_ptrb = True\n else:\n if ptrb_.format != \"q\":\n ptrb_ = memoryview(array.array(\"q\",ptrb))\n _copyback_ptrb = True\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptrb has wrong length\")\n _copyback_ptre = False\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 _copyback_ptre = True\n else:\n if ptre_.format != \"q\":\n ptre_ = memoryview(array.array(\"q\",ptre))\n _copyback_ptre = True\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptre has wrong length\")\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(\"i\",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 != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n _copyback_sub = True\n if sub_ is not None and len(sub_) != (maxnumnz_):\n raise ValueError(\"Array argument sub 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.getaslice64(accmode_,first_,last_,maxnumnz_,len(sub),ptrb_,ptre_,sub_,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_sub:\n sub[:] = _tmparr_sub\n if _copyback_ptre:\n ptre[:] = _tmparr_ptre\n if _copyback_ptrb:\n ptrb[:] = _tmparr_ptrb","def __getslice__(self,i,j):\n return self.x[i:j]","def slice_matrix(m,i,j):\n return np.take(np.take(m,i,0),j,1)","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 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 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 __getslice__(self, i, j):\n return self.__getitem__(slice(i,j))","def __getslice__(self, i, j):\n return self.__getitem__(slice(i, j))","def gather_rows_1(tt_mat, inds):\n cores = tt_mat.tt_cores\n slices = []\n batch_size = int(inds[0].shape[0])\n\n\n ranks = [int(tt_core.shape[0]) for tt_core in tt_mat.tt_cores] + [1, ]\n\n\n for k, core in enumerate(cores):\n i = inds[k]\n #core = core.permute(1, 0, 2, 3).to(inds.device)\n\n cur_slice = torch.index_select(core, 1, i)\n\n if k == 0:\n res = cur_slice\n\n else:\n res = res.view(batch_size, -1, ranks[k])\n curr_core = cur_slice.view(ranks[k], batch_size, -1)\n res = torch.einsum('oqb,bow->oqw', (res, curr_core))\n\n return res\n\n #slices.append(torch.index_select(core, 1, i).permute(1, 0, 2, 3))","def __getslice__(self,i,j):\n nv=_Matr()\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\n nv.__maj()\n return nv","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 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 getRow(self, i):\n return self.data[:,i]","def __getslice__(self, start, stop):\n return self.__getitem__(slice(start, stop, None))","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 __getslice__(self,i,j):\n nv=_Matr()\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\n nv._Matr__maj()\n return nv","def getarowslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def __getitem__(self, index: Union[int, slice]) -> Union[D2TXTRow, List[D2TXTRow]]:\n return self._rows[index]","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 row_slice(self, xt, nproc):\n if nproc is None: nproc = self.nproc\n cs = xt.shape[0]//nproc #chuncksize\n tmp = [xt[i*cs:cs*i+cs,:] for i in range(nproc)]\n if nproc*cs != xt.shape[0]:\n tmp[-1] = np.concatenate((tmp[-1],xt[nproc*cs:xt.shape[0],:]),axis=0)\n return tmp","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 _slice(tensor, size, i):\n return tensor[:, i * size : (i + 1) * size]","def get_diagonal_subtensor_view(x, i0, i1):\r\n # We have to cast i0 and i0 to int because python 2.4 (and maybe later)\r\n # do not support indexing with 0-dim, 'int*' ndarrays.\r\n i0 = int(i0)\r\n i1 = int(i1)\r\n if x.shape[i0] < x.shape[i1]:\r\n raise NotImplementedError('is this allowed?')\r\n idx = [slice(None)] * x.ndim\r\n idx[i0] = slice(x.shape[i1] - 1, None, None)\r\n xview = x.__getitem__(tuple(idx))\r\n strides = list(xview.strides)\r\n strides[i1] -= strides[i0]\r\n xview.strides = strides\r\n return xview","def slice(A,rowrange,colrange):\n\n\treturn [[get_elem(A,j,i) for j in rowrange] for i in colrange]","def subset(arr, start, end):\n return [[row_data for row_data in row[start[1]:end[1]]] for row in arr[start[0]:end[0]]]","def slice2(self, cvars=None,ctuple=None):\n return self.condition2(cvars,ctuple)","def slice(tensor):\n out = tensor[:, 444:524, :]\n return out","def get_subset(self, times=None, varids=None):\n if times is None:\n dat = self.ix[:,varids]\n times = self.times\n elif varids is None:\n dat = self.ix[times,:]\n varids = self.varids\n else:\n dat = self.ix[times,varids]\n subtraj = Trajectory(dat=dat, times=times, varids=varids) \n return subtraj","def get_row( self, rowIndex ):\n return self.data[rowIndex,:]","def relay_array_getitem(c, a, start, stop, strides):\n assert start.is_constant(tuple)\n assert stop.is_constant(tuple)\n assert strides.is_constant(tuple)\n return relay.op.transform.strided_slice(c.ref(a), start.value, stop.value,\n strides.value)","def subset_(self):\n return self.X.ix[:, self.important_features_]","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 slice(self, i, j, n, m):\n return Table(rows=[list.__getitem__(self, i)[j:j+m] for i in range(i, i+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","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 slice2(self, vs=None,xs=None):\n return self.condition2(vs,xs)","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 to_slice(self):\n return np.index_exp[self.start[2]:self.end[2], #\n self.start[1]:self.end[1], #\n self.start[0]:self.end[0]]","def getbarablocktriplet(self,subi_,subj_,subk_,subl_,valijkl_):\n maxnum_ = self.getnumbarablocktriplets()\n num_ = ctypes.c_int64()\n _subi_minlength = (maxnum_)\n if (maxnum_) > 0 and subi_ is not None and len(subi_) != (maxnum_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnum_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\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 = (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 _valijkl_minlength = (maxnum_)\n if (maxnum_) > 0 and valijkl_ is not None and len(valijkl_) != (maxnum_):\n raise ValueError(\"Array argument valijkl is not long enough: Is %d, expected %d\" % (len(valijkl_),(maxnum_)))\n if isinstance(valijkl_,numpy.ndarray) and not valijkl_.flags.writeable:\n raise ValueError(\"Argument valijkl must be writable\")\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_getbarablocktriplet(self.__nativep,maxnum_,ctypes.byref(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 num_ = num_.value\n _num_return_value = num_\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\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 _valijkl_copyarray:\n valijkl_[:] = _valijkl_np_tmp\n return (_num_return_value)","def getSlice(properties=None, **kw):","def tail(self, rows, as_list=False):\n indexes = self.index[-int(rows) :]\n return self.get_table(indexes, as_list=as_list)","def sub_rows(arr, sub_size):\n rows, cols = arr.shape\n for i in range(rows):\n for j in range(cols - sub_size + 1):\n yield arr[i, range(j, j + sub_size)]","def getrow(self, i):\n new = lil_matrix((1, self.shape[1]), dtype=self.dtype)\n new.rows[0] = self.rows[i][:]\n new.data[0] = self.data[i][:]\n return new","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 __getitem__(self, ind):\n try:\n\n if isinstance(ind, slice):\n if ind.start is None:\n start = self.increments\n else:\n start = ind.start + self.increments\n\n if ind.stop is not None:\n stop = ind.stop + self.increments\n\n ind = slice(start, stop)\n else:\n ind += self.increments\n\n return self.data.iloc[ind,:]\n\n except IndexError:\n warning('DataEngine: Index out of bounds')\n return None","def cqt_slices(record, t_len, shuffle=True, auto_restart=True,\n add_noise=True, random_seed=None,\n slice_logger=None):\n def cqt_slicer(cqt, idx, counter, t_len):\n obs = utils.slice_ndarray(cqt, idx[counter], length=t_len, axis=1)\n return obs[np.newaxis, ...]\n\n for cqt_slice in base_slicer(\n record, t_len, cqt_slicer,\n shuffle=shuffle, auto_restart=auto_restart,\n add_noise=add_noise, random_seed=random_seed,\n npz_data_key='cqt',\n slice_logger=slice_logger):\n yield cqt_slice","def __getitem__(self,k):\n if type(k) is IntType: return self.data[k, 0]\n \n vec = [type(x) is SliceType for x in k]\n \n if True in vec: #suppose only one slice\n ii=vec.index(True)\n indices=[]\n k = list(k)\n import numpy\n rep = numpy.zeros((self.dims[ii],), 'd')\n for i in range(self.dims[ii]):\n k[ii] = i\n rep[i] = self.data[self.comp(k), 0]\n return rep\n else:\n return self.data[self.comp(k), 0]","def sub_major_diags(arr, sub_size):\n rows, cols = arr.shape\n for i in range(rows - sub_size + 1):\n for j in range(cols - sub_size + 1):\n yield arr[range(i, i + sub_size), range(j, j + sub_size)]","def test_slice_single_first(self):\n self.table.append(['Tom', 26])\n self.assertEqual(self.table[0], ['Tom', 26])","def get_slice(self):\n return self.locs[tuple(self.indices), :]","def slicer(seq, start=None, stop=None, step=None):\n return seq[start:stop:step]","def as_slice(self):\n # slice for accessing arrays of values\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)","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 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 RowPart(self, *args):\n return _hypre.HypreParMatrix_RowPart(self, *args)","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 __getslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___getslice__(self, *args)","def _ixs(self, i, axis=0):\n result = super()._ixs(i, axis=axis)\n\n # Override columns\n if axis == 0:\n if isinstance(i, slice):\n return self[i]\n else:\n label = self.index[i]\n if isinstance(label, Index):\n # a location index by definition\n result = self.take(i, axis=axis)\n copy = True\n else:\n new_values = self._data.fast_xs(i)\n\n # if we are a copy, mark as such\n copy = (\n isinstance(new_values, np.ndarray) and new_values.base is None\n )\n result = self._constructor_sliced(\n new_values,\n index=self.columns,\n name=self.index[i],\n dtype=new_values.dtype,\n au_columns=self.au_columns,\n emotion_columns=self.emotion_columns,\n facebox_columns=self.facebox_columns,\n landmark_columns=self.landmark_columns,\n facepose_columns=self.facepose_columns,\n gaze_columns=self.gaze_columns,\n time_columns=self.time_columns,\n design_columns=self.design_columns,\n filename=self.filename,\n sampling_freq=self.sampling_freq,\n detector=self.detector,\n features=self.features,\n sessions=self.sessions,\n )\n result._set_is_copy(self, copy=copy)\n return result\n\n else:\n \"\"\"\n Notes\n -----\n If slice passed, the resulting data will be a view\n \"\"\"\n\n label = self.columns[i]\n if isinstance(i, slice):\n # need to return view\n lab_slice = slice(label[0], label[-1])\n return self.loc[:, lab_slice]\n else:\n if isinstance(label, Index):\n return self._take(i, axis=1, convert=True)\n\n index_len = len(self.index)\n\n # if the values returned are not the same length\n # as the index (iow a not found value), iget returns\n # a 0-len ndarray. This is effectively catching\n # a numpy error (as numpy should really raise)\n values = self._data.iget(i)\n\n if index_len and not len(values):\n values = np.array([np.nan] * index_len, dtype=object)\n result = self._constructor_sliced(\n values,\n index=self.index,\n name=label,\n fastpath=True,\n au_columns=self.au_columns,\n emotion_columns=self.emotion_columns,\n facebox_columns=self.facebox_columns,\n landmark_columns=self.landmark_columns,\n facepose_columns=self.facepose_columns,\n gaze_columns=self.gaze_columns,\n time_columns=self.time_columns,\n design_columns=self.design_columns,\n filename=self.filename,\n sampling_freq=self.sampling_freq,\n detector=self.detector,\n features=self.features,\n sessions=self.sessions,\n )\n\n # this is a cached value, mark it so\n result._set_as_cached(label, self)\n return result","def slice(data, size):\n\treturn dice(data, size).T","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 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 getRow(self, idx):\r\n m=self.mlist\r\n if idx==0: return _vec4(m[0], m[1], m[2], m[3])\r\n elif idx==1: return _vec4(m[4], m[5], m[6], m[7])\r\n elif idx==2: return _vec4(m[8], m[9], m[10], m[11])\r\n elif idx==3: return _vec4(m[12], m[13], m[14], m[15])\r\n else:\r\n raise IndexError,\"index out of range\"","def get_row_slice(xlrd_sheet, start_row):\r\n num_rows = xlrd_sheet.nrows\r\n\r\n for _ in range(num_rows):\r\n # print start_row\r\n yield xlrd_sheet.row_slice(rowx=start_row, start_colx=0, end_colx=3)\r\n start_row += 1","def __getitem__(self, val):\n start = datetime_to_millis(val.start)\n stop = datetime_to_millis(val.stop)\n return TimeSeriesRDD(None, None, self._jtsrdd.slice(start, stop), self.ctx)","def __getslice__( self, *args):\n return array.array.__getslice__(self, *args).tostring()","def __getslice__(self, i, j):\n if self.__pepth__ != 0:\n if '__warned__' not in plist.__getslice__.__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.__getslice__.__dict__['__warned__'] = True\n return plist.__getattr__(self, '__getslice__')(i, j)\n try:\n if self is self.__root__:\n return plist(list.__getslice__(self, i, j))\n return plist(list.__getslice__(self, i, j), root=plist(list.__getslice__(self.__root__, i, j)))\n except Exception:\n return plist.__getitem__(self, slice(i, j))","def slice0(A,rowrange,colrange):\n\treturn [[A[i][j] for j in range(rowrange[0],rowrange[1])] for i in range(colrange[0],colrange[1])]","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 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 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 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","def _get_slice(series, start, length):\n return [ int(s) for s in series[start:start+length] ]","def __getitem__(self, idx):\n return self.data.iloc[idx]","def test_slice(self):\n r = self.RNA(\"UCAGG\")\n self.assertEqual(r[0], \"U\")\n self.assertEqual(r[-1], \"G\")\n self.assertEqual(r[1:3], \"CA\")","def sub_columns(arr, sub_size):\n return sub_rows(arr.T, sub_size)","def slice(self, pred):\n return NotImplemented","def get_row(A: Matrix, i: int) -> Vector:\n return A[i]","def get_row(A: Matrix, i: int) -> Vector:\n return A[i]","def get_row(A: Matrix, i: int) -> Vector:\n return A[i]","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 get_slice(self, start: Optional[Index] = None, end: Optional[Index] = None):\n index = self._slice_index(slice(start, end))\n return self.get_table(index, self._columns)","def __getitem__(self, idx):\n if idx >= len(self):\n raise StopIteration\n\n _, timesteps, height, width = self.data.shape\n\n # If time=True, return an entire time slice,\n # otherwise return a single time step\n t = slice(None)\n if not self.time:\n t = idx % timesteps\n idx = idx // timesteps\n\n if self.subset == 'train':\n # Right quadrants\n # Return a random subset\n xl = width // 2\n xr = width - self.size\n x = random.randint(xl, xr)\n\n yu = 0\n yl = height - self.size\n y = random.randint(yu, yl)\n elif self.subset == 'val':\n # Bottom left quadrant\n # Convert the index to a (row, col) location\n row = idx // (width // 2 // self.size)\n col = idx % (width // 2 // self.size)\n\n # Find the exact coordinates in the array\n y = row * self.size + (height // 2)\n x = col * self.size\n elif self.subset == 'test':\n # Top left quadrant\n # Convert the index to a (row, col) location\n row = idx // (width // 2 // self.size)\n col = idx % (width // 2 // self.size)\n\n # Find the exact coordinates in the array\n y = row * self.size\n x = col * self.size\n else:\n # All quadrants\n # Convert the index to a (row, col) location\n row = idx // (width // self.size)\n col = idx % (width // self.size)\n\n # Find the exact coordinates in the array\n y = row * self.size\n x = col * self.size\n\n data = self.data[:, t, y:y + self.size, x:x + self.size]\n target = self.segmentation[y:y + self.size, x:x + self.size]\n\n # Apply any requested transforms\n if self.transform:\n data = self.transform(data)\n\n if self.target_transform:\n target = self.target_transform(target)\n\n if isinstance(t, slice):\n t = np.zeros_like(y) - 1\n\n return data, target, t, y, x","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 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 _slice(self, start, stop, step=None):\n\n slices = [slice(None)] * self.data.ndim\n slices[self.axis] = slice(start, stop, step)\n return tuple(slices)","def putarowslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\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 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 _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 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_putarowslice64(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 __getslice__(self, i, j):\n return OutputGroup(list.__getslice__(self, i, j))","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 wcqt_slices(record, t_len, shuffle=True, auto_restart=True, add_noise=True,\n p_len=54, p_stride=36, random_seed=None, slice_logger=None):\n def wcqt_slicer(cqt, idx, counter, t_len):\n # Grab the obs\n obs = utils.slice_ndarray(cqt, idx[counter], length=t_len, axis=1)\n # Convert it to WCQT\n wcqt = utils.fold_array(obs[0], length=p_len, stride=p_stride)\n # Fix the shape.s\n return wcqt[np.newaxis, ...]\n\n for wcqt_slice in base_slicer(\n record, t_len, wcqt_slicer,\n shuffle=shuffle, auto_restart=auto_restart,\n add_noise=add_noise, random_seed=random_seed,\n npz_data_key='cqt',\n slice_logger=slice_logger):\n yield wcqt_slice","def getrowview(self, i):\n new = lil_matrix((1, self.shape[1]), dtype=self.dtype)\n new.rows[0] = self.rows[i]\n new.data[0] = self.data[i]\n return new","def __getitem__(self, idx):\n if len(idx) == 1:\n return self.rows[idx[0]]\n else:\n return self.rows[idx[0]][idx[1]]","def est_row_2_base_row(i):\n row = np.zeros(n)\n for j in range(k):\n row[est_inx_2_base_inx[j]] = est_T[i,j]\n return row","def test_slice_second(self):\n self.table.append(['Tom', 26])\n self.table.append(['Chantelle', 24])\n self.assertEqual(self.table[1], ['Chantelle', 24])","def Rt(X):\n return X[:2,:2], X[:2, 2]","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"],"string":"[\n \"def getarowslicetrip(self,first_,last_,subi,subj,val): # 3\\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 getacolslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\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 __getslice__(self, i, j):\\n return self.dtrs[i:j]\",\n \"def getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\\n maxnumnz_ = self.getarowslicenumnz((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_getarowslice64(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 getaslice(self,accmode_,first_,last_,ptrb,ptre,sub,val): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n maxnumnz_ = self.getaslicenumnz((accmode_),(first_),(last_))\\n _copyback_ptrb = False\\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 _copyback_ptrb = True\\n else:\\n if ptrb_.format != \\\"q\\\":\\n ptrb_ = memoryview(array.array(\\\"q\\\",ptrb))\\n _copyback_ptrb = True\\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptrb has wrong length\\\")\\n _copyback_ptre = False\\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 _copyback_ptre = True\\n else:\\n if ptre_.format != \\\"q\\\":\\n ptre_ = memoryview(array.array(\\\"q\\\",ptre))\\n _copyback_ptre = True\\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptre has wrong length\\\")\\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(\\\"i\\\",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 != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n _copyback_sub = True\\n if sub_ is not None and len(sub_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument sub 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.getaslice64(accmode_,first_,last_,maxnumnz_,len(sub),ptrb_,ptre_,sub_,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_sub:\\n sub[:] = _tmparr_sub\\n if _copyback_ptre:\\n ptre[:] = _tmparr_ptre\\n if _copyback_ptrb:\\n ptrb[:] = _tmparr_ptrb\",\n \"def __getslice__(self,i,j):\\n return self.x[i:j]\",\n \"def slice_matrix(m,i,j):\\n return np.take(np.take(m,i,0),j,1)\",\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 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 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 __getslice__(self, i, j):\\n return self.__getitem__(slice(i,j))\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i, j))\",\n \"def gather_rows_1(tt_mat, inds):\\n cores = tt_mat.tt_cores\\n slices = []\\n batch_size = int(inds[0].shape[0])\\n\\n\\n ranks = [int(tt_core.shape[0]) for tt_core in tt_mat.tt_cores] + [1, ]\\n\\n\\n for k, core in enumerate(cores):\\n i = inds[k]\\n #core = core.permute(1, 0, 2, 3).to(inds.device)\\n\\n cur_slice = torch.index_select(core, 1, i)\\n\\n if k == 0:\\n res = cur_slice\\n\\n else:\\n res = res.view(batch_size, -1, ranks[k])\\n curr_core = cur_slice.view(ranks[k], batch_size, -1)\\n res = torch.einsum('oqb,bow->oqw', (res, curr_core))\\n\\n return res\\n\\n #slices.append(torch.index_select(core, 1, i).permute(1, 0, 2, 3))\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\\n nv.__maj()\\n return nv\",\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 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 getRow(self, i):\\n return self.data[:,i]\",\n \"def __getslice__(self, start, stop):\\n return self.__getitem__(slice(start, stop, None))\",\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 __getslice__(self,i,j):\\n nv=_Matr()\\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\\n nv._Matr__maj()\\n return nv\",\n \"def getarowslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def __getitem__(self, index: Union[int, slice]) -> Union[D2TXTRow, List[D2TXTRow]]:\\n return self._rows[index]\",\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 row_slice(self, xt, nproc):\\n if nproc is None: nproc = self.nproc\\n cs = xt.shape[0]//nproc #chuncksize\\n tmp = [xt[i*cs:cs*i+cs,:] for i in range(nproc)]\\n if nproc*cs != xt.shape[0]:\\n tmp[-1] = np.concatenate((tmp[-1],xt[nproc*cs:xt.shape[0],:]),axis=0)\\n return tmp\",\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 _slice(tensor, size, i):\\n return tensor[:, i * size : (i + 1) * size]\",\n \"def get_diagonal_subtensor_view(x, i0, i1):\\r\\n # We have to cast i0 and i0 to int because python 2.4 (and maybe later)\\r\\n # do not support indexing with 0-dim, 'int*' ndarrays.\\r\\n i0 = int(i0)\\r\\n i1 = int(i1)\\r\\n if x.shape[i0] < x.shape[i1]:\\r\\n raise NotImplementedError('is this allowed?')\\r\\n idx = [slice(None)] * x.ndim\\r\\n idx[i0] = slice(x.shape[i1] - 1, None, None)\\r\\n xview = x.__getitem__(tuple(idx))\\r\\n strides = list(xview.strides)\\r\\n strides[i1] -= strides[i0]\\r\\n xview.strides = strides\\r\\n return xview\",\n \"def slice(A,rowrange,colrange):\\n\\n\\treturn [[get_elem(A,j,i) for j in rowrange] for i in colrange]\",\n \"def subset(arr, start, end):\\n return [[row_data for row_data in row[start[1]:end[1]]] for row in arr[start[0]:end[0]]]\",\n \"def slice2(self, cvars=None,ctuple=None):\\n return self.condition2(cvars,ctuple)\",\n \"def slice(tensor):\\n out = tensor[:, 444:524, :]\\n return out\",\n \"def get_subset(self, times=None, varids=None):\\n if times is None:\\n dat = self.ix[:,varids]\\n times = self.times\\n elif varids is None:\\n dat = self.ix[times,:]\\n varids = self.varids\\n else:\\n dat = self.ix[times,varids]\\n subtraj = Trajectory(dat=dat, times=times, varids=varids) \\n return subtraj\",\n \"def get_row( self, rowIndex ):\\n return self.data[rowIndex,:]\",\n \"def relay_array_getitem(c, a, start, stop, strides):\\n assert start.is_constant(tuple)\\n assert stop.is_constant(tuple)\\n assert strides.is_constant(tuple)\\n return relay.op.transform.strided_slice(c.ref(a), start.value, stop.value,\\n strides.value)\",\n \"def subset_(self):\\n return self.X.ix[:, self.important_features_]\",\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 slice(self, i, j, n, m):\\n return Table(rows=[list.__getitem__(self, i)[j:j+m] for i in range(i, i+n)])\",\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 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 slice2(self, vs=None,xs=None):\\n return self.condition2(vs,xs)\",\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 to_slice(self):\\n return np.index_exp[self.start[2]:self.end[2], #\\n self.start[1]:self.end[1], #\\n self.start[0]:self.end[0]]\",\n \"def getbarablocktriplet(self,subi_,subj_,subk_,subl_,valijkl_):\\n maxnum_ = self.getnumbarablocktriplets()\\n num_ = ctypes.c_int64()\\n _subi_minlength = (maxnum_)\\n if (maxnum_) > 0 and subi_ is not None and len(subi_) != (maxnum_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnum_)))\\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\\n raise ValueError(\\\"Argument subi must be writable\\\")\\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 = (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 _valijkl_minlength = (maxnum_)\\n if (maxnum_) > 0 and valijkl_ is not None and len(valijkl_) != (maxnum_):\\n raise ValueError(\\\"Array argument valijkl is not long enough: Is %d, expected %d\\\" % (len(valijkl_),(maxnum_)))\\n if isinstance(valijkl_,numpy.ndarray) and not valijkl_.flags.writeable:\\n raise ValueError(\\\"Argument valijkl must be writable\\\")\\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_getbarablocktriplet(self.__nativep,maxnum_,ctypes.byref(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 num_ = num_.value\\n _num_return_value = num_\\n if _subi_copyarray:\\n subi_[:] = _subi_np_tmp\\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 _valijkl_copyarray:\\n valijkl_[:] = _valijkl_np_tmp\\n return (_num_return_value)\",\n \"def getSlice(properties=None, **kw):\",\n \"def tail(self, rows, as_list=False):\\n indexes = self.index[-int(rows) :]\\n return self.get_table(indexes, as_list=as_list)\",\n \"def sub_rows(arr, sub_size):\\n rows, cols = arr.shape\\n for i in range(rows):\\n for j in range(cols - sub_size + 1):\\n yield arr[i, range(j, j + sub_size)]\",\n \"def getrow(self, i):\\n new = lil_matrix((1, self.shape[1]), dtype=self.dtype)\\n new.rows[0] = self.rows[i][:]\\n new.data[0] = self.data[i][:]\\n return new\",\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 __getitem__(self, ind):\\n try:\\n\\n if isinstance(ind, slice):\\n if ind.start is None:\\n start = self.increments\\n else:\\n start = ind.start + self.increments\\n\\n if ind.stop is not None:\\n stop = ind.stop + self.increments\\n\\n ind = slice(start, stop)\\n else:\\n ind += self.increments\\n\\n return self.data.iloc[ind,:]\\n\\n except IndexError:\\n warning('DataEngine: Index out of bounds')\\n return None\",\n \"def cqt_slices(record, t_len, shuffle=True, auto_restart=True,\\n add_noise=True, random_seed=None,\\n slice_logger=None):\\n def cqt_slicer(cqt, idx, counter, t_len):\\n obs = utils.slice_ndarray(cqt, idx[counter], length=t_len, axis=1)\\n return obs[np.newaxis, ...]\\n\\n for cqt_slice in base_slicer(\\n record, t_len, cqt_slicer,\\n shuffle=shuffle, auto_restart=auto_restart,\\n add_noise=add_noise, random_seed=random_seed,\\n npz_data_key='cqt',\\n slice_logger=slice_logger):\\n yield cqt_slice\",\n \"def __getitem__(self,k):\\n if type(k) is IntType: return self.data[k, 0]\\n \\n vec = [type(x) is SliceType for x in k]\\n \\n if True in vec: #suppose only one slice\\n ii=vec.index(True)\\n indices=[]\\n k = list(k)\\n import numpy\\n rep = numpy.zeros((self.dims[ii],), 'd')\\n for i in range(self.dims[ii]):\\n k[ii] = i\\n rep[i] = self.data[self.comp(k), 0]\\n return rep\\n else:\\n return self.data[self.comp(k), 0]\",\n \"def sub_major_diags(arr, sub_size):\\n rows, cols = arr.shape\\n for i in range(rows - sub_size + 1):\\n for j in range(cols - sub_size + 1):\\n yield arr[range(i, i + sub_size), range(j, j + sub_size)]\",\n \"def test_slice_single_first(self):\\n self.table.append(['Tom', 26])\\n self.assertEqual(self.table[0], ['Tom', 26])\",\n \"def get_slice(self):\\n return self.locs[tuple(self.indices), :]\",\n \"def slicer(seq, start=None, stop=None, step=None):\\n return seq[start:stop:step]\",\n \"def as_slice(self):\\n # slice for accessing arrays of values\\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)\",\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 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 RowPart(self, *args):\\n return _hypre.HypreParMatrix_RowPart(self, *args)\",\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 __getslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___getslice__(self, *args)\",\n \"def _ixs(self, i, axis=0):\\n result = super()._ixs(i, axis=axis)\\n\\n # Override columns\\n if axis == 0:\\n if isinstance(i, slice):\\n return self[i]\\n else:\\n label = self.index[i]\\n if isinstance(label, Index):\\n # a location index by definition\\n result = self.take(i, axis=axis)\\n copy = True\\n else:\\n new_values = self._data.fast_xs(i)\\n\\n # if we are a copy, mark as such\\n copy = (\\n isinstance(new_values, np.ndarray) and new_values.base is None\\n )\\n result = self._constructor_sliced(\\n new_values,\\n index=self.columns,\\n name=self.index[i],\\n dtype=new_values.dtype,\\n au_columns=self.au_columns,\\n emotion_columns=self.emotion_columns,\\n facebox_columns=self.facebox_columns,\\n landmark_columns=self.landmark_columns,\\n facepose_columns=self.facepose_columns,\\n gaze_columns=self.gaze_columns,\\n time_columns=self.time_columns,\\n design_columns=self.design_columns,\\n filename=self.filename,\\n sampling_freq=self.sampling_freq,\\n detector=self.detector,\\n features=self.features,\\n sessions=self.sessions,\\n )\\n result._set_is_copy(self, copy=copy)\\n return result\\n\\n else:\\n \\\"\\\"\\\"\\n Notes\\n -----\\n If slice passed, the resulting data will be a view\\n \\\"\\\"\\\"\\n\\n label = self.columns[i]\\n if isinstance(i, slice):\\n # need to return view\\n lab_slice = slice(label[0], label[-1])\\n return self.loc[:, lab_slice]\\n else:\\n if isinstance(label, Index):\\n return self._take(i, axis=1, convert=True)\\n\\n index_len = len(self.index)\\n\\n # if the values returned are not the same length\\n # as the index (iow a not found value), iget returns\\n # a 0-len ndarray. This is effectively catching\\n # a numpy error (as numpy should really raise)\\n values = self._data.iget(i)\\n\\n if index_len and not len(values):\\n values = np.array([np.nan] * index_len, dtype=object)\\n result = self._constructor_sliced(\\n values,\\n index=self.index,\\n name=label,\\n fastpath=True,\\n au_columns=self.au_columns,\\n emotion_columns=self.emotion_columns,\\n facebox_columns=self.facebox_columns,\\n landmark_columns=self.landmark_columns,\\n facepose_columns=self.facepose_columns,\\n gaze_columns=self.gaze_columns,\\n time_columns=self.time_columns,\\n design_columns=self.design_columns,\\n filename=self.filename,\\n sampling_freq=self.sampling_freq,\\n detector=self.detector,\\n features=self.features,\\n sessions=self.sessions,\\n )\\n\\n # this is a cached value, mark it so\\n result._set_as_cached(label, self)\\n return result\",\n \"def slice(data, size):\\n\\treturn dice(data, size).T\",\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 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 getRow(self, idx):\\r\\n m=self.mlist\\r\\n if idx==0: return _vec4(m[0], m[1], m[2], m[3])\\r\\n elif idx==1: return _vec4(m[4], m[5], m[6], m[7])\\r\\n elif idx==2: return _vec4(m[8], m[9], m[10], m[11])\\r\\n elif idx==3: return _vec4(m[12], m[13], m[14], m[15])\\r\\n else:\\r\\n raise IndexError,\\\"index out of range\\\"\",\n \"def get_row_slice(xlrd_sheet, start_row):\\r\\n num_rows = xlrd_sheet.nrows\\r\\n\\r\\n for _ in range(num_rows):\\r\\n # print start_row\\r\\n yield xlrd_sheet.row_slice(rowx=start_row, start_colx=0, end_colx=3)\\r\\n start_row += 1\",\n \"def __getitem__(self, val):\\n start = datetime_to_millis(val.start)\\n stop = datetime_to_millis(val.stop)\\n return TimeSeriesRDD(None, None, self._jtsrdd.slice(start, stop), self.ctx)\",\n \"def __getslice__( self, *args):\\n return array.array.__getslice__(self, *args).tostring()\",\n \"def __getslice__(self, i, j):\\n if self.__pepth__ != 0:\\n if '__warned__' not in plist.__getslice__.__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.__getslice__.__dict__['__warned__'] = True\\n return plist.__getattr__(self, '__getslice__')(i, j)\\n try:\\n if self is self.__root__:\\n return plist(list.__getslice__(self, i, j))\\n return plist(list.__getslice__(self, i, j), root=plist(list.__getslice__(self.__root__, i, j)))\\n except Exception:\\n return plist.__getitem__(self, slice(i, j))\",\n \"def slice0(A,rowrange,colrange):\\n\\treturn [[A[i][j] for j in range(rowrange[0],rowrange[1])] for i in range(colrange[0],colrange[1])]\",\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 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 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 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 \"def _get_slice(series, start, length):\\n return [ int(s) for s in series[start:start+length] ]\",\n \"def __getitem__(self, idx):\\n return self.data.iloc[idx]\",\n \"def test_slice(self):\\n r = self.RNA(\\\"UCAGG\\\")\\n self.assertEqual(r[0], \\\"U\\\")\\n self.assertEqual(r[-1], \\\"G\\\")\\n self.assertEqual(r[1:3], \\\"CA\\\")\",\n \"def sub_columns(arr, sub_size):\\n return sub_rows(arr.T, sub_size)\",\n \"def slice(self, pred):\\n return NotImplemented\",\n \"def get_row(A: Matrix, i: int) -> Vector:\\n return A[i]\",\n \"def get_row(A: Matrix, i: int) -> Vector:\\n return A[i]\",\n \"def get_row(A: Matrix, i: int) -> Vector:\\n return A[i]\",\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 get_slice(self, start: Optional[Index] = None, end: Optional[Index] = None):\\n index = self._slice_index(slice(start, end))\\n return self.get_table(index, self._columns)\",\n \"def __getitem__(self, idx):\\n if idx >= len(self):\\n raise StopIteration\\n\\n _, timesteps, height, width = self.data.shape\\n\\n # If time=True, return an entire time slice,\\n # otherwise return a single time step\\n t = slice(None)\\n if not self.time:\\n t = idx % timesteps\\n idx = idx // timesteps\\n\\n if self.subset == 'train':\\n # Right quadrants\\n # Return a random subset\\n xl = width // 2\\n xr = width - self.size\\n x = random.randint(xl, xr)\\n\\n yu = 0\\n yl = height - self.size\\n y = random.randint(yu, yl)\\n elif self.subset == 'val':\\n # Bottom left quadrant\\n # Convert the index to a (row, col) location\\n row = idx // (width // 2 // self.size)\\n col = idx % (width // 2 // self.size)\\n\\n # Find the exact coordinates in the array\\n y = row * self.size + (height // 2)\\n x = col * self.size\\n elif self.subset == 'test':\\n # Top left quadrant\\n # Convert the index to a (row, col) location\\n row = idx // (width // 2 // self.size)\\n col = idx % (width // 2 // self.size)\\n\\n # Find the exact coordinates in the array\\n y = row * self.size\\n x = col * self.size\\n else:\\n # All quadrants\\n # Convert the index to a (row, col) location\\n row = idx // (width // self.size)\\n col = idx % (width // self.size)\\n\\n # Find the exact coordinates in the array\\n y = row * self.size\\n x = col * self.size\\n\\n data = self.data[:, t, y:y + self.size, x:x + self.size]\\n target = self.segmentation[y:y + self.size, x:x + self.size]\\n\\n # Apply any requested transforms\\n if self.transform:\\n data = self.transform(data)\\n\\n if self.target_transform:\\n target = self.target_transform(target)\\n\\n if isinstance(t, slice):\\n t = np.zeros_like(y) - 1\\n\\n return data, target, t, y, x\",\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 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 _slice(self, start, stop, step=None):\\n\\n slices = [slice(None)] * self.data.ndim\\n slices[self.axis] = slice(start, stop, step)\\n return tuple(slices)\",\n \"def putarowslice(self,first_,last_,ptrb_,ptre_,asub_,aval_):\\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 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 _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 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_putarowslice64(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 __getslice__(self, i, j):\\n return OutputGroup(list.__getslice__(self, i, j))\",\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 wcqt_slices(record, t_len, shuffle=True, auto_restart=True, add_noise=True,\\n p_len=54, p_stride=36, random_seed=None, slice_logger=None):\\n def wcqt_slicer(cqt, idx, counter, t_len):\\n # Grab the obs\\n obs = utils.slice_ndarray(cqt, idx[counter], length=t_len, axis=1)\\n # Convert it to WCQT\\n wcqt = utils.fold_array(obs[0], length=p_len, stride=p_stride)\\n # Fix the shape.s\\n return wcqt[np.newaxis, ...]\\n\\n for wcqt_slice in base_slicer(\\n record, t_len, wcqt_slicer,\\n shuffle=shuffle, auto_restart=auto_restart,\\n add_noise=add_noise, random_seed=random_seed,\\n npz_data_key='cqt',\\n slice_logger=slice_logger):\\n yield wcqt_slice\",\n \"def getrowview(self, i):\\n new = lil_matrix((1, self.shape[1]), dtype=self.dtype)\\n new.rows[0] = self.rows[i]\\n new.data[0] = self.data[i]\\n return new\",\n \"def __getitem__(self, idx):\\n if len(idx) == 1:\\n return self.rows[idx[0]]\\n else:\\n return self.rows[idx[0]][idx[1]]\",\n \"def est_row_2_base_row(i):\\n row = np.zeros(n)\\n for j in range(k):\\n row[est_inx_2_base_inx[j]] = est_T[i,j]\\n return row\",\n \"def test_slice_second(self):\\n self.table.append(['Tom', 26])\\n self.table.append(['Chantelle', 24])\\n self.assertEqual(self.table[1], ['Chantelle', 24])\",\n \"def Rt(X):\\n return X[:2,:2], X[:2, 2]\",\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]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7290529","0.70560914","0.6958755","0.61386895","0.5720467","0.5619456","0.56170875","0.5535816","0.5501307","0.5492698","0.5491768","0.54850245","0.5449865","0.5407904","0.5361041","0.5322481","0.53132904","0.5291811","0.5257537","0.5243826","0.5230079","0.5227893","0.5225116","0.5212413","0.5178705","0.5102134","0.50922775","0.5070457","0.5059146","0.5037361","0.5022031","0.5018565","0.50085884","0.50018793","0.49946266","0.49852595","0.4980938","0.49792734","0.49260086","0.49251404","0.49170855","0.49141198","0.48840308","0.4878556","0.48775026","0.48676577","0.486147","0.48609212","0.48535255","0.48392805","0.48381594","0.48376667","0.48374835","0.4829348","0.48072368","0.4801195","0.4795078","0.4785154","0.47648153","0.47619703","0.47510692","0.47390231","0.4732333","0.47262445","0.47192302","0.4705666","0.46936563","0.46902063","0.46897912","0.46881935","0.46730042","0.4671538","0.46694443","0.4667809","0.46634942","0.46626464","0.46586797","0.46567798","0.4655009","0.4651536","0.46435994","0.46375906","0.46375906","0.46375906","0.4631723","0.46302107","0.462255","0.46196052","0.46194038","0.46069685","0.4598893","0.45973733","0.45908678","0.4588241","0.45832747","0.45786405","0.4574643","0.45742998","0.45679143","0.45672446"],"string":"[\n \"0.7290529\",\n \"0.70560914\",\n \"0.6958755\",\n \"0.61386895\",\n \"0.5720467\",\n \"0.5619456\",\n \"0.56170875\",\n \"0.5535816\",\n \"0.5501307\",\n \"0.5492698\",\n \"0.5491768\",\n \"0.54850245\",\n \"0.5449865\",\n \"0.5407904\",\n \"0.5361041\",\n \"0.5322481\",\n \"0.53132904\",\n \"0.5291811\",\n \"0.5257537\",\n \"0.5243826\",\n \"0.5230079\",\n \"0.5227893\",\n \"0.5225116\",\n \"0.5212413\",\n \"0.5178705\",\n \"0.5102134\",\n \"0.50922775\",\n \"0.5070457\",\n \"0.5059146\",\n \"0.5037361\",\n \"0.5022031\",\n \"0.5018565\",\n \"0.50085884\",\n \"0.50018793\",\n \"0.49946266\",\n \"0.49852595\",\n \"0.4980938\",\n \"0.49792734\",\n \"0.49260086\",\n \"0.49251404\",\n \"0.49170855\",\n \"0.49141198\",\n \"0.48840308\",\n \"0.4878556\",\n \"0.48775026\",\n \"0.48676577\",\n \"0.486147\",\n \"0.48609212\",\n \"0.48535255\",\n \"0.48392805\",\n \"0.48381594\",\n \"0.48376667\",\n \"0.48374835\",\n \"0.4829348\",\n \"0.48072368\",\n \"0.4801195\",\n \"0.4795078\",\n \"0.4785154\",\n \"0.47648153\",\n \"0.47619703\",\n \"0.47510692\",\n \"0.47390231\",\n \"0.4732333\",\n \"0.47262445\",\n \"0.47192302\",\n \"0.4705666\",\n \"0.46936563\",\n \"0.46902063\",\n \"0.46897912\",\n \"0.46881935\",\n \"0.46730042\",\n \"0.4671538\",\n \"0.46694443\",\n \"0.4667809\",\n \"0.46634942\",\n \"0.46626464\",\n \"0.46586797\",\n \"0.46567798\",\n \"0.4655009\",\n \"0.4651536\",\n \"0.46435994\",\n \"0.46375906\",\n \"0.46375906\",\n \"0.46375906\",\n \"0.4631723\",\n \"0.46302107\",\n \"0.462255\",\n \"0.46196052\",\n \"0.46194038\",\n \"0.46069685\",\n \"0.4598893\",\n \"0.45973733\",\n \"0.45908678\",\n \"0.4588241\",\n \"0.45832747\",\n \"0.45786405\",\n \"0.4574643\",\n \"0.45742998\",\n \"0.45679143\",\n \"0.45672446\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7172589"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11784,"cells":{"query":{"kind":"string","value":"Obtains a sequence of columns from the coefficient matrix in triplet format. getacolslicetrip(self,first_,last_,subi_,subj_,val_)"},"document":{"kind":"string","value":"def getacolslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getacolslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getacolslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_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 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 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 getarowslicetrip(self,first_,last_,subi,subj,val): # 3\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 getarowslicetrip(self,first_,last_,subi_,subj_,val_):\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\n _subi_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxnumnz_)))\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 = (maxnumnz_)\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxnumnz_)))\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 _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 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(_subi_minlength)\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\n val_[:] = _val_np_tmp","def __getslice__(self, i, j):\n return self.dtrs[i:j]","def sub_columns(arr, sub_size):\n return sub_rows(arr.T, sub_size)","def ColPart(self, *args):\n return _hypre.HypreParMatrix_ColPart(self, *args)","def getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\n maxnumnz_ = self.getarowslicenumnz((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_getarowslice64(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 getarowslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def __getslice__(self,i,j):\n return self.x[i:j]","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 getacolslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\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 return (_numnz_return_value)","def getacol(self,j_,subj,valj): # 3\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_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument subj has wrong length\")\n if valj is None: raise TypeError(\"Invalid type for argument valj\")\n _copyback_valj = False\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 _copyback_valj = True\n else:\n if valj_.format != \"d\":\n valj_ = memoryview(array.array(\"d\",valj))\n _copyback_valj = True\n if valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument valj has wrong length\")\n res,resargs = self.__obj.getacol(j_,subj_,valj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n if _copyback_valj:\n valj[:] = _tmparr_valj\n if _copyback_subj:\n subj[:] = _tmparr_subj\n return _nzj_return_value","def __getslice__(self,i,j):\n nv=_Matr()\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\n nv._Matr__maj()\n return nv","def __getslice__(self,i,j):\n nv=_Matr()\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\n nv.__maj()\n return nv","def get_col(b, ci):\r\n return [b[0][ci], b[1][ci], b[2][ci]]","def slice_matrix(m,i,j):\n return np.take(np.take(m,i,0),j,1)","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 getacol(self,j_,subj_,valj_):\n nzj_ = ctypes.c_int32()\n _subj_minlength = self.getacolnumnz((j_))\n if self.getacolnumnz((j_)) > 0 and subj_ is not None and len(subj_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),self.getacolnumnz((j_))))\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 _valj_minlength = self.getacolnumnz((j_))\n if self.getacolnumnz((j_)) > 0 and valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\n raise ValueError(\"Array argument valj is not long enough: Is %d, expected %d\" % (len(valj_),self.getacolnumnz((j_))))\n if isinstance(valj_,numpy.ndarray) and not valj_.flags.writeable:\n raise ValueError(\"Argument valj must be writable\")\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_getacol(self.__nativep,j_,ctypes.byref(nzj_),_subj_tmp,_valj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valj_copyarray:\n valj_[:] = _valj_np_tmp\n return (_nzj_return_value)","def get_cols(self) :\n\n return list(self.cols)[1:]","def slice2(self, cvars=None,ctuple=None):\n return self.condition2(cvars,ctuple)","def slice(A,rowrange,colrange):\n\n\treturn [[get_elem(A,j,i) for j in rowrange] for i in colrange]","def GetColPartArray(self):\n return _hypre.HypreParMatrix_GetColPartArray(self)","def col(self, i):\n return Vector([row[i] for row in self.data])","def __getslice__(self, i, j):\n return self.__getitem__(slice(i,j))","def getacolnumnz(self,i_): # 3\n res,resargs = self.__obj.getacolnumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n return _nzj_return_value","def __getslice__(self, i, j):\n return self.__getitem__(slice(i, j))","def fast_get_col(self,j):\n col = self.col_view[:,j].copy()\n col.data = self.X.data[col.data]\n return col","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 get_col(A,r=0):\n\treturn list(A[r])","def __substr(self, begin, length):\n col = _VirtualCategoricalColumn(\n df_name=self.thisptr[\"df_name_\"],\n operator=\"substr\",\n operand1=self,\n operand2=None\n )\n col.thisptr[\"begin_\"] = begin\n col.thisptr[\"len_\"] = length\n return col","def getaslice(self,accmode_,first_,last_,ptrb,ptre,sub,val): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n maxnumnz_ = self.getaslicenumnz((accmode_),(first_),(last_))\n _copyback_ptrb = False\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 _copyback_ptrb = True\n else:\n if ptrb_.format != \"q\":\n ptrb_ = memoryview(array.array(\"q\",ptrb))\n _copyback_ptrb = True\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptrb has wrong length\")\n _copyback_ptre = False\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 _copyback_ptre = True\n else:\n if ptre_.format != \"q\":\n ptre_ = memoryview(array.array(\"q\",ptre))\n _copyback_ptre = True\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptre has wrong length\")\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(\"i\",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 != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n _copyback_sub = True\n if sub_ is not None and len(sub_) != (maxnumnz_):\n raise ValueError(\"Array argument sub 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.getaslice64(accmode_,first_,last_,maxnumnz_,len(sub),ptrb_,ptre_,sub_,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_sub:\n sub[:] = _tmparr_sub\n if _copyback_ptre:\n ptre[:] = _tmparr_ptre\n if _copyback_ptrb:\n ptrb[:] = _tmparr_ptrb","def _slice(tensor, size, i):\n return tensor[:, i * size : (i + 1) * size]","def __getslice__( self, *args):\n return array.array.__getslice__(self, *args).tostring()","def slice(tensor):\n out = tensor[:, 444:524, :]\n return out","def iter_col(self, inclusive=False, **kwargs):\n ret = self.sudoku.col(self.col, **kwargs)\n if not inclusive:\n ret -= self\n return ret","def col(self, col: tuple) -> list:\n return self.grid[col::9]","def __getslice__(self, start, stop):\n return self.__getitem__(slice(start, stop, None))","def _colvec(x):\n x = np.atleast_1d(x)\n return x[:, None]","def GetColStarts(self):\n return _hypre.HypreParMatrix_GetColStarts(self)","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 _slice(self, start, stop, step=None):\n\n slices = [slice(None)] * self.data.ndim\n slices[self.axis] = slice(start, stop, step)\n return tuple(slices)","def column(self, i):\n return [self.data[ self.columns * row + (i - 1)] for row in range(self.rows)]","def col_subset(self, patt, rmprefix=None):\n cols = self.widedf.columns\n want = [bool(re.search('^sesid$|^age$|'+patt, x)) for x in cols]\n subset = self.widedf.iloc[:, want]\n if rmprefix is None:\n # assume\n # * the best prefix to remove is from the first non-{age,id} column \n # * prefix is any text before the first '_'\n subset_specifc_cols = [x for x in cols[want] if x not in ['sesid','age']]\n rmprefix = subset_specifc_cols[0].split(\"_\")[0]\n if rmprefix:\n subset.columns = [re.sub(f'^{rmprefix}_','',x) for x in subset.columns]\n return subset.dropna()","def test03(self):\n a = np.arange(1, 101)\n b = bcolz.carray(a)\n c = b[[1.1, 3.3]]\n r = a[[1, 3]]\n assert_array_equal(c, r, \"fancy indexing does not work correctly\")","def sub_minor_diags(arr, sub_size):\n return sub_major_diags(arr[:, ::-1], sub_size)","def test_slice_second_last(self):\n self.table.append(['Tom', 26])\n self.table.append(['Chantelle', 24])\n self.assertEqual(self.table[-2], ['Tom', 26])","def getacolnumnz(self,i_):\n nzj_ = ctypes.c_int32()\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n return (_nzj_return_value)","def get_slice(self, start: Optional[Index] = None, end: Optional[Index] = None):\n index = self._slice_index(slice(start, end))\n return self.get_table(index, self._columns)","def column (self, i):\n return Vector(tuple(zip(*self._m))[i])","def matPart(mat, rs, re, cs, ce):\n return [[matGet(mat,x,y) for y in range(cs,ce)] \\\n for x in range(rs,re)]","def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value","def get_slice(self):\n return self.locs[tuple(self.indices), :]","def last_col_filtered(self):\n data = []\n for row in range(self.proxyModel3.rowCount()):\n data.append([])\n for column in range(self.proxyModel3.columnCount()):\n index = self.proxyModel3.index(row, column)\n data[row].append(str(self.proxyModel3.data(index)))\n data2 = [col[6] for col in data]\n # print(data)\n # print(data2)\n return data2","def test_slice_second(self):\n self.table.append(['Tom', 26])\n self.table.append(['Chantelle', 24])\n self.assertEqual(self.table[1], ['Chantelle', 24])","def slice2(self, vs=None,xs=None):\n return self.condition2(vs,xs)","def relay_array_getitem(c, a, start, stop, strides):\n assert start.is_constant(tuple)\n assert stop.is_constant(tuple)\n assert strides.is_constant(tuple)\n return relay.op.transform.strided_slice(c.ref(a), start.value, stop.value,\n strides.value)","def sudoku_getcol(A, j):\r\n n = len(A)\r\n B = [0 for i in range(n)]\r\n for i in range(n):\r\n B[i] = A[i][j]\r\n return B","def slice(self, i, j, n, m):\n return Table(rows=[list.__getitem__(self, i)[j:j+m] for i in range(i, i+n)])","def row_slice(self, xt, nproc):\n if nproc is None: nproc = self.nproc\n cs = xt.shape[0]//nproc #chuncksize\n tmp = [xt[i*cs:cs*i+cs,:] for i in range(nproc)]\n if nproc*cs != xt.shape[0]:\n tmp[-1] = np.concatenate((tmp[-1],xt[nproc*cs:xt.shape[0],:]),axis=0)\n return tmp","def _get_col(self, idx):\n return self.line[self._fwf.column_slices[idx]]","def to_slice(self):\n return np.index_exp[self.start[2]:self.end[2], #\n self.start[1]:self.end[1], #\n self.start[0]:self.end[0]]","def get_tile_slice(self, x_min, x_max, y_min, y_max):\n result = self.tiles[x_min:x_max]\n for column in result:\n column = column[y_min:y_max]\n return result","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 test02d(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(-3, -1)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def collocation(lhs, rhs, basis, nterms, domain=(0, 1), x_col=None):\n x0, x1 = domain\n if x_col is None:\n dx = S(x1 - x0)/(nterms - 2)\n x_col = [dx + dx*cont for cont in range(nterms)]\n x = symbols(\"x\")\n A_mat = zeros(nterms, nterms)\n b_vec = zeros(nterms, 1)\n for row in range(nterms):\n b_vec[row] = rhs(x_col[row])\n for col in range(nterms):\n phi_j = basis(x, col)\n A_mat[row, col] = lhs(phi_j, x).subs(x, x_col[row])\n return A_mat, b_vec","def test_slice(self):\n r = self.RNA(\"UCAGG\")\n self.assertEqual(r[0], \"U\")\n self.assertEqual(r[-1], \"G\")\n self.assertEqual(r[1:3], \"CA\")","def get_column(self, from_int=None, to_int=None):\n\n try:\n if from_int is None and to_int is None: \n return list(self.df_input)\n else:\n get_col_arr = list(self.df_input)\n column_arr = []\n while from_int < to_int:\n column_arr.append(get_col_arr[from_int])\n from_int += 1\n return column_arr\n except Exception as e:\n print(e)","def column(self, label):\n dis = []\n for x in self.rows:\n dis = dis + [x[self.column_labels.index(label)]]\n return dis\n # return self.rows[self.column_labels.index(label)]","def cols(self):\n return self.col","def get_cols(self, *dims):\n dims_not_found = [d for d in dims if not d in self.dims]\n if dims_not_found:\n raise Exception('Some dims were not found.\\n Dims not found: %s\\n Dims in table: %s' % (str(dims_not_found), str(self.dims)))\n \n return self.get_points(*dims).T","def get_columns(self, *args):\n get = lambda val: self[val[0]] or [val[1]]*self.num_rows \\\n if isinstance(val, tuple) else self[val]\n return tuple(get(var) for var in args)","def get_columns(self, *args, **kw):\n\n v = self.namespace[self._name]\n while True:\n if callable(v):\n if isinstance(v, (Term, Formula)):\n v = copy.copy(v)\n v.namespace = self.namespace\n v = v(*args, **kw)\n else: break\n\n n = len(v)\n\n if self.ordinal:\n col = [float(self.keys.index(v[i])) for i in range(n)]\n return np.array(col)\n\n else:\n value = []\n for key in self.keys:\n col = [float((v[i] == key)) for i in range(n)]\n value.append(col)\n return np.array(value)","def test00(self):\n a = np.arange(1, 111)\n b = bcolz.carray(a)\n c = b[[3, 1]]\n r = a[[3, 1]]\n assert_array_equal(c, r, \"fancy indexing does not work correctly\")","def test02c(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(1, -3)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def getColsInPatch(self, patchNo):\n return _patchExtractor.patchExtractor_getColsInPatch(self, patchNo)","def slice( mat, n, pt ):\n \n lc, uc = TemplateMatcher.hinge( pt.x, n ) ## column limits\n lr, ur = TemplateMatcher.hinge( pt.y, n )\n nr, nc = np.shape( mat )\n nr = nr - 1\n nc = nc - 1\n \n alc, auc = lc, uc \n alr, aur = lr, ur \n rpb, rpa, cpl, cpr = [0,0,0,0]\n\n if lc < 0:\n alc = 0\n cpl = -lc \n\n if uc > nc:\n auc = nc \n cpr = uc - auc \n\n if lr < 0:\n alr = 0\n rpb = -lr\n\n if ur > nr:\n aur = nr \n rpa = ur - aur \n\n return np.pad( mat[ alr : aur + 1 , alc : auc + 1 ], (( rpb, rpa ),( cpl, cpr )), mode ='constant' )","def dctColUels(pdct, j):\n return _dctmcc.dctColUels(pdct, j)","def extractComponent(img, cols, fromX, toX, fromY, toY):\n\n mask = maskByColors(img, cols)\n return mask[fromY: toY, fromX: toX]\n # return mask[fromY: toY+1, fromX: toX+1]","def subset_(self):\n return self.X.ix[:, self.important_features_]","def slice0(A,rowrange,colrange):\n\treturn [[A[i][j] for j in range(rowrange[0],rowrange[1])] for i in range(colrange[0],colrange[1])]","def getcols(d,l,*args):\n ret = []\n for i in range(len(args)):\n ret = ret + [getcol(d[args[i]],l)]\n return ret","def get_cols(self):\n collist = []\n if self.direction == \"vertical\":\n if len(self.position[0]) == 4:\n print('TODO')\n elif len(self.position[0]) == 5:\n print('TODO')\n else:\n collist.append(int(self.position[0][2]))\n return collist\n else:\n # check for 3 or 4 length string\n if len(self.position[0]) == 4: # 11.1 or 1.11 ?\n if self.position[0][1] == '.': #1.11\n newstr = self.position[0][2] + self.position[0][3]\n collist.append(int(newstr))\n else: \n collist.append(int(self.position[0][3]))\n print(self.position)\n print(self.position[0])\n elif len(self.position[0]) == 5:\n newstr1 = self.position[0][3] + self.position[0][4]\n collist.append(int(newstr1))\n newstr2 = self.position[1][3] + self.position[1][4]\n collist.append(int(newstr2))\n else:\n collist.append(int(self.position[0][2]))\n collist.append(int(self.position[1][2]))\n if self.size == 3:\n if len(self.position[0]) == 4:\n if self.position[0][1] == '.': #1.11\n print(\"hia\")\n collist.append(int(self.position[2][3]))\n elif len(self.position[0]) == 5:\n newstr = self.position[2][3] + self.position[2][4]\n collist.append(int(newstr))\n else:\n collist.append(int(self.position[2][2]))\n return collist","def cut(takenColumns, sep):\n tempList = map(list, zip(*takenColumns))\n for i in tempList:\n print sep.join(i)","def get_time_cols(self):\n # Retrieve first column only\n time_cols = list()\n n = self.col_count\n for i in range(n): # check every column/attribute for time format\n row_data = str(self.data[0][i])\n try:\n time_ok, t_stamp = DataGP.test_time(row_data)\n if time_ok:\n time_cols.append(i)\n except ValueError:\n continue\n return np.array(time_cols)","def test05(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(None, None, -3)\n # print \"b[sl]->\", `b[sl]`\n self.assertRaises(NotImplementedError, b.__getitem__, sl)","def test_get_second_slice(self):\n self.init()\n assert np.all(get_second_slice(self.i64_3) == self.i64_3[:,:,1])\n assert np.all(get_second_slice(self.fi64_3) == self.fi64_3[:,:,1])\n assert np.all(get_second_slice(self.f64_3) == self.f64_3[:,:,1])\n assert np.all(get_second_slice(self.ff64_3) == self.ff64_3[:,:,1])\n assert get_second_slice(self.i64_3).shape == (3,3)\n assert get_second_slice(self.fi64_3).shape == (3,3)\n assert get_second_slice(self.f64_3).shape == (3,3)\n assert get_second_slice(self.ff64_3).shape == (3,3)\n assert get_second_slice(self.i64_3).dtype == 'float64'\n assert get_second_slice(self.fi64_3).dtype == 'float64'\n assert get_second_slice(self.f64_3).dtype == 'float64'\n assert get_second_slice(self.ff64_3).dtype == 'float64'\n assert get_second_slice(self.i64_3).flags['F_CONTIGUOUS'] == True\n assert get_second_slice(self.fi64_3).flags['F_CONTIGUOUS'] == True\n assert get_second_slice(self.f64_3).flags['F_CONTIGUOUS'] == True\n assert get_second_slice(self.ff64_3).flags['F_CONTIGUOUS'] == True","def get_substr(self, i, j):\n return self._val[(i-1):j]","def get_columns(self, *args, **kw):\n\n v = self.namespace[self._name]\n while True:\n if callable(v): \n if hasattr(v, \"namespace\"):\n v.namespace = self.namespace\n v = v(*args, **kw)\n else: break \n\n if self.ordinal:\n col = [float(self.keys.index(v[i])) for i in range(len(self.keys))]\n return N.array(col)\n\n else:\n n = len(v)\n value = []\n for key in self.keys:\n col = [float((v[i] == key)) for i in range(n)]\n value.append(col)\n return N.array(value)","def get_column(A: Matrix, j: int) -> Vector:\n return [A_i[j] for A_i in A]","def get_column(A: Matrix, j: int) -> Vector:\n return [A_i[j] for A_i in A]","def get_column(A: Matrix, j: int) -> Vector:\n return [A_i[j] for A_i in A]","def _pos2col(self, start, cpos, **opts):\n tw = opts.get('tab_width', self.TAB_WIDTH)\n tt = opts.get('tab_type', 'stop')\n if tt == 'fixed':\n\n def advance(p):\n return p + tw\n else:\n\n def advance(p):\n return tw * ((p + tw) // tw)\n\n colnum = 0\n while cpos > 0:\n if self.input[start] == '\\t':\n colnum = advance(colnum)\n else:\n colnum += 1\n start += 1\n cpos -= 1\n return colnum","def cell_slice(payload):\n payload_len = len(payload)\n if payload_len < 7: # (payload too small, need data)\n return None, payload\n\n cmd = cell_get_cmd(payload)\n\n if cell_is_variable_length(cmd):\n cell_len = 7 + cell_get_len(payload)\n else:\n cell_len = 514\n\n if payload_len < cell_len:\n return None, payload\n\n cell = payload[:cell_len]\n\n return cell, payload[cell_len:]","def cols(self, state):\n columns = []\n col_word = []\n g = state\n z = len(g)\n for x in range(z):\n for y in range(z):\n col_word.append(g[y][x])\n columns.append(col_word)\n col_word = []\n \n print \"rows: \"\n for i in range(len(g)):\n print g[i]\n \n print \"cols: \"\n for i in range(len(columns)):\n print columns[i]\n\n return columns","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 _get_col(self, idx):\n return self.text[self._fwf.column_slices[idx]]","def _slice_at_axis(sl, axis):\n return (slice(None),) * axis + (sl,) + (...,)","def collect_columns():\n return ((x, y) for x in range(72) for y in range(x + 9, 81, 9))","def as_slice(self):\n # slice for accessing arrays of values\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)"],"string":"[\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 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 getarowslicetrip(self,first_,last_,subi,subj,val): # 3\\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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.getarowslicetrip(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 getarowslicetrip(self,first_,last_,subi_,subj_,val_):\\n maxnumnz_ = self.getarowslicenumnz((first_),(last_))\\n _subi_minlength = (maxnumnz_)\\n if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subi is not long enough: Is %d, expected %d\\\" % (len(subi_),(maxnumnz_)))\\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 = (maxnumnz_)\\n if (maxnumnz_) > 0 and subj_ is not None and len(subj_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),(maxnumnz_)))\\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 _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 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(_subi_minlength)\\n res = __library__.MSK_XX_getarowslicetrip(self.__nativep,first_,last_,maxnumnz_,ctypes.byref(surp_),_subi_tmp,_subj_tmp,_val_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 _val_copyarray:\\n val_[:] = _val_np_tmp\",\n \"def __getslice__(self, i, j):\\n return self.dtrs[i:j]\",\n \"def sub_columns(arr, sub_size):\\n return sub_rows(arr.T, sub_size)\",\n \"def ColPart(self, *args):\\n return _hypre.HypreParMatrix_ColPart(self, *args)\",\n \"def getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_):\\n maxnumnz_ = self.getarowslicenumnz((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_getarowslice64(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 getarowslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def __getslice__(self,i,j):\\n return self.x[i:j]\",\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 getacolslicenumnz(self,first_,last_):\\n numnz_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\\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 return (_numnz_return_value)\",\n \"def getacol(self,j_,subj,valj): # 3\\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_) != self.getacolnumnz((j_)):\\n raise ValueError(\\\"Array argument subj has wrong length\\\")\\n if valj is None: raise TypeError(\\\"Invalid type for argument valj\\\")\\n _copyback_valj = False\\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 _copyback_valj = True\\n else:\\n if valj_.format != \\\"d\\\":\\n valj_ = memoryview(array.array(\\\"d\\\",valj))\\n _copyback_valj = True\\n if valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\\n raise ValueError(\\\"Array argument valj has wrong length\\\")\\n res,resargs = self.__obj.getacol(j_,subj_,valj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n if _copyback_valj:\\n valj[:] = _tmparr_valj\\n if _copyback_subj:\\n subj[:] = _tmparr_subj\\n return _nzj_return_value\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\\n nv._Matr__maj()\\n return nv\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv.__c_elem().recup_rel(self.__c_elem(),i,j)\\n nv.__maj()\\n return nv\",\n \"def get_col(b, ci):\\r\\n return [b[0][ci], b[1][ci], b[2][ci]]\",\n \"def slice_matrix(m,i,j):\\n return np.take(np.take(m,i,0),j,1)\",\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 getacol(self,j_,subj_,valj_):\\n nzj_ = ctypes.c_int32()\\n _subj_minlength = self.getacolnumnz((j_))\\n if self.getacolnumnz((j_)) > 0 and subj_ is not None and len(subj_) != self.getacolnumnz((j_)):\\n raise ValueError(\\\"Array argument subj is not long enough: Is %d, expected %d\\\" % (len(subj_),self.getacolnumnz((j_))))\\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 _valj_minlength = self.getacolnumnz((j_))\\n if self.getacolnumnz((j_)) > 0 and valj_ is not None and len(valj_) != self.getacolnumnz((j_)):\\n raise ValueError(\\\"Array argument valj is not long enough: Is %d, expected %d\\\" % (len(valj_),self.getacolnumnz((j_))))\\n if isinstance(valj_,numpy.ndarray) and not valj_.flags.writeable:\\n raise ValueError(\\\"Argument valj must be writable\\\")\\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_getacol(self.__nativep,j_,ctypes.byref(nzj_),_subj_tmp,_valj_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzj_ = nzj_.value\\n _nzj_return_value = nzj_\\n if _subj_copyarray:\\n subj_[:] = _subj_np_tmp\\n if _valj_copyarray:\\n valj_[:] = _valj_np_tmp\\n return (_nzj_return_value)\",\n \"def get_cols(self) :\\n\\n return list(self.cols)[1:]\",\n \"def slice2(self, cvars=None,ctuple=None):\\n return self.condition2(cvars,ctuple)\",\n \"def slice(A,rowrange,colrange):\\n\\n\\treturn [[get_elem(A,j,i) for j in rowrange] for i in colrange]\",\n \"def GetColPartArray(self):\\n return _hypre.HypreParMatrix_GetColPartArray(self)\",\n \"def col(self, i):\\n return Vector([row[i] for row in self.data])\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i,j))\",\n \"def getacolnumnz(self,i_): # 3\\n res,resargs = self.__obj.getacolnumnz(i_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _nzj_return_value = resargs\\n return _nzj_return_value\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i, j))\",\n \"def fast_get_col(self,j):\\n col = self.col_view[:,j].copy()\\n col.data = self.X.data[col.data]\\n return col\",\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 get_col(A,r=0):\\n\\treturn list(A[r])\",\n \"def __substr(self, begin, length):\\n col = _VirtualCategoricalColumn(\\n df_name=self.thisptr[\\\"df_name_\\\"],\\n operator=\\\"substr\\\",\\n operand1=self,\\n operand2=None\\n )\\n col.thisptr[\\\"begin_\\\"] = begin\\n col.thisptr[\\\"len_\\\"] = length\\n return col\",\n \"def getaslice(self,accmode_,first_,last_,ptrb,ptre,sub,val): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n maxnumnz_ = self.getaslicenumnz((accmode_),(first_),(last_))\\n _copyback_ptrb = False\\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 _copyback_ptrb = True\\n else:\\n if ptrb_.format != \\\"q\\\":\\n ptrb_ = memoryview(array.array(\\\"q\\\",ptrb))\\n _copyback_ptrb = True\\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptrb has wrong length\\\")\\n _copyback_ptre = False\\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 _copyback_ptre = True\\n else:\\n if ptre_.format != \\\"q\\\":\\n ptre_ = memoryview(array.array(\\\"q\\\",ptre))\\n _copyback_ptre = True\\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptre has wrong length\\\")\\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(\\\"i\\\",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 != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n _copyback_sub = True\\n if sub_ is not None and len(sub_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument sub 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.getaslice64(accmode_,first_,last_,maxnumnz_,len(sub),ptrb_,ptre_,sub_,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_sub:\\n sub[:] = _tmparr_sub\\n if _copyback_ptre:\\n ptre[:] = _tmparr_ptre\\n if _copyback_ptrb:\\n ptrb[:] = _tmparr_ptrb\",\n \"def _slice(tensor, size, i):\\n return tensor[:, i * size : (i + 1) * size]\",\n \"def __getslice__( self, *args):\\n return array.array.__getslice__(self, *args).tostring()\",\n \"def slice(tensor):\\n out = tensor[:, 444:524, :]\\n return out\",\n \"def iter_col(self, inclusive=False, **kwargs):\\n ret = self.sudoku.col(self.col, **kwargs)\\n if not inclusive:\\n ret -= self\\n return ret\",\n \"def col(self, col: tuple) -> list:\\n return self.grid[col::9]\",\n \"def __getslice__(self, start, stop):\\n return self.__getitem__(slice(start, stop, None))\",\n \"def _colvec(x):\\n x = np.atleast_1d(x)\\n return x[:, None]\",\n \"def GetColStarts(self):\\n return _hypre.HypreParMatrix_GetColStarts(self)\",\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 _slice(self, start, stop, step=None):\\n\\n slices = [slice(None)] * self.data.ndim\\n slices[self.axis] = slice(start, stop, step)\\n return tuple(slices)\",\n \"def column(self, i):\\n return [self.data[ self.columns * row + (i - 1)] for row in range(self.rows)]\",\n \"def col_subset(self, patt, rmprefix=None):\\n cols = self.widedf.columns\\n want = [bool(re.search('^sesid$|^age$|'+patt, x)) for x in cols]\\n subset = self.widedf.iloc[:, want]\\n if rmprefix is None:\\n # assume\\n # * the best prefix to remove is from the first non-{age,id} column \\n # * prefix is any text before the first '_'\\n subset_specifc_cols = [x for x in cols[want] if x not in ['sesid','age']]\\n rmprefix = subset_specifc_cols[0].split(\\\"_\\\")[0]\\n if rmprefix:\\n subset.columns = [re.sub(f'^{rmprefix}_','',x) for x in subset.columns]\\n return subset.dropna()\",\n \"def test03(self):\\n a = np.arange(1, 101)\\n b = bcolz.carray(a)\\n c = b[[1.1, 3.3]]\\n r = a[[1, 3]]\\n assert_array_equal(c, r, \\\"fancy indexing does not work correctly\\\")\",\n \"def sub_minor_diags(arr, sub_size):\\n return sub_major_diags(arr[:, ::-1], sub_size)\",\n \"def test_slice_second_last(self):\\n self.table.append(['Tom', 26])\\n self.table.append(['Chantelle', 24])\\n self.assertEqual(self.table[-2], ['Tom', 26])\",\n \"def getacolnumnz(self,i_):\\n nzj_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nzj_ = nzj_.value\\n _nzj_return_value = nzj_\\n return (_nzj_return_value)\",\n \"def get_slice(self, start: Optional[Index] = None, end: Optional[Index] = None):\\n index = self._slice_index(slice(start, end))\\n return self.get_table(index, self._columns)\",\n \"def column (self, i):\\n return Vector(tuple(zip(*self._m))[i])\",\n \"def matPart(mat, rs, re, cs, ce):\\n return [[matGet(mat,x,y) for y in range(cs,ce)] \\\\\\n for x in range(rs,re)]\",\n \"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _numnz_return_value = resargs\\n return _numnz_return_value\",\n \"def get_slice(self):\\n return self.locs[tuple(self.indices), :]\",\n \"def last_col_filtered(self):\\n data = []\\n for row in range(self.proxyModel3.rowCount()):\\n data.append([])\\n for column in range(self.proxyModel3.columnCount()):\\n index = self.proxyModel3.index(row, column)\\n data[row].append(str(self.proxyModel3.data(index)))\\n data2 = [col[6] for col in data]\\n # print(data)\\n # print(data2)\\n return data2\",\n \"def test_slice_second(self):\\n self.table.append(['Tom', 26])\\n self.table.append(['Chantelle', 24])\\n self.assertEqual(self.table[1], ['Chantelle', 24])\",\n \"def slice2(self, vs=None,xs=None):\\n return self.condition2(vs,xs)\",\n \"def relay_array_getitem(c, a, start, stop, strides):\\n assert start.is_constant(tuple)\\n assert stop.is_constant(tuple)\\n assert strides.is_constant(tuple)\\n return relay.op.transform.strided_slice(c.ref(a), start.value, stop.value,\\n strides.value)\",\n \"def sudoku_getcol(A, j):\\r\\n n = len(A)\\r\\n B = [0 for i in range(n)]\\r\\n for i in range(n):\\r\\n B[i] = A[i][j]\\r\\n return B\",\n \"def slice(self, i, j, n, m):\\n return Table(rows=[list.__getitem__(self, i)[j:j+m] for i in range(i, i+n)])\",\n \"def row_slice(self, xt, nproc):\\n if nproc is None: nproc = self.nproc\\n cs = xt.shape[0]//nproc #chuncksize\\n tmp = [xt[i*cs:cs*i+cs,:] for i in range(nproc)]\\n if nproc*cs != xt.shape[0]:\\n tmp[-1] = np.concatenate((tmp[-1],xt[nproc*cs:xt.shape[0],:]),axis=0)\\n return tmp\",\n \"def _get_col(self, idx):\\n return self.line[self._fwf.column_slices[idx]]\",\n \"def to_slice(self):\\n return np.index_exp[self.start[2]:self.end[2], #\\n self.start[1]:self.end[1], #\\n self.start[0]:self.end[0]]\",\n \"def get_tile_slice(self, x_min, x_max, y_min, y_max):\\n result = self.tiles[x_min:x_max]\\n for column in result:\\n column = column[y_min:y_max]\\n return result\",\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 test02d(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(-3, -1)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def collocation(lhs, rhs, basis, nterms, domain=(0, 1), x_col=None):\\n x0, x1 = domain\\n if x_col is None:\\n dx = S(x1 - x0)/(nterms - 2)\\n x_col = [dx + dx*cont for cont in range(nterms)]\\n x = symbols(\\\"x\\\")\\n A_mat = zeros(nterms, nterms)\\n b_vec = zeros(nterms, 1)\\n for row in range(nterms):\\n b_vec[row] = rhs(x_col[row])\\n for col in range(nterms):\\n phi_j = basis(x, col)\\n A_mat[row, col] = lhs(phi_j, x).subs(x, x_col[row])\\n return A_mat, b_vec\",\n \"def test_slice(self):\\n r = self.RNA(\\\"UCAGG\\\")\\n self.assertEqual(r[0], \\\"U\\\")\\n self.assertEqual(r[-1], \\\"G\\\")\\n self.assertEqual(r[1:3], \\\"CA\\\")\",\n \"def get_column(self, from_int=None, to_int=None):\\n\\n try:\\n if from_int is None and to_int is None: \\n return list(self.df_input)\\n else:\\n get_col_arr = list(self.df_input)\\n column_arr = []\\n while from_int < to_int:\\n column_arr.append(get_col_arr[from_int])\\n from_int += 1\\n return column_arr\\n except Exception as e:\\n print(e)\",\n \"def column(self, label):\\n dis = []\\n for x in self.rows:\\n dis = dis + [x[self.column_labels.index(label)]]\\n return dis\\n # return self.rows[self.column_labels.index(label)]\",\n \"def cols(self):\\n return self.col\",\n \"def get_cols(self, *dims):\\n dims_not_found = [d for d in dims if not d in self.dims]\\n if dims_not_found:\\n raise Exception('Some dims were not found.\\\\n Dims not found: %s\\\\n Dims in table: %s' % (str(dims_not_found), str(self.dims)))\\n \\n return self.get_points(*dims).T\",\n \"def get_columns(self, *args):\\n get = lambda val: self[val[0]] or [val[1]]*self.num_rows \\\\\\n if isinstance(val, tuple) else self[val]\\n return tuple(get(var) for var in args)\",\n \"def get_columns(self, *args, **kw):\\n\\n v = self.namespace[self._name]\\n while True:\\n if callable(v):\\n if isinstance(v, (Term, Formula)):\\n v = copy.copy(v)\\n v.namespace = self.namespace\\n v = v(*args, **kw)\\n else: break\\n\\n n = len(v)\\n\\n if self.ordinal:\\n col = [float(self.keys.index(v[i])) for i in range(n)]\\n return np.array(col)\\n\\n else:\\n value = []\\n for key in self.keys:\\n col = [float((v[i] == key)) for i in range(n)]\\n value.append(col)\\n return np.array(value)\",\n \"def test00(self):\\n a = np.arange(1, 111)\\n b = bcolz.carray(a)\\n c = b[[3, 1]]\\n r = a[[3, 1]]\\n assert_array_equal(c, r, \\\"fancy indexing does not work correctly\\\")\",\n \"def test02c(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(1, -3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def getColsInPatch(self, patchNo):\\n return _patchExtractor.patchExtractor_getColsInPatch(self, patchNo)\",\n \"def slice( mat, n, pt ):\\n \\n lc, uc = TemplateMatcher.hinge( pt.x, n ) ## column limits\\n lr, ur = TemplateMatcher.hinge( pt.y, n )\\n nr, nc = np.shape( mat )\\n nr = nr - 1\\n nc = nc - 1\\n \\n alc, auc = lc, uc \\n alr, aur = lr, ur \\n rpb, rpa, cpl, cpr = [0,0,0,0]\\n\\n if lc < 0:\\n alc = 0\\n cpl = -lc \\n\\n if uc > nc:\\n auc = nc \\n cpr = uc - auc \\n\\n if lr < 0:\\n alr = 0\\n rpb = -lr\\n\\n if ur > nr:\\n aur = nr \\n rpa = ur - aur \\n\\n return np.pad( mat[ alr : aur + 1 , alc : auc + 1 ], (( rpb, rpa ),( cpl, cpr )), mode ='constant' )\",\n \"def dctColUels(pdct, j):\\n return _dctmcc.dctColUels(pdct, j)\",\n \"def extractComponent(img, cols, fromX, toX, fromY, toY):\\n\\n mask = maskByColors(img, cols)\\n return mask[fromY: toY, fromX: toX]\\n # return mask[fromY: toY+1, fromX: toX+1]\",\n \"def subset_(self):\\n return self.X.ix[:, self.important_features_]\",\n \"def slice0(A,rowrange,colrange):\\n\\treturn [[A[i][j] for j in range(rowrange[0],rowrange[1])] for i in range(colrange[0],colrange[1])]\",\n \"def getcols(d,l,*args):\\n ret = []\\n for i in range(len(args)):\\n ret = ret + [getcol(d[args[i]],l)]\\n return ret\",\n \"def get_cols(self):\\n collist = []\\n if self.direction == \\\"vertical\\\":\\n if len(self.position[0]) == 4:\\n print('TODO')\\n elif len(self.position[0]) == 5:\\n print('TODO')\\n else:\\n collist.append(int(self.position[0][2]))\\n return collist\\n else:\\n # check for 3 or 4 length string\\n if len(self.position[0]) == 4: # 11.1 or 1.11 ?\\n if self.position[0][1] == '.': #1.11\\n newstr = self.position[0][2] + self.position[0][3]\\n collist.append(int(newstr))\\n else: \\n collist.append(int(self.position[0][3]))\\n print(self.position)\\n print(self.position[0])\\n elif len(self.position[0]) == 5:\\n newstr1 = self.position[0][3] + self.position[0][4]\\n collist.append(int(newstr1))\\n newstr2 = self.position[1][3] + self.position[1][4]\\n collist.append(int(newstr2))\\n else:\\n collist.append(int(self.position[0][2]))\\n collist.append(int(self.position[1][2]))\\n if self.size == 3:\\n if len(self.position[0]) == 4:\\n if self.position[0][1] == '.': #1.11\\n print(\\\"hia\\\")\\n collist.append(int(self.position[2][3]))\\n elif len(self.position[0]) == 5:\\n newstr = self.position[2][3] + self.position[2][4]\\n collist.append(int(newstr))\\n else:\\n collist.append(int(self.position[2][2]))\\n return collist\",\n \"def cut(takenColumns, sep):\\n tempList = map(list, zip(*takenColumns))\\n for i in tempList:\\n print sep.join(i)\",\n \"def get_time_cols(self):\\n # Retrieve first column only\\n time_cols = list()\\n n = self.col_count\\n for i in range(n): # check every column/attribute for time format\\n row_data = str(self.data[0][i])\\n try:\\n time_ok, t_stamp = DataGP.test_time(row_data)\\n if time_ok:\\n time_cols.append(i)\\n except ValueError:\\n continue\\n return np.array(time_cols)\",\n \"def test05(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(None, None, -3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n self.assertRaises(NotImplementedError, b.__getitem__, sl)\",\n \"def test_get_second_slice(self):\\n self.init()\\n assert np.all(get_second_slice(self.i64_3) == self.i64_3[:,:,1])\\n assert np.all(get_second_slice(self.fi64_3) == self.fi64_3[:,:,1])\\n assert np.all(get_second_slice(self.f64_3) == self.f64_3[:,:,1])\\n assert np.all(get_second_slice(self.ff64_3) == self.ff64_3[:,:,1])\\n assert get_second_slice(self.i64_3).shape == (3,3)\\n assert get_second_slice(self.fi64_3).shape == (3,3)\\n assert get_second_slice(self.f64_3).shape == (3,3)\\n assert get_second_slice(self.ff64_3).shape == (3,3)\\n assert get_second_slice(self.i64_3).dtype == 'float64'\\n assert get_second_slice(self.fi64_3).dtype == 'float64'\\n assert get_second_slice(self.f64_3).dtype == 'float64'\\n assert get_second_slice(self.ff64_3).dtype == 'float64'\\n assert get_second_slice(self.i64_3).flags['F_CONTIGUOUS'] == True\\n assert get_second_slice(self.fi64_3).flags['F_CONTIGUOUS'] == True\\n assert get_second_slice(self.f64_3).flags['F_CONTIGUOUS'] == True\\n assert get_second_slice(self.ff64_3).flags['F_CONTIGUOUS'] == True\",\n \"def get_substr(self, i, j):\\n return self._val[(i-1):j]\",\n \"def get_columns(self, *args, **kw):\\n\\n v = self.namespace[self._name]\\n while True:\\n if callable(v): \\n if hasattr(v, \\\"namespace\\\"):\\n v.namespace = self.namespace\\n v = v(*args, **kw)\\n else: break \\n\\n if self.ordinal:\\n col = [float(self.keys.index(v[i])) for i in range(len(self.keys))]\\n return N.array(col)\\n\\n else:\\n n = len(v)\\n value = []\\n for key in self.keys:\\n col = [float((v[i] == key)) for i in range(n)]\\n value.append(col)\\n return N.array(value)\",\n \"def get_column(A: Matrix, j: int) -> Vector:\\n return [A_i[j] for A_i in A]\",\n \"def get_column(A: Matrix, j: int) -> Vector:\\n return [A_i[j] for A_i in A]\",\n \"def get_column(A: Matrix, j: int) -> Vector:\\n return [A_i[j] for A_i in A]\",\n \"def _pos2col(self, start, cpos, **opts):\\n tw = opts.get('tab_width', self.TAB_WIDTH)\\n tt = opts.get('tab_type', 'stop')\\n if tt == 'fixed':\\n\\n def advance(p):\\n return p + tw\\n else:\\n\\n def advance(p):\\n return tw * ((p + tw) // tw)\\n\\n colnum = 0\\n while cpos > 0:\\n if self.input[start] == '\\\\t':\\n colnum = advance(colnum)\\n else:\\n colnum += 1\\n start += 1\\n cpos -= 1\\n return colnum\",\n \"def cell_slice(payload):\\n payload_len = len(payload)\\n if payload_len < 7: # (payload too small, need data)\\n return None, payload\\n\\n cmd = cell_get_cmd(payload)\\n\\n if cell_is_variable_length(cmd):\\n cell_len = 7 + cell_get_len(payload)\\n else:\\n cell_len = 514\\n\\n if payload_len < cell_len:\\n return None, payload\\n\\n cell = payload[:cell_len]\\n\\n return cell, payload[cell_len:]\",\n \"def cols(self, state):\\n columns = []\\n col_word = []\\n g = state\\n z = len(g)\\n for x in range(z):\\n for y in range(z):\\n col_word.append(g[y][x])\\n columns.append(col_word)\\n col_word = []\\n \\n print \\\"rows: \\\"\\n for i in range(len(g)):\\n print g[i]\\n \\n print \\\"cols: \\\"\\n for i in range(len(columns)):\\n print columns[i]\\n\\n return columns\",\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 _get_col(self, idx):\\n return self.text[self._fwf.column_slices[idx]]\",\n \"def _slice_at_axis(sl, axis):\\n return (slice(None),) * axis + (sl,) + (...,)\",\n \"def collect_columns():\\n return ((x, y) for x in range(72) for y in range(x + 9, 81, 9))\",\n \"def as_slice(self):\\n # slice for accessing arrays of values\\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77380747","0.69819945","0.67847955","0.6653259","0.6169519","0.5882231","0.58708996","0.57938963","0.570199","0.56744456","0.56735504","0.5606898","0.5576777","0.5566366","0.5544074","0.55073357","0.5492098","0.54868627","0.5441892","0.5436611","0.5434624","0.5387297","0.53826","0.53708196","0.53248143","0.5302407","0.5276967","0.5262948","0.52569854","0.52178645","0.5172142","0.5172124","0.51231045","0.50457025","0.5038119","0.5036696","0.5025969","0.5023949","0.50234723","0.49772432","0.49644536","0.49594408","0.49402815","0.49091634","0.48908487","0.48708957","0.48540115","0.48517734","0.48480922","0.48463544","0.4845305","0.48375887","0.48370028","0.48171338","0.48158684","0.4814291","0.48136517","0.48100916","0.480679","0.48056415","0.48012632","0.4801195","0.47921318","0.4791596","0.47899815","0.4788048","0.4778796","0.47781408","0.47732234","0.47728252","0.47718683","0.47688362","0.47649342","0.47604623","0.4757069","0.47506982","0.4750691","0.47489473","0.47472087","0.4742711","0.47390905","0.47376722","0.47256","0.47246462","0.4722639","0.47192055","0.47100702","0.47060573","0.47023624","0.47012705","0.47012705","0.47012705","0.46985912","0.46919158","0.469044","0.4689048","0.4683405","0.46830088","0.46808204","0.46762824"],"string":"[\n \"0.77380747\",\n \"0.69819945\",\n \"0.67847955\",\n \"0.6653259\",\n \"0.6169519\",\n \"0.5882231\",\n \"0.58708996\",\n \"0.57938963\",\n \"0.570199\",\n \"0.56744456\",\n \"0.56735504\",\n \"0.5606898\",\n \"0.5576777\",\n \"0.5566366\",\n \"0.5544074\",\n \"0.55073357\",\n \"0.5492098\",\n \"0.54868627\",\n \"0.5441892\",\n \"0.5436611\",\n \"0.5434624\",\n \"0.5387297\",\n \"0.53826\",\n \"0.53708196\",\n \"0.53248143\",\n \"0.5302407\",\n \"0.5276967\",\n \"0.5262948\",\n \"0.52569854\",\n \"0.52178645\",\n \"0.5172142\",\n \"0.5172124\",\n \"0.51231045\",\n \"0.50457025\",\n \"0.5038119\",\n \"0.5036696\",\n \"0.5025969\",\n \"0.5023949\",\n \"0.50234723\",\n \"0.49772432\",\n \"0.49644536\",\n \"0.49594408\",\n \"0.49402815\",\n \"0.49091634\",\n \"0.48908487\",\n \"0.48708957\",\n \"0.48540115\",\n \"0.48517734\",\n \"0.48480922\",\n \"0.48463544\",\n \"0.4845305\",\n \"0.48375887\",\n \"0.48370028\",\n \"0.48171338\",\n \"0.48158684\",\n \"0.4814291\",\n \"0.48136517\",\n \"0.48100916\",\n \"0.480679\",\n \"0.48056415\",\n \"0.48012632\",\n \"0.4801195\",\n \"0.47921318\",\n \"0.4791596\",\n \"0.47899815\",\n \"0.4788048\",\n \"0.4778796\",\n \"0.47781408\",\n \"0.47732234\",\n \"0.47728252\",\n \"0.47718683\",\n \"0.47688362\",\n \"0.47649342\",\n \"0.47604623\",\n \"0.4757069\",\n \"0.47506982\",\n \"0.4750691\",\n \"0.47489473\",\n \"0.47472087\",\n \"0.4742711\",\n \"0.47390905\",\n \"0.47376722\",\n \"0.47256\",\n \"0.47246462\",\n \"0.4722639\",\n \"0.47192055\",\n \"0.47100702\",\n \"0.47060573\",\n \"0.47023624\",\n \"0.47012705\",\n \"0.47012705\",\n \"0.47012705\",\n \"0.46985912\",\n \"0.46919158\",\n \"0.469044\",\n \"0.4689048\",\n \"0.4683405\",\n \"0.46830088\",\n \"0.46808204\",\n \"0.46762824\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7907325"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11785,"cells":{"query":{"kind":"string","value":"Obtains bound information for one constraint. getconbound(self,i_)"},"document":{"kind":"string","value":"def getconbound(self,i_):\n bk_ = ctypes.c_int32()\n bl_ = ctypes.c_double()\n bu_ = ctypes.c_double()\n res = __library__.MSK_XX_getconbound(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)"},"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 getconbound(self,i_): # 3\n res,resargs = self.__obj.getconbound(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 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 getbound(self,accmode_,i_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n res,resargs = self.__obj.getbound(accmode_,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 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 constraint(self) -> Constraint:\n return self._constraint","def _get_one_bound(self, param_name):\n return getattr(self, '__' + param_name + '_bounds')","def boundary_of_set(i):\n b = self.args[i].boundary\n for j, a in enumerate(self.args):\n if j != i:\n b = b - a.interior\n return b","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self, p_int, p_int_1, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\n ...","def chgconbound(self,i_,lower_,finite_,value_): # 3\n res = self.__obj.chgconbound(i_,lower_,finite_,value_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def getConstraint(self):\n return self.gk, self.g_mink, self.g_maxk","def constraints(self):\n return self._constraints","def constraints(self):\n return self._constraints","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 get_constraints(self):\n return self.constraints","def constraints(self):\n ...","def getConstraint(self, *args):\n return _libsbml.Model_getConstraint(self, *args)","def get_bounds(self):\n raise Exception(\"Non-implemented base class method.\")","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 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 _getBound(self):\n if self._colormap is not None:\n bound = self._getRawBound()\n\n if bound is None:\n bound = self._getColormapRange()[self._index]\n return bound\n else:\n return 1. # Fallback","def get_bound_circuit(self, theta):\r\n param_dict = self.get_param_dict(theta)\r\n\r\n return self.circuit.bind_parameters(param_dict)","def bounds(self):\n return self._bboxes[0][0] #TODO: merge all coverages","def bounds(self): # -> tuple[()]:\n ...","def condition_bounds(self) -> Tuple[float, float]:\n raise NotImplementedError","def get_basicConstraints(self):\n\n return self.get_POW().getBasicConstraints()","def get_constraints(self):\n\n return vertcat(*self.g), self.g_min, self.g_max","def bounds(self):\n return self.kernel.bounds","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 constraintData(self):\n pass","def bounds(self):\n return self._bboxes[0][0] #TODO: merge all coverages","def constraints(self, x):\n pass","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 constraints(self) -> Tuple[NDArray, NDArray]:","def bounds(self):\n if self.change_dimensionality:\n return [self._bounds[0]] * self.N\n else:\n return self._bounds","def bounds(self):\n return self.GetBounds()","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 build_constraints_boundaries(self):\n\n # Trapezoidal and Hermite-Simpson methods can't compute\n # defects at the last node contrary to pseudospectral methods\n coll_method = self.options['tr_method'] in [\n 'trapezoidal', 'hermite-simpson']\n n_nodes = self.problem.prm['n_nodes'] - \\\n 1 if coll_method else self.problem.prm['n_nodes']\n\n # Defects lower and upper boundaries\n defects_low = np.zeros(\n self.problem.prm['n_states'] * n_nodes)\n defects_upp = np.zeros(\n self.problem.prm['n_states'] * n_nodes)\n\n # Path lower and upper boundaries\n path_low = np.hstack([self.problem.low_bnd.path]\n * (self.problem.prm['n_nodes']))\n path_upp = np.hstack([self.problem.upp_bnd.path]\n * (self.problem.prm['n_nodes']))\n\n # Events lower and upper boundaries\n event_low = self.problem.low_bnd.event\n event_upp = self.problem.upp_bnd.event\n\n # Assembly of the lower and upper boundaries vectors\n low = np.concatenate((defects_low, path_low, event_low))\n upp = np.concatenate((defects_upp, path_upp, event_upp))\n\n return low, upp","def get_params_bounds(self) -> np.array:\n pass","def bounds(self):\n return [(2, None)]","def bounds(self):\n return self._bounds","def get_lower_bound(self):\n return self._lower_bound","def get_lower_bound(self):\n return self._lower_bound","def bounds(self) -> devices.PrimaryBounds:\n if self._bounds is None:\n print(self.__class__.bounds.__doc__)\n raise SilSubProblemError(\n \"The *bounds* property has not been set (see above).\"\n )\n return self._bounds","def calc_bounds(roi: np.ndarray) -> Dict[int, BoundInfo]:\n try:\n min_bounds, max_bounds = calc_bounds(roi)\n return {\n num: BoundInfo(lower=lower, upper=upper)\n for num, (lower, upper) in enumerate(zip(min_bounds, max_bounds))\n if num != 0 and upper[0] != -1\n }\n except KeyError:\n bound_info = {}\n points = np.nonzero(roi)\n comp_num = roi[points]\n point_dict = defaultdict(list)\n for num, point in zip(comp_num, np.transpose(points)):\n point_dict[num].append(point)\n for num, points_for_num in point_dict.items():\n lower = np.min(points_for_num, 0)\n upper = np.max(points_for_num, 0)\n bound_info[num] = BoundInfo(lower=lower, upper=upper)\n return bound_info","def constraints(self):\n # Turn softmax output to categories.\n predictions = (1 + tf.sign(self.predictions)) / 2\n\n # Set the constraint to zero.\n self.constraint = 0\n ct = list()\n\n # Compute DIDI constraint.\n for I in self.I_train:\n N = tf.reduce_sum(tf.cast(I >= 0, dtype=tf.float32))\n Np = tf.reduce_sum(I)\n a = (tf.reduce_sum(predictions) / N)\n b = (tf.reduce_sum(I * predictions) / Np)\n\n tmp = tf.cond(Np > 0, lambda: 2 * (a - b), lambda: 0.0)\n ct.append(tf.abs(tmp))\n\n # ConstrainedMinimizationProblems must always provide their constraints in\n # the form (tensor <= 0).\n # return self.constraint - self.constraint_value\n return sum(ct) - self.constraint_value","def get_upper_bound(self):\n return self._upper_bound","def get_bounds(self, channel_indx=None):\n if channel_indx is None:\n upper_bound = self.upper_bound.reshape(self.batch_size, -1)\n lower_bound = self.lower_bound.reshape(self.batch_size, -1)\n else:\n upper_bound = self.upper_bound[:, channel_indx, :].reshape(\n self.batch_size, -1\n )\n lower_bound = self.lower_bound[:, channel_indx, :].reshape(\n self.batch_size, -1\n )\n return upper_bound, lower_bound","def _get_bond_constraint(self, atom1, atom2, system):\n # TODO: This algorithm is incredibly inefficient.\n # Instead, generate a dictionary lookup of constrained distances.\n\n atom_indices = set([atom1.idx, atom2.idx])\n n_constraints = system.getNumConstraints()\n constraint = None\n for i in range(n_constraints):\n p1, p2, length = system.getConstraintParameters(i)\n constraint_atoms = set([p1, p2])\n if len(constraint_atoms.intersection(atom_indices))==2:\n constraint = length\n\n if constraint is not None:\n check_dimensionality(constraint, unit.nanometers)\n return constraint","def get_constraint_array(self, x):\n return np.array(self.constraint.get_g(x)).reshape((-1, 1))","def get_constraints(self, scaled=True, use_indices=True):\n return self._get_variables_of_type('constraint', scaled, use_indices)","def cluster_constraint(self):\n return self._cluster_constraint","def get_constraints(self, prev_layer):\n constraints = []\n if self.activation is not None:\n constraints += self.activation.get_constraints(self, prev_layer)\n else:\n # for linear activations\n current_constraints = []\n for channel_indx in range(self.n_in_channels):\n upper_bound, _ = prev_layer.get_bounds(channel_indx)\n critical_prob = prev_layer.get_critical_neurons(channel_indx)\n if critical_prob is None:\n keep_upper_bound = 0\n else:\n keep_upper_bound = cp.multiply(1 - critical_prob, upper_bound)\n\n current_constraints += [\n self.layer_input[channel_indx]\n == prev_layer.get_computation_layer(channel_indx) - keep_upper_bound\n ]\n constraints += self.create_constraint(\n f\"{self.name}_linear\", current_constraints\n )\n if prev_layer.compute_critical_neurons:\n constraints += self.create_constraint(\n f\"neuron_importance_bounds_{prev_layer.name}\",\n [prev_layer.neuron_importance >= 0, prev_layer.neuron_importance <= 1],\n )\n return constraints","def bounds(self, pos):","def _parse_initbound(self) :\n\t\tlogging.debug(\"Parsing initbound soft constraints\")","def ikle_bnd(self):\n if self._ikle_bnd is None:\n if self.boundary_file is not None:\n self._ikle_bnd = self.get_bnd_connectivity()\n else:\n raise TelemacException(\\\n \"Can not read ikle_bnd no boundary file was given\")\n\n return self._ikle_bnd","def get_constraint(self, attribute_name):\n\n for constraint in self.constraints:\n if constraint.key == attribute_name:\n return constraint\n\n # If it can't be found, return None.\n return None","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 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 constraints(self) -> constraints.QuantumCircuitConstraints:\n return self._constraints","def get_bound_adapter_index(self):\n\t\treturn call_sdk_function('PrlVmDevNet_GetBoundAdapterIndex', self.handle)","def GetIndexBounds(self, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\n ...","def _bs(self, X, t):\n for i, const in self._constraints_df.iterrows():\n X_local = X[const['Xidxs']]\n\n def b(u):\n return const['constraint_obj'].b(X_local, u, t)\n\n yield b","def get_constr_pars_high(self):\n constrHi = numpy.empty(self.get_num_parameters())\n i = 0\n for p in self.parameters:\n constrHi[i] = p.get_constraint_high()\n i += 1\n return constrHi","def compute_bounds(self, x=None, aux=None, C=None, method='backward', IBP=False, forward=False, \n bound_lower=True, bound_upper=True, reuse_ibp=False,\n return_A=False, needed_A_list=None, final_node_name=None, average_A=False, new_interval=None,\n return_b=False, b_dict=None, reference_bounds=None, intermediate_constr=None, alpha_idx=None):\n\n # Several shortcuts.\n method = method.lower() if method is not None else method\n if method == 'ibp':\n # Pure IBP bounds.\n method = None\n IBP = True\n elif method == 'ibp+backward' or method == 'ibp+crown' or method == 'crown-ibp':\n method = 'backward'\n IBP = True\n elif method == 'crown':\n method = 'backward'\n elif method == 'forward':\n forward = True\n elif method == 'forward+backward':\n method = 'backward'\n forward = True\n elif method == \"crown-optimized\" or method == 'alpha-crown':\n if bound_lower:\n ret1 = self.get_optimized_bounds(x=x, IBP=False, C=C, method='backward', new_interval=new_interval, reference_bounds=reference_bounds,\n bound_lower=bound_lower, bound_upper=False, return_A=return_A)\n if bound_upper:\n ret2 = self.get_optimized_bounds(x=x, IBP=False, C=C, method='backward', new_interval=new_interval, reference_bounds=reference_bounds,\n bound_lower=False, bound_upper=bound_upper, return_A=return_A)\n if bound_upper and bound_upper:\n assert return_A is False\n return ret1[0], ret2[1]\n elif bound_lower:\n return ret1\n elif bound_upper:\n return ret2\n else:\n raise NotImplementedError\n\n # If y in self.backward_node_pairs[x], then node y is visited when \n # doing backward bound propagation starting from node x.\n self.backward_from = dict([(node, []) for node in self._modules])\n\n if not bound_lower and not bound_upper:\n raise ValueError('At least one of bound_lower and bound_upper in compute_bounds should be True')\n A_dict = {} if return_A else None\n\n if x is not None:\n self._set_input(*x, new_interval=new_interval)\n\n if IBP and method is None and reuse_ibp:\n # directly return the previously saved ibp bounds\n return self.ibp_lower, self.ibp_upper\n root = [self._modules[name] for name in self.root_name]\n batch_size = root[0].value.shape[0]\n dim_in = 0\n\n for i in range(len(root)):\n value = root[i].forward()\n if hasattr(root[i], 'perturbation') and root[i].perturbation is not None: \n root[i].linear, root[i].center, root[i].aux = \\\n root[i].perturbation.init(value, aux=aux, forward=forward)\n # This input/parameter has perturbation. Create an interval object.\n if self.ibp_relative:\n root[i].interval = Interval(\n None, None, \n root[i].linear.nominal, root[i].linear.lower_offset, root[i].linear.upper_offset)\n else:\n root[i].interval = \\\n Interval(root[i].linear.lower, root[i].linear.upper, ptb=root[i].perturbation)\n if forward:\n root[i].dim = root[i].linear.lw.shape[1]\n dim_in += root[i].dim\n else:\n if self.ibp_relative:\n root[i].interval = Interval(\n None, None, \n value, torch.zeros_like(value), torch.zeros_like(value)) \n else:\n # This inpute/parameter does not has perturbation. \n # Use plain tuple defaulting to Linf perturbation.\n root[i].interval = (value, value)\n root[i].forward_value = root[i].forward_value = root[i].value = root[i].lower = root[i].upper = value\n\n if self.ibp_relative:\n root[i].lower = root[i].interval.lower\n root[i].upper = root[i].interval.upper\n else:\n root[i].lower, root[i].upper = root[i].interval\n\n if forward:\n self._init_forward(root, dim_in)\n\n final = self._modules[self.final_name] if final_node_name is None else self._modules[final_node_name]\n logger.debug('Final node {}[{}]'.format(final, final.name))\n\n if IBP:\n res = self._IBP_general(node=final, C=C)\n if self.ibp_relative:\n self.ibp_lower, self.ibp_upper = res.lower, res.upper\n else:\n self.ibp_lower, self.ibp_upper = res\n\n if method is None:\n return self.ibp_lower, self.ibp_upper \n\n if C is None:\n # C is an identity matrix by default \n if final.output_shape is None:\n raise ValueError('C is not provided while node {} has no default shape'.format(final.shape))\n dim_output = int(np.prod(final.output_shape[1:]))\n C = torch.eye(dim_output, device=self.device).unsqueeze(0).repeat(batch_size, 1, 1) # TODO: use an eyeC object here.\n\n # check whether weights are perturbed and set nonlinear for the BoundMatMul operation\n for n in self._modules.values():\n if isinstance(n, (BoundLinear, BoundConv, BoundBatchNormalization)):\n n.nonlinear = False\n for l_name in n.input_name[1:]:\n node = self._modules[l_name]\n if hasattr(node, 'perturbation'):\n if node.perturbation is not None:\n n.nonlinear = True\n\n # BFS to find out whether each node is used given the current final node\n if final != self.last_final_node:\n self.last_final_node = final\n for i in self._modules.values():\n i.used = False\n final.used = True\n queue = deque([final])\n while len(queue) > 0:\n n = queue.popleft()\n for n_pre_name in n.input_name:\n n_pre = self._modules[n_pre_name]\n if not n_pre.used:\n n_pre.used = True\n queue.append(n_pre)\n\n for i in self._modules.values(): # for all nodes\n if not i.used:\n continue\n if hasattr(i, 'nonlinear') and i.nonlinear:\n for l_name in i.input_name:\n node = self._modules[l_name]\n # print('node', node, 'lower', hasattr(node, 'lower'), 'perturbed', node.perturbed, 'forward_value', hasattr(node, 'forward_value'), 'from_input', node.from_input)\n if not hasattr(node, 'lower'):\n assert not IBP, 'There should be no missing intermediate bounds when IBP is enabled'\n if not node.perturbed and hasattr(node, 'forward_value'):\n node.interval = node.lower, node.upper = \\\n node.forward_value, node.forward_value\n continue\n # FIXME check that weight perturbation is not affected\n # (from_input=True should be set for weights)\n if not node.from_input and hasattr(node, 'forward_value'):\n node.lower = node.upper = node.forward_value\n continue\n if forward:\n l, u = self._forward_general(\n node=node, root=root, dim_in=dim_in, concretize=True)\n else:\n # assign concretized bound for ReLU layer to save computational cost\n # FIXME: Put ReLU after reshape will cause problem!\n if (isinstance(node, BoundActivation) or isinstance(node, BoundTranspose)) and hasattr(\n self._modules[node.input_name[0]], 'lower'):\n node.lower = node.forward(self._modules[node.input_name[0]].lower)\n node.upper = node.forward(self._modules[node.input_name[0]].upper)\n elif isinstance(node, BoundReshape) and \\\n hasattr(self._modules[node.input_name[0]], 'lower') and \\\n hasattr(self._modules[node.input_name[1]], 'value'):\n # Node for input value.\n val_input = self._modules[node.input_name[0]]\n # Node for input parameter (e.g., shape, permute)\n arg_input = self._modules[node.input_name[1]]\n node.lower = node.forward(val_input.lower, arg_input.value)\n node.upper = node.forward(val_input.upper, arg_input.value)\n else:\n first_layer_flag = False\n # This is the list of all intermediate layers where we need to refine.\n if intermediate_constr is not None:\n intermediate_beta_enabled_layers = [k for v in intermediate_constr.values() for k in v]\n else:\n intermediate_beta_enabled_layers = []\n # Here we avoid creating a big C matrix in the first linear layer.\n # Disable this optimization when we have beta for intermediate layer bounds.\n if type(node) == BoundLinear or type(node) == BoundConv and node.name not in intermediate_beta_enabled_layers:\n for l_pre in node.input_name:\n if type(self._modules[l_pre]) == BoundInput:\n node.lower, node.upper = self._IBP_general(node)\n first_layer_flag = True\n break\n if not first_layer_flag:\n reduced_dim = False # Only partial neurons (unstable neurons) are bounded.\n unstable_idx = None\n dim = int(np.prod(node.output_shape[1:]))\n # FIXME: C matrix shape incorrect for BoundParams.\n if (isinstance(node, BoundLinear) or isinstance(node, BoundMatMul)) and int(\n os.environ.get('AUTOLIRPA_USE_FULL_C', 0)) == 0:\n if intermediate_constr is not None and node.name in reference_bounds:\n # If we are doing bound refinement and reference bounds are given, we only refine unstable neurons.\n # For each batch element, we find the unstable neurons.\n unstable_masks = torch.logical_and(reference_bounds[node.name][0] < 0, reference_bounds[node.name][1] > 0)\n # unstable_masks = torch.ones(dtype=torch.bool, size=(batch_size, dim), device=self.device)\n # For simplicity, merge unstable locations for all elements in this batch. TODO: use individual unstable mask.\n unstable_locs = unstable_masks.sum(dim=0).bool()\n # This is a 1-d indices, shared by all elements in this batch.\n unstable_idx = unstable_locs.nonzero().squeeze()\n # Number of unstable neurons after merging.\n max_non_zero = unstable_locs.sum()\n # Create an abstract C matrix, the unstable_idx are the non-zero elements in specifications for all batches.\n newC = OneHotC([batch_size, max_non_zero, *node.output_shape[1:]], self.device, unstable_idx, None)\n reduced_dim = True\n # print(f'layer {node.name} total {dim} unstable {max_non_zero} newC {newC.shape}')\n \"\"\"\n newC = torch.eye(dim, device=self.device) \\\n .unsqueeze(0).repeat(batch_size, 1, 1) \\\n .view(batch_size, dim, *node.output_shape[1:])\n print(f'creating new C {newC.size()}')\n if int(os.environ.get('USE_EYE_C', 0)) == 1:\n newC = eyeC([batch_size, dim, *node.output_shape[1:]], self.device)\n \"\"\"\n else:\n newC = eyeC([batch_size, dim, *node.output_shape[1:]], self.device)\n elif (isinstance(node, BoundConv) or isinstance(node,\n BoundBatchNormalization)) and node.mode == \"patches\":\n # import pdb; pdb.set_trace()\n # Here we create an Identity Patches object \n newC = Patches(None, 1, 0,\n [batch_size, node.output_shape[-2] * node.output_shape[-1],\n node.output_shape[-3], node.output_shape[-3], 1, 1], 1)\n elif isinstance(node, BoundAdd) and node.mode == \"patches\":\n num_channel = node.output_shape[-3]\n L = node.output_shape[-2] * node.output_shape[-1]\n patches = (torch.eye(num_channel, device=self.device)).unsqueeze(0).unsqueeze(\n 0).unsqueeze(4).unsqueeze(5).expand(batch_size, L, num_channel, num_channel, 1, 1) # now [1 * 1 * in_C * in_C * 1 * 1]\n newC = Patches(patches, 1, 0, [batch_size] + list(patches.shape[1:]))\n else:\n if intermediate_constr is not None and node.name in reference_bounds:\n # If we are doing bound refinement and reference bounds are given, we only refine unstable neurons.\n # For each batch element, we find the unstable neurons.\n unstable_masks = torch.logical_and(reference_bounds[node.name][0] < 0, reference_bounds[node.name][1] > 0)\n # Flatten the conv layer shape.\n unstable_masks = unstable_masks.view(unstable_masks.size(0), -1)\n # unstable_masks = torch.ones(dtype=torch.bool, size=(batch_size, dim), device=self.device)\n # For simplicity, merge unstable locations for all elements in this batch. TODO: use individual unstable mask.\n unstable_locs = unstable_masks.sum(dim=0).bool()\n # This is always a 1-d indices. For conv layers it's flattened.\n unstable_idx = unstable_locs.nonzero().squeeze()\n # Number of unstable neurons after merging.\n max_non_zero = unstable_locs.sum()\n # Create a C matrix.\n newC = torch.zeros([1, max_non_zero, dim], device=self.device)\n # Fill the corresponding elements to 1.0\n newC[0, torch.arange(max_non_zero), unstable_idx] = 1.0\n newC = newC.repeat(batch_size, 1, 1).view(batch_size, max_non_zero, *node.output_shape[1:])\n reduced_dim = True\n # print(f'layer {node.name} total {dim} unstable {max_non_zero} newC {newC.size()}')\n else:\n if dim > 1000:\n warnings.warn(f\"Creating an identity matrix with size {dim}x{dim} for node {node}. This may indicate poor performance for bound computation. If you see this message on a small network please submit a bug report.\", stacklevel=2)\n newC = torch.eye(dim, device=self.device) \\\n .unsqueeze(0).repeat(batch_size, 1, 1) \\\n .view(batch_size, dim, *node.output_shape[1:])\n # print('Creating new C', type(newC), 'for', node)\n if False: # TODO: only return A_dict of final layer\n _, _, A_dict = self._backward_general(C=newC, node=node, root=root,\n return_A=return_A, A_dict=A_dict, intermedaite_constr=intermediate_constr)\n else:\n self._backward_general(C=newC, node=node, root=root, return_A=False, intermediate_constr=intermediate_constr, unstable_idx=unstable_idx)\n\n if reduced_dim:\n # If we only calculated unstable neurons, we need to scatter the results back based on reference bounds.\n new_lower = reference_bounds[node.name][0].detach().clone().view(batch_size, -1)\n new_lower[:, unstable_idx] = node.lower.view(batch_size, -1)\n node.lower = new_lower.view(batch_size, *node.output_shape[1:])\n new_upper = reference_bounds[node.name][1].detach().clone().view(batch_size, -1)\n new_upper[:, unstable_idx] = node.upper.view(batch_size, -1)\n node.upper = new_upper.view(batch_size, *node.output_shape[1:])\n # node.lower and node.upper (intermediate bounds) are computed in the above function.\n # If we have bound references, we set them here to always obtain a better set of bounds.\n if reference_bounds is not None and node.name in reference_bounds:\n # Initially, the reference bound and the computed bound can be exactly the same when intermediate layer beta is 0. This will prevent gradients flow. So we need a small guard here.\n if Check_against_base_lp:\n if node.name == Check_against_base_lp_layer:\n pass\n # print(reference_bounds[node.name][0][1,0,0,3].item(), node.lower[1,0,0,3].item())\n # node.lower = torch.max(reference_bounds[node.name][0] - 1e-5, node.lower)\n # node.upper = torch.min(reference_bounds[node.name][1] + 1e-5, node.upper)\n else:\n # For LP checking, fix all other intermediate layer bounds.\n node.lower = reference_bounds[node.name][0]\n node.upper = reference_bounds[node.name][1]\n else:\n # Setting reference bounds are actually incorrect. Because the split constraints are computed using slightly\n # different alpha (not the optimal), they can be slightly worse than original at the beginning.\n # So we only update bounds to reference if they cross zero (split constraints).\n node.lower = torch.max(reference_bounds[node.name][0] - 1e-5,\n node.lower)\n node.upper = torch.min(reference_bounds[node.name][1] + 1e-5,\n node.upper)\n \"\"\"\n update_lower = reference_bounds[node.name][0] >= 0\n node.lower[update_lower] = reference_bounds[node.name][0][update_lower]\n update_upper = reference_bounds[node.name][0] <= 0\n node.upper[update_upper] = reference_bounds[node.name][1][update_upper]\n \"\"\"\n\n if method == 'backward':\n # This is for the final output bound. No need to pass in intermediate layer beta constraints.\n return self._backward_general(C=C, node=final, root=root, bound_lower=bound_lower, bound_upper=bound_upper,\n return_A=return_A, needed_A_list=needed_A_list, average_A=average_A, A_dict=A_dict,\n return_b=return_b, b_dict=b_dict, unstable_idx=alpha_idx)\n elif method == 'forward':\n return self._forward_general(C=C, node=final, root=root, dim_in=dim_in, concretize=True)\n else:\n raise NotImplementedError","def _Bs(self, X, t):\n for i, const in self._constraints_df.iterrows():\n X_local = X[const['Xidxs']]\n\n def B(u):\n return const['constraint_obj'].B(X_local, u, t)\n yield B","def init_constraint_list(self):\n constraints = []\n for row, equ_val, rhs_val in \\\n zip(self.matrix, self.equ_vec, self.rhs_vec):\n\n constraints.append({'type': self.get_eq_type(equ_val),\n 'fun': lambda x: rhs_val - np.dot(row, x)})\n\n bounds = Bounds(self.low_bounds, self.upper_bounds)\n\n return constraints, bounds","def get_constraint_list(self):\n constraints = []\n for i in xrange(self.num_repeats):\n # Using start_index, start each domain at the correct index when flattening out points in COBYLA.\n constraints.extend(self._domain.get_constraint_list(start_index=self.dim * i))\n return constraints","def bound_for(self, name):\n if '.' in name:\n module, name = name.split('.', 1)\n if module in self._modules:\n return self.__getattr__(module).bound_for(name)\n else:\n raise AttributeError('Invalid bound name %s. '\n '%s has no module %s' % (name, type(self).__name__, module))\n else:\n if name in self._parameters:\n return self._bounds[name]\n else:\n raise AttributeError('Invalid bound name %s. '\n '%s has no parameter %s' % (name, type(self).__name__, module))","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 getInterval(self) -> float:\n\t\treturn self[self._bcni]","def get_cluster_bounds(i, cluster_bounds):\n con1 = np.where(i >= cluster_bounds)[0]\n j = con1[len(con1) -1]+1\n\n # for j in range(1, len(cluster_bounds)):\n # if(i < cluster_bounds[j] and i >= cluster_bounds[j-1]):\n # break\n return np.array([cluster_bounds[j-1], cluster_bounds[j]], dtype=np.int64)","def bounds(self):\n return self.xmin, self.xmax, self.ymin, self.ymax","def bounds(self):\n return self.substrates.bounds","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 input_bounds(self):\n return self.__input_bounds","def get_constraints(model):\n with connection.cursor() as cursor:\n return connection.introspection.get_constraints(cursor, model._meta.db_table)","def _process_individual_bound(self, val):\n if(val not in [True, False]):\n raise ValueError('For composition bounds expected are iether True' \n '(free function) or False (fixed function) not %s' % (str(val)))\n return val","def _boundary_constraint_fence(\n self,\n x: np.ndarray,\n ) -> np.ndarray:\n # clip dimensions to fit within the boundary\n x_constrained = np.clip(\n x,\n self.boundary_fence['min'],\n self.boundary_fence['max'],\n )\n return x_constrained","def get_lower_bound(self, method):\n sol = method.bound(self)\n print('The lower bound is {}'.format(sol['primal objective']))\n self.lower_bounds[method.__class__.__name__] = sol['primal objective']\n return sol","def getconname(self,i_): # 3\n sizename_ = (1 + self.getconnamelen((i_)))\n arr_name = array.array(\"b\",[0]*((sizename_)))\n memview_arr_name = memoryview(arr_name)\n res,resargs = self.__obj.getconname(i_,sizename_,memview_arr_name)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n retarg_name = resargs\n retarg_name = arr_name.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return retarg_name","def parameter_bounds(self):\n for name, bound in self.named_parameter_bounds():\n yield bound","def getConstraints(self, nStates, nParams):\n # currently untested and unused\n raise NotImplementedError(\n \"constraints have not been implemented for this Experiment\")","def get_bounds(self):\n log.debug(str(inspect.stack()[1][3]) + \"--> OC.get_bounds()\")\n\n # TODO: Move the operation out of here.\n\n xmin = Inf\n ymin = Inf\n xmax = -Inf\n ymax = -Inf\n\n # for obj in self.object_list:\n for obj in self.get_list():\n try:\n gxmin, gymin, gxmax, gymax = obj.bounds()\n xmin = min([xmin, gxmin])\n ymin = min([ymin, gymin])\n xmax = max([xmax, gxmax])\n ymax = max([ymax, gymax])\n except Exception as e:\n log.warning(\"DEV WARNING: Tried to get bounds of empty geometry. %s\" % str(e))\n\n return [xmin, ymin, xmax, ymax]","def _get_i_bnds(self, wave_bounds=None):\n\n wave_grid = self.wave_grid\n i_bounds = self.i_bounds\n\n # Check if wave_bounds given\n if wave_bounds is None:\n wave_bounds = []\n for i in range(self.n_orders):\n wave = self.wave_map[i][~self.mask_ord[i]]\n wave_bounds.append([wave.min(), wave.max()])\n\n # What we need is the boundary position\n # on the wavelength grid.\n i_bnds_new = []\n for bounds, i_bnds in zip(wave_bounds, i_bounds):\n\n a = np.min(np.where(wave_grid >= bounds[0])[0])\n b = np.max(np.where(wave_grid <= bounds[1])[0]) + 1\n\n # Take the most restrictive bound\n a = np.maximum(a, i_bnds[0])\n b = np.minimum(b, i_bnds[1])\n\n # Keep value\n i_bnds_new.append([a, b])\n\n return i_bnds_new","def get_bounds(self):\n return ([self.t_min] * self.dim,[self.t_max] * self.dim)","def _check_bound(self, q):\n mat = ur_utils.forward(q, self._ik_params)\n xyz = mat[:3, 3]\n inside_bound = np.all(self._end_effector_low <= xyz) and np.all(xyz <= self._end_effector_high)\n inside_buffer_bound = (np.all(self._end_effector_low + self._box_bound_buffer <= xyz) and \\\n np.all(xyz <= self._end_effector_high - self._box_bound_buffer))\n return inside_bound, inside_buffer_bound, mat, xyz","def constraints(self):\n constraints = np.concatenate( (np.ravel(self.noise_var_constraint), \n self.kern.constraints), axis=0)\n return constraints","def _get_bound(self):\n\n if self.totensor:\n max_ = 1.0\n min_ = 0.0\n else:\n max_ = 255.0\n min_ = 0.0\n\n upper = (max_ - torch.tensor(self.img_norm['mean'])) / torch.tensor(self.img_norm['std'])\n lower = min_ - torch.tensor(self.img_norm['mean']) / torch.tensor(self.img_norm['std'])\n \n return upper, lower","def find_B(self):\n max_lb = 0\n for arc in self.arcs():\n lb = self.arc_info[arc[0]]['lower_bound']\n max_lb = max(max_lb, lb)\n n = len(self)\n m = len(list(self.edges()))\n return((m - n + 2)*max_lb)","def _constraints_other(self):\n pass","def _process_individual_bound(self, val):\n if(val == True):\n res = self._DEF_BOUNDS\n \n elif(val in [False, None]):\n res = val\n else:\n if(len(val) != 2):\n raise ValueError('Bound value is not recognized. '% (str(val)))\n if(val[0] > val[1]):\n raise ValueError('Bound values are inverted '% (str(val)))\n res = val\n return res","def _set_constraint(self):\n pass"],"string":"[\n \"def getconbound(self,i_): # 3\\n res,resargs = self.__obj.getconbound(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 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 getbound(self,accmode_,i_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n res,resargs = self.__obj.getbound(accmode_,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 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 constraint(self) -> Constraint:\\n return self._constraint\",\n \"def _get_one_bound(self, param_name):\\n return getattr(self, '__' + param_name + '_bounds')\",\n \"def boundary_of_set(i):\\n b = self.args[i].boundary\\n for j, a in enumerate(self.args):\\n if j != i:\\n b = b - a.interior\\n return b\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self, p_int, p_int_1, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def chgconbound(self,i_,lower_,finite_,value_): # 3\\n res = self.__obj.chgconbound(i_,lower_,finite_,value_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def getConstraint(self):\\n return self.gk, self.g_mink, self.g_maxk\",\n \"def constraints(self):\\n return self._constraints\",\n \"def constraints(self):\\n return self._constraints\",\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 get_constraints(self):\\n return self.constraints\",\n \"def constraints(self):\\n ...\",\n \"def getConstraint(self, *args):\\n return _libsbml.Model_getConstraint(self, *args)\",\n \"def get_bounds(self):\\n raise Exception(\\\"Non-implemented base class method.\\\")\",\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 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 _getBound(self):\\n if self._colormap is not None:\\n bound = self._getRawBound()\\n\\n if bound is None:\\n bound = self._getColormapRange()[self._index]\\n return bound\\n else:\\n return 1. # Fallback\",\n \"def get_bound_circuit(self, theta):\\r\\n param_dict = self.get_param_dict(theta)\\r\\n\\r\\n return self.circuit.bind_parameters(param_dict)\",\n \"def bounds(self):\\n return self._bboxes[0][0] #TODO: merge all coverages\",\n \"def bounds(self): # -> tuple[()]:\\n ...\",\n \"def condition_bounds(self) -> Tuple[float, float]:\\n raise NotImplementedError\",\n \"def get_basicConstraints(self):\\n\\n return self.get_POW().getBasicConstraints()\",\n \"def get_constraints(self):\\n\\n return vertcat(*self.g), self.g_min, self.g_max\",\n \"def bounds(self):\\n return self.kernel.bounds\",\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 constraintData(self):\\n pass\",\n \"def bounds(self):\\n return self._bboxes[0][0] #TODO: merge all coverages\",\n \"def constraints(self, x):\\n pass\",\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 constraints(self) -> Tuple[NDArray, NDArray]:\",\n \"def bounds(self):\\n if self.change_dimensionality:\\n return [self._bounds[0]] * self.N\\n else:\\n return self._bounds\",\n \"def bounds(self):\\n return self.GetBounds()\",\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 build_constraints_boundaries(self):\\n\\n # Trapezoidal and Hermite-Simpson methods can't compute\\n # defects at the last node contrary to pseudospectral methods\\n coll_method = self.options['tr_method'] in [\\n 'trapezoidal', 'hermite-simpson']\\n n_nodes = self.problem.prm['n_nodes'] - \\\\\\n 1 if coll_method else self.problem.prm['n_nodes']\\n\\n # Defects lower and upper boundaries\\n defects_low = np.zeros(\\n self.problem.prm['n_states'] * n_nodes)\\n defects_upp = np.zeros(\\n self.problem.prm['n_states'] * n_nodes)\\n\\n # Path lower and upper boundaries\\n path_low = np.hstack([self.problem.low_bnd.path]\\n * (self.problem.prm['n_nodes']))\\n path_upp = np.hstack([self.problem.upp_bnd.path]\\n * (self.problem.prm['n_nodes']))\\n\\n # Events lower and upper boundaries\\n event_low = self.problem.low_bnd.event\\n event_upp = self.problem.upp_bnd.event\\n\\n # Assembly of the lower and upper boundaries vectors\\n low = np.concatenate((defects_low, path_low, event_low))\\n upp = np.concatenate((defects_upp, path_upp, event_upp))\\n\\n return low, upp\",\n \"def get_params_bounds(self) -> np.array:\\n pass\",\n \"def bounds(self):\\n return [(2, None)]\",\n \"def bounds(self):\\n return self._bounds\",\n \"def get_lower_bound(self):\\n return self._lower_bound\",\n \"def get_lower_bound(self):\\n return self._lower_bound\",\n \"def bounds(self) -> devices.PrimaryBounds:\\n if self._bounds is None:\\n print(self.__class__.bounds.__doc__)\\n raise SilSubProblemError(\\n \\\"The *bounds* property has not been set (see above).\\\"\\n )\\n return self._bounds\",\n \"def calc_bounds(roi: np.ndarray) -> Dict[int, BoundInfo]:\\n try:\\n min_bounds, max_bounds = calc_bounds(roi)\\n return {\\n num: BoundInfo(lower=lower, upper=upper)\\n for num, (lower, upper) in enumerate(zip(min_bounds, max_bounds))\\n if num != 0 and upper[0] != -1\\n }\\n except KeyError:\\n bound_info = {}\\n points = np.nonzero(roi)\\n comp_num = roi[points]\\n point_dict = defaultdict(list)\\n for num, point in zip(comp_num, np.transpose(points)):\\n point_dict[num].append(point)\\n for num, points_for_num in point_dict.items():\\n lower = np.min(points_for_num, 0)\\n upper = np.max(points_for_num, 0)\\n bound_info[num] = BoundInfo(lower=lower, upper=upper)\\n return bound_info\",\n \"def constraints(self):\\n # Turn softmax output to categories.\\n predictions = (1 + tf.sign(self.predictions)) / 2\\n\\n # Set the constraint to zero.\\n self.constraint = 0\\n ct = list()\\n\\n # Compute DIDI constraint.\\n for I in self.I_train:\\n N = tf.reduce_sum(tf.cast(I >= 0, dtype=tf.float32))\\n Np = tf.reduce_sum(I)\\n a = (tf.reduce_sum(predictions) / N)\\n b = (tf.reduce_sum(I * predictions) / Np)\\n\\n tmp = tf.cond(Np > 0, lambda: 2 * (a - b), lambda: 0.0)\\n ct.append(tf.abs(tmp))\\n\\n # ConstrainedMinimizationProblems must always provide their constraints in\\n # the form (tensor <= 0).\\n # return self.constraint - self.constraint_value\\n return sum(ct) - self.constraint_value\",\n \"def get_upper_bound(self):\\n return self._upper_bound\",\n \"def get_bounds(self, channel_indx=None):\\n if channel_indx is None:\\n upper_bound = self.upper_bound.reshape(self.batch_size, -1)\\n lower_bound = self.lower_bound.reshape(self.batch_size, -1)\\n else:\\n upper_bound = self.upper_bound[:, channel_indx, :].reshape(\\n self.batch_size, -1\\n )\\n lower_bound = self.lower_bound[:, channel_indx, :].reshape(\\n self.batch_size, -1\\n )\\n return upper_bound, lower_bound\",\n \"def _get_bond_constraint(self, atom1, atom2, system):\\n # TODO: This algorithm is incredibly inefficient.\\n # Instead, generate a dictionary lookup of constrained distances.\\n\\n atom_indices = set([atom1.idx, atom2.idx])\\n n_constraints = system.getNumConstraints()\\n constraint = None\\n for i in range(n_constraints):\\n p1, p2, length = system.getConstraintParameters(i)\\n constraint_atoms = set([p1, p2])\\n if len(constraint_atoms.intersection(atom_indices))==2:\\n constraint = length\\n\\n if constraint is not None:\\n check_dimensionality(constraint, unit.nanometers)\\n return constraint\",\n \"def get_constraint_array(self, x):\\n return np.array(self.constraint.get_g(x)).reshape((-1, 1))\",\n \"def get_constraints(self, scaled=True, use_indices=True):\\n return self._get_variables_of_type('constraint', scaled, use_indices)\",\n \"def cluster_constraint(self):\\n return self._cluster_constraint\",\n \"def get_constraints(self, prev_layer):\\n constraints = []\\n if self.activation is not None:\\n constraints += self.activation.get_constraints(self, prev_layer)\\n else:\\n # for linear activations\\n current_constraints = []\\n for channel_indx in range(self.n_in_channels):\\n upper_bound, _ = prev_layer.get_bounds(channel_indx)\\n critical_prob = prev_layer.get_critical_neurons(channel_indx)\\n if critical_prob is None:\\n keep_upper_bound = 0\\n else:\\n keep_upper_bound = cp.multiply(1 - critical_prob, upper_bound)\\n\\n current_constraints += [\\n self.layer_input[channel_indx]\\n == prev_layer.get_computation_layer(channel_indx) - keep_upper_bound\\n ]\\n constraints += self.create_constraint(\\n f\\\"{self.name}_linear\\\", current_constraints\\n )\\n if prev_layer.compute_critical_neurons:\\n constraints += self.create_constraint(\\n f\\\"neuron_importance_bounds_{prev_layer.name}\\\",\\n [prev_layer.neuron_importance >= 0, prev_layer.neuron_importance <= 1],\\n )\\n return constraints\",\n \"def bounds(self, pos):\",\n \"def _parse_initbound(self) :\\n\\t\\tlogging.debug(\\\"Parsing initbound soft constraints\\\")\",\n \"def ikle_bnd(self):\\n if self._ikle_bnd is None:\\n if self.boundary_file is not None:\\n self._ikle_bnd = self.get_bnd_connectivity()\\n else:\\n raise TelemacException(\\\\\\n \\\"Can not read ikle_bnd no boundary file was given\\\")\\n\\n return self._ikle_bnd\",\n \"def get_constraint(self, attribute_name):\\n\\n for constraint in self.constraints:\\n if constraint.key == attribute_name:\\n return constraint\\n\\n # If it can't be found, return None.\\n return None\",\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 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 constraints(self) -> constraints.QuantumCircuitConstraints:\\n return self._constraints\",\n \"def get_bound_adapter_index(self):\\n\\t\\treturn call_sdk_function('PrlVmDevNet_GetBoundAdapterIndex', self.handle)\",\n \"def GetIndexBounds(self, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def _bs(self, X, t):\\n for i, const in self._constraints_df.iterrows():\\n X_local = X[const['Xidxs']]\\n\\n def b(u):\\n return const['constraint_obj'].b(X_local, u, t)\\n\\n yield b\",\n \"def get_constr_pars_high(self):\\n constrHi = numpy.empty(self.get_num_parameters())\\n i = 0\\n for p in self.parameters:\\n constrHi[i] = p.get_constraint_high()\\n i += 1\\n return constrHi\",\n \"def compute_bounds(self, x=None, aux=None, C=None, method='backward', IBP=False, forward=False, \\n bound_lower=True, bound_upper=True, reuse_ibp=False,\\n return_A=False, needed_A_list=None, final_node_name=None, average_A=False, new_interval=None,\\n return_b=False, b_dict=None, reference_bounds=None, intermediate_constr=None, alpha_idx=None):\\n\\n # Several shortcuts.\\n method = method.lower() if method is not None else method\\n if method == 'ibp':\\n # Pure IBP bounds.\\n method = None\\n IBP = True\\n elif method == 'ibp+backward' or method == 'ibp+crown' or method == 'crown-ibp':\\n method = 'backward'\\n IBP = True\\n elif method == 'crown':\\n method = 'backward'\\n elif method == 'forward':\\n forward = True\\n elif method == 'forward+backward':\\n method = 'backward'\\n forward = True\\n elif method == \\\"crown-optimized\\\" or method == 'alpha-crown':\\n if bound_lower:\\n ret1 = self.get_optimized_bounds(x=x, IBP=False, C=C, method='backward', new_interval=new_interval, reference_bounds=reference_bounds,\\n bound_lower=bound_lower, bound_upper=False, return_A=return_A)\\n if bound_upper:\\n ret2 = self.get_optimized_bounds(x=x, IBP=False, C=C, method='backward', new_interval=new_interval, reference_bounds=reference_bounds,\\n bound_lower=False, bound_upper=bound_upper, return_A=return_A)\\n if bound_upper and bound_upper:\\n assert return_A is False\\n return ret1[0], ret2[1]\\n elif bound_lower:\\n return ret1\\n elif bound_upper:\\n return ret2\\n else:\\n raise NotImplementedError\\n\\n # If y in self.backward_node_pairs[x], then node y is visited when \\n # doing backward bound propagation starting from node x.\\n self.backward_from = dict([(node, []) for node in self._modules])\\n\\n if not bound_lower and not bound_upper:\\n raise ValueError('At least one of bound_lower and bound_upper in compute_bounds should be True')\\n A_dict = {} if return_A else None\\n\\n if x is not None:\\n self._set_input(*x, new_interval=new_interval)\\n\\n if IBP and method is None and reuse_ibp:\\n # directly return the previously saved ibp bounds\\n return self.ibp_lower, self.ibp_upper\\n root = [self._modules[name] for name in self.root_name]\\n batch_size = root[0].value.shape[0]\\n dim_in = 0\\n\\n for i in range(len(root)):\\n value = root[i].forward()\\n if hasattr(root[i], 'perturbation') and root[i].perturbation is not None: \\n root[i].linear, root[i].center, root[i].aux = \\\\\\n root[i].perturbation.init(value, aux=aux, forward=forward)\\n # This input/parameter has perturbation. Create an interval object.\\n if self.ibp_relative:\\n root[i].interval = Interval(\\n None, None, \\n root[i].linear.nominal, root[i].linear.lower_offset, root[i].linear.upper_offset)\\n else:\\n root[i].interval = \\\\\\n Interval(root[i].linear.lower, root[i].linear.upper, ptb=root[i].perturbation)\\n if forward:\\n root[i].dim = root[i].linear.lw.shape[1]\\n dim_in += root[i].dim\\n else:\\n if self.ibp_relative:\\n root[i].interval = Interval(\\n None, None, \\n value, torch.zeros_like(value), torch.zeros_like(value)) \\n else:\\n # This inpute/parameter does not has perturbation. \\n # Use plain tuple defaulting to Linf perturbation.\\n root[i].interval = (value, value)\\n root[i].forward_value = root[i].forward_value = root[i].value = root[i].lower = root[i].upper = value\\n\\n if self.ibp_relative:\\n root[i].lower = root[i].interval.lower\\n root[i].upper = root[i].interval.upper\\n else:\\n root[i].lower, root[i].upper = root[i].interval\\n\\n if forward:\\n self._init_forward(root, dim_in)\\n\\n final = self._modules[self.final_name] if final_node_name is None else self._modules[final_node_name]\\n logger.debug('Final node {}[{}]'.format(final, final.name))\\n\\n if IBP:\\n res = self._IBP_general(node=final, C=C)\\n if self.ibp_relative:\\n self.ibp_lower, self.ibp_upper = res.lower, res.upper\\n else:\\n self.ibp_lower, self.ibp_upper = res\\n\\n if method is None:\\n return self.ibp_lower, self.ibp_upper \\n\\n if C is None:\\n # C is an identity matrix by default \\n if final.output_shape is None:\\n raise ValueError('C is not provided while node {} has no default shape'.format(final.shape))\\n dim_output = int(np.prod(final.output_shape[1:]))\\n C = torch.eye(dim_output, device=self.device).unsqueeze(0).repeat(batch_size, 1, 1) # TODO: use an eyeC object here.\\n\\n # check whether weights are perturbed and set nonlinear for the BoundMatMul operation\\n for n in self._modules.values():\\n if isinstance(n, (BoundLinear, BoundConv, BoundBatchNormalization)):\\n n.nonlinear = False\\n for l_name in n.input_name[1:]:\\n node = self._modules[l_name]\\n if hasattr(node, 'perturbation'):\\n if node.perturbation is not None:\\n n.nonlinear = True\\n\\n # BFS to find out whether each node is used given the current final node\\n if final != self.last_final_node:\\n self.last_final_node = final\\n for i in self._modules.values():\\n i.used = False\\n final.used = True\\n queue = deque([final])\\n while len(queue) > 0:\\n n = queue.popleft()\\n for n_pre_name in n.input_name:\\n n_pre = self._modules[n_pre_name]\\n if not n_pre.used:\\n n_pre.used = True\\n queue.append(n_pre)\\n\\n for i in self._modules.values(): # for all nodes\\n if not i.used:\\n continue\\n if hasattr(i, 'nonlinear') and i.nonlinear:\\n for l_name in i.input_name:\\n node = self._modules[l_name]\\n # print('node', node, 'lower', hasattr(node, 'lower'), 'perturbed', node.perturbed, 'forward_value', hasattr(node, 'forward_value'), 'from_input', node.from_input)\\n if not hasattr(node, 'lower'):\\n assert not IBP, 'There should be no missing intermediate bounds when IBP is enabled'\\n if not node.perturbed and hasattr(node, 'forward_value'):\\n node.interval = node.lower, node.upper = \\\\\\n node.forward_value, node.forward_value\\n continue\\n # FIXME check that weight perturbation is not affected\\n # (from_input=True should be set for weights)\\n if not node.from_input and hasattr(node, 'forward_value'):\\n node.lower = node.upper = node.forward_value\\n continue\\n if forward:\\n l, u = self._forward_general(\\n node=node, root=root, dim_in=dim_in, concretize=True)\\n else:\\n # assign concretized bound for ReLU layer to save computational cost\\n # FIXME: Put ReLU after reshape will cause problem!\\n if (isinstance(node, BoundActivation) or isinstance(node, BoundTranspose)) and hasattr(\\n self._modules[node.input_name[0]], 'lower'):\\n node.lower = node.forward(self._modules[node.input_name[0]].lower)\\n node.upper = node.forward(self._modules[node.input_name[0]].upper)\\n elif isinstance(node, BoundReshape) and \\\\\\n hasattr(self._modules[node.input_name[0]], 'lower') and \\\\\\n hasattr(self._modules[node.input_name[1]], 'value'):\\n # Node for input value.\\n val_input = self._modules[node.input_name[0]]\\n # Node for input parameter (e.g., shape, permute)\\n arg_input = self._modules[node.input_name[1]]\\n node.lower = node.forward(val_input.lower, arg_input.value)\\n node.upper = node.forward(val_input.upper, arg_input.value)\\n else:\\n first_layer_flag = False\\n # This is the list of all intermediate layers where we need to refine.\\n if intermediate_constr is not None:\\n intermediate_beta_enabled_layers = [k for v in intermediate_constr.values() for k in v]\\n else:\\n intermediate_beta_enabled_layers = []\\n # Here we avoid creating a big C matrix in the first linear layer.\\n # Disable this optimization when we have beta for intermediate layer bounds.\\n if type(node) == BoundLinear or type(node) == BoundConv and node.name not in intermediate_beta_enabled_layers:\\n for l_pre in node.input_name:\\n if type(self._modules[l_pre]) == BoundInput:\\n node.lower, node.upper = self._IBP_general(node)\\n first_layer_flag = True\\n break\\n if not first_layer_flag:\\n reduced_dim = False # Only partial neurons (unstable neurons) are bounded.\\n unstable_idx = None\\n dim = int(np.prod(node.output_shape[1:]))\\n # FIXME: C matrix shape incorrect for BoundParams.\\n if (isinstance(node, BoundLinear) or isinstance(node, BoundMatMul)) and int(\\n os.environ.get('AUTOLIRPA_USE_FULL_C', 0)) == 0:\\n if intermediate_constr is not None and node.name in reference_bounds:\\n # If we are doing bound refinement and reference bounds are given, we only refine unstable neurons.\\n # For each batch element, we find the unstable neurons.\\n unstable_masks = torch.logical_and(reference_bounds[node.name][0] < 0, reference_bounds[node.name][1] > 0)\\n # unstable_masks = torch.ones(dtype=torch.bool, size=(batch_size, dim), device=self.device)\\n # For simplicity, merge unstable locations for all elements in this batch. TODO: use individual unstable mask.\\n unstable_locs = unstable_masks.sum(dim=0).bool()\\n # This is a 1-d indices, shared by all elements in this batch.\\n unstable_idx = unstable_locs.nonzero().squeeze()\\n # Number of unstable neurons after merging.\\n max_non_zero = unstable_locs.sum()\\n # Create an abstract C matrix, the unstable_idx are the non-zero elements in specifications for all batches.\\n newC = OneHotC([batch_size, max_non_zero, *node.output_shape[1:]], self.device, unstable_idx, None)\\n reduced_dim = True\\n # print(f'layer {node.name} total {dim} unstable {max_non_zero} newC {newC.shape}')\\n \\\"\\\"\\\"\\n newC = torch.eye(dim, device=self.device) \\\\\\n .unsqueeze(0).repeat(batch_size, 1, 1) \\\\\\n .view(batch_size, dim, *node.output_shape[1:])\\n print(f'creating new C {newC.size()}')\\n if int(os.environ.get('USE_EYE_C', 0)) == 1:\\n newC = eyeC([batch_size, dim, *node.output_shape[1:]], self.device)\\n \\\"\\\"\\\"\\n else:\\n newC = eyeC([batch_size, dim, *node.output_shape[1:]], self.device)\\n elif (isinstance(node, BoundConv) or isinstance(node,\\n BoundBatchNormalization)) and node.mode == \\\"patches\\\":\\n # import pdb; pdb.set_trace()\\n # Here we create an Identity Patches object \\n newC = Patches(None, 1, 0,\\n [batch_size, node.output_shape[-2] * node.output_shape[-1],\\n node.output_shape[-3], node.output_shape[-3], 1, 1], 1)\\n elif isinstance(node, BoundAdd) and node.mode == \\\"patches\\\":\\n num_channel = node.output_shape[-3]\\n L = node.output_shape[-2] * node.output_shape[-1]\\n patches = (torch.eye(num_channel, device=self.device)).unsqueeze(0).unsqueeze(\\n 0).unsqueeze(4).unsqueeze(5).expand(batch_size, L, num_channel, num_channel, 1, 1) # now [1 * 1 * in_C * in_C * 1 * 1]\\n newC = Patches(patches, 1, 0, [batch_size] + list(patches.shape[1:]))\\n else:\\n if intermediate_constr is not None and node.name in reference_bounds:\\n # If we are doing bound refinement and reference bounds are given, we only refine unstable neurons.\\n # For each batch element, we find the unstable neurons.\\n unstable_masks = torch.logical_and(reference_bounds[node.name][0] < 0, reference_bounds[node.name][1] > 0)\\n # Flatten the conv layer shape.\\n unstable_masks = unstable_masks.view(unstable_masks.size(0), -1)\\n # unstable_masks = torch.ones(dtype=torch.bool, size=(batch_size, dim), device=self.device)\\n # For simplicity, merge unstable locations for all elements in this batch. TODO: use individual unstable mask.\\n unstable_locs = unstable_masks.sum(dim=0).bool()\\n # This is always a 1-d indices. For conv layers it's flattened.\\n unstable_idx = unstable_locs.nonzero().squeeze()\\n # Number of unstable neurons after merging.\\n max_non_zero = unstable_locs.sum()\\n # Create a C matrix.\\n newC = torch.zeros([1, max_non_zero, dim], device=self.device)\\n # Fill the corresponding elements to 1.0\\n newC[0, torch.arange(max_non_zero), unstable_idx] = 1.0\\n newC = newC.repeat(batch_size, 1, 1).view(batch_size, max_non_zero, *node.output_shape[1:])\\n reduced_dim = True\\n # print(f'layer {node.name} total {dim} unstable {max_non_zero} newC {newC.size()}')\\n else:\\n if dim > 1000:\\n warnings.warn(f\\\"Creating an identity matrix with size {dim}x{dim} for node {node}. This may indicate poor performance for bound computation. If you see this message on a small network please submit a bug report.\\\", stacklevel=2)\\n newC = torch.eye(dim, device=self.device) \\\\\\n .unsqueeze(0).repeat(batch_size, 1, 1) \\\\\\n .view(batch_size, dim, *node.output_shape[1:])\\n # print('Creating new C', type(newC), 'for', node)\\n if False: # TODO: only return A_dict of final layer\\n _, _, A_dict = self._backward_general(C=newC, node=node, root=root,\\n return_A=return_A, A_dict=A_dict, intermedaite_constr=intermediate_constr)\\n else:\\n self._backward_general(C=newC, node=node, root=root, return_A=False, intermediate_constr=intermediate_constr, unstable_idx=unstable_idx)\\n\\n if reduced_dim:\\n # If we only calculated unstable neurons, we need to scatter the results back based on reference bounds.\\n new_lower = reference_bounds[node.name][0].detach().clone().view(batch_size, -1)\\n new_lower[:, unstable_idx] = node.lower.view(batch_size, -1)\\n node.lower = new_lower.view(batch_size, *node.output_shape[1:])\\n new_upper = reference_bounds[node.name][1].detach().clone().view(batch_size, -1)\\n new_upper[:, unstable_idx] = node.upper.view(batch_size, -1)\\n node.upper = new_upper.view(batch_size, *node.output_shape[1:])\\n # node.lower and node.upper (intermediate bounds) are computed in the above function.\\n # If we have bound references, we set them here to always obtain a better set of bounds.\\n if reference_bounds is not None and node.name in reference_bounds:\\n # Initially, the reference bound and the computed bound can be exactly the same when intermediate layer beta is 0. This will prevent gradients flow. So we need a small guard here.\\n if Check_against_base_lp:\\n if node.name == Check_against_base_lp_layer:\\n pass\\n # print(reference_bounds[node.name][0][1,0,0,3].item(), node.lower[1,0,0,3].item())\\n # node.lower = torch.max(reference_bounds[node.name][0] - 1e-5, node.lower)\\n # node.upper = torch.min(reference_bounds[node.name][1] + 1e-5, node.upper)\\n else:\\n # For LP checking, fix all other intermediate layer bounds.\\n node.lower = reference_bounds[node.name][0]\\n node.upper = reference_bounds[node.name][1]\\n else:\\n # Setting reference bounds are actually incorrect. Because the split constraints are computed using slightly\\n # different alpha (not the optimal), they can be slightly worse than original at the beginning.\\n # So we only update bounds to reference if they cross zero (split constraints).\\n node.lower = torch.max(reference_bounds[node.name][0] - 1e-5,\\n node.lower)\\n node.upper = torch.min(reference_bounds[node.name][1] + 1e-5,\\n node.upper)\\n \\\"\\\"\\\"\\n update_lower = reference_bounds[node.name][0] >= 0\\n node.lower[update_lower] = reference_bounds[node.name][0][update_lower]\\n update_upper = reference_bounds[node.name][0] <= 0\\n node.upper[update_upper] = reference_bounds[node.name][1][update_upper]\\n \\\"\\\"\\\"\\n\\n if method == 'backward':\\n # This is for the final output bound. No need to pass in intermediate layer beta constraints.\\n return self._backward_general(C=C, node=final, root=root, bound_lower=bound_lower, bound_upper=bound_upper,\\n return_A=return_A, needed_A_list=needed_A_list, average_A=average_A, A_dict=A_dict,\\n return_b=return_b, b_dict=b_dict, unstable_idx=alpha_idx)\\n elif method == 'forward':\\n return self._forward_general(C=C, node=final, root=root, dim_in=dim_in, concretize=True)\\n else:\\n raise NotImplementedError\",\n \"def _Bs(self, X, t):\\n for i, const in self._constraints_df.iterrows():\\n X_local = X[const['Xidxs']]\\n\\n def B(u):\\n return const['constraint_obj'].B(X_local, u, t)\\n yield B\",\n \"def init_constraint_list(self):\\n constraints = []\\n for row, equ_val, rhs_val in \\\\\\n zip(self.matrix, self.equ_vec, self.rhs_vec):\\n\\n constraints.append({'type': self.get_eq_type(equ_val),\\n 'fun': lambda x: rhs_val - np.dot(row, x)})\\n\\n bounds = Bounds(self.low_bounds, self.upper_bounds)\\n\\n return constraints, bounds\",\n \"def get_constraint_list(self):\\n constraints = []\\n for i in xrange(self.num_repeats):\\n # Using start_index, start each domain at the correct index when flattening out points in COBYLA.\\n constraints.extend(self._domain.get_constraint_list(start_index=self.dim * i))\\n return constraints\",\n \"def bound_for(self, name):\\n if '.' in name:\\n module, name = name.split('.', 1)\\n if module in self._modules:\\n return self.__getattr__(module).bound_for(name)\\n else:\\n raise AttributeError('Invalid bound name %s. '\\n '%s has no module %s' % (name, type(self).__name__, module))\\n else:\\n if name in self._parameters:\\n return self._bounds[name]\\n else:\\n raise AttributeError('Invalid bound name %s. '\\n '%s has no parameter %s' % (name, type(self).__name__, module))\",\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 getInterval(self) -> float:\\n\\t\\treturn self[self._bcni]\",\n \"def get_cluster_bounds(i, cluster_bounds):\\n con1 = np.where(i >= cluster_bounds)[0]\\n j = con1[len(con1) -1]+1\\n\\n # for j in range(1, len(cluster_bounds)):\\n # if(i < cluster_bounds[j] and i >= cluster_bounds[j-1]):\\n # break\\n return np.array([cluster_bounds[j-1], cluster_bounds[j]], dtype=np.int64)\",\n \"def bounds(self):\\n return self.xmin, self.xmax, self.ymin, self.ymax\",\n \"def bounds(self):\\n return self.substrates.bounds\",\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 input_bounds(self):\\n return self.__input_bounds\",\n \"def get_constraints(model):\\n with connection.cursor() as cursor:\\n return connection.introspection.get_constraints(cursor, model._meta.db_table)\",\n \"def _process_individual_bound(self, val):\\n if(val not in [True, False]):\\n raise ValueError('For composition bounds expected are iether True' \\n '(free function) or False (fixed function) not %s' % (str(val)))\\n return val\",\n \"def _boundary_constraint_fence(\\n self,\\n x: np.ndarray,\\n ) -> np.ndarray:\\n # clip dimensions to fit within the boundary\\n x_constrained = np.clip(\\n x,\\n self.boundary_fence['min'],\\n self.boundary_fence['max'],\\n )\\n return x_constrained\",\n \"def get_lower_bound(self, method):\\n sol = method.bound(self)\\n print('The lower bound is {}'.format(sol['primal objective']))\\n self.lower_bounds[method.__class__.__name__] = sol['primal objective']\\n return sol\",\n \"def getconname(self,i_): # 3\\n sizename_ = (1 + self.getconnamelen((i_)))\\n arr_name = array.array(\\\"b\\\",[0]*((sizename_)))\\n memview_arr_name = memoryview(arr_name)\\n res,resargs = self.__obj.getconname(i_,sizename_,memview_arr_name)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n retarg_name = resargs\\n retarg_name = arr_name.tobytes()[:-1].decode(\\\"utf-8\\\",errors=\\\"ignore\\\")\\n return retarg_name\",\n \"def parameter_bounds(self):\\n for name, bound in self.named_parameter_bounds():\\n yield bound\",\n \"def getConstraints(self, nStates, nParams):\\n # currently untested and unused\\n raise NotImplementedError(\\n \\\"constraints have not been implemented for this Experiment\\\")\",\n \"def get_bounds(self):\\n log.debug(str(inspect.stack()[1][3]) + \\\"--> OC.get_bounds()\\\")\\n\\n # TODO: Move the operation out of here.\\n\\n xmin = Inf\\n ymin = Inf\\n xmax = -Inf\\n ymax = -Inf\\n\\n # for obj in self.object_list:\\n for obj in self.get_list():\\n try:\\n gxmin, gymin, gxmax, gymax = obj.bounds()\\n xmin = min([xmin, gxmin])\\n ymin = min([ymin, gymin])\\n xmax = max([xmax, gxmax])\\n ymax = max([ymax, gymax])\\n except Exception as e:\\n log.warning(\\\"DEV WARNING: Tried to get bounds of empty geometry. %s\\\" % str(e))\\n\\n return [xmin, ymin, xmax, ymax]\",\n \"def _get_i_bnds(self, wave_bounds=None):\\n\\n wave_grid = self.wave_grid\\n i_bounds = self.i_bounds\\n\\n # Check if wave_bounds given\\n if wave_bounds is None:\\n wave_bounds = []\\n for i in range(self.n_orders):\\n wave = self.wave_map[i][~self.mask_ord[i]]\\n wave_bounds.append([wave.min(), wave.max()])\\n\\n # What we need is the boundary position\\n # on the wavelength grid.\\n i_bnds_new = []\\n for bounds, i_bnds in zip(wave_bounds, i_bounds):\\n\\n a = np.min(np.where(wave_grid >= bounds[0])[0])\\n b = np.max(np.where(wave_grid <= bounds[1])[0]) + 1\\n\\n # Take the most restrictive bound\\n a = np.maximum(a, i_bnds[0])\\n b = np.minimum(b, i_bnds[1])\\n\\n # Keep value\\n i_bnds_new.append([a, b])\\n\\n return i_bnds_new\",\n \"def get_bounds(self):\\n return ([self.t_min] * self.dim,[self.t_max] * self.dim)\",\n \"def _check_bound(self, q):\\n mat = ur_utils.forward(q, self._ik_params)\\n xyz = mat[:3, 3]\\n inside_bound = np.all(self._end_effector_low <= xyz) and np.all(xyz <= self._end_effector_high)\\n inside_buffer_bound = (np.all(self._end_effector_low + self._box_bound_buffer <= xyz) and \\\\\\n np.all(xyz <= self._end_effector_high - self._box_bound_buffer))\\n return inside_bound, inside_buffer_bound, mat, xyz\",\n \"def constraints(self):\\n constraints = np.concatenate( (np.ravel(self.noise_var_constraint), \\n self.kern.constraints), axis=0)\\n return constraints\",\n \"def _get_bound(self):\\n\\n if self.totensor:\\n max_ = 1.0\\n min_ = 0.0\\n else:\\n max_ = 255.0\\n min_ = 0.0\\n\\n upper = (max_ - torch.tensor(self.img_norm['mean'])) / torch.tensor(self.img_norm['std'])\\n lower = min_ - torch.tensor(self.img_norm['mean']) / torch.tensor(self.img_norm['std'])\\n \\n return upper, lower\",\n \"def find_B(self):\\n max_lb = 0\\n for arc in self.arcs():\\n lb = self.arc_info[arc[0]]['lower_bound']\\n max_lb = max(max_lb, lb)\\n n = len(self)\\n m = len(list(self.edges()))\\n return((m - n + 2)*max_lb)\",\n \"def _constraints_other(self):\\n pass\",\n \"def _process_individual_bound(self, val):\\n if(val == True):\\n res = self._DEF_BOUNDS\\n \\n elif(val in [False, None]):\\n res = val\\n else:\\n if(len(val) != 2):\\n raise ValueError('Bound value is not recognized. '% (str(val)))\\n if(val[0] > val[1]):\\n raise ValueError('Bound values are inverted '% (str(val)))\\n res = val\\n return res\",\n \"def _set_constraint(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8117996","0.67920166","0.6751894","0.67239743","0.6467672","0.62063897","0.6088784","0.6081014","0.6081014","0.6081014","0.6081014","0.6081014","0.6081014","0.6081014","0.6081014","0.6065061","0.5971675","0.59263587","0.59000784","0.59000784","0.5864605","0.5833809","0.58046234","0.58005166","0.57962644","0.5790428","0.57882977","0.5761463","0.5748048","0.57475996","0.5730459","0.57260936","0.57090944","0.5701016","0.5700625","0.569139","0.56876725","0.56755054","0.5665304","0.5665092","0.5651186","0.5617601","0.56054986","0.5601497","0.559867","0.5562886","0.5528449","0.5506756","0.5503395","0.5503395","0.5485272","0.54783744","0.5474651","0.5453048","0.5440484","0.54390323","0.5420435","0.54132754","0.5410493","0.53931737","0.53797406","0.53762835","0.5376075","0.5362073","0.53460056","0.534551","0.5343918","0.53362143","0.5332808","0.5329605","0.5321181","0.5313329","0.53020936","0.52811533","0.5279522","0.52770454","0.52599806","0.5251989","0.525163","0.5250591","0.52406526","0.52380246","0.5226604","0.52229875","0.51994294","0.5187023","0.5186294","0.5172542","0.5169757","0.51681","0.51629114","0.5154481","0.5147902","0.51470643","0.51375276","0.51272035","0.5125693","0.5121061","0.5116208","0.5107365"],"string":"[\n \"0.8117996\",\n \"0.67920166\",\n \"0.6751894\",\n \"0.67239743\",\n \"0.6467672\",\n \"0.62063897\",\n \"0.6088784\",\n \"0.6081014\",\n \"0.6081014\",\n \"0.6081014\",\n \"0.6081014\",\n \"0.6081014\",\n \"0.6081014\",\n \"0.6081014\",\n \"0.6081014\",\n \"0.6065061\",\n \"0.5971675\",\n \"0.59263587\",\n \"0.59000784\",\n \"0.59000784\",\n \"0.5864605\",\n \"0.5833809\",\n \"0.58046234\",\n \"0.58005166\",\n \"0.57962644\",\n \"0.5790428\",\n \"0.57882977\",\n \"0.5761463\",\n \"0.5748048\",\n \"0.57475996\",\n \"0.5730459\",\n \"0.57260936\",\n \"0.57090944\",\n \"0.5701016\",\n \"0.5700625\",\n \"0.569139\",\n \"0.56876725\",\n \"0.56755054\",\n \"0.5665304\",\n \"0.5665092\",\n \"0.5651186\",\n \"0.5617601\",\n \"0.56054986\",\n \"0.5601497\",\n \"0.559867\",\n \"0.5562886\",\n \"0.5528449\",\n \"0.5506756\",\n \"0.5503395\",\n \"0.5503395\",\n \"0.5485272\",\n \"0.54783744\",\n \"0.5474651\",\n \"0.5453048\",\n \"0.5440484\",\n \"0.54390323\",\n \"0.5420435\",\n \"0.54132754\",\n \"0.5410493\",\n \"0.53931737\",\n \"0.53797406\",\n \"0.53762835\",\n \"0.5376075\",\n \"0.5362073\",\n \"0.53460056\",\n \"0.534551\",\n \"0.5343918\",\n \"0.53362143\",\n \"0.5332808\",\n \"0.5329605\",\n \"0.5321181\",\n \"0.5313329\",\n \"0.53020936\",\n \"0.52811533\",\n \"0.5279522\",\n \"0.52770454\",\n \"0.52599806\",\n \"0.5251989\",\n \"0.525163\",\n \"0.5250591\",\n \"0.52406526\",\n \"0.52380246\",\n \"0.5226604\",\n \"0.52229875\",\n \"0.51994294\",\n \"0.5187023\",\n \"0.5186294\",\n \"0.5172542\",\n \"0.5169757\",\n \"0.51681\",\n \"0.51629114\",\n \"0.5154481\",\n \"0.5147902\",\n \"0.51470643\",\n \"0.51375276\",\n \"0.51272035\",\n \"0.5125693\",\n \"0.5121061\",\n \"0.5116208\",\n \"0.5107365\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7855516"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11786,"cells":{"query":{"kind":"string","value":"Obtains bound information for one variable. getvarbound(self,i_)"},"document":{"kind":"string","value":"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)"},"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 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 getconbound(self,i_): # 3\n res,resargs = self.__obj.getconbound(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 getconbound(self,i_):\n bk_ = ctypes.c_int32()\n bl_ = ctypes.c_double()\n bu_ = ctypes.c_double()\n res = __library__.MSK_XX_getconbound(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 _get_one_bound(self, param_name):\n return getattr(self, '__' + param_name + '_bounds')","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 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 getbound(self,accmode_,i_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n res,resargs = self.__obj.getbound(accmode_,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 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 lookup_var(self, var):\n if var in self.binding:\n return self.binding[var]\n elif self.parent is not None:\n return self.parent.lookup_var(var)\n else:\n raise Environment.Unbound('unbound variable \"%s\"' % var)","def bound(name):","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 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 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 GetBounds(self, p_int, p_int_1, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\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","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 var(self,i): # TODO: change to property to access (read only?) X?\n return Var(i,self.dims[i])","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 dynamically_bound(*args, **kwargs):\n return DynamicVar(*args, **kwargs)","def num_vars(self):\n return len(self.bounds.lb)","def get_bindable_vars(self):\n return self.local_vars.keys() + self.parent.get_bindable_vars()","def bound_for(self, name):\n if '.' in name:\n module, name = name.split('.', 1)\n if module in self._modules:\n return self.__getattr__(module).bound_for(name)\n else:\n raise AttributeError('Invalid bound name %s. '\n '%s has no module %s' % (name, type(self).__name__, module))\n else:\n if name in self._parameters:\n return self._bounds[name]\n else:\n raise AttributeError('Invalid bound name %s. '\n '%s has no parameter %s' % (name, type(self).__name__, module))","def nvar(self):\n return len(self.__vars)","def _get_reference_by_variable(self, var):\n if not var[0] == consts.VARIABLE:\n raise Exception('Internal error: Expected a variable, got: \"%r\"' % var)\n res = self._bindings.get(var, var)\n if res == consts.TOPIC_IN_FOCUS:\n res = self.focus\n while res[0] == consts.VARIABLE and self.parent:\n res = self.parent._get_reference_by_variable(res) #pylint: disable-msg=W0212\n if res == consts.TOPIC_IN_FOCUS:\n res = self.focus\n return res","def getbarvarname(self,i_): # 3\n sizename_ = (1 + self.getbarvarnamelen((i_)))\n arr_name = array.array(\"b\",[0]*((sizename_)))\n memview_arr_name = memoryview(arr_name)\n res,resargs = self.__obj.getbarvarname(i_,sizename_,memview_arr_name)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n retarg_name = resargs\n retarg_name = arr_name.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return retarg_name","def vars(self) -> {(EVar, Pool)}:\n raise NotImplementedError()","def _getBound(self):\n if self._colormap is not None:\n bound = self._getRawBound()\n\n if bound is None:\n bound = self._getColormapRange()[self._index]\n return bound\n else:\n return 1. # Fallback","def named_parameter_bounds(self):\n for name, _ in self.named_parameters():\n yield name, self.bound_for(name)","def variable_integrate(self, variable, *bounds):\n try:\n index = list(self.variables).index(variable)\n except ValueError:\n index = None\n if index is not None:\n indef_integral = self.head.integrate_indefinite_index(type(self), self.data, self, index)\n if bounds:\n low, high = bounds\n return indef_integral.variable_subs(variable, high) - indef_integral.variable_subs(variable, low)\n return indef_integral\n raise NotImplementedError(`self.variables, variable, index`)","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 GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def isBvar(self):\n return _libsbml.ASTNode_isBvar(self)","def _check_bound(self, q):\n mat = ur_utils.forward(q, self._ik_params)\n xyz = mat[:3, 3]\n inside_bound = np.all(self._end_effector_low <= xyz) and np.all(xyz <= self._end_effector_high)\n inside_buffer_bound = (np.all(self._end_effector_low + self._box_bound_buffer <= xyz) and \\\n np.all(xyz <= self._end_effector_high - self._box_bound_buffer))\n return inside_bound, inside_buffer_bound, mat, xyz","def parameter_bounds(self):\n for name, bound in self.named_parameter_bounds():\n yield bound","def _process_individual_bound(self, val):\n if(val not in [True, False]):\n raise ValueError('For composition bounds expected are iether True' \n '(free function) or False (fixed function) not %s' % (str(val)))\n return val","def xvar ( self ) :\n return self.__xvar","def is_bound(self):\n if self._value is UninitializedBinding:\n return False\n else:\n return True","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 bounding_var(self, field):\n if field not in self.bounding_fields:\n raise KeyError(\"Invalid field name: %s. Valid fields are: %s\"%\n (field, self.bounding_fields))\n fname = os.path.join(self.logs_dir, \"bounding_\"+field+\".dat\")\n data = np.loadtxt(fname, skiprows=2)\n return data","def getvar(obj):\n class VarDict(dict):\n \"\"\"wrapper of var dict\"\"\"\n def __getitem__(self, key):\n # expression may be set a var in this dict\n if key in self:\n return super(VarDict, self).__getitem__(key)\n if hastag(obj, key):\n return gettag(obj, key)\n # maybe some build-in object\n try:\n return eval(key, {}, {})\n except:\n return False\n\n return VarDict()","def _process_individual_bound(self, val):\n if(val == True):\n res = self._DEF_BOUNDS\n \n elif(val in [False, None]):\n res = val\n else:\n if(len(val) != 2):\n raise ValueError('Bound value is not recognized. '% (str(val)))\n if(val[0] > val[1]):\n raise ValueError('Bound values are inverted '% (str(val)))\n res = val\n return res","def get_var_grid(self, var_name, var_val):\n var_val = self._var_grid[var_name]\n return True","def compute_bounds(self, field):\n # Call general-purpose bound computation.\n super(VertexBasedP1DGLimiter, self).compute_bounds(field)\n\n # Add the average of lateral boundary facets to min/max fields\n # NOTE this just computes the arithmetic mean of nodal values on the facet,\n # which in general is not equivalent to the mean of the field over the bnd facet.\n # This is OK for P1DG triangles, but not exact for the extruded case (quad facets)\n from finat.finiteelementbase import entity_support_dofs\n\n if self.extruded:\n entity_dim = (self.dim-2, 1) # get vertical facets\n else:\n entity_dim = self.dim-1\n boundary_dofs = entity_support_dofs(self.P1DG.finat_element, entity_dim)\n local_facet_nodes = np.array([boundary_dofs[e] for e in sorted(boundary_dofs.keys())])\n n_bnd_nodes = local_facet_nodes.shape[1]\n local_facet_idx = op2.Global(local_facet_nodes.shape, local_facet_nodes, dtype=np.int32, name='local_facet_idx')\n code = \"\"\"\n void my_kernel(double *qmax, double *qmin, double *field, unsigned int *facet, unsigned int *local_facet_idx)\n {\n double face_mean = 0.0;\n for (int i = 0; i < %(nnodes)d; i++) {\n unsigned int idx = local_facet_idx[facet[0]*%(nnodes)d + i];\n face_mean += field[idx];\n }\n face_mean /= %(nnodes)d;\n for (int i = 0; i < %(nnodes)d; i++) {\n unsigned int idx = local_facet_idx[facet[0]*%(nnodes)d + i];\n qmax[idx] = fmax(qmax[idx], face_mean);\n qmin[idx] = fmin(qmin[idx], face_mean);\n }\n }\"\"\"\n bnd_kernel = op2.Kernel(code % {'nnodes': n_bnd_nodes}, 'my_kernel')\n op2.par_loop(bnd_kernel,\n self.P1DG.mesh().exterior_facets.set,\n self.max_field.dat(op2.MAX, self.max_field.exterior_facet_node_map()),\n self.min_field.dat(op2.MIN, self.min_field.exterior_facet_node_map()),\n field.dat(op2.READ, field.exterior_facet_node_map()),\n self.P1DG.mesh().exterior_facets.local_facet_dat(op2.READ),\n local_facet_idx(op2.READ))\n if self.extruded:\n # Add nodal values from surface/bottom boundaries\n # NOTE calling firedrake par_loop with measure=ds_t raises an error\n bottom_nodes = get_facet_mask(self.P1CG, 'bottom')\n top_nodes = get_facet_mask(self.P1CG, 'top')\n bottom_idx = op2.Global(len(bottom_nodes), bottom_nodes, dtype=np.int32, name='node_idx')\n top_idx = op2.Global(len(top_nodes), top_nodes, dtype=np.int32, name='node_idx')\n code = \"\"\"\n void my_kernel(double *qmax, double *qmin, double *field, int *idx) {\n double face_mean = 0;\n for (int i=0; i<%(nnodes)d; i++) {\n face_mean += field[idx[i]];\n }\n face_mean /= %(nnodes)d;\n for (int i=0; i<%(nnodes)d; i++) {\n qmax[idx[i]] = fmax(qmax[idx[i]], face_mean);\n qmin[idx[i]] = fmin(qmin[idx[i]], face_mean);\n }\n }\"\"\"\n kernel = op2.Kernel(code % {'nnodes': len(bottom_nodes)}, 'my_kernel')\n\n op2.par_loop(kernel, self.mesh.cell_set,\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\n field.dat(op2.READ, field.function_space().cell_node_map()),\n bottom_idx(op2.READ),\n iteration_region=op2.ON_BOTTOM)\n\n op2.par_loop(kernel, self.mesh.cell_set,\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\n field.dat(op2.READ, field.function_space().cell_node_map()),\n top_idx(op2.READ),\n iteration_region=op2.ON_TOP)\n if self.squeezed_triangles:\n code = \"\"\"\n void my_kernel(double *qmax, double *qmin, double *marker) {\n float min_val, max_val;\n for (int i=0; i<%(nnodes)d; i++) {\n if (marker[i] > 0) {\n max_val = qmax[i];\n min_val = qmin[i];\n break;\n }\n }\n for (int i=i+1; i<%(nnodes)d; i++) {\n if (marker[i] > 0) {\n max_val = fmax(qmax[i], max_val);\n min_val = fmin(qmin[i], min_val);\n }\n }\n for (int i=0; i<%(nnodes)d; i++) {\n if (marker[i] > 0) {\n qmax[i] = max_val;\n qmin[i] = min_val;\n }\n }\n }\"\"\"\n cnode_map = self.min_field.function_space().cell_node_map()\n kernel = op2.Kernel(code % {'nnodes': cnode_map.shape[1]}, 'my_kernel')\n\n marker = self.squeezed_filter.marker\n\n # NOTE: for multiple squeezed triangle on top (e.g. ice front!) this currently only\n # works at the top, under the assumption that cells are iterated\n # over in each column bottom to top:\n op2.par_loop(kernel, self.mesh.cell_set,\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\n marker.dat(op2.READ, marker.function_space().cell_node_map()))","def getLinIterVarIndx( self, var ):\n \n self.updateAdb( )\n\n if var in self.iterNames:\n return self.iterNames[ var ]\n elif var in self.iterNames.values():\n return var\n else:\n return -1","def get_params_bounds(self) -> np.array:\n pass","def vars(self):\n return self.v","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 declare_variable(var, bound_variables):\n varname = var.name\n vartype = var.vartype\n\n # check if it is bound and has already been seen\n if bound_variables is not None and varname in bound_variables:\n yvar = bound_variables[varname].yices_term\n var.bound = True\n return yvar\n\n # check if it has already been seen\n yvar = Terms.get_by_name(varname)\n if yvar is not None:\n #now we need to see if it is free or bound\n tag = Terms.constructor(yvar)\n if tag == Constructor.VARIABLE:\n var.bound = True\n bound_variables[varname] = var\n return yvar\n\n type_term = vartype.yices_term\n type_name = vartype.name\n\n var_term = None\n\n if bound_variables is not None:\n # we need to make a yices variable not an uninterpreted term\n var_term = Terms.new_variable(type_term, varname)\n if var_term is None:\n sys.stderr.write(f'declare_variable: Term.new_variable failed {Yices.error_string()}\\n')\n return None\n bound_variables[varname] = var\n var.bound = True\n else:\n var_term = Terms.new_uninterpreted_term(type_term, varname)\n if var_term is None:\n sys.stderr.write(f'declare_variable: Term.new_uninterpreted_term failed {Yices.error_string()}\\n')\n return None\n\n YicesSignature.types_to_variables[type_name].add(var)\n\n return var_term","def lookup(self, variable):\n for binding in self.bindings:\n if binding.variable.name == variable:\n return binding\n if self.static_link is not None:\n return self.enclosing_frame.lookup(variable)\n return None","def var(self, name):\n return self.get_ground_vector('!Var:{}'.format(name))","def var(self, name):\n return self.get_ground_vector('!Var:{}'.format(name))","def var(self, name):\n return self.get_ground_vector('!Var:{}'.format(name))","def get_var_at_iter(self, n_iter):\n if self.freeze_after_iters > 0 and n_iter >= self.freeze_after_iters:\n return self.x_freeze\n else:\n return self._get_var_at_iter(n_iter)","def bounds(self, pos):","def vars(self):\n return [Var(i,self.dims[i]) for i in range(self.nvar)] # TODO: use stored state info (=1 sometimes)","def getNumBvars(self):\n return _libsbml.ASTNode_getNumBvars(self)","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 value(self):\n if self.is_bound():\n return self._value\n else:\n raise UninitializedBinding('{} is unbound.'.format(self))","def get_binding(self, v: str) -> Optional[str]:\n assert is_var(v)\n t = self\n ret = t.binding.get(v)\n while not ret and t.parent:\n t = t.parent\n ret = t.binding.get(v)\n return ret","def getbarvarnamelen(self,i_):\n len_ = ctypes.c_int32()\n res = __library__.MSK_XX_getbarvarnamelen(self.__nativep,i_,ctypes.byref(len_))\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 return (_len_return_value)","def get_bindable_vars(self):\n return (self.linkable_vars + self.local_vars.keys() +\n self.external_vars.keys())","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 variables(self):\n return {u for u in self if u.type == 'var'}","def nvar(self):\n return len(self.v)","def bounds(self):\n return self.GetBounds()","def bounds(self): # -> tuple[()]:\n ...","def bounds(self):\n if self.change_dimensionality:\n return [self._bounds[0]] * self.N\n else:\n return self._bounds","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 __getLoopBoundNames(self):\n lb_name = \"lb%s\" % self.counter\n ub_name = \"ub%s\" % self.counter\n self.counter += 1\n return (lb_name, ub_name)","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_return_value","def bounds(self):\n return self.kernel.bounds","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 find_lvar(self, *args):\n return _ida_hexrays.lvars_t_find_lvar(self, *args)","def bound_variables(*terms):\n\n t = terms[0] if len(terms) == 1 else terms\n\n if type(t) is Var:\n return frozenset()\n\n elif type(t) in (tuple, Const, Apply, Eq, Ite, Not, And, Or,\n Implies, Iff):\n return union(*(bound_variables(x) for x in t))\n\n elif type(t) in (ForAll, Exists, Lambda, NamedBinder):\n return union(bound_variables(t.body), t.variables)\n\n elif hasattr(t,'args'):\n return union(*(bound_variables(x) for x in t.args))\n\n else:\n assert False, type(t)","def get_lower_bound(self):\n return self._lower_bound","def get_lower_bound(self):\n return self._lower_bound","def getGridVarInfo(self):\n return self.gridVars","def get_upper_bound(self):\n return self._upper_bound","def __getitem__(self, key):\n return self.variables[key]","def bounds(self):\n return self._bounds","def find_bindings(self, bindName):\n try:\n return self.bind2index[bindName]\n except:\n raise KeyError(\n f\"The binding {bindName} is not in the general list... check your input file!\")","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 __getitem__(self, item):\n return foreign_get(self.vars, item)","def getlenbarvarj(self,j_): # 3\n res,resargs = self.__obj.getlenbarvarj(j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _lenbarvarj_return_value = resargs\n return _lenbarvarj_return_value","def _get_bound(self):\n\n if self.totensor:\n max_ = 1.0\n min_ = 0.0\n else:\n max_ = 255.0\n min_ = 0.0\n\n upper = (max_ - torch.tensor(self.img_norm['mean'])) / torch.tensor(self.img_norm['std'])\n lower = min_ - torch.tensor(self.img_norm['mean']) / torch.tensor(self.img_norm['std'])\n \n return upper, lower","def IsBound(self) -> bool:","def bound(cls):\n return cls(\n classes={Var: ((), None)},\n data_classes={\n Var._ComponentDataClass: ((\"lb\", \"ub\"), None),\n },\n )","def visit_bound_predicate(self, predicate) -> T:","def get_bound(box_list):\n box_xyxy_list = []\n for box in box_list:\n box_xyxy = xywh2xyxy(box)\n box_xyxy_list.append(box_xyxy)\n\n box_xyxy_list = np.array(box_xyxy_list)\n x1max, y1max, x2max, y2max = np.amax(box_xyxy_list, axis=0)\n x1min, y1min, x2min, y2min = np.amin(box_xyxy_list, axis=0)\n\n boundbox = xyxy2xywh([x1min, y1min, x2max, y2max])\n return boundbox","def _isintvar(self, index):\n return 251 <= self._typlist[index] <= 253","def binding(model, binding):\n var = model.binding(binding)\n if var is None:\n raise myokit.IncompatibleModelError(\n model.name(),\n 'No variable found with binding \"' + str(binding) + '\".')\n return var","def getbarvarnamelen(self,i_): # 3\n res,resargs = self.__obj.getbarvarnamelen(i_)\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 _parse_initbound(self) :\n\t\tlogging.debug(\"Parsing initbound soft constraints\")"],"string":"[\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 getconbound(self,i_): # 3\\n res,resargs = self.__obj.getconbound(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 getconbound(self,i_):\\n bk_ = ctypes.c_int32()\\n bl_ = ctypes.c_double()\\n bu_ = ctypes.c_double()\\n res = __library__.MSK_XX_getconbound(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 _get_one_bound(self, param_name):\\n return getattr(self, '__' + param_name + '_bounds')\",\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 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 getbound(self,accmode_,i_): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n res,resargs = self.__obj.getbound(accmode_,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 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 lookup_var(self, var):\\n if var in self.binding:\\n return self.binding[var]\\n elif self.parent is not None:\\n return self.parent.lookup_var(var)\\n else:\\n raise Environment.Unbound('unbound variable \\\"%s\\\"' % var)\",\n \"def bound(name):\",\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 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 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 GetBounds(self, p_int, p_int_1, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\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 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 var(self,i): # TODO: change to property to access (read only?) X?\\n return Var(i,self.dims[i])\",\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 dynamically_bound(*args, **kwargs):\\n return DynamicVar(*args, **kwargs)\",\n \"def num_vars(self):\\n return len(self.bounds.lb)\",\n \"def get_bindable_vars(self):\\n return self.local_vars.keys() + self.parent.get_bindable_vars()\",\n \"def bound_for(self, name):\\n if '.' in name:\\n module, name = name.split('.', 1)\\n if module in self._modules:\\n return self.__getattr__(module).bound_for(name)\\n else:\\n raise AttributeError('Invalid bound name %s. '\\n '%s has no module %s' % (name, type(self).__name__, module))\\n else:\\n if name in self._parameters:\\n return self._bounds[name]\\n else:\\n raise AttributeError('Invalid bound name %s. '\\n '%s has no parameter %s' % (name, type(self).__name__, module))\",\n \"def nvar(self):\\n return len(self.__vars)\",\n \"def _get_reference_by_variable(self, var):\\n if not var[0] == consts.VARIABLE:\\n raise Exception('Internal error: Expected a variable, got: \\\"%r\\\"' % var)\\n res = self._bindings.get(var, var)\\n if res == consts.TOPIC_IN_FOCUS:\\n res = self.focus\\n while res[0] == consts.VARIABLE and self.parent:\\n res = self.parent._get_reference_by_variable(res) #pylint: disable-msg=W0212\\n if res == consts.TOPIC_IN_FOCUS:\\n res = self.focus\\n return res\",\n \"def getbarvarname(self,i_): # 3\\n sizename_ = (1 + self.getbarvarnamelen((i_)))\\n arr_name = array.array(\\\"b\\\",[0]*((sizename_)))\\n memview_arr_name = memoryview(arr_name)\\n res,resargs = self.__obj.getbarvarname(i_,sizename_,memview_arr_name)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n retarg_name = resargs\\n retarg_name = arr_name.tobytes()[:-1].decode(\\\"utf-8\\\",errors=\\\"ignore\\\")\\n return retarg_name\",\n \"def vars(self) -> {(EVar, Pool)}:\\n raise NotImplementedError()\",\n \"def _getBound(self):\\n if self._colormap is not None:\\n bound = self._getRawBound()\\n\\n if bound is None:\\n bound = self._getColormapRange()[self._index]\\n return bound\\n else:\\n return 1. # Fallback\",\n \"def named_parameter_bounds(self):\\n for name, _ in self.named_parameters():\\n yield name, self.bound_for(name)\",\n \"def variable_integrate(self, variable, *bounds):\\n try:\\n index = list(self.variables).index(variable)\\n except ValueError:\\n index = None\\n if index is not None:\\n indef_integral = self.head.integrate_indefinite_index(type(self), self.data, self, index)\\n if bounds:\\n low, high = bounds\\n return indef_integral.variable_subs(variable, high) - indef_integral.variable_subs(variable, low)\\n return indef_integral\\n raise NotImplementedError(`self.variables, variable, index`)\",\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 GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def isBvar(self):\\n return _libsbml.ASTNode_isBvar(self)\",\n \"def _check_bound(self, q):\\n mat = ur_utils.forward(q, self._ik_params)\\n xyz = mat[:3, 3]\\n inside_bound = np.all(self._end_effector_low <= xyz) and np.all(xyz <= self._end_effector_high)\\n inside_buffer_bound = (np.all(self._end_effector_low + self._box_bound_buffer <= xyz) and \\\\\\n np.all(xyz <= self._end_effector_high - self._box_bound_buffer))\\n return inside_bound, inside_buffer_bound, mat, xyz\",\n \"def parameter_bounds(self):\\n for name, bound in self.named_parameter_bounds():\\n yield bound\",\n \"def _process_individual_bound(self, val):\\n if(val not in [True, False]):\\n raise ValueError('For composition bounds expected are iether True' \\n '(free function) or False (fixed function) not %s' % (str(val)))\\n return val\",\n \"def xvar ( self ) :\\n return self.__xvar\",\n \"def is_bound(self):\\n if self._value is UninitializedBinding:\\n return False\\n else:\\n return True\",\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 bounding_var(self, field):\\n if field not in self.bounding_fields:\\n raise KeyError(\\\"Invalid field name: %s. Valid fields are: %s\\\"%\\n (field, self.bounding_fields))\\n fname = os.path.join(self.logs_dir, \\\"bounding_\\\"+field+\\\".dat\\\")\\n data = np.loadtxt(fname, skiprows=2)\\n return data\",\n \"def getvar(obj):\\n class VarDict(dict):\\n \\\"\\\"\\\"wrapper of var dict\\\"\\\"\\\"\\n def __getitem__(self, key):\\n # expression may be set a var in this dict\\n if key in self:\\n return super(VarDict, self).__getitem__(key)\\n if hastag(obj, key):\\n return gettag(obj, key)\\n # maybe some build-in object\\n try:\\n return eval(key, {}, {})\\n except:\\n return False\\n\\n return VarDict()\",\n \"def _process_individual_bound(self, val):\\n if(val == True):\\n res = self._DEF_BOUNDS\\n \\n elif(val in [False, None]):\\n res = val\\n else:\\n if(len(val) != 2):\\n raise ValueError('Bound value is not recognized. '% (str(val)))\\n if(val[0] > val[1]):\\n raise ValueError('Bound values are inverted '% (str(val)))\\n res = val\\n return res\",\n \"def get_var_grid(self, var_name, var_val):\\n var_val = self._var_grid[var_name]\\n return True\",\n \"def compute_bounds(self, field):\\n # Call general-purpose bound computation.\\n super(VertexBasedP1DGLimiter, self).compute_bounds(field)\\n\\n # Add the average of lateral boundary facets to min/max fields\\n # NOTE this just computes the arithmetic mean of nodal values on the facet,\\n # which in general is not equivalent to the mean of the field over the bnd facet.\\n # This is OK for P1DG triangles, but not exact for the extruded case (quad facets)\\n from finat.finiteelementbase import entity_support_dofs\\n\\n if self.extruded:\\n entity_dim = (self.dim-2, 1) # get vertical facets\\n else:\\n entity_dim = self.dim-1\\n boundary_dofs = entity_support_dofs(self.P1DG.finat_element, entity_dim)\\n local_facet_nodes = np.array([boundary_dofs[e] for e in sorted(boundary_dofs.keys())])\\n n_bnd_nodes = local_facet_nodes.shape[1]\\n local_facet_idx = op2.Global(local_facet_nodes.shape, local_facet_nodes, dtype=np.int32, name='local_facet_idx')\\n code = \\\"\\\"\\\"\\n void my_kernel(double *qmax, double *qmin, double *field, unsigned int *facet, unsigned int *local_facet_idx)\\n {\\n double face_mean = 0.0;\\n for (int i = 0; i < %(nnodes)d; i++) {\\n unsigned int idx = local_facet_idx[facet[0]*%(nnodes)d + i];\\n face_mean += field[idx];\\n }\\n face_mean /= %(nnodes)d;\\n for (int i = 0; i < %(nnodes)d; i++) {\\n unsigned int idx = local_facet_idx[facet[0]*%(nnodes)d + i];\\n qmax[idx] = fmax(qmax[idx], face_mean);\\n qmin[idx] = fmin(qmin[idx], face_mean);\\n }\\n }\\\"\\\"\\\"\\n bnd_kernel = op2.Kernel(code % {'nnodes': n_bnd_nodes}, 'my_kernel')\\n op2.par_loop(bnd_kernel,\\n self.P1DG.mesh().exterior_facets.set,\\n self.max_field.dat(op2.MAX, self.max_field.exterior_facet_node_map()),\\n self.min_field.dat(op2.MIN, self.min_field.exterior_facet_node_map()),\\n field.dat(op2.READ, field.exterior_facet_node_map()),\\n self.P1DG.mesh().exterior_facets.local_facet_dat(op2.READ),\\n local_facet_idx(op2.READ))\\n if self.extruded:\\n # Add nodal values from surface/bottom boundaries\\n # NOTE calling firedrake par_loop with measure=ds_t raises an error\\n bottom_nodes = get_facet_mask(self.P1CG, 'bottom')\\n top_nodes = get_facet_mask(self.P1CG, 'top')\\n bottom_idx = op2.Global(len(bottom_nodes), bottom_nodes, dtype=np.int32, name='node_idx')\\n top_idx = op2.Global(len(top_nodes), top_nodes, dtype=np.int32, name='node_idx')\\n code = \\\"\\\"\\\"\\n void my_kernel(double *qmax, double *qmin, double *field, int *idx) {\\n double face_mean = 0;\\n for (int i=0; i<%(nnodes)d; i++) {\\n face_mean += field[idx[i]];\\n }\\n face_mean /= %(nnodes)d;\\n for (int i=0; i<%(nnodes)d; i++) {\\n qmax[idx[i]] = fmax(qmax[idx[i]], face_mean);\\n qmin[idx[i]] = fmin(qmin[idx[i]], face_mean);\\n }\\n }\\\"\\\"\\\"\\n kernel = op2.Kernel(code % {'nnodes': len(bottom_nodes)}, 'my_kernel')\\n\\n op2.par_loop(kernel, self.mesh.cell_set,\\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\\n field.dat(op2.READ, field.function_space().cell_node_map()),\\n bottom_idx(op2.READ),\\n iteration_region=op2.ON_BOTTOM)\\n\\n op2.par_loop(kernel, self.mesh.cell_set,\\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\\n field.dat(op2.READ, field.function_space().cell_node_map()),\\n top_idx(op2.READ),\\n iteration_region=op2.ON_TOP)\\n if self.squeezed_triangles:\\n code = \\\"\\\"\\\"\\n void my_kernel(double *qmax, double *qmin, double *marker) {\\n float min_val, max_val;\\n for (int i=0; i<%(nnodes)d; i++) {\\n if (marker[i] > 0) {\\n max_val = qmax[i];\\n min_val = qmin[i];\\n break;\\n }\\n }\\n for (int i=i+1; i<%(nnodes)d; i++) {\\n if (marker[i] > 0) {\\n max_val = fmax(qmax[i], max_val);\\n min_val = fmin(qmin[i], min_val);\\n }\\n }\\n for (int i=0; i<%(nnodes)d; i++) {\\n if (marker[i] > 0) {\\n qmax[i] = max_val;\\n qmin[i] = min_val;\\n }\\n }\\n }\\\"\\\"\\\"\\n cnode_map = self.min_field.function_space().cell_node_map()\\n kernel = op2.Kernel(code % {'nnodes': cnode_map.shape[1]}, 'my_kernel')\\n\\n marker = self.squeezed_filter.marker\\n\\n # NOTE: for multiple squeezed triangle on top (e.g. ice front!) this currently only\\n # works at the top, under the assumption that cells are iterated\\n # over in each column bottom to top:\\n op2.par_loop(kernel, self.mesh.cell_set,\\n self.max_field.dat(op2.MAX, self.max_field.function_space().cell_node_map()),\\n self.min_field.dat(op2.MIN, self.min_field.function_space().cell_node_map()),\\n marker.dat(op2.READ, marker.function_space().cell_node_map()))\",\n \"def getLinIterVarIndx( self, var ):\\n \\n self.updateAdb( )\\n\\n if var in self.iterNames:\\n return self.iterNames[ var ]\\n elif var in self.iterNames.values():\\n return var\\n else:\\n return -1\",\n \"def get_params_bounds(self) -> np.array:\\n pass\",\n \"def vars(self):\\n return self.v\",\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 declare_variable(var, bound_variables):\\n varname = var.name\\n vartype = var.vartype\\n\\n # check if it is bound and has already been seen\\n if bound_variables is not None and varname in bound_variables:\\n yvar = bound_variables[varname].yices_term\\n var.bound = True\\n return yvar\\n\\n # check if it has already been seen\\n yvar = Terms.get_by_name(varname)\\n if yvar is not None:\\n #now we need to see if it is free or bound\\n tag = Terms.constructor(yvar)\\n if tag == Constructor.VARIABLE:\\n var.bound = True\\n bound_variables[varname] = var\\n return yvar\\n\\n type_term = vartype.yices_term\\n type_name = vartype.name\\n\\n var_term = None\\n\\n if bound_variables is not None:\\n # we need to make a yices variable not an uninterpreted term\\n var_term = Terms.new_variable(type_term, varname)\\n if var_term is None:\\n sys.stderr.write(f'declare_variable: Term.new_variable failed {Yices.error_string()}\\\\n')\\n return None\\n bound_variables[varname] = var\\n var.bound = True\\n else:\\n var_term = Terms.new_uninterpreted_term(type_term, varname)\\n if var_term is None:\\n sys.stderr.write(f'declare_variable: Term.new_uninterpreted_term failed {Yices.error_string()}\\\\n')\\n return None\\n\\n YicesSignature.types_to_variables[type_name].add(var)\\n\\n return var_term\",\n \"def lookup(self, variable):\\n for binding in self.bindings:\\n if binding.variable.name == variable:\\n return binding\\n if self.static_link is not None:\\n return self.enclosing_frame.lookup(variable)\\n return None\",\n \"def var(self, name):\\n return self.get_ground_vector('!Var:{}'.format(name))\",\n \"def var(self, name):\\n return self.get_ground_vector('!Var:{}'.format(name))\",\n \"def var(self, name):\\n return self.get_ground_vector('!Var:{}'.format(name))\",\n \"def get_var_at_iter(self, n_iter):\\n if self.freeze_after_iters > 0 and n_iter >= self.freeze_after_iters:\\n return self.x_freeze\\n else:\\n return self._get_var_at_iter(n_iter)\",\n \"def bounds(self, pos):\",\n \"def vars(self):\\n return [Var(i,self.dims[i]) for i in range(self.nvar)] # TODO: use stored state info (=1 sometimes)\",\n \"def getNumBvars(self):\\n return _libsbml.ASTNode_getNumBvars(self)\",\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 value(self):\\n if self.is_bound():\\n return self._value\\n else:\\n raise UninitializedBinding('{} is unbound.'.format(self))\",\n \"def get_binding(self, v: str) -> Optional[str]:\\n assert is_var(v)\\n t = self\\n ret = t.binding.get(v)\\n while not ret and t.parent:\\n t = t.parent\\n ret = t.binding.get(v)\\n return ret\",\n \"def getbarvarnamelen(self,i_):\\n len_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getbarvarnamelen(self.__nativep,i_,ctypes.byref(len_))\\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 return (_len_return_value)\",\n \"def get_bindable_vars(self):\\n return (self.linkable_vars + self.local_vars.keys() +\\n self.external_vars.keys())\",\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 variables(self):\\n return {u for u in self if u.type == 'var'}\",\n \"def nvar(self):\\n return len(self.v)\",\n \"def bounds(self):\\n return self.GetBounds()\",\n \"def bounds(self): # -> tuple[()]:\\n ...\",\n \"def bounds(self):\\n if self.change_dimensionality:\\n return [self._bounds[0]] * self.N\\n else:\\n return self._bounds\",\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 __getLoopBoundNames(self):\\n lb_name = \\\"lb%s\\\" % self.counter\\n ub_name = \\\"ub%s\\\" % self.counter\\n self.counter += 1\\n return (lb_name, ub_name)\",\n \"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_return_value\",\n \"def bounds(self):\\n return self.kernel.bounds\",\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 find_lvar(self, *args):\\n return _ida_hexrays.lvars_t_find_lvar(self, *args)\",\n \"def bound_variables(*terms):\\n\\n t = terms[0] if len(terms) == 1 else terms\\n\\n if type(t) is Var:\\n return frozenset()\\n\\n elif type(t) in (tuple, Const, Apply, Eq, Ite, Not, And, Or,\\n Implies, Iff):\\n return union(*(bound_variables(x) for x in t))\\n\\n elif type(t) in (ForAll, Exists, Lambda, NamedBinder):\\n return union(bound_variables(t.body), t.variables)\\n\\n elif hasattr(t,'args'):\\n return union(*(bound_variables(x) for x in t.args))\\n\\n else:\\n assert False, type(t)\",\n \"def get_lower_bound(self):\\n return self._lower_bound\",\n \"def get_lower_bound(self):\\n return self._lower_bound\",\n \"def getGridVarInfo(self):\\n return self.gridVars\",\n \"def get_upper_bound(self):\\n return self._upper_bound\",\n \"def __getitem__(self, key):\\n return self.variables[key]\",\n \"def bounds(self):\\n return self._bounds\",\n \"def find_bindings(self, bindName):\\n try:\\n return self.bind2index[bindName]\\n except:\\n raise KeyError(\\n f\\\"The binding {bindName} is not in the general list... check your input file!\\\")\",\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 __getitem__(self, item):\\n return foreign_get(self.vars, item)\",\n \"def getlenbarvarj(self,j_): # 3\\n res,resargs = self.__obj.getlenbarvarj(j_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _lenbarvarj_return_value = resargs\\n return _lenbarvarj_return_value\",\n \"def _get_bound(self):\\n\\n if self.totensor:\\n max_ = 1.0\\n min_ = 0.0\\n else:\\n max_ = 255.0\\n min_ = 0.0\\n\\n upper = (max_ - torch.tensor(self.img_norm['mean'])) / torch.tensor(self.img_norm['std'])\\n lower = min_ - torch.tensor(self.img_norm['mean']) / torch.tensor(self.img_norm['std'])\\n \\n return upper, lower\",\n \"def IsBound(self) -> bool:\",\n \"def bound(cls):\\n return cls(\\n classes={Var: ((), None)},\\n data_classes={\\n Var._ComponentDataClass: ((\\\"lb\\\", \\\"ub\\\"), None),\\n },\\n )\",\n \"def visit_bound_predicate(self, predicate) -> T:\",\n \"def get_bound(box_list):\\n box_xyxy_list = []\\n for box in box_list:\\n box_xyxy = xywh2xyxy(box)\\n box_xyxy_list.append(box_xyxy)\\n\\n box_xyxy_list = np.array(box_xyxy_list)\\n x1max, y1max, x2max, y2max = np.amax(box_xyxy_list, axis=0)\\n x1min, y1min, x2min, y2min = np.amin(box_xyxy_list, axis=0)\\n\\n boundbox = xyxy2xywh([x1min, y1min, x2max, y2max])\\n return boundbox\",\n \"def _isintvar(self, index):\\n return 251 <= self._typlist[index] <= 253\",\n \"def binding(model, binding):\\n var = model.binding(binding)\\n if var is None:\\n raise myokit.IncompatibleModelError(\\n model.name(),\\n 'No variable found with binding \\\"' + str(binding) + '\\\".')\\n return var\",\n \"def getbarvarnamelen(self,i_): # 3\\n res,resargs = self.__obj.getbarvarnamelen(i_)\\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\",\n \"def _parse_initbound(self) :\\n\\t\\tlogging.debug(\\\"Parsing initbound soft constraints\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.88780814","0.6618329","0.6337327","0.6297966","0.6271888","0.6069767","0.606333","0.603762","0.59093964","0.5864278","0.5855671","0.57791764","0.5742324","0.5674004","0.56727177","0.56428105","0.5599474","0.55750924","0.5573686","0.5553733","0.554883","0.5534935","0.54801404","0.54507244","0.5406407","0.53483266","0.5333609","0.53182","0.53138274","0.53069663","0.52981305","0.52981305","0.52981305","0.52981305","0.52981305","0.52981305","0.52981305","0.52981305","0.52760994","0.52737916","0.5254301","0.5247517","0.5245741","0.5239353","0.5238339","0.52306694","0.52157587","0.5211321","0.5205398","0.5198518","0.5198453","0.5190257","0.51739526","0.51707363","0.51670134","0.51624626","0.5158725","0.5158725","0.5158725","0.51561415","0.51422095","0.51360697","0.5133617","0.5131035","0.51253104","0.5110564","0.50999206","0.5098646","0.5095481","0.5089299","0.5081946","0.5081445","0.5078792","0.50747347","0.5069113","0.5063264","0.5054912","0.5054227","0.50519717","0.5049283","0.5047142","0.5044322","0.5044322","0.504395","0.5033237","0.5031334","0.50267816","0.5023143","0.5019743","0.501836","0.5015075","0.50136757","0.5005428","0.4998335","0.49967796","0.49954012","0.49939683","0.49930838","0.49873835","0.49852154"],"string":"[\n \"0.88780814\",\n \"0.6618329\",\n \"0.6337327\",\n \"0.6297966\",\n \"0.6271888\",\n \"0.6069767\",\n \"0.606333\",\n \"0.603762\",\n \"0.59093964\",\n \"0.5864278\",\n \"0.5855671\",\n \"0.57791764\",\n \"0.5742324\",\n \"0.5674004\",\n \"0.56727177\",\n \"0.56428105\",\n \"0.5599474\",\n \"0.55750924\",\n \"0.5573686\",\n \"0.5553733\",\n \"0.554883\",\n \"0.5534935\",\n \"0.54801404\",\n \"0.54507244\",\n \"0.5406407\",\n \"0.53483266\",\n \"0.5333609\",\n \"0.53182\",\n \"0.53138274\",\n \"0.53069663\",\n \"0.52981305\",\n \"0.52981305\",\n \"0.52981305\",\n \"0.52981305\",\n \"0.52981305\",\n \"0.52981305\",\n \"0.52981305\",\n \"0.52981305\",\n \"0.52760994\",\n \"0.52737916\",\n \"0.5254301\",\n \"0.5247517\",\n \"0.5245741\",\n \"0.5239353\",\n \"0.5238339\",\n \"0.52306694\",\n \"0.52157587\",\n \"0.5211321\",\n \"0.5205398\",\n \"0.5198518\",\n \"0.5198453\",\n \"0.5190257\",\n \"0.51739526\",\n \"0.51707363\",\n \"0.51670134\",\n \"0.51624626\",\n \"0.5158725\",\n \"0.5158725\",\n \"0.5158725\",\n \"0.51561415\",\n \"0.51422095\",\n \"0.51360697\",\n \"0.5133617\",\n \"0.5131035\",\n \"0.51253104\",\n \"0.5110564\",\n \"0.50999206\",\n \"0.5098646\",\n \"0.5095481\",\n \"0.5089299\",\n \"0.5081946\",\n \"0.5081445\",\n \"0.5078792\",\n \"0.50747347\",\n \"0.5069113\",\n \"0.5063264\",\n \"0.5054912\",\n \"0.5054227\",\n \"0.50519717\",\n \"0.5049283\",\n \"0.5047142\",\n \"0.5044322\",\n \"0.5044322\",\n \"0.504395\",\n \"0.5033237\",\n \"0.5031334\",\n \"0.50267816\",\n \"0.5023143\",\n \"0.5019743\",\n \"0.501836\",\n \"0.5015075\",\n \"0.50136757\",\n \"0.5005428\",\n \"0.4998335\",\n \"0.49967796\",\n \"0.49954012\",\n \"0.49939683\",\n \"0.49930838\",\n \"0.49873835\",\n \"0.49852154\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8567469"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11787,"cells":{"query":{"kind":"string","value":"Obtains bounds information for a slice of the constraints. getconboundslice(self,first_,last_,bk_,bl_,bu_)"},"document":{"kind":"string","value":"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"},"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 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 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 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 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 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 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 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_,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 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 GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def bounds(self): # -> tuple[()]:\n ...","def bounds(self):\n return self._bboxes[0][0] #TODO: merge all coverages","def get_bounds(self):\n raise Exception(\"Non-implemented base class method.\")","def get_bounds(self):\n return ([self.t_min] * self.dim,[self.t_max] * self.dim)","def bounds(self):\n return self._bboxes[0][0] #TODO: merge all coverages","def bounds(self):\n return self.substrates.bounds","def get_bounds(self):\r\n left, bottom, front = 10000, 10000, 10000\r\n right, top, back = -10000, -10000, -10000\r\n for b in self.buf:\r\n for v in b.vertices:\r\n if v[0] < left:\r\n left = v[0]\r\n if v[0] > right:\r\n right = v[0]\r\n if v[1] < bottom:\r\n bottom = v[1]\r\n if v[1] > top:\r\n top = v[1]\r\n if v[2] < front:\r\n front = v[2]\r\n if v[2] > back:\r\n back = v[2]\r\n\r\n return (left, bottom, front, right, top, back)","def get_bounds():\n return [0.00], [1.00]","def fetchbounds(self):\n pnts = [x for x in [self.out_start, self.start, self.in_start, \\\n self.in_end, self.end, self.out_end] \\\n if x is not None]\n return min(pnts), max(pnts)","def bounds(self, start=None, finish=None):\n lower = start if start is not None else self.limits[0]\n upper = finish if finish is not None else self.limits[1]\n\n lower = lower + self.offsets[0]\n upper = upper + self.offsets[1]\n\n return (lower, upper)","def get_bounds(self):\n log.debug(str(inspect.stack()[1][3]) + \"--> OC.get_bounds()\")\n\n # TODO: Move the operation out of here.\n\n xmin = Inf\n ymin = Inf\n xmax = -Inf\n ymax = -Inf\n\n # for obj in self.object_list:\n for obj in self.get_list():\n try:\n gxmin, gymin, gxmax, gymax = obj.bounds()\n xmin = min([xmin, gxmin])\n ymin = min([ymin, gymin])\n xmax = max([xmax, gxmax])\n ymax = max([ymax, gymax])\n except Exception as e:\n log.warning(\"DEV WARNING: Tried to get bounds of empty geometry. %s\" % str(e))\n\n return [xmin, ymin, xmax, ymax]","def bounds(self, pos):","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 bounds(self):\n if self.change_dimensionality:\n return [self._bounds[0]] * self.N\n else:\n return self._bounds","def GetBounds(self, p_int, p_int_1, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\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","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 get_bounds():\n bounds = [\n (0.1, 0.5), # Omega_m\n (0.05, 0.15) # beta\n ]\n return np.array(bounds)","def get_bounds(self, channel_indx=None):\n if channel_indx is None:\n upper_bound = self.upper_bound.reshape(self.batch_size, -1)\n lower_bound = self.lower_bound.reshape(self.batch_size, -1)\n else:\n upper_bound = self.upper_bound[:, channel_indx, :].reshape(\n self.batch_size, -1\n )\n lower_bound = self.lower_bound[:, channel_indx, :].reshape(\n self.batch_size, -1\n )\n return upper_bound, lower_bound","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 bounds(self):\n return self._bounds","def __nc_geo_bounds(self, extent, data_sel):\n indx = None\n if extent is not None:\n if len(extent) != 4:\n raise ValueError('parameter extent must have 4 elements')\n\n lats = self.fid['/instrument/latitude_center'][:].reshape(\n self.scanline, self.ground_pixel)\n lons = self.fid['/instrument/longitude_center'][:].reshape(\n self.scanline, self.ground_pixel)\n\n indx = np.where((lons >= extent[0]) & (lons <= extent[1])\n & (lats >= extent[2]) & (lats <= extent[3]))\n data_sel = np.s_[indx[0].min():indx[0].max(),\n indx[1].min():indx[1].max()]\n\n gid = self.fid['/instrument']\n lat_bounds = gid['latitude_corners'][:].data.reshape(\n self.scanline, self.ground_pixel, 4)\n lon_bounds = gid['longitude_corners'][:].data.reshape(\n self.scanline, self.ground_pixel, 4)\n if data_sel is not None:\n lat_bounds = lat_bounds[data_sel + (slice(None),)]\n lon_bounds = lon_bounds[data_sel + (slice(None),)]\n\n return (data_sel, lon_bounds, lat_bounds)","def get_bounds(self):\n bottom_right = np.asarray([self.coords[k][0] for k in range(self.dim)])\n upper_left = np.asarray([self.coords[k][-1] for k in range(self.dim)])\n return bottom_right, upper_left","def bounds(self):\n return self.GetBounds()","def bounds(self):\n b = []\n\n for dim in self.dimensions:\n if dim.size == 1:\n b.append(dim.bounds)\n else:\n b.extend(dim.bounds)\n\n return b","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 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 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 bounds(self):\n return self.xmin, self.xmax, self.ymin, self.ymax","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 getconbound(self,i_):\n bk_ = ctypes.c_int32()\n bl_ = ctypes.c_double()\n bu_ = ctypes.c_double()\n res = __library__.MSK_XX_getconbound(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 getSampleBounds(args, matrix):\n bounds = matrix.parameters['sample_boundaries']\n if args.samples is None:\n return np.arange(0, matrix.matrix.matrix.shape[1])\n else:\n o = list()\n for sample in args.samples:\n if sample not in matrix.matrix.sample_labels:\n sys.exit(\"Error: '{0}' is not a valid sample\\n\".format(sample))\n idx = matrix.matrix.sample_labels.index(sample)\n o.extend(range(bounds[idx], bounds[idx + 1]))\n return o","def get_raw_bounds(self) -> [Vector, Vector]:\n\t\tverts = np.array([v.co for mesh in self._meshes for v in mesh.data.vertices])\n\t\tbbox_min = Vector([*np.min(verts, axis=0)])\n\t\tbbox_max = Vector([*np.max(verts, axis=0)])\n\t\treturn bbox_min, bbox_max","def bounds(self):\n return [(2, None)]","def GetIndexBounds(self, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\n ...","def get_bounds(shape, affine):\n adim, bdim, cdim = shape\n adim -= 1\n bdim -= 1\n cdim -= 1\n # form a collection of vectors for each 8 corners of the box\n box = np.array([[0., 0, 0, 1],\n [adim, 0, 0, 1],\n [0, bdim, 0, 1],\n [0, 0, cdim, 1],\n [adim, bdim, 0, 1],\n [adim, 0, cdim, 1],\n [0, bdim, cdim, 1],\n [adim, bdim, cdim, 1]]).T\n box = np.dot(affine, box)[:3]\n return zip(box.min(axis=-1), box.max(axis=-1))","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 compute_bounds(self, x=None, aux=None, C=None, method='backward', IBP=False, forward=False, \n bound_lower=True, bound_upper=True, reuse_ibp=False,\n return_A=False, needed_A_list=None, final_node_name=None, average_A=False, new_interval=None,\n return_b=False, b_dict=None, reference_bounds=None, intermediate_constr=None, alpha_idx=None):\n\n # Several shortcuts.\n method = method.lower() if method is not None else method\n if method == 'ibp':\n # Pure IBP bounds.\n method = None\n IBP = True\n elif method == 'ibp+backward' or method == 'ibp+crown' or method == 'crown-ibp':\n method = 'backward'\n IBP = True\n elif method == 'crown':\n method = 'backward'\n elif method == 'forward':\n forward = True\n elif method == 'forward+backward':\n method = 'backward'\n forward = True\n elif method == \"crown-optimized\" or method == 'alpha-crown':\n if bound_lower:\n ret1 = self.get_optimized_bounds(x=x, IBP=False, C=C, method='backward', new_interval=new_interval, reference_bounds=reference_bounds,\n bound_lower=bound_lower, bound_upper=False, return_A=return_A)\n if bound_upper:\n ret2 = self.get_optimized_bounds(x=x, IBP=False, C=C, method='backward', new_interval=new_interval, reference_bounds=reference_bounds,\n bound_lower=False, bound_upper=bound_upper, return_A=return_A)\n if bound_upper and bound_upper:\n assert return_A is False\n return ret1[0], ret2[1]\n elif bound_lower:\n return ret1\n elif bound_upper:\n return ret2\n else:\n raise NotImplementedError\n\n # If y in self.backward_node_pairs[x], then node y is visited when \n # doing backward bound propagation starting from node x.\n self.backward_from = dict([(node, []) for node in self._modules])\n\n if not bound_lower and not bound_upper:\n raise ValueError('At least one of bound_lower and bound_upper in compute_bounds should be True')\n A_dict = {} if return_A else None\n\n if x is not None:\n self._set_input(*x, new_interval=new_interval)\n\n if IBP and method is None and reuse_ibp:\n # directly return the previously saved ibp bounds\n return self.ibp_lower, self.ibp_upper\n root = [self._modules[name] for name in self.root_name]\n batch_size = root[0].value.shape[0]\n dim_in = 0\n\n for i in range(len(root)):\n value = root[i].forward()\n if hasattr(root[i], 'perturbation') and root[i].perturbation is not None: \n root[i].linear, root[i].center, root[i].aux = \\\n root[i].perturbation.init(value, aux=aux, forward=forward)\n # This input/parameter has perturbation. Create an interval object.\n if self.ibp_relative:\n root[i].interval = Interval(\n None, None, \n root[i].linear.nominal, root[i].linear.lower_offset, root[i].linear.upper_offset)\n else:\n root[i].interval = \\\n Interval(root[i].linear.lower, root[i].linear.upper, ptb=root[i].perturbation)\n if forward:\n root[i].dim = root[i].linear.lw.shape[1]\n dim_in += root[i].dim\n else:\n if self.ibp_relative:\n root[i].interval = Interval(\n None, None, \n value, torch.zeros_like(value), torch.zeros_like(value)) \n else:\n # This inpute/parameter does not has perturbation. \n # Use plain tuple defaulting to Linf perturbation.\n root[i].interval = (value, value)\n root[i].forward_value = root[i].forward_value = root[i].value = root[i].lower = root[i].upper = value\n\n if self.ibp_relative:\n root[i].lower = root[i].interval.lower\n root[i].upper = root[i].interval.upper\n else:\n root[i].lower, root[i].upper = root[i].interval\n\n if forward:\n self._init_forward(root, dim_in)\n\n final = self._modules[self.final_name] if final_node_name is None else self._modules[final_node_name]\n logger.debug('Final node {}[{}]'.format(final, final.name))\n\n if IBP:\n res = self._IBP_general(node=final, C=C)\n if self.ibp_relative:\n self.ibp_lower, self.ibp_upper = res.lower, res.upper\n else:\n self.ibp_lower, self.ibp_upper = res\n\n if method is None:\n return self.ibp_lower, self.ibp_upper \n\n if C is None:\n # C is an identity matrix by default \n if final.output_shape is None:\n raise ValueError('C is not provided while node {} has no default shape'.format(final.shape))\n dim_output = int(np.prod(final.output_shape[1:]))\n C = torch.eye(dim_output, device=self.device).unsqueeze(0).repeat(batch_size, 1, 1) # TODO: use an eyeC object here.\n\n # check whether weights are perturbed and set nonlinear for the BoundMatMul operation\n for n in self._modules.values():\n if isinstance(n, (BoundLinear, BoundConv, BoundBatchNormalization)):\n n.nonlinear = False\n for l_name in n.input_name[1:]:\n node = self._modules[l_name]\n if hasattr(node, 'perturbation'):\n if node.perturbation is not None:\n n.nonlinear = True\n\n # BFS to find out whether each node is used given the current final node\n if final != self.last_final_node:\n self.last_final_node = final\n for i in self._modules.values():\n i.used = False\n final.used = True\n queue = deque([final])\n while len(queue) > 0:\n n = queue.popleft()\n for n_pre_name in n.input_name:\n n_pre = self._modules[n_pre_name]\n if not n_pre.used:\n n_pre.used = True\n queue.append(n_pre)\n\n for i in self._modules.values(): # for all nodes\n if not i.used:\n continue\n if hasattr(i, 'nonlinear') and i.nonlinear:\n for l_name in i.input_name:\n node = self._modules[l_name]\n # print('node', node, 'lower', hasattr(node, 'lower'), 'perturbed', node.perturbed, 'forward_value', hasattr(node, 'forward_value'), 'from_input', node.from_input)\n if not hasattr(node, 'lower'):\n assert not IBP, 'There should be no missing intermediate bounds when IBP is enabled'\n if not node.perturbed and hasattr(node, 'forward_value'):\n node.interval = node.lower, node.upper = \\\n node.forward_value, node.forward_value\n continue\n # FIXME check that weight perturbation is not affected\n # (from_input=True should be set for weights)\n if not node.from_input and hasattr(node, 'forward_value'):\n node.lower = node.upper = node.forward_value\n continue\n if forward:\n l, u = self._forward_general(\n node=node, root=root, dim_in=dim_in, concretize=True)\n else:\n # assign concretized bound for ReLU layer to save computational cost\n # FIXME: Put ReLU after reshape will cause problem!\n if (isinstance(node, BoundActivation) or isinstance(node, BoundTranspose)) and hasattr(\n self._modules[node.input_name[0]], 'lower'):\n node.lower = node.forward(self._modules[node.input_name[0]].lower)\n node.upper = node.forward(self._modules[node.input_name[0]].upper)\n elif isinstance(node, BoundReshape) and \\\n hasattr(self._modules[node.input_name[0]], 'lower') and \\\n hasattr(self._modules[node.input_name[1]], 'value'):\n # Node for input value.\n val_input = self._modules[node.input_name[0]]\n # Node for input parameter (e.g., shape, permute)\n arg_input = self._modules[node.input_name[1]]\n node.lower = node.forward(val_input.lower, arg_input.value)\n node.upper = node.forward(val_input.upper, arg_input.value)\n else:\n first_layer_flag = False\n # This is the list of all intermediate layers where we need to refine.\n if intermediate_constr is not None:\n intermediate_beta_enabled_layers = [k for v in intermediate_constr.values() for k in v]\n else:\n intermediate_beta_enabled_layers = []\n # Here we avoid creating a big C matrix in the first linear layer.\n # Disable this optimization when we have beta for intermediate layer bounds.\n if type(node) == BoundLinear or type(node) == BoundConv and node.name not in intermediate_beta_enabled_layers:\n for l_pre in node.input_name:\n if type(self._modules[l_pre]) == BoundInput:\n node.lower, node.upper = self._IBP_general(node)\n first_layer_flag = True\n break\n if not first_layer_flag:\n reduced_dim = False # Only partial neurons (unstable neurons) are bounded.\n unstable_idx = None\n dim = int(np.prod(node.output_shape[1:]))\n # FIXME: C matrix shape incorrect for BoundParams.\n if (isinstance(node, BoundLinear) or isinstance(node, BoundMatMul)) and int(\n os.environ.get('AUTOLIRPA_USE_FULL_C', 0)) == 0:\n if intermediate_constr is not None and node.name in reference_bounds:\n # If we are doing bound refinement and reference bounds are given, we only refine unstable neurons.\n # For each batch element, we find the unstable neurons.\n unstable_masks = torch.logical_and(reference_bounds[node.name][0] < 0, reference_bounds[node.name][1] > 0)\n # unstable_masks = torch.ones(dtype=torch.bool, size=(batch_size, dim), device=self.device)\n # For simplicity, merge unstable locations for all elements in this batch. TODO: use individual unstable mask.\n unstable_locs = unstable_masks.sum(dim=0).bool()\n # This is a 1-d indices, shared by all elements in this batch.\n unstable_idx = unstable_locs.nonzero().squeeze()\n # Number of unstable neurons after merging.\n max_non_zero = unstable_locs.sum()\n # Create an abstract C matrix, the unstable_idx are the non-zero elements in specifications for all batches.\n newC = OneHotC([batch_size, max_non_zero, *node.output_shape[1:]], self.device, unstable_idx, None)\n reduced_dim = True\n # print(f'layer {node.name} total {dim} unstable {max_non_zero} newC {newC.shape}')\n \"\"\"\n newC = torch.eye(dim, device=self.device) \\\n .unsqueeze(0).repeat(batch_size, 1, 1) \\\n .view(batch_size, dim, *node.output_shape[1:])\n print(f'creating new C {newC.size()}')\n if int(os.environ.get('USE_EYE_C', 0)) == 1:\n newC = eyeC([batch_size, dim, *node.output_shape[1:]], self.device)\n \"\"\"\n else:\n newC = eyeC([batch_size, dim, *node.output_shape[1:]], self.device)\n elif (isinstance(node, BoundConv) or isinstance(node,\n BoundBatchNormalization)) and node.mode == \"patches\":\n # import pdb; pdb.set_trace()\n # Here we create an Identity Patches object \n newC = Patches(None, 1, 0,\n [batch_size, node.output_shape[-2] * node.output_shape[-1],\n node.output_shape[-3], node.output_shape[-3], 1, 1], 1)\n elif isinstance(node, BoundAdd) and node.mode == \"patches\":\n num_channel = node.output_shape[-3]\n L = node.output_shape[-2] * node.output_shape[-1]\n patches = (torch.eye(num_channel, device=self.device)).unsqueeze(0).unsqueeze(\n 0).unsqueeze(4).unsqueeze(5).expand(batch_size, L, num_channel, num_channel, 1, 1) # now [1 * 1 * in_C * in_C * 1 * 1]\n newC = Patches(patches, 1, 0, [batch_size] + list(patches.shape[1:]))\n else:\n if intermediate_constr is not None and node.name in reference_bounds:\n # If we are doing bound refinement and reference bounds are given, we only refine unstable neurons.\n # For each batch element, we find the unstable neurons.\n unstable_masks = torch.logical_and(reference_bounds[node.name][0] < 0, reference_bounds[node.name][1] > 0)\n # Flatten the conv layer shape.\n unstable_masks = unstable_masks.view(unstable_masks.size(0), -1)\n # unstable_masks = torch.ones(dtype=torch.bool, size=(batch_size, dim), device=self.device)\n # For simplicity, merge unstable locations for all elements in this batch. TODO: use individual unstable mask.\n unstable_locs = unstable_masks.sum(dim=0).bool()\n # This is always a 1-d indices. For conv layers it's flattened.\n unstable_idx = unstable_locs.nonzero().squeeze()\n # Number of unstable neurons after merging.\n max_non_zero = unstable_locs.sum()\n # Create a C matrix.\n newC = torch.zeros([1, max_non_zero, dim], device=self.device)\n # Fill the corresponding elements to 1.0\n newC[0, torch.arange(max_non_zero), unstable_idx] = 1.0\n newC = newC.repeat(batch_size, 1, 1).view(batch_size, max_non_zero, *node.output_shape[1:])\n reduced_dim = True\n # print(f'layer {node.name} total {dim} unstable {max_non_zero} newC {newC.size()}')\n else:\n if dim > 1000:\n warnings.warn(f\"Creating an identity matrix with size {dim}x{dim} for node {node}. This may indicate poor performance for bound computation. If you see this message on a small network please submit a bug report.\", stacklevel=2)\n newC = torch.eye(dim, device=self.device) \\\n .unsqueeze(0).repeat(batch_size, 1, 1) \\\n .view(batch_size, dim, *node.output_shape[1:])\n # print('Creating new C', type(newC), 'for', node)\n if False: # TODO: only return A_dict of final layer\n _, _, A_dict = self._backward_general(C=newC, node=node, root=root,\n return_A=return_A, A_dict=A_dict, intermedaite_constr=intermediate_constr)\n else:\n self._backward_general(C=newC, node=node, root=root, return_A=False, intermediate_constr=intermediate_constr, unstable_idx=unstable_idx)\n\n if reduced_dim:\n # If we only calculated unstable neurons, we need to scatter the results back based on reference bounds.\n new_lower = reference_bounds[node.name][0].detach().clone().view(batch_size, -1)\n new_lower[:, unstable_idx] = node.lower.view(batch_size, -1)\n node.lower = new_lower.view(batch_size, *node.output_shape[1:])\n new_upper = reference_bounds[node.name][1].detach().clone().view(batch_size, -1)\n new_upper[:, unstable_idx] = node.upper.view(batch_size, -1)\n node.upper = new_upper.view(batch_size, *node.output_shape[1:])\n # node.lower and node.upper (intermediate bounds) are computed in the above function.\n # If we have bound references, we set them here to always obtain a better set of bounds.\n if reference_bounds is not None and node.name in reference_bounds:\n # Initially, the reference bound and the computed bound can be exactly the same when intermediate layer beta is 0. This will prevent gradients flow. So we need a small guard here.\n if Check_against_base_lp:\n if node.name == Check_against_base_lp_layer:\n pass\n # print(reference_bounds[node.name][0][1,0,0,3].item(), node.lower[1,0,0,3].item())\n # node.lower = torch.max(reference_bounds[node.name][0] - 1e-5, node.lower)\n # node.upper = torch.min(reference_bounds[node.name][1] + 1e-5, node.upper)\n else:\n # For LP checking, fix all other intermediate layer bounds.\n node.lower = reference_bounds[node.name][0]\n node.upper = reference_bounds[node.name][1]\n else:\n # Setting reference bounds are actually incorrect. Because the split constraints are computed using slightly\n # different alpha (not the optimal), they can be slightly worse than original at the beginning.\n # So we only update bounds to reference if they cross zero (split constraints).\n node.lower = torch.max(reference_bounds[node.name][0] - 1e-5,\n node.lower)\n node.upper = torch.min(reference_bounds[node.name][1] + 1e-5,\n node.upper)\n \"\"\"\n update_lower = reference_bounds[node.name][0] >= 0\n node.lower[update_lower] = reference_bounds[node.name][0][update_lower]\n update_upper = reference_bounds[node.name][0] <= 0\n node.upper[update_upper] = reference_bounds[node.name][1][update_upper]\n \"\"\"\n\n if method == 'backward':\n # This is for the final output bound. No need to pass in intermediate layer beta constraints.\n return self._backward_general(C=C, node=final, root=root, bound_lower=bound_lower, bound_upper=bound_upper,\n return_A=return_A, needed_A_list=needed_A_list, average_A=average_A, A_dict=A_dict,\n return_b=return_b, b_dict=b_dict, unstable_idx=alpha_idx)\n elif method == 'forward':\n return self._forward_general(C=C, node=final, root=root, dim_in=dim_in, concretize=True)\n else:\n raise NotImplementedError","def bounds(self):\n return self.kernel.bounds","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 axis_bounds(pc, axis):\n try:\n bounds = pc.bounds\n except AttributeError:\n bounds = pc\n \n return tuple([getattr(bounds, b + axis) for b in ('min', 'max')])","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 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 _compute_bounds(self, axis, view):\n return None","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 condition_bounds(self) -> Tuple[float, float]:\n raise NotImplementedError","def bounds(self):\n return self.min_col, self.min_row, self.max_col, self.max_row","def bounds(self) -> devices.PrimaryBounds:\n if self._bounds is None:\n print(self.__class__.bounds.__doc__)\n raise SilSubProblemError(\n \"The *bounds* property has not been set (see above).\"\n )\n return self._bounds","def bounds(self):\n return (\n self.x, self.y,\n self.x, self.y\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","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","def compute_bb(self):\n all_shapes = list(self.parts.values()) + list(self.edges.values())\n bbox_vertices = cascaded_union(all_shapes).envelope.exterior.coords.xy\n min_x = min(bbox_vertices[0])\n max_x = max(bbox_vertices[0])\n min_y = min(bbox_vertices[1])\n max_y = max(bbox_vertices[1])\n return [min_x, max_x,min_y, max_y]","def get_params_bounds(self) -> np.array:\n pass","def getconbound(self,i_): # 3\n res,resargs = self.__obj.getconbound(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 bounds(self):\n \n return self.osmdb.bounds()","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 bounds(self):\n x,y,z = self._arr\n try:\n return Bounds(x.min(), y.min(), z.min(),\n x.max(), y.max(), z.max())\n except ValueError:\n raise simulocloud.exceptions.EmptyPointCloud(\n \"len 0 PointCloud has no Bounds\")","def bounds(self, resids: NDArray) -> List[Tuple[float, float]]:","def _process_bounds(self, param_name, bounds):\n l = self.n_elements_one_param(param_name)\n if(ut.is_iter(bounds)):\n if(len(bounds) == l):\n if(l!=2):\n res = [self._process_individual_bound(b) for b in bounds]\n else:\n try:\n res = [self._process_individual_bound(b) for b in bounds]\n except:\n res_one = self._process_individual_bound(bounds)\n res = [res_one for _ in range(l)]\n\n elif(len(bounds) == 2):\n # slightly tricky as l can be = 2\n res_one = self._process_individual_bound(bounds)\n res = [res_one for _ in range(l)]\n\n else:\n raise ValueError('Bounds length (%s) is not recognized. '% (len(bounds)))\n else:\n res_one = self._process_individual_bound(bounds)\n res = [res_one for _ in range(l)]\n \n return res","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 _getBounds(self, request):\n start = _getBound(request.args, \"start\")\n stop = _getBound(request.args, \"stop\", self._collection.pageSize)\n return start, stop","def EndsToBounds(chargeData, dischargeData,chargeBounds,dischargeBounds):\n global cut_off_time\n \n def cut_off_end_func(data,boundaries):\n for i in range(boundaries.shape[0]):\n try:\n start=boundaries[i,0]\n end=boundaries[i,1]\n c1=data[start:end]\n \n #c1 is the segmented array\n temp=[c1==0][0].astype(int)\n temp=np.diff(temp) \n last_element=np.where(temp==1)[0][-1]\n length_to_end=len(c1)-last_element\n \n if length_to_end>cut_off_time and np.all(c1[int(-length_to_end+1):]==0):\n boundaries[i,1]=start+last_element\n except:\n pass\n return(boundaries)\n \n chargeBounds=cut_off_end_func(chargeData,chargeBounds)\n dischargeBounds=cut_off_end_func(dischargeData,dischargeBounds)\n return(chargeBounds,dischargeBounds)","def bounds_riodataset(raster: DatasetReader) -> box:\n return box(*list(raster.bounds))","def get_cluster_bounds(i, cluster_bounds):\n con1 = np.where(i >= cluster_bounds)[0]\n j = con1[len(con1) -1]+1\n\n # for j in range(1, len(cluster_bounds)):\n # if(i < cluster_bounds[j] and i >= cluster_bounds[j-1]):\n # break\n return np.array([cluster_bounds[j-1], cluster_bounds[j]], dtype=np.int64)","def draw_bounds():\n\n pass","def _parse_bounds(self, bounds):\n try:\n if bounds == None:\n return None\n elif not isinstance(bounds[0], tuple):\n if len(bounds)==2:\n return [tuple(bounds) for i in range(self.params.size)]\n else:\n raise ValueError\n elif len(bounds) == self.params.size:\n if all([len(b)==2 for b in bounds]):\n return bounds\n else:\n raise ValueError\n else:\n raise ValueError\n except:\n raise ValueError(\"'bounds' should be a list of two elements \"\n \"[lb, ub], or a list of the same length as the number of \"\n \"parameters where each element is a tuple (lb, ub)\")","def getGroupBounds(args, matrix):\n bounds = matrix.parameters['group_boundaries']\n if args.groups is None:\n return range(0, matrix.matrix.matrix.shape[0]), np.array(bounds)\n else:\n o = list()\n obounds = [0]\n for group in args.groups:\n if group not in matrix.matrix.group_labels:\n sys.exit(\"Error: '{0}' is not a valid group\\n\".format(group))\n idx = matrix.matrix.group_labels.index(group)\n o.extend(range(bounds[idx], bounds[idx + 1]))\n obounds.append(bounds[idx + 1] - bounds[idx])\n return o, np.cumsum(obounds)","def compute_bounds(self, space):\n bounds = np.zeros((len(space), 2))\n\n for idx, param in enumerate(space):\n\n if TYPE[param[\"type\"]] is TYPE.FLOAT or \\\n TYPE[param[\"type\"]] is TYPE.INTEGER:\n bounds[idx] = (param[\"min\"], param[\"max\"])\n\n elif TYPE[param[\"type\"]] is TYPE.DISCRETE or \\\n TYPE[param[\"type\"]] is TYPE.DISCRETE:\n bounds[idx] = (0, len(param['values']))\n\n return bounds","def build_constraints_boundaries(self):\n\n # Trapezoidal and Hermite-Simpson methods can't compute\n # defects at the last node contrary to pseudospectral methods\n coll_method = self.options['tr_method'] in [\n 'trapezoidal', 'hermite-simpson']\n n_nodes = self.problem.prm['n_nodes'] - \\\n 1 if coll_method else self.problem.prm['n_nodes']\n\n # Defects lower and upper boundaries\n defects_low = np.zeros(\n self.problem.prm['n_states'] * n_nodes)\n defects_upp = np.zeros(\n self.problem.prm['n_states'] * n_nodes)\n\n # Path lower and upper boundaries\n path_low = np.hstack([self.problem.low_bnd.path]\n * (self.problem.prm['n_nodes']))\n path_upp = np.hstack([self.problem.upp_bnd.path]\n * (self.problem.prm['n_nodes']))\n\n # Events lower and upper boundaries\n event_low = self.problem.low_bnd.event\n event_upp = self.problem.upp_bnd.event\n\n # Assembly of the lower and upper boundaries vectors\n low = np.concatenate((defects_low, path_low, event_low))\n upp = np.concatenate((defects_upp, path_upp, event_upp))\n\n return low, upp","def _get_bounds(x, y, size):\n x = np.array(np.atleast_1d(x))\n y = np.array(np.atleast_1d(y))\n\n lower_x = np.rint(x - size[0]/2)\n lower_y = np.rint(y - size[1]/2)\n\n return np.stack((np.stack((lower_x, lower_x + size[0]), axis=1),\n np.stack((lower_y, lower_y + size[1]), axis=1)), axis=1).astype(int)","def bounds2slices(\n bounds: tuple[int, int, int, int],\n stride: int | tuple[int, int, tuple[int, int]] = 1,\n) -> tuple[slice]:\n if np.size(stride) not in [1, 2]:\n msg = \"Invalid stride shape.\"\n raise ValueError(msg)\n if np.size(stride) == 1:\n stride = np.tile(stride, 4)\n elif np.size(stride) == 2: # pragma: no cover # noqa: PLR2004\n stride = np.tile(stride, 2)\n\n start, stop = np.reshape(bounds, (2, -1)).astype(int)\n slice_array = np.stack([start[::-1], stop[::-1]], axis=1)\n return tuple(slice(*x, s) for x, s in zip(slice_array, stride))","def get_bounds(self, A: list, c: int) -> (int, int):\r\n\r\n # This implementation uses two binary search algorithms to find\r\n # the upper and lower bound.\r\n # First step is to isolate the upper_bound.\r\n\r\n L = 0\r\n R = len(A)\r\n while L < R:\r\n # Find the middle value\r\n m = math.floor((L + R) / 2)\r\n v = A[m]\r\n\r\n # Check if |A[i] - i| < c:\r\n if abs(v - m) > c:\r\n # This step is important, if we are on a negative number\r\n # We need to move right instead of left.\r\n if v < 0 or (v - m) < 0:\r\n L = m + 1\r\n else:\r\n # Else, we need to move towards the left.\r\n R = m\r\n else:\r\n # If it matches the condition, move the left up because we're\r\n # going towards the lowest number.\r\n L = m + 1\r\n upper_bound = R\r\n\r\n # Now that we have the upper bound, we only need to\r\n # Binary search for the lower bound between index 0 and upper_bound.\r\n L = 0\r\n R = upper_bound\r\n while L < R:\r\n # find the middle\r\n m = math.floor((L + R) / 2)\r\n if abs(A[m] - m) > c:\r\n # If it's greater, move the left up.\r\n L = m + 1\r\n else:\r\n # Else, move the right down.\r\n R = m\r\n\r\n # Finally we have the lower bound.\r\n lower_bound = L\r\n\r\n # Return the lower bound and the upper bound index\r\n # Note the -1 because the upper bound will give the\r\n # size of the array in worst case.\r\n return lower_bound, upper_bound - 1","def parameter_bounds(self):\n for name, bound in self.named_parameter_bounds():\n yield bound","def cfdGetBoundaryElementsSubArrayForBoundaryPatch(self):\r\n\r\n for iBPatch, theBCInfo in self.cfdBoundaryPatchesArray.items():\r\n \r\n startBElement=self.numberOfElements+self.cfdBoundaryPatchesArray[iBPatch]['startFaceIndex']-self.numberOfInteriorFaces\r\n endBElement=startBElement+self.cfdBoundaryPatchesArray[iBPatch]['numberOfBFaces']\r\n \r\n self.cfdBoundaryPatchesArray[iBPatch]['iBElements']=list(range(int(startBElement),int(endBElement)))","def calc_bounds(roi: np.ndarray) -> Dict[int, BoundInfo]:\n try:\n min_bounds, max_bounds = calc_bounds(roi)\n return {\n num: BoundInfo(lower=lower, upper=upper)\n for num, (lower, upper) in enumerate(zip(min_bounds, max_bounds))\n if num != 0 and upper[0] != -1\n }\n except KeyError:\n bound_info = {}\n points = np.nonzero(roi)\n comp_num = roi[points]\n point_dict = defaultdict(list)\n for num, point in zip(comp_num, np.transpose(points)):\n point_dict[num].append(point)\n for num, points_for_num in point_dict.items():\n lower = np.min(points_for_num, 0)\n upper = np.max(points_for_num, 0)\n bound_info[num] = BoundInfo(lower=lower, upper=upper)\n return bound_info","def test_calculate_crow_bounds_shape_parameter_type1(self):\n\n _bounds = calculate_crow_bounds(22, 620.0, 0.4239, 0.6142, 0.9, 1, 1)\n self.assertAlmostEqual(_bounds[0], 0.4356064)\n self.assertAlmostEqual(_bounds[1], 0.8844610)","def _get_i_bnds(self, wave_bounds=None):\n\n wave_grid = self.wave_grid\n i_bounds = self.i_bounds\n\n # Check if wave_bounds given\n if wave_bounds is None:\n wave_bounds = []\n for i in range(self.n_orders):\n wave = self.wave_map[i][~self.mask_ord[i]]\n wave_bounds.append([wave.min(), wave.max()])\n\n # What we need is the boundary position\n # on the wavelength grid.\n i_bnds_new = []\n for bounds, i_bnds in zip(wave_bounds, i_bounds):\n\n a = np.min(np.where(wave_grid >= bounds[0])[0])\n b = np.max(np.where(wave_grid <= bounds[1])[0]) + 1\n\n # Take the most restrictive bound\n a = np.maximum(a, i_bnds[0])\n b = np.minimum(b, i_bnds[1])\n\n # Keep value\n i_bnds_new.append([a, b])\n\n return i_bnds_new","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 calc_bounds(segmentation: np.ndarray) -> Dict[int, BoundInfo]:\n bound_info = {}\n count = np.max(segmentation)\n for i in range(1, count + 1):\n component = np.array(segmentation == i)\n if np.any(component):\n points = np.nonzero(component)\n lower = np.min(points, 1)\n upper = np.max(points, 1)\n bound_info[i] = BoundInfo(lower=lower, upper=upper)\n return bound_info","def get_bounds(self, t_index):\n mean = self.get_mean(t_index)\n std = self.get_std()\n return mean - std, mean + std","def getBounds(self, nStates, nParams):\n raise NotImplementedError(\n \"bounds have not been implemented for this Experiment\")"],"string":"[\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 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 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 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 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 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 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_,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 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 GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def bounds(self): # -> tuple[()]:\\n ...\",\n \"def bounds(self):\\n return self._bboxes[0][0] #TODO: merge all coverages\",\n \"def get_bounds(self):\\n raise Exception(\\\"Non-implemented base class method.\\\")\",\n \"def get_bounds(self):\\n return ([self.t_min] * self.dim,[self.t_max] * self.dim)\",\n \"def bounds(self):\\n return self._bboxes[0][0] #TODO: merge all coverages\",\n \"def bounds(self):\\n return self.substrates.bounds\",\n \"def get_bounds(self):\\r\\n left, bottom, front = 10000, 10000, 10000\\r\\n right, top, back = -10000, -10000, -10000\\r\\n for b in self.buf:\\r\\n for v in b.vertices:\\r\\n if v[0] < left:\\r\\n left = v[0]\\r\\n if v[0] > right:\\r\\n right = v[0]\\r\\n if v[1] < bottom:\\r\\n bottom = v[1]\\r\\n if v[1] > top:\\r\\n top = v[1]\\r\\n if v[2] < front:\\r\\n front = v[2]\\r\\n if v[2] > back:\\r\\n back = v[2]\\r\\n\\r\\n return (left, bottom, front, right, top, back)\",\n \"def get_bounds():\\n return [0.00], [1.00]\",\n \"def fetchbounds(self):\\n pnts = [x for x in [self.out_start, self.start, self.in_start, \\\\\\n self.in_end, self.end, self.out_end] \\\\\\n if x is not None]\\n return min(pnts), max(pnts)\",\n \"def bounds(self, start=None, finish=None):\\n lower = start if start is not None else self.limits[0]\\n upper = finish if finish is not None else self.limits[1]\\n\\n lower = lower + self.offsets[0]\\n upper = upper + self.offsets[1]\\n\\n return (lower, upper)\",\n \"def get_bounds(self):\\n log.debug(str(inspect.stack()[1][3]) + \\\"--> OC.get_bounds()\\\")\\n\\n # TODO: Move the operation out of here.\\n\\n xmin = Inf\\n ymin = Inf\\n xmax = -Inf\\n ymax = -Inf\\n\\n # for obj in self.object_list:\\n for obj in self.get_list():\\n try:\\n gxmin, gymin, gxmax, gymax = obj.bounds()\\n xmin = min([xmin, gxmin])\\n ymin = min([ymin, gymin])\\n xmax = max([xmax, gxmax])\\n ymax = max([ymax, gymax])\\n except Exception as e:\\n log.warning(\\\"DEV WARNING: Tried to get bounds of empty geometry. %s\\\" % str(e))\\n\\n return [xmin, ymin, xmax, ymax]\",\n \"def bounds(self, pos):\",\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 bounds(self):\\n if self.change_dimensionality:\\n return [self._bounds[0]] * self.N\\n else:\\n return self._bounds\",\n \"def GetBounds(self, p_int, p_int_1, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\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 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 get_bounds():\\n bounds = [\\n (0.1, 0.5), # Omega_m\\n (0.05, 0.15) # beta\\n ]\\n return np.array(bounds)\",\n \"def get_bounds(self, channel_indx=None):\\n if channel_indx is None:\\n upper_bound = self.upper_bound.reshape(self.batch_size, -1)\\n lower_bound = self.lower_bound.reshape(self.batch_size, -1)\\n else:\\n upper_bound = self.upper_bound[:, channel_indx, :].reshape(\\n self.batch_size, -1\\n )\\n lower_bound = self.lower_bound[:, channel_indx, :].reshape(\\n self.batch_size, -1\\n )\\n return upper_bound, lower_bound\",\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 bounds(self):\\n return self._bounds\",\n \"def __nc_geo_bounds(self, extent, data_sel):\\n indx = None\\n if extent is not None:\\n if len(extent) != 4:\\n raise ValueError('parameter extent must have 4 elements')\\n\\n lats = self.fid['/instrument/latitude_center'][:].reshape(\\n self.scanline, self.ground_pixel)\\n lons = self.fid['/instrument/longitude_center'][:].reshape(\\n self.scanline, self.ground_pixel)\\n\\n indx = np.where((lons >= extent[0]) & (lons <= extent[1])\\n & (lats >= extent[2]) & (lats <= extent[3]))\\n data_sel = np.s_[indx[0].min():indx[0].max(),\\n indx[1].min():indx[1].max()]\\n\\n gid = self.fid['/instrument']\\n lat_bounds = gid['latitude_corners'][:].data.reshape(\\n self.scanline, self.ground_pixel, 4)\\n lon_bounds = gid['longitude_corners'][:].data.reshape(\\n self.scanline, self.ground_pixel, 4)\\n if data_sel is not None:\\n lat_bounds = lat_bounds[data_sel + (slice(None),)]\\n lon_bounds = lon_bounds[data_sel + (slice(None),)]\\n\\n return (data_sel, lon_bounds, lat_bounds)\",\n \"def get_bounds(self):\\n bottom_right = np.asarray([self.coords[k][0] for k in range(self.dim)])\\n upper_left = np.asarray([self.coords[k][-1] for k in range(self.dim)])\\n return bottom_right, upper_left\",\n \"def bounds(self):\\n return self.GetBounds()\",\n \"def bounds(self):\\n b = []\\n\\n for dim in self.dimensions:\\n if dim.size == 1:\\n b.append(dim.bounds)\\n else:\\n b.extend(dim.bounds)\\n\\n return b\",\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 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 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 bounds(self):\\n return self.xmin, self.xmax, self.ymin, self.ymax\",\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 getconbound(self,i_):\\n bk_ = ctypes.c_int32()\\n bl_ = ctypes.c_double()\\n bu_ = ctypes.c_double()\\n res = __library__.MSK_XX_getconbound(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 getSampleBounds(args, matrix):\\n bounds = matrix.parameters['sample_boundaries']\\n if args.samples is None:\\n return np.arange(0, matrix.matrix.matrix.shape[1])\\n else:\\n o = list()\\n for sample in args.samples:\\n if sample not in matrix.matrix.sample_labels:\\n sys.exit(\\\"Error: '{0}' is not a valid sample\\\\n\\\".format(sample))\\n idx = matrix.matrix.sample_labels.index(sample)\\n o.extend(range(bounds[idx], bounds[idx + 1]))\\n return o\",\n \"def get_raw_bounds(self) -> [Vector, Vector]:\\n\\t\\tverts = np.array([v.co for mesh in self._meshes for v in mesh.data.vertices])\\n\\t\\tbbox_min = Vector([*np.min(verts, axis=0)])\\n\\t\\tbbox_max = Vector([*np.max(verts, axis=0)])\\n\\t\\treturn bbox_min, bbox_max\",\n \"def bounds(self):\\n return [(2, None)]\",\n \"def GetIndexBounds(self, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def get_bounds(shape, affine):\\n adim, bdim, cdim = shape\\n adim -= 1\\n bdim -= 1\\n cdim -= 1\\n # form a collection of vectors for each 8 corners of the box\\n box = np.array([[0., 0, 0, 1],\\n [adim, 0, 0, 1],\\n [0, bdim, 0, 1],\\n [0, 0, cdim, 1],\\n [adim, bdim, 0, 1],\\n [adim, 0, cdim, 1],\\n [0, bdim, cdim, 1],\\n [adim, bdim, cdim, 1]]).T\\n box = np.dot(affine, box)[:3]\\n return zip(box.min(axis=-1), box.max(axis=-1))\",\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 compute_bounds(self, x=None, aux=None, C=None, method='backward', IBP=False, forward=False, \\n bound_lower=True, bound_upper=True, reuse_ibp=False,\\n return_A=False, needed_A_list=None, final_node_name=None, average_A=False, new_interval=None,\\n return_b=False, b_dict=None, reference_bounds=None, intermediate_constr=None, alpha_idx=None):\\n\\n # Several shortcuts.\\n method = method.lower() if method is not None else method\\n if method == 'ibp':\\n # Pure IBP bounds.\\n method = None\\n IBP = True\\n elif method == 'ibp+backward' or method == 'ibp+crown' or method == 'crown-ibp':\\n method = 'backward'\\n IBP = True\\n elif method == 'crown':\\n method = 'backward'\\n elif method == 'forward':\\n forward = True\\n elif method == 'forward+backward':\\n method = 'backward'\\n forward = True\\n elif method == \\\"crown-optimized\\\" or method == 'alpha-crown':\\n if bound_lower:\\n ret1 = self.get_optimized_bounds(x=x, IBP=False, C=C, method='backward', new_interval=new_interval, reference_bounds=reference_bounds,\\n bound_lower=bound_lower, bound_upper=False, return_A=return_A)\\n if bound_upper:\\n ret2 = self.get_optimized_bounds(x=x, IBP=False, C=C, method='backward', new_interval=new_interval, reference_bounds=reference_bounds,\\n bound_lower=False, bound_upper=bound_upper, return_A=return_A)\\n if bound_upper and bound_upper:\\n assert return_A is False\\n return ret1[0], ret2[1]\\n elif bound_lower:\\n return ret1\\n elif bound_upper:\\n return ret2\\n else:\\n raise NotImplementedError\\n\\n # If y in self.backward_node_pairs[x], then node y is visited when \\n # doing backward bound propagation starting from node x.\\n self.backward_from = dict([(node, []) for node in self._modules])\\n\\n if not bound_lower and not bound_upper:\\n raise ValueError('At least one of bound_lower and bound_upper in compute_bounds should be True')\\n A_dict = {} if return_A else None\\n\\n if x is not None:\\n self._set_input(*x, new_interval=new_interval)\\n\\n if IBP and method is None and reuse_ibp:\\n # directly return the previously saved ibp bounds\\n return self.ibp_lower, self.ibp_upper\\n root = [self._modules[name] for name in self.root_name]\\n batch_size = root[0].value.shape[0]\\n dim_in = 0\\n\\n for i in range(len(root)):\\n value = root[i].forward()\\n if hasattr(root[i], 'perturbation') and root[i].perturbation is not None: \\n root[i].linear, root[i].center, root[i].aux = \\\\\\n root[i].perturbation.init(value, aux=aux, forward=forward)\\n # This input/parameter has perturbation. Create an interval object.\\n if self.ibp_relative:\\n root[i].interval = Interval(\\n None, None, \\n root[i].linear.nominal, root[i].linear.lower_offset, root[i].linear.upper_offset)\\n else:\\n root[i].interval = \\\\\\n Interval(root[i].linear.lower, root[i].linear.upper, ptb=root[i].perturbation)\\n if forward:\\n root[i].dim = root[i].linear.lw.shape[1]\\n dim_in += root[i].dim\\n else:\\n if self.ibp_relative:\\n root[i].interval = Interval(\\n None, None, \\n value, torch.zeros_like(value), torch.zeros_like(value)) \\n else:\\n # This inpute/parameter does not has perturbation. \\n # Use plain tuple defaulting to Linf perturbation.\\n root[i].interval = (value, value)\\n root[i].forward_value = root[i].forward_value = root[i].value = root[i].lower = root[i].upper = value\\n\\n if self.ibp_relative:\\n root[i].lower = root[i].interval.lower\\n root[i].upper = root[i].interval.upper\\n else:\\n root[i].lower, root[i].upper = root[i].interval\\n\\n if forward:\\n self._init_forward(root, dim_in)\\n\\n final = self._modules[self.final_name] if final_node_name is None else self._modules[final_node_name]\\n logger.debug('Final node {}[{}]'.format(final, final.name))\\n\\n if IBP:\\n res = self._IBP_general(node=final, C=C)\\n if self.ibp_relative:\\n self.ibp_lower, self.ibp_upper = res.lower, res.upper\\n else:\\n self.ibp_lower, self.ibp_upper = res\\n\\n if method is None:\\n return self.ibp_lower, self.ibp_upper \\n\\n if C is None:\\n # C is an identity matrix by default \\n if final.output_shape is None:\\n raise ValueError('C is not provided while node {} has no default shape'.format(final.shape))\\n dim_output = int(np.prod(final.output_shape[1:]))\\n C = torch.eye(dim_output, device=self.device).unsqueeze(0).repeat(batch_size, 1, 1) # TODO: use an eyeC object here.\\n\\n # check whether weights are perturbed and set nonlinear for the BoundMatMul operation\\n for n in self._modules.values():\\n if isinstance(n, (BoundLinear, BoundConv, BoundBatchNormalization)):\\n n.nonlinear = False\\n for l_name in n.input_name[1:]:\\n node = self._modules[l_name]\\n if hasattr(node, 'perturbation'):\\n if node.perturbation is not None:\\n n.nonlinear = True\\n\\n # BFS to find out whether each node is used given the current final node\\n if final != self.last_final_node:\\n self.last_final_node = final\\n for i in self._modules.values():\\n i.used = False\\n final.used = True\\n queue = deque([final])\\n while len(queue) > 0:\\n n = queue.popleft()\\n for n_pre_name in n.input_name:\\n n_pre = self._modules[n_pre_name]\\n if not n_pre.used:\\n n_pre.used = True\\n queue.append(n_pre)\\n\\n for i in self._modules.values(): # for all nodes\\n if not i.used:\\n continue\\n if hasattr(i, 'nonlinear') and i.nonlinear:\\n for l_name in i.input_name:\\n node = self._modules[l_name]\\n # print('node', node, 'lower', hasattr(node, 'lower'), 'perturbed', node.perturbed, 'forward_value', hasattr(node, 'forward_value'), 'from_input', node.from_input)\\n if not hasattr(node, 'lower'):\\n assert not IBP, 'There should be no missing intermediate bounds when IBP is enabled'\\n if not node.perturbed and hasattr(node, 'forward_value'):\\n node.interval = node.lower, node.upper = \\\\\\n node.forward_value, node.forward_value\\n continue\\n # FIXME check that weight perturbation is not affected\\n # (from_input=True should be set for weights)\\n if not node.from_input and hasattr(node, 'forward_value'):\\n node.lower = node.upper = node.forward_value\\n continue\\n if forward:\\n l, u = self._forward_general(\\n node=node, root=root, dim_in=dim_in, concretize=True)\\n else:\\n # assign concretized bound for ReLU layer to save computational cost\\n # FIXME: Put ReLU after reshape will cause problem!\\n if (isinstance(node, BoundActivation) or isinstance(node, BoundTranspose)) and hasattr(\\n self._modules[node.input_name[0]], 'lower'):\\n node.lower = node.forward(self._modules[node.input_name[0]].lower)\\n node.upper = node.forward(self._modules[node.input_name[0]].upper)\\n elif isinstance(node, BoundReshape) and \\\\\\n hasattr(self._modules[node.input_name[0]], 'lower') and \\\\\\n hasattr(self._modules[node.input_name[1]], 'value'):\\n # Node for input value.\\n val_input = self._modules[node.input_name[0]]\\n # Node for input parameter (e.g., shape, permute)\\n arg_input = self._modules[node.input_name[1]]\\n node.lower = node.forward(val_input.lower, arg_input.value)\\n node.upper = node.forward(val_input.upper, arg_input.value)\\n else:\\n first_layer_flag = False\\n # This is the list of all intermediate layers where we need to refine.\\n if intermediate_constr is not None:\\n intermediate_beta_enabled_layers = [k for v in intermediate_constr.values() for k in v]\\n else:\\n intermediate_beta_enabled_layers = []\\n # Here we avoid creating a big C matrix in the first linear layer.\\n # Disable this optimization when we have beta for intermediate layer bounds.\\n if type(node) == BoundLinear or type(node) == BoundConv and node.name not in intermediate_beta_enabled_layers:\\n for l_pre in node.input_name:\\n if type(self._modules[l_pre]) == BoundInput:\\n node.lower, node.upper = self._IBP_general(node)\\n first_layer_flag = True\\n break\\n if not first_layer_flag:\\n reduced_dim = False # Only partial neurons (unstable neurons) are bounded.\\n unstable_idx = None\\n dim = int(np.prod(node.output_shape[1:]))\\n # FIXME: C matrix shape incorrect for BoundParams.\\n if (isinstance(node, BoundLinear) or isinstance(node, BoundMatMul)) and int(\\n os.environ.get('AUTOLIRPA_USE_FULL_C', 0)) == 0:\\n if intermediate_constr is not None and node.name in reference_bounds:\\n # If we are doing bound refinement and reference bounds are given, we only refine unstable neurons.\\n # For each batch element, we find the unstable neurons.\\n unstable_masks = torch.logical_and(reference_bounds[node.name][0] < 0, reference_bounds[node.name][1] > 0)\\n # unstable_masks = torch.ones(dtype=torch.bool, size=(batch_size, dim), device=self.device)\\n # For simplicity, merge unstable locations for all elements in this batch. TODO: use individual unstable mask.\\n unstable_locs = unstable_masks.sum(dim=0).bool()\\n # This is a 1-d indices, shared by all elements in this batch.\\n unstable_idx = unstable_locs.nonzero().squeeze()\\n # Number of unstable neurons after merging.\\n max_non_zero = unstable_locs.sum()\\n # Create an abstract C matrix, the unstable_idx are the non-zero elements in specifications for all batches.\\n newC = OneHotC([batch_size, max_non_zero, *node.output_shape[1:]], self.device, unstable_idx, None)\\n reduced_dim = True\\n # print(f'layer {node.name} total {dim} unstable {max_non_zero} newC {newC.shape}')\\n \\\"\\\"\\\"\\n newC = torch.eye(dim, device=self.device) \\\\\\n .unsqueeze(0).repeat(batch_size, 1, 1) \\\\\\n .view(batch_size, dim, *node.output_shape[1:])\\n print(f'creating new C {newC.size()}')\\n if int(os.environ.get('USE_EYE_C', 0)) == 1:\\n newC = eyeC([batch_size, dim, *node.output_shape[1:]], self.device)\\n \\\"\\\"\\\"\\n else:\\n newC = eyeC([batch_size, dim, *node.output_shape[1:]], self.device)\\n elif (isinstance(node, BoundConv) or isinstance(node,\\n BoundBatchNormalization)) and node.mode == \\\"patches\\\":\\n # import pdb; pdb.set_trace()\\n # Here we create an Identity Patches object \\n newC = Patches(None, 1, 0,\\n [batch_size, node.output_shape[-2] * node.output_shape[-1],\\n node.output_shape[-3], node.output_shape[-3], 1, 1], 1)\\n elif isinstance(node, BoundAdd) and node.mode == \\\"patches\\\":\\n num_channel = node.output_shape[-3]\\n L = node.output_shape[-2] * node.output_shape[-1]\\n patches = (torch.eye(num_channel, device=self.device)).unsqueeze(0).unsqueeze(\\n 0).unsqueeze(4).unsqueeze(5).expand(batch_size, L, num_channel, num_channel, 1, 1) # now [1 * 1 * in_C * in_C * 1 * 1]\\n newC = Patches(patches, 1, 0, [batch_size] + list(patches.shape[1:]))\\n else:\\n if intermediate_constr is not None and node.name in reference_bounds:\\n # If we are doing bound refinement and reference bounds are given, we only refine unstable neurons.\\n # For each batch element, we find the unstable neurons.\\n unstable_masks = torch.logical_and(reference_bounds[node.name][0] < 0, reference_bounds[node.name][1] > 0)\\n # Flatten the conv layer shape.\\n unstable_masks = unstable_masks.view(unstable_masks.size(0), -1)\\n # unstable_masks = torch.ones(dtype=torch.bool, size=(batch_size, dim), device=self.device)\\n # For simplicity, merge unstable locations for all elements in this batch. TODO: use individual unstable mask.\\n unstable_locs = unstable_masks.sum(dim=0).bool()\\n # This is always a 1-d indices. For conv layers it's flattened.\\n unstable_idx = unstable_locs.nonzero().squeeze()\\n # Number of unstable neurons after merging.\\n max_non_zero = unstable_locs.sum()\\n # Create a C matrix.\\n newC = torch.zeros([1, max_non_zero, dim], device=self.device)\\n # Fill the corresponding elements to 1.0\\n newC[0, torch.arange(max_non_zero), unstable_idx] = 1.0\\n newC = newC.repeat(batch_size, 1, 1).view(batch_size, max_non_zero, *node.output_shape[1:])\\n reduced_dim = True\\n # print(f'layer {node.name} total {dim} unstable {max_non_zero} newC {newC.size()}')\\n else:\\n if dim > 1000:\\n warnings.warn(f\\\"Creating an identity matrix with size {dim}x{dim} for node {node}. This may indicate poor performance for bound computation. If you see this message on a small network please submit a bug report.\\\", stacklevel=2)\\n newC = torch.eye(dim, device=self.device) \\\\\\n .unsqueeze(0).repeat(batch_size, 1, 1) \\\\\\n .view(batch_size, dim, *node.output_shape[1:])\\n # print('Creating new C', type(newC), 'for', node)\\n if False: # TODO: only return A_dict of final layer\\n _, _, A_dict = self._backward_general(C=newC, node=node, root=root,\\n return_A=return_A, A_dict=A_dict, intermedaite_constr=intermediate_constr)\\n else:\\n self._backward_general(C=newC, node=node, root=root, return_A=False, intermediate_constr=intermediate_constr, unstable_idx=unstable_idx)\\n\\n if reduced_dim:\\n # If we only calculated unstable neurons, we need to scatter the results back based on reference bounds.\\n new_lower = reference_bounds[node.name][0].detach().clone().view(batch_size, -1)\\n new_lower[:, unstable_idx] = node.lower.view(batch_size, -1)\\n node.lower = new_lower.view(batch_size, *node.output_shape[1:])\\n new_upper = reference_bounds[node.name][1].detach().clone().view(batch_size, -1)\\n new_upper[:, unstable_idx] = node.upper.view(batch_size, -1)\\n node.upper = new_upper.view(batch_size, *node.output_shape[1:])\\n # node.lower and node.upper (intermediate bounds) are computed in the above function.\\n # If we have bound references, we set them here to always obtain a better set of bounds.\\n if reference_bounds is not None and node.name in reference_bounds:\\n # Initially, the reference bound and the computed bound can be exactly the same when intermediate layer beta is 0. This will prevent gradients flow. So we need a small guard here.\\n if Check_against_base_lp:\\n if node.name == Check_against_base_lp_layer:\\n pass\\n # print(reference_bounds[node.name][0][1,0,0,3].item(), node.lower[1,0,0,3].item())\\n # node.lower = torch.max(reference_bounds[node.name][0] - 1e-5, node.lower)\\n # node.upper = torch.min(reference_bounds[node.name][1] + 1e-5, node.upper)\\n else:\\n # For LP checking, fix all other intermediate layer bounds.\\n node.lower = reference_bounds[node.name][0]\\n node.upper = reference_bounds[node.name][1]\\n else:\\n # Setting reference bounds are actually incorrect. Because the split constraints are computed using slightly\\n # different alpha (not the optimal), they can be slightly worse than original at the beginning.\\n # So we only update bounds to reference if they cross zero (split constraints).\\n node.lower = torch.max(reference_bounds[node.name][0] - 1e-5,\\n node.lower)\\n node.upper = torch.min(reference_bounds[node.name][1] + 1e-5,\\n node.upper)\\n \\\"\\\"\\\"\\n update_lower = reference_bounds[node.name][0] >= 0\\n node.lower[update_lower] = reference_bounds[node.name][0][update_lower]\\n update_upper = reference_bounds[node.name][0] <= 0\\n node.upper[update_upper] = reference_bounds[node.name][1][update_upper]\\n \\\"\\\"\\\"\\n\\n if method == 'backward':\\n # This is for the final output bound. No need to pass in intermediate layer beta constraints.\\n return self._backward_general(C=C, node=final, root=root, bound_lower=bound_lower, bound_upper=bound_upper,\\n return_A=return_A, needed_A_list=needed_A_list, average_A=average_A, A_dict=A_dict,\\n return_b=return_b, b_dict=b_dict, unstable_idx=alpha_idx)\\n elif method == 'forward':\\n return self._forward_general(C=C, node=final, root=root, dim_in=dim_in, concretize=True)\\n else:\\n raise NotImplementedError\",\n \"def bounds(self):\\n return self.kernel.bounds\",\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 axis_bounds(pc, axis):\\n try:\\n bounds = pc.bounds\\n except AttributeError:\\n bounds = pc\\n \\n return tuple([getattr(bounds, b + axis) for b in ('min', 'max')])\",\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 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 _compute_bounds(self, axis, view):\\n return None\",\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 condition_bounds(self) -> Tuple[float, float]:\\n raise NotImplementedError\",\n \"def bounds(self):\\n return self.min_col, self.min_row, self.max_col, self.max_row\",\n \"def bounds(self) -> devices.PrimaryBounds:\\n if self._bounds is None:\\n print(self.__class__.bounds.__doc__)\\n raise SilSubProblemError(\\n \\\"The *bounds* property has not been set (see above).\\\"\\n )\\n return self._bounds\",\n \"def bounds(self):\\n return (\\n self.x, self.y,\\n self.x, self.y\\n )\",\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 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 \"def compute_bb(self):\\n all_shapes = list(self.parts.values()) + list(self.edges.values())\\n bbox_vertices = cascaded_union(all_shapes).envelope.exterior.coords.xy\\n min_x = min(bbox_vertices[0])\\n max_x = max(bbox_vertices[0])\\n min_y = min(bbox_vertices[1])\\n max_y = max(bbox_vertices[1])\\n return [min_x, max_x,min_y, max_y]\",\n \"def get_params_bounds(self) -> np.array:\\n pass\",\n \"def getconbound(self,i_): # 3\\n res,resargs = self.__obj.getconbound(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 bounds(self):\\n \\n return self.osmdb.bounds()\",\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 bounds(self):\\n x,y,z = self._arr\\n try:\\n return Bounds(x.min(), y.min(), z.min(),\\n x.max(), y.max(), z.max())\\n except ValueError:\\n raise simulocloud.exceptions.EmptyPointCloud(\\n \\\"len 0 PointCloud has no Bounds\\\")\",\n \"def bounds(self, resids: NDArray) -> List[Tuple[float, float]]:\",\n \"def _process_bounds(self, param_name, bounds):\\n l = self.n_elements_one_param(param_name)\\n if(ut.is_iter(bounds)):\\n if(len(bounds) == l):\\n if(l!=2):\\n res = [self._process_individual_bound(b) for b in bounds]\\n else:\\n try:\\n res = [self._process_individual_bound(b) for b in bounds]\\n except:\\n res_one = self._process_individual_bound(bounds)\\n res = [res_one for _ in range(l)]\\n\\n elif(len(bounds) == 2):\\n # slightly tricky as l can be = 2\\n res_one = self._process_individual_bound(bounds)\\n res = [res_one for _ in range(l)]\\n\\n else:\\n raise ValueError('Bounds length (%s) is not recognized. '% (len(bounds)))\\n else:\\n res_one = self._process_individual_bound(bounds)\\n res = [res_one for _ in range(l)]\\n \\n return res\",\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 _getBounds(self, request):\\n start = _getBound(request.args, \\\"start\\\")\\n stop = _getBound(request.args, \\\"stop\\\", self._collection.pageSize)\\n return start, stop\",\n \"def EndsToBounds(chargeData, dischargeData,chargeBounds,dischargeBounds):\\n global cut_off_time\\n \\n def cut_off_end_func(data,boundaries):\\n for i in range(boundaries.shape[0]):\\n try:\\n start=boundaries[i,0]\\n end=boundaries[i,1]\\n c1=data[start:end]\\n \\n #c1 is the segmented array\\n temp=[c1==0][0].astype(int)\\n temp=np.diff(temp) \\n last_element=np.where(temp==1)[0][-1]\\n length_to_end=len(c1)-last_element\\n \\n if length_to_end>cut_off_time and np.all(c1[int(-length_to_end+1):]==0):\\n boundaries[i,1]=start+last_element\\n except:\\n pass\\n return(boundaries)\\n \\n chargeBounds=cut_off_end_func(chargeData,chargeBounds)\\n dischargeBounds=cut_off_end_func(dischargeData,dischargeBounds)\\n return(chargeBounds,dischargeBounds)\",\n \"def bounds_riodataset(raster: DatasetReader) -> box:\\n return box(*list(raster.bounds))\",\n \"def get_cluster_bounds(i, cluster_bounds):\\n con1 = np.where(i >= cluster_bounds)[0]\\n j = con1[len(con1) -1]+1\\n\\n # for j in range(1, len(cluster_bounds)):\\n # if(i < cluster_bounds[j] and i >= cluster_bounds[j-1]):\\n # break\\n return np.array([cluster_bounds[j-1], cluster_bounds[j]], dtype=np.int64)\",\n \"def draw_bounds():\\n\\n pass\",\n \"def _parse_bounds(self, bounds):\\n try:\\n if bounds == None:\\n return None\\n elif not isinstance(bounds[0], tuple):\\n if len(bounds)==2:\\n return [tuple(bounds) for i in range(self.params.size)]\\n else:\\n raise ValueError\\n elif len(bounds) == self.params.size:\\n if all([len(b)==2 for b in bounds]):\\n return bounds\\n else:\\n raise ValueError\\n else:\\n raise ValueError\\n except:\\n raise ValueError(\\\"'bounds' should be a list of two elements \\\"\\n \\\"[lb, ub], or a list of the same length as the number of \\\"\\n \\\"parameters where each element is a tuple (lb, ub)\\\")\",\n \"def getGroupBounds(args, matrix):\\n bounds = matrix.parameters['group_boundaries']\\n if args.groups is None:\\n return range(0, matrix.matrix.matrix.shape[0]), np.array(bounds)\\n else:\\n o = list()\\n obounds = [0]\\n for group in args.groups:\\n if group not in matrix.matrix.group_labels:\\n sys.exit(\\\"Error: '{0}' is not a valid group\\\\n\\\".format(group))\\n idx = matrix.matrix.group_labels.index(group)\\n o.extend(range(bounds[idx], bounds[idx + 1]))\\n obounds.append(bounds[idx + 1] - bounds[idx])\\n return o, np.cumsum(obounds)\",\n \"def compute_bounds(self, space):\\n bounds = np.zeros((len(space), 2))\\n\\n for idx, param in enumerate(space):\\n\\n if TYPE[param[\\\"type\\\"]] is TYPE.FLOAT or \\\\\\n TYPE[param[\\\"type\\\"]] is TYPE.INTEGER:\\n bounds[idx] = (param[\\\"min\\\"], param[\\\"max\\\"])\\n\\n elif TYPE[param[\\\"type\\\"]] is TYPE.DISCRETE or \\\\\\n TYPE[param[\\\"type\\\"]] is TYPE.DISCRETE:\\n bounds[idx] = (0, len(param['values']))\\n\\n return bounds\",\n \"def build_constraints_boundaries(self):\\n\\n # Trapezoidal and Hermite-Simpson methods can't compute\\n # defects at the last node contrary to pseudospectral methods\\n coll_method = self.options['tr_method'] in [\\n 'trapezoidal', 'hermite-simpson']\\n n_nodes = self.problem.prm['n_nodes'] - \\\\\\n 1 if coll_method else self.problem.prm['n_nodes']\\n\\n # Defects lower and upper boundaries\\n defects_low = np.zeros(\\n self.problem.prm['n_states'] * n_nodes)\\n defects_upp = np.zeros(\\n self.problem.prm['n_states'] * n_nodes)\\n\\n # Path lower and upper boundaries\\n path_low = np.hstack([self.problem.low_bnd.path]\\n * (self.problem.prm['n_nodes']))\\n path_upp = np.hstack([self.problem.upp_bnd.path]\\n * (self.problem.prm['n_nodes']))\\n\\n # Events lower and upper boundaries\\n event_low = self.problem.low_bnd.event\\n event_upp = self.problem.upp_bnd.event\\n\\n # Assembly of the lower and upper boundaries vectors\\n low = np.concatenate((defects_low, path_low, event_low))\\n upp = np.concatenate((defects_upp, path_upp, event_upp))\\n\\n return low, upp\",\n \"def _get_bounds(x, y, size):\\n x = np.array(np.atleast_1d(x))\\n y = np.array(np.atleast_1d(y))\\n\\n lower_x = np.rint(x - size[0]/2)\\n lower_y = np.rint(y - size[1]/2)\\n\\n return np.stack((np.stack((lower_x, lower_x + size[0]), axis=1),\\n np.stack((lower_y, lower_y + size[1]), axis=1)), axis=1).astype(int)\",\n \"def bounds2slices(\\n bounds: tuple[int, int, int, int],\\n stride: int | tuple[int, int, tuple[int, int]] = 1,\\n) -> tuple[slice]:\\n if np.size(stride) not in [1, 2]:\\n msg = \\\"Invalid stride shape.\\\"\\n raise ValueError(msg)\\n if np.size(stride) == 1:\\n stride = np.tile(stride, 4)\\n elif np.size(stride) == 2: # pragma: no cover # noqa: PLR2004\\n stride = np.tile(stride, 2)\\n\\n start, stop = np.reshape(bounds, (2, -1)).astype(int)\\n slice_array = np.stack([start[::-1], stop[::-1]], axis=1)\\n return tuple(slice(*x, s) for x, s in zip(slice_array, stride))\",\n \"def get_bounds(self, A: list, c: int) -> (int, int):\\r\\n\\r\\n # This implementation uses two binary search algorithms to find\\r\\n # the upper and lower bound.\\r\\n # First step is to isolate the upper_bound.\\r\\n\\r\\n L = 0\\r\\n R = len(A)\\r\\n while L < R:\\r\\n # Find the middle value\\r\\n m = math.floor((L + R) / 2)\\r\\n v = A[m]\\r\\n\\r\\n # Check if |A[i] - i| < c:\\r\\n if abs(v - m) > c:\\r\\n # This step is important, if we are on a negative number\\r\\n # We need to move right instead of left.\\r\\n if v < 0 or (v - m) < 0:\\r\\n L = m + 1\\r\\n else:\\r\\n # Else, we need to move towards the left.\\r\\n R = m\\r\\n else:\\r\\n # If it matches the condition, move the left up because we're\\r\\n # going towards the lowest number.\\r\\n L = m + 1\\r\\n upper_bound = R\\r\\n\\r\\n # Now that we have the upper bound, we only need to\\r\\n # Binary search for the lower bound between index 0 and upper_bound.\\r\\n L = 0\\r\\n R = upper_bound\\r\\n while L < R:\\r\\n # find the middle\\r\\n m = math.floor((L + R) / 2)\\r\\n if abs(A[m] - m) > c:\\r\\n # If it's greater, move the left up.\\r\\n L = m + 1\\r\\n else:\\r\\n # Else, move the right down.\\r\\n R = m\\r\\n\\r\\n # Finally we have the lower bound.\\r\\n lower_bound = L\\r\\n\\r\\n # Return the lower bound and the upper bound index\\r\\n # Note the -1 because the upper bound will give the\\r\\n # size of the array in worst case.\\r\\n return lower_bound, upper_bound - 1\",\n \"def parameter_bounds(self):\\n for name, bound in self.named_parameter_bounds():\\n yield bound\",\n \"def cfdGetBoundaryElementsSubArrayForBoundaryPatch(self):\\r\\n\\r\\n for iBPatch, theBCInfo in self.cfdBoundaryPatchesArray.items():\\r\\n \\r\\n startBElement=self.numberOfElements+self.cfdBoundaryPatchesArray[iBPatch]['startFaceIndex']-self.numberOfInteriorFaces\\r\\n endBElement=startBElement+self.cfdBoundaryPatchesArray[iBPatch]['numberOfBFaces']\\r\\n \\r\\n self.cfdBoundaryPatchesArray[iBPatch]['iBElements']=list(range(int(startBElement),int(endBElement)))\",\n \"def calc_bounds(roi: np.ndarray) -> Dict[int, BoundInfo]:\\n try:\\n min_bounds, max_bounds = calc_bounds(roi)\\n return {\\n num: BoundInfo(lower=lower, upper=upper)\\n for num, (lower, upper) in enumerate(zip(min_bounds, max_bounds))\\n if num != 0 and upper[0] != -1\\n }\\n except KeyError:\\n bound_info = {}\\n points = np.nonzero(roi)\\n comp_num = roi[points]\\n point_dict = defaultdict(list)\\n for num, point in zip(comp_num, np.transpose(points)):\\n point_dict[num].append(point)\\n for num, points_for_num in point_dict.items():\\n lower = np.min(points_for_num, 0)\\n upper = np.max(points_for_num, 0)\\n bound_info[num] = BoundInfo(lower=lower, upper=upper)\\n return bound_info\",\n \"def test_calculate_crow_bounds_shape_parameter_type1(self):\\n\\n _bounds = calculate_crow_bounds(22, 620.0, 0.4239, 0.6142, 0.9, 1, 1)\\n self.assertAlmostEqual(_bounds[0], 0.4356064)\\n self.assertAlmostEqual(_bounds[1], 0.8844610)\",\n \"def _get_i_bnds(self, wave_bounds=None):\\n\\n wave_grid = self.wave_grid\\n i_bounds = self.i_bounds\\n\\n # Check if wave_bounds given\\n if wave_bounds is None:\\n wave_bounds = []\\n for i in range(self.n_orders):\\n wave = self.wave_map[i][~self.mask_ord[i]]\\n wave_bounds.append([wave.min(), wave.max()])\\n\\n # What we need is the boundary position\\n # on the wavelength grid.\\n i_bnds_new = []\\n for bounds, i_bnds in zip(wave_bounds, i_bounds):\\n\\n a = np.min(np.where(wave_grid >= bounds[0])[0])\\n b = np.max(np.where(wave_grid <= bounds[1])[0]) + 1\\n\\n # Take the most restrictive bound\\n a = np.maximum(a, i_bnds[0])\\n b = np.minimum(b, i_bnds[1])\\n\\n # Keep value\\n i_bnds_new.append([a, b])\\n\\n return i_bnds_new\",\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 calc_bounds(segmentation: np.ndarray) -> Dict[int, BoundInfo]:\\n bound_info = {}\\n count = np.max(segmentation)\\n for i in range(1, count + 1):\\n component = np.array(segmentation == i)\\n if np.any(component):\\n points = np.nonzero(component)\\n lower = np.min(points, 1)\\n upper = np.max(points, 1)\\n bound_info[i] = BoundInfo(lower=lower, upper=upper)\\n return bound_info\",\n \"def get_bounds(self, t_index):\\n mean = self.get_mean(t_index)\\n std = self.get_std()\\n return mean - std, mean + std\",\n \"def getBounds(self, nStates, nParams):\\n raise NotImplementedError(\\n \\\"bounds have not been implemented for this Experiment\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.83687973","0.7650801","0.7564731","0.7493438","0.74723506","0.7434169","0.7281272","0.72708595","0.69236696","0.6754937","0.67351925","0.6625706","0.6625706","0.6625706","0.6625706","0.6625706","0.6625706","0.6625706","0.6625706","0.64139324","0.6381346","0.6343208","0.6342877","0.63336277","0.630237","0.6283226","0.6239908","0.6231917","0.62097055","0.61555845","0.6113585","0.60982466","0.60787934","0.6064894","0.5998576","0.5988536","0.5979846","0.5954305","0.59228534","0.59029204","0.5902284","0.58978885","0.5889266","0.5886108","0.58769995","0.5862163","0.58510786","0.58429825","0.5828907","0.5822036","0.58102775","0.5800296","0.57666147","0.5749157","0.5748543","0.5748312","0.5742578","0.5741421","0.5728013","0.5708123","0.56936216","0.5685041","0.56828326","0.56798697","0.56750864","0.5662254","0.56488305","0.5643827","0.5643514","0.5626695","0.55913544","0.55876964","0.5576961","0.55639046","0.5558375","0.55421185","0.55341077","0.5531512","0.552808","0.5522321","0.5517698","0.55170846","0.55169773","0.5516831","0.5513822","0.55091596","0.54957247","0.5493045","0.54778975","0.546388","0.5451354","0.5443191","0.54415876","0.54361033","0.54326105","0.54307544","0.54242617","0.5421646","0.5418403","0.54091036"],"string":"[\n \"0.83687973\",\n \"0.7650801\",\n \"0.7564731\",\n \"0.7493438\",\n \"0.74723506\",\n \"0.7434169\",\n \"0.7281272\",\n \"0.72708595\",\n \"0.69236696\",\n \"0.6754937\",\n \"0.67351925\",\n \"0.6625706\",\n \"0.6625706\",\n \"0.6625706\",\n \"0.6625706\",\n \"0.6625706\",\n \"0.6625706\",\n \"0.6625706\",\n \"0.6625706\",\n \"0.64139324\",\n \"0.6381346\",\n \"0.6343208\",\n \"0.6342877\",\n \"0.63336277\",\n \"0.630237\",\n \"0.6283226\",\n \"0.6239908\",\n \"0.6231917\",\n \"0.62097055\",\n \"0.61555845\",\n \"0.6113585\",\n \"0.60982466\",\n \"0.60787934\",\n \"0.6064894\",\n \"0.5998576\",\n \"0.5988536\",\n \"0.5979846\",\n \"0.5954305\",\n \"0.59228534\",\n \"0.59029204\",\n \"0.5902284\",\n \"0.58978885\",\n \"0.5889266\",\n \"0.5886108\",\n \"0.58769995\",\n \"0.5862163\",\n \"0.58510786\",\n \"0.58429825\",\n \"0.5828907\",\n \"0.5822036\",\n \"0.58102775\",\n \"0.5800296\",\n \"0.57666147\",\n \"0.5749157\",\n \"0.5748543\",\n \"0.5748312\",\n \"0.5742578\",\n \"0.5741421\",\n \"0.5728013\",\n \"0.5708123\",\n \"0.56936216\",\n \"0.5685041\",\n \"0.56828326\",\n \"0.56798697\",\n \"0.56750864\",\n \"0.5662254\",\n \"0.56488305\",\n \"0.5643827\",\n \"0.5643514\",\n \"0.5626695\",\n \"0.55913544\",\n \"0.55876964\",\n \"0.5576961\",\n \"0.55639046\",\n \"0.5558375\",\n \"0.55421185\",\n \"0.55341077\",\n \"0.5531512\",\n \"0.552808\",\n \"0.5522321\",\n \"0.5517698\",\n \"0.55170846\",\n \"0.55169773\",\n \"0.5516831\",\n \"0.5513822\",\n \"0.55091596\",\n \"0.54957247\",\n \"0.5493045\",\n \"0.54778975\",\n \"0.546388\",\n \"0.5451354\",\n \"0.5443191\",\n \"0.54415876\",\n \"0.54361033\",\n \"0.54326105\",\n \"0.54307544\",\n \"0.54242617\",\n \"0.5421646\",\n \"0.5418403\",\n \"0.54091036\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80538183"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11788,"cells":{"query":{"kind":"string","value":"Obtains bounds information for a slice of the variables. getvarboundslice(self,first_,last_,bk_,bl_,bu_)"},"document":{"kind":"string","value":"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"},"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 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 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 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 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 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 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 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 get_bounds(self):\n return ([self.t_min] * self.dim,[self.t_max] * self.dim)","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 get_bounds():\n bounds = [\n (0.1, 0.5), # Omega_m\n (0.05, 0.15) # beta\n ]\n return np.array(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 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 get_bounds():\n return [0.00], [1.00]","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def GetBounds(self):\n ...","def bounds(self, pos):","def get_bounds(self):\n raise Exception(\"Non-implemented base class method.\")","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 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 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 bounds(self):\n return self._bboxes[0][0] #TODO: merge all coverages","def fetchbounds(self):\n pnts = [x for x in [self.out_start, self.start, self.in_start, \\\n self.in_end, self.end, self.out_end] \\\n if x is not None]\n return min(pnts), max(pnts)","def bounds(self): # -> tuple[()]:\n ...","def bounds(self):\n if self.change_dimensionality:\n return [self._bounds[0]] * self.N\n else:\n return self._bounds","def bounds(self):\n return self._bboxes[0][0] #TODO: merge all coverages","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 get_bounds(self):\n log.debug(str(inspect.stack()[1][3]) + \"--> OC.get_bounds()\")\n\n # TODO: Move the operation out of here.\n\n xmin = Inf\n ymin = Inf\n xmax = -Inf\n ymax = -Inf\n\n # for obj in self.object_list:\n for obj in self.get_list():\n try:\n gxmin, gymin, gxmax, gymax = obj.bounds()\n xmin = min([xmin, gxmin])\n ymin = min([ymin, gymin])\n xmax = max([xmax, gxmax])\n ymax = max([ymax, gymax])\n except Exception as e:\n log.warning(\"DEV WARNING: Tried to get bounds of empty geometry. %s\" % str(e))\n\n return [xmin, ymin, xmax, ymax]","def GetBounds(self, p_int, p_int_1, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\n ...","def get_bounds(self, channel_indx=None):\n if channel_indx is None:\n upper_bound = self.upper_bound.reshape(self.batch_size, -1)\n lower_bound = self.lower_bound.reshape(self.batch_size, -1)\n else:\n upper_bound = self.upper_bound[:, channel_indx, :].reshape(\n self.batch_size, -1\n )\n lower_bound = self.lower_bound[:, channel_indx, :].reshape(\n self.batch_size, -1\n )\n return upper_bound, lower_bound","def bounds(self):\n return self.substrates.bounds","def bounds(self):\n return self.xmin, self.xmax, self.ymin, self.ymax","def get_bounds(self, t_index):\n mean = self.get_mean(t_index)\n std = self.get_std()\n return mean - std, mean + std","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 bounds(self, start=None, finish=None):\n lower = start if start is not None else self.limits[0]\n upper = finish if finish is not None else self.limits[1]\n\n lower = lower + self.offsets[0]\n upper = upper + self.offsets[1]\n\n return (lower, upper)","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 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 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 get_bounds(self):\r\n left, bottom, front = 10000, 10000, 10000\r\n right, top, back = -10000, -10000, -10000\r\n for b in self.buf:\r\n for v in b.vertices:\r\n if v[0] < left:\r\n left = v[0]\r\n if v[0] > right:\r\n right = v[0]\r\n if v[1] < bottom:\r\n bottom = v[1]\r\n if v[1] > top:\r\n top = v[1]\r\n if v[2] < front:\r\n front = v[2]\r\n if v[2] > back:\r\n back = v[2]\r\n\r\n return (left, bottom, front, right, top, back)","def get_bounds(self):\n x_max = self.data['x'].max()\n y_max = self.data['y'].max()\n z_max = self.data['z'].max()\n print(\"x={}; y={}; z={}\".format(x_max, y_max, z_max))\n return (x_max, y_max, z_max)","def get_bounds(self):\n bottom_right = np.asarray([self.coords[k][0] for k in range(self.dim)])\n upper_left = np.asarray([self.coords[k][-1] for k in range(self.dim)])\n return bottom_right, upper_left","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):\n return self._bounds","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 get_raw_bounds(self) -> [Vector, Vector]:\n\t\tverts = np.array([v.co for mesh in self._meshes for v in mesh.data.vertices])\n\t\tbbox_min = Vector([*np.min(verts, axis=0)])\n\t\tbbox_max = Vector([*np.max(verts, axis=0)])\n\t\treturn bbox_min, bbox_max","def bounds(self):\n b = []\n\n for dim in self.dimensions:\n if dim.size == 1:\n b.append(dim.bounds)\n else:\n b.extend(dim.bounds)\n\n return b","def get_params_bounds(self) -> np.array:\n pass","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 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 compute_bounds(self, space):\n bounds = np.zeros((len(space), 2))\n\n for idx, param in enumerate(space):\n\n if TYPE[param[\"type\"]] is TYPE.FLOAT or \\\n TYPE[param[\"type\"]] is TYPE.INTEGER:\n bounds[idx] = (param[\"min\"], param[\"max\"])\n\n elif TYPE[param[\"type\"]] is TYPE.DISCRETE or \\\n TYPE[param[\"type\"]] is TYPE.DISCRETE:\n bounds[idx] = (0, len(param['values']))\n\n return bounds","def domain_bounds(self):\n return self._xmin, self._xmax, self._ymin, self._ymax, self._zmin, self._zmax","def bounds(self):\n return self.GetBounds()","def getBounds(self, nStates, nParams):\n raise NotImplementedError(\n \"bounds have not been implemented for this Experiment\")","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 GetIndexBounds(self, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\n ...","def axis_bounds(pc, axis):\n try:\n bounds = pc.bounds\n except AttributeError:\n bounds = pc\n \n return tuple([getattr(bounds, b + axis) for b in ('min', 'max')])","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 bounds(self):\n return self.min_col, self.min_row, self.max_col, self.max_row","def bounds(self):\n x,y,z = self._arr\n try:\n return Bounds(x.min(), y.min(), z.min(),\n x.max(), y.max(), z.max())\n except ValueError:\n raise simulocloud.exceptions.EmptyPointCloud(\n \"len 0 PointCloud has no Bounds\")","def _compute_bounds(self, axis, view):\n return None","def _get_las_bounds(fpath):\n with laspy.file.File(fpath) as f:\n return Bounds(*(f.header.min + f.header.max))","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 bounds(self):\n return self.kernel.bounds","def parameter_bounds(self):\n for name, bound in self.named_parameter_bounds():\n yield bound","def _parse_bounds(self, bounds):\n try:\n if bounds == None:\n return None\n elif not isinstance(bounds[0], tuple):\n if len(bounds)==2:\n return [tuple(bounds) for i in range(self.params.size)]\n else:\n raise ValueError\n elif len(bounds) == self.params.size:\n if all([len(b)==2 for b in bounds]):\n return bounds\n else:\n raise ValueError\n else:\n raise ValueError\n except:\n raise ValueError(\"'bounds' should be a list of two elements \"\n \"[lb, ub], or a list of the same length as the number of \"\n \"parameters where each element is a tuple (lb, ub)\")","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 bounds(self, resids: NDArray) -> List[Tuple[float, float]]:","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 variable_range(examples, var):\n if var[1] == 'd':\n range = set()\n for datum in examples:\n range.add(datum[var[0]])\n return range\n else:\n range_min, range_max = 0, 0\n for datum in examples:\n data_val = float(datum[var[0]])\n range_min, range_max = min(range_min, data_val), max(range_max, data_val)\n return (range_min, range_max)","def getSampleBounds(args, matrix):\n bounds = matrix.parameters['sample_boundaries']\n if args.samples is None:\n return np.arange(0, matrix.matrix.matrix.shape[1])\n else:\n o = list()\n for sample in args.samples:\n if sample not in matrix.matrix.sample_labels:\n sys.exit(\"Error: '{0}' is not a valid sample\\n\".format(sample))\n idx = matrix.matrix.sample_labels.index(sample)\n o.extend(range(bounds[idx], bounds[idx + 1]))\n return o","def GetBounds(self, vtkAMRBox, , , p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\n ...","def get_bounds(dimension):\n bounds = np.tile(np.nan, [dimension, 2])\n bounds[:, 0], bounds[:, 1] = -10, 10\n return bounds","def __nc_geo_bounds(self, extent, data_sel):\n indx = None\n if extent is not None:\n if len(extent) != 4:\n raise ValueError('parameter extent must have 4 elements')\n\n lats = self.fid['/instrument/latitude_center'][:].reshape(\n self.scanline, self.ground_pixel)\n lons = self.fid['/instrument/longitude_center'][:].reshape(\n self.scanline, self.ground_pixel)\n\n indx = np.where((lons >= extent[0]) & (lons <= extent[1])\n & (lats >= extent[2]) & (lats <= extent[3]))\n data_sel = np.s_[indx[0].min():indx[0].max(),\n indx[1].min():indx[1].max()]\n\n gid = self.fid['/instrument']\n lat_bounds = gid['latitude_corners'][:].data.reshape(\n self.scanline, self.ground_pixel, 4)\n lon_bounds = gid['longitude_corners'][:].data.reshape(\n self.scanline, self.ground_pixel, 4)\n if data_sel is not None:\n lat_bounds = lat_bounds[data_sel + (slice(None),)]\n lon_bounds = lon_bounds[data_sel + (slice(None),)]\n\n return (data_sel, lon_bounds, lat_bounds)","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 _getBounds(self, request):\n start = _getBound(request.args, \"start\")\n stop = _getBound(request.args, \"stop\", self._collection.pageSize)\n return start, stop","def get_bounds():\n lower_bound = 0\n upper_bound = input(\"Please enter a whole number: \")\n domain = [lower_bound, upper_bound]\n return domain","def bounds(self):\n \n return self.osmdb.bounds()","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 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 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 _process_bounds(self, param_name, bounds):\n l = self.n_elements_one_param(param_name)\n if(ut.is_iter(bounds)):\n if(len(bounds) == l):\n if(l!=2):\n res = [self._process_individual_bound(b) for b in bounds]\n else:\n try:\n res = [self._process_individual_bound(b) for b in bounds]\n except:\n res_one = self._process_individual_bound(bounds)\n res = [res_one for _ in range(l)]\n\n elif(len(bounds) == 2):\n # slightly tricky as l can be = 2\n res_one = self._process_individual_bound(bounds)\n res = [res_one for _ in range(l)]\n\n else:\n raise ValueError('Bounds length (%s) is not recognized. '% (len(bounds)))\n else:\n res_one = self._process_individual_bound(bounds)\n res = [res_one for _ in range(l)]\n \n return res","def bounds(self):\n return (\n self.x, self.y,\n self.x, self.y\n )","def get_physical_bounds(dim):\n dim = basename(dim)\n\n if dim == \"coszen\":\n trunc_low = -1.\n trunc_high = 1.\n\n elif dim == \"energy\":\n trunc_low = 0.\n trunc_high = None\n\n elif dim == \"azimuth\":\n trunc_low = 0.\n trunc_high = 2*np.pi\n\n else:\n raise ValueError(\"No physical bounds for dimension '%s' available.\"%dim)\n\n return trunc_low, trunc_high","def get_bounds(shakefile, parameter='pga', threshold=2.0):\n shakemap = ShakeGrid.load(shakefile, adjust='res')\n if parameter == 'pga':\n vals = shakemap.getLayer('pga')\n elif parameter == 'pgv':\n vals = shakemap.getLayer('pgv')\n else:\n raise Exception('parameter not valid')\n xmin, xmax, ymin, ymax = vals.getBounds()\n lons = np.linspace(xmin, xmax, vals.getGeoDict().nx)\n lats = np.linspace(ymax, ymin, vals.getGeoDict().ny)\n row, col = np.where(vals.getData() > float(threshold))\n lonmin = lons[col].min()\n lonmax = lons[col].max()\n latmin = lats[row].min()\n latmax = lats[row].max()\n\n # dummy fillers, only really care about bounds\n boundaries1 = {'dx': 100, 'dy': 100., 'nx': 100., 'ny': 100}\n\n if xmin < lonmin:\n boundaries1['xmin'] = lonmin\n else:\n boundaries1['xmin'] = xmin\n if xmax > lonmax:\n boundaries1['xmax'] = lonmax\n else:\n boundaries1['xmax'] = xmax\n if ymin < latmin:\n boundaries1['ymin'] = latmin\n else:\n boundaries1['ymin'] = ymin\n if ymax > latmax:\n boundaries1['ymax'] = latmax\n else:\n boundaries1['ymax'] = ymax\n\n return boundaries1","def get_bounds(f, lb=0, ub=None):\r\n lb_idx = np.searchsorted(f, lb, 'left')\r\n if ub == None:\r\n ub_idx = len(f)\r\n else:\r\n ub_idx = np.searchsorted(f, ub, 'right')\r\n\r\n return lb_idx, ub_idx","def bounds(self):\n return [(2, None)]","def internal_bounds(self) -> tuple[float, float, float, float]:\n xres, yres = self.res\n w, s, e, n = self.bounds\n y0, y1 = (n, s) if yres < 0 else (s, n)\n x0, x1 = (e, w) if xres < 0 else (w, e)\n return x0, y0, x1, y1","def bounds(self):\n\n if self.size == 0:\n lo, hi = np.nan, np.nan\n elif self.is_monotonic:\n lo, hi = sorted([self.coordinates[0], self.coordinates[-1]])\n elif self.dtype is np.datetime64:\n lo, hi = np.min(self.coordinates), np.max(self.coordinates)\n else:\n lo, hi = np.nanmin(self.coordinates), np.nanmax(self.coordinates)\n\n return lo, hi","def get_bounds(shakefile, parameter='pga', threshold=2):\n from mapio.shake import ShakeGrid\n\n shakemap = ShakeGrid.load(shakefile, adjust='res')\n if parameter == 'pga':\n vals = shakemap.getLayer('pga')\n elif parameter == 'pgv':\n vals = shakemap.getLayer('pgv')\n else:\n raise Exception('parameter not valid')\n xmin, xmax, ymin, ymax = vals.getBounds()\n lons = np.linspace(xmin, xmax, vals.getGeoDict().nx)\n lats = np.linspace(ymax, ymin, vals.getGeoDict().ny) # backwards so it plots right\n row, col = np.where(vals.getData() > float(threshold))\n lonmin = lons[col].min()\n lonmax = lons[col].max()\n latmin = lats[row].min()\n latmax = lats[row].max()\n\n boundaries1 = {'dx': 100, 'dy': 100., 'nx': 100., 'ny': 100} # dummy fillers, only really care about bounds\n if xmin < lonmin:\n boundaries1['xmin'] = lonmin\n else:\n boundaries1['xmin'] = xmin\n if xmax > lonmax:\n boundaries1['xmax'] = lonmax\n else:\n boundaries1['xmax'] = xmax\n if ymin < latmin:\n boundaries1['ymin'] = latmin\n else:\n boundaries1['ymin'] = ymin\n if ymax > latmax:\n boundaries1['ymax'] = latmax\n else:\n boundaries1['ymax'] = ymax\n\n return boundaries1","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 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 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 __getslice__(self,i,j):\n return self.x[i:j]"],"string":"[\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 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 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 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 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 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 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 get_bounds(self):\\n return ([self.t_min] * self.dim,[self.t_max] * self.dim)\",\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 get_bounds():\\n bounds = [\\n (0.1, 0.5), # Omega_m\\n (0.05, 0.15) # beta\\n ]\\n return np.array(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 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 get_bounds():\\n return [0.00], [1.00]\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def GetBounds(self):\\n ...\",\n \"def bounds(self, pos):\",\n \"def get_bounds(self):\\n raise Exception(\\\"Non-implemented base class method.\\\")\",\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 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 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 bounds(self):\\n return self._bboxes[0][0] #TODO: merge all coverages\",\n \"def fetchbounds(self):\\n pnts = [x for x in [self.out_start, self.start, self.in_start, \\\\\\n self.in_end, self.end, self.out_end] \\\\\\n if x is not None]\\n return min(pnts), max(pnts)\",\n \"def bounds(self): # -> tuple[()]:\\n ...\",\n \"def bounds(self):\\n if self.change_dimensionality:\\n return [self._bounds[0]] * self.N\\n else:\\n return self._bounds\",\n \"def bounds(self):\\n return self._bboxes[0][0] #TODO: merge all coverages\",\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 get_bounds(self):\\n log.debug(str(inspect.stack()[1][3]) + \\\"--> OC.get_bounds()\\\")\\n\\n # TODO: Move the operation out of here.\\n\\n xmin = Inf\\n ymin = Inf\\n xmax = -Inf\\n ymax = -Inf\\n\\n # for obj in self.object_list:\\n for obj in self.get_list():\\n try:\\n gxmin, gymin, gxmax, gymax = obj.bounds()\\n xmin = min([xmin, gxmin])\\n ymin = min([ymin, gymin])\\n xmax = max([xmax, gxmax])\\n ymax = max([ymax, gymax])\\n except Exception as e:\\n log.warning(\\\"DEV WARNING: Tried to get bounds of empty geometry. %s\\\" % str(e))\\n\\n return [xmin, ymin, xmax, ymax]\",\n \"def GetBounds(self, p_int, p_int_1, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def get_bounds(self, channel_indx=None):\\n if channel_indx is None:\\n upper_bound = self.upper_bound.reshape(self.batch_size, -1)\\n lower_bound = self.lower_bound.reshape(self.batch_size, -1)\\n else:\\n upper_bound = self.upper_bound[:, channel_indx, :].reshape(\\n self.batch_size, -1\\n )\\n lower_bound = self.lower_bound[:, channel_indx, :].reshape(\\n self.batch_size, -1\\n )\\n return upper_bound, lower_bound\",\n \"def bounds(self):\\n return self.substrates.bounds\",\n \"def bounds(self):\\n return self.xmin, self.xmax, self.ymin, self.ymax\",\n \"def get_bounds(self, t_index):\\n mean = self.get_mean(t_index)\\n std = self.get_std()\\n return mean - std, mean + std\",\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 bounds(self, start=None, finish=None):\\n lower = start if start is not None else self.limits[0]\\n upper = finish if finish is not None else self.limits[1]\\n\\n lower = lower + self.offsets[0]\\n upper = upper + self.offsets[1]\\n\\n return (lower, upper)\",\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 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 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 get_bounds(self):\\r\\n left, bottom, front = 10000, 10000, 10000\\r\\n right, top, back = -10000, -10000, -10000\\r\\n for b in self.buf:\\r\\n for v in b.vertices:\\r\\n if v[0] < left:\\r\\n left = v[0]\\r\\n if v[0] > right:\\r\\n right = v[0]\\r\\n if v[1] < bottom:\\r\\n bottom = v[1]\\r\\n if v[1] > top:\\r\\n top = v[1]\\r\\n if v[2] < front:\\r\\n front = v[2]\\r\\n if v[2] > back:\\r\\n back = v[2]\\r\\n\\r\\n return (left, bottom, front, right, top, back)\",\n \"def get_bounds(self):\\n x_max = self.data['x'].max()\\n y_max = self.data['y'].max()\\n z_max = self.data['z'].max()\\n print(\\\"x={}; y={}; z={}\\\".format(x_max, y_max, z_max))\\n return (x_max, y_max, z_max)\",\n \"def get_bounds(self):\\n bottom_right = np.asarray([self.coords[k][0] for k in range(self.dim)])\\n upper_left = np.asarray([self.coords[k][-1] for k in range(self.dim)])\\n return bottom_right, upper_left\",\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):\\n return self._bounds\",\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 get_raw_bounds(self) -> [Vector, Vector]:\\n\\t\\tverts = np.array([v.co for mesh in self._meshes for v in mesh.data.vertices])\\n\\t\\tbbox_min = Vector([*np.min(verts, axis=0)])\\n\\t\\tbbox_max = Vector([*np.max(verts, axis=0)])\\n\\t\\treturn bbox_min, bbox_max\",\n \"def bounds(self):\\n b = []\\n\\n for dim in self.dimensions:\\n if dim.size == 1:\\n b.append(dim.bounds)\\n else:\\n b.extend(dim.bounds)\\n\\n return b\",\n \"def get_params_bounds(self) -> np.array:\\n pass\",\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 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 compute_bounds(self, space):\\n bounds = np.zeros((len(space), 2))\\n\\n for idx, param in enumerate(space):\\n\\n if TYPE[param[\\\"type\\\"]] is TYPE.FLOAT or \\\\\\n TYPE[param[\\\"type\\\"]] is TYPE.INTEGER:\\n bounds[idx] = (param[\\\"min\\\"], param[\\\"max\\\"])\\n\\n elif TYPE[param[\\\"type\\\"]] is TYPE.DISCRETE or \\\\\\n TYPE[param[\\\"type\\\"]] is TYPE.DISCRETE:\\n bounds[idx] = (0, len(param['values']))\\n\\n return bounds\",\n \"def domain_bounds(self):\\n return self._xmin, self._xmax, self._ymin, self._ymax, self._zmin, self._zmax\",\n \"def bounds(self):\\n return self.GetBounds()\",\n \"def getBounds(self, nStates, nParams):\\n raise NotImplementedError(\\n \\\"bounds have not been implemented for this Experiment\\\")\",\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 GetIndexBounds(self, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def axis_bounds(pc, axis):\\n try:\\n bounds = pc.bounds\\n except AttributeError:\\n bounds = pc\\n \\n return tuple([getattr(bounds, b + axis) for b in ('min', 'max')])\",\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 bounds(self):\\n return self.min_col, self.min_row, self.max_col, self.max_row\",\n \"def bounds(self):\\n x,y,z = self._arr\\n try:\\n return Bounds(x.min(), y.min(), z.min(),\\n x.max(), y.max(), z.max())\\n except ValueError:\\n raise simulocloud.exceptions.EmptyPointCloud(\\n \\\"len 0 PointCloud has no Bounds\\\")\",\n \"def _compute_bounds(self, axis, view):\\n return None\",\n \"def _get_las_bounds(fpath):\\n with laspy.file.File(fpath) as f:\\n return Bounds(*(f.header.min + f.header.max))\",\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 bounds(self):\\n return self.kernel.bounds\",\n \"def parameter_bounds(self):\\n for name, bound in self.named_parameter_bounds():\\n yield bound\",\n \"def _parse_bounds(self, bounds):\\n try:\\n if bounds == None:\\n return None\\n elif not isinstance(bounds[0], tuple):\\n if len(bounds)==2:\\n return [tuple(bounds) for i in range(self.params.size)]\\n else:\\n raise ValueError\\n elif len(bounds) == self.params.size:\\n if all([len(b)==2 for b in bounds]):\\n return bounds\\n else:\\n raise ValueError\\n else:\\n raise ValueError\\n except:\\n raise ValueError(\\\"'bounds' should be a list of two elements \\\"\\n \\\"[lb, ub], or a list of the same length as the number of \\\"\\n \\\"parameters where each element is a tuple (lb, ub)\\\")\",\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 bounds(self, resids: NDArray) -> List[Tuple[float, float]]:\",\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 variable_range(examples, var):\\n if var[1] == 'd':\\n range = set()\\n for datum in examples:\\n range.add(datum[var[0]])\\n return range\\n else:\\n range_min, range_max = 0, 0\\n for datum in examples:\\n data_val = float(datum[var[0]])\\n range_min, range_max = min(range_min, data_val), max(range_max, data_val)\\n return (range_min, range_max)\",\n \"def getSampleBounds(args, matrix):\\n bounds = matrix.parameters['sample_boundaries']\\n if args.samples is None:\\n return np.arange(0, matrix.matrix.matrix.shape[1])\\n else:\\n o = list()\\n for sample in args.samples:\\n if sample not in matrix.matrix.sample_labels:\\n sys.exit(\\\"Error: '{0}' is not a valid sample\\\\n\\\".format(sample))\\n idx = matrix.matrix.sample_labels.index(sample)\\n o.extend(range(bounds[idx], bounds[idx + 1]))\\n return o\",\n \"def GetBounds(self, vtkAMRBox, , , p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def get_bounds(dimension):\\n bounds = np.tile(np.nan, [dimension, 2])\\n bounds[:, 0], bounds[:, 1] = -10, 10\\n return bounds\",\n \"def __nc_geo_bounds(self, extent, data_sel):\\n indx = None\\n if extent is not None:\\n if len(extent) != 4:\\n raise ValueError('parameter extent must have 4 elements')\\n\\n lats = self.fid['/instrument/latitude_center'][:].reshape(\\n self.scanline, self.ground_pixel)\\n lons = self.fid['/instrument/longitude_center'][:].reshape(\\n self.scanline, self.ground_pixel)\\n\\n indx = np.where((lons >= extent[0]) & (lons <= extent[1])\\n & (lats >= extent[2]) & (lats <= extent[3]))\\n data_sel = np.s_[indx[0].min():indx[0].max(),\\n indx[1].min():indx[1].max()]\\n\\n gid = self.fid['/instrument']\\n lat_bounds = gid['latitude_corners'][:].data.reshape(\\n self.scanline, self.ground_pixel, 4)\\n lon_bounds = gid['longitude_corners'][:].data.reshape(\\n self.scanline, self.ground_pixel, 4)\\n if data_sel is not None:\\n lat_bounds = lat_bounds[data_sel + (slice(None),)]\\n lon_bounds = lon_bounds[data_sel + (slice(None),)]\\n\\n return (data_sel, lon_bounds, lat_bounds)\",\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 _getBounds(self, request):\\n start = _getBound(request.args, \\\"start\\\")\\n stop = _getBound(request.args, \\\"stop\\\", self._collection.pageSize)\\n return start, stop\",\n \"def get_bounds():\\n lower_bound = 0\\n upper_bound = input(\\\"Please enter a whole number: \\\")\\n domain = [lower_bound, upper_bound]\\n return domain\",\n \"def bounds(self):\\n \\n return self.osmdb.bounds()\",\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 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 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 _process_bounds(self, param_name, bounds):\\n l = self.n_elements_one_param(param_name)\\n if(ut.is_iter(bounds)):\\n if(len(bounds) == l):\\n if(l!=2):\\n res = [self._process_individual_bound(b) for b in bounds]\\n else:\\n try:\\n res = [self._process_individual_bound(b) for b in bounds]\\n except:\\n res_one = self._process_individual_bound(bounds)\\n res = [res_one for _ in range(l)]\\n\\n elif(len(bounds) == 2):\\n # slightly tricky as l can be = 2\\n res_one = self._process_individual_bound(bounds)\\n res = [res_one for _ in range(l)]\\n\\n else:\\n raise ValueError('Bounds length (%s) is not recognized. '% (len(bounds)))\\n else:\\n res_one = self._process_individual_bound(bounds)\\n res = [res_one for _ in range(l)]\\n \\n return res\",\n \"def bounds(self):\\n return (\\n self.x, self.y,\\n self.x, self.y\\n )\",\n \"def get_physical_bounds(dim):\\n dim = basename(dim)\\n\\n if dim == \\\"coszen\\\":\\n trunc_low = -1.\\n trunc_high = 1.\\n\\n elif dim == \\\"energy\\\":\\n trunc_low = 0.\\n trunc_high = None\\n\\n elif dim == \\\"azimuth\\\":\\n trunc_low = 0.\\n trunc_high = 2*np.pi\\n\\n else:\\n raise ValueError(\\\"No physical bounds for dimension '%s' available.\\\"%dim)\\n\\n return trunc_low, trunc_high\",\n \"def get_bounds(shakefile, parameter='pga', threshold=2.0):\\n shakemap = ShakeGrid.load(shakefile, adjust='res')\\n if parameter == 'pga':\\n vals = shakemap.getLayer('pga')\\n elif parameter == 'pgv':\\n vals = shakemap.getLayer('pgv')\\n else:\\n raise Exception('parameter not valid')\\n xmin, xmax, ymin, ymax = vals.getBounds()\\n lons = np.linspace(xmin, xmax, vals.getGeoDict().nx)\\n lats = np.linspace(ymax, ymin, vals.getGeoDict().ny)\\n row, col = np.where(vals.getData() > float(threshold))\\n lonmin = lons[col].min()\\n lonmax = lons[col].max()\\n latmin = lats[row].min()\\n latmax = lats[row].max()\\n\\n # dummy fillers, only really care about bounds\\n boundaries1 = {'dx': 100, 'dy': 100., 'nx': 100., 'ny': 100}\\n\\n if xmin < lonmin:\\n boundaries1['xmin'] = lonmin\\n else:\\n boundaries1['xmin'] = xmin\\n if xmax > lonmax:\\n boundaries1['xmax'] = lonmax\\n else:\\n boundaries1['xmax'] = xmax\\n if ymin < latmin:\\n boundaries1['ymin'] = latmin\\n else:\\n boundaries1['ymin'] = ymin\\n if ymax > latmax:\\n boundaries1['ymax'] = latmax\\n else:\\n boundaries1['ymax'] = ymax\\n\\n return boundaries1\",\n \"def get_bounds(f, lb=0, ub=None):\\r\\n lb_idx = np.searchsorted(f, lb, 'left')\\r\\n if ub == None:\\r\\n ub_idx = len(f)\\r\\n else:\\r\\n ub_idx = np.searchsorted(f, ub, 'right')\\r\\n\\r\\n return lb_idx, ub_idx\",\n \"def bounds(self):\\n return [(2, None)]\",\n \"def internal_bounds(self) -> tuple[float, float, float, float]:\\n xres, yres = self.res\\n w, s, e, n = self.bounds\\n y0, y1 = (n, s) if yres < 0 else (s, n)\\n x0, x1 = (e, w) if xres < 0 else (w, e)\\n return x0, y0, x1, y1\",\n \"def bounds(self):\\n\\n if self.size == 0:\\n lo, hi = np.nan, np.nan\\n elif self.is_monotonic:\\n lo, hi = sorted([self.coordinates[0], self.coordinates[-1]])\\n elif self.dtype is np.datetime64:\\n lo, hi = np.min(self.coordinates), np.max(self.coordinates)\\n else:\\n lo, hi = np.nanmin(self.coordinates), np.nanmax(self.coordinates)\\n\\n return lo, hi\",\n \"def get_bounds(shakefile, parameter='pga', threshold=2):\\n from mapio.shake import ShakeGrid\\n\\n shakemap = ShakeGrid.load(shakefile, adjust='res')\\n if parameter == 'pga':\\n vals = shakemap.getLayer('pga')\\n elif parameter == 'pgv':\\n vals = shakemap.getLayer('pgv')\\n else:\\n raise Exception('parameter not valid')\\n xmin, xmax, ymin, ymax = vals.getBounds()\\n lons = np.linspace(xmin, xmax, vals.getGeoDict().nx)\\n lats = np.linspace(ymax, ymin, vals.getGeoDict().ny) # backwards so it plots right\\n row, col = np.where(vals.getData() > float(threshold))\\n lonmin = lons[col].min()\\n lonmax = lons[col].max()\\n latmin = lats[row].min()\\n latmax = lats[row].max()\\n\\n boundaries1 = {'dx': 100, 'dy': 100., 'nx': 100., 'ny': 100} # dummy fillers, only really care about bounds\\n if xmin < lonmin:\\n boundaries1['xmin'] = lonmin\\n else:\\n boundaries1['xmin'] = xmin\\n if xmax > lonmax:\\n boundaries1['xmax'] = lonmax\\n else:\\n boundaries1['xmax'] = xmax\\n if ymin < latmin:\\n boundaries1['ymin'] = latmin\\n else:\\n boundaries1['ymin'] = ymin\\n if ymax > latmax:\\n boundaries1['ymax'] = latmax\\n else:\\n boundaries1['ymax'] = ymax\\n\\n return boundaries1\",\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 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 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 __getslice__(self,i,j):\\n return self.x[i:j]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8427269","0.76399803","0.7581138","0.72763824","0.7077902","0.6877357","0.67139685","0.65998864","0.64918834","0.63755333","0.63566226","0.6347929","0.63300616","0.62415","0.6238382","0.62190634","0.62190634","0.62190634","0.62190634","0.62190634","0.62190634","0.62190634","0.62190634","0.61316663","0.6107121","0.61058193","0.6092169","0.60883737","0.6042942","0.60274386","0.6023994","0.59988314","0.5978658","0.5968661","0.5952762","0.5941958","0.5934352","0.593161","0.59252864","0.5923613","0.59229064","0.5916907","0.5903668","0.5902876","0.5871236","0.58558494","0.58493215","0.58412904","0.5830712","0.58072525","0.57956517","0.5787716","0.5785892","0.5757721","0.5757356","0.57503766","0.57384235","0.5738325","0.57176864","0.5706217","0.5669492","0.56646687","0.56570804","0.56550795","0.5655037","0.56431836","0.56171715","0.56063396","0.560034","0.55991155","0.5596555","0.5577612","0.55599135","0.55502945","0.5549012","0.5522083","0.55185044","0.55046296","0.54937106","0.54841065","0.5482936","0.5472924","0.5472611","0.5472243","0.5469359","0.5466046","0.54650736","0.54593223","0.5451791","0.5447194","0.54278386","0.54217446","0.54191417","0.5418646","0.5403056","0.5396103","0.53925335","0.53731483","0.53676575","0.5365481"],"string":"[\n \"0.8427269\",\n \"0.76399803\",\n \"0.7581138\",\n \"0.72763824\",\n \"0.7077902\",\n \"0.6877357\",\n \"0.67139685\",\n \"0.65998864\",\n \"0.64918834\",\n \"0.63755333\",\n \"0.63566226\",\n \"0.6347929\",\n \"0.63300616\",\n \"0.62415\",\n \"0.6238382\",\n \"0.62190634\",\n \"0.62190634\",\n \"0.62190634\",\n \"0.62190634\",\n \"0.62190634\",\n \"0.62190634\",\n \"0.62190634\",\n \"0.62190634\",\n \"0.61316663\",\n \"0.6107121\",\n \"0.61058193\",\n \"0.6092169\",\n \"0.60883737\",\n \"0.6042942\",\n \"0.60274386\",\n \"0.6023994\",\n \"0.59988314\",\n \"0.5978658\",\n \"0.5968661\",\n \"0.5952762\",\n \"0.5941958\",\n \"0.5934352\",\n \"0.593161\",\n \"0.59252864\",\n \"0.5923613\",\n \"0.59229064\",\n \"0.5916907\",\n \"0.5903668\",\n \"0.5902876\",\n \"0.5871236\",\n \"0.58558494\",\n \"0.58493215\",\n \"0.58412904\",\n \"0.5830712\",\n \"0.58072525\",\n \"0.57956517\",\n \"0.5787716\",\n \"0.5785892\",\n \"0.5757721\",\n \"0.5757356\",\n \"0.57503766\",\n \"0.57384235\",\n \"0.5738325\",\n \"0.57176864\",\n \"0.5706217\",\n \"0.5669492\",\n \"0.56646687\",\n \"0.56570804\",\n \"0.56550795\",\n \"0.5655037\",\n \"0.56431836\",\n \"0.56171715\",\n \"0.56063396\",\n \"0.560034\",\n \"0.55991155\",\n \"0.5596555\",\n \"0.5577612\",\n \"0.55599135\",\n \"0.55502945\",\n \"0.5549012\",\n \"0.5522083\",\n \"0.55185044\",\n \"0.55046296\",\n \"0.54937106\",\n \"0.54841065\",\n \"0.5482936\",\n \"0.5472924\",\n \"0.5472611\",\n \"0.5472243\",\n \"0.5469359\",\n \"0.5466046\",\n \"0.54650736\",\n \"0.54593223\",\n \"0.5451791\",\n \"0.5447194\",\n \"0.54278386\",\n \"0.54217446\",\n \"0.54191417\",\n \"0.5418646\",\n \"0.5403056\",\n \"0.5396103\",\n \"0.53925335\",\n \"0.53731483\",\n \"0.53676575\",\n \"0.5365481\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8042054"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11789,"cells":{"query":{"kind":"string","value":"Obtains one objective coefficient. getcj(self,j_)"},"document":{"kind":"string","value":"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)"},"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 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 jac(self):\n return self.unit_jac if self._jac is None else self._jac","def getCoefficient(self):\n return _libsbml.FluxObjective_getCoefficient(self)","def J(self):\n return self._J","def J(self) -> float:\n return self._J","def jaccard_coeff(self):\n a, c, _, b = self.to_ccw()\n return _div(a, a + b + c)","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 jval(self):\n return self.q * self.model.nobs_moms","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 GetJ(self, *args):\n return _table.Table_GetJ(self, *args)","def c(self,j,i_j):\n \"\"\" The index j of the chains goes from 0 to k-1 (where k is the \n number of chains in our decomposition \"\"\"\n assert j < len(self.lcd), \"j must be the index of a chain\"\n \"\"\" The index i_j goes from 0 to len(lcd[j]) this range is one longer\n than the length of the chain because we go from {} to the full chain. \"\"\"\n assert i_j <= self.lcd_dims[j], \"i_j = {}, dims[j] = {}\".format(i_j, self.lcd_dims[j])\n if i_j == 0:\n return None\n else:\n return self.lcd[j][i_j-1]","def objective(self):\n return self._objective","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 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 C_ret(img):\r\n return solveJ(img, 0, tc(img, ignore_ch=2))","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 jacobian(self, c):\n\n raise NotImplementedError","def coefficient(self) -> float:\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)","def jacobian_c(self, x, out=None, **kwargs):\n return empty_matrix(0, self.nx)","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 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 conj(self, o): \n return (o.inv()) * self * o","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 objective(self):\n pass","def convolved_j0(self, _x, delta_x):\n return (\n j0(_x - 0.5 * delta_x) +\n 4.*j0(_x) +\n j0(_x + 0.5 * delta_x)\n ) / 6.","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 jacobian_c(self, x, out=None, **kwargs):\n return self._base_nlp.jacobian_c(x, out=out, **kwargs)","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 coeff(self):\n return self._coeff","def convolved_j1(self, _x, delta_x):\n return (\n j1(_x-0.5*delta_x) +\n 4.*j1(_x) +\n j1(_x+0.5*delta_x)\n )/6.","def objective_val(self):\n return self.m.ObjVal","def jacobian(self, x):\n pass","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 _jz(j, *, dtype=None):\n dtype = dtype or settings.core[\"default_dtype\"] or _data.CSR\n N = int(2*j + 1)\n data = np.array([j-k for k in range(N)], dtype=complex)\n return _data.diag[dtype](data, 0)","def value_comp(self, x: float, j: int) -> complex:\n return np.interp([x], self.x, self.components[j])[0]","def int_convolved_j1(self, _x, delta_x):\n return -(\n j0(_x - 0.5*delta_x) +\n 4.*j0(_x) +\n j0(_x + 0.5*delta_x)\n )/6.","def ckm(i,j):\n if i >= 1 and i <= 3 and j >= 1 and j <= 3:\n return _ckm_abs[i-1, j-1]\n else:\n raise(ValueError('Wrong generation index in CKM matrix: ({},{}).'.format(i,j)))","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 setCoefficient(self, *args):\n return _libsbml.FluxObjective_setCoefficient(self, *args)","def objective(self, x):\n rvs = frozenset(map(frozenset, self._rvs))\n joint = self.construct_joint(x)\n joint = joint.sum(axis=self._others, keepdims=True)\n crv = joint.sum(axis=tuple(flatten(rvs)))\n\n H_crv = h(crv.ravel())\n H = h(joint.ravel()) - H_crv\n\n def I_P(part):\n margs = [ joint.sum(axis=tuple(flatten(rvs - p))) for p in part ]\n a = sum(h(marg.ravel()) - H_crv for marg in margs)\n return (a - H)/(len(part) - 1)\n\n parts = [p for p in partitions(map(frozenset, rvs)) if len(p) > 1]\n\n caekl = min(I_P(p) for p in parts)\n\n return caekl","def get_con_elem0(self, j0):\n return self.je[ self.ie[j0] : self.ie[j0+1] ]","def get_rho_from_J(self, J):\n return J[self.iOmX]/J[0]","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 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 jac_pos(self):\n J = self.sim.data.get_body_jacp(self.end_effector)\n J = J.reshape(3, -1)[:, 0:7].T\n return J","def c(self, i):\n value = self.b(i)\n if i == self.N:\n return value\n else:\n for j in range(i+1, self.N+1):\n value = value - self.a(i,j) * self.C[j]\n return value","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 GetCoefficient(self):\n self.coefficient = max([max([j for j in self.price[i]]) for i in self.price])","def DRate_j(eta,Pap,Pec,exp_loss_jt):\n return (1 + Pap)*(1 - (1 - 2*Pec)*exp_loss_jt)","def fd_jacobian(self,y):\n res0 = self.residual(y)\n eps = 1e-6\n dofs = y.shape[0]\n jac_approx = np.zeros((dofs,dofs))\n for i in range(dofs):\n y_temp = np.copy(y)\n y_temp[i]+=eps\n\n r2 = self.residual(y_temp)\n dr = (r2-res0)/eps\n for j in range(dofs):\n jac_approx[j,i] = dr[j]\n \n return jac_approx","def jacobian(self, dt):\n return self._F_cache","def jaccard_coef(y_true, y_pred):\n smooth = 1e-12\n intersection = K.sum(y_true * y_pred)\n sum_ = K.sum(y_true + y_pred)\n jac = (intersection + smooth) / (sum_ - intersection + smooth)\n return K.mean(jac)","def get_con_sf(self, j):\n return self.je[ self.ie[j] : self.ie[j+1] ]","def ergsetc2jansky(self):\n return self._ergsetc2jansky","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 provideJ(self):\n # Derivatives are simple\n return","def provideJ(self):\n # Derivatives are simple\n return","def objective(self, x):\n pass","def objective(self, x):\n pass","def getGradient(self,j):\n i = int(self.indicator['term'][j])\n r = int(self.indicator['row'][j])\n c = int(self.indicator['col'][j])\n rv = -np.kron(self.Fstar()[i][:,[r]],self.Astar()[i][[c],:])\n return rv","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 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 get_gain_activity_j(state: types.StateSpace, j: int,\n buffer_processing_matrix: types.BufferMatrix,\n weight_per_buffer: np.ndarray) -> float:\n current_buffers_cost = np.multiply(weight_per_buffer, state)\n theta_j = - np.sum(np.multiply(current_buffers_cost, buffer_processing_matrix[:, j][:, None]))\n return theta_j","def get_jacobian_spatial(self, qs=None) -> np.ndarray:\n if qs is None:\n qs = self.get_current_joint_position()\n return self.robot.jacob0(qs)","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 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 vectorized_result(j):\n\te = np.zeros((3, 1))\n\te[j] = 1.0\n\treturn e","def coef(self):\n return self._coef","def jacobian_i(self, x):\n return np.matrix([-x**3, -x**2, -x, -1])","def _get_minibatch_jacobian(y, x):\n assert y.shape[0] == x.shape[0]\n y = y.view(y.shape[0], -1)\n\n # Compute Jacobian row by row.\n jac = []\n for j in range(y.shape[1]):\n dy_j_dx = torch.autograd.grad(y[:, j], x, torch.ones_like(y[:, j]), retain_graph=True,\n create_graph=True)[0].view(x.shape[0], -1)\n jac.append(torch.unsqueeze(dy_j_dx, 1))\n jac = torch.cat(jac, 1)\n return jac","def __getslice__(self,i,j):\n nv=_Matr()\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\n nv._Matr__maj()\n return nv","def _C(self):\n\n # Find the local x and y coordinates at each node\n xi = 0\n yi = 0\n xj = self.width()\n yj = 0\n xm = xj\n ym = self.height()\n xn = 0\n yn = ym\n\n # Calculate the [C] coefficient matrix\n C = array([[1, xi, yi, xi**2, xi*yi, yi**2, xi**3, xi**2*yi, xi*yi**2, yi**3, xi**3*yi, xi*yi**3],\n [0, 0, 1, 0, xi, 2*yi, 0, xi**2, 2*xi*yi, 3*yi**2, xi**3, 3*xi*yi**2],\n [0, -1, 0, -2*xi, -yi, 0, -3*xi**2, -2*xi*yi, -yi**2, 0, -3*xi**2*yi, -yi**3],\n \n [1, xj, yj, xj**2, xj*yj, yj**2, xj**3, xj**2*yj, xj*yj**2, yj**3, xj**3*yj, xj*yj**3],\n [0, 0, 1, 0, xj, 2*yj, 0, xj**2, 2*xj*yj, 3*yj**2, xj**3, 3*xj*yj**2],\n [0, -1, 0, -2*xj, -yj, 0, -3*xj**2, -2*xj*yj, -yj**2, 0, -3*xj**2*yj, -yj**3],\n\n [1, xm, ym, xm**2, xm*ym, ym**2, xm**3, xm**2*ym, xm*ym**2, ym**3, xm**3*ym, xm*ym**3],\n [0, 0, 1, 0, xm, 2*ym, 0, xm**2, 2*xm*ym, 3*ym**2, xm**3, 3*xm*ym**2],\n [0, -1, 0, -2*xm, -ym, 0, -3*xm**2, -2*xm*ym, -ym**2, 0, -3*xm**2*ym, -ym**3],\n\n [1, xn, yn, xn**2, xn*yn, yn**2, xn**3, xn**2*yn, xn*yn**2, yn**3, xn**3*yn, xn*yn**3],\n [0, 0, 1, 0, xn, 2*yn, 0, xn**2, 2*xn*yn, 3*yn**2, xn**3, 3*xn*yn**2],\n [0, -1, 0, -2*xn, -yn, 0, -3*xn**2, -2*xn*yn, -yn**2, 0, -3*xn**2*yn, -yn**3]])\n \n # Return the coefficient matrix\n return C","def calc_cophenetic_coeff(self):\n c, d = cophenet(self.__linkage, self.__distance_matrix)\n return round(c, 3)","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 clebsch_gordan((J1,M1),(J2,M2),(J3,M3)):\n cg=(-1)**(J2-J1-M3)*math.sqrt(2*J3+1)*pygsl.sf.coupling_3j(int(2*J1), int(2*J2), int(2*J3), int(2*M1), int(2*M2),int(-2*M3))[0]\n #\n return cg","def substitute_cost(self, i, j):\n raise NotImplementedError","def jmat(ind: int):\n return _jm[ind - 1]","def JConUACalc(self):\n if hasattr(self,\"jcon\"): return self.jcon\n st = self.uacalc_format(\"A\"+str(self.index))\n writefile('tmpalgCon.ua',st)\n os.system('java -classpath '+clspth+'uacalc/classes/ org.uacalc.example.JConUACalc tmpalgCon.ua >tmpoutCon.txt')\n st = readfile('tmpoutCon.txt')\n while st[0]==\"k\": st = st[st.index(\"\\n\")+1:] # remove diagnostic output\n self.jcon = eval(st)\n return self.jcon","def covar(self):\n wwt = self.ww.copy()\n wwt[self.ww>0] = 1.0/self.ww[self.ww>0]\n covar = np.zeros((self.nstar,self.nstar),dtype=self.ww.dtype)\n for i in range(self.nstar):\n for j in range(i+1):\n covar[i,j] = np.sum(wwt * self.vv[:,i] * self.vv[:,j])\n covar[j,i] = covar[i,j]\n return covar","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 conductivity(self):\n m = 1.67296736e-02 # Determined from optimisation\n c = 8.54665149e-05 # Determined from optimisation\n return m * self.concentration + c","def J (self, n):","def cost_function(self, u):\n # enforce that the input is a numpy array\n u = np.asarray(u)\n\n # Reshape the control input to (mxT). Vector input is required for some optimization libraries\n T = int(len(u)/2)\n u = u.reshape((2,T))\n\n J = - self.rho(u)\n\n return J","def J_minus_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_minus_coefficient(j, m)","def conj(self):\n return np.conj(self)","def EvaluateJacobian(x):\n j = np.zeros((NOBSERVATIONS, 3))\n\n for i in range(NOBSERVATIONS):\n base = np.exp(-x[0] * t[i]) / (x[1] + x[2] * t[i])\n\n j[i][0] = t[i] * base\n j[i][1] = base / (x[1] + x[2] * t[i])\n j[i][2] = base * t[i] / (x[1] + x[2] * t[i])\n\n return j","def EllipticCurve_from_j(j, minimal_twist=True):\n return EllipticCurve(coefficients_from_j(j, minimal_twist))","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 cost_fun(x, problem):\n j = 0\n if problem['use_log_bar']:\n c = ineqconstr(x, problem)\n j += np.sum(logbarrierfunc(0.1, c, problem['use_sigma']))\n\n x, t_final = matrify(x, problem)\n if problem['T']!=0:\n j += np.sum([problem['cost_fun_single'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])\n else:\n j = t_final\n return j","def jacobi(self, lattice):\n kernel = np.array([[[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]],\n [[0.0,1.0,0.0],[1.0,0.0,1.0],[0.0,1.0,0.0]],\n [[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]])\n return ((signal.fftconvolve(lattice, kernel, mode='same') + self.J)/ 6.0)","def _get_J(self, omega, y):\n x = y[:-1]\n newt_lambda = y[-1]\n J = np.zeros([len(x)+1, len(x)+1])\n J[:-1, :-1] = omega + newt_lambda*np.diagflat(1/(x**2))\n J[:-1, -1] = -1/x.ravel()\n J[-1, :-1] = 1\n return J","def evaluate_jacobian(self, x, V):\n jac = self._numeric_jacobian(x, V, *self.model.params.values())\n return jac","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 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 AB_zero_Jy(self):\n return self._get_mean_and_samples_attribute('AB_zero_Jy')","def _J(self):\n pd = self.particle_distribution(self._Ep * u.GeV)\n return pd.to('1/GeV').value","def getC(self):\n\t\treturn self.c","def coherency(self):\r\n coherency = tsa.cache_to_coherency(self.cache, self.ij)\r\n\r\n return coherency","def calc_jacobian(*args, **kwargs):\n try:\n tag = kwargs[\"tag\"]\n except:\n tag = 0\n\n try:\n sparse = kwargs[\"sparse\"]\n except:\n sparse = True\n\n if sparse:\n try:\n shape = kwargs[\"shape\"]\n except:\n raise ValueError(\"'shape' should be passed to calculate sparse jacobian!\")\n\n \n options = np.array([0,0,0,0],dtype=int)\n result = ad.colpack.sparse_jac_no_repeat(tag, *args, options=options)\n nnz = result[0]\n ridx = result[1]\n cidx = result[2]\n values = result[3]\n assert nnz > 0\n jac = sp.csr_matrix((values, (ridx, cidx)), shape=shape)\n jac = jac.toarray()\n else:\n jac = ad.jacobian(tag, *args)\n return jac"],"string":"[\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 jac(self):\\n return self.unit_jac if self._jac is None else self._jac\",\n \"def getCoefficient(self):\\n return _libsbml.FluxObjective_getCoefficient(self)\",\n \"def J(self):\\n return self._J\",\n \"def J(self) -> float:\\n return self._J\",\n \"def jaccard_coeff(self):\\n a, c, _, b = self.to_ccw()\\n return _div(a, a + b + c)\",\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 jval(self):\\n return self.q * self.model.nobs_moms\",\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 GetJ(self, *args):\\n return _table.Table_GetJ(self, *args)\",\n \"def c(self,j,i_j):\\n \\\"\\\"\\\" The index j of the chains goes from 0 to k-1 (where k is the \\n number of chains in our decomposition \\\"\\\"\\\"\\n assert j < len(self.lcd), \\\"j must be the index of a chain\\\"\\n \\\"\\\"\\\" The index i_j goes from 0 to len(lcd[j]) this range is one longer\\n than the length of the chain because we go from {} to the full chain. \\\"\\\"\\\"\\n assert i_j <= self.lcd_dims[j], \\\"i_j = {}, dims[j] = {}\\\".format(i_j, self.lcd_dims[j])\\n if i_j == 0:\\n return None\\n else:\\n return self.lcd[j][i_j-1]\",\n \"def objective(self):\\n return self._objective\",\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 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 C_ret(img):\\r\\n return solveJ(img, 0, tc(img, ignore_ch=2))\",\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 jacobian(self, c):\\n\\n raise NotImplementedError\",\n \"def coefficient(self) -> float:\\n ...\",\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_c(self, x, out=None, **kwargs):\\n return empty_matrix(0, self.nx)\",\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 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 conj(self, o): \\n return (o.inv()) * self * o\",\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 objective(self):\\n pass\",\n \"def convolved_j0(self, _x, delta_x):\\n return (\\n j0(_x - 0.5 * delta_x) +\\n 4.*j0(_x) +\\n j0(_x + 0.5 * delta_x)\\n ) / 6.\",\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 jacobian_c(self, x, out=None, **kwargs):\\n return self._base_nlp.jacobian_c(x, out=out, **kwargs)\",\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 coeff(self):\\n return self._coeff\",\n \"def convolved_j1(self, _x, delta_x):\\n return (\\n j1(_x-0.5*delta_x) +\\n 4.*j1(_x) +\\n j1(_x+0.5*delta_x)\\n )/6.\",\n \"def objective_val(self):\\n return self.m.ObjVal\",\n \"def jacobian(self, x):\\n pass\",\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 _jz(j, *, dtype=None):\\n dtype = dtype or settings.core[\\\"default_dtype\\\"] or _data.CSR\\n N = int(2*j + 1)\\n data = np.array([j-k for k in range(N)], dtype=complex)\\n return _data.diag[dtype](data, 0)\",\n \"def value_comp(self, x: float, j: int) -> complex:\\n return np.interp([x], self.x, self.components[j])[0]\",\n \"def int_convolved_j1(self, _x, delta_x):\\n return -(\\n j0(_x - 0.5*delta_x) +\\n 4.*j0(_x) +\\n j0(_x + 0.5*delta_x)\\n )/6.\",\n \"def ckm(i,j):\\n if i >= 1 and i <= 3 and j >= 1 and j <= 3:\\n return _ckm_abs[i-1, j-1]\\n else:\\n raise(ValueError('Wrong generation index in CKM matrix: ({},{}).'.format(i,j)))\",\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 setCoefficient(self, *args):\\n return _libsbml.FluxObjective_setCoefficient(self, *args)\",\n \"def objective(self, x):\\n rvs = frozenset(map(frozenset, self._rvs))\\n joint = self.construct_joint(x)\\n joint = joint.sum(axis=self._others, keepdims=True)\\n crv = joint.sum(axis=tuple(flatten(rvs)))\\n\\n H_crv = h(crv.ravel())\\n H = h(joint.ravel()) - H_crv\\n\\n def I_P(part):\\n margs = [ joint.sum(axis=tuple(flatten(rvs - p))) for p in part ]\\n a = sum(h(marg.ravel()) - H_crv for marg in margs)\\n return (a - H)/(len(part) - 1)\\n\\n parts = [p for p in partitions(map(frozenset, rvs)) if len(p) > 1]\\n\\n caekl = min(I_P(p) for p in parts)\\n\\n return caekl\",\n \"def get_con_elem0(self, j0):\\n return self.je[ self.ie[j0] : self.ie[j0+1] ]\",\n \"def get_rho_from_J(self, J):\\n return J[self.iOmX]/J[0]\",\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 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 jac_pos(self):\\n J = self.sim.data.get_body_jacp(self.end_effector)\\n J = J.reshape(3, -1)[:, 0:7].T\\n return J\",\n \"def c(self, i):\\n value = self.b(i)\\n if i == self.N:\\n return value\\n else:\\n for j in range(i+1, self.N+1):\\n value = value - self.a(i,j) * self.C[j]\\n return value\",\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 GetCoefficient(self):\\n self.coefficient = max([max([j for j in self.price[i]]) for i in self.price])\",\n \"def DRate_j(eta,Pap,Pec,exp_loss_jt):\\n return (1 + Pap)*(1 - (1 - 2*Pec)*exp_loss_jt)\",\n \"def fd_jacobian(self,y):\\n res0 = self.residual(y)\\n eps = 1e-6\\n dofs = y.shape[0]\\n jac_approx = np.zeros((dofs,dofs))\\n for i in range(dofs):\\n y_temp = np.copy(y)\\n y_temp[i]+=eps\\n\\n r2 = self.residual(y_temp)\\n dr = (r2-res0)/eps\\n for j in range(dofs):\\n jac_approx[j,i] = dr[j]\\n \\n return jac_approx\",\n \"def jacobian(self, dt):\\n return self._F_cache\",\n \"def jaccard_coef(y_true, y_pred):\\n smooth = 1e-12\\n intersection = K.sum(y_true * y_pred)\\n sum_ = K.sum(y_true + y_pred)\\n jac = (intersection + smooth) / (sum_ - intersection + smooth)\\n return K.mean(jac)\",\n \"def get_con_sf(self, j):\\n return self.je[ self.ie[j] : self.ie[j+1] ]\",\n \"def ergsetc2jansky(self):\\n return self._ergsetc2jansky\",\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 provideJ(self):\\n # Derivatives are simple\\n return\",\n \"def provideJ(self):\\n # Derivatives are simple\\n return\",\n \"def objective(self, x):\\n pass\",\n \"def objective(self, x):\\n pass\",\n \"def getGradient(self,j):\\n i = int(self.indicator['term'][j])\\n r = int(self.indicator['row'][j])\\n c = int(self.indicator['col'][j])\\n rv = -np.kron(self.Fstar()[i][:,[r]],self.Astar()[i][[c],:])\\n return rv\",\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 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 get_gain_activity_j(state: types.StateSpace, j: int,\\n buffer_processing_matrix: types.BufferMatrix,\\n weight_per_buffer: np.ndarray) -> float:\\n current_buffers_cost = np.multiply(weight_per_buffer, state)\\n theta_j = - np.sum(np.multiply(current_buffers_cost, buffer_processing_matrix[:, j][:, None]))\\n return theta_j\",\n \"def get_jacobian_spatial(self, qs=None) -> np.ndarray:\\n if qs is None:\\n qs = self.get_current_joint_position()\\n return self.robot.jacob0(qs)\",\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 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 vectorized_result(j):\\n\\te = np.zeros((3, 1))\\n\\te[j] = 1.0\\n\\treturn e\",\n \"def coef(self):\\n return self._coef\",\n \"def jacobian_i(self, x):\\n return np.matrix([-x**3, -x**2, -x, -1])\",\n \"def _get_minibatch_jacobian(y, x):\\n assert y.shape[0] == x.shape[0]\\n y = y.view(y.shape[0], -1)\\n\\n # Compute Jacobian row by row.\\n jac = []\\n for j in range(y.shape[1]):\\n dy_j_dx = torch.autograd.grad(y[:, j], x, torch.ones_like(y[:, j]), retain_graph=True,\\n create_graph=True)[0].view(x.shape[0], -1)\\n jac.append(torch.unsqueeze(dy_j_dx, 1))\\n jac = torch.cat(jac, 1)\\n return jac\",\n \"def __getslice__(self,i,j):\\n nv=_Matr()\\n nv._Matr__c_elem().recup_relC(self._Matr__c_elem(),i,j)\\n nv._Matr__maj()\\n return nv\",\n \"def _C(self):\\n\\n # Find the local x and y coordinates at each node\\n xi = 0\\n yi = 0\\n xj = self.width()\\n yj = 0\\n xm = xj\\n ym = self.height()\\n xn = 0\\n yn = ym\\n\\n # Calculate the [C] coefficient matrix\\n C = array([[1, xi, yi, xi**2, xi*yi, yi**2, xi**3, xi**2*yi, xi*yi**2, yi**3, xi**3*yi, xi*yi**3],\\n [0, 0, 1, 0, xi, 2*yi, 0, xi**2, 2*xi*yi, 3*yi**2, xi**3, 3*xi*yi**2],\\n [0, -1, 0, -2*xi, -yi, 0, -3*xi**2, -2*xi*yi, -yi**2, 0, -3*xi**2*yi, -yi**3],\\n \\n [1, xj, yj, xj**2, xj*yj, yj**2, xj**3, xj**2*yj, xj*yj**2, yj**3, xj**3*yj, xj*yj**3],\\n [0, 0, 1, 0, xj, 2*yj, 0, xj**2, 2*xj*yj, 3*yj**2, xj**3, 3*xj*yj**2],\\n [0, -1, 0, -2*xj, -yj, 0, -3*xj**2, -2*xj*yj, -yj**2, 0, -3*xj**2*yj, -yj**3],\\n\\n [1, xm, ym, xm**2, xm*ym, ym**2, xm**3, xm**2*ym, xm*ym**2, ym**3, xm**3*ym, xm*ym**3],\\n [0, 0, 1, 0, xm, 2*ym, 0, xm**2, 2*xm*ym, 3*ym**2, xm**3, 3*xm*ym**2],\\n [0, -1, 0, -2*xm, -ym, 0, -3*xm**2, -2*xm*ym, -ym**2, 0, -3*xm**2*ym, -ym**3],\\n\\n [1, xn, yn, xn**2, xn*yn, yn**2, xn**3, xn**2*yn, xn*yn**2, yn**3, xn**3*yn, xn*yn**3],\\n [0, 0, 1, 0, xn, 2*yn, 0, xn**2, 2*xn*yn, 3*yn**2, xn**3, 3*xn*yn**2],\\n [0, -1, 0, -2*xn, -yn, 0, -3*xn**2, -2*xn*yn, -yn**2, 0, -3*xn**2*yn, -yn**3]])\\n \\n # Return the coefficient matrix\\n return C\",\n \"def calc_cophenetic_coeff(self):\\n c, d = cophenet(self.__linkage, self.__distance_matrix)\\n return round(c, 3)\",\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 clebsch_gordan((J1,M1),(J2,M2),(J3,M3)):\\n cg=(-1)**(J2-J1-M3)*math.sqrt(2*J3+1)*pygsl.sf.coupling_3j(int(2*J1), int(2*J2), int(2*J3), int(2*M1), int(2*M2),int(-2*M3))[0]\\n #\\n return cg\",\n \"def substitute_cost(self, i, j):\\n raise NotImplementedError\",\n \"def jmat(ind: int):\\n return _jm[ind - 1]\",\n \"def JConUACalc(self):\\n if hasattr(self,\\\"jcon\\\"): return self.jcon\\n st = self.uacalc_format(\\\"A\\\"+str(self.index))\\n writefile('tmpalgCon.ua',st)\\n os.system('java -classpath '+clspth+'uacalc/classes/ org.uacalc.example.JConUACalc tmpalgCon.ua >tmpoutCon.txt')\\n st = readfile('tmpoutCon.txt')\\n while st[0]==\\\"k\\\": st = st[st.index(\\\"\\\\n\\\")+1:] # remove diagnostic output\\n self.jcon = eval(st)\\n return self.jcon\",\n \"def covar(self):\\n wwt = self.ww.copy()\\n wwt[self.ww>0] = 1.0/self.ww[self.ww>0]\\n covar = np.zeros((self.nstar,self.nstar),dtype=self.ww.dtype)\\n for i in range(self.nstar):\\n for j in range(i+1):\\n covar[i,j] = np.sum(wwt * self.vv[:,i] * self.vv[:,j])\\n covar[j,i] = covar[i,j]\\n return covar\",\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 conductivity(self):\\n m = 1.67296736e-02 # Determined from optimisation\\n c = 8.54665149e-05 # Determined from optimisation\\n return m * self.concentration + c\",\n \"def J (self, n):\",\n \"def cost_function(self, u):\\n # enforce that the input is a numpy array\\n u = np.asarray(u)\\n\\n # Reshape the control input to (mxT). Vector input is required for some optimization libraries\\n T = int(len(u)/2)\\n u = u.reshape((2,T))\\n\\n J = - self.rho(u)\\n\\n return J\",\n \"def J_minus_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_minus_coefficient(j, m)\",\n \"def conj(self):\\n return np.conj(self)\",\n \"def EvaluateJacobian(x):\\n j = np.zeros((NOBSERVATIONS, 3))\\n\\n for i in range(NOBSERVATIONS):\\n base = np.exp(-x[0] * t[i]) / (x[1] + x[2] * t[i])\\n\\n j[i][0] = t[i] * base\\n j[i][1] = base / (x[1] + x[2] * t[i])\\n j[i][2] = base * t[i] / (x[1] + x[2] * t[i])\\n\\n return j\",\n \"def EllipticCurve_from_j(j, minimal_twist=True):\\n return EllipticCurve(coefficients_from_j(j, minimal_twist))\",\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 cost_fun(x, problem):\\n j = 0\\n if problem['use_log_bar']:\\n c = ineqconstr(x, problem)\\n j += np.sum(logbarrierfunc(0.1, c, problem['use_sigma']))\\n\\n x, t_final = matrify(x, problem)\\n if problem['T']!=0:\\n j += np.sum([problem['cost_fun_single'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])\\n else:\\n j = t_final\\n return j\",\n \"def jacobi(self, lattice):\\n kernel = np.array([[[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]],\\n [[0.0,1.0,0.0],[1.0,0.0,1.0],[0.0,1.0,0.0]],\\n [[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]])\\n return ((signal.fftconvolve(lattice, kernel, mode='same') + self.J)/ 6.0)\",\n \"def _get_J(self, omega, y):\\n x = y[:-1]\\n newt_lambda = y[-1]\\n J = np.zeros([len(x)+1, len(x)+1])\\n J[:-1, :-1] = omega + newt_lambda*np.diagflat(1/(x**2))\\n J[:-1, -1] = -1/x.ravel()\\n J[-1, :-1] = 1\\n return J\",\n \"def evaluate_jacobian(self, x, V):\\n jac = self._numeric_jacobian(x, V, *self.model.params.values())\\n return jac\",\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 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 AB_zero_Jy(self):\\n return self._get_mean_and_samples_attribute('AB_zero_Jy')\",\n \"def _J(self):\\n pd = self.particle_distribution(self._Ep * u.GeV)\\n return pd.to('1/GeV').value\",\n \"def getC(self):\\n\\t\\treturn self.c\",\n \"def coherency(self):\\r\\n coherency = tsa.cache_to_coherency(self.cache, self.ij)\\r\\n\\r\\n return coherency\",\n \"def calc_jacobian(*args, **kwargs):\\n try:\\n tag = kwargs[\\\"tag\\\"]\\n except:\\n tag = 0\\n\\n try:\\n sparse = kwargs[\\\"sparse\\\"]\\n except:\\n sparse = True\\n\\n if sparse:\\n try:\\n shape = kwargs[\\\"shape\\\"]\\n except:\\n raise ValueError(\\\"'shape' should be passed to calculate sparse jacobian!\\\")\\n\\n \\n options = np.array([0,0,0,0],dtype=int)\\n result = ad.colpack.sparse_jac_no_repeat(tag, *args, options=options)\\n nnz = result[0]\\n ridx = result[1]\\n cidx = result[2]\\n values = result[3]\\n assert nnz > 0\\n jac = sp.csr_matrix((values, (ridx, cidx)), shape=shape)\\n jac = jac.toarray()\\n else:\\n jac = ad.jacobian(tag, *args)\\n return jac\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7897348","0.694601","0.649423","0.64774793","0.6358544","0.6241598","0.62322664","0.62282366","0.61798847","0.6176942","0.60854423","0.6068824","0.60608375","0.6059429","0.60535663","0.6037667","0.60261023","0.6021581","0.6002902","0.5994126","0.5985048","0.5962831","0.5952473","0.58765996","0.5872884","0.58555174","0.58223677","0.5809507","0.58034927","0.5784401","0.5760709","0.57553977","0.5751856","0.5751661","0.5725528","0.5711645","0.5664418","0.56492895","0.56487054","0.56380427","0.56218493","0.5618528","0.5611612","0.5610528","0.56082785","0.5605543","0.55886984","0.55742717","0.5568488","0.5556939","0.55553854","0.55540967","0.5534041","0.55290455","0.55233425","0.5518016","0.5511836","0.5511836","0.5502753","0.5502753","0.5499173","0.54987967","0.5488937","0.5478828","0.5474177","0.5470079","0.54689085","0.54536015","0.5452449","0.54489285","0.5448083","0.5443881","0.54388565","0.54281384","0.54272825","0.54247206","0.5419907","0.5416731","0.54111755","0.54075116","0.5405205","0.5405032","0.54044557","0.53886837","0.5387361","0.5384637","0.5380441","0.53792334","0.5375679","0.5372422","0.5365718","0.5358154","0.5357653","0.53561604","0.53431964","0.534187","0.5338704","0.532301","0.5318802","0.5318527"],"string":"[\n \"0.7897348\",\n \"0.694601\",\n \"0.649423\",\n \"0.64774793\",\n \"0.6358544\",\n \"0.6241598\",\n \"0.62322664\",\n \"0.62282366\",\n \"0.61798847\",\n \"0.6176942\",\n \"0.60854423\",\n \"0.6068824\",\n \"0.60608375\",\n \"0.6059429\",\n \"0.60535663\",\n \"0.6037667\",\n \"0.60261023\",\n \"0.6021581\",\n \"0.6002902\",\n \"0.5994126\",\n \"0.5985048\",\n \"0.5962831\",\n \"0.5952473\",\n \"0.58765996\",\n \"0.5872884\",\n \"0.58555174\",\n \"0.58223677\",\n \"0.5809507\",\n \"0.58034927\",\n \"0.5784401\",\n \"0.5760709\",\n \"0.57553977\",\n \"0.5751856\",\n \"0.5751661\",\n \"0.5725528\",\n \"0.5711645\",\n \"0.5664418\",\n \"0.56492895\",\n \"0.56487054\",\n \"0.56380427\",\n \"0.56218493\",\n \"0.5618528\",\n \"0.5611612\",\n \"0.5610528\",\n \"0.56082785\",\n \"0.5605543\",\n \"0.55886984\",\n \"0.55742717\",\n \"0.5568488\",\n \"0.5556939\",\n \"0.55553854\",\n \"0.55540967\",\n \"0.5534041\",\n \"0.55290455\",\n \"0.55233425\",\n \"0.5518016\",\n \"0.5511836\",\n \"0.5511836\",\n \"0.5502753\",\n \"0.5502753\",\n \"0.5499173\",\n \"0.54987967\",\n \"0.5488937\",\n \"0.5478828\",\n \"0.5474177\",\n \"0.5470079\",\n \"0.54689085\",\n \"0.54536015\",\n \"0.5452449\",\n \"0.54489285\",\n \"0.5448083\",\n \"0.5443881\",\n \"0.54388565\",\n \"0.54281384\",\n \"0.54272825\",\n \"0.54247206\",\n \"0.5419907\",\n \"0.5416731\",\n \"0.54111755\",\n \"0.54075116\",\n \"0.5405205\",\n \"0.5405032\",\n \"0.54044557\",\n \"0.53886837\",\n \"0.5387361\",\n \"0.5384637\",\n \"0.5380441\",\n \"0.53792334\",\n \"0.5375679\",\n \"0.5372422\",\n \"0.5365718\",\n \"0.5358154\",\n \"0.5357653\",\n \"0.53561604\",\n \"0.53431964\",\n \"0.534187\",\n \"0.5338704\",\n \"0.532301\",\n \"0.5318802\",\n \"0.5318527\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7652591"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11790,"cells":{"query":{"kind":"string","value":"Obtains all objective coefficients. getc(self,c_)"},"document":{"kind":"string","value":"def getc(self,c_):\n _c_minlength = self.getnumvar()\n if self.getnumvar() > 0 and c_ is not None and len(c_) != self.getnumvar():\n raise ValueError(\"Array argument c is not long enough: Is %d, expected %d\" % (len(c_),self.getnumvar()))\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_getc(self.__nativep,_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"},"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 coefficients(self) :\n return self.__coefficients","def coefficients(self) :\n raise NotImplementedError","def coefficients(self):\r\n return self.coef_['x']","def coefficients(self):\n return self._coefficients","def coefficients(self):\n return self._coefficients","def c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\n\tC = c_matrix(x1,x2,x3)\n\ty = y_vector(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\n\tCCoefficients = np.dot(inv(C),y)\n\treturn(CCoefficients)","def getC(self):\n\t\treturn self.c","def get_coefficients(self):\n return self.coefficients","def get_coefficients(self):\n return self.coefficients","def coefficients(self) -> np.ndarray:\n return self._coefficients","def c(self):\n if self.__c is not None:\n return self.__c\n else:\n raise ValueError(\"Run .fit() first!\")","def _C(self):\n\n # Find the local x and y coordinates at each node\n xi = 0\n yi = 0\n xj = self.width()\n yj = 0\n xm = xj\n ym = self.height()\n xn = 0\n yn = ym\n\n # Calculate the [C] coefficient matrix\n C = array([[1, xi, yi, xi**2, xi*yi, yi**2, xi**3, xi**2*yi, xi*yi**2, yi**3, xi**3*yi, xi*yi**3],\n [0, 0, 1, 0, xi, 2*yi, 0, xi**2, 2*xi*yi, 3*yi**2, xi**3, 3*xi*yi**2],\n [0, -1, 0, -2*xi, -yi, 0, -3*xi**2, -2*xi*yi, -yi**2, 0, -3*xi**2*yi, -yi**3],\n \n [1, xj, yj, xj**2, xj*yj, yj**2, xj**3, xj**2*yj, xj*yj**2, yj**3, xj**3*yj, xj*yj**3],\n [0, 0, 1, 0, xj, 2*yj, 0, xj**2, 2*xj*yj, 3*yj**2, xj**3, 3*xj*yj**2],\n [0, -1, 0, -2*xj, -yj, 0, -3*xj**2, -2*xj*yj, -yj**2, 0, -3*xj**2*yj, -yj**3],\n\n [1, xm, ym, xm**2, xm*ym, ym**2, xm**3, xm**2*ym, xm*ym**2, ym**3, xm**3*ym, xm*ym**3],\n [0, 0, 1, 0, xm, 2*ym, 0, xm**2, 2*xm*ym, 3*ym**2, xm**3, 3*xm*ym**2],\n [0, -1, 0, -2*xm, -ym, 0, -3*xm**2, -2*xm*ym, -ym**2, 0, -3*xm**2*ym, -ym**3],\n\n [1, xn, yn, xn**2, xn*yn, yn**2, xn**3, xn**2*yn, xn*yn**2, yn**3, xn**3*yn, xn*yn**3],\n [0, 0, 1, 0, xn, 2*yn, 0, xn**2, 2*xn*yn, 3*yn**2, xn**3, 3*xn*yn**2],\n [0, -1, 0, -2*xn, -yn, 0, -3*xn**2, -2*xn*yn, -yn**2, 0, -3*xn**2*yn, -yn**3]])\n \n # Return the coefficient matrix\n return C","def getCoefficient(self):\n return _libsbml.FluxObjective_getCoefficient(self)","def coeff(self):\n return self._coeff","def coefficients(self):\n if self._coefficients is None:\n return np.hstack([c.coefficients for c in self._traces])\n return self._coefficients","def get_coeffs(self):\n\n return self._coeff_to_dict()","def m_c(self) -> np.ndarray:\n assert self._k is not None, \"camera must be calibrated\"\n return forge_projective_matrix(self._k)","def C(self, r, n=None):\n return self.get_coefficient(r, n)","def jacobian(self, c):\n\n raise NotImplementedError","def c(self):\r\n return self.__c","def getc(self,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_) != self.getnumvar():\n raise ValueError(\"Array argument c has wrong length\")\n res = self.__obj.getc(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 c(self) -> np.ndarray:\n return self._vector[10:12]","def coef(self):\n return self._coef","def c_():\r\n c = np.array([[0, 0], [0, 100], [100, 100], [100, 80], [20, 80],\r\n [20, 20], [100, 20], [100, 0], [0, 0]])\r\n return c","def coefficient(self) -> float:\n ...","def return_coef(self):\n try:\n model_coef = self.cmodel.coef_\n except:\n model_coef = 'Model does not have a .coef_ attribute.'\n\n return model_coef","def all_c(self):\n for i in range( self.N, 0, -1):\n self.C[i] = self.c(i)\n self.ok = True","def coefficients(self, force_characters = False) :\n if len(self.__coefficients) == 0 :\n return dict()\n elif not force_characters and len(self.__coefficients) == 1 :\n return self.__coefficients.values()[0] \n else :\n return self.__coefficients","def get_variables(self) -> np.array:\n return np.array([self.m, self.c])","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 find_coefficients(self):\n self.make_matrix()\n self.coeffs = np.linalg.solve(self.global_matrix,self.global_vector)\n self.coeffs = np.append(self.coeffs, self.D) #Initial condition","def c(self):\n return self._c","def setCoefficient(self, *args):\n return _libsbml.FluxObjective_setCoefficient(self, *args)","def evaluate_c(self, x, out=None, **kwargs):\n return np.zeros(0)","def get_C(self, observation, area):\n if observation:\n C = []\n for Theta in area.Thetas:\n C.append(Theta @ self.Omega)\n else:\n C = [self.Omega]\n return C","def reduce_C(self, C_on_basis_vecs):\n self.C_reduced = np.mat(np.array(C_on_basis_vecs, ndmin=2))\n return self.C_reduced","def circpol(self):\n return self._circpol","def proximal(self):\n return proximal_cconj_kl(space=self.domain, g=self.prior)","def covariates(self):\n return None","def reduce_C(self, C_on_basis_vecs):\n self.C_reduced = np.mat(np.array(C_on_basis_vecs, ndmin=2).T)\n return self.C_reduced","def coefficients(k, xi, x):\n\n import pyweno.cnonuniform\n\n x = np.asarray(x, np.float64)\n xi = np.asarray(xi, np.float64)\n\n nc = len(x) - 1\n n = len(xi)\n c = np.zeros((nc, n, k, k), np.float64)\n beta = np.zeros((nc, k, k, k), np.float64)\n varpi = np.zeros((nc, n, k), np.float64)\n\n pyweno.cnonuniform.nonuniform_coeffs(k, xi, x, c, beta, varpi)\n\n return c, beta, varpi","def calculate_coefficients(self):\n for i in range(0, self.nz):\n zno = i * self.dz\n self.z[0][i] = zno\n plot_eccentricity_error = False\n position = -1\n for j in range(0, self.ntheta):\n # fmt: off\n self.gama[i][j] = j * self.dtheta + (np.pi - self.beta)\n [radius_external, self.xre[i][j], self.yre[i][j]] = \\\n self.external_radius_function(self.gama[i][j])\n [radius_internal, self.xri[i][j], self.yri[i][j]] = \\\n self.internal_radius_function(zno, self.gama[i][j])\n self.re[i][j] = radius_external\n self.ri[i][j] = radius_internal\n\n w = self.omega * self.ri[i][j]\n\n k = (self.re[i][j] ** 2 * (np.log(self.re[i][j]) - 1 / 2) - self.ri[i][j] ** 2 *\n (np.log(self.ri[i][j]) - 1 / 2)) / (self.ri[i][j] ** 2 - self.re[i][j] ** 2)\n\n self.c1[i][j] = (1 / (4 * self.viscosity)) * ((self.re[i][j] ** 2 * np.log(self.re[i][j]) -\n self.ri[i][j] ** 2 * np.log(self.ri[i][j]) +\n (self.re[i][j] ** 2 - self.ri[i][j] ** 2) *\n (k - 1)) - 2 * self.re[i][j] ** 2 * (\n (np.log(self.re[i][j]) + k - 1 / 2) * np.log(\n self.re[i][j] / self.ri[i][j])))\n\n self.c2[i][j] = (- self.ri[i][j] ** 2) / (8 * self.viscosity) * \\\n ((self.re[i][j] ** 2 - self.ri[i][j] ** 2 -\n (self.re[i][j] ** 4 - self.ri[i][j] ** 4) /\n (2 * self.ri[i][j] ** 2)) +\n ((self.re[i][j] ** 2 - self.ri[i][j] ** 2) /\n (self.ri[i][j] ** 2 *\n np.log(self.re[i][j] / self.ri[i][j]))) *\n (self.re[i][j] ** 2 * np.log(self.re[i][j] / self.ri[i][j]) -\n (self.re[i][j] ** 2 - self.ri[i][j] ** 2) / 2))\n\n self.c0w[i][j] = (- w * self.ri[i][j] *\n (np.log(self.re[i][j] / self.ri[i][j]) *\n (1 + (self.ri[i][j] ** 2) / (self.re[i][j] ** 2 - self.ri[i][j] ** 2)) - 1 / 2))\n # fmt: on\n if not plot_eccentricity_error:\n if abs(self.xri[i][j]) > abs(self.xre[i][j]) or abs(\n self.yri[i][j]\n ) > abs(self.yre[i][j]):\n plot_eccentricity_error = True\n position = i\n if plot_eccentricity_error:\n self.plot_eccentricity(position)\n sys.exit(\n \"Error: The given parameters create a rotor that is not inside the stator. \"\n \"Check the plotted figure and fix accordingly.\"\n )","def aic_c(self):\n if hasattr(self, '_aic_c'):\n return self._aic_c\n else:\n k = len(self.params)\n n = self.data['n'].sum()\n self._aic_c = self.aic() + (2*k**2 + 2*k)/(n - k - 1)\n return self._aic_c","def get_vv10_coef(self):\n\n if self._nlc_b is False:\n raise ValueError(\"get_vv10_coeff can only be called on -V functionals.\")\n\n return (self._nlc_b, self._nlc_C)","def c(self, z, y, r, t):\n \n u = np.zeros( self.m ) \n \n return u","def coefficients(self, force_characters = False) :\n raise NotImplementedError","def jacobian_c(self, x, out=None, **kwargs):\n return self._base_nlp.jacobian_c(x, out=out, **kwargs)","def objective(self):\n pass","def compCoeff_CGP(i, A, c, N):\n Ap = np.copy(A)\n out = c[i, 0] * np.eye(N)\n j = 1\n while j <= i:\n # compute A to the power p\n if j > 1:\n Ap = Ap.dot(A)\n\n # add to the polynome\n out += c[i, j] * Ap\n j += 1\n\n return out","def calc_cophenetic_coeff(self):\n c, d = cophenet(self.__linkage, self.__distance_matrix)\n return round(c, 3)","def c(self, i):\n value = self.b(i)\n if i == self.N:\n return value\n else:\n for j in range(i+1, self.N+1):\n value = value - self.a(i,j) * self.C[j]\n return value","def cole_coeff(self):\n return self.diseq_coeff(standardize=True)","def __getitem__(self, s) :\n try :\n return self.coefficients()[s]\n except KeyError :\n return self.parent().coefficient_domain().zero_element()","def coefC(x0,y0,x1,y1):\n return (x1*y0-x0*y1)/(x1-x0)","def jacobian_c(self, x, out=None, **kwargs):\n return empty_matrix(0, self.nx)","def xcoeff(self):\n a, c, d, b = self.to_ccw()\n p1, q1 = a + b, c + d\n p2, q2 = a + c, b + d\n n = p1 + q1\n\n cov = self.covar()\n\n if n == 0:\n return np.nan\n elif a == n or d == n:\n return 0.5\n elif b == n or c == n:\n return -1.0\n elif cov > 0.0:\n return _div(cov, min(p1 * q2, p2 * q1))\n elif cov < 0.0:\n return _div(cov, min(n * c, n * b))\n else:\n return 0.0","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 vnC(self):\n return np.array(\n [x for x in [self.nCx, self.nCy, self.nCz] if x is not None],\n dtype=int\n )","def acoeff(self):\n return np.dot(self.mmi,np.dot(self.mmatrix.T,self.bvec))","def c(self):\n pass","def c(self):\n pass","def get_C(n_c,CV_matrix):\n C = np.zeros((n_c, n_c), dtype=np.float32)\n for i in range(3):\n C += np.asfortranarray(CV_matrix[:, :, i]) @ np.asfortranarray(CV_matrix[:, :, np.mod(i + 2, 3)].T)\n C = (C != 0).astype(np.int32)\n return C","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 conductivity(self):\n m = 1.67296736e-02 # Determined from optimisation\n c = 8.54665149e-05 # Determined from optimisation\n return m * self.concentration + c","def cget(self, *args, **kwargs):\n return self._canvas.cget(*args, **kwargs)","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 get_occr(self):\n return self._occr_array","def getModel(self):\n m,n = self.m,self.n\n A = np.zeros((m,m))\n B = np.zeros((m,n))\n C = np.zeros(m)\n Apattern,Bpattern,Cpattern = self.coeffPattern\n for i,e in enumerate(self.estimators):\n aofs = 0\n bofs = m\n cofs = m+n\n if Apattern==None:\n ai = e.x[aofs:m+aofs]\n else:\n bofs=aofs\n ai = []\n for j,pj in enumerate(Apattern[i]):\n if pj == None:\n ai.append(e.x[bofs])\n bofs += 1\n else:\n ai.append(pj)\n if Bpattern==None:\n bi = e.x[bofs:n+bofs]\n else:\n cofs=bofs\n bi = []\n for j,pj in enumerate(Bpattern[i]):\n if pj == None:\n bi.append(e.x[cofs])\n cofs += 1\n else:\n bi.append(pj)\n if Cpattern==None:\n ci = e.x[cofs]\n cofs+=1\n else:\n if Cpattern[i] == None:\n ci = e.x[cofs]\n cofs+=1\n else:\n ci = Cpattern[i]\n assert(cofs == e.n)\n assert len(ai)==m\n assert len(bi)==n\n A[i,:] = ai\n B[i,:] = bi\n C[i] = ci\n return (A,B,C)","def objective_function(T_cr: np.ndarray) -> np.ndarray:\n \n Tm = (T_r + T_cr)/2.0\n ks = thermal_conductivity(Tm)\n T_cr_update = cooled_surface_temp_actual(T_r,q,delta_tc,ks)\n \n return np.linalg.norm(T_cr_update - T_cr,ord = 1)","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 test_coefficients(self):\n\n coefs = self.cs.coefficients\n\n self.assertEqual(coefs, (1, 0, 1, 0, 0, -1))","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 p_mx_c(pm,px,py,pyx_c,pym_c,beta):\n \n pmx_c = np.zeros((pm.size,px.size)) # P(M|X) matrix to be returned\n for mi in range(pm.size):\n for xi in range(px.size):\n pmx_c[mi,xi] = pm[mi] * np.exp(-beta * entropy(pyx_c[:,xi], pym_c[:,mi], base=2))\n z = pmx_c.sum(axis=0)\n pmx_c /= z #Normalize\n \n \t\n return pmx_c, z","def _get_eci_chem_pot(self):\n bf = self.atoms.get_calculator().BC.basis_functions\n bf_change_vec = np.zeros((1, len(bf)))\n for i, func in enumerate(bf):\n for key, num in self.groups[0].items():\n bf_change_vec[0, i] += func[key] * num\n\n for key, num in self.groups[1].items():\n bf_change_vec[0, i] -= func[key] * num\n pinv = np.linalg.pinv(bf_change_vec)\n mu_vec = pinv.dot(np.array([self._chem_pot]))\n\n chem_pot_dict = {}\n for i in range(len(mu_vec)):\n chem_pot_dict[\"c1_{}\".format(i)] = mu_vec[i]\n return chem_pot_dict","def covar(self):\n wwt = self.ww.copy()\n wwt[self.ww>0] = 1.0/self.ww[self.ww>0]\n covar = np.zeros((self.nstar,self.nstar),dtype=self.ww.dtype)\n for i in range(self.nstar):\n for j in range(i+1):\n covar[i,j] = np.sum(wwt * self.vv[:,i] * self.vv[:,j])\n covar[j,i] = covar[i,j]\n return covar","def cost_matrix(self):\n return self.__cost_matrix","def p_ym_c(pm,px,py,pyx_c,pmx_c):\n pym_c = np.zeros((py.size,pm.size))\n for yi in range(py.size):\n for mi in range(pm.size):\n for xi in range(px.size):\n pym_c[yi,mi] += (1./pm[mi])*pyx_c[yi,xi]*pmx_c[mi,xi]*px[xi]\n return pym_c","def covar(self) -> np.ndarray:\n if self._covar is None:\n self._covar = batched_inv_spd(batched_cholesky(self._inv_covar))\n return self._covar","def getCC(self, var, idx = None, translation = None, other = None,\\\n verbose = 1, version = \"pearson\", ab = []):\n\n if idx is None: idx = np.arange(self.atoms.shape[0])\n if translation is None: translation = [0]\n if isinstance(translation, (int, np.integer)): translation = [translation]\n\n data, lbl = self.getData(var = var, idx = idx, translation = translation,\\\n verbose = verbose, ab = ab, other = other)[::2]\n string = \"\"\n for i, item in enumerate(data):\n if i == 0:\n values = np.zeros((len(data), np.shape(data[i])[0]))\n\n if np.ndim(data[i]) == 1:\n values[i, :] = data[i]\n else:\n values[i, :] = data[i][:, 0]\n\n if \"pearson\".startswith(version.lower()):\n ccoef = np.zeros((values.shape[0], values.shape[0]))\n rho = np.zeros((values.shape[0], values.shape[0]))\n\n for i in range(0, values.shape[0]):\n for j in range(i + 1, values.shape[0]):\n ccoef[i, j], rho[i, j] = stats.pearsonr(values[i, :], values[j, :])\n ccoef[j, i] = ccoef[i, j]\n rho[j, i] = rho[i, j]\n\n ccoef += np.identity(values.shape[0])\n\n elif \"spearman\".startswith(version.lower()):\n ccoef, rho = stats.spearmanr(values, axis = 1)\n\n if verbose > 0:\n head = \"Cor-Coef for the following variables:\"\n ut.infoPrint(head, sep_after = False)\n print(\"-\" * len(head))\n for i, string in enumerate(var):\n ut.infoPrint(\"%s\" % string, sep_before = False, sep_after = False)\n print(\"=\" * len(head))\n\n return ccoef, rho, lbl","def get_occr_jacobian(self, flattened=True):\n\n if flattened:\n return np.reshape(a=self._occr_jacobian,\n newshape=(np.prod(self.shape), self.shape[0]))\n else:\n return self._occr_jacobian","def cmm(self) -> Optional[np.ndarray]:\n if self.sensorsz is None:\n return None\n return self.c * self.sensorsz / self.imgsz","def coef_(self):\n assert self.sess is not None, \"Model has not been fitted yet!\"\n return self.sess.run(self.W_lst)[0]","def __getitem__( self, l ) :\n\n return( self.coefficients[l] )","def zzx_mul_const(f, c):\n if not c or not f:\n return []\n else:\n return [ c * coeff for coeff in f ]","def _get_covar(minuit):\n n = len(minuit.parameters)\n m = np.zeros((n, n))\n for i1, k1 in enumerate(minuit.parameters):\n for i2, k2 in enumerate(minuit.parameters):\n if set([k1, k2]).issubset(minuit.list_of_vary_param()):\n m[i1, i2] = minuit.covariance[(k1, k2)]\n return m","def getObjective(self, *args):\n return _libsbml.FbcModelPlugin_getObjective(self, *args)","def equation(self):\n mat = np.empty((self.nunknowns, self.model.neq))\n # rhs = np.zeros(self.nunknowns) # Needs to be initialized to zero\n rhs = self.pc.copy()\n for icp in range(self.ncp):\n istart = icp * self.nlayers\n # rhs[istart:istart+self.nlayers] = self.pc[]\n ieq = 0\n for e in self.model.elementlist:\n if e.nunknowns > 0:\n mat[istart:istart + self.nlayers, ieq:ieq + e.nunknowns] = \\\n e.potinflayers(self.xc[icp], self.yc[icp], self.layers)\n if e == self:\n mat[istart:istart + self.nlayers, ieq:ieq + e.nunknowns] -= self.resfac[icp]\n ieq += e.nunknowns\n else:\n rhs[istart:istart + self.nlayers] -= \\\n e.potentiallayers(self.xc[icp], self.yc[icp],\n self.layers) # Pretty cool that this works, really\n return mat, rhs","def conditional_component_covs(self):\n return np.array([d.conditional_cov() for d in self.conditionalMVNs])","def get_coeff(self):\n return bernoulli(self.degree+1) / factorial(self.degree + 1)","def GetCoefficient(self):\n self.coefficient = max([max([j for j in self.price[i]]) for i in self.price])","def evaluate_c(self, x, out=None, **kwargs):\n return self._base_nlp.evaluate_c(x, out=out, **kwargs)","def getCL(self):\r\n return self.cL;","def __init__(self, coefficients):\n self.coefficients = coefficients","def all_coeffs(f):\n return dmp_all_coeffs(f.rep, f.lev, f.dom)","def _update_c_and_beta(self):\n labels = np.log(self._gv())\n if self.update_c:\n clf = sklearn.linear_model.LinearRegression(\n fit_intercept=False).fit(self.ones_logx, labels)\n log_beta = clf.coef_[0]\n self.c = clf.coef_[1]\n else:\n log_beta = np.mean(labels - self.ones_logx[:, 1] * self.c)\n self.beta = np.exp(log_beta)","def Cvec(self):\n return vec(self.xc, self.yc)","def cofactorMatrix(self):\n returnvalue = Matrix()\n for i in range(self._height):\n newRow = list()\n for j in range(self._width):\n newRow.append(self.cofactor(i, j))\n returnvalue.addRow(*newRow)\n return returnvalue","def corrcoef(self):\r\n return np.corrcoef(self.input.data)","def equation(self):\n mat = np.empty((self.nunknowns, self.model.neq))\n rhs = np.zeros(self.nunknowns) # Needs to be initialized to zero\n for icp in range(self.ncp):\n istart = icp * self.nlayers\n rhs[istart:istart + self.nlayers] = self.pc\n ieq = 0\n for e in self.model.elementlist:\n if e.nunknowns > 0:\n mat[istart:istart + self.nlayers, ieq:ieq + e.nunknowns] = \\\n e.potinflayers(self.xc[icp], self.yc[icp], self.layers)\n ieq += e.nunknowns\n else:\n rhs[istart:istart + self.nlayers] -= \\\n e.potentiallayers(self.xc[icp], self.yc[icp],\n self.layers) # Pretty cool that this works, really\n return mat, rhs","def make_coco_labels(real_c):\n y = np.eye(real_c.size(1))\n\n fixed_c_list = []\n\n # single object addition and removal\n for i in range(2*real_c.size(1)):\n fixed_c = real_c.clone()\n for c in fixed_c:\n if i%2:\n c[i//2] = 0.\n else:\n c[i//2] = 1.\n fixed_c_list.append(Variable(fixed_c, volatile=True).cuda())\n\n # multi-attribute transfer (H+G, H+A, G+A, H+G+A)\n #if self.dataset == 'CelebA':\n # for i in range(4):\n # fixed_c = real_c.clone()\n # for c in fixed_c:\n # if i in [0, 1, 3]: # Hair color to brown\n # c[:3] = y[2]\n # if i in [0, 2, 3]: # Gender\n # c[3] = 0 if c[3] == 1 else 1\n # if i in [1, 2, 3]: # Aged\n # c[4] = 0 if c[4] == 1 else 1\n # fixed_c_list.append(self.to_var(fixed_c, volatile=True))\n return fixed_c_list"],"string":"[\n \"def coefficients(self) :\\n return self.__coefficients\",\n \"def coefficients(self) :\\n raise NotImplementedError\",\n \"def coefficients(self):\\r\\n return self.coef_['x']\",\n \"def coefficients(self):\\n return self._coefficients\",\n \"def coefficients(self):\\n return self._coefficients\",\n \"def c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\\n\\tC = c_matrix(x1,x2,x3)\\n\\ty = y_vector(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\\n\\tCCoefficients = np.dot(inv(C),y)\\n\\treturn(CCoefficients)\",\n \"def getC(self):\\n\\t\\treturn self.c\",\n \"def get_coefficients(self):\\n return self.coefficients\",\n \"def get_coefficients(self):\\n return self.coefficients\",\n \"def coefficients(self) -> np.ndarray:\\n return self._coefficients\",\n \"def c(self):\\n if self.__c is not None:\\n return self.__c\\n else:\\n raise ValueError(\\\"Run .fit() first!\\\")\",\n \"def _C(self):\\n\\n # Find the local x and y coordinates at each node\\n xi = 0\\n yi = 0\\n xj = self.width()\\n yj = 0\\n xm = xj\\n ym = self.height()\\n xn = 0\\n yn = ym\\n\\n # Calculate the [C] coefficient matrix\\n C = array([[1, xi, yi, xi**2, xi*yi, yi**2, xi**3, xi**2*yi, xi*yi**2, yi**3, xi**3*yi, xi*yi**3],\\n [0, 0, 1, 0, xi, 2*yi, 0, xi**2, 2*xi*yi, 3*yi**2, xi**3, 3*xi*yi**2],\\n [0, -1, 0, -2*xi, -yi, 0, -3*xi**2, -2*xi*yi, -yi**2, 0, -3*xi**2*yi, -yi**3],\\n \\n [1, xj, yj, xj**2, xj*yj, yj**2, xj**3, xj**2*yj, xj*yj**2, yj**3, xj**3*yj, xj*yj**3],\\n [0, 0, 1, 0, xj, 2*yj, 0, xj**2, 2*xj*yj, 3*yj**2, xj**3, 3*xj*yj**2],\\n [0, -1, 0, -2*xj, -yj, 0, -3*xj**2, -2*xj*yj, -yj**2, 0, -3*xj**2*yj, -yj**3],\\n\\n [1, xm, ym, xm**2, xm*ym, ym**2, xm**3, xm**2*ym, xm*ym**2, ym**3, xm**3*ym, xm*ym**3],\\n [0, 0, 1, 0, xm, 2*ym, 0, xm**2, 2*xm*ym, 3*ym**2, xm**3, 3*xm*ym**2],\\n [0, -1, 0, -2*xm, -ym, 0, -3*xm**2, -2*xm*ym, -ym**2, 0, -3*xm**2*ym, -ym**3],\\n\\n [1, xn, yn, xn**2, xn*yn, yn**2, xn**3, xn**2*yn, xn*yn**2, yn**3, xn**3*yn, xn*yn**3],\\n [0, 0, 1, 0, xn, 2*yn, 0, xn**2, 2*xn*yn, 3*yn**2, xn**3, 3*xn*yn**2],\\n [0, -1, 0, -2*xn, -yn, 0, -3*xn**2, -2*xn*yn, -yn**2, 0, -3*xn**2*yn, -yn**3]])\\n \\n # Return the coefficient matrix\\n return C\",\n \"def getCoefficient(self):\\n return _libsbml.FluxObjective_getCoefficient(self)\",\n \"def coeff(self):\\n return self._coeff\",\n \"def coefficients(self):\\n if self._coefficients is None:\\n return np.hstack([c.coefficients for c in self._traces])\\n return self._coefficients\",\n \"def get_coeffs(self):\\n\\n return self._coeff_to_dict()\",\n \"def m_c(self) -> np.ndarray:\\n assert self._k is not None, \\\"camera must be calibrated\\\"\\n return forge_projective_matrix(self._k)\",\n \"def C(self, r, n=None):\\n return self.get_coefficient(r, n)\",\n \"def jacobian(self, c):\\n\\n raise NotImplementedError\",\n \"def c(self):\\r\\n return self.__c\",\n \"def getc(self,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_) != self.getnumvar():\\n raise ValueError(\\\"Array argument c has wrong length\\\")\\n res = self.__obj.getc(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 c(self) -> np.ndarray:\\n return self._vector[10:12]\",\n \"def coef(self):\\n return self._coef\",\n \"def c_():\\r\\n c = np.array([[0, 0], [0, 100], [100, 100], [100, 80], [20, 80],\\r\\n [20, 20], [100, 20], [100, 0], [0, 0]])\\r\\n return c\",\n \"def coefficient(self) -> float:\\n ...\",\n \"def return_coef(self):\\n try:\\n model_coef = self.cmodel.coef_\\n except:\\n model_coef = 'Model does not have a .coef_ attribute.'\\n\\n return model_coef\",\n \"def all_c(self):\\n for i in range( self.N, 0, -1):\\n self.C[i] = self.c(i)\\n self.ok = True\",\n \"def coefficients(self, force_characters = False) :\\n if len(self.__coefficients) == 0 :\\n return dict()\\n elif not force_characters and len(self.__coefficients) == 1 :\\n return self.__coefficients.values()[0] \\n else :\\n return self.__coefficients\",\n \"def get_variables(self) -> np.array:\\n return np.array([self.m, self.c])\",\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 find_coefficients(self):\\n self.make_matrix()\\n self.coeffs = np.linalg.solve(self.global_matrix,self.global_vector)\\n self.coeffs = np.append(self.coeffs, self.D) #Initial condition\",\n \"def c(self):\\n return self._c\",\n \"def setCoefficient(self, *args):\\n return _libsbml.FluxObjective_setCoefficient(self, *args)\",\n \"def evaluate_c(self, x, out=None, **kwargs):\\n return np.zeros(0)\",\n \"def get_C(self, observation, area):\\n if observation:\\n C = []\\n for Theta in area.Thetas:\\n C.append(Theta @ self.Omega)\\n else:\\n C = [self.Omega]\\n return C\",\n \"def reduce_C(self, C_on_basis_vecs):\\n self.C_reduced = np.mat(np.array(C_on_basis_vecs, ndmin=2))\\n return self.C_reduced\",\n \"def circpol(self):\\n return self._circpol\",\n \"def proximal(self):\\n return proximal_cconj_kl(space=self.domain, g=self.prior)\",\n \"def covariates(self):\\n return None\",\n \"def reduce_C(self, C_on_basis_vecs):\\n self.C_reduced = np.mat(np.array(C_on_basis_vecs, ndmin=2).T)\\n return self.C_reduced\",\n \"def coefficients(k, xi, x):\\n\\n import pyweno.cnonuniform\\n\\n x = np.asarray(x, np.float64)\\n xi = np.asarray(xi, np.float64)\\n\\n nc = len(x) - 1\\n n = len(xi)\\n c = np.zeros((nc, n, k, k), np.float64)\\n beta = np.zeros((nc, k, k, k), np.float64)\\n varpi = np.zeros((nc, n, k), np.float64)\\n\\n pyweno.cnonuniform.nonuniform_coeffs(k, xi, x, c, beta, varpi)\\n\\n return c, beta, varpi\",\n \"def calculate_coefficients(self):\\n for i in range(0, self.nz):\\n zno = i * self.dz\\n self.z[0][i] = zno\\n plot_eccentricity_error = False\\n position = -1\\n for j in range(0, self.ntheta):\\n # fmt: off\\n self.gama[i][j] = j * self.dtheta + (np.pi - self.beta)\\n [radius_external, self.xre[i][j], self.yre[i][j]] = \\\\\\n self.external_radius_function(self.gama[i][j])\\n [radius_internal, self.xri[i][j], self.yri[i][j]] = \\\\\\n self.internal_radius_function(zno, self.gama[i][j])\\n self.re[i][j] = radius_external\\n self.ri[i][j] = radius_internal\\n\\n w = self.omega * self.ri[i][j]\\n\\n k = (self.re[i][j] ** 2 * (np.log(self.re[i][j]) - 1 / 2) - self.ri[i][j] ** 2 *\\n (np.log(self.ri[i][j]) - 1 / 2)) / (self.ri[i][j] ** 2 - self.re[i][j] ** 2)\\n\\n self.c1[i][j] = (1 / (4 * self.viscosity)) * ((self.re[i][j] ** 2 * np.log(self.re[i][j]) -\\n self.ri[i][j] ** 2 * np.log(self.ri[i][j]) +\\n (self.re[i][j] ** 2 - self.ri[i][j] ** 2) *\\n (k - 1)) - 2 * self.re[i][j] ** 2 * (\\n (np.log(self.re[i][j]) + k - 1 / 2) * np.log(\\n self.re[i][j] / self.ri[i][j])))\\n\\n self.c2[i][j] = (- self.ri[i][j] ** 2) / (8 * self.viscosity) * \\\\\\n ((self.re[i][j] ** 2 - self.ri[i][j] ** 2 -\\n (self.re[i][j] ** 4 - self.ri[i][j] ** 4) /\\n (2 * self.ri[i][j] ** 2)) +\\n ((self.re[i][j] ** 2 - self.ri[i][j] ** 2) /\\n (self.ri[i][j] ** 2 *\\n np.log(self.re[i][j] / self.ri[i][j]))) *\\n (self.re[i][j] ** 2 * np.log(self.re[i][j] / self.ri[i][j]) -\\n (self.re[i][j] ** 2 - self.ri[i][j] ** 2) / 2))\\n\\n self.c0w[i][j] = (- w * self.ri[i][j] *\\n (np.log(self.re[i][j] / self.ri[i][j]) *\\n (1 + (self.ri[i][j] ** 2) / (self.re[i][j] ** 2 - self.ri[i][j] ** 2)) - 1 / 2))\\n # fmt: on\\n if not plot_eccentricity_error:\\n if abs(self.xri[i][j]) > abs(self.xre[i][j]) or abs(\\n self.yri[i][j]\\n ) > abs(self.yre[i][j]):\\n plot_eccentricity_error = True\\n position = i\\n if plot_eccentricity_error:\\n self.plot_eccentricity(position)\\n sys.exit(\\n \\\"Error: The given parameters create a rotor that is not inside the stator. \\\"\\n \\\"Check the plotted figure and fix accordingly.\\\"\\n )\",\n \"def aic_c(self):\\n if hasattr(self, '_aic_c'):\\n return self._aic_c\\n else:\\n k = len(self.params)\\n n = self.data['n'].sum()\\n self._aic_c = self.aic() + (2*k**2 + 2*k)/(n - k - 1)\\n return self._aic_c\",\n \"def get_vv10_coef(self):\\n\\n if self._nlc_b is False:\\n raise ValueError(\\\"get_vv10_coeff can only be called on -V functionals.\\\")\\n\\n return (self._nlc_b, self._nlc_C)\",\n \"def c(self, z, y, r, t):\\n \\n u = np.zeros( self.m ) \\n \\n return u\",\n \"def coefficients(self, force_characters = False) :\\n raise NotImplementedError\",\n \"def jacobian_c(self, x, out=None, **kwargs):\\n return self._base_nlp.jacobian_c(x, out=out, **kwargs)\",\n \"def objective(self):\\n pass\",\n \"def compCoeff_CGP(i, A, c, N):\\n Ap = np.copy(A)\\n out = c[i, 0] * np.eye(N)\\n j = 1\\n while j <= i:\\n # compute A to the power p\\n if j > 1:\\n Ap = Ap.dot(A)\\n\\n # add to the polynome\\n out += c[i, j] * Ap\\n j += 1\\n\\n return out\",\n \"def calc_cophenetic_coeff(self):\\n c, d = cophenet(self.__linkage, self.__distance_matrix)\\n return round(c, 3)\",\n \"def c(self, i):\\n value = self.b(i)\\n if i == self.N:\\n return value\\n else:\\n for j in range(i+1, self.N+1):\\n value = value - self.a(i,j) * self.C[j]\\n return value\",\n \"def cole_coeff(self):\\n return self.diseq_coeff(standardize=True)\",\n \"def __getitem__(self, s) :\\n try :\\n return self.coefficients()[s]\\n except KeyError :\\n return self.parent().coefficient_domain().zero_element()\",\n \"def coefC(x0,y0,x1,y1):\\n return (x1*y0-x0*y1)/(x1-x0)\",\n \"def jacobian_c(self, x, out=None, **kwargs):\\n return empty_matrix(0, self.nx)\",\n \"def xcoeff(self):\\n a, c, d, b = self.to_ccw()\\n p1, q1 = a + b, c + d\\n p2, q2 = a + c, b + d\\n n = p1 + q1\\n\\n cov = self.covar()\\n\\n if n == 0:\\n return np.nan\\n elif a == n or d == n:\\n return 0.5\\n elif b == n or c == n:\\n return -1.0\\n elif cov > 0.0:\\n return _div(cov, min(p1 * q2, p2 * q1))\\n elif cov < 0.0:\\n return _div(cov, min(n * c, n * b))\\n else:\\n return 0.0\",\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 vnC(self):\\n return np.array(\\n [x for x in [self.nCx, self.nCy, self.nCz] if x is not None],\\n dtype=int\\n )\",\n \"def acoeff(self):\\n return np.dot(self.mmi,np.dot(self.mmatrix.T,self.bvec))\",\n \"def c(self):\\n pass\",\n \"def c(self):\\n pass\",\n \"def get_C(n_c,CV_matrix):\\n C = np.zeros((n_c, n_c), dtype=np.float32)\\n for i in range(3):\\n C += np.asfortranarray(CV_matrix[:, :, i]) @ np.asfortranarray(CV_matrix[:, :, np.mod(i + 2, 3)].T)\\n C = (C != 0).astype(np.int32)\\n return C\",\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 conductivity(self):\\n m = 1.67296736e-02 # Determined from optimisation\\n c = 8.54665149e-05 # Determined from optimisation\\n return m * self.concentration + c\",\n \"def cget(self, *args, **kwargs):\\n return self._canvas.cget(*args, **kwargs)\",\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 get_occr(self):\\n return self._occr_array\",\n \"def getModel(self):\\n m,n = self.m,self.n\\n A = np.zeros((m,m))\\n B = np.zeros((m,n))\\n C = np.zeros(m)\\n Apattern,Bpattern,Cpattern = self.coeffPattern\\n for i,e in enumerate(self.estimators):\\n aofs = 0\\n bofs = m\\n cofs = m+n\\n if Apattern==None:\\n ai = e.x[aofs:m+aofs]\\n else:\\n bofs=aofs\\n ai = []\\n for j,pj in enumerate(Apattern[i]):\\n if pj == None:\\n ai.append(e.x[bofs])\\n bofs += 1\\n else:\\n ai.append(pj)\\n if Bpattern==None:\\n bi = e.x[bofs:n+bofs]\\n else:\\n cofs=bofs\\n bi = []\\n for j,pj in enumerate(Bpattern[i]):\\n if pj == None:\\n bi.append(e.x[cofs])\\n cofs += 1\\n else:\\n bi.append(pj)\\n if Cpattern==None:\\n ci = e.x[cofs]\\n cofs+=1\\n else:\\n if Cpattern[i] == None:\\n ci = e.x[cofs]\\n cofs+=1\\n else:\\n ci = Cpattern[i]\\n assert(cofs == e.n)\\n assert len(ai)==m\\n assert len(bi)==n\\n A[i,:] = ai\\n B[i,:] = bi\\n C[i] = ci\\n return (A,B,C)\",\n \"def objective_function(T_cr: np.ndarray) -> np.ndarray:\\n \\n Tm = (T_r + T_cr)/2.0\\n ks = thermal_conductivity(Tm)\\n T_cr_update = cooled_surface_temp_actual(T_r,q,delta_tc,ks)\\n \\n return np.linalg.norm(T_cr_update - T_cr,ord = 1)\",\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 test_coefficients(self):\\n\\n coefs = self.cs.coefficients\\n\\n self.assertEqual(coefs, (1, 0, 1, 0, 0, -1))\",\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 p_mx_c(pm,px,py,pyx_c,pym_c,beta):\\n \\n pmx_c = np.zeros((pm.size,px.size)) # P(M|X) matrix to be returned\\n for mi in range(pm.size):\\n for xi in range(px.size):\\n pmx_c[mi,xi] = pm[mi] * np.exp(-beta * entropy(pyx_c[:,xi], pym_c[:,mi], base=2))\\n z = pmx_c.sum(axis=0)\\n pmx_c /= z #Normalize\\n \\n \\t\\n return pmx_c, z\",\n \"def _get_eci_chem_pot(self):\\n bf = self.atoms.get_calculator().BC.basis_functions\\n bf_change_vec = np.zeros((1, len(bf)))\\n for i, func in enumerate(bf):\\n for key, num in self.groups[0].items():\\n bf_change_vec[0, i] += func[key] * num\\n\\n for key, num in self.groups[1].items():\\n bf_change_vec[0, i] -= func[key] * num\\n pinv = np.linalg.pinv(bf_change_vec)\\n mu_vec = pinv.dot(np.array([self._chem_pot]))\\n\\n chem_pot_dict = {}\\n for i in range(len(mu_vec)):\\n chem_pot_dict[\\\"c1_{}\\\".format(i)] = mu_vec[i]\\n return chem_pot_dict\",\n \"def covar(self):\\n wwt = self.ww.copy()\\n wwt[self.ww>0] = 1.0/self.ww[self.ww>0]\\n covar = np.zeros((self.nstar,self.nstar),dtype=self.ww.dtype)\\n for i in range(self.nstar):\\n for j in range(i+1):\\n covar[i,j] = np.sum(wwt * self.vv[:,i] * self.vv[:,j])\\n covar[j,i] = covar[i,j]\\n return covar\",\n \"def cost_matrix(self):\\n return self.__cost_matrix\",\n \"def p_ym_c(pm,px,py,pyx_c,pmx_c):\\n pym_c = np.zeros((py.size,pm.size))\\n for yi in range(py.size):\\n for mi in range(pm.size):\\n for xi in range(px.size):\\n pym_c[yi,mi] += (1./pm[mi])*pyx_c[yi,xi]*pmx_c[mi,xi]*px[xi]\\n return pym_c\",\n \"def covar(self) -> np.ndarray:\\n if self._covar is None:\\n self._covar = batched_inv_spd(batched_cholesky(self._inv_covar))\\n return self._covar\",\n \"def getCC(self, var, idx = None, translation = None, other = None,\\\\\\n verbose = 1, version = \\\"pearson\\\", ab = []):\\n\\n if idx is None: idx = np.arange(self.atoms.shape[0])\\n if translation is None: translation = [0]\\n if isinstance(translation, (int, np.integer)): translation = [translation]\\n\\n data, lbl = self.getData(var = var, idx = idx, translation = translation,\\\\\\n verbose = verbose, ab = ab, other = other)[::2]\\n string = \\\"\\\"\\n for i, item in enumerate(data):\\n if i == 0:\\n values = np.zeros((len(data), np.shape(data[i])[0]))\\n\\n if np.ndim(data[i]) == 1:\\n values[i, :] = data[i]\\n else:\\n values[i, :] = data[i][:, 0]\\n\\n if \\\"pearson\\\".startswith(version.lower()):\\n ccoef = np.zeros((values.shape[0], values.shape[0]))\\n rho = np.zeros((values.shape[0], values.shape[0]))\\n\\n for i in range(0, values.shape[0]):\\n for j in range(i + 1, values.shape[0]):\\n ccoef[i, j], rho[i, j] = stats.pearsonr(values[i, :], values[j, :])\\n ccoef[j, i] = ccoef[i, j]\\n rho[j, i] = rho[i, j]\\n\\n ccoef += np.identity(values.shape[0])\\n\\n elif \\\"spearman\\\".startswith(version.lower()):\\n ccoef, rho = stats.spearmanr(values, axis = 1)\\n\\n if verbose > 0:\\n head = \\\"Cor-Coef for the following variables:\\\"\\n ut.infoPrint(head, sep_after = False)\\n print(\\\"-\\\" * len(head))\\n for i, string in enumerate(var):\\n ut.infoPrint(\\\"%s\\\" % string, sep_before = False, sep_after = False)\\n print(\\\"=\\\" * len(head))\\n\\n return ccoef, rho, lbl\",\n \"def get_occr_jacobian(self, flattened=True):\\n\\n if flattened:\\n return np.reshape(a=self._occr_jacobian,\\n newshape=(np.prod(self.shape), self.shape[0]))\\n else:\\n return self._occr_jacobian\",\n \"def cmm(self) -> Optional[np.ndarray]:\\n if self.sensorsz is None:\\n return None\\n return self.c * self.sensorsz / self.imgsz\",\n \"def coef_(self):\\n assert self.sess is not None, \\\"Model has not been fitted yet!\\\"\\n return self.sess.run(self.W_lst)[0]\",\n \"def __getitem__( self, l ) :\\n\\n return( self.coefficients[l] )\",\n \"def zzx_mul_const(f, c):\\n if not c or not f:\\n return []\\n else:\\n return [ c * coeff for coeff in f ]\",\n \"def _get_covar(minuit):\\n n = len(minuit.parameters)\\n m = np.zeros((n, n))\\n for i1, k1 in enumerate(minuit.parameters):\\n for i2, k2 in enumerate(minuit.parameters):\\n if set([k1, k2]).issubset(minuit.list_of_vary_param()):\\n m[i1, i2] = minuit.covariance[(k1, k2)]\\n return m\",\n \"def getObjective(self, *args):\\n return _libsbml.FbcModelPlugin_getObjective(self, *args)\",\n \"def equation(self):\\n mat = np.empty((self.nunknowns, self.model.neq))\\n # rhs = np.zeros(self.nunknowns) # Needs to be initialized to zero\\n rhs = self.pc.copy()\\n for icp in range(self.ncp):\\n istart = icp * self.nlayers\\n # rhs[istart:istart+self.nlayers] = self.pc[]\\n ieq = 0\\n for e in self.model.elementlist:\\n if e.nunknowns > 0:\\n mat[istart:istart + self.nlayers, ieq:ieq + e.nunknowns] = \\\\\\n e.potinflayers(self.xc[icp], self.yc[icp], self.layers)\\n if e == self:\\n mat[istart:istart + self.nlayers, ieq:ieq + e.nunknowns] -= self.resfac[icp]\\n ieq += e.nunknowns\\n else:\\n rhs[istart:istart + self.nlayers] -= \\\\\\n e.potentiallayers(self.xc[icp], self.yc[icp],\\n self.layers) # Pretty cool that this works, really\\n return mat, rhs\",\n \"def conditional_component_covs(self):\\n return np.array([d.conditional_cov() for d in self.conditionalMVNs])\",\n \"def get_coeff(self):\\n return bernoulli(self.degree+1) / factorial(self.degree + 1)\",\n \"def GetCoefficient(self):\\n self.coefficient = max([max([j for j in self.price[i]]) for i in self.price])\",\n \"def evaluate_c(self, x, out=None, **kwargs):\\n return self._base_nlp.evaluate_c(x, out=out, **kwargs)\",\n \"def getCL(self):\\r\\n return self.cL;\",\n \"def __init__(self, coefficients):\\n self.coefficients = coefficients\",\n \"def all_coeffs(f):\\n return dmp_all_coeffs(f.rep, f.lev, f.dom)\",\n \"def _update_c_and_beta(self):\\n labels = np.log(self._gv())\\n if self.update_c:\\n clf = sklearn.linear_model.LinearRegression(\\n fit_intercept=False).fit(self.ones_logx, labels)\\n log_beta = clf.coef_[0]\\n self.c = clf.coef_[1]\\n else:\\n log_beta = np.mean(labels - self.ones_logx[:, 1] * self.c)\\n self.beta = np.exp(log_beta)\",\n \"def Cvec(self):\\n return vec(self.xc, self.yc)\",\n \"def cofactorMatrix(self):\\n returnvalue = Matrix()\\n for i in range(self._height):\\n newRow = list()\\n for j in range(self._width):\\n newRow.append(self.cofactor(i, j))\\n returnvalue.addRow(*newRow)\\n return returnvalue\",\n \"def corrcoef(self):\\r\\n return np.corrcoef(self.input.data)\",\n \"def equation(self):\\n mat = np.empty((self.nunknowns, self.model.neq))\\n rhs = np.zeros(self.nunknowns) # Needs to be initialized to zero\\n for icp in range(self.ncp):\\n istart = icp * self.nlayers\\n rhs[istart:istart + self.nlayers] = self.pc\\n ieq = 0\\n for e in self.model.elementlist:\\n if e.nunknowns > 0:\\n mat[istart:istart + self.nlayers, ieq:ieq + e.nunknowns] = \\\\\\n e.potinflayers(self.xc[icp], self.yc[icp], self.layers)\\n ieq += e.nunknowns\\n else:\\n rhs[istart:istart + self.nlayers] -= \\\\\\n e.potentiallayers(self.xc[icp], self.yc[icp],\\n self.layers) # Pretty cool that this works, really\\n return mat, rhs\",\n \"def make_coco_labels(real_c):\\n y = np.eye(real_c.size(1))\\n\\n fixed_c_list = []\\n\\n # single object addition and removal\\n for i in range(2*real_c.size(1)):\\n fixed_c = real_c.clone()\\n for c in fixed_c:\\n if i%2:\\n c[i//2] = 0.\\n else:\\n c[i//2] = 1.\\n fixed_c_list.append(Variable(fixed_c, volatile=True).cuda())\\n\\n # multi-attribute transfer (H+G, H+A, G+A, H+G+A)\\n #if self.dataset == 'CelebA':\\n # for i in range(4):\\n # fixed_c = real_c.clone()\\n # for c in fixed_c:\\n # if i in [0, 1, 3]: # Hair color to brown\\n # c[:3] = y[2]\\n # if i in [0, 2, 3]: # Gender\\n # c[3] = 0 if c[3] == 1 else 1\\n # if i in [1, 2, 3]: # Aged\\n # c[4] = 0 if c[4] == 1 else 1\\n # fixed_c_list.append(self.to_var(fixed_c, volatile=True))\\n return fixed_c_list\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65427005","0.63974696","0.63769525","0.635677","0.635677","0.625591","0.62489706","0.6245763","0.6245763","0.61773825","0.61445075","0.612955","0.6082035","0.60051066","0.5921834","0.5798229","0.57925135","0.5790028","0.57677597","0.5748355","0.56996524","0.56866884","0.5678868","0.5660289","0.56551886","0.5645334","0.5631907","0.56194377","0.55952287","0.5585196","0.5554225","0.5535612","0.5509219","0.5486452","0.5471713","0.5414589","0.5400946","0.5390036","0.5389999","0.5389698","0.53864163","0.538137","0.5367144","0.5362608","0.5360937","0.5359996","0.5352507","0.532132","0.5320458","0.52973205","0.5296805","0.52850145","0.5283538","0.5276012","0.5273244","0.52631235","0.52587265","0.5257479","0.5250512","0.5249792","0.5249792","0.52393186","0.5231366","0.5226637","0.5221549","0.5212776","0.5204631","0.52026063","0.5198298","0.5197507","0.51969194","0.5188116","0.5184303","0.5175637","0.5174216","0.5173539","0.5172179","0.5171057","0.51512486","0.51365334","0.5131517","0.5127003","0.512656","0.5124153","0.51238847","0.51214623","0.5119889","0.5117269","0.5114192","0.5113708","0.51017445","0.5097059","0.50959885","0.50936913","0.50929534","0.5090983","0.5082793","0.50759983","0.5073936","0.5073173"],"string":"[\n \"0.65427005\",\n \"0.63974696\",\n \"0.63769525\",\n \"0.635677\",\n \"0.635677\",\n \"0.625591\",\n \"0.62489706\",\n \"0.6245763\",\n \"0.6245763\",\n \"0.61773825\",\n \"0.61445075\",\n \"0.612955\",\n \"0.6082035\",\n \"0.60051066\",\n \"0.5921834\",\n \"0.5798229\",\n \"0.57925135\",\n \"0.5790028\",\n \"0.57677597\",\n \"0.5748355\",\n \"0.56996524\",\n \"0.56866884\",\n \"0.5678868\",\n \"0.5660289\",\n \"0.56551886\",\n \"0.5645334\",\n \"0.5631907\",\n \"0.56194377\",\n \"0.55952287\",\n \"0.5585196\",\n \"0.5554225\",\n \"0.5535612\",\n \"0.5509219\",\n \"0.5486452\",\n \"0.5471713\",\n \"0.5414589\",\n \"0.5400946\",\n \"0.5390036\",\n \"0.5389999\",\n \"0.5389698\",\n \"0.53864163\",\n \"0.538137\",\n \"0.5367144\",\n \"0.5362608\",\n \"0.5360937\",\n \"0.5359996\",\n \"0.5352507\",\n \"0.532132\",\n \"0.5320458\",\n \"0.52973205\",\n \"0.5296805\",\n \"0.52850145\",\n \"0.5283538\",\n \"0.5276012\",\n \"0.5273244\",\n \"0.52631235\",\n \"0.52587265\",\n \"0.5257479\",\n \"0.5250512\",\n \"0.5249792\",\n \"0.5249792\",\n \"0.52393186\",\n \"0.5231366\",\n \"0.5226637\",\n \"0.5221549\",\n \"0.5212776\",\n \"0.5204631\",\n \"0.52026063\",\n \"0.5198298\",\n \"0.5197507\",\n \"0.51969194\",\n \"0.5188116\",\n \"0.5184303\",\n \"0.5175637\",\n \"0.5174216\",\n \"0.5173539\",\n \"0.5172179\",\n \"0.5171057\",\n \"0.51512486\",\n \"0.51365334\",\n \"0.5131517\",\n \"0.5127003\",\n \"0.512656\",\n \"0.5124153\",\n \"0.51238847\",\n \"0.51214623\",\n \"0.5119889\",\n \"0.5117269\",\n \"0.5114192\",\n \"0.5113708\",\n \"0.51017445\",\n \"0.5097059\",\n \"0.50959885\",\n \"0.50936913\",\n \"0.50929534\",\n \"0.5090983\",\n \"0.5082793\",\n \"0.50759983\",\n \"0.5073936\",\n \"0.5073173\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61323375"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":11791,"cells":{"query":{"kind":"string","value":"Obtains the fixed term in the objective. getcfix(self)"},"document":{"kind":"string","value":"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)"},"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 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 getCF(self):\r\n return self.lEq;","def CFL(self):\n return self.__CFL","def fixed(self):\n return self.f_fixed().m_fixed()","def fcp(self, var='x'):\n return self.charpoly(var).factor()","def fx(self) -> float:\n\n return self.intrinsic_matrix[0, 0]","def getCoefficient(self):\n return _libsbml.FluxObjective_getCoefficient(self)","def fixed_cost(self):\n return np.einsum('i->', self.c[self.f])","def tctfdfc(x):\n if isinstance(x,Fdf) :\n pass\n else : \n x = Fdf.constant(x)\n return x","def cf(self):\n if hasattr(self, \"_cf_cache\"):\n return self._cf_cache\n return np.array([conf.cf for conf in self.configurations], dtype=int)","def cpf(self):\n return self._cpf","def get_fx(self):\n return self.fx[:self.nump, :]","def get_ctf(ima):\n\tfrom EMAN2 import EMAN2Ctf\n\tctf_params = ima.get_attr(\"ctf\")\t\n\treturn ctf_params.defocus, ctf_params.cs, ctf_params.voltage, ctf_params.apix, ctf_params.bfactor, ctf_params.ampcont, ctf_params.dfdiff, ctf_params.dfang","def couleur_fond(self):\n return self.fond * self.ka","def get_icdf(self, xx):\n return self.parent.ppf(xx)","def cole_coeff(self):\n return self.diseq_coeff(standardize=True)","def pareto_front_cut(self):\n return self.NDA([kernel.objective_values for kernel in self.kernels \\\n if kernel.objective_values is not None],\n self.reference_point)","def C(self, y, x):\n return self.minor(y,x).det()*(-1.0)**(y+x+2.0)","def getFactura(self): \n return self.caja","def getFactura(self): \n return self.caja","def cdf(self, x):\n from scipy.special import betainc\n sq_x = x * x\n return np.where(\n sq_x < 1., betainc(self.m / 2.0, self.n / 2.0, sq_x),\n np.ones_like(x))","def getCDF(self):\n return self.cdfSample","def getTraitCovarFun(self, term_i):\n assert term_i < self.n_randEffs, 'VarianceDecomposition:: specied term out of range'\n return self.trait_covars[term_i]","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 cdf(self, x):\n\n if type(x) is np.float64:\n x = np.array([x])\n\n ndx = [np.argmin(np.abs(self.xs - x[i])) for i in range(x.size)]\n\n return self.ys[ndx]","def cdf(self,x):\n if self.base == 'natural':\n cdfValue = (math.log(x)-self.lowerBound)/(self.upperBound-self.lowerBound)\n else:\n cdfValue = (math.log10(x)-self.lowerBound)/(self.upperBound-self.lowerBound)\n return cdfValue","def n_cf(self):\n return self._configurations[0].n_cf","def f_vector(self):\n try:\n return self._f_vector\n except AttributeError:\n self._f_vector = vector(ZZ,[len(x) for x in self.face_lattice().level_sets()])\n return self._f_vector","def cdf(self,x):\n sortedMapping = sorted(self.mapping.items(), key=operator.itemgetter(0))\n if x == sortedMapping[-1][0]:\n return 1.0\n if x in self.values:\n cumulative=0.0\n for element in sortedMapping:\n cumulative += element[1]\n if x == ( float(element[0]) if self.isFloat else element[0] ):\n return cumulative\n else:\n if self.isFloat:\n cumulative=0.0\n for element in sortedMapping:\n cumulative += element[1]\n if x >= element[0]:\n return cumulative\n # if we reach this point we must error out\n self.raiseAnError(IOError,'Categorical distribution cannot calculate cdf for ' + str(x))","def xcoeff(self):\n a, c, d, b = self.to_ccw()\n p1, q1 = a + b, c + d\n p2, q2 = a + c, b + d\n n = p1 + q1\n\n cov = self.covar()\n\n if n == 0:\n return np.nan\n elif a == n or d == n:\n return 0.5\n elif b == n or c == n:\n return -1.0\n elif cov > 0.0:\n return _div(cov, min(p1 * q2, p2 * q1))\n elif cov < 0.0:\n return _div(cov, min(n * c, n * b))\n else:\n return 0.0","def focn(self):\n return self.table[1, 1] / (self.table[1, 0] + self.table[1, 1])","def _excitonic_coft_old(self,SS,AG,n):\n \n # FIXME: works only for 2 level molecules\n \n c0 = AG.monomers[0].get_egcf((0,1))\n Nt = len(c0)\n \n # SystemBathInteraction\n sbi = AG.get_SystemBathInteraction()\n # CorrelationFunctionMatrix\n cfm = sbi.CC\n \n # get number of monomeric basis states\n Na = 0\n for monomer in AG.monomers:\n Na += monomer.nel-1\n \n ct = numpy.zeros((Nt),dtype=numpy.complex128)\n #Na = AG.nmono\n for kk in range(Na):\n \n #nkk = AG.monomers[kk].egcf_mapping[0]\n \n for ll in range(Na):\n \n #nll = AG.monomers[ll].egcf_mapping[0]\n \n ct += ((SS[kk+1,n+1]**2)*(SS[ll+1,n+1]**2)*cfm.get_coft(kk,ll))\n #*AG.egcf_matrix.get_coft(nkk,nll))\n \n return ct","def cci(self) -> float:\n return self._cci","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 get_cov_re(self):\n pa = self._params[self.k_fe:]\n\n cov_re = np.zeros((self.k_re, self.k_re))\n cov_re[self._ix] = pa\n if self.use_sqrt:\n cov_re = np.dot(cov_re, cov_re.T)\n else:\n cov_re = (cov_re + cov_re.T) - np.diag(np.diag(cov_re))\n\n return cov_re","def _fv(self):\n return self.beta * (self.x ** self.c)","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 ffb_nc (cls):\r\n b=(bin(c0+jmax_raw)[-(cls)])\r\n return -(2*int(b)-1)","def coefficient(self) -> float:\n ...","def getAffix(self,):\n\t\treturn self.affix;","def get_cf(self, cf_name):\n return self._cf_dict.get(cf_name)","def coeff(self):\n return self._coeff","def SolveFcn(self):\n return _hypre.HypreSolver_SolveFcn(self)","def calculate(self):\n\n return self.confusion_matrix.fp","def get_cfi(data):\n if data is None:\n raise EmptyDataError('[!] Invalid data value')\n\n result = TA.CFI(data)\n if result is None:\n raise IndicatorException\n return result","def get_function(self):\n return self.element.get_basis_functions()[self.n]","def _get_concentration(self, state):\n return self.fc(state.float_features).exp() + self.EPSILON","def cdf(self,x):\n if self.functionType == 'cdf':\n cdfValue = self.cdfFunc(x)\n else:\n cdfValue = self.pdfFunc.integral(self.data[0][0],x)\n return cdfValue","def c(self) -> np.ndarray:\n return self._vector[10:12]","def comptcptotalquantum(self) :\n\t\ttry :\n\t\t\treturn self._comptcptotalquantum\n\t\texcept Exception as e:\n\t\t\traise e","def SolveFcn(self):\n return _hypre.HypreDiagScale_SolveFcn(self)","def getCVTerm(self, *args):\n return _libsbml.SBase_getCVTerm(self, *args)","def score_cutoff_(self):\n return self.predictor.score_cutoff_fun(self.scores_,\n self.score_coefficient)","def isotropic_correction_front(self):\n return self.cartesian_map_array(self.IsotropicCorrection(self,'front'))","def fy(self) -> float:\n return self.intrinsic_matrix[1, 1]","def getBasis(self):\n return self.__basis","def F(self, x):\n F = np.zeros((1, x.shape[-1]))\n # Column indices of state vectors that fulfill the condition z<z0\n contact = x[1, :]<self.z0\n F[0, ~contact] = -self.H*self.R/(6*x[1, ~contact]**2)\n F[0, contact] = (4/3)*self.E*np.sqrt(self.R)*(self.z0 - x[1, contact])**1.5 - self.H*self.R/(6*self.z0**2)\n\n return F","def icdf(self, value):\n return self._normal.icdf(value)","def getC(self):\n\t\treturn self.c","def CFL(self, dt):\r\n # get the diameter of each cell of the mesh, as dx\r\n h = CellDiameter(self.mesh)\r\n\r\n # set as a Constant for speed\r\n dt = Constant(dt)\r\n\r\n # compute the CFL number cell wise\r\n # set function space for math purposes\r\n DG = FunctionSpace(self.mesh, \"DG\", 0)\r\n\r\n # compute the CFL criterion\r\n CFL = project(sqrt(inner(self.u, self.u))*dt/h, DG)\r\n\r\n # return the maximum CFL criterion over the body\r\n return(np.max(CFL.vector()[:]))","def dof(self):\n return (self.sqivar > 0).sum() - self.nstar","def c(self):\n if self.__c is not None:\n return self.__c\n else:\n raise ValueError(\"Run .fit() first!\")","def eci(self):\n num_ext_terms = len(self._subspace.external_terms) # check for extra terms\n coefs = self.coefs[:-num_ext_terms] if num_ext_terms else self.coefs[:]\n eci = coefs.copy()\n eci = eci / self._subspace.function_total_multiplicities\n return eci","def covar(self):\n a, c, d, b = self.to_ccw()\n return a * d - b * c","def cofactors(self,x,y):\r\n return self.factorset(x) & self.factorset(y)","def c(self, i):\n value = self.b(i)\n if i == self.N:\n return value\n else:\n for j in range(i+1, self.N+1):\n value = value - self.a(i,j) * self.C[j]\n return value","def __float__(self) -> float:\n\n if self.isScalar():\n return float(self[()])\n else:\n raise ValueError(\"non-scalar coefficients are non-zero\")","def get_cfft(self):\n return self.get_rfft().get_cfft()","def cmask(self):\n mask = np.zeros(18)\n if 'full' in self.CONS: mask[:] = 1\n if 'f0' in self.CONS: mask[0] = 1\n if 'f1' in self.CONS: mask[1:4] = 1\n if 'f2' in self.CONS: mask[4:10] = 1\n if 'vx' in self.CONS: mask[10] = 1\n if 'vy' in self.CONS: mask[11] = 1\n if 'vz' in self.CONS: mask[12] = 1\n if 'TG' in self.CONS: mask[13:18] = 1\n return mask>0","def get_flux(self):\n if self.contour_exists:\n return compute_obj_intensity(self.data, self.largest_cont)\n else:\n raise ValueError(\"No large bright object exists to measure the flux of!\")","def SolveFcn(self):\n return _hypre.HypreFGMRES_SolveFcn(self)","def get_exact_classical_fval_solution(qubit_operator, offset):\n result = get_classical_solver_result(qubit_operator, offset)\n return result.fval","def _fixed_indicies(self):\n fixed_inds = self.constraints == 'fixed'\n return fixed_inds","def cdf(x, point):\n raise NotImplementedError(\"The cdf method has not yet been implemented.\")","def SolveFcn(self):\n return _hypre.HyprePCG_SolveFcn(self)","def estimate_diffusion_coefficient(n_CFx: int) -> t.Diffusivity:\n return t.Diffusivity(10 ** (-4.5360 + -0.1088 * n_CFx), \"cm^2/s\")","def getFluxObjective(self, *args):\n return _libsbml.Objective_getFluxObjective(self, *args)","def test_get_cl_fix():\n fix = Fix.get_fixes('CMIP5', 'MIROC-ESM', 'Amon', 'cl')\n assert fix == [Cl(None), AllVars(None)]","def Coaxial(movableAxis: str, fixedAxis: str, flip: Boolean) -> \"Feature\":\n return Feature()","def functional_margin(self, index):\n y = self.targets[index, :].copy()\n y[y == 0] = -1\n fm = y.T.dot(\n self.hypothesis.features[index, :].dot(self.get_parameters()))\n return fm","def clim(self):\n return self._clim","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 cofactor(self, row, column):\n if not self.isSquare():\n raise ValueError(\"Cofactor is not defined for a non-square matrix\")\n return ((-1) ** (row + column)) * self.minor(row, column)","def dof(self):\n if self._dof is None:\n self._dof = self.tri.nbr_cells - (self.tri.columns.size - 1) + self.tri.index.size\n return(self._dof)","def calc_qcorr(self) -> Dict[int, float]:\n return self._calc_qcorr","def c(self):\r\n return self.__c","def cigtab(self, y):\r\n X = [y] if np.isscalar(y[0]) else y\r\n f = [1e-4 * x[0]**2 + 1e4 * x[1]**2 + sum(x[2:]**2) for x in X]\r\n return f if len(f) > 1 else f[0]","def counit(self, element):\n return element.coefficient([])","def Initialize(self):\n return _gmat_py.CSFixed_Initialize(self)","def csi(self):\n return self.table[0, 0] / (self.table[0, 0] + self.table[0, 1] + self.table[1, 0])","def cdf(self,x):\n if self.method == 'spline':\n coordinate = distribution1D.vectord_cxx(len(x))\n for i in range(len(x)):\n coordinate[i] = x[i]\n cdfValue = self._distribution.cdf(coordinate)\n else:\n self.raiseAnError(NotImplementedError,'cdf not yet implemented for ' + self.method + ' method')\n return cdfValue","def calculate(self):\n\n return self.confusion_matrix.fn","def scalar(self):\n return self.q[0]","def get_correlation(self, f, peaks):\n c = 0\n for s in peaks:\n c += e ** -((2 * (f - s[0]) / self.spacing) ** 2)\n \n return c","def C(self,X):\n return(\n 0\n )","def cofactorMatrix(self):\n returnvalue = Matrix()\n for i in range(self._height):\n newRow = list()\n for j in range(self._width):\n newRow.append(self.cofactor(i, j))\n returnvalue.addRow(*newRow)\n return returnvalue","def cie_lab(self):\n K = Fraction(1, 3) * Fraction(29, 6) ** 2\n e = Fraction(6, 29) ** 3\n x, y, z = (n / m for n, m in zip(self.cie_xyz, D65))\n fx, fy, fz = (\n n ** Fraction(1, 3) if n > e else K * n + Fraction(4, 29)\n for n in (x, y, z)\n )\n return (116 * fy - 16, 500 * (fx - fy), 200 * (fy - fz))","def coefficients(self):\r\n return self.coef_['x']","def C(self, q, dq):\n # check for function in dictionary\n if self._C is None:\n self._C = self._calc_C()\n parameters = tuple(q) + tuple(dq)\n return np.array(self._C(*parameters), dtype='float32')","def npix(self):\n return self._npix"],"string":"[\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 getCF(self):\\r\\n return self.lEq;\",\n \"def CFL(self):\\n return self.__CFL\",\n \"def fixed(self):\\n return self.f_fixed().m_fixed()\",\n \"def fcp(self, var='x'):\\n return self.charpoly(var).factor()\",\n \"def fx(self) -> float:\\n\\n return self.intrinsic_matrix[0, 0]\",\n \"def getCoefficient(self):\\n return _libsbml.FluxObjective_getCoefficient(self)\",\n \"def fixed_cost(self):\\n return np.einsum('i->', self.c[self.f])\",\n \"def tctfdfc(x):\\n if isinstance(x,Fdf) :\\n pass\\n else : \\n x = Fdf.constant(x)\\n return x\",\n \"def cf(self):\\n if hasattr(self, \\\"_cf_cache\\\"):\\n return self._cf_cache\\n return np.array([conf.cf for conf in self.configurations], dtype=int)\",\n \"def cpf(self):\\n return self._cpf\",\n \"def get_fx(self):\\n return self.fx[:self.nump, :]\",\n \"def get_ctf(ima):\\n\\tfrom EMAN2 import EMAN2Ctf\\n\\tctf_params = ima.get_attr(\\\"ctf\\\")\\t\\n\\treturn ctf_params.defocus, ctf_params.cs, ctf_params.voltage, ctf_params.apix, ctf_params.bfactor, ctf_params.ampcont, ctf_params.dfdiff, ctf_params.dfang\",\n \"def couleur_fond(self):\\n return self.fond * self.ka\",\n \"def get_icdf(self, xx):\\n return self.parent.ppf(xx)\",\n \"def cole_coeff(self):\\n return self.diseq_coeff(standardize=True)\",\n \"def pareto_front_cut(self):\\n return self.NDA([kernel.objective_values for kernel in self.kernels \\\\\\n if kernel.objective_values is not None],\\n self.reference_point)\",\n \"def C(self, y, x):\\n return self.minor(y,x).det()*(-1.0)**(y+x+2.0)\",\n \"def getFactura(self): \\n return self.caja\",\n \"def getFactura(self): \\n return self.caja\",\n \"def cdf(self, x):\\n from scipy.special import betainc\\n sq_x = x * x\\n return np.where(\\n sq_x < 1., betainc(self.m / 2.0, self.n / 2.0, sq_x),\\n np.ones_like(x))\",\n \"def getCDF(self):\\n return self.cdfSample\",\n \"def getTraitCovarFun(self, term_i):\\n assert term_i < self.n_randEffs, 'VarianceDecomposition:: specied term out of range'\\n return self.trait_covars[term_i]\",\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 cdf(self, x):\\n\\n if type(x) is np.float64:\\n x = np.array([x])\\n\\n ndx = [np.argmin(np.abs(self.xs - x[i])) for i in range(x.size)]\\n\\n return self.ys[ndx]\",\n \"def cdf(self,x):\\n if self.base == 'natural':\\n cdfValue = (math.log(x)-self.lowerBound)/(self.upperBound-self.lowerBound)\\n else:\\n cdfValue = (math.log10(x)-self.lowerBound)/(self.upperBound-self.lowerBound)\\n return cdfValue\",\n \"def n_cf(self):\\n return self._configurations[0].n_cf\",\n \"def f_vector(self):\\n try:\\n return self._f_vector\\n except AttributeError:\\n self._f_vector = vector(ZZ,[len(x) for x in self.face_lattice().level_sets()])\\n return self._f_vector\",\n \"def cdf(self,x):\\n sortedMapping = sorted(self.mapping.items(), key=operator.itemgetter(0))\\n if x == sortedMapping[-1][0]:\\n return 1.0\\n if x in self.values:\\n cumulative=0.0\\n for element in sortedMapping:\\n cumulative += element[1]\\n if x == ( float(element[0]) if self.isFloat else element[0] ):\\n return cumulative\\n else:\\n if self.isFloat:\\n cumulative=0.0\\n for element in sortedMapping:\\n cumulative += element[1]\\n if x >= element[0]:\\n return cumulative\\n # if we reach this point we must error out\\n self.raiseAnError(IOError,'Categorical distribution cannot calculate cdf for ' + str(x))\",\n \"def xcoeff(self):\\n a, c, d, b = self.to_ccw()\\n p1, q1 = a + b, c + d\\n p2, q2 = a + c, b + d\\n n = p1 + q1\\n\\n cov = self.covar()\\n\\n if n == 0:\\n return np.nan\\n elif a == n or d == n:\\n return 0.5\\n elif b == n or c == n:\\n return -1.0\\n elif cov > 0.0:\\n return _div(cov, min(p1 * q2, p2 * q1))\\n elif cov < 0.0:\\n return _div(cov, min(n * c, n * b))\\n else:\\n return 0.0\",\n \"def focn(self):\\n return self.table[1, 1] / (self.table[1, 0] + self.table[1, 1])\",\n \"def _excitonic_coft_old(self,SS,AG,n):\\n \\n # FIXME: works only for 2 level molecules\\n \\n c0 = AG.monomers[0].get_egcf((0,1))\\n Nt = len(c0)\\n \\n # SystemBathInteraction\\n sbi = AG.get_SystemBathInteraction()\\n # CorrelationFunctionMatrix\\n cfm = sbi.CC\\n \\n # get number of monomeric basis states\\n Na = 0\\n for monomer in AG.monomers:\\n Na += monomer.nel-1\\n \\n ct = numpy.zeros((Nt),dtype=numpy.complex128)\\n #Na = AG.nmono\\n for kk in range(Na):\\n \\n #nkk = AG.monomers[kk].egcf_mapping[0]\\n \\n for ll in range(Na):\\n \\n #nll = AG.monomers[ll].egcf_mapping[0]\\n \\n ct += ((SS[kk+1,n+1]**2)*(SS[ll+1,n+1]**2)*cfm.get_coft(kk,ll))\\n #*AG.egcf_matrix.get_coft(nkk,nll))\\n \\n return ct\",\n \"def cci(self) -> float:\\n return self._cci\",\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 get_cov_re(self):\\n pa = self._params[self.k_fe:]\\n\\n cov_re = np.zeros((self.k_re, self.k_re))\\n cov_re[self._ix] = pa\\n if self.use_sqrt:\\n cov_re = np.dot(cov_re, cov_re.T)\\n else:\\n cov_re = (cov_re + cov_re.T) - np.diag(np.diag(cov_re))\\n\\n return cov_re\",\n \"def _fv(self):\\n return self.beta * (self.x ** self.c)\",\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 ffb_nc (cls):\\r\\n b=(bin(c0+jmax_raw)[-(cls)])\\r\\n return -(2*int(b)-1)\",\n \"def coefficient(self) -> float:\\n ...\",\n \"def getAffix(self,):\\n\\t\\treturn self.affix;\",\n \"def get_cf(self, cf_name):\\n return self._cf_dict.get(cf_name)\",\n \"def coeff(self):\\n return self._coeff\",\n \"def SolveFcn(self):\\n return _hypre.HypreSolver_SolveFcn(self)\",\n \"def calculate(self):\\n\\n return self.confusion_matrix.fp\",\n \"def get_cfi(data):\\n if data is None:\\n raise EmptyDataError('[!] Invalid data value')\\n\\n result = TA.CFI(data)\\n if result is None:\\n raise IndicatorException\\n return result\",\n \"def get_function(self):\\n return self.element.get_basis_functions()[self.n]\",\n \"def _get_concentration(self, state):\\n return self.fc(state.float_features).exp() + self.EPSILON\",\n \"def cdf(self,x):\\n if self.functionType == 'cdf':\\n cdfValue = self.cdfFunc(x)\\n else:\\n cdfValue = self.pdfFunc.integral(self.data[0][0],x)\\n return cdfValue\",\n \"def c(self) -> np.ndarray:\\n return self._vector[10:12]\",\n \"def comptcptotalquantum(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._comptcptotalquantum\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def SolveFcn(self):\\n return _hypre.HypreDiagScale_SolveFcn(self)\",\n \"def getCVTerm(self, *args):\\n return _libsbml.SBase_getCVTerm(self, *args)\",\n \"def score_cutoff_(self):\\n return self.predictor.score_cutoff_fun(self.scores_,\\n self.score_coefficient)\",\n \"def isotropic_correction_front(self):\\n return self.cartesian_map_array(self.IsotropicCorrection(self,'front'))\",\n \"def fy(self) -> float:\\n return self.intrinsic_matrix[1, 1]\",\n \"def getBasis(self):\\n return self.__basis\",\n \"def F(self, x):\\n F = np.zeros((1, x.shape[-1]))\\n # Column indices of state vectors that fulfill the condition z<z0\\n contact = x[1, :]<self.z0\\n F[0, ~contact] = -self.H*self.R/(6*x[1, ~contact]**2)\\n F[0, contact] = (4/3)*self.E*np.sqrt(self.R)*(self.z0 - x[1, contact])**1.5 - self.H*self.R/(6*self.z0**2)\\n\\n return F\",\n \"def icdf(self, value):\\n return self._normal.icdf(value)\",\n \"def getC(self):\\n\\t\\treturn self.c\",\n \"def CFL(self, dt):\\r\\n # get the diameter of each cell of the mesh, as dx\\r\\n h = CellDiameter(self.mesh)\\r\\n\\r\\n # set as a Constant for speed\\r\\n dt = Constant(dt)\\r\\n\\r\\n # compute the CFL number cell wise\\r\\n # set function space for math purposes\\r\\n DG = FunctionSpace(self.mesh, \\\"DG\\\", 0)\\r\\n\\r\\n # compute the CFL criterion\\r\\n CFL = project(sqrt(inner(self.u, self.u))*dt/h, DG)\\r\\n\\r\\n # return the maximum CFL criterion over the body\\r\\n return(np.max(CFL.vector()[:]))\",\n \"def dof(self):\\n return (self.sqivar > 0).sum() - self.nstar\",\n \"def c(self):\\n if self.__c is not None:\\n return self.__c\\n else:\\n raise ValueError(\\\"Run .fit() first!\\\")\",\n \"def eci(self):\\n num_ext_terms = len(self._subspace.external_terms) # check for extra terms\\n coefs = self.coefs[:-num_ext_terms] if num_ext_terms else self.coefs[:]\\n eci = coefs.copy()\\n eci = eci / self._subspace.function_total_multiplicities\\n return eci\",\n \"def covar(self):\\n a, c, d, b = self.to_ccw()\\n return a * d - b * c\",\n \"def cofactors(self,x,y):\\r\\n return self.factorset(x) & self.factorset(y)\",\n \"def c(self, i):\\n value = self.b(i)\\n if i == self.N:\\n return value\\n else:\\n for j in range(i+1, self.N+1):\\n value = value - self.a(i,j) * self.C[j]\\n return value\",\n \"def __float__(self) -> float:\\n\\n if self.isScalar():\\n return float(self[()])\\n else:\\n raise ValueError(\\\"non-scalar coefficients are non-zero\\\")\",\n \"def get_cfft(self):\\n return self.get_rfft().get_cfft()\",\n \"def cmask(self):\\n mask = np.zeros(18)\\n if 'full' in self.CONS: mask[:] = 1\\n if 'f0' in self.CONS: mask[0] = 1\\n if 'f1' in self.CONS: mask[1:4] = 1\\n if 'f2' in self.CONS: mask[4:10] = 1\\n if 'vx' in self.CONS: mask[10] = 1\\n if 'vy' in self.CONS: mask[11] = 1\\n if 'vz' in self.CONS: mask[12] = 1\\n if 'TG' in self.CONS: mask[13:18] = 1\\n return mask>0\",\n \"def get_flux(self):\\n if self.contour_exists:\\n return compute_obj_intensity(self.data, self.largest_cont)\\n else:\\n raise ValueError(\\\"No large bright object exists to measure the flux of!\\\")\",\n \"def SolveFcn(self):\\n return _hypre.HypreFGMRES_SolveFcn(self)\",\n \"def get_exact_classical_fval_solution(qubit_operator, offset):\\n result = get_classical_solver_result(qubit_operator, offset)\\n return result.fval\",\n \"def _fixed_indicies(self):\\n fixed_inds = self.constraints == 'fixed'\\n return fixed_inds\",\n \"def cdf(x, point):\\n raise NotImplementedError(\\\"The cdf method has not yet been implemented.\\\")\",\n \"def SolveFcn(self):\\n return _hypre.HyprePCG_SolveFcn(self)\",\n \"def estimate_diffusion_coefficient(n_CFx: int) -> t.Diffusivity:\\n return t.Diffusivity(10 ** (-4.5360 + -0.1088 * n_CFx), \\\"cm^2/s\\\")\",\n \"def getFluxObjective(self, *args):\\n return _libsbml.Objective_getFluxObjective(self, *args)\",\n \"def test_get_cl_fix():\\n fix = Fix.get_fixes('CMIP5', 'MIROC-ESM', 'Amon', 'cl')\\n assert fix == [Cl(None), AllVars(None)]\",\n \"def Coaxial(movableAxis: str, fixedAxis: str, flip: Boolean) -> \\\"Feature\\\":\\n return Feature()\",\n \"def functional_margin(self, index):\\n y = self.targets[index, :].copy()\\n y[y == 0] = -1\\n fm = y.T.dot(\\n self.hypothesis.features[index, :].dot(self.get_parameters()))\\n return fm\",\n \"def clim(self):\\n return self._clim\",\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 cofactor(self, row, column):\\n if not self.isSquare():\\n raise ValueError(\\\"Cofactor is not defined for a non-square matrix\\\")\\n return ((-1) ** (row + column)) * self.minor(row, column)\",\n \"def dof(self):\\n if self._dof is None:\\n self._dof = self.tri.nbr_cells - (self.tri.columns.size - 1) + self.tri.index.size\\n return(self._dof)\",\n \"def calc_qcorr(self) -> Dict[int, float]:\\n return self._calc_qcorr\",\n \"def c(self):\\r\\n return self.__c\",\n \"def cigtab(self, y):\\r\\n X = [y] if np.isscalar(y[0]) else y\\r\\n f = [1e-4 * x[0]**2 + 1e4 * x[1]**2 + sum(x[2:]**2) for x in X]\\r\\n return f if len(f) > 1 else f[0]\",\n \"def counit(self, element):\\n return element.coefficient([])\",\n \"def Initialize(self):\\n return _gmat_py.CSFixed_Initialize(self)\",\n \"def csi(self):\\n return self.table[0, 0] / (self.table[0, 0] + self.table[0, 1] + self.table[1, 0])\",\n \"def cdf(self,x):\\n if self.method == 'spline':\\n coordinate = distribution1D.vectord_cxx(len(x))\\n for i in range(len(x)):\\n coordinate[i] = x[i]\\n cdfValue = self._distribution.cdf(coordinate)\\n else:\\n self.raiseAnError(NotImplementedError,'cdf not yet implemented for ' + self.method + ' method')\\n return cdfValue\",\n \"def calculate(self):\\n\\n return self.confusion_matrix.fn\",\n \"def scalar(self):\\n return self.q[0]\",\n \"def get_correlation(self, f, peaks):\\n c = 0\\n for s in peaks:\\n c += e ** -((2 * (f - s[0]) / self.spacing) ** 2)\\n \\n return c\",\n \"def C(self,X):\\n return(\\n 0\\n )\",\n \"def cofactorMatrix(self):\\n returnvalue = Matrix()\\n for i in range(self._height):\\n newRow = list()\\n for j in range(self._width):\\n newRow.append(self.cofactor(i, j))\\n returnvalue.addRow(*newRow)\\n return returnvalue\",\n \"def cie_lab(self):\\n K = Fraction(1, 3) * Fraction(29, 6) ** 2\\n e = Fraction(6, 29) ** 3\\n x, y, z = (n / m for n, m in zip(self.cie_xyz, D65))\\n fx, fy, fz = (\\n n ** Fraction(1, 3) if n > e else K * n + Fraction(4, 29)\\n for n in (x, y, z)\\n )\\n return (116 * fy - 16, 500 * (fx - fy), 200 * (fy - fz))\",\n \"def coefficients(self):\\r\\n return self.coef_['x']\",\n \"def C(self, q, dq):\\n # check for function in dictionary\\n if self._C is None:\\n self._C = self._calc_C()\\n parameters = tuple(q) + tuple(dq)\\n return np.array(self._C(*parameters), dtype='float32')\",\n \"def npix(self):\\n return self._npix\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7102011","0.69387436","0.61814743","0.61706793","0.6118419","0.6102831","0.60178024","0.59100634","0.58950984","0.5895022","0.5848309","0.58352464","0.5788822","0.5725575","0.57214016","0.56842697","0.56441844","0.56353074","0.56272084","0.56272084","0.5606963","0.5579843","0.55653703","0.55517346","0.55301386","0.5513291","0.5496895","0.54896843","0.5489501","0.5454264","0.54370135","0.54327995","0.54309833","0.5427201","0.5414566","0.54093546","0.54074436","0.5389023","0.53844833","0.5382565","0.53780174","0.53728247","0.5372717","0.5365975","0.53613645","0.53610957","0.53421676","0.53406155","0.5331689","0.5320945","0.53207177","0.53115994","0.5291615","0.52913934","0.5278633","0.5277659","0.52767324","0.5275833","0.52585775","0.5256626","0.5249408","0.524889","0.5248628","0.52472466","0.5246858","0.52433604","0.52419406","0.52418786","0.5236949","0.52257615","0.52242935","0.52234876","0.52218693","0.5215242","0.52120936","0.52087134","0.52084213","0.5197368","0.5194448","0.5190678","0.5189421","0.5185293","0.51852393","0.51850134","0.5181232","0.51800376","0.5179203","0.51769954","0.51735497","0.5162491","0.51548266","0.5154436","0.51503503","0.51502347","0.51467353","0.51453924","0.51434004","0.5142701","0.5138506","0.5138171"],"string":"[\n \"0.7102011\",\n \"0.69387436\",\n \"0.61814743\",\n \"0.61706793\",\n \"0.6118419\",\n \"0.6102831\",\n \"0.60178024\",\n \"0.59100634\",\n \"0.58950984\",\n \"0.5895022\",\n \"0.5848309\",\n \"0.58352464\",\n \"0.5788822\",\n \"0.5725575\",\n \"0.57214016\",\n \"0.56842697\",\n \"0.56441844\",\n \"0.56353074\",\n \"0.56272084\",\n \"0.56272084\",\n \"0.5606963\",\n \"0.5579843\",\n \"0.55653703\",\n \"0.55517346\",\n \"0.55301386\",\n \"0.5513291\",\n \"0.5496895\",\n \"0.54896843\",\n \"0.5489501\",\n \"0.5454264\",\n \"0.54370135\",\n \"0.54327995\",\n \"0.54309833\",\n \"0.5427201\",\n \"0.5414566\",\n \"0.54093546\",\n \"0.54074436\",\n \"0.5389023\",\n \"0.53844833\",\n \"0.5382565\",\n \"0.53780174\",\n \"0.53728247\",\n \"0.5372717\",\n \"0.5365975\",\n \"0.53613645\",\n \"0.53610957\",\n \"0.53421676\",\n \"0.53406155\",\n \"0.5331689\",\n \"0.5320945\",\n \"0.53207177\",\n \"0.53115994\",\n \"0.5291615\",\n \"0.52913934\",\n \"0.5278633\",\n \"0.5277659\",\n \"0.52767324\",\n \"0.5275833\",\n \"0.52585775\",\n \"0.5256626\",\n \"0.5249408\",\n \"0.524889\",\n \"0.5248628\",\n \"0.52472466\",\n \"0.5246858\",\n \"0.52433604\",\n \"0.52419406\",\n \"0.52418786\",\n \"0.5236949\",\n \"0.52257615\",\n \"0.52242935\",\n \"0.52234876\",\n \"0.52218693\",\n \"0.5215242\",\n \"0.52120936\",\n \"0.52087134\",\n \"0.52084213\",\n \"0.5197368\",\n \"0.5194448\",\n \"0.5190678\",\n \"0.5189421\",\n \"0.5185293\",\n \"0.51852393\",\n \"0.51850134\",\n \"0.5181232\",\n \"0.51800376\",\n \"0.5179203\",\n \"0.51769954\",\n \"0.51735497\",\n \"0.5162491\",\n \"0.51548266\",\n \"0.5154436\",\n \"0.51503503\",\n \"0.51502347\",\n \"0.51467353\",\n \"0.51453924\",\n \"0.51434004\",\n \"0.5142701\",\n \"0.5138506\",\n \"0.5138171\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.723974"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11792,"cells":{"query":{"kind":"string","value":"Obtains a cone. getcone(self,k_,submem_)"},"document":{"kind":"string","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)"},"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 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 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 getconeinfo(self,k_):\n ct_ = ctypes.c_int32()\n conepar_ = ctypes.c_double()\n nummem_ = ctypes.c_int32()\n res = __library__.MSK_XX_getconeinfo(self.__nativep,k_,ctypes.byref(ct_),ctypes.byref(conepar_),ctypes.byref(nummem_))\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 return (_ct_return_value,_conepar_return_value,_nummem_return_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 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 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 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 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 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 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 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 cone(df, mu, N, alphacov=2.0):\r\n return alphacov / ((N + 1.3)**2 + mu)","def cget(self, *args, **kwargs):\n return self._canvas.cget(*args, **kwargs)","def caget(PV):\n return epics.caget(PV)","def getC(self):\n\t\treturn self.c","def cole_coeff(self):\n return self.diseq_coeff(standardize=True)","def get_coft(self,n,m):\n if self.aggregate is None:\n \n return self.CC.get_coft(n,m)\n \n else:\n \n bn = self.aggregate.which_band[n]\n bm = self.aggregate.which_band[m]\n \n if ((bn == 0) and (bm == 0)):\n \n #print(bn,\"::\",n,m)\n return self.CC._cofts[0,:]\n \n elif ((bn == 1) and (bm == 1)):\n #print(bn,\"::\",n-1,m-1)\n \n return self.CC.get_coft(n-1,m-1)\n \n else:\n \n return self.CC._cofts[0,:]","def getnumconemem(self,k_):\n nummem_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumconemem(self.__nativep,k_,ctypes.byref(nummem_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nummem_ = nummem_.value\n _nummem_return_value = nummem_\n return (_nummem_return_value)","def create_cone(base=1.0, height=1.0, lat=20, lng=20, color=COLOR_WHITE):\n if lat >= 3 and lng >= 10:\n circlebase = create_circle(base - 0.05, 0.1, [0.0, 0.0, -1.0], color)\n obj = glGenLists(1)\n glNewList(obj, GL_COMPILE)\n glPushMatrix()\n glColor4fv(color)\n try:\n glutSolidCone(base, height, lat, lng)\n except:\n if not _ERRS[3]:\n printGLError(\n \"la version actual de OpenGL no posee la funcion glutSolidCone\")\n _ERRS[3] = True\n glCallList(circlebase)\n glPopMatrix()\n glEndList()\n return obj\n else:\n raise Exception(\n \"La latitud y longitud de la figura deben ser mayores a 3\")","def aveEy2CC(self):\n if self.dim < 2:\n return None\n if getattr(self, '_aveEy2CC', None) is None:\n # The number of cell centers in each direction\n n = self.vnC\n if(self.dim == 2):\n self._aveEy2CC = sp.kron(speye(n[1]), av(n[0]))\n elif(self.dim == 3):\n self._aveEy2CC = kron3(av(n[2]), speye(n[1]), av(n[0]))\n return self._aveEy2CC","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 cone(individual, position, height, width):\n value = 0.0\n for x, p in zip(individual, position):\n value += (x - p)**2\n return height - width * math.sqrt(value)","def forward_cone(self, p):\n return to_rec(zip(p, self.top))","def ppl_positive_cone(n):\n gs = ppl.Generator_System(ppl_zero_point(n))\n l = [0]*n\n for i in range(n):\n gs.insert(ppl.ray(ppl.Variable(i)))\n return ppl.C_Polyhedron(gs)","def get_coe_cluster(self, name_or_id, filters=None):\n return _utils._get_entity(self, 'coe_cluster', name_or_id, filters)","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 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 conj(self, o): \n return (o.inv()) * self * o","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 aveEx2CC(self):\n if getattr(self, '_aveEx2CC', None) is None:\n # The number of cell centers in each direction\n n = self.vnC\n if(self.dim == 1):\n self._aveEx2CC = speye(n[0])\n elif(self.dim == 2):\n self._aveEx2CC = sp.kron(av(n[1]), speye(n[0]))\n elif(self.dim == 3):\n self._aveEx2CC = kron3(av(n[2]), av(n[1]), speye(n[0]))\n return self._aveEx2CC","def ppl_cone(rays):\n n = len(rays[0])\n gs = ppl.Generator_System(ppl_zero_point(n))\n for r in rays:\n gs.insert(ppl.ray(sum(int(j) * ppl.Variable(i) for i,j in enumerate(r))))\n return ppl.C_Polyhedron(gs)","def get_cov_re(self):\n pa = self._params[self.k_fe:]\n\n cov_re = np.zeros((self.k_re, self.k_re))\n cov_re[self._ix] = pa\n if self.use_sqrt:\n cov_re = np.dot(cov_re, cov_re.T)\n else:\n cov_re = (cov_re + cov_re.T) - np.diag(np.diag(cov_re))\n\n return cov_re","def Ncen(self, m):\n pass","def get_coft_elsig(self,n_sig,m_sig): \n \n nb = numpy.sum(n_sig)\n mb = numpy.sum(m_sig)\n \n indices = []\n if mb == nb:\n ni = 0\n for na in n_sig:\n mi = 0\n for ma in m_sig:\n if ((na == 1) and (ma == 1)):\n indices.append([ni,mi]) \n mi += 1\n ni += 1\n \n ret = numpy.zeros((self.TimeAxis.length),dtype=numpy.complex128)\n for ind in indices:\n #print(nb,\":\",ind[0],ind[1])\n ret += self.get_coft(ind[0],ind[1]) \n \n \n return ret \n \n else:\n return self.CC._cofts[0,:]","def aveEz2CC(self):\n if self.dim < 3:\n return None\n if getattr(self, '_aveEz2CC', None) is None:\n # The number of cell centers in each direction\n n = self.vnC\n if(self.dim == 3):\n self._aveEz2CC = kron3(speye(n[2]), av(n[1]), av(n[0]))\n return self._aveEz2CC","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 cz(control: QubitInput, target: QubitInput) -> Instruction:\n return Instruction(CZ(), target=[control, target])","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 k_c(self, tl):\n\t\treturn self.KC0*exp(self.HKC/(R*self.TO)*(1. - self.TO/tl))","def C(self, r, n=None):\n return self.get_coefficient(r, n)","def cole(Te, nev):\n e4e2me2=eV2J**(2.5)/epsilon**(2.0)/np.sqrt(me)/4.0/np.pi/2.0**(1.5)\n return e4e2me2*nev*lnlambda(Te,nev)/Te**(1.5)","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 get_covec(self, key, oov_policy='err'):\n if self.W is None:\n raise ValueError(\"This one-sided embedding has no co-vectors.\")\n if self.handle_out_of_vocab(key, oov_policy):\n return self.unkW\n slice_obj = self._as_slice(key)\n return self.W[slice_obj]","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 getKeyWordConjByCoord(self, idCoord):\n kWord = KeyWord()\n i = 0\n while i < len(self.sentence):\n if self.sentence[i].getDependenceId() == idCoord and self.sentence[i].getDependenceType() == \"conj\":\n kWord = self.sentence[i]\n i += 1\n return kWord","def heights_cone(self, side=None):\n I = self.intersection_matrix()\n C = self.suspension_cone(side)\n\n from sage.geometry.polyhedron.constructor import Polyhedron\n return Polyhedron(rays=[-I*c.vector() for c in C.rays()])","def get_kc(data):\n if data is None:\n raise EmptyDataError('[!] Invalid data value')\n\n result = TA.KC(data)\n if result is None:\n raise IndicatorException\n return result","def cone(self, length):\n X, Y, Z = baseFromAngles(self.angles)\n result = polyhedron([], [], [])\n assert result.pnFacesInPoly()\n f = result.addVertex(self.focalPoint)\n for s in self.segments:\n vect0 = (self.focalDist * X +\n s[0][0] * Z +\n s[0][1] * Y).normalize()\n vect1 = (self.focalDist * X +\n s[1][0] * Z +\n s[1][1] * Y).normalize()\n assert result.pnFacesInPoly()\n vert0 = result.addVertex(vector(self.focalPoint) +\n length * vect0)\n assert result.pnFacesInPoly()\n vert1 = result.addVertex(vector(self.focalPoint) +\n length * vect1)\n assert result.pnFacesInPoly()\n try:\n result.addFace([f, vert1, vert0])\n except ValueError:\n result.plot()\n p = polyhedron([], [], [])\n p.addFace([f, vert1, vert0])\n p.union(result).plot()\n self.plot()\n assert False\n assert result.pnFacesInPoly()\n # WARNING : the cone is not closed at its top.\n # I'm not sure if this can cause issues.\n return result","def c(self):\r\n return self.__c","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 ckn(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"ckn\")","def cones_and_cylinders(\n target,\n pore_diameter='pore.diameter',\n throat_diameter='throat.diameter'\n):\n from openpnm.models.geometry import conduit_lengths\n out = conduit_lengths.cones_and_cylinders(\n target, pore_diameter=pore_diameter, throat_diameter=throat_diameter\n )\n return out[:, 1]","def conToCelc(faren):\n return (faren - 32)*(5/9)","def suspension_cone(self, winner=None):\n n = len(self)\n ieqs = []\n\n for i in range(1,len(self)):\n ieq = [0]*(n+1)\n for j in range(i):\n ieq[self._labels[0][j]+1] = 1\n ieqs.append(ieq)\n\n ieq = [0]*(n+1)\n for j in range(i):\n ieq[self._labels[1][j]+1] = -1\n ieqs.append(ieq)\n\n if winner is not None:\n winner = interval_conversion(winner)\n if winner == 0:\n ieqs.append([0] + [1] * len(self))\n elif winner == 1:\n ieqs.append([0] + [-1] * len(self))\n\n from sage.geometry.polyhedron.constructor import Polyhedron\n return Polyhedron(ieqs=ieqs)","def aic_c(self):\n if hasattr(self, '_aic_c'):\n return self._aic_c\n else:\n k = len(self.params)\n n = self.data['n'].sum()\n self._aic_c = self.aic() + (2*k**2 + 2*k)/(n - k - 1)\n return self._aic_c","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 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 robot_view_cone( Px, Py, yaw):\n d = 4 # max monitored distance: reasonably not more than 3.5-4m\n alpha = 1 # field of view: 57 deg kinect, 58 xtion, we can use exactly 1 rad (=57.3 deg)\n Lx = Px + d * (math.cos((yaw-alpha)/2))\n Ly = Py + d * (math.cos((yaw-alpha)/2))\n Rx = Px + d * (math.cos((yaw+alpha)/2))\n Ry = Py + d * (math.cos((yaw+alpha)/2))\n return [ [Lx, Ly], [Rx, Ry], [Px, Py] ]","def arccosh(self):\n return type(self)(self.parent(),\n self._simplify(self._express.arccosh()))","def concentration(self, num, step, doses=None):\n if doses is None:\n doses = {0: self.c_0}\n else:\n doses = {offset: dose * self.c_0 for offset, dose in doses.items()}\n return self._concentration(num, step, self.hl_a, self.hl_e, doses)","def get_canvas(self, key) -> tk.Canvas:\n try:\n canvas = self._Canvases[key]\n except KeyError:\n canvas = self._Canvases[key] = tk.Canvas()\n return canvas","def _excitonic_coft_old(self,SS,AG,n):\n \n # FIXME: works only for 2 level molecules\n \n c0 = AG.monomers[0].get_egcf((0,1))\n Nt = len(c0)\n \n # SystemBathInteraction\n sbi = AG.get_SystemBathInteraction()\n # CorrelationFunctionMatrix\n cfm = sbi.CC\n \n # get number of monomeric basis states\n Na = 0\n for monomer in AG.monomers:\n Na += monomer.nel-1\n \n ct = numpy.zeros((Nt),dtype=numpy.complex128)\n #Na = AG.nmono\n for kk in range(Na):\n \n #nkk = AG.monomers[kk].egcf_mapping[0]\n \n for ll in range(Na):\n \n #nll = AG.monomers[ll].egcf_mapping[0]\n \n ct += ((SS[kk+1,n+1]**2)*(SS[ll+1,n+1]**2)*cfm.get_coft(kk,ll))\n #*AG.egcf_matrix.get_coft(nkk,nll))\n \n return ct","def cci(self, n, array=False, length=None):\n \n if array:\n if length is not None:\n result = talib.CCI(self.high[-length:], self.low[-length:], self.close[-length:], n)\n else:\n result = talib.CCI(self.high, self.low, self.close, n)\n return result\n else:\n l = n+1\n result = talib.CCI(self.high[-l:], self.low[-l:], self.close[-l:], n)\n return result[-1]","def get_circuit(self, params):\n raise NotImplementedError","def polyCone(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True,\n constructionHistory: bool=True, createUVs: Union[int, bool]=2, height: Union[float,\n bool]=2.0, name: AnyStr=\"\", nodeState: Union[int, bool]=0, object: bool=True,\n radius: Union[float, bool]=1.0, roundCap: bool=False, subdivisionsAxis: Union[int,\n bool]=20, subdivisionsCap: Union[int, bool]=0, subdivisionsHeight: Union[int,\n bool]=1, subdivisionsX: Union[int, bool]=20, subdivisionsY: Union[int, bool]=1,\n subdivisionsZ: Union[int, bool]=0, texture: bool=True, q=True, query=True, e=True,\n edit=True, **kwargs)->Union[List[AnyStr], Any]:\n pass","def chol_covar(self) -> np.ndarray:\n if self._chol_covar is None:\n self._chol_covar = batched_cholesky(self.covar)\n return self._chol_covar","def createCone( basePoint=(0,-1,0), tipPoint=(0,1,0), radius = 1.0, colour=(0.6,0.6,0.6), samples = 20 ):\r\n \r\n basePoint = PyUtils.toPoint3d(basePoint)\r\n tipPoint = PyUtils.toPoint3d(tipPoint)\r\n baseToTipVector = Vector3d(basePoint,tipPoint)\r\n if baseToTipVector.isZeroVector() :\r\n raise ValueError( 'Invalid points for cylinder: base and tip are equal!' )\r\n baseToTipUnitVector = baseToTipVector.unit()\r\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,0,1) )\r\n if xUnitVector.length() < 0.5 :\r\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,-1,0) )\r\n xUnitVector.toUnit()\r\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(-1,0,0) )\r\n if yUnitVector.length() < 0.5 :\r\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,1,0) )\r\n yUnitVector.toUnit()\r\n\r\n vertices = []\r\n for i in range(samples):\r\n theta = i * 2 * math.pi / float(samples)\r\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\r\n for i in range(samples):\r\n theta = i * 2 * math.pi / float(samples)\r\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\r\n vertices.append( tipPoint )\r\n \r\n faces = [ range(0,samples) ]\r\n for i in range(0,samples) :\r\n base = samples\r\n size = samples\r\n faces.append( (base+i, base+(i+1)%size, 2*samples ) )\r\n \r\n return create( vertices, faces, colour )","def get_circuit(self):\n return self._q_bnn_circ","def busca(self, k):\n x = self.getRaiz()\n while x is not None and k != x.getChave():\n if k < x.getChave():\n x = x.getEsquerdo()\n else:\n x = x.getDireito()\n return x","def GetConicMode(self, *args):\n return _ShapeUpgrade.ShapeUpgrade_ConvertCurve3dToBezier_GetConicMode(self, *args)","def get_crd_ec(self, axis, shaped=False):\n return self._src_crds.get_ec(axis, shaped=shaped)","def contract(self, depth, kernel_cc_weight=0.0):\n\n def composite_layer(x):\n name = 'contr_%d' % depth\n num_filters = self.base_num_filters * 2 ** depth\n x = self.conv_depth(num_filters=num_filters, name=name, kernel_cc_weight=kernel_cc_weight)(x)\n self.contr_tensors[depth] = x\n x = MaxPoolingND(x)(x)\n return x\n\n return composite_layer","def cy(control: QubitInput, target: QubitInput) -> Instruction:\n return Instruction(CY(), target=[control, target])","def get_eccentricity(self, h_arr, k_arr):\n n = len(self.planets)\n h, k = h_arr, k_arr\n eccentricities = []\n for j in range(n): \n # eccentricities.append(np.sqrt(h[j]**2+k[j]**2))\n eccentricities.append(np.real(np.sqrt(h[j]*np.conjugate(h[j])+k[j]*np.conjugate(k[j]))))\n return np.array(eccentricities)","def get_Ecc_n(self, eccType=\"ed\", r_power=2, order=2, where=\"\", orderBy=\"event_id\"):\n eccArray = self.getEccentricities(eccType=eccType, r_power=r_power, order=order, orderBy=orderBy)\n return eccArray[:,0] + 1j*eccArray[:,1]","def cz(self, q0, q1, ctrl=None):\n self.__add_quantum_gate(kind=CONTROLLED_Z, qid=[q0,q1], ctrl=ctrl)\n return self","def c(self):\n return self._c","def get_coulomb_info(self):\n return","def equivalence_query(self, hypothesis):\n if self.samples_cex_strategy:\n cex = None\n if self.samples_cex_strategy == 'bfs':\n cex = self.bfs_for_cex_in_tree(hypothesis)\n elif self.samples_cex_strategy.startswith('random'):\n split_strategy = self.samples_cex_strategy.split(\":\")\n try:\n nr_traces = int(split_strategy[1])\n stop_prob = float(split_strategy[2])\n cex = self.dfs_for_cex_in_tree(hypothesis, nr_traces, stop_prob)\n except Exception as e:\n print(\"Problem in random DFS for cex in samples:\", e)\n if cex:\n self.last_tree_cex = cex\n self.eq_oracle.reset_counter()\n return cex\n\n # Repeat same cex if it did not lead to state size increase\n # if self.last_cex and len(hypothesis.states) == self.last_hyp_size:\n # if random() <= 0.33:\n # cex = self.eq_oracle.find_cex(hypothesis)\n # if cex and len(cex) < len(self.last_cex):\n # self.last_cex = cex[:-1]\n # return self.last_cex\n\n self.last_hyp_size = len(hypothesis.states)\n\n cex = self.eq_oracle.find_cex(hypothesis)\n if cex: # remove last output\n cex = cex[:-1]\n self.last_cex = cex\n return cex","def _concentration(num, step, hl_a, hl_e, doses, return_diff=False):\n k_a = np.log(2) / hl_a\n k_e = np.log(2) / hl_e\n mat = np.float64([[-k_a, k_a, 0], [0, -k_e, k_e], [0, 0, 0]])\n mat_step = expm(mat * step)\n solution = np.zeros((num, 3))\n if return_diff:\n mat_tangent = np.copy(mat)\n diff = np.zeros(num)\n try:\n indexed_doses = {int(round(offset / step)): dose for offset, dose in doses.items()}\n except ZeroDivisionError:\n indexed_doses = {0: sum(doses.values())}\n for i in range(num):\n if i:\n solution[i] = mat_step.T @ solution[i-1]\n if i in indexed_doses:\n solution[i, 0] += indexed_doses[i]\n if return_diff:\n diff[i] = mat_tangent[0, 1] * solution[0, 0]\n mat_tangent[...] = mat_tangent @ mat_step\n if return_diff:\n return solution[:, 1], diff\n return solution[:, 1]","def cc_trie (cls) :\n return Trie (cls.cc_map)","def ccNew(self, cs, ta, qa, z, m):\n\t\treturn self.cmNew(cs, ta, qa) + self.f_c(z, m)*self.C0","def ccNew(self, cs, ta, qa, z, m):\n\t\treturn self.cmNew(cs, ta, qa) + self.f_c(z, m)*self.C0","def m_c(self) -> np.ndarray:\n assert self._k is not None, \"camera must be calibrated\"\n return forge_projective_matrix(self._k)","def calc_cophenetic_coeff(self):\n c, d = cophenet(self.__linkage, self.__distance_matrix)\n return round(c, 3)","def get_crds_ec(self, axes=None, shaped=False):\n return self._src_crds.get_crds_ec(axes=axes, shaped=shaped)","def circpol(self):\n return self._circpol","def CL(self):","def kkCon(*args): \n if (None in args):\n return None\n return args[0]","def sample_from_cone(self, cone, num_samples=1):\n num_faces = cone.shape[1]\n v_samples = np.empty((num_samples, 3))\n for i in range(num_samples):\n lambdas = np.random.gamma(self.dir_prior, self.dir_prior, num_faces)\n lambdas = lambdas / sum(lambdas)\n v_sample = lambdas * cone\n v_samples[i, :] = np.sum(v_sample, 1)\n return v_samples","def _C(self):\n\n # Find the local x and y coordinates at each node\n xi = 0\n yi = 0\n xj = self.width()\n yj = 0\n xm = xj\n ym = self.height()\n xn = 0\n yn = ym\n\n # Calculate the [C] coefficient matrix\n C = array([[1, xi, yi, xi**2, xi*yi, yi**2, xi**3, xi**2*yi, xi*yi**2, yi**3, xi**3*yi, xi*yi**3],\n [0, 0, 1, 0, xi, 2*yi, 0, xi**2, 2*xi*yi, 3*yi**2, xi**3, 3*xi*yi**2],\n [0, -1, 0, -2*xi, -yi, 0, -3*xi**2, -2*xi*yi, -yi**2, 0, -3*xi**2*yi, -yi**3],\n \n [1, xj, yj, xj**2, xj*yj, yj**2, xj**3, xj**2*yj, xj*yj**2, yj**3, xj**3*yj, xj*yj**3],\n [0, 0, 1, 0, xj, 2*yj, 0, xj**2, 2*xj*yj, 3*yj**2, xj**3, 3*xj*yj**2],\n [0, -1, 0, -2*xj, -yj, 0, -3*xj**2, -2*xj*yj, -yj**2, 0, -3*xj**2*yj, -yj**3],\n\n [1, xm, ym, xm**2, xm*ym, ym**2, xm**3, xm**2*ym, xm*ym**2, ym**3, xm**3*ym, xm*ym**3],\n [0, 0, 1, 0, xm, 2*ym, 0, xm**2, 2*xm*ym, 3*ym**2, xm**3, 3*xm*ym**2],\n [0, -1, 0, -2*xm, -ym, 0, -3*xm**2, -2*xm*ym, -ym**2, 0, -3*xm**2*ym, -ym**3],\n\n [1, xn, yn, xn**2, xn*yn, yn**2, xn**3, xn**2*yn, xn*yn**2, yn**3, xn**3*yn, xn*yn**3],\n [0, 0, 1, 0, xn, 2*yn, 0, xn**2, 2*xn*yn, 3*yn**2, xn**3, 3*xn*yn**2],\n [0, -1, 0, -2*xn, -yn, 0, -3*xn**2, -2*xn*yn, -yn**2, 0, -3*xn**2*yn, -yn**3]])\n \n # Return the coefficient matrix\n return C","def ckn(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"ckn\")","def ckn(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"ckn\")","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 convex_conj(self):\n return KullbackLeiblerCrossEntropyConvexConj(self.domain, self.prior)","def cc(self):\n return MultiterminalDevice(\n self.center.cc(),\n list(i.cc() for i in self.leads),\n list(i.conj() for i in self.connections),\n )","def calc_CO2_compensation_point(self, Tleaf):\n\n # Rubisco specificity for CO2 relative to O2\n tau = self.Q10_func(self.gamma25, self.Q10_gamma, Tleaf)\n gamma = self.Oa / (2.0 * tau)\n\n return gamma","def __getitem__(self, key):\n self._checkForSlicesInKey(key)\n\n if isinstance(key, tuple):\n x, y = self._checkKey(key)\n return self._chars[x][y]\n\n elif isinstance(key, slice):\n x1, y1, x2, y2, xStep, yStep = self._normalizeKeySlice(key)\n\n # create the new Canvas object\n subWidth = math.ceil((x2 - x1) / float(xStep))\n subHeight = math.ceil((y2 - y1) / float(yStep))\n\n subcanvas = Canvas(width=subWidth, height=subHeight)\n\n # copy the characters to the new Canvas object\n for ix, xoffset in enumerate(range(0, subWidth, xStep)):\n for iy, yoffset in enumerate(range(0, subHeight, yStep)):\n subcanvas[ix, iy] = self[x1 + xoffset, y1 + yoffset]\n return subcanvas\n\n else:\n raise PyTextCanvasException('key must be a tuple of two ints')","def conj(traj):\r\n return Trajectory(np.conj(traj.modes))","def test_get_concise_contract():\n contract_concise = ContractHandler.get_concise_contract(\"DataTokenTemplate\")\n assert isinstance(contract_concise, ConciseContract)"],"string":"[\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 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 getconeinfo(self,k_):\\n ct_ = ctypes.c_int32()\\n conepar_ = ctypes.c_double()\\n nummem_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getconeinfo(self.__nativep,k_,ctypes.byref(ct_),ctypes.byref(conepar_),ctypes.byref(nummem_))\\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 return (_ct_return_value,_conepar_return_value,_nummem_return_value)\",\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 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 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 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 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 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 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 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 cone(df, mu, N, alphacov=2.0):\\r\\n return alphacov / ((N + 1.3)**2 + mu)\",\n \"def cget(self, *args, **kwargs):\\n return self._canvas.cget(*args, **kwargs)\",\n \"def caget(PV):\\n return epics.caget(PV)\",\n \"def getC(self):\\n\\t\\treturn self.c\",\n \"def cole_coeff(self):\\n return self.diseq_coeff(standardize=True)\",\n \"def get_coft(self,n,m):\\n if self.aggregate is None:\\n \\n return self.CC.get_coft(n,m)\\n \\n else:\\n \\n bn = self.aggregate.which_band[n]\\n bm = self.aggregate.which_band[m]\\n \\n if ((bn == 0) and (bm == 0)):\\n \\n #print(bn,\\\"::\\\",n,m)\\n return self.CC._cofts[0,:]\\n \\n elif ((bn == 1) and (bm == 1)):\\n #print(bn,\\\"::\\\",n-1,m-1)\\n \\n return self.CC.get_coft(n-1,m-1)\\n \\n else:\\n \\n return self.CC._cofts[0,:]\",\n \"def getnumconemem(self,k_):\\n nummem_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getnumconemem(self.__nativep,k_,ctypes.byref(nummem_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nummem_ = nummem_.value\\n _nummem_return_value = nummem_\\n return (_nummem_return_value)\",\n \"def create_cone(base=1.0, height=1.0, lat=20, lng=20, color=COLOR_WHITE):\\n if lat >= 3 and lng >= 10:\\n circlebase = create_circle(base - 0.05, 0.1, [0.0, 0.0, -1.0], color)\\n obj = glGenLists(1)\\n glNewList(obj, GL_COMPILE)\\n glPushMatrix()\\n glColor4fv(color)\\n try:\\n glutSolidCone(base, height, lat, lng)\\n except:\\n if not _ERRS[3]:\\n printGLError(\\n \\\"la version actual de OpenGL no posee la funcion glutSolidCone\\\")\\n _ERRS[3] = True\\n glCallList(circlebase)\\n glPopMatrix()\\n glEndList()\\n return obj\\n else:\\n raise Exception(\\n \\\"La latitud y longitud de la figura deben ser mayores a 3\\\")\",\n \"def aveEy2CC(self):\\n if self.dim < 2:\\n return None\\n if getattr(self, '_aveEy2CC', None) is None:\\n # The number of cell centers in each direction\\n n = self.vnC\\n if(self.dim == 2):\\n self._aveEy2CC = sp.kron(speye(n[1]), av(n[0]))\\n elif(self.dim == 3):\\n self._aveEy2CC = kron3(av(n[2]), speye(n[1]), av(n[0]))\\n return self._aveEy2CC\",\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 cone(individual, position, height, width):\\n value = 0.0\\n for x, p in zip(individual, position):\\n value += (x - p)**2\\n return height - width * math.sqrt(value)\",\n \"def forward_cone(self, p):\\n return to_rec(zip(p, self.top))\",\n \"def ppl_positive_cone(n):\\n gs = ppl.Generator_System(ppl_zero_point(n))\\n l = [0]*n\\n for i in range(n):\\n gs.insert(ppl.ray(ppl.Variable(i)))\\n return ppl.C_Polyhedron(gs)\",\n \"def get_coe_cluster(self, name_or_id, filters=None):\\n return _utils._get_entity(self, 'coe_cluster', name_or_id, filters)\",\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 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 conj(self, o): \\n return (o.inv()) * self * o\",\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 aveEx2CC(self):\\n if getattr(self, '_aveEx2CC', None) is None:\\n # The number of cell centers in each direction\\n n = self.vnC\\n if(self.dim == 1):\\n self._aveEx2CC = speye(n[0])\\n elif(self.dim == 2):\\n self._aveEx2CC = sp.kron(av(n[1]), speye(n[0]))\\n elif(self.dim == 3):\\n self._aveEx2CC = kron3(av(n[2]), av(n[1]), speye(n[0]))\\n return self._aveEx2CC\",\n \"def ppl_cone(rays):\\n n = len(rays[0])\\n gs = ppl.Generator_System(ppl_zero_point(n))\\n for r in rays:\\n gs.insert(ppl.ray(sum(int(j) * ppl.Variable(i) for i,j in enumerate(r))))\\n return ppl.C_Polyhedron(gs)\",\n \"def get_cov_re(self):\\n pa = self._params[self.k_fe:]\\n\\n cov_re = np.zeros((self.k_re, self.k_re))\\n cov_re[self._ix] = pa\\n if self.use_sqrt:\\n cov_re = np.dot(cov_re, cov_re.T)\\n else:\\n cov_re = (cov_re + cov_re.T) - np.diag(np.diag(cov_re))\\n\\n return cov_re\",\n \"def Ncen(self, m):\\n pass\",\n \"def get_coft_elsig(self,n_sig,m_sig): \\n \\n nb = numpy.sum(n_sig)\\n mb = numpy.sum(m_sig)\\n \\n indices = []\\n if mb == nb:\\n ni = 0\\n for na in n_sig:\\n mi = 0\\n for ma in m_sig:\\n if ((na == 1) and (ma == 1)):\\n indices.append([ni,mi]) \\n mi += 1\\n ni += 1\\n \\n ret = numpy.zeros((self.TimeAxis.length),dtype=numpy.complex128)\\n for ind in indices:\\n #print(nb,\\\":\\\",ind[0],ind[1])\\n ret += self.get_coft(ind[0],ind[1]) \\n \\n \\n return ret \\n \\n else:\\n return self.CC._cofts[0,:]\",\n \"def aveEz2CC(self):\\n if self.dim < 3:\\n return None\\n if getattr(self, '_aveEz2CC', None) is None:\\n # The number of cell centers in each direction\\n n = self.vnC\\n if(self.dim == 3):\\n self._aveEz2CC = kron3(speye(n[2]), av(n[1]), av(n[0]))\\n return self._aveEz2CC\",\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 cz(control: QubitInput, target: QubitInput) -> Instruction:\\n return Instruction(CZ(), target=[control, target])\",\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 k_c(self, tl):\\n\\t\\treturn self.KC0*exp(self.HKC/(R*self.TO)*(1. - self.TO/tl))\",\n \"def C(self, r, n=None):\\n return self.get_coefficient(r, n)\",\n \"def cole(Te, nev):\\n e4e2me2=eV2J**(2.5)/epsilon**(2.0)/np.sqrt(me)/4.0/np.pi/2.0**(1.5)\\n return e4e2me2*nev*lnlambda(Te,nev)/Te**(1.5)\",\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 get_covec(self, key, oov_policy='err'):\\n if self.W is None:\\n raise ValueError(\\\"This one-sided embedding has no co-vectors.\\\")\\n if self.handle_out_of_vocab(key, oov_policy):\\n return self.unkW\\n slice_obj = self._as_slice(key)\\n return self.W[slice_obj]\",\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 getKeyWordConjByCoord(self, idCoord):\\n kWord = KeyWord()\\n i = 0\\n while i < len(self.sentence):\\n if self.sentence[i].getDependenceId() == idCoord and self.sentence[i].getDependenceType() == \\\"conj\\\":\\n kWord = self.sentence[i]\\n i += 1\\n return kWord\",\n \"def heights_cone(self, side=None):\\n I = self.intersection_matrix()\\n C = self.suspension_cone(side)\\n\\n from sage.geometry.polyhedron.constructor import Polyhedron\\n return Polyhedron(rays=[-I*c.vector() for c in C.rays()])\",\n \"def get_kc(data):\\n if data is None:\\n raise EmptyDataError('[!] Invalid data value')\\n\\n result = TA.KC(data)\\n if result is None:\\n raise IndicatorException\\n return result\",\n \"def cone(self, length):\\n X, Y, Z = baseFromAngles(self.angles)\\n result = polyhedron([], [], [])\\n assert result.pnFacesInPoly()\\n f = result.addVertex(self.focalPoint)\\n for s in self.segments:\\n vect0 = (self.focalDist * X +\\n s[0][0] * Z +\\n s[0][1] * Y).normalize()\\n vect1 = (self.focalDist * X +\\n s[1][0] * Z +\\n s[1][1] * Y).normalize()\\n assert result.pnFacesInPoly()\\n vert0 = result.addVertex(vector(self.focalPoint) +\\n length * vect0)\\n assert result.pnFacesInPoly()\\n vert1 = result.addVertex(vector(self.focalPoint) +\\n length * vect1)\\n assert result.pnFacesInPoly()\\n try:\\n result.addFace([f, vert1, vert0])\\n except ValueError:\\n result.plot()\\n p = polyhedron([], [], [])\\n p.addFace([f, vert1, vert0])\\n p.union(result).plot()\\n self.plot()\\n assert False\\n assert result.pnFacesInPoly()\\n # WARNING : the cone is not closed at its top.\\n # I'm not sure if this can cause issues.\\n return result\",\n \"def c(self):\\r\\n return self.__c\",\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 ckn(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"ckn\\\")\",\n \"def cones_and_cylinders(\\n target,\\n pore_diameter='pore.diameter',\\n throat_diameter='throat.diameter'\\n):\\n from openpnm.models.geometry import conduit_lengths\\n out = conduit_lengths.cones_and_cylinders(\\n target, pore_diameter=pore_diameter, throat_diameter=throat_diameter\\n )\\n return out[:, 1]\",\n \"def conToCelc(faren):\\n return (faren - 32)*(5/9)\",\n \"def suspension_cone(self, winner=None):\\n n = len(self)\\n ieqs = []\\n\\n for i in range(1,len(self)):\\n ieq = [0]*(n+1)\\n for j in range(i):\\n ieq[self._labels[0][j]+1] = 1\\n ieqs.append(ieq)\\n\\n ieq = [0]*(n+1)\\n for j in range(i):\\n ieq[self._labels[1][j]+1] = -1\\n ieqs.append(ieq)\\n\\n if winner is not None:\\n winner = interval_conversion(winner)\\n if winner == 0:\\n ieqs.append([0] + [1] * len(self))\\n elif winner == 1:\\n ieqs.append([0] + [-1] * len(self))\\n\\n from sage.geometry.polyhedron.constructor import Polyhedron\\n return Polyhedron(ieqs=ieqs)\",\n \"def aic_c(self):\\n if hasattr(self, '_aic_c'):\\n return self._aic_c\\n else:\\n k = len(self.params)\\n n = self.data['n'].sum()\\n self._aic_c = self.aic() + (2*k**2 + 2*k)/(n - k - 1)\\n return self._aic_c\",\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 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 robot_view_cone( Px, Py, yaw):\\n d = 4 # max monitored distance: reasonably not more than 3.5-4m\\n alpha = 1 # field of view: 57 deg kinect, 58 xtion, we can use exactly 1 rad (=57.3 deg)\\n Lx = Px + d * (math.cos((yaw-alpha)/2))\\n Ly = Py + d * (math.cos((yaw-alpha)/2))\\n Rx = Px + d * (math.cos((yaw+alpha)/2))\\n Ry = Py + d * (math.cos((yaw+alpha)/2))\\n return [ [Lx, Ly], [Rx, Ry], [Px, Py] ]\",\n \"def arccosh(self):\\n return type(self)(self.parent(),\\n self._simplify(self._express.arccosh()))\",\n \"def concentration(self, num, step, doses=None):\\n if doses is None:\\n doses = {0: self.c_0}\\n else:\\n doses = {offset: dose * self.c_0 for offset, dose in doses.items()}\\n return self._concentration(num, step, self.hl_a, self.hl_e, doses)\",\n \"def get_canvas(self, key) -> tk.Canvas:\\n try:\\n canvas = self._Canvases[key]\\n except KeyError:\\n canvas = self._Canvases[key] = tk.Canvas()\\n return canvas\",\n \"def _excitonic_coft_old(self,SS,AG,n):\\n \\n # FIXME: works only for 2 level molecules\\n \\n c0 = AG.monomers[0].get_egcf((0,1))\\n Nt = len(c0)\\n \\n # SystemBathInteraction\\n sbi = AG.get_SystemBathInteraction()\\n # CorrelationFunctionMatrix\\n cfm = sbi.CC\\n \\n # get number of monomeric basis states\\n Na = 0\\n for monomer in AG.monomers:\\n Na += monomer.nel-1\\n \\n ct = numpy.zeros((Nt),dtype=numpy.complex128)\\n #Na = AG.nmono\\n for kk in range(Na):\\n \\n #nkk = AG.monomers[kk].egcf_mapping[0]\\n \\n for ll in range(Na):\\n \\n #nll = AG.monomers[ll].egcf_mapping[0]\\n \\n ct += ((SS[kk+1,n+1]**2)*(SS[ll+1,n+1]**2)*cfm.get_coft(kk,ll))\\n #*AG.egcf_matrix.get_coft(nkk,nll))\\n \\n return ct\",\n \"def cci(self, n, array=False, length=None):\\n \\n if array:\\n if length is not None:\\n result = talib.CCI(self.high[-length:], self.low[-length:], self.close[-length:], n)\\n else:\\n result = talib.CCI(self.high, self.low, self.close, n)\\n return result\\n else:\\n l = n+1\\n result = talib.CCI(self.high[-l:], self.low[-l:], self.close[-l:], n)\\n return result[-1]\",\n \"def get_circuit(self, params):\\n raise NotImplementedError\",\n \"def polyCone(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True,\\n constructionHistory: bool=True, createUVs: Union[int, bool]=2, height: Union[float,\\n bool]=2.0, name: AnyStr=\\\"\\\", nodeState: Union[int, bool]=0, object: bool=True,\\n radius: Union[float, bool]=1.0, roundCap: bool=False, subdivisionsAxis: Union[int,\\n bool]=20, subdivisionsCap: Union[int, bool]=0, subdivisionsHeight: Union[int,\\n bool]=1, subdivisionsX: Union[int, bool]=20, subdivisionsY: Union[int, bool]=1,\\n subdivisionsZ: Union[int, bool]=0, texture: bool=True, q=True, query=True, e=True,\\n edit=True, **kwargs)->Union[List[AnyStr], Any]:\\n pass\",\n \"def chol_covar(self) -> np.ndarray:\\n if self._chol_covar is None:\\n self._chol_covar = batched_cholesky(self.covar)\\n return self._chol_covar\",\n \"def createCone( basePoint=(0,-1,0), tipPoint=(0,1,0), radius = 1.0, colour=(0.6,0.6,0.6), samples = 20 ):\\r\\n \\r\\n basePoint = PyUtils.toPoint3d(basePoint)\\r\\n tipPoint = PyUtils.toPoint3d(tipPoint)\\r\\n baseToTipVector = Vector3d(basePoint,tipPoint)\\r\\n if baseToTipVector.isZeroVector() :\\r\\n raise ValueError( 'Invalid points for cylinder: base and tip are equal!' )\\r\\n baseToTipUnitVector = baseToTipVector.unit()\\r\\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,0,1) )\\r\\n if xUnitVector.length() < 0.5 :\\r\\n xUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,-1,0) )\\r\\n xUnitVector.toUnit()\\r\\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(-1,0,0) )\\r\\n if yUnitVector.length() < 0.5 :\\r\\n yUnitVector = baseToTipUnitVector.crossProductWith( Vector3d(0,1,0) )\\r\\n yUnitVector.toUnit()\\r\\n\\r\\n vertices = []\\r\\n for i in range(samples):\\r\\n theta = i * 2 * math.pi / float(samples)\\r\\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\\r\\n for i in range(samples):\\r\\n theta = i * 2 * math.pi / float(samples)\\r\\n vertices.append( basePoint + xUnitVector * math.cos(theta) * radius + yUnitVector * math.sin(theta) * radius )\\r\\n vertices.append( tipPoint )\\r\\n \\r\\n faces = [ range(0,samples) ]\\r\\n for i in range(0,samples) :\\r\\n base = samples\\r\\n size = samples\\r\\n faces.append( (base+i, base+(i+1)%size, 2*samples ) )\\r\\n \\r\\n return create( vertices, faces, colour )\",\n \"def get_circuit(self):\\n return self._q_bnn_circ\",\n \"def busca(self, k):\\n x = self.getRaiz()\\n while x is not None and k != x.getChave():\\n if k < x.getChave():\\n x = x.getEsquerdo()\\n else:\\n x = x.getDireito()\\n return x\",\n \"def GetConicMode(self, *args):\\n return _ShapeUpgrade.ShapeUpgrade_ConvertCurve3dToBezier_GetConicMode(self, *args)\",\n \"def get_crd_ec(self, axis, shaped=False):\\n return self._src_crds.get_ec(axis, shaped=shaped)\",\n \"def contract(self, depth, kernel_cc_weight=0.0):\\n\\n def composite_layer(x):\\n name = 'contr_%d' % depth\\n num_filters = self.base_num_filters * 2 ** depth\\n x = self.conv_depth(num_filters=num_filters, name=name, kernel_cc_weight=kernel_cc_weight)(x)\\n self.contr_tensors[depth] = x\\n x = MaxPoolingND(x)(x)\\n return x\\n\\n return composite_layer\",\n \"def cy(control: QubitInput, target: QubitInput) -> Instruction:\\n return Instruction(CY(), target=[control, target])\",\n \"def get_eccentricity(self, h_arr, k_arr):\\n n = len(self.planets)\\n h, k = h_arr, k_arr\\n eccentricities = []\\n for j in range(n): \\n # eccentricities.append(np.sqrt(h[j]**2+k[j]**2))\\n eccentricities.append(np.real(np.sqrt(h[j]*np.conjugate(h[j])+k[j]*np.conjugate(k[j]))))\\n return np.array(eccentricities)\",\n \"def get_Ecc_n(self, eccType=\\\"ed\\\", r_power=2, order=2, where=\\\"\\\", orderBy=\\\"event_id\\\"):\\n eccArray = self.getEccentricities(eccType=eccType, r_power=r_power, order=order, orderBy=orderBy)\\n return eccArray[:,0] + 1j*eccArray[:,1]\",\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 c(self):\\n return self._c\",\n \"def get_coulomb_info(self):\\n return\",\n \"def equivalence_query(self, hypothesis):\\n if self.samples_cex_strategy:\\n cex = None\\n if self.samples_cex_strategy == 'bfs':\\n cex = self.bfs_for_cex_in_tree(hypothesis)\\n elif self.samples_cex_strategy.startswith('random'):\\n split_strategy = self.samples_cex_strategy.split(\\\":\\\")\\n try:\\n nr_traces = int(split_strategy[1])\\n stop_prob = float(split_strategy[2])\\n cex = self.dfs_for_cex_in_tree(hypothesis, nr_traces, stop_prob)\\n except Exception as e:\\n print(\\\"Problem in random DFS for cex in samples:\\\", e)\\n if cex:\\n self.last_tree_cex = cex\\n self.eq_oracle.reset_counter()\\n return cex\\n\\n # Repeat same cex if it did not lead to state size increase\\n # if self.last_cex and len(hypothesis.states) == self.last_hyp_size:\\n # if random() <= 0.33:\\n # cex = self.eq_oracle.find_cex(hypothesis)\\n # if cex and len(cex) < len(self.last_cex):\\n # self.last_cex = cex[:-1]\\n # return self.last_cex\\n\\n self.last_hyp_size = len(hypothesis.states)\\n\\n cex = self.eq_oracle.find_cex(hypothesis)\\n if cex: # remove last output\\n cex = cex[:-1]\\n self.last_cex = cex\\n return cex\",\n \"def _concentration(num, step, hl_a, hl_e, doses, return_diff=False):\\n k_a = np.log(2) / hl_a\\n k_e = np.log(2) / hl_e\\n mat = np.float64([[-k_a, k_a, 0], [0, -k_e, k_e], [0, 0, 0]])\\n mat_step = expm(mat * step)\\n solution = np.zeros((num, 3))\\n if return_diff:\\n mat_tangent = np.copy(mat)\\n diff = np.zeros(num)\\n try:\\n indexed_doses = {int(round(offset / step)): dose for offset, dose in doses.items()}\\n except ZeroDivisionError:\\n indexed_doses = {0: sum(doses.values())}\\n for i in range(num):\\n if i:\\n solution[i] = mat_step.T @ solution[i-1]\\n if i in indexed_doses:\\n solution[i, 0] += indexed_doses[i]\\n if return_diff:\\n diff[i] = mat_tangent[0, 1] * solution[0, 0]\\n mat_tangent[...] = mat_tangent @ mat_step\\n if return_diff:\\n return solution[:, 1], diff\\n return solution[:, 1]\",\n \"def cc_trie (cls) :\\n return Trie (cls.cc_map)\",\n \"def ccNew(self, cs, ta, qa, z, m):\\n\\t\\treturn self.cmNew(cs, ta, qa) + self.f_c(z, m)*self.C0\",\n \"def ccNew(self, cs, ta, qa, z, m):\\n\\t\\treturn self.cmNew(cs, ta, qa) + self.f_c(z, m)*self.C0\",\n \"def m_c(self) -> np.ndarray:\\n assert self._k is not None, \\\"camera must be calibrated\\\"\\n return forge_projective_matrix(self._k)\",\n \"def calc_cophenetic_coeff(self):\\n c, d = cophenet(self.__linkage, self.__distance_matrix)\\n return round(c, 3)\",\n \"def get_crds_ec(self, axes=None, shaped=False):\\n return self._src_crds.get_crds_ec(axes=axes, shaped=shaped)\",\n \"def circpol(self):\\n return self._circpol\",\n \"def CL(self):\",\n \"def kkCon(*args): \\n if (None in args):\\n return None\\n return args[0]\",\n \"def sample_from_cone(self, cone, num_samples=1):\\n num_faces = cone.shape[1]\\n v_samples = np.empty((num_samples, 3))\\n for i in range(num_samples):\\n lambdas = np.random.gamma(self.dir_prior, self.dir_prior, num_faces)\\n lambdas = lambdas / sum(lambdas)\\n v_sample = lambdas * cone\\n v_samples[i, :] = np.sum(v_sample, 1)\\n return v_samples\",\n \"def _C(self):\\n\\n # Find the local x and y coordinates at each node\\n xi = 0\\n yi = 0\\n xj = self.width()\\n yj = 0\\n xm = xj\\n ym = self.height()\\n xn = 0\\n yn = ym\\n\\n # Calculate the [C] coefficient matrix\\n C = array([[1, xi, yi, xi**2, xi*yi, yi**2, xi**3, xi**2*yi, xi*yi**2, yi**3, xi**3*yi, xi*yi**3],\\n [0, 0, 1, 0, xi, 2*yi, 0, xi**2, 2*xi*yi, 3*yi**2, xi**3, 3*xi*yi**2],\\n [0, -1, 0, -2*xi, -yi, 0, -3*xi**2, -2*xi*yi, -yi**2, 0, -3*xi**2*yi, -yi**3],\\n \\n [1, xj, yj, xj**2, xj*yj, yj**2, xj**3, xj**2*yj, xj*yj**2, yj**3, xj**3*yj, xj*yj**3],\\n [0, 0, 1, 0, xj, 2*yj, 0, xj**2, 2*xj*yj, 3*yj**2, xj**3, 3*xj*yj**2],\\n [0, -1, 0, -2*xj, -yj, 0, -3*xj**2, -2*xj*yj, -yj**2, 0, -3*xj**2*yj, -yj**3],\\n\\n [1, xm, ym, xm**2, xm*ym, ym**2, xm**3, xm**2*ym, xm*ym**2, ym**3, xm**3*ym, xm*ym**3],\\n [0, 0, 1, 0, xm, 2*ym, 0, xm**2, 2*xm*ym, 3*ym**2, xm**3, 3*xm*ym**2],\\n [0, -1, 0, -2*xm, -ym, 0, -3*xm**2, -2*xm*ym, -ym**2, 0, -3*xm**2*ym, -ym**3],\\n\\n [1, xn, yn, xn**2, xn*yn, yn**2, xn**3, xn**2*yn, xn*yn**2, yn**3, xn**3*yn, xn*yn**3],\\n [0, 0, 1, 0, xn, 2*yn, 0, xn**2, 2*xn*yn, 3*yn**2, xn**3, 3*xn*yn**2],\\n [0, -1, 0, -2*xn, -yn, 0, -3*xn**2, -2*xn*yn, -yn**2, 0, -3*xn**2*yn, -yn**3]])\\n \\n # Return the coefficient matrix\\n return C\",\n \"def ckn(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"ckn\\\")\",\n \"def ckn(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"ckn\\\")\",\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 convex_conj(self):\\n return KullbackLeiblerCrossEntropyConvexConj(self.domain, self.prior)\",\n \"def cc(self):\\n return MultiterminalDevice(\\n self.center.cc(),\\n list(i.cc() for i in self.leads),\\n list(i.conj() for i in self.connections),\\n )\",\n \"def calc_CO2_compensation_point(self, Tleaf):\\n\\n # Rubisco specificity for CO2 relative to O2\\n tau = self.Q10_func(self.gamma25, self.Q10_gamma, Tleaf)\\n gamma = self.Oa / (2.0 * tau)\\n\\n return gamma\",\n \"def __getitem__(self, key):\\n self._checkForSlicesInKey(key)\\n\\n if isinstance(key, tuple):\\n x, y = self._checkKey(key)\\n return self._chars[x][y]\\n\\n elif isinstance(key, slice):\\n x1, y1, x2, y2, xStep, yStep = self._normalizeKeySlice(key)\\n\\n # create the new Canvas object\\n subWidth = math.ceil((x2 - x1) / float(xStep))\\n subHeight = math.ceil((y2 - y1) / float(yStep))\\n\\n subcanvas = Canvas(width=subWidth, height=subHeight)\\n\\n # copy the characters to the new Canvas object\\n for ix, xoffset in enumerate(range(0, subWidth, xStep)):\\n for iy, yoffset in enumerate(range(0, subHeight, yStep)):\\n subcanvas[ix, iy] = self[x1 + xoffset, y1 + yoffset]\\n return subcanvas\\n\\n else:\\n raise PyTextCanvasException('key must be a tuple of two ints')\",\n \"def conj(traj):\\r\\n return Trajectory(np.conj(traj.modes))\",\n \"def test_get_concise_contract():\\n contract_concise = ContractHandler.get_concise_contract(\\\"DataTokenTemplate\\\")\\n assert isinstance(contract_concise, ConciseContract)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.85851794","0.74542636","0.6922491","0.6714365","0.6663073","0.6131412","0.61234295","0.60180366","0.59645844","0.5906362","0.5844101","0.55410284","0.5464269","0.5432204","0.54281247","0.54012233","0.5308824","0.5305274","0.5274296","0.52731514","0.5224948","0.5197121","0.51587415","0.51538336","0.51530814","0.5150834","0.5145124","0.51368","0.51285815","0.5122779","0.51115614","0.5098631","0.5097309","0.509639","0.5076305","0.5065587","0.5060322","0.5046609","0.50145364","0.500268","0.4993288","0.49847355","0.49772894","0.49745637","0.4973588","0.49676633","0.49671906","0.49519363","0.49466875","0.4913884","0.49042594","0.49034557","0.48880187","0.48853794","0.4878515","0.48696417","0.4864338","0.48615083","0.48608863","0.4854602","0.48517212","0.484528","0.4844483","0.48423466","0.48135585","0.48036486","0.48027548","0.48014775","0.47982624","0.47954056","0.47913063","0.47902578","0.4783857","0.4780527","0.47747573","0.4770162","0.47650152","0.47614127","0.4760473","0.4751463","0.47454464","0.47433212","0.47433212","0.47374746","0.4735643","0.47328833","0.4721226","0.47210097","0.47195423","0.4712708","0.4697777","0.46950793","0.46950793","0.46950555","0.4691092","0.46904346","0.46889225","0.46866426","0.46801078","0.46781647"],"string":"[\n \"0.85851794\",\n \"0.74542636\",\n \"0.6922491\",\n \"0.6714365\",\n \"0.6663073\",\n \"0.6131412\",\n \"0.61234295\",\n \"0.60180366\",\n \"0.59645844\",\n \"0.5906362\",\n \"0.5844101\",\n \"0.55410284\",\n \"0.5464269\",\n \"0.5432204\",\n \"0.54281247\",\n \"0.54012233\",\n \"0.5308824\",\n \"0.5305274\",\n \"0.5274296\",\n \"0.52731514\",\n \"0.5224948\",\n \"0.5197121\",\n \"0.51587415\",\n \"0.51538336\",\n \"0.51530814\",\n \"0.5150834\",\n \"0.5145124\",\n \"0.51368\",\n \"0.51285815\",\n \"0.5122779\",\n \"0.51115614\",\n \"0.5098631\",\n \"0.5097309\",\n \"0.509639\",\n \"0.5076305\",\n \"0.5065587\",\n \"0.5060322\",\n \"0.5046609\",\n \"0.50145364\",\n \"0.500268\",\n \"0.4993288\",\n \"0.49847355\",\n \"0.49772894\",\n \"0.49745637\",\n \"0.4973588\",\n \"0.49676633\",\n \"0.49671906\",\n \"0.49519363\",\n \"0.49466875\",\n \"0.4913884\",\n \"0.49042594\",\n \"0.49034557\",\n \"0.48880187\",\n \"0.48853794\",\n \"0.4878515\",\n \"0.48696417\",\n \"0.4864338\",\n \"0.48615083\",\n \"0.48608863\",\n \"0.4854602\",\n \"0.48517212\",\n \"0.484528\",\n \"0.4844483\",\n \"0.48423466\",\n \"0.48135585\",\n \"0.48036486\",\n \"0.48027548\",\n \"0.48014775\",\n \"0.47982624\",\n \"0.47954056\",\n \"0.47913063\",\n \"0.47902578\",\n \"0.4783857\",\n \"0.4780527\",\n \"0.47747573\",\n \"0.4770162\",\n \"0.47650152\",\n \"0.47614127\",\n \"0.4760473\",\n \"0.4751463\",\n \"0.47454464\",\n \"0.47433212\",\n \"0.47433212\",\n \"0.47374746\",\n \"0.4735643\",\n \"0.47328833\",\n \"0.4721226\",\n \"0.47210097\",\n \"0.47195423\",\n \"0.4712708\",\n \"0.4697777\",\n \"0.46950793\",\n \"0.46950793\",\n \"0.46950555\",\n \"0.4691092\",\n \"0.46904346\",\n \"0.46889225\",\n \"0.46866426\",\n \"0.46801078\",\n \"0.46781647\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.783578"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11793,"cells":{"query":{"kind":"string","value":"Obtains information about a cone. getconeinfo(self,k_)"},"document":{"kind":"string","value":"def getconeinfo(self,k_):\n ct_ = ctypes.c_int32()\n conepar_ = ctypes.c_double()\n nummem_ = ctypes.c_int32()\n res = __library__.MSK_XX_getconeinfo(self.__nativep,k_,ctypes.byref(ct_),ctypes.byref(conepar_),ctypes.byref(nummem_))\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 return (_ct_return_value,_conepar_return_value,_nummem_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 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 get_coulomb_info(self):\n return","def cfInfo(self, key):\n\n return self.execute_command(self.CF_INFO, key)","def cainfo(PV):\n return epics.cainfo(PV, False)","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 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 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 get_cp_info(self):\n return self.get(COMMAND_CPM, 'GetCpInfo')","def getC(self):\n\t\treturn self.c","def getInfo():","def mychem_info(self):\n return self._mychem_info","def get_info(self):\n pass","def get_info(self):\n pass","def _get_econt_info(self, out_log):\n f = open_general(out_log)\n tmptxt = f.readlines()\n f.close()\n econt = {}\n itmp = search_string('[read_energy] number of energy points', tmptxt)\n if itmp>=0: econt['Nepts'] = int(tmptxt.pop(itmp).split()[-1])\n itmp = search_string('energies and weights are:', tmptxt)\n if itmp>=0:\n tmp = []\n for ie in range(econt['Nepts']):\n tmpline = tmptxt[itmp+4+ie].split()[1:]\n tmp.append([float(tmpline[0]), float(tmpline[1]), float(tmpline[2]), float(tmpline[3])])\n tmp = array(tmp)\n econt['epts'] = tmp[:,:2]\n econt['weights'] = tmp[:,2:]\n econt['emin'] = tmp[0,0]\n return econt","def ckn(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"ckn\")","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 get_info(self):\n return \"TODO !\"","def get_cluster_info(self) -> Dict[str, Any]:\n pass","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 _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 info(self):","def info(self):","def get_customer_information(self):\n return self._request_json(\"/api/consumer\")[\"consumer\"]","def getConc(fileID, spc):\r\n\r\n dataKey = rmn.fstinf(fileID, nomvar=spc, ip1=ip1)['key']\r\n dataRec = rmn.fstluk(dataKey)\r\n concData = dataRec['d']\r\n return concData, dataKey, dataRec","def info(self) -> dict:","def get_info():\r\n app = application.Application()\r\n\r\n app.start(r\"C:\\\\AL50022\\\\Circ\\\\bin\\\\Circ.exe\")\r\n\r\n app.Circ.menu_select(\"View\")","def closeness_centrality(self):\n try:\n self.logger.info('正在计算网络的接近中心性 ...')\n return self.order_dict(nx.closeness_centrality(self.G), index=1)\n except Exception as e:\n self.logger.error(\"计算失败，原因：{0}\".format(e))","def getInfo(self):\n return self.info","def get_info(self):\n return None","def info(self):\n print 'A= ', self.application\n print 'C= ', self.city\n print 'D= ', self.dataset.shape","def got_info(self, cloud_obj):","def get_info(self):\n out = ''\n for k in sorted(self.components.keys()):\n out += '{:s}: {:s}'.format(k, self.info[k]) + '\\n'\n return(out)","def get_info(self, info):\r\n pass","def _get_information(self):\n pass","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 get_eco_details(self, pgn_data):\n result = eco_mapping['unknown']\n\n try:\n moves = self.get_moves(pgn_data)\n current_sequence = ''\n\n for move in moves:\n half_move = '.'.join([move[0], move[1]])\n current_sequence += half_move\n\n if current_sequence in eco_mapping:\n result = eco_mapping[current_sequence]\n else:\n break\n\n current_sequence = ' '.join([current_sequence, move[2]])\n\n if current_sequence in eco_mapping:\n result = eco_mapping[current_sequence]\n else:\n break\n\n current_sequence += ' '\n except:\n pass\n\n return result","def coin_info(self):\n res = r.get(self.url + self.coin)\n return self.execute(res)","def GetConicMode(self, *args):\n return _ShapeUpgrade.ShapeUpgrade_ConvertCurve3dToBezier_GetConicMode(self, *args)","def caget(PV):\n return epics.caget(PV)","def get_config(self):\n return 'c2Key', self.key","def cluster_info(self) -> ClusterInfoResult:\n if not self.connected:\n raise RuntimeError(\n \"Cluster is not connected, cannot get cluster info.\")\n cluster_info = None\n cluster_info = self._get_cluster_info()\n self._cluster_info = cluster_info\n return cluster_info","def get_perfect_information(self):\n raise NotImplementedError","def get_kc(data):\n if data is None:\n raise EmptyDataError('[!] Invalid data value')\n\n result = TA.KC(data)\n if result is None:\n raise IndicatorException\n return result","def concentration(self):\n return self._gev_bijector.concentration","def get_info(self):\n raise NotImplementedError(\"Robot.get_info\")","def info(self):\n return self.nfo","def get_info(self) -> str:\n raise NotImplementedError()","def get_cs(self,name):\n return self.mfp.get_cs(name)","def getInfo(self):\n return self._info","def cget(self, *args, **kwargs):\n return self._canvas.cget(*args, **kwargs)","def robot_view_cone( Px, Py, yaw):\n d = 4 # max monitored distance: reasonably not more than 3.5-4m\n alpha = 1 # field of view: 57 deg kinect, 58 xtion, we can use exactly 1 rad (=57.3 deg)\n Lx = Px + d * (math.cos((yaw-alpha)/2))\n Ly = Py + d * (math.cos((yaw-alpha)/2))\n Rx = Px + d * (math.cos((yaw+alpha)/2))\n Ry = Py + d * (math.cos((yaw+alpha)/2))\n return [ [Lx, Ly], [Rx, Ry], [Px, Py] ]","def ConnectionInfo(self):\n if (self._accountKind == \"azure\"):\n print(\"%s: %s\" % (DataConnection.accountName, self._accountName))\n print(\"%s: %s\" % (DataConnection.accountKind, self._accountKind))\n else:\n raise NotImplementedError(DataConnection.notYetImplementedMsg)","def get_coft_elsig(self,n_sig,m_sig): \n \n nb = numpy.sum(n_sig)\n mb = numpy.sum(m_sig)\n \n indices = []\n if mb == nb:\n ni = 0\n for na in n_sig:\n mi = 0\n for ma in m_sig:\n if ((na == 1) and (ma == 1)):\n indices.append([ni,mi]) \n mi += 1\n ni += 1\n \n ret = numpy.zeros((self.TimeAxis.length),dtype=numpy.complex128)\n for ind in indices:\n #print(nb,\":\",ind[0],ind[1])\n ret += self.get_coft(ind[0],ind[1]) \n \n \n return ret \n \n else:\n return self.CC._cofts[0,:]","def get_info(self) -> str:\n return self.info","def info():\n return r\"\"\"Tseng, Lin-Yu, and Chun Chen. \"Multiple trajectory search for unconstrained/constrained multi-objective optimization.\" Evolutionary Computation, 2009. CEC'09. IEEE Congress on. IEEE, 2009.\"\"\"","def c(self):\r\n return self.__c","def info() -> None:","def cep(self):\n return self._cep","def obtem_ciclo_in(self):\n\n return self.ciclo_in","def cmsInfo(self, key):\n\n return self.execute_command(self.CMS_INFO, key)","def ckn(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"ckn\")","def ckn(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"ckn\")","def c(self):\n return self._c","def test_ctcpQuery_CLIENTINFO(self):\n self.client.ctcpQuery_CLIENTINFO(self.user, self.channel, \"\")\n self.client.ctcpQuery_CLIENTINFO(self.user, self.channel, \"PING PONG\")\n info = (\n \"ACTION CLIENTINFO DCC ERRMSG FINGER PING SOURCE TIME \" \"USERINFO VERSION\"\n )\n self.assertEqual(\n self.client.methods,\n [\n (\"ctcpMakeReply\", (\"Wolf\", [(\"CLIENTINFO\", info)])),\n (\"ctcpMakeReply\", (\"Wolf\", [(\"CLIENTINFO\", None)])),\n ],\n )","def info(self, key):\n return self.execute_command(self.INFO_CMD, key)","def num_cones(self):\n return self._shape_count(_sff.cone)","def info(**kwargs):\n cluster_call(\"editor_info\", **kwargs)","def aic_c(self):\n if hasattr(self, '_aic_c'):\n return self._aic_c\n else:\n k = len(self.params)\n n = self.data['n'].sum()\n self._aic_c = self.aic() + (2*k**2 + 2*k)/(n - k - 1)\n return self._aic_c","def getdata(self):\n return self.cwt","def info(self):\n return self._info","def info(self):\r\n return self._get('info', {})","def _default_get_information(self, k=None):\n raise Exception(\"Information not set in pst.Neighs_Info.\")","def get_info(self) -> types.NestedArray:\n raise NotImplementedError('No support of get_info for this environment.')","def Ncen(self, m):\n pass","def cone(df, mu, N, alphacov=2.0):\r\n return alphacov / ((N + 1.3)**2 + mu)","def get_pbc_info(self):\n return","def get_torsion_info(self):\n return","def info(self):\n\n print(\"pixellisation:\", self.pixel)\n print(\"number of components:\", self.ncomp)\n print(\"number of pixels:\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\n print(\"nside:\", self.nside)\n print(\"geometry:\", self.geometry)\n print(\"coordinates:\", self.coordinate)","def get_info(self, name):\n return self.info[name]","def get_eccentricity(self, h_arr, k_arr):\n n = len(self.planets)\n h, k = h_arr, k_arr\n eccentricities = []\n for j in range(n): \n # eccentricities.append(np.sqrt(h[j]**2+k[j]**2))\n eccentricities.append(np.real(np.sqrt(h[j]*np.conjugate(h[j])+k[j]*np.conjugate(k[j]))))\n return np.array(eccentricities)","def getnumconemem(self,k_):\n nummem_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumconemem(self.__nativep,k_,ctypes.byref(nummem_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nummem_ = nummem_.value\n _nummem_return_value = nummem_\n return (_nummem_return_value)","def csc():\n endcaps = [1,2]\n disks = [1,2,3,4]\n rings = {1:[1,2,3], # different rings for different disks\n 2:[1,2], \n 3:[1,2],\n 4:[1,2]}\n\n csc_info = {\n \"endcaps\":endcaps,\n \"disks\": disks,\n \"rings\": rings}\n\n return csc_info","def cauto(self):\n return self[\"cauto\"]","def info():\n return buildcat.info()","def eci(self):\n return self.__eci","def info(self):\n return self._info","def info(self):\n print self.id, self.type, self.xyz.get_xyz","def n_cs(self):\n pass","def get_ctf(ima):\n\tfrom EMAN2 import EMAN2Ctf\n\tctf_params = ima.get_attr(\"ctf\")\t\n\treturn ctf_params.defocus, ctf_params.cs, ctf_params.voltage, ctf_params.apix, ctf_params.bfactor, ctf_params.ampcont, ctf_params.dfdiff, ctf_params.dfang","def topkInfo(self, key):\n \n return self.execute_command(self.TOPK_INFO, key)","def k_c(self, tl):\n\t\treturn self.KC0*exp(self.HKC/(R*self.TO)*(1. - self.TO/tl))","def get_info(self):\r\n return np.array([self.ypos, self.xpos, self.radius, self.count_sum, self.bg_galaxy, self.no_count])","def _get_cus_info(self):\n label_enc = LabelEncoder()\n customer_info = self._inv.drop_duplicates(['customer_code'], keep='last')\n customer_info = customer_info[['customer_code', 'customer_name', 'sales_cen_code',\n 'sales_cen_name', 'sales_region_name', 'province',\n 'city', 'district', 'customer_type', 'is_usable', 'channel_level']]\n customer_info['customer_id'] = label_enc.fit_transform(customer_info['customer_code'])\n customer_info['sales_cen_id'] = label_enc.fit_transform(customer_info['sales_cen_code'])\n customer_info['sales_region_id'] = label_enc.fit_transform(customer_info['sales_region_name'])\n customer_info['province_id'] = label_enc.fit_transform(customer_info['province'])\n customer_info['city_id'] = label_enc.fit_transform(customer_info['city'])\n customer_info['district_id'] = label_enc.fit_transform(customer_info['district'])\n customer_info['customer_type'] = label_enc.fit_transform(customer_info['customer_type'])\n customer_info['is_usable'] = label_enc.fit_transform(customer_info['is_usable'])\n customer_info['channel_level'] = label_enc.fit_transform(customer_info['channel_level'])\n customer_info_encoded = customer_info.drop(\n columns=['customer_name', 'sales_cen_code', 'sales_cen_name',\n 'sales_region_name', 'province', 'city', 'district']\n ).set_index('customer_code')\n customer_info.set_index('customer_code', inplace=True)\n customer_info_encoded = customer_info_encoded.reindex(self._index.get_level_values(0))\n return customer_info, customer_info_encoded","def ocpc(self):\n return self._ocpc","def circleInfo(r):\n c = 2 * 3.14159 * r\n a = 3.14159 * r * r\n return (c, a)","def bfInfo(self, key):\n\n return self.execute_command(self.BF_INFO, key)","def get_coe_cluster(self, name_or_id, filters=None):\n return _utils._get_entity(self, 'coe_cluster', name_or_id, filters)","def _get_concentration(self, state):\n return self.fc(state.float_features).exp() + self.EPSILON","def return_info(self):\n\t\treturn self.info","def get_fcc_level(self):\n raise NotImplementedError"],"string":"[\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 get_coulomb_info(self):\\n return\",\n \"def cfInfo(self, key):\\n\\n return self.execute_command(self.CF_INFO, key)\",\n \"def cainfo(PV):\\n return epics.cainfo(PV, False)\",\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 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 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 get_cp_info(self):\\n return self.get(COMMAND_CPM, 'GetCpInfo')\",\n \"def getC(self):\\n\\t\\treturn self.c\",\n \"def getInfo():\",\n \"def mychem_info(self):\\n return self._mychem_info\",\n \"def get_info(self):\\n pass\",\n \"def get_info(self):\\n pass\",\n \"def _get_econt_info(self, out_log):\\n f = open_general(out_log)\\n tmptxt = f.readlines()\\n f.close()\\n econt = {}\\n itmp = search_string('[read_energy] number of energy points', tmptxt)\\n if itmp>=0: econt['Nepts'] = int(tmptxt.pop(itmp).split()[-1])\\n itmp = search_string('energies and weights are:', tmptxt)\\n if itmp>=0:\\n tmp = []\\n for ie in range(econt['Nepts']):\\n tmpline = tmptxt[itmp+4+ie].split()[1:]\\n tmp.append([float(tmpline[0]), float(tmpline[1]), float(tmpline[2]), float(tmpline[3])])\\n tmp = array(tmp)\\n econt['epts'] = tmp[:,:2]\\n econt['weights'] = tmp[:,2:]\\n econt['emin'] = tmp[0,0]\\n return econt\",\n \"def ckn(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"ckn\\\")\",\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 get_info(self):\\n return \\\"TODO !\\\"\",\n \"def get_cluster_info(self) -> Dict[str, Any]:\\n pass\",\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 _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 info(self):\",\n \"def info(self):\",\n \"def get_customer_information(self):\\n return self._request_json(\\\"/api/consumer\\\")[\\\"consumer\\\"]\",\n \"def getConc(fileID, spc):\\r\\n\\r\\n dataKey = rmn.fstinf(fileID, nomvar=spc, ip1=ip1)['key']\\r\\n dataRec = rmn.fstluk(dataKey)\\r\\n concData = dataRec['d']\\r\\n return concData, dataKey, dataRec\",\n \"def info(self) -> dict:\",\n \"def get_info():\\r\\n app = application.Application()\\r\\n\\r\\n app.start(r\\\"C:\\\\\\\\AL50022\\\\\\\\Circ\\\\\\\\bin\\\\\\\\Circ.exe\\\")\\r\\n\\r\\n app.Circ.menu_select(\\\"View\\\")\",\n \"def closeness_centrality(self):\\n try:\\n self.logger.info('正在计算网络的接近中心性 ...')\\n return self.order_dict(nx.closeness_centrality(self.G), index=1)\\n except Exception as e:\\n self.logger.error(\\\"计算失败，原因：{0}\\\".format(e))\",\n \"def getInfo(self):\\n return self.info\",\n \"def get_info(self):\\n return None\",\n \"def info(self):\\n print 'A= ', self.application\\n print 'C= ', self.city\\n print 'D= ', self.dataset.shape\",\n \"def got_info(self, cloud_obj):\",\n \"def get_info(self):\\n out = ''\\n for k in sorted(self.components.keys()):\\n out += '{:s}: {:s}'.format(k, self.info[k]) + '\\\\n'\\n return(out)\",\n \"def get_info(self, info):\\r\\n pass\",\n \"def _get_information(self):\\n pass\",\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 get_eco_details(self, pgn_data):\\n result = eco_mapping['unknown']\\n\\n try:\\n moves = self.get_moves(pgn_data)\\n current_sequence = ''\\n\\n for move in moves:\\n half_move = '.'.join([move[0], move[1]])\\n current_sequence += half_move\\n\\n if current_sequence in eco_mapping:\\n result = eco_mapping[current_sequence]\\n else:\\n break\\n\\n current_sequence = ' '.join([current_sequence, move[2]])\\n\\n if current_sequence in eco_mapping:\\n result = eco_mapping[current_sequence]\\n else:\\n break\\n\\n current_sequence += ' '\\n except:\\n pass\\n\\n return result\",\n \"def coin_info(self):\\n res = r.get(self.url + self.coin)\\n return self.execute(res)\",\n \"def GetConicMode(self, *args):\\n return _ShapeUpgrade.ShapeUpgrade_ConvertCurve3dToBezier_GetConicMode(self, *args)\",\n \"def caget(PV):\\n return epics.caget(PV)\",\n \"def get_config(self):\\n return 'c2Key', self.key\",\n \"def cluster_info(self) -> ClusterInfoResult:\\n if not self.connected:\\n raise RuntimeError(\\n \\\"Cluster is not connected, cannot get cluster info.\\\")\\n cluster_info = None\\n cluster_info = self._get_cluster_info()\\n self._cluster_info = cluster_info\\n return cluster_info\",\n \"def get_perfect_information(self):\\n raise NotImplementedError\",\n \"def get_kc(data):\\n if data is None:\\n raise EmptyDataError('[!] Invalid data value')\\n\\n result = TA.KC(data)\\n if result is None:\\n raise IndicatorException\\n return result\",\n \"def concentration(self):\\n return self._gev_bijector.concentration\",\n \"def get_info(self):\\n raise NotImplementedError(\\\"Robot.get_info\\\")\",\n \"def info(self):\\n return self.nfo\",\n \"def get_info(self) -> str:\\n raise NotImplementedError()\",\n \"def get_cs(self,name):\\n return self.mfp.get_cs(name)\",\n \"def getInfo(self):\\n return self._info\",\n \"def cget(self, *args, **kwargs):\\n return self._canvas.cget(*args, **kwargs)\",\n \"def robot_view_cone( Px, Py, yaw):\\n d = 4 # max monitored distance: reasonably not more than 3.5-4m\\n alpha = 1 # field of view: 57 deg kinect, 58 xtion, we can use exactly 1 rad (=57.3 deg)\\n Lx = Px + d * (math.cos((yaw-alpha)/2))\\n Ly = Py + d * (math.cos((yaw-alpha)/2))\\n Rx = Px + d * (math.cos((yaw+alpha)/2))\\n Ry = Py + d * (math.cos((yaw+alpha)/2))\\n return [ [Lx, Ly], [Rx, Ry], [Px, Py] ]\",\n \"def ConnectionInfo(self):\\n if (self._accountKind == \\\"azure\\\"):\\n print(\\\"%s: %s\\\" % (DataConnection.accountName, self._accountName))\\n print(\\\"%s: %s\\\" % (DataConnection.accountKind, self._accountKind))\\n else:\\n raise NotImplementedError(DataConnection.notYetImplementedMsg)\",\n \"def get_coft_elsig(self,n_sig,m_sig): \\n \\n nb = numpy.sum(n_sig)\\n mb = numpy.sum(m_sig)\\n \\n indices = []\\n if mb == nb:\\n ni = 0\\n for na in n_sig:\\n mi = 0\\n for ma in m_sig:\\n if ((na == 1) and (ma == 1)):\\n indices.append([ni,mi]) \\n mi += 1\\n ni += 1\\n \\n ret = numpy.zeros((self.TimeAxis.length),dtype=numpy.complex128)\\n for ind in indices:\\n #print(nb,\\\":\\\",ind[0],ind[1])\\n ret += self.get_coft(ind[0],ind[1]) \\n \\n \\n return ret \\n \\n else:\\n return self.CC._cofts[0,:]\",\n \"def get_info(self) -> str:\\n return self.info\",\n \"def info():\\n return r\\\"\\\"\\\"Tseng, Lin-Yu, and Chun Chen. \\\"Multiple trajectory search for unconstrained/constrained multi-objective optimization.\\\" Evolutionary Computation, 2009. CEC'09. IEEE Congress on. IEEE, 2009.\\\"\\\"\\\"\",\n \"def c(self):\\r\\n return self.__c\",\n \"def info() -> None:\",\n \"def cep(self):\\n return self._cep\",\n \"def obtem_ciclo_in(self):\\n\\n return self.ciclo_in\",\n \"def cmsInfo(self, key):\\n\\n return self.execute_command(self.CMS_INFO, key)\",\n \"def ckn(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"ckn\\\")\",\n \"def ckn(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"ckn\\\")\",\n \"def c(self):\\n return self._c\",\n \"def test_ctcpQuery_CLIENTINFO(self):\\n self.client.ctcpQuery_CLIENTINFO(self.user, self.channel, \\\"\\\")\\n self.client.ctcpQuery_CLIENTINFO(self.user, self.channel, \\\"PING PONG\\\")\\n info = (\\n \\\"ACTION CLIENTINFO DCC ERRMSG FINGER PING SOURCE TIME \\\" \\\"USERINFO VERSION\\\"\\n )\\n self.assertEqual(\\n self.client.methods,\\n [\\n (\\\"ctcpMakeReply\\\", (\\\"Wolf\\\", [(\\\"CLIENTINFO\\\", info)])),\\n (\\\"ctcpMakeReply\\\", (\\\"Wolf\\\", [(\\\"CLIENTINFO\\\", None)])),\\n ],\\n )\",\n \"def info(self, key):\\n return self.execute_command(self.INFO_CMD, key)\",\n \"def num_cones(self):\\n return self._shape_count(_sff.cone)\",\n \"def info(**kwargs):\\n cluster_call(\\\"editor_info\\\", **kwargs)\",\n \"def aic_c(self):\\n if hasattr(self, '_aic_c'):\\n return self._aic_c\\n else:\\n k = len(self.params)\\n n = self.data['n'].sum()\\n self._aic_c = self.aic() + (2*k**2 + 2*k)/(n - k - 1)\\n return self._aic_c\",\n \"def getdata(self):\\n return self.cwt\",\n \"def info(self):\\n return self._info\",\n \"def info(self):\\r\\n return self._get('info', {})\",\n \"def _default_get_information(self, k=None):\\n raise Exception(\\\"Information not set in pst.Neighs_Info.\\\")\",\n \"def get_info(self) -> types.NestedArray:\\n raise NotImplementedError('No support of get_info for this environment.')\",\n \"def Ncen(self, m):\\n pass\",\n \"def cone(df, mu, N, alphacov=2.0):\\r\\n return alphacov / ((N + 1.3)**2 + mu)\",\n \"def get_pbc_info(self):\\n return\",\n \"def get_torsion_info(self):\\n return\",\n \"def info(self):\\n\\n print(\\\"pixellisation:\\\", self.pixel)\\n print(\\\"number of components:\\\", self.ncomp)\\n print(\\\"number of pixels:\\\", self.data.shape[:] if self.ncomp == 1 else self.data.shape[1:])\\n print(\\\"nside:\\\", self.nside)\\n print(\\\"geometry:\\\", self.geometry)\\n print(\\\"coordinates:\\\", self.coordinate)\",\n \"def get_info(self, name):\\n return self.info[name]\",\n \"def get_eccentricity(self, h_arr, k_arr):\\n n = len(self.planets)\\n h, k = h_arr, k_arr\\n eccentricities = []\\n for j in range(n): \\n # eccentricities.append(np.sqrt(h[j]**2+k[j]**2))\\n eccentricities.append(np.real(np.sqrt(h[j]*np.conjugate(h[j])+k[j]*np.conjugate(k[j]))))\\n return np.array(eccentricities)\",\n \"def getnumconemem(self,k_):\\n nummem_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getnumconemem(self.__nativep,k_,ctypes.byref(nummem_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nummem_ = nummem_.value\\n _nummem_return_value = nummem_\\n return (_nummem_return_value)\",\n \"def csc():\\n endcaps = [1,2]\\n disks = [1,2,3,4]\\n rings = {1:[1,2,3], # different rings for different disks\\n 2:[1,2], \\n 3:[1,2],\\n 4:[1,2]}\\n\\n csc_info = {\\n \\\"endcaps\\\":endcaps,\\n \\\"disks\\\": disks,\\n \\\"rings\\\": rings}\\n\\n return csc_info\",\n \"def cauto(self):\\n return self[\\\"cauto\\\"]\",\n \"def info():\\n return buildcat.info()\",\n \"def eci(self):\\n return self.__eci\",\n \"def info(self):\\n return self._info\",\n \"def info(self):\\n print self.id, self.type, self.xyz.get_xyz\",\n \"def n_cs(self):\\n pass\",\n \"def get_ctf(ima):\\n\\tfrom EMAN2 import EMAN2Ctf\\n\\tctf_params = ima.get_attr(\\\"ctf\\\")\\t\\n\\treturn ctf_params.defocus, ctf_params.cs, ctf_params.voltage, ctf_params.apix, ctf_params.bfactor, ctf_params.ampcont, ctf_params.dfdiff, ctf_params.dfang\",\n \"def topkInfo(self, key):\\n \\n return self.execute_command(self.TOPK_INFO, key)\",\n \"def k_c(self, tl):\\n\\t\\treturn self.KC0*exp(self.HKC/(R*self.TO)*(1. - self.TO/tl))\",\n \"def get_info(self):\\r\\n return np.array([self.ypos, self.xpos, self.radius, self.count_sum, self.bg_galaxy, self.no_count])\",\n \"def _get_cus_info(self):\\n label_enc = LabelEncoder()\\n customer_info = self._inv.drop_duplicates(['customer_code'], keep='last')\\n customer_info = customer_info[['customer_code', 'customer_name', 'sales_cen_code',\\n 'sales_cen_name', 'sales_region_name', 'province',\\n 'city', 'district', 'customer_type', 'is_usable', 'channel_level']]\\n customer_info['customer_id'] = label_enc.fit_transform(customer_info['customer_code'])\\n customer_info['sales_cen_id'] = label_enc.fit_transform(customer_info['sales_cen_code'])\\n customer_info['sales_region_id'] = label_enc.fit_transform(customer_info['sales_region_name'])\\n customer_info['province_id'] = label_enc.fit_transform(customer_info['province'])\\n customer_info['city_id'] = label_enc.fit_transform(customer_info['city'])\\n customer_info['district_id'] = label_enc.fit_transform(customer_info['district'])\\n customer_info['customer_type'] = label_enc.fit_transform(customer_info['customer_type'])\\n customer_info['is_usable'] = label_enc.fit_transform(customer_info['is_usable'])\\n customer_info['channel_level'] = label_enc.fit_transform(customer_info['channel_level'])\\n customer_info_encoded = customer_info.drop(\\n columns=['customer_name', 'sales_cen_code', 'sales_cen_name',\\n 'sales_region_name', 'province', 'city', 'district']\\n ).set_index('customer_code')\\n customer_info.set_index('customer_code', inplace=True)\\n customer_info_encoded = customer_info_encoded.reindex(self._index.get_level_values(0))\\n return customer_info, customer_info_encoded\",\n \"def ocpc(self):\\n return self._ocpc\",\n \"def circleInfo(r):\\n c = 2 * 3.14159 * r\\n a = 3.14159 * r * r\\n return (c, a)\",\n \"def bfInfo(self, key):\\n\\n return self.execute_command(self.BF_INFO, key)\",\n \"def get_coe_cluster(self, name_or_id, filters=None):\\n return _utils._get_entity(self, 'coe_cluster', name_or_id, filters)\",\n \"def _get_concentration(self, state):\\n return self.fc(state.float_features).exp() + self.EPSILON\",\n \"def return_info(self):\\n\\t\\treturn self.info\",\n \"def get_fcc_level(self):\\n raise NotImplementedError\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.88562346","0.65607196","0.6062003","0.6057762","0.6046421","0.6028648","0.57615036","0.57012206","0.5669752","0.56304246","0.5603641","0.5487404","0.5487404","0.53959966","0.53923273","0.53695613","0.5355254","0.5324863","0.531304","0.5291991","0.5281199","0.5281199","0.5227771","0.5225178","0.5184261","0.5177437","0.5126269","0.5116734","0.5110722","0.51098424","0.5104889","0.5103509","0.5102614","0.51006854","0.5089726","0.5086579","0.50772655","0.50594807","0.50444996","0.50301087","0.50287265","0.502467","0.5023052","0.5009344","0.4997767","0.4987462","0.49841508","0.49791738","0.49768692","0.4973516","0.49731702","0.4966109","0.49645576","0.49556527","0.49458858","0.49436778","0.49402702","0.4937848","0.493673","0.49315715","0.49242693","0.49242693","0.49222112","0.4912517","0.49100935","0.48984146","0.4891185","0.4889042","0.48849136","0.4873552","0.48604408","0.48358232","0.4830027","0.48237276","0.48227343","0.48195517","0.48176795","0.48158243","0.48157668","0.48144737","0.4802676","0.47969484","0.47904783","0.4783972","0.47820282","0.4777229","0.4768425","0.47677723","0.4762376","0.47613642","0.47608864","0.47504932","0.47504562","0.47462064","0.47445148","0.4743959","0.47421917","0.47350913","0.47339895","0.47335935"],"string":"[\n \"0.88562346\",\n \"0.65607196\",\n \"0.6062003\",\n \"0.6057762\",\n \"0.6046421\",\n \"0.6028648\",\n \"0.57615036\",\n \"0.57012206\",\n \"0.5669752\",\n \"0.56304246\",\n \"0.5603641\",\n \"0.5487404\",\n \"0.5487404\",\n \"0.53959966\",\n \"0.53923273\",\n \"0.53695613\",\n \"0.5355254\",\n \"0.5324863\",\n \"0.531304\",\n \"0.5291991\",\n \"0.5281199\",\n \"0.5281199\",\n \"0.5227771\",\n \"0.5225178\",\n \"0.5184261\",\n \"0.5177437\",\n \"0.5126269\",\n \"0.5116734\",\n \"0.5110722\",\n \"0.51098424\",\n \"0.5104889\",\n \"0.5103509\",\n \"0.5102614\",\n \"0.51006854\",\n \"0.5089726\",\n \"0.5086579\",\n \"0.50772655\",\n \"0.50594807\",\n \"0.50444996\",\n \"0.50301087\",\n \"0.50287265\",\n \"0.502467\",\n \"0.5023052\",\n \"0.5009344\",\n \"0.4997767\",\n \"0.4987462\",\n \"0.49841508\",\n \"0.49791738\",\n \"0.49768692\",\n \"0.4973516\",\n \"0.49731702\",\n \"0.4966109\",\n \"0.49645576\",\n \"0.49556527\",\n \"0.49458858\",\n \"0.49436778\",\n \"0.49402702\",\n \"0.4937848\",\n \"0.493673\",\n \"0.49315715\",\n \"0.49242693\",\n \"0.49242693\",\n \"0.49222112\",\n \"0.4912517\",\n \"0.49100935\",\n \"0.48984146\",\n \"0.4891185\",\n \"0.4889042\",\n \"0.48849136\",\n \"0.4873552\",\n \"0.48604408\",\n \"0.48358232\",\n \"0.4830027\",\n \"0.48237276\",\n \"0.48227343\",\n \"0.48195517\",\n \"0.48176795\",\n \"0.48158243\",\n \"0.48157668\",\n \"0.48144737\",\n \"0.4802676\",\n \"0.47969484\",\n \"0.47904783\",\n \"0.4783972\",\n \"0.47820282\",\n \"0.4777229\",\n \"0.4768425\",\n \"0.47677723\",\n \"0.4762376\",\n \"0.47613642\",\n \"0.47608864\",\n \"0.47504932\",\n \"0.47504562\",\n \"0.47462064\",\n \"0.47445148\",\n \"0.4743959\",\n \"0.47421917\",\n \"0.47350913\",\n \"0.47339895\",\n \"0.47335935\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.85250115"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11794,"cells":{"query":{"kind":"string","value":"Obtains a sequence of coefficients from the objective. getclist(self,subj_,c_)"},"document":{"kind":"string","value":"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"},"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 coefficients(self) :\n return self.__coefficients","def coefficients(self):\n return self._coefficients","def coefficients(self):\n return self._coefficients","def coefficients(self) :\n raise NotImplementedError","def coefficients(self):\r\n return self.coef_['x']","def get_coefficients(self):\n return self.coefficients","def get_coefficients(self):\n return self.coefficients","def coefficients(self) -> np.ndarray:\n return self._coefficients","def getListOfCompartments(self, *args):\n return _libsbml.Model_getListOfCompartments(self, *args)","def get_C(self, observation, area):\n if observation:\n C = []\n for Theta in area.Thetas:\n C.append(Theta @ self.Omega)\n else:\n C = [self.Omega]\n return C","def _clist(slist):\n retList = []\n if slist == None:\n return retList\n for p in slist:\n aobj = {}\n for prop in p.allProperties():\n if prop in IGNORED_PROPS:\n continue\n tmpval = p.valueForProperty_(prop)\n if type(tmpval) == ABMultiValueCoreDataWrapper:\n aval = [(_getVal(tmpval.labelAtIndex_(i)),\n _getVal(tmpval.valueAtIndex_(i)))\n for i in range(0, tmpval.count())]\n else:\n aval = _getVal(tmpval)\n if aval is not None:\n aobj[prop.lower()] = aval\n retList.append(aobj)\n return retList","def get_base_coefs(mv):\n\trs = []\n\tfor bs in bases:\n\t\tt = []\n\t\tfor b in bs:\n\t\t\tt.append(mv.coef(b))\n\t\t\t\t\t\n\t\trs.append(t)\t\t\n\treturn rs","def get_coefficients(poles):\n\n poles = np.array(poles)\n s = sp.symbols('s')\n poly = 1\n for s_i in poles:\n poly = (s - s_i) * poly\n poly = poly.expand()\n\n # calculate the coefficient of characteristic polynomial\n n = len(poles)\n p = []\n for i in range(n):\n p.append(poly.subs([(s, 0)]))\n poly = poly - p[i]\n poly = poly / s\n poly = poly.expand()\n\n # convert numbers and complex objects from multiplication to a complex number\n p = [complex(x) for x in p]\n # if imaginary part if greater than the boundary, then set imaginary part null\n boundary = 1e-12\n for idx, val in enumerate(p):\n val = complex(val)\n if abs(val.imag) > boundary:\n msg = \"Imaginary Part of the coefficient p[\" + \\\n str(idx) + \"] is not null (\" + str(val.imag) + \") for a given boundary of \" + \\\n str(boundary)\n warnings.warn(msg)\n p[idx] = val.real\n\n return np.array([p], dtype=float)","def list_coefficients(self, format='components', fd=True, pos=True, neg=True, printimag=False, norm_neg=True, nmin=0, nmax=0, latex=False, nd=0, Lv=False, prime=False):\n M = self._space\n WR = M.WR\n C = self._coeffs\n if format[0] == \"C\" or format[0] == \"c\":\n self._list_coefficients_by_components(fd, pos, neg, printimag, norm_neg, nmin, nmax, latex, nd, Lv, prime)\n else:\n self._list_coefficients_by_discriminant(fd, pos, neg, printimag, norm_neg, nmin, nmax, latex, nd, Lv, prime)","def tolist (self) :\r\n if self.complex :\r\n result = []\r\n for x in xrange(0,len(self)) :\r\n result.append(self[x])\r\n return result\r\n else :\r\n return self.impl.tolist()","def get(self, *args):\n return _libsbml.ListOfCompartments_get(self, *args)","def coefficients(self):\n if self._coefficients is None:\n return np.hstack([c.coefficients for c in self._traces])\n return self._coefficients","def _pco_list(self, hdr, name, pos):\n if name not in ('poly', 'champs', 'offset'):\n raise TypeError('Name must be one of \"poly\", \"champs\", or \"offset\".')\n\n hdr.seek(pos + 16)\n length = unpack(self._bo + 'i', hdr.read(4))[0]\n d = []\n for p in range(length):\n if name == 'poly':\n d.append(self._species(hdr))\n else:\n raise NotImplementedError(\n '{}List is non-null, don\\'t know how to read.'\n ''.format(name.capitalize()))\n hdr.seek(4, 1)\n return d","def sublistsC (seq):\n if seq:\n sublists = [([seq[0]] + a, b) for a, b in sublistsC(seq[1:])]\n return sublists + [(b, a) for a, b in sublists]\n else:\n return [([], [])]","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 getListOfConstraints(self, *args):\n return _libsbml.Model_getListOfConstraints(self, *args)","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 get_cids(self):\n return tuple(getattr(self, name + \"_cid\") for name in self.__argnames__)","def __getitem__( self, l ) :\n\n return( self.coefficients[l] )","def c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\n\tC = c_matrix(x1,x2,x3)\n\ty = y_vector(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\n\tCCoefficients = np.dot(inv(C),y)\n\treturn(CCoefficients)","def get_coeffs(self):\n\n return self._coeff_to_dict()","def findCurvePoints(self, x, y, c):\n\t\tyCurve = []\n\t\tfor xi in x:\n\t\t\tyi = self.polynomialFunct(c, xi)\n\t\t\t\n\t\t\tyCurve.append( yi )\n\t\t\n\t\treturn np.asarray(yCurve)","def getListOfCompartments(self):\n return self.model.getListOfCompartments()","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 cinters_circle(self, c):\r\n if self.__segments == None:\r\n self.__load_segments()\r\n \r\n result = []\r\n for segment in self.__segments:\r\n points = c.inters_segment(segment)\r\n for p in points:\r\n result.append(p) \r\n \r\n return result","def getCC(self, var, idx = None, translation = None, other = None,\\\n verbose = 1, version = \"pearson\", ab = []):\n\n if idx is None: idx = np.arange(self.atoms.shape[0])\n if translation is None: translation = [0]\n if isinstance(translation, (int, np.integer)): translation = [translation]\n\n data, lbl = self.getData(var = var, idx = idx, translation = translation,\\\n verbose = verbose, ab = ab, other = other)[::2]\n string = \"\"\n for i, item in enumerate(data):\n if i == 0:\n values = np.zeros((len(data), np.shape(data[i])[0]))\n\n if np.ndim(data[i]) == 1:\n values[i, :] = data[i]\n else:\n values[i, :] = data[i][:, 0]\n\n if \"pearson\".startswith(version.lower()):\n ccoef = np.zeros((values.shape[0], values.shape[0]))\n rho = np.zeros((values.shape[0], values.shape[0]))\n\n for i in range(0, values.shape[0]):\n for j in range(i + 1, values.shape[0]):\n ccoef[i, j], rho[i, j] = stats.pearsonr(values[i, :], values[j, :])\n ccoef[j, i] = ccoef[i, j]\n rho[j, i] = rho[i, j]\n\n ccoef += np.identity(values.shape[0])\n\n elif \"spearman\".startswith(version.lower()):\n ccoef, rho = stats.spearmanr(values, axis = 1)\n\n if verbose > 0:\n head = \"Cor-Coef for the following variables:\"\n ut.infoPrint(head, sep_after = False)\n print(\"-\" * len(head))\n for i, string in enumerate(var):\n ut.infoPrint(\"%s\" % string, sep_before = False, sep_after = False)\n print(\"=\" * len(head))\n\n return ccoef, rho, lbl","def getListNestedCVTerms(self, *args):\n return _libsbml.CVTerm_getListNestedCVTerms(self, *args)","def getDisciplinesList():\n return Gw2Spidy._request('disciplines')['results']","def getCVTerms(self):\n return _libsbml.SBase_getCVTerms(self)","def get(self, *args):\n return _libsbml.ListOfConstraints_get(self, *args)","def get_constraint_list(self):\n constraints = []\n for i in xrange(self.num_repeats):\n # Using start_index, start each domain at the correct index when flattening out points in COBYLA.\n constraints.extend(self._domain.get_constraint_list(start_index=self.dim * i))\n return constraints","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 circuit_list(self):\r\n return self.circuits.itervalues()","def getListPunctCmplx(self):\n\t\treturn self.list_punct_cmplx","def get_compounds(self, ctx, params):\n # ctx is the context object\n # return variables are: out_compounds\n #BEGIN get_compounds\n self._check_param(params, ['compounds'])\n out_compounds = []\n for x in params['compounds']:\n id = x.split('/')[-1]\n comp = self.compounds.get(id, None)\n if comp:\n comp['aliases'] = self.comp_aliases.get(id, '')\n out_compounds.append(comp)\n #END get_compounds\n\n # At some point might do deeper type checking...\n if not isinstance(out_compounds, list):\n raise ValueError('Method get_compounds return value ' +\n 'out_compounds is not type list as required.')\n # return the results\n return [out_compounds]","def coefficients(self, force_characters = False) :\n if len(self.__coefficients) == 0 :\n return dict()\n elif not force_characters and len(self.__coefficients) == 1 :\n return self.__coefficients.values()[0] \n else :\n return self.__coefficients","def _get_labels_for_subject(file_ptr):\n labels = []\n\n #Open CSC\n reader = csv.reader(file_ptr, delimiter=',')\n\n for i, row in enumerate(reader):\n facs = []\n for j, intensity in enumerate(row):\n if j == 0:\n continue\n\n facs.append(int(intensity))\n\n labels.append(facs)\n \n return labels","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 get_subject_cn(self):\n subject = self.get_subject()\n cns = subject.get_entries_by_oid(name.OID_commonName)\n return [cn.get_value() for cn in cns]","def completions (cls, cc_prefix) :\n return cls.cc_trie.completions (cc_prefix)","def getList(x, swipl):\n\n t = swipl.PL_copy_term_ref(x)\n head = swipl.PL_new_term_ref()\n result = []\n while swipl.PL_get_list(t, head, t):\n result.append(getTerm(head, swipl))\n head = swipl.PL_new_term_ref()\n\n return result","def _generate_poly_array(self, nchan, coeff=[]):\n if nchan < 0:\n raise ValueError, \"nchan should be >=0\"\n if len(coeff)==0:\n if nchan ==0: return []\n else: raise ValueError, \"No valid coefficient given.\"\n polyarr = numpy.zeros(nchan)\n for iorder in range(len(coeff)):\n polyarr += coeff[iorder]*numpy.array(xrange(nchan))**iorder\n return polyarr","def _to_cc_list(collection):\n return \"{\" + \", \".join(collection) + \"}\"","def get_cgo(self, dot_mode=0, dot_radius=0.03):\n cgolist = []\n\n # COLOR\n cgolist.extend(_cgo_color(self.color))\n\n if dot_mode == 0: # spheres\n logger.debug(\"Adding dot to cgolist...\")\n cgolist.extend(_cgo_sphere(self.coords, dot_radius))\n logger.debug(\"Finished adding dot to cgolist.\")\n\n if dot_mode == 1: # quads\n logger.debug(\"Adding quad to cgolist...\")\n normal = cpv.normalize(cpv.sub(self.coords, self.atom['coords']))\n cgolist.extend(_cgo_quad(self.coords, normal, dot_radius * 1.5))\n logger.debug(\"Finished adding quad to cgolist.\")\n\n return cgolist","def coefficients(self, force_characters = False) :\n raise NotImplementedError","def getComicsListFromFile(filename):\n h = open(filename)\n contents = \"\\n\".join(h.readlines())\n expr = re.compile(\"([a-z0-9]+)\")\n return expr.findall(contents)","def get_complaints(visit):\r\n return visit.complaints.all()","def concentration(self):\n return [node.concentration for node in self]","def get_subjects(self, subj_type='included'):\n if subj_type == 'included':\n scode = 'subjectswithcode'\n elif subj_type == 'excluded':\n scode = 'excludedsubjectswithcode'\n else:\n raise NameError(\"\"\"subj_type must be either 'included' or\n 'excluded'\"\"\")\n\n url = scode + '?' + self._login_code + \\\n '&projectCode=' + self.proj_code\n output = self._send_request(url)\n\n # Split at '\\n'\n subj_list = output.split('\\n')\n # Remove any empty entries!\n subj_list = [x for x in subj_list if x]\n\n return(subj_list)","def get_course_results(self,subj,cnum=\"\"):\n\t\tinfo = self.info # take info from self\n\n\t\turl = info['url_base'] + info['url_action']\n\t\tvals = PostData()\n\t\tvals.add_item('TRM'\t\t\t\t, \"U\")\n\t\tvals.add_item('term_in'\t\t\t, \"201501\")\n\t\tvals.add_item('sel_subj'\t\t, \"dummy\")\n\t\tvals.add_item('sel_day'\t\t\t, \"dummy\")\n\t\tvals.add_item('sel_schd'\t\t, \"dummy\")\n\t\tvals.add_item('sel_insm'\t\t, \"dummy\")\n\t\tvals.add_item('sel_camp'\t\t, \"dummy\")\n\t\tvals.add_item('sel_levl'\t\t, \"dummy\")\n\t\tvals.add_item('sel_sess'\t\t, \"dummy\")\n\t\tvals.add_item('sel_instr'\t\t, \"dummy\")\n\t\tvals.add_item('sel_ptrm'\t\t, \"dummy\")\n\t\tvals.add_item('sel_attr'\t\t, \"dummy\")\n\t\tvals.add_item('sel_subj'\t\t, subj)\n\t\tvals.add_item('sel_crse'\t\t, cnum)\n\t\tvals.add_item('sel_title'\t\t, \"\")\n\t\tvals.add_item('sel_schd'\t\t, \"%\")\n\t\tvals.add_item('sel_insm'\t\t, \"%\")\n\t\tvals.add_item('sel_from_cred'\t, \"\")\n\t\tvals.add_item('sel_to_cred'\t\t, \"\")\n\t\tvals.add_item('sel_camp'\t\t, \"%\")\n\t\tvals.add_item('begin_hh'\t\t, \"0\")\n\t\tvals.add_item('begin_mi'\t\t, \"0\")\n\t\tvals.add_item('begin_ap'\t\t, \"a\")\n\t\tvals.add_item('end_hh'\t\t\t, \"0\")\n\t\tvals.add_item('end_mi'\t\t\t, \"0\")\n\t\tvals.add_item('end_ap'\t\t\t, \"a\")\n\n\t\t#data = urllib.urlencode(vals.get_string())\n\t\tdata = vals.get_string()\n\t\treq = urllib2.Request(url, data=data)\n\t\tprint(\"=== Headers ===\")\n\t\tprint(req.headers)\n\t\tprint(\"=== Data ===\")\n\t\tprint(req.data)\n\t\tresponse = urllib2.urlopen(req)\n\t\treturn response.read()","def allcJPDs(net,cond_node,cond_states):\r\n \r\n onodes = net.Outputnodes()\r\n onodes = sorted(onodes)\r\n combinations = [i for i in itertools.combinations(onodes,2)]\r\n JPDdatas = [] \r\n progress = 0\r\n for cs in cond_states:\r\n JPDdata = []\r\n for c in combinations:\r\n print 'Progress: ' +str(int(100*float(progress)/float(len(combinations)*len(cond_states))))+ ' %'\r\n priorA = getCondPrior(net,c[0],cond_node,cs)\r\n print priorA\r\n priorB = getCondPrior(net,c[1],cond_node,cs) \r\n print priorB\r\n JPD = cJPD(net,c,cond_node,cs)\r\n cov = Covariance (priorA,priorB,JPD)\r\n cor = Correlation (priorA,priorB,cov) \r\n JPDdata.append([c[0],c[1],JPD,cov,cor])\r\n progress+=1\r\n JPDdatas.append(JPDdata)\r\n return JPDdatas","def covariates(self) -> List[str]:\n return self._obj._names[\"covariates\"]","def getC(self):\n\t\treturn self.c","def getcitations(self, pmcid, source):\n import requests\n r = requests.get((self.citationurl).format(source=source, pmcid=pmcid))\n return r.content","def charpoly(self):\n M = self\n K = M.domain\n\n factors = M.charpoly_factor_blocks()\n\n cp = [K.one]\n\n for f, mult in factors:\n for _ in range(mult):\n cp = dup_mul(cp, f, K)\n\n return cp","def ceps_coeff(sig,coefNumber):\n\n est=lpc_coef(sig,coefNumber)\n cc=lpcar2cc(est)\n if len(cc)==1:\n cc=float(cc)\n else:\n cc=tuple(cc)\n\n return cc","def tc_mcdc(foo):\r\n\r\n # get number of input parameters (=conditions)\r\n sig=signature(foo)\r\n n=len(sig.parameters)\r\n \r\n # generate all possible test cases\r\n all_test_cases=tc_gen(n)\r\n\r\n # generate all subsets of test cases\r\n allsubsets=powerset(all_test_cases)\r\n\r\n # create list with all subsets that satisfy cc and their length\r\n satisfies_mcdc=[]\r\n for subset in allsubsets:\r\n #print(subset)\r\n if is_mc(subset,foo) and is_cc(subset) and is_dc(subset,foo):\r\n satisfies_mcdc.append((subset,len(subset)))\r\n satisfies_mcdc.sort(key=lambda x: x[1])\r\n return satisfies_mcdc[0][0]","def zzx_mul_const(f, c):\n if not c or not f:\n return []\n else:\n return [ c * coeff for coeff in f ]","def c(self) -> np.ndarray:\n return self._vector[10:12]","def get_proj_coeffs(self, src):\n self.proj_coeffs = parallel.call_and_bcast(self.get_array, src)","def getc(self,c_):\n _c_minlength = self.getnumvar()\n if self.getnumvar() > 0 and c_ is not None and len(c_) != self.getnumvar():\n raise ValueError(\"Array argument c is not long enough: Is %d, expected %d\" % (len(c_),self.getnumvar()))\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_getc(self.__nativep,_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 get_clarifications_mctaco(ex, nlp, comet_model):\n context = ex['context']\n personx, _ = get_personx(nlp, context)\n \n if len(personx) == 0:\n return []\n \n outputs = {category: comet_model.predict(context, category, num_beams=5) for category in comet_model.categories}\n\n curr_events = []\n for category, prefix in CATEGORY_TO_PREFIX.items():\n for out_event in outputs[category]:\n if out_event != \"none\" and out_event != \"\":\n if not out_event.lower().startswith(\"person\") and not out_event.lower().startswith(\"other\"):\n out_event = \" \".join((prefix, out_event))\n\n out_event = re.sub(\"personx\", personx, out_event, flags=re.I)\n out_event = re.sub(\"person x\", personx, out_event, flags=re.I)\n out_event = re.sub(\"persony\", \"others\", out_event, flags=re.I)\n out_event = re.sub(\"person y\", \"others\", out_event, flags=re.I)\n\n question = CATEGORY_TO_QUESTION[category].replace(\"PersonX\", personx)\n curr_events.append((question, out_event))\n\n return curr_events","def get_cm_list ( self ) :\n cm_name_list = []\n stmt = \"select name from sdb_continuousmodel where sys002 =\\'T\\'\"\n self.oracle_cursor.arraysize = 100000\n self.oracle_cursor.execute(stmt)\n resultset = self.oracle_cursor.fetchmany()\n if resultset :\n for row in resultset :\n cm_name_list.append(str(row[0]))\n return cm_name_list","def tc_cc(foo):\r\n\r\n # get number of input parameters (=conditions)\r\n sig=signature(foo)\r\n n=len(sig.parameters)\r\n \r\n # generate all possible test cases\r\n all_test_cases=tc_gen(n)\r\n\r\n # generate all subsets of test cases\r\n allsubsets=powerset(all_test_cases)\r\n\r\n # create list with all subsets that satisfy cc and their length\r\n satisfies_cc=[]\r\n for subset in allsubsets:\r\n if is_cc(subset):\r\n satisfies_cc.append((subset,len(subset)))\r\n satisfies_cc.sort(key=lambda x: x[1])\r\n return satisfies_cc[0][1]","def get(self, *args):\n return _libsbml.ListOfCompartmentReferences_get(self, *args)","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 get_cffts(self):\n return [\n rfft(self.nx, self.dx, fft=self.tfft, ny=self.ny,\n dy=self.dy).get_cfft(),\n rfft(self.nx, self.dx, fft=self.efft, ny=self.ny,\n dy=self.dy).get_cfft(),\n rfft(self.nx, self.dx, fft=self.bfft, ny=self.ny,\n dy=self.dy).get_cfft()\n ]","def read_pccp(fname, seq):\n pccp = phy7(fname)\n return np.array([pccp[i] for i in seq])","def get_cgo(self, radius=0.03):\n cgolist = []\n\n # Set colors\n rgb0 = colors.get_color_rgb(self.color[0])\n rgb1 = colors.get_color_rgb(self.color[1])\n\n if True: # cylinders\n logger.debug(\"Adding vector to cgolist...\")\n # Cylinder\n cgolist.extend(_cgo_cylinder(self.coords[0], self.coords[1],\n radius, rgb0, rgb1))\n # Caps\n cgolist.extend(_cgo_color(self.color[0]))\n cgolist.extend(_cgo_sphere(self.coords[0], radius))\n cgolist.extend(_cgo_color(self.color[1]))\n cgolist.extend(_cgo_sphere(self.coords[1], radius))\n\n logger.debug(\"Finished adding vector to cgolist.\")\n\n return cgolist","def get(self) -> list:\n return self.__cogs","def test_coefficients(self):\n\n coefs = self.cs.coefficients\n\n self.assertEqual(coefs, (1, 0, 1, 0, 0, -1))","def coeff(self):\n return self._coeff","def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n coherence_values =[]\n model_list =[]\n for i in range(start,limit,step):\n model=gensim.models.ldamodel.LdaModel(doc_term_matrix, num_topics=i, random_state=2, id2word=dictionary, iterations=10)\n model_list.append(model)\n coherence_model =CoherenceModel(model=model, texts=texts, dictionary=dictionary,coherence='c_v')\n coherence_values.append(coherence_model.get_coherence())\n return model_list,coherence_values","def list_cas():\n cas = []\n for ca in settings.ACM_PRIVATE_CA_SETTINGS:\n _ca = get_ca(ca)\n cas.append(_ca.get_certificate_authority_certificate())\n return cas","def construct_sequence_list(self):\n return list(self.iter_sequence())","def complexCompose(self,coefficients,t=1):\n c=coefficients\n N=len(c)//2\n s=lambda t,n:c[n+N]*cmath.exp(1j*n*t)\n a=0\n g=[]\n z=[]\n\n #for i in range(len(c)):\n # if i==0: n=0\n # elif i%2==1: n=(i+1)//2\n # elif i%2==0: n=-i//2\n # pass\n\n #print([a[1] for a in z])\n #z=sorted(z,key=lambda x:1,reverse=True)\n #print([a[1] for a in z])\n #z=[a[0] for a in z]\n\n for n in range(-N,N+1):\n a+=s(t,n)\n g.append((a.real,a.imag))\n\n return g","def clts(sequence):\n return [_token2clts(segment)[1] for segment in sequence]","def coeffs(f):\n return dmp_coeffs(f.rep, f.lev, f.dom)","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 evaluation_cc(self, property='clustering-coeff'):\n\n if property == 'clustering-coeff':\n rw_cc = [np.mean(clustering_coef_wu(self.rw_data[t])) for t in range(0, self.T)]\n smth_cc = [np.mean(clustering_coef_wu(self.smth_data[t])) for t in range(0, self.T)]\n elif property == 'transitivity':\n rw_cc = [np.mean(transitivity_wu(self.rw_data[t])) for t in range(0, self.T)]\n smth_cc = [np.mean(transitivity_wu(self.smth_data[t])) for t in range(0, self.T)]\n elif property == 'coreness':\n rw_cc = [np.mean(core.core_periphery_dir(self.rw_data[t])) for t in range(0, self.T)]\n smth_cc = [np.mean(core.core_periphery_dir(self.smth_data[t])) for t in range(0, self.T)]\n elif property == 'assortativity':\n rw_cc = [np.mean(core.assortativity_wei(self.rw_data[t], 0)) for t in range(0, self.T)]\n smth_cc = [np.mean(core.assortativity_wei(self.smth_data[t], 0)) for t in range(0, self.T)]\n elif property == 'modularity':\n rw_cc, _ = get_number_of_components(self.rw_data)\n smth_cc, _ = get_number_of_components(self.smth_data)\n elif property == 'path_length':\n rw_cc = [charpath(rw)[0] for rw in self.rw_data]\n smth_cc = [charpath(sm)[0] for sm in self.smth_data]\n\n # rw_cc_ent = get_entropy_list(rw_cc)\n # smth_cc_ent = get_entropy_list(smth_cc)\n\n return rw_cc, smth_cc","def X(self)->list:","def get_critics(self):\n actors = [ddpg_agent.critic for ddpg_agent in self.maddpg_agent]\n return actors","def __init__(self, coefficients):\n self.coefficients = coefficients","def get_circuits(self) -> List[Circuit]:\n return [Circuit(c, self, self.easee) for c in self[\"circuits\"]]","def get_courses(self):\r\n\r\n return self.def_ms.get_courses()","def params(self):\n return [p for sublist in [o.params for o in self.obs] for p in sublist]","def get_occr(self):\n return self._occr_array","def getCodonSeqs(self):\r\n combinations = list(self.codonTable[aa] for aa in self.peptide) # creates a list of possible codons based on AA\r\n self.allPepSeqs = list(''.join(codon) for codon in itertools.product(*combinations)) # creates list of peptides\r\n return","def __getitem__(self, s) :\n try :\n return self.coefficients()[s]\n except KeyError :\n return self.parent().coefficient_domain().zero_element()","def all_coeffs(f):\n return dmp_all_coeffs(f.rep, f.lev, f.dom)","def _get_all_contrs(term, contractor, resolvers):\n\n vecs = term.vecs\n n_vecs = len(vecs)\n contrs = []\n sums_dict = term.dumms\n\n for i in range(n_vecs):\n curr_contrs = {}\n for j in range(i, n_vecs):\n vec_prev = vecs[i]\n vec_lat = vecs[j]\n contr_amp, contr_substs = simplify_deltas_in_expr(\n sums_dict, contractor(vec_prev, vec_lat, term), resolvers\n )\n if contr_amp != 0:\n curr_contrs[j] = (contr_amp, tuple(contr_substs.items()))\n continue\n contrs.append(curr_contrs)\n continue\n\n return contrs","def specCoeffsInReaction(r):\n c = []\n for k in range(__data.nsp):\n nu = (__data.g.product_stoich_coeff(k,r) -\n __data.g.reactant_stoich_coeff(k,r))\n if (nu <> 0):\n c.append((__data.g.species_name(k),nu))\n return c","def cvars(occs):\n names = []\n canonicals = []\n for occ in occs:\n if occ.name in names:\n continue\n canonicals.append(occ)\n return canonicals","def getAllCoPilots(self):\n staffObject_list = self.mainObject.getStaffIO()\n coPilotObject_list = []\n for staffMember in staffObject_list:\n if staffMember.getRank() == 'copilot':\n coPilotObject_list.append(staffMember)\n return coPilotObject_list","def _coefficients(regression_df):\n coeff_names = ('mindist', 'x_j', 'f_is', 'v_is')\n coefficients = {x: _get_coefficient(regression_df, x) for x in coeff_names}\n return coefficients"],"string":"[\n \"def coefficients(self) :\\n return self.__coefficients\",\n \"def coefficients(self):\\n return self._coefficients\",\n \"def coefficients(self):\\n return self._coefficients\",\n \"def coefficients(self) :\\n raise NotImplementedError\",\n \"def coefficients(self):\\r\\n return self.coef_['x']\",\n \"def get_coefficients(self):\\n return self.coefficients\",\n \"def get_coefficients(self):\\n return self.coefficients\",\n \"def coefficients(self) -> np.ndarray:\\n return self._coefficients\",\n \"def getListOfCompartments(self, *args):\\n return _libsbml.Model_getListOfCompartments(self, *args)\",\n \"def get_C(self, observation, area):\\n if observation:\\n C = []\\n for Theta in area.Thetas:\\n C.append(Theta @ self.Omega)\\n else:\\n C = [self.Omega]\\n return C\",\n \"def _clist(slist):\\n retList = []\\n if slist == None:\\n return retList\\n for p in slist:\\n aobj = {}\\n for prop in p.allProperties():\\n if prop in IGNORED_PROPS:\\n continue\\n tmpval = p.valueForProperty_(prop)\\n if type(tmpval) == ABMultiValueCoreDataWrapper:\\n aval = [(_getVal(tmpval.labelAtIndex_(i)),\\n _getVal(tmpval.valueAtIndex_(i)))\\n for i in range(0, tmpval.count())]\\n else:\\n aval = _getVal(tmpval)\\n if aval is not None:\\n aobj[prop.lower()] = aval\\n retList.append(aobj)\\n return retList\",\n \"def get_base_coefs(mv):\\n\\trs = []\\n\\tfor bs in bases:\\n\\t\\tt = []\\n\\t\\tfor b in bs:\\n\\t\\t\\tt.append(mv.coef(b))\\n\\t\\t\\t\\t\\t\\n\\t\\trs.append(t)\\t\\t\\n\\treturn rs\",\n \"def get_coefficients(poles):\\n\\n poles = np.array(poles)\\n s = sp.symbols('s')\\n poly = 1\\n for s_i in poles:\\n poly = (s - s_i) * poly\\n poly = poly.expand()\\n\\n # calculate the coefficient of characteristic polynomial\\n n = len(poles)\\n p = []\\n for i in range(n):\\n p.append(poly.subs([(s, 0)]))\\n poly = poly - p[i]\\n poly = poly / s\\n poly = poly.expand()\\n\\n # convert numbers and complex objects from multiplication to a complex number\\n p = [complex(x) for x in p]\\n # if imaginary part if greater than the boundary, then set imaginary part null\\n boundary = 1e-12\\n for idx, val in enumerate(p):\\n val = complex(val)\\n if abs(val.imag) > boundary:\\n msg = \\\"Imaginary Part of the coefficient p[\\\" + \\\\\\n str(idx) + \\\"] is not null (\\\" + str(val.imag) + \\\") for a given boundary of \\\" + \\\\\\n str(boundary)\\n warnings.warn(msg)\\n p[idx] = val.real\\n\\n return np.array([p], dtype=float)\",\n \"def list_coefficients(self, format='components', fd=True, pos=True, neg=True, printimag=False, norm_neg=True, nmin=0, nmax=0, latex=False, nd=0, Lv=False, prime=False):\\n M = self._space\\n WR = M.WR\\n C = self._coeffs\\n if format[0] == \\\"C\\\" or format[0] == \\\"c\\\":\\n self._list_coefficients_by_components(fd, pos, neg, printimag, norm_neg, nmin, nmax, latex, nd, Lv, prime)\\n else:\\n self._list_coefficients_by_discriminant(fd, pos, neg, printimag, norm_neg, nmin, nmax, latex, nd, Lv, prime)\",\n \"def tolist (self) :\\r\\n if self.complex :\\r\\n result = []\\r\\n for x in xrange(0,len(self)) :\\r\\n result.append(self[x])\\r\\n return result\\r\\n else :\\r\\n return self.impl.tolist()\",\n \"def get(self, *args):\\n return _libsbml.ListOfCompartments_get(self, *args)\",\n \"def coefficients(self):\\n if self._coefficients is None:\\n return np.hstack([c.coefficients for c in self._traces])\\n return self._coefficients\",\n \"def _pco_list(self, hdr, name, pos):\\n if name not in ('poly', 'champs', 'offset'):\\n raise TypeError('Name must be one of \\\"poly\\\", \\\"champs\\\", or \\\"offset\\\".')\\n\\n hdr.seek(pos + 16)\\n length = unpack(self._bo + 'i', hdr.read(4))[0]\\n d = []\\n for p in range(length):\\n if name == 'poly':\\n d.append(self._species(hdr))\\n else:\\n raise NotImplementedError(\\n '{}List is non-null, don\\\\'t know how to read.'\\n ''.format(name.capitalize()))\\n hdr.seek(4, 1)\\n return d\",\n \"def sublistsC (seq):\\n if seq:\\n sublists = [([seq[0]] + a, b) for a, b in sublistsC(seq[1:])]\\n return sublists + [(b, a) for a, b in sublists]\\n else:\\n return [([], [])]\",\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 getListOfConstraints(self, *args):\\n return _libsbml.Model_getListOfConstraints(self, *args)\",\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 get_cids(self):\\n return tuple(getattr(self, name + \\\"_cid\\\") for name in self.__argnames__)\",\n \"def __getitem__( self, l ) :\\n\\n return( self.coefficients[l] )\",\n \"def c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\\n\\tC = c_matrix(x1,x2,x3)\\n\\ty = y_vector(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\\n\\tCCoefficients = np.dot(inv(C),y)\\n\\treturn(CCoefficients)\",\n \"def get_coeffs(self):\\n\\n return self._coeff_to_dict()\",\n \"def findCurvePoints(self, x, y, c):\\n\\t\\tyCurve = []\\n\\t\\tfor xi in x:\\n\\t\\t\\tyi = self.polynomialFunct(c, xi)\\n\\t\\t\\t\\n\\t\\t\\tyCurve.append( yi )\\n\\t\\t\\n\\t\\treturn np.asarray(yCurve)\",\n \"def getListOfCompartments(self):\\n return self.model.getListOfCompartments()\",\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 cinters_circle(self, c):\\r\\n if self.__segments == None:\\r\\n self.__load_segments()\\r\\n \\r\\n result = []\\r\\n for segment in self.__segments:\\r\\n points = c.inters_segment(segment)\\r\\n for p in points:\\r\\n result.append(p) \\r\\n \\r\\n return result\",\n \"def getCC(self, var, idx = None, translation = None, other = None,\\\\\\n verbose = 1, version = \\\"pearson\\\", ab = []):\\n\\n if idx is None: idx = np.arange(self.atoms.shape[0])\\n if translation is None: translation = [0]\\n if isinstance(translation, (int, np.integer)): translation = [translation]\\n\\n data, lbl = self.getData(var = var, idx = idx, translation = translation,\\\\\\n verbose = verbose, ab = ab, other = other)[::2]\\n string = \\\"\\\"\\n for i, item in enumerate(data):\\n if i == 0:\\n values = np.zeros((len(data), np.shape(data[i])[0]))\\n\\n if np.ndim(data[i]) == 1:\\n values[i, :] = data[i]\\n else:\\n values[i, :] = data[i][:, 0]\\n\\n if \\\"pearson\\\".startswith(version.lower()):\\n ccoef = np.zeros((values.shape[0], values.shape[0]))\\n rho = np.zeros((values.shape[0], values.shape[0]))\\n\\n for i in range(0, values.shape[0]):\\n for j in range(i + 1, values.shape[0]):\\n ccoef[i, j], rho[i, j] = stats.pearsonr(values[i, :], values[j, :])\\n ccoef[j, i] = ccoef[i, j]\\n rho[j, i] = rho[i, j]\\n\\n ccoef += np.identity(values.shape[0])\\n\\n elif \\\"spearman\\\".startswith(version.lower()):\\n ccoef, rho = stats.spearmanr(values, axis = 1)\\n\\n if verbose > 0:\\n head = \\\"Cor-Coef for the following variables:\\\"\\n ut.infoPrint(head, sep_after = False)\\n print(\\\"-\\\" * len(head))\\n for i, string in enumerate(var):\\n ut.infoPrint(\\\"%s\\\" % string, sep_before = False, sep_after = False)\\n print(\\\"=\\\" * len(head))\\n\\n return ccoef, rho, lbl\",\n \"def getListNestedCVTerms(self, *args):\\n return _libsbml.CVTerm_getListNestedCVTerms(self, *args)\",\n \"def getDisciplinesList():\\n return Gw2Spidy._request('disciplines')['results']\",\n \"def getCVTerms(self):\\n return _libsbml.SBase_getCVTerms(self)\",\n \"def get(self, *args):\\n return _libsbml.ListOfConstraints_get(self, *args)\",\n \"def get_constraint_list(self):\\n constraints = []\\n for i in xrange(self.num_repeats):\\n # Using start_index, start each domain at the correct index when flattening out points in COBYLA.\\n constraints.extend(self._domain.get_constraint_list(start_index=self.dim * i))\\n return constraints\",\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 circuit_list(self):\\r\\n return self.circuits.itervalues()\",\n \"def getListPunctCmplx(self):\\n\\t\\treturn self.list_punct_cmplx\",\n \"def get_compounds(self, ctx, params):\\n # ctx is the context object\\n # return variables are: out_compounds\\n #BEGIN get_compounds\\n self._check_param(params, ['compounds'])\\n out_compounds = []\\n for x in params['compounds']:\\n id = x.split('/')[-1]\\n comp = self.compounds.get(id, None)\\n if comp:\\n comp['aliases'] = self.comp_aliases.get(id, '')\\n out_compounds.append(comp)\\n #END get_compounds\\n\\n # At some point might do deeper type checking...\\n if not isinstance(out_compounds, list):\\n raise ValueError('Method get_compounds return value ' +\\n 'out_compounds is not type list as required.')\\n # return the results\\n return [out_compounds]\",\n \"def coefficients(self, force_characters = False) :\\n if len(self.__coefficients) == 0 :\\n return dict()\\n elif not force_characters and len(self.__coefficients) == 1 :\\n return self.__coefficients.values()[0] \\n else :\\n return self.__coefficients\",\n \"def _get_labels_for_subject(file_ptr):\\n labels = []\\n\\n #Open CSC\\n reader = csv.reader(file_ptr, delimiter=',')\\n\\n for i, row in enumerate(reader):\\n facs = []\\n for j, intensity in enumerate(row):\\n if j == 0:\\n continue\\n\\n facs.append(int(intensity))\\n\\n labels.append(facs)\\n \\n return labels\",\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 get_subject_cn(self):\\n subject = self.get_subject()\\n cns = subject.get_entries_by_oid(name.OID_commonName)\\n return [cn.get_value() for cn in cns]\",\n \"def completions (cls, cc_prefix) :\\n return cls.cc_trie.completions (cc_prefix)\",\n \"def getList(x, swipl):\\n\\n t = swipl.PL_copy_term_ref(x)\\n head = swipl.PL_new_term_ref()\\n result = []\\n while swipl.PL_get_list(t, head, t):\\n result.append(getTerm(head, swipl))\\n head = swipl.PL_new_term_ref()\\n\\n return result\",\n \"def _generate_poly_array(self, nchan, coeff=[]):\\n if nchan < 0:\\n raise ValueError, \\\"nchan should be >=0\\\"\\n if len(coeff)==0:\\n if nchan ==0: return []\\n else: raise ValueError, \\\"No valid coefficient given.\\\"\\n polyarr = numpy.zeros(nchan)\\n for iorder in range(len(coeff)):\\n polyarr += coeff[iorder]*numpy.array(xrange(nchan))**iorder\\n return polyarr\",\n \"def _to_cc_list(collection):\\n return \\\"{\\\" + \\\", \\\".join(collection) + \\\"}\\\"\",\n \"def get_cgo(self, dot_mode=0, dot_radius=0.03):\\n cgolist = []\\n\\n # COLOR\\n cgolist.extend(_cgo_color(self.color))\\n\\n if dot_mode == 0: # spheres\\n logger.debug(\\\"Adding dot to cgolist...\\\")\\n cgolist.extend(_cgo_sphere(self.coords, dot_radius))\\n logger.debug(\\\"Finished adding dot to cgolist.\\\")\\n\\n if dot_mode == 1: # quads\\n logger.debug(\\\"Adding quad to cgolist...\\\")\\n normal = cpv.normalize(cpv.sub(self.coords, self.atom['coords']))\\n cgolist.extend(_cgo_quad(self.coords, normal, dot_radius * 1.5))\\n logger.debug(\\\"Finished adding quad to cgolist.\\\")\\n\\n return cgolist\",\n \"def coefficients(self, force_characters = False) :\\n raise NotImplementedError\",\n \"def getComicsListFromFile(filename):\\n h = open(filename)\\n contents = \\\"\\\\n\\\".join(h.readlines())\\n expr = re.compile(\\\"([a-z0-9]+)\\\")\\n return expr.findall(contents)\",\n \"def get_complaints(visit):\\r\\n return visit.complaints.all()\",\n \"def concentration(self):\\n return [node.concentration for node in self]\",\n \"def get_subjects(self, subj_type='included'):\\n if subj_type == 'included':\\n scode = 'subjectswithcode'\\n elif subj_type == 'excluded':\\n scode = 'excludedsubjectswithcode'\\n else:\\n raise NameError(\\\"\\\"\\\"subj_type must be either 'included' or\\n 'excluded'\\\"\\\"\\\")\\n\\n url = scode + '?' + self._login_code + \\\\\\n '&projectCode=' + self.proj_code\\n output = self._send_request(url)\\n\\n # Split at '\\\\n'\\n subj_list = output.split('\\\\n')\\n # Remove any empty entries!\\n subj_list = [x for x in subj_list if x]\\n\\n return(subj_list)\",\n \"def get_course_results(self,subj,cnum=\\\"\\\"):\\n\\t\\tinfo = self.info # take info from self\\n\\n\\t\\turl = info['url_base'] + info['url_action']\\n\\t\\tvals = PostData()\\n\\t\\tvals.add_item('TRM'\\t\\t\\t\\t, \\\"U\\\")\\n\\t\\tvals.add_item('term_in'\\t\\t\\t, \\\"201501\\\")\\n\\t\\tvals.add_item('sel_subj'\\t\\t, \\\"dummy\\\")\\n\\t\\tvals.add_item('sel_day'\\t\\t\\t, \\\"dummy\\\")\\n\\t\\tvals.add_item('sel_schd'\\t\\t, \\\"dummy\\\")\\n\\t\\tvals.add_item('sel_insm'\\t\\t, \\\"dummy\\\")\\n\\t\\tvals.add_item('sel_camp'\\t\\t, \\\"dummy\\\")\\n\\t\\tvals.add_item('sel_levl'\\t\\t, \\\"dummy\\\")\\n\\t\\tvals.add_item('sel_sess'\\t\\t, \\\"dummy\\\")\\n\\t\\tvals.add_item('sel_instr'\\t\\t, \\\"dummy\\\")\\n\\t\\tvals.add_item('sel_ptrm'\\t\\t, \\\"dummy\\\")\\n\\t\\tvals.add_item('sel_attr'\\t\\t, \\\"dummy\\\")\\n\\t\\tvals.add_item('sel_subj'\\t\\t, subj)\\n\\t\\tvals.add_item('sel_crse'\\t\\t, cnum)\\n\\t\\tvals.add_item('sel_title'\\t\\t, \\\"\\\")\\n\\t\\tvals.add_item('sel_schd'\\t\\t, \\\"%\\\")\\n\\t\\tvals.add_item('sel_insm'\\t\\t, \\\"%\\\")\\n\\t\\tvals.add_item('sel_from_cred'\\t, \\\"\\\")\\n\\t\\tvals.add_item('sel_to_cred'\\t\\t, \\\"\\\")\\n\\t\\tvals.add_item('sel_camp'\\t\\t, \\\"%\\\")\\n\\t\\tvals.add_item('begin_hh'\\t\\t, \\\"0\\\")\\n\\t\\tvals.add_item('begin_mi'\\t\\t, \\\"0\\\")\\n\\t\\tvals.add_item('begin_ap'\\t\\t, \\\"a\\\")\\n\\t\\tvals.add_item('end_hh'\\t\\t\\t, \\\"0\\\")\\n\\t\\tvals.add_item('end_mi'\\t\\t\\t, \\\"0\\\")\\n\\t\\tvals.add_item('end_ap'\\t\\t\\t, \\\"a\\\")\\n\\n\\t\\t#data = urllib.urlencode(vals.get_string())\\n\\t\\tdata = vals.get_string()\\n\\t\\treq = urllib2.Request(url, data=data)\\n\\t\\tprint(\\\"=== Headers ===\\\")\\n\\t\\tprint(req.headers)\\n\\t\\tprint(\\\"=== Data ===\\\")\\n\\t\\tprint(req.data)\\n\\t\\tresponse = urllib2.urlopen(req)\\n\\t\\treturn response.read()\",\n \"def allcJPDs(net,cond_node,cond_states):\\r\\n \\r\\n onodes = net.Outputnodes()\\r\\n onodes = sorted(onodes)\\r\\n combinations = [i for i in itertools.combinations(onodes,2)]\\r\\n JPDdatas = [] \\r\\n progress = 0\\r\\n for cs in cond_states:\\r\\n JPDdata = []\\r\\n for c in combinations:\\r\\n print 'Progress: ' +str(int(100*float(progress)/float(len(combinations)*len(cond_states))))+ ' %'\\r\\n priorA = getCondPrior(net,c[0],cond_node,cs)\\r\\n print priorA\\r\\n priorB = getCondPrior(net,c[1],cond_node,cs) \\r\\n print priorB\\r\\n JPD = cJPD(net,c,cond_node,cs)\\r\\n cov = Covariance (priorA,priorB,JPD)\\r\\n cor = Correlation (priorA,priorB,cov) \\r\\n JPDdata.append([c[0],c[1],JPD,cov,cor])\\r\\n progress+=1\\r\\n JPDdatas.append(JPDdata)\\r\\n return JPDdatas\",\n \"def covariates(self) -> List[str]:\\n return self._obj._names[\\\"covariates\\\"]\",\n \"def getC(self):\\n\\t\\treturn self.c\",\n \"def getcitations(self, pmcid, source):\\n import requests\\n r = requests.get((self.citationurl).format(source=source, pmcid=pmcid))\\n return r.content\",\n \"def charpoly(self):\\n M = self\\n K = M.domain\\n\\n factors = M.charpoly_factor_blocks()\\n\\n cp = [K.one]\\n\\n for f, mult in factors:\\n for _ in range(mult):\\n cp = dup_mul(cp, f, K)\\n\\n return cp\",\n \"def ceps_coeff(sig,coefNumber):\\n\\n est=lpc_coef(sig,coefNumber)\\n cc=lpcar2cc(est)\\n if len(cc)==1:\\n cc=float(cc)\\n else:\\n cc=tuple(cc)\\n\\n return cc\",\n \"def tc_mcdc(foo):\\r\\n\\r\\n # get number of input parameters (=conditions)\\r\\n sig=signature(foo)\\r\\n n=len(sig.parameters)\\r\\n \\r\\n # generate all possible test cases\\r\\n all_test_cases=tc_gen(n)\\r\\n\\r\\n # generate all subsets of test cases\\r\\n allsubsets=powerset(all_test_cases)\\r\\n\\r\\n # create list with all subsets that satisfy cc and their length\\r\\n satisfies_mcdc=[]\\r\\n for subset in allsubsets:\\r\\n #print(subset)\\r\\n if is_mc(subset,foo) and is_cc(subset) and is_dc(subset,foo):\\r\\n satisfies_mcdc.append((subset,len(subset)))\\r\\n satisfies_mcdc.sort(key=lambda x: x[1])\\r\\n return satisfies_mcdc[0][0]\",\n \"def zzx_mul_const(f, c):\\n if not c or not f:\\n return []\\n else:\\n return [ c * coeff for coeff in f ]\",\n \"def c(self) -> np.ndarray:\\n return self._vector[10:12]\",\n \"def get_proj_coeffs(self, src):\\n self.proj_coeffs = parallel.call_and_bcast(self.get_array, src)\",\n \"def getc(self,c_):\\n _c_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and c_ is not None and len(c_) != self.getnumvar():\\n raise ValueError(\\\"Array argument c is not long enough: Is %d, expected %d\\\" % (len(c_),self.getnumvar()))\\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_getc(self.__nativep,_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 get_clarifications_mctaco(ex, nlp, comet_model):\\n context = ex['context']\\n personx, _ = get_personx(nlp, context)\\n \\n if len(personx) == 0:\\n return []\\n \\n outputs = {category: comet_model.predict(context, category, num_beams=5) for category in comet_model.categories}\\n\\n curr_events = []\\n for category, prefix in CATEGORY_TO_PREFIX.items():\\n for out_event in outputs[category]:\\n if out_event != \\\"none\\\" and out_event != \\\"\\\":\\n if not out_event.lower().startswith(\\\"person\\\") and not out_event.lower().startswith(\\\"other\\\"):\\n out_event = \\\" \\\".join((prefix, out_event))\\n\\n out_event = re.sub(\\\"personx\\\", personx, out_event, flags=re.I)\\n out_event = re.sub(\\\"person x\\\", personx, out_event, flags=re.I)\\n out_event = re.sub(\\\"persony\\\", \\\"others\\\", out_event, flags=re.I)\\n out_event = re.sub(\\\"person y\\\", \\\"others\\\", out_event, flags=re.I)\\n\\n question = CATEGORY_TO_QUESTION[category].replace(\\\"PersonX\\\", personx)\\n curr_events.append((question, out_event))\\n\\n return curr_events\",\n \"def get_cm_list ( self ) :\\n cm_name_list = []\\n stmt = \\\"select name from sdb_continuousmodel where sys002 =\\\\'T\\\\'\\\"\\n self.oracle_cursor.arraysize = 100000\\n self.oracle_cursor.execute(stmt)\\n resultset = self.oracle_cursor.fetchmany()\\n if resultset :\\n for row in resultset :\\n cm_name_list.append(str(row[0]))\\n return cm_name_list\",\n \"def tc_cc(foo):\\r\\n\\r\\n # get number of input parameters (=conditions)\\r\\n sig=signature(foo)\\r\\n n=len(sig.parameters)\\r\\n \\r\\n # generate all possible test cases\\r\\n all_test_cases=tc_gen(n)\\r\\n\\r\\n # generate all subsets of test cases\\r\\n allsubsets=powerset(all_test_cases)\\r\\n\\r\\n # create list with all subsets that satisfy cc and their length\\r\\n satisfies_cc=[]\\r\\n for subset in allsubsets:\\r\\n if is_cc(subset):\\r\\n satisfies_cc.append((subset,len(subset)))\\r\\n satisfies_cc.sort(key=lambda x: x[1])\\r\\n return satisfies_cc[0][1]\",\n \"def get(self, *args):\\n return _libsbml.ListOfCompartmentReferences_get(self, *args)\",\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 get_cffts(self):\\n return [\\n rfft(self.nx, self.dx, fft=self.tfft, ny=self.ny,\\n dy=self.dy).get_cfft(),\\n rfft(self.nx, self.dx, fft=self.efft, ny=self.ny,\\n dy=self.dy).get_cfft(),\\n rfft(self.nx, self.dx, fft=self.bfft, ny=self.ny,\\n dy=self.dy).get_cfft()\\n ]\",\n \"def read_pccp(fname, seq):\\n pccp = phy7(fname)\\n return np.array([pccp[i] for i in seq])\",\n \"def get_cgo(self, radius=0.03):\\n cgolist = []\\n\\n # Set colors\\n rgb0 = colors.get_color_rgb(self.color[0])\\n rgb1 = colors.get_color_rgb(self.color[1])\\n\\n if True: # cylinders\\n logger.debug(\\\"Adding vector to cgolist...\\\")\\n # Cylinder\\n cgolist.extend(_cgo_cylinder(self.coords[0], self.coords[1],\\n radius, rgb0, rgb1))\\n # Caps\\n cgolist.extend(_cgo_color(self.color[0]))\\n cgolist.extend(_cgo_sphere(self.coords[0], radius))\\n cgolist.extend(_cgo_color(self.color[1]))\\n cgolist.extend(_cgo_sphere(self.coords[1], radius))\\n\\n logger.debug(\\\"Finished adding vector to cgolist.\\\")\\n\\n return cgolist\",\n \"def get(self) -> list:\\n return self.__cogs\",\n \"def test_coefficients(self):\\n\\n coefs = self.cs.coefficients\\n\\n self.assertEqual(coefs, (1, 0, 1, 0, 0, -1))\",\n \"def coeff(self):\\n return self._coeff\",\n \"def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\\n coherence_values =[]\\n model_list =[]\\n for i in range(start,limit,step):\\n model=gensim.models.ldamodel.LdaModel(doc_term_matrix, num_topics=i, random_state=2, id2word=dictionary, iterations=10)\\n model_list.append(model)\\n coherence_model =CoherenceModel(model=model, texts=texts, dictionary=dictionary,coherence='c_v')\\n coherence_values.append(coherence_model.get_coherence())\\n return model_list,coherence_values\",\n \"def list_cas():\\n cas = []\\n for ca in settings.ACM_PRIVATE_CA_SETTINGS:\\n _ca = get_ca(ca)\\n cas.append(_ca.get_certificate_authority_certificate())\\n return cas\",\n \"def construct_sequence_list(self):\\n return list(self.iter_sequence())\",\n \"def complexCompose(self,coefficients,t=1):\\n c=coefficients\\n N=len(c)//2\\n s=lambda t,n:c[n+N]*cmath.exp(1j*n*t)\\n a=0\\n g=[]\\n z=[]\\n\\n #for i in range(len(c)):\\n # if i==0: n=0\\n # elif i%2==1: n=(i+1)//2\\n # elif i%2==0: n=-i//2\\n # pass\\n\\n #print([a[1] for a in z])\\n #z=sorted(z,key=lambda x:1,reverse=True)\\n #print([a[1] for a in z])\\n #z=[a[0] for a in z]\\n\\n for n in range(-N,N+1):\\n a+=s(t,n)\\n g.append((a.real,a.imag))\\n\\n return g\",\n \"def clts(sequence):\\n return [_token2clts(segment)[1] for segment in sequence]\",\n \"def coeffs(f):\\n return dmp_coeffs(f.rep, f.lev, f.dom)\",\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 evaluation_cc(self, property='clustering-coeff'):\\n\\n if property == 'clustering-coeff':\\n rw_cc = [np.mean(clustering_coef_wu(self.rw_data[t])) for t in range(0, self.T)]\\n smth_cc = [np.mean(clustering_coef_wu(self.smth_data[t])) for t in range(0, self.T)]\\n elif property == 'transitivity':\\n rw_cc = [np.mean(transitivity_wu(self.rw_data[t])) for t in range(0, self.T)]\\n smth_cc = [np.mean(transitivity_wu(self.smth_data[t])) for t in range(0, self.T)]\\n elif property == 'coreness':\\n rw_cc = [np.mean(core.core_periphery_dir(self.rw_data[t])) for t in range(0, self.T)]\\n smth_cc = [np.mean(core.core_periphery_dir(self.smth_data[t])) for t in range(0, self.T)]\\n elif property == 'assortativity':\\n rw_cc = [np.mean(core.assortativity_wei(self.rw_data[t], 0)) for t in range(0, self.T)]\\n smth_cc = [np.mean(core.assortativity_wei(self.smth_data[t], 0)) for t in range(0, self.T)]\\n elif property == 'modularity':\\n rw_cc, _ = get_number_of_components(self.rw_data)\\n smth_cc, _ = get_number_of_components(self.smth_data)\\n elif property == 'path_length':\\n rw_cc = [charpath(rw)[0] for rw in self.rw_data]\\n smth_cc = [charpath(sm)[0] for sm in self.smth_data]\\n\\n # rw_cc_ent = get_entropy_list(rw_cc)\\n # smth_cc_ent = get_entropy_list(smth_cc)\\n\\n return rw_cc, smth_cc\",\n \"def X(self)->list:\",\n \"def get_critics(self):\\n actors = [ddpg_agent.critic for ddpg_agent in self.maddpg_agent]\\n return actors\",\n \"def __init__(self, coefficients):\\n self.coefficients = coefficients\",\n \"def get_circuits(self) -> List[Circuit]:\\n return [Circuit(c, self, self.easee) for c in self[\\\"circuits\\\"]]\",\n \"def get_courses(self):\\r\\n\\r\\n return self.def_ms.get_courses()\",\n \"def params(self):\\n return [p for sublist in [o.params for o in self.obs] for p in sublist]\",\n \"def get_occr(self):\\n return self._occr_array\",\n \"def getCodonSeqs(self):\\r\\n combinations = list(self.codonTable[aa] for aa in self.peptide) # creates a list of possible codons based on AA\\r\\n self.allPepSeqs = list(''.join(codon) for codon in itertools.product(*combinations)) # creates list of peptides\\r\\n return\",\n \"def __getitem__(self, s) :\\n try :\\n return self.coefficients()[s]\\n except KeyError :\\n return self.parent().coefficient_domain().zero_element()\",\n \"def all_coeffs(f):\\n return dmp_all_coeffs(f.rep, f.lev, f.dom)\",\n \"def _get_all_contrs(term, contractor, resolvers):\\n\\n vecs = term.vecs\\n n_vecs = len(vecs)\\n contrs = []\\n sums_dict = term.dumms\\n\\n for i in range(n_vecs):\\n curr_contrs = {}\\n for j in range(i, n_vecs):\\n vec_prev = vecs[i]\\n vec_lat = vecs[j]\\n contr_amp, contr_substs = simplify_deltas_in_expr(\\n sums_dict, contractor(vec_prev, vec_lat, term), resolvers\\n )\\n if contr_amp != 0:\\n curr_contrs[j] = (contr_amp, tuple(contr_substs.items()))\\n continue\\n contrs.append(curr_contrs)\\n continue\\n\\n return contrs\",\n \"def specCoeffsInReaction(r):\\n c = []\\n for k in range(__data.nsp):\\n nu = (__data.g.product_stoich_coeff(k,r) -\\n __data.g.reactant_stoich_coeff(k,r))\\n if (nu <> 0):\\n c.append((__data.g.species_name(k),nu))\\n return c\",\n \"def cvars(occs):\\n names = []\\n canonicals = []\\n for occ in occs:\\n if occ.name in names:\\n continue\\n canonicals.append(occ)\\n return canonicals\",\n \"def getAllCoPilots(self):\\n staffObject_list = self.mainObject.getStaffIO()\\n coPilotObject_list = []\\n for staffMember in staffObject_list:\\n if staffMember.getRank() == 'copilot':\\n coPilotObject_list.append(staffMember)\\n return coPilotObject_list\",\n \"def _coefficients(regression_df):\\n coeff_names = ('mindist', 'x_j', 'f_is', 'v_is')\\n coefficients = {x: _get_coefficient(regression_df, x) for x in coeff_names}\\n return coefficients\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61023015","0.60017306","0.60017306","0.6000582","0.58433247","0.581803","0.581803","0.57937485","0.5704129","0.55387217","0.5518362","0.5512744","0.5497052","0.54852647","0.5466663","0.54118663","0.54016364","0.537931","0.5366499","0.5345296","0.5335419","0.5313061","0.5308713","0.5303487","0.5299834","0.5295104","0.5264533","0.5242031","0.5236824","0.5223935","0.5217212","0.52139115","0.5208613","0.5193126","0.5188315","0.5184072","0.51713246","0.51681376","0.5163396","0.51479596","0.5141753","0.51399785","0.5118169","0.50943196","0.5087257","0.5081466","0.5062596","0.5058682","0.5052837","0.5049328","0.5039167","0.5036281","0.5027094","0.5016533","0.5008952","0.50068295","0.49939042","0.49902472","0.49901226","0.49791428","0.49771708","0.49720618","0.49699053","0.4963675","0.49634585","0.4961017","0.4957171","0.49555987","0.49544153","0.49535227","0.4947198","0.49466917","0.49335366","0.4933283","0.49248427","0.49241257","0.4923784","0.49129072","0.4905232","0.49001452","0.48981467","0.48965985","0.4891783","0.48902753","0.4888406","0.48882258","0.48868233","0.4884756","0.4882677","0.48659065","0.4848978","0.48453498","0.48407903","0.4834663","0.48340678","0.4831973","0.48266098","0.48248008","0.48245698","0.4823423"],"string":"[\n \"0.61023015\",\n \"0.60017306\",\n \"0.60017306\",\n \"0.6000582\",\n \"0.58433247\",\n \"0.581803\",\n \"0.581803\",\n \"0.57937485\",\n \"0.5704129\",\n \"0.55387217\",\n \"0.5518362\",\n \"0.5512744\",\n \"0.5497052\",\n \"0.54852647\",\n \"0.5466663\",\n \"0.54118663\",\n \"0.54016364\",\n \"0.537931\",\n \"0.5366499\",\n \"0.5345296\",\n \"0.5335419\",\n \"0.5313061\",\n \"0.5308713\",\n \"0.5303487\",\n \"0.5299834\",\n \"0.5295104\",\n \"0.5264533\",\n \"0.5242031\",\n \"0.5236824\",\n \"0.5223935\",\n \"0.5217212\",\n \"0.52139115\",\n \"0.5208613\",\n \"0.5193126\",\n \"0.5188315\",\n \"0.5184072\",\n \"0.51713246\",\n \"0.51681376\",\n \"0.5163396\",\n \"0.51479596\",\n \"0.5141753\",\n \"0.51399785\",\n \"0.5118169\",\n \"0.50943196\",\n \"0.5087257\",\n \"0.5081466\",\n \"0.5062596\",\n \"0.5058682\",\n \"0.5052837\",\n \"0.5049328\",\n \"0.5039167\",\n \"0.5036281\",\n \"0.5027094\",\n \"0.5016533\",\n \"0.5008952\",\n \"0.50068295\",\n \"0.49939042\",\n \"0.49902472\",\n \"0.49901226\",\n \"0.49791428\",\n \"0.49771708\",\n \"0.49720618\",\n \"0.49699053\",\n \"0.4963675\",\n \"0.49634585\",\n \"0.4961017\",\n \"0.4957171\",\n \"0.49555987\",\n \"0.49544153\",\n \"0.49535227\",\n \"0.4947198\",\n \"0.49466917\",\n \"0.49335366\",\n \"0.4933283\",\n \"0.49248427\",\n \"0.49241257\",\n \"0.4923784\",\n \"0.49129072\",\n \"0.4905232\",\n \"0.49001452\",\n \"0.48981467\",\n \"0.48965985\",\n \"0.4891783\",\n \"0.48902753\",\n \"0.4888406\",\n \"0.48882258\",\n \"0.48868233\",\n \"0.4884756\",\n \"0.4882677\",\n \"0.48659065\",\n \"0.4848978\",\n \"0.48453498\",\n \"0.48407903\",\n \"0.4834663\",\n \"0.48340678\",\n \"0.4831973\",\n \"0.48266098\",\n \"0.48248008\",\n \"0.48245698\",\n \"0.4823423\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70591795"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11795,"cells":{"query":{"kind":"string","value":"Obtains a sequence of coefficients from the objective. getcslice(self,first_,last_,c_)"},"document":{"kind":"string","value":"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"},"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 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 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 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 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 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 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 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 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 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 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 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 coefficients(self) :\n raise NotImplementedError","def __getitem__( self, l ) :\n\n return( self.coefficients[l] )","def c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\n\tC = c_matrix(x1,x2,x3)\n\ty = y_vector(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\n\tCCoefficients = np.dot(inv(C),y)\n\treturn(CCoefficients)","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 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 c(self) -> np.ndarray:\n return self._vector[10:12]","def getsolutionslice(self,whichsol_,solitem_,first_,last_,values_):\n _values_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and values_ is not None and len(values_) != ((last_) - (first_)):\n raise ValueError(\"Array argument values is not long enough: Is %d, expected %d\" % (len(values_),((last_) - (first_))))\n if isinstance(values_,numpy.ndarray) and not values_.flags.writeable:\n raise ValueError(\"Argument values must be writable\")\n if isinstance(values_, numpy.ndarray) and values_.dtype is numpy.dtype(numpy.float64) and values_.flags.contiguous:\n _values_copyarray = False\n _values_tmp = ctypes.cast(values_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif values_ is not None:\n _values_copyarray = True\n _values_np_tmp = numpy.zeros(len(values_),numpy.dtype(numpy.float64))\n _values_np_tmp[:] = values_\n assert _values_np_tmp.flags.contiguous\n _values_tmp = ctypes.cast(_values_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _values_copyarray = False\n _values_tmp = None\n \n res = __library__.MSK_XX_getsolutionslice(self.__nativep,whichsol_,solitem_,first_,last_,_values_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _values_copyarray:\n values_[:] = _values_np_tmp","def coefficients(self) :\n return self.__coefficients","def __setslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___setslice__(self, *args)","def coeff(self, pt):\n if pt < self.start or pt > self.stop:\n raise IndexError(\"Index %s out of bounds %s\" % (pt, self.interval))\n return self._eval_coeff(pt)","def __getitem__(self, s) :\n try :\n return self.coefficients()[s]\n except KeyError :\n return self.parent().coefficient_domain().zero_element()","def __getslice__(self, start, stop):\n return self.__getitem__(slice(start, stop, None))","def coefficients(self):\r\n return self.coef_['x']","def __getslice__(self,i,j):\n return self.x[i:j]","def __getitem__(self, c, *rest):\n if isinstance(c, Partition):\n assert len(rest) == 0\n else:\n if len(rest) > 0 or isinstance(c,(int,Integer)):\n c = self._kbounded_partitions.element_class(self._kbounded_partitions, [c]+list(rest))\n else:\n c = self._kbounded_partitions.element_class(self._kbounded_partitions, list(c))\n if len(c) != 0 and c[0] > self.k:\n raise ValueError(\"Partition is not %d-bounded\"%self.k)\n return self.monomial(c)","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 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 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 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 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 is None:\n values_ = None\n else:\n try:\n values_ = memoryview(values)\n except TypeError:\n try:\n _tmparr_values = array.array(\"d\",values)\n except TypeError:\n raise TypeError(\"Argument values has wrong type\")\n else:\n values_ = memoryview(_tmparr_values)\n _copyback_values = True\n else:\n if values_.format != \"d\":\n values_ = memoryview(array.array(\"d\",values))\n _copyback_values = True\n if values_ is not None and len(values_) != ((last_) - (first_)):\n raise ValueError(\"Array argument values has wrong length\")\n res = self.__obj.getsolutionslice(whichsol_,solitem_,first_,last_,values_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_values:\n values[:] = _tmparr_values","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 coefficients(self):\n return self._coefficients","def coefficients(self):\n return self._coefficients","def get_cs(self, model=1, limits=None, prev_aa=None, next_aa=None,\n piqc=False, sigma_n=None, like_ss=True, debug=False):\n\n # Validate input.\n aa = self.correlation.aa\n if aa not in aminoacids.aa_list:\n mesg = '{} is not a supported amino-acid!'.format(aa)\n raise ValueError(mesg)\n\n for atom in self.correlation.atoms:\n if atom not in aminoacids.aa_atoms[aa]:\n raise ValueError('{} is not an atom in {}'.format(atom, aa))\n if prev_aa:\n prev_aa = tuple(set(prev_aa))\n for prev_aa_i in prev_aa:\n if prev_aa_i not in aminoacids.aa_list:\n mesg = '{} is not an amino-acid!'.format(prev_aa_i)\n raise ValueError(mesg)\n if next_aa:\n next_aa = tuple(set(next_aa))\n for next_aa_i in next_aa:\n if next_aa_i not in aminoacids.aa_list:\n mesg = '{} is not an amino-acid!'.format(next_aa_i)\n raise ValueError(mesg)\n\n if piqc and not self.database.table_exist('SEQ_CS_DB'):\n mesg = 'The PIQC table is not in {}.'.format(self.database)\n raise ValueError(mesg)\n\n ss = self.correlation.ss\n n = self.dims\n\n # Scary sub-query to find unique secondary structure or the most common\n # secondary structure. I am sorry ...\n if model == 'all':\n sub_sql = \"\"\"INNER JOIN(\n SELECT DISTINCT c.KEY_ID, c.SND_STRC FROM {0}_strc_db as c\n INNER JOIN (\n SELECT KEY_ID, COUNT(a.KEY_ID) as count FROM (\n SELECT DISTINCT KEY_ID, SND_STRC FROM {0}_strc_db\n GROUP BY KEY_ID, SND_STRC)as a\n GROUP BY KEY_ID\n HAVING count = 1\n ) as b ON c.KEY_ID = b.KEY_ID ) as strc\n ON cs_0.KEY_ID = strc.KEY_ID\"\"\".format(aa)\n\n if model == 'most':\n sub_sql = \"\"\"INNER JOIN (SELECT KEY_ID,\n SUBSTRING_INDEX(GROUP_CONCAT(x.SND_STRC\n ORDER BY x.count DESC SEPARATOR ':::'), ':::', 1) AS snd_strc_mode\n FROM (SELECT KEY_ID, SND_STRC, COUNT(*) as count FROM {0}_strc_db\n GROUP BY KEY_ID, SND_STRC) as x\n GROUP BY x.KEY_ID ) as xx\n ON cs_0.KEY_ID = xx.KEY_ID\"\"\".format(aa)\n\n # Build SQL query\n # Select Chemical Shifts From Sub Table(s)\n cs = 'cs_{0}.C_SHIFT as cs{0}'\n sql = [(\"SELECT \" + ', '.join([cs.format(x) for x in range(n)])),\n \"FROM {0}_cs_db as cs_0\".format(aa)]\n\n # Join other sub table for chemical shift.\n for ni in range(1, n):\n sql.append(\"INNER JOIN {0}_cs_db AS cs_{1} \".format(aa, ni))\n sql.append(\"ON cs_0.KEY_ID = cs_{0}.KEY_ID\".format(ni))\n\n # Join other sub table for structure\n if ss and ss != 'X':\n if model in {'all', 'most'}:\n sql.append(sub_sql)\n else:\n sql.append(\"INNER JOIN {0}_strc_db AS strc\".format(aa))\n sql.append(\"ON cs_0.KEY_ID = strc.KEY_ID\")\n\n if prev_aa or next_aa:\n sql.append(\"INNER JOIN {0}_db AS info\".format(aa))\n sql.append(\"ON cs_0.KEY_ID = info.KEY_ID\")\n\n if piqc:\n sql.append(\"INNER JOIN SEQ_CS_DB \")\n sql.append(\"ON cs_0.FIRSTKEY_ID = SEQ_CS_DB.KEY_ID\")\n\n # Start of the where statements.\n # Atoms and Limits for first atom\n atom_0 = self.correlation.atoms[0]\n sql.append(\"WHERE cs_0.ATOM_NAME = '{0}'\".format(atom_0))\n\n if prev_aa:\n if len(prev_aa) == 1:\n sql.append(\"AND info.PREV_X = '{}'\".format(prev_aa[0]))\n else:\n sql.append(\"AND info.PREV_X in {}\".format(prev_aa))\n\n if next_aa:\n if len(next_aa) == 1:\n sql.append(\"AND info.NEXT_X = '{}'\".format(next_aa[0]))\n else:\n sql.append(\"AND info.NEXT_X in {}\".format(next_aa))\n\n if limits:\n sql.append(\"AND cs_0.C_SHIFT\")\n limits_0 = (limits[0][0], limits[0][1])\n sql.append(\"BETWEEN {0} AND {1}\".format(limits_0))\n\n # Atoms and Limits for rest of the atom\n for ni in list(range(1, n)):\n sql.append(\"AND cs_{0}.ATOM_NAME = '{1}'\".format(\n ni, self.correlation.atoms[ni]))\n\n if limits:\n sql.append(\"AND cs_{0}.C_SHIFT \".format(ni))\n limits_n = (limits[ni][0], limits[ni][1])\n sql.append(\"BETWEEN {0} AND {1}\".format(limits_n))\n\n # If secondary structure\n if ss and ss != 'X':\n try:\n ss_list = aminoacids.similar_sndstr[ss]\n except KeyError:\n raise ValueError('{} is not a valid sndstr'.format(ss))\n\n if model == 'all':\n if like_ss:\n sql.append(\"AND strc.SND_STRC IN {0}\".format(ss_list))\n else:\n sql.append(\"AND strc.SND_STRC = '{}'\".format(ss))\n elif model == 'most':\n if like_ss:\n sql.append(\"AND xx.snd_strc_mode IN {0}\".format(ss_list))\n else:\n sql.append(\"AND xx.snd_strc_mode = '{0}'\".format(ss))\n else:\n if like_ss:\n sql.append(\"AND SND_STRC IN {0}\".format(ss_list))\n else:\n sql.append(\"AND SND_STRC = '{0}'\".format(ss))\n sql.append(\"AND MODEL_NO={0}\".format(model))\n\n if piqc:\n sql.append(\"AND ELEMENT='C'\")\n sql.append(\"AND PIQC = 1\")\n\n sql = [x.strip() for x in sql]\n sql = '\\n'.join(sql)\n\n if debug:\n print(sql)\n\n cs = self.database.query(sql)\n\n if not cs:\n raise ValueError\n\n if sigma_n:\n avg = np.mean(cs, axis=0)\n std = np.std(cs, axis=0)\n ind = np.all(np.abs(cs-avg) <= std*sigma_n, axis=1)\n cs = np.compress(ind, cs, axis=0)\n\n return cs","def getc(self,c_):\n _c_minlength = self.getnumvar()\n if self.getnumvar() > 0 and c_ is not None and len(c_) != self.getnumvar():\n raise ValueError(\"Array argument c is not long enough: Is %d, expected %d\" % (len(c_),self.getnumvar()))\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_getc(self.__nativep,_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 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 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 __delslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___delslice__(self, *args)","def get_coefficients(self):\n return self.coefficients","def get_coefficients(self):\n return self.coefficients","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 calculate_coefficients(self, start, end):\n A = np.array([\n [self.deltaT**3, self.deltaT**4, self.deltaT**5],\n [3 * self.deltaT**2, 4 * self.deltaT**3, 5 * self.deltaT**4],\n [6 * self.deltaT, 12 * self.deltaT**2, 20 * self.deltaT**3],\n ])\n\n a_0, a_1, a_2 = start[0], start[1], start[2] / 2.0\n c_0 = a_0 + a_1 * self.deltaT + a_2 * self.deltaT**2\n c_1 = a_1 + 2 * a_2 * self.deltaT\n c_2 = 2 * a_2\n\n B = np.array([\n end[0] - c_0,\n end[1] - c_1,\n end[2] - c_2\n ])\n\n a_3_4_5 = np.linalg.solve(A, B)\n coeff = np.concatenate((np.array([a_0, a_1, a_2]), a_3_4_5))\n\n return coeff","def __getslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___getslice__(self, *args)","def coefficients(self) -> np.ndarray:\n return self._coefficients","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 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 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 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 cinters_circle(self, c):\r\n if self.__segments == None:\r\n self.__load_segments()\r\n \r\n result = []\r\n for segment in self.__segments:\r\n points = c.inters_segment(segment)\r\n for p in points:\r\n result.append(p) \r\n \r\n return result","def spline_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\n\tC = c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\n\tD = d_coefficients(x1,x2,x3,C)\n\tB = b_coefficients(x1,x2,x3,y1,y2,y3,C,D)\n\tA = a_coefficients(y1,y2)\n\treturn(A,B,C[:2],D)","def findCurvePoints(self, x, y, c):\n\t\tyCurve = []\n\t\tfor xi in x:\n\t\t\tyi = self.polynomialFunct(c, xi)\n\t\t\t\n\t\t\tyCurve.append( yi )\n\t\t\n\t\treturn np.asarray(yCurve)","def test05(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(None, None, -3)\n # print \"b[sl]->\", `b[sl]`\n self.assertRaises(NotImplementedError, b.__getitem__, sl)","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 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 __setslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___setslice__(self, *args)","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 relay_array_getitem(c, a, start, stop, strides):\n assert start.is_constant(tuple)\n assert stop.is_constant(tuple)\n assert strides.is_constant(tuple)\n return relay.op.transform.strided_slice(c.ref(a), start.value, stop.value,\n strides.value)","def __setslice__(self, *args):\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___setslice__(self, *args)","def __getslice__(self, i, j):\n return self.dtrs[i:j]","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","def slice2(self, cvars=None,ctuple=None):\n return self.condition2(cvars,ctuple)","def c(self, i):\n value = self.b(i)\n if i == self.N:\n return value\n else:\n for j in range(i+1, self.N+1):\n value = value - self.a(i,j) * self.C[j]\n return value","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 get_cd_samples(self):\n \n if \"PCD\" in self.algorithm:\n \n input_vars = []\n \n given_vars = []\n \n else:\n \n input_vars = [self.minibatch_set]\n \n given_vars = {self.x_gibbs: self.train_inputs[self.minibatch_set,:]} \n \n get_samples = theano.function(inputs = input_vars,\n outputs = [self.p_xi_given_x_[-1], \n self.gibbs_samples[-1]\n ], \n givens = given_vars,\n #start the chain at the data distribution\n updates = self.gibbs_updates)\n \n return get_samples","def lookup_subset(self, c):\n p = self\n for start in range(len(c)):\n if p.tail.has_key(c[start]):\n p = p.tail[c[start]]\n\n return p.result","def getaslice(self,accmode_,first_,last_,ptrb,ptre,sub,val): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n maxnumnz_ = self.getaslicenumnz((accmode_),(first_),(last_))\n _copyback_ptrb = False\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 _copyback_ptrb = True\n else:\n if ptrb_.format != \"q\":\n ptrb_ = memoryview(array.array(\"q\",ptrb))\n _copyback_ptrb = True\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptrb has wrong length\")\n _copyback_ptre = False\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 _copyback_ptre = True\n else:\n if ptre_.format != \"q\":\n ptre_ = memoryview(array.array(\"q\",ptre))\n _copyback_ptre = True\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\n raise ValueError(\"Array argument ptre has wrong length\")\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(\"i\",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 != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n _copyback_sub = True\n if sub_ is not None and len(sub_) != (maxnumnz_):\n raise ValueError(\"Array argument sub 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.getaslice64(accmode_,first_,last_,maxnumnz_,len(sub),ptrb_,ptre_,sub_,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_sub:\n sub[:] = _tmparr_sub\n if _copyback_ptre:\n ptre[:] = _tmparr_ptre\n if _copyback_ptrb:\n ptrb[:] = _tmparr_ptrb","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 getc(self,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_) != self.getnumvar():\n raise ValueError(\"Array argument c has wrong length\")\n res = self.__obj.getc(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 __getitem__(self, i):\n return eos80.cp(\n self.nc.variables['SSS'].__getitem__(i),\n self.nc.variables['SST'].__getitem__(i),\n self.p)","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 slicer(seq, start=None, stop=None, step=None):\n return seq[start:stop:step]","def getSlice(properties=None, **kw):","def get_items(self, start, stop, next_position=None):","def wave_grid_c(self, i_order):\n\n index = slice(*self.i_bounds[i_order])\n\n return self.wave_grid[index]","def test02c(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(1, -3)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def items():\n for i in self._iter_restrict(zeros, ones):\n yield self.pcdata[i]","def getsnxslice(self,whichsol_,first_,last_,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\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_) != ((last_) - (first_)):\n raise ValueError(\"Array argument snx has wrong length\")\n res = self.__obj.getsnxslice(whichsol_,first_,last_,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 compute_coefficients(self, nrange, irange=None, prec=10, ef=True, Qadd=0):\n # we first need an initial set of coefficients\n C = self._coeffs\n P = self._principal_part\n M = self._space\n WR = M.multiplier()\n weight = M.weight()\n if(self.prec >= prec or len(C) > 0):\n # presumable we already have good coefficients\n pass\n else:\n # Need initial set first\n print(\"Computing initial set of coefficients!\")\n self.prec = prec\n [Y, M0] = self._space.get_Y_and_M(P, weight, prec)\n Q = M0 + 10\n W = vv_harmonic_wmwf_setupV(WR, P, Y, M0, Q, weight, self._space._sym_type, verbose=self._space._verbose)\n if (0, 0) in P:\n N = self._space.set_norm()\n # N=set_norm_vv_harmonic_weak_maass_forms(WR,cusp_form=True,holomorphic=self._holomorphic)\n else:\n N = self._space.set_norm()\n # N=set_norm_vv_harmonic_weak_maass_forms(WR,cusp_form=False,holomorphic=self._holomorphic)\n C = solve_system_for_vv_harmonic_weak_Maass_waveforms(W, N, verbose=self._verbose)\n\n # endif\n # check if we have all coefficients we wanted\n maxc = max(C[list(C.keys())[0]].keys())\n if maxc >= max(nrange):\n print(\"Have all we need!\")\n pass\n else:\n # we do not have all coefficients we need\n print(\"Need to compute more!!\")\n Ns = nrange # [maxc,max(nrange)]\n if irange is not None:\n Is = irange\n else:\n Is = [min(M.D()), max(M.D())]\n\n # Try to find good Y\n # Recall that the error in the negative part is usually smaller than in the positive part\n M_minus = abs(min(self._coeffs[list(self._coeffs.keys())[0]]))\n M_plus = abs(max(self._coeffs[list(self._coeffs.keys())[0]]))\n # Assume we computed these coefficients at (almost) the highest horocycle\n Y0 = mpmath.sqrt(3) / mpmath.mpf(2) * mpmath.mpf(0.995)\n [err_minus, err_plus] = self.get_error_estimates(Y0, M_minus, M_plus)\n kint = mpmath.mp.mpf(1 - self._space.weight())\n print(\"original:\")\n print(\"err-={0}\".format(err_minus))\n print(\"err+={0}\".format(err_plus))\n Y0 = mpmath.mpf(0.5)\n Yin = Y0\n for j in range(5000):\n Y = Y0 * mpmath.power(mpmath.mpf(0.99), j)\n t = mpmath.pi() * 2 * Y * abs(Ns[0])\n tmp1 = mpmath.exp(t)\n err1 = err_plus * tmp1\n # print \"err+=\",err1\n tmp2 = mpmath.gammainc(kint, 2 * t)\n err2 = err_plus * mpmath.exp(-t) / tmp2\n # print \"err-=\",err2\n if(max(err1, err2) < mpmath.power(10, -prec)):\n Yin = Y\n break\n # t=max(1.0,abs(mpmath.log10(prec)-mpmath.log10(self.prec)))\n # Yin=t/mpmath.mpf(Ns[0]+Ns[1])*mpmath.mpf(2.0) ## This should be good on average\n # Yin=Yin*mpmath.mpf(0.2)\n print(\"err={0}\".format(max(err1, err2)))\n print(\"Yin={0}\".format(Yin))\n sys.stdout.flush()\n # Qadd=40\n try:\n if(ef):\n CC = vv_harmonic_wmwf_phase2_2_ef(self, Ns, Is, prec, Yin, Qadd_in=Qadd)\n else:\n CC = vv_harmonic_wmwf_phase2_2(M, P, C, Ns, Is, prec, Yin)\n for x in CC.keys():\n C[x] = CC[x]\n except KeyboardInterrupt:\n print(\"Manually stopping...\")","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 c(self):\n if self.__c is not None:\n return self.__c\n else:\n raise ValueError(\"Run .fit() first!\")","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 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 get_vars_and_coefficients(elements, start=3):\n return [var for var in elements[start + 1::2]], [float(coeff) for coeff in elements[start::2]]","def coefficients(self, force_characters = False) :\n if len(self.__coefficients) == 0 :\n return dict()\n elif not force_characters and len(self.__coefficients) == 1 :\n return self.__coefficients.values()[0] \n else :\n return self.__coefficients","def get_model(self, last=True, a=None, c=None):\n \n if (a is not None) and (c is not None) and (not last):\n return np.outer(a, c)\n elif last and (a is None) and (c is None):\n if len(self.rijs) == 0:\n raise(PE.PyAValError(\"Last model requested, but no model exists yet.\", \\\n where=\"SysRem\", \\\n solution=\"Call 'iterate' first.\"))\n a, c = self.ac[-1][0], self.ac[-1][1]\n return np.outer(a, c)\n else:\n raise(PE.PyAValError(\"Unknown combination of parameters given.\", \\\n where=\"SysRem.get_model\", \\\n solution=\"Specify either a and c or use 'last=True'.\"))","def __getslice__(self, i, j):\n return self.__getitem__(slice(i,j))","def splineval(x,C,X):\n m = len(X)\n i = findsubintervals(x,X) \n G = zeros(m)\n for j in range(m):\n k = i[j]\n t = X[j] - x[k]\n G[j]=C[k,:]* t**array([[0],[1],[2],[3]])\n return G","def __delslice__(self, *args):\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___delslice__(self, *args)","def __delslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___delslice__(self, *args)","def __call__(self, start):\r\n return self._iterate(start)","def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n coherence_values =[]\n model_list =[]\n for i in range(start,limit,step):\n model=gensim.models.ldamodel.LdaModel(doc_term_matrix, num_topics=i, random_state=2, id2word=dictionary, iterations=10)\n model_list.append(model)\n coherence_model =CoherenceModel(model=model, texts=texts, dictionary=dictionary,coherence='c_v')\n coherence_values.append(coherence_model.get_coherence())\n return model_list,coherence_values","def coeff(self):\n return self._coeff","def getC(self):\n\t\treturn self.c","def test10b(self):\n a = np.arange(10)\n b = bcolz.carray(a, rootdir=self.rootdir)\n bi = b.iter(2, 10, 2)\n ai = iter(a[2:10:2])\n # print \"result:\", [i for i in bi]\n self.assertEqual([i for i in ai], [i for i in bi])\n self.assertEqual([i for i in ai], [i for i in bi])","def get_proj_coeffs(self, src):\n self.proj_coeffs = parallel.call_and_bcast(self.get_array, src)","def get_controlpoints(self, index):\n if index < 2: # is index in very beginning\n current_controlpoints = self.controlpoints[0:4] # use first points\n elif index > len(self.controlpoints) - 2: # is index in very end\n current_controlpoints = self.controlpoints[-4:] # use last points\n else:\n current_controlpoints = self.controlpoints[index - 2:index + 2]\n return current_controlpoints","def getContents(self):\r\n cont=[]\r\n for i in range (len(self._indices)):\r\n cont.append(self._dataset.getPoint(self._indices[i]))\r\n return cont","def test03c(self):\n a = np.arange(1e3)\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\n sl = slice(990, 998, 2)\n # print \"b[sl]->\", `b[sl]`\n assert_array_equal(a[sl], b[sl], \"Arrays are not equal\")","def __getslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___getslice__(self, *args)"],"string":"[\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 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 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 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 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 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 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 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 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 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 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 coefficients(self) :\\n raise NotImplementedError\",\n \"def __getitem__( self, l ) :\\n\\n return( self.coefficients[l] )\",\n \"def c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\\n\\tC = c_matrix(x1,x2,x3)\\n\\ty = y_vector(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\\n\\tCCoefficients = np.dot(inv(C),y)\\n\\treturn(CCoefficients)\",\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 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 c(self) -> np.ndarray:\\n return self._vector[10:12]\",\n \"def getsolutionslice(self,whichsol_,solitem_,first_,last_,values_):\\n _values_minlength = ((last_) - (first_))\\n if ((last_) - (first_)) > 0 and values_ is not None and len(values_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument values is not long enough: Is %d, expected %d\\\" % (len(values_),((last_) - (first_))))\\n if isinstance(values_,numpy.ndarray) and not values_.flags.writeable:\\n raise ValueError(\\\"Argument values must be writable\\\")\\n if isinstance(values_, numpy.ndarray) and values_.dtype is numpy.dtype(numpy.float64) and values_.flags.contiguous:\\n _values_copyarray = False\\n _values_tmp = ctypes.cast(values_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n elif values_ is not None:\\n _values_copyarray = True\\n _values_np_tmp = numpy.zeros(len(values_),numpy.dtype(numpy.float64))\\n _values_np_tmp[:] = values_\\n assert _values_np_tmp.flags.contiguous\\n _values_tmp = ctypes.cast(_values_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\\n else:\\n _values_copyarray = False\\n _values_tmp = None\\n \\n res = __library__.MSK_XX_getsolutionslice(self.__nativep,whichsol_,solitem_,first_,last_,_values_tmp)\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _values_copyarray:\\n values_[:] = _values_np_tmp\",\n \"def coefficients(self) :\\n return self.__coefficients\",\n \"def __setslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___setslice__(self, *args)\",\n \"def coeff(self, pt):\\n if pt < self.start or pt > self.stop:\\n raise IndexError(\\\"Index %s out of bounds %s\\\" % (pt, self.interval))\\n return self._eval_coeff(pt)\",\n \"def __getitem__(self, s) :\\n try :\\n return self.coefficients()[s]\\n except KeyError :\\n return self.parent().coefficient_domain().zero_element()\",\n \"def __getslice__(self, start, stop):\\n return self.__getitem__(slice(start, stop, None))\",\n \"def coefficients(self):\\r\\n return self.coef_['x']\",\n \"def __getslice__(self,i,j):\\n return self.x[i:j]\",\n \"def __getitem__(self, c, *rest):\\n if isinstance(c, Partition):\\n assert len(rest) == 0\\n else:\\n if len(rest) > 0 or isinstance(c,(int,Integer)):\\n c = self._kbounded_partitions.element_class(self._kbounded_partitions, [c]+list(rest))\\n else:\\n c = self._kbounded_partitions.element_class(self._kbounded_partitions, list(c))\\n if len(c) != 0 and c[0] > self.k:\\n raise ValueError(\\\"Partition is not %d-bounded\\\"%self.k)\\n return self.monomial(c)\",\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 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 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 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 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 is None:\\n values_ = None\\n else:\\n try:\\n values_ = memoryview(values)\\n except TypeError:\\n try:\\n _tmparr_values = array.array(\\\"d\\\",values)\\n except TypeError:\\n raise TypeError(\\\"Argument values has wrong type\\\")\\n else:\\n values_ = memoryview(_tmparr_values)\\n _copyback_values = True\\n else:\\n if values_.format != \\\"d\\\":\\n values_ = memoryview(array.array(\\\"d\\\",values))\\n _copyback_values = True\\n if values_ is not None and len(values_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument values has wrong length\\\")\\n res = self.__obj.getsolutionslice(whichsol_,solitem_,first_,last_,values_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n if _copyback_values:\\n values[:] = _tmparr_values\",\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 coefficients(self):\\n return self._coefficients\",\n \"def coefficients(self):\\n return self._coefficients\",\n \"def get_cs(self, model=1, limits=None, prev_aa=None, next_aa=None,\\n piqc=False, sigma_n=None, like_ss=True, debug=False):\\n\\n # Validate input.\\n aa = self.correlation.aa\\n if aa not in aminoacids.aa_list:\\n mesg = '{} is not a supported amino-acid!'.format(aa)\\n raise ValueError(mesg)\\n\\n for atom in self.correlation.atoms:\\n if atom not in aminoacids.aa_atoms[aa]:\\n raise ValueError('{} is not an atom in {}'.format(atom, aa))\\n if prev_aa:\\n prev_aa = tuple(set(prev_aa))\\n for prev_aa_i in prev_aa:\\n if prev_aa_i not in aminoacids.aa_list:\\n mesg = '{} is not an amino-acid!'.format(prev_aa_i)\\n raise ValueError(mesg)\\n if next_aa:\\n next_aa = tuple(set(next_aa))\\n for next_aa_i in next_aa:\\n if next_aa_i not in aminoacids.aa_list:\\n mesg = '{} is not an amino-acid!'.format(next_aa_i)\\n raise ValueError(mesg)\\n\\n if piqc and not self.database.table_exist('SEQ_CS_DB'):\\n mesg = 'The PIQC table is not in {}.'.format(self.database)\\n raise ValueError(mesg)\\n\\n ss = self.correlation.ss\\n n = self.dims\\n\\n # Scary sub-query to find unique secondary structure or the most common\\n # secondary structure. I am sorry ...\\n if model == 'all':\\n sub_sql = \\\"\\\"\\\"INNER JOIN(\\n SELECT DISTINCT c.KEY_ID, c.SND_STRC FROM {0}_strc_db as c\\n INNER JOIN (\\n SELECT KEY_ID, COUNT(a.KEY_ID) as count FROM (\\n SELECT DISTINCT KEY_ID, SND_STRC FROM {0}_strc_db\\n GROUP BY KEY_ID, SND_STRC)as a\\n GROUP BY KEY_ID\\n HAVING count = 1\\n ) as b ON c.KEY_ID = b.KEY_ID ) as strc\\n ON cs_0.KEY_ID = strc.KEY_ID\\\"\\\"\\\".format(aa)\\n\\n if model == 'most':\\n sub_sql = \\\"\\\"\\\"INNER JOIN (SELECT KEY_ID,\\n SUBSTRING_INDEX(GROUP_CONCAT(x.SND_STRC\\n ORDER BY x.count DESC SEPARATOR ':::'), ':::', 1) AS snd_strc_mode\\n FROM (SELECT KEY_ID, SND_STRC, COUNT(*) as count FROM {0}_strc_db\\n GROUP BY KEY_ID, SND_STRC) as x\\n GROUP BY x.KEY_ID ) as xx\\n ON cs_0.KEY_ID = xx.KEY_ID\\\"\\\"\\\".format(aa)\\n\\n # Build SQL query\\n # Select Chemical Shifts From Sub Table(s)\\n cs = 'cs_{0}.C_SHIFT as cs{0}'\\n sql = [(\\\"SELECT \\\" + ', '.join([cs.format(x) for x in range(n)])),\\n \\\"FROM {0}_cs_db as cs_0\\\".format(aa)]\\n\\n # Join other sub table for chemical shift.\\n for ni in range(1, n):\\n sql.append(\\\"INNER JOIN {0}_cs_db AS cs_{1} \\\".format(aa, ni))\\n sql.append(\\\"ON cs_0.KEY_ID = cs_{0}.KEY_ID\\\".format(ni))\\n\\n # Join other sub table for structure\\n if ss and ss != 'X':\\n if model in {'all', 'most'}:\\n sql.append(sub_sql)\\n else:\\n sql.append(\\\"INNER JOIN {0}_strc_db AS strc\\\".format(aa))\\n sql.append(\\\"ON cs_0.KEY_ID = strc.KEY_ID\\\")\\n\\n if prev_aa or next_aa:\\n sql.append(\\\"INNER JOIN {0}_db AS info\\\".format(aa))\\n sql.append(\\\"ON cs_0.KEY_ID = info.KEY_ID\\\")\\n\\n if piqc:\\n sql.append(\\\"INNER JOIN SEQ_CS_DB \\\")\\n sql.append(\\\"ON cs_0.FIRSTKEY_ID = SEQ_CS_DB.KEY_ID\\\")\\n\\n # Start of the where statements.\\n # Atoms and Limits for first atom\\n atom_0 = self.correlation.atoms[0]\\n sql.append(\\\"WHERE cs_0.ATOM_NAME = '{0}'\\\".format(atom_0))\\n\\n if prev_aa:\\n if len(prev_aa) == 1:\\n sql.append(\\\"AND info.PREV_X = '{}'\\\".format(prev_aa[0]))\\n else:\\n sql.append(\\\"AND info.PREV_X in {}\\\".format(prev_aa))\\n\\n if next_aa:\\n if len(next_aa) == 1:\\n sql.append(\\\"AND info.NEXT_X = '{}'\\\".format(next_aa[0]))\\n else:\\n sql.append(\\\"AND info.NEXT_X in {}\\\".format(next_aa))\\n\\n if limits:\\n sql.append(\\\"AND cs_0.C_SHIFT\\\")\\n limits_0 = (limits[0][0], limits[0][1])\\n sql.append(\\\"BETWEEN {0} AND {1}\\\".format(limits_0))\\n\\n # Atoms and Limits for rest of the atom\\n for ni in list(range(1, n)):\\n sql.append(\\\"AND cs_{0}.ATOM_NAME = '{1}'\\\".format(\\n ni, self.correlation.atoms[ni]))\\n\\n if limits:\\n sql.append(\\\"AND cs_{0}.C_SHIFT \\\".format(ni))\\n limits_n = (limits[ni][0], limits[ni][1])\\n sql.append(\\\"BETWEEN {0} AND {1}\\\".format(limits_n))\\n\\n # If secondary structure\\n if ss and ss != 'X':\\n try:\\n ss_list = aminoacids.similar_sndstr[ss]\\n except KeyError:\\n raise ValueError('{} is not a valid sndstr'.format(ss))\\n\\n if model == 'all':\\n if like_ss:\\n sql.append(\\\"AND strc.SND_STRC IN {0}\\\".format(ss_list))\\n else:\\n sql.append(\\\"AND strc.SND_STRC = '{}'\\\".format(ss))\\n elif model == 'most':\\n if like_ss:\\n sql.append(\\\"AND xx.snd_strc_mode IN {0}\\\".format(ss_list))\\n else:\\n sql.append(\\\"AND xx.snd_strc_mode = '{0}'\\\".format(ss))\\n else:\\n if like_ss:\\n sql.append(\\\"AND SND_STRC IN {0}\\\".format(ss_list))\\n else:\\n sql.append(\\\"AND SND_STRC = '{0}'\\\".format(ss))\\n sql.append(\\\"AND MODEL_NO={0}\\\".format(model))\\n\\n if piqc:\\n sql.append(\\\"AND ELEMENT='C'\\\")\\n sql.append(\\\"AND PIQC = 1\\\")\\n\\n sql = [x.strip() for x in sql]\\n sql = '\\\\n'.join(sql)\\n\\n if debug:\\n print(sql)\\n\\n cs = self.database.query(sql)\\n\\n if not cs:\\n raise ValueError\\n\\n if sigma_n:\\n avg = np.mean(cs, axis=0)\\n std = np.std(cs, axis=0)\\n ind = np.all(np.abs(cs-avg) <= std*sigma_n, axis=1)\\n cs = np.compress(ind, cs, axis=0)\\n\\n return cs\",\n \"def getc(self,c_):\\n _c_minlength = self.getnumvar()\\n if self.getnumvar() > 0 and c_ is not None and len(c_) != self.getnumvar():\\n raise ValueError(\\\"Array argument c is not long enough: Is %d, expected %d\\\" % (len(c_),self.getnumvar()))\\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_getc(self.__nativep,_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 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 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 __delslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___delslice__(self, *args)\",\n \"def get_coefficients(self):\\n return self.coefficients\",\n \"def get_coefficients(self):\\n return self.coefficients\",\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 calculate_coefficients(self, start, end):\\n A = np.array([\\n [self.deltaT**3, self.deltaT**4, self.deltaT**5],\\n [3 * self.deltaT**2, 4 * self.deltaT**3, 5 * self.deltaT**4],\\n [6 * self.deltaT, 12 * self.deltaT**2, 20 * self.deltaT**3],\\n ])\\n\\n a_0, a_1, a_2 = start[0], start[1], start[2] / 2.0\\n c_0 = a_0 + a_1 * self.deltaT + a_2 * self.deltaT**2\\n c_1 = a_1 + 2 * a_2 * self.deltaT\\n c_2 = 2 * a_2\\n\\n B = np.array([\\n end[0] - c_0,\\n end[1] - c_1,\\n end[2] - c_2\\n ])\\n\\n a_3_4_5 = np.linalg.solve(A, B)\\n coeff = np.concatenate((np.array([a_0, a_1, a_2]), a_3_4_5))\\n\\n return coeff\",\n \"def __getslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___getslice__(self, *args)\",\n \"def coefficients(self) -> np.ndarray:\\n return self._coefficients\",\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 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 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 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 cinters_circle(self, c):\\r\\n if self.__segments == None:\\r\\n self.__load_segments()\\r\\n \\r\\n result = []\\r\\n for segment in self.__segments:\\r\\n points = c.inters_segment(segment)\\r\\n for p in points:\\r\\n result.append(p) \\r\\n \\r\\n return result\",\n \"def spline_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\\n\\tC = c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\\n\\tD = d_coefficients(x1,x2,x3,C)\\n\\tB = b_coefficients(x1,x2,x3,y1,y2,y3,C,D)\\n\\tA = a_coefficients(y1,y2)\\n\\treturn(A,B,C[:2],D)\",\n \"def findCurvePoints(self, x, y, c):\\n\\t\\tyCurve = []\\n\\t\\tfor xi in x:\\n\\t\\t\\tyi = self.polynomialFunct(c, xi)\\n\\t\\t\\t\\n\\t\\t\\tyCurve.append( yi )\\n\\t\\t\\n\\t\\treturn np.asarray(yCurve)\",\n \"def test05(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(None, None, -3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n self.assertRaises(NotImplementedError, b.__getitem__, sl)\",\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 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 __setslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___setslice__(self, *args)\",\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 relay_array_getitem(c, a, start, stop, strides):\\n assert start.is_constant(tuple)\\n assert stop.is_constant(tuple)\\n assert strides.is_constant(tuple)\\n return relay.op.transform.strided_slice(c.ref(a), start.value, stop.value,\\n strides.value)\",\n \"def __setslice__(self, *args):\\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___setslice__(self, *args)\",\n \"def __getslice__(self, i, j):\\n return self.dtrs[i:j]\",\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 \"def slice2(self, cvars=None,ctuple=None):\\n return self.condition2(cvars,ctuple)\",\n \"def c(self, i):\\n value = self.b(i)\\n if i == self.N:\\n return value\\n else:\\n for j in range(i+1, self.N+1):\\n value = value - self.a(i,j) * self.C[j]\\n return value\",\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 get_cd_samples(self):\\n \\n if \\\"PCD\\\" in self.algorithm:\\n \\n input_vars = []\\n \\n given_vars = []\\n \\n else:\\n \\n input_vars = [self.minibatch_set]\\n \\n given_vars = {self.x_gibbs: self.train_inputs[self.minibatch_set,:]} \\n \\n get_samples = theano.function(inputs = input_vars,\\n outputs = [self.p_xi_given_x_[-1], \\n self.gibbs_samples[-1]\\n ], \\n givens = given_vars,\\n #start the chain at the data distribution\\n updates = self.gibbs_updates)\\n \\n return get_samples\",\n \"def lookup_subset(self, c):\\n p = self\\n for start in range(len(c)):\\n if p.tail.has_key(c[start]):\\n p = p.tail[c[start]]\\n\\n return p.result\",\n \"def getaslice(self,accmode_,first_,last_,ptrb,ptre,sub,val): # 3\\n if not isinstance(accmode_,accmode): raise TypeError(\\\"Argument accmode has wrong type\\\")\\n maxnumnz_ = self.getaslicenumnz((accmode_),(first_),(last_))\\n _copyback_ptrb = False\\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 _copyback_ptrb = True\\n else:\\n if ptrb_.format != \\\"q\\\":\\n ptrb_ = memoryview(array.array(\\\"q\\\",ptrb))\\n _copyback_ptrb = True\\n if ptrb_ is not None and len(ptrb_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptrb has wrong length\\\")\\n _copyback_ptre = False\\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 _copyback_ptre = True\\n else:\\n if ptre_.format != \\\"q\\\":\\n ptre_ = memoryview(array.array(\\\"q\\\",ptre))\\n _copyback_ptre = True\\n if ptre_ is not None and len(ptre_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument ptre has wrong length\\\")\\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(\\\"i\\\",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 != \\\"i\\\":\\n sub_ = memoryview(array.array(\\\"i\\\",sub))\\n _copyback_sub = True\\n if sub_ is not None and len(sub_) != (maxnumnz_):\\n raise ValueError(\\\"Array argument sub 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.getaslice64(accmode_,first_,last_,maxnumnz_,len(sub),ptrb_,ptre_,sub_,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_sub:\\n sub[:] = _tmparr_sub\\n if _copyback_ptre:\\n ptre[:] = _tmparr_ptre\\n if _copyback_ptrb:\\n ptrb[:] = _tmparr_ptrb\",\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 getc(self,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_) != self.getnumvar():\\n raise ValueError(\\\"Array argument c has wrong length\\\")\\n res = self.__obj.getc(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 __getitem__(self, i):\\n return eos80.cp(\\n self.nc.variables['SSS'].__getitem__(i),\\n self.nc.variables['SST'].__getitem__(i),\\n self.p)\",\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 slicer(seq, start=None, stop=None, step=None):\\n return seq[start:stop:step]\",\n \"def getSlice(properties=None, **kw):\",\n \"def get_items(self, start, stop, next_position=None):\",\n \"def wave_grid_c(self, i_order):\\n\\n index = slice(*self.i_bounds[i_order])\\n\\n return self.wave_grid[index]\",\n \"def test02c(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(1, -3)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def items():\\n for i in self._iter_restrict(zeros, ones):\\n yield self.pcdata[i]\",\n \"def getsnxslice(self,whichsol_,first_,last_,snx): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\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_) != ((last_) - (first_)):\\n raise ValueError(\\\"Array argument snx has wrong length\\\")\\n res = self.__obj.getsnxslice(whichsol_,first_,last_,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 compute_coefficients(self, nrange, irange=None, prec=10, ef=True, Qadd=0):\\n # we first need an initial set of coefficients\\n C = self._coeffs\\n P = self._principal_part\\n M = self._space\\n WR = M.multiplier()\\n weight = M.weight()\\n if(self.prec >= prec or len(C) > 0):\\n # presumable we already have good coefficients\\n pass\\n else:\\n # Need initial set first\\n print(\\\"Computing initial set of coefficients!\\\")\\n self.prec = prec\\n [Y, M0] = self._space.get_Y_and_M(P, weight, prec)\\n Q = M0 + 10\\n W = vv_harmonic_wmwf_setupV(WR, P, Y, M0, Q, weight, self._space._sym_type, verbose=self._space._verbose)\\n if (0, 0) in P:\\n N = self._space.set_norm()\\n # N=set_norm_vv_harmonic_weak_maass_forms(WR,cusp_form=True,holomorphic=self._holomorphic)\\n else:\\n N = self._space.set_norm()\\n # N=set_norm_vv_harmonic_weak_maass_forms(WR,cusp_form=False,holomorphic=self._holomorphic)\\n C = solve_system_for_vv_harmonic_weak_Maass_waveforms(W, N, verbose=self._verbose)\\n\\n # endif\\n # check if we have all coefficients we wanted\\n maxc = max(C[list(C.keys())[0]].keys())\\n if maxc >= max(nrange):\\n print(\\\"Have all we need!\\\")\\n pass\\n else:\\n # we do not have all coefficients we need\\n print(\\\"Need to compute more!!\\\")\\n Ns = nrange # [maxc,max(nrange)]\\n if irange is not None:\\n Is = irange\\n else:\\n Is = [min(M.D()), max(M.D())]\\n\\n # Try to find good Y\\n # Recall that the error in the negative part is usually smaller than in the positive part\\n M_minus = abs(min(self._coeffs[list(self._coeffs.keys())[0]]))\\n M_plus = abs(max(self._coeffs[list(self._coeffs.keys())[0]]))\\n # Assume we computed these coefficients at (almost) the highest horocycle\\n Y0 = mpmath.sqrt(3) / mpmath.mpf(2) * mpmath.mpf(0.995)\\n [err_minus, err_plus] = self.get_error_estimates(Y0, M_minus, M_plus)\\n kint = mpmath.mp.mpf(1 - self._space.weight())\\n print(\\\"original:\\\")\\n print(\\\"err-={0}\\\".format(err_minus))\\n print(\\\"err+={0}\\\".format(err_plus))\\n Y0 = mpmath.mpf(0.5)\\n Yin = Y0\\n for j in range(5000):\\n Y = Y0 * mpmath.power(mpmath.mpf(0.99), j)\\n t = mpmath.pi() * 2 * Y * abs(Ns[0])\\n tmp1 = mpmath.exp(t)\\n err1 = err_plus * tmp1\\n # print \\\"err+=\\\",err1\\n tmp2 = mpmath.gammainc(kint, 2 * t)\\n err2 = err_plus * mpmath.exp(-t) / tmp2\\n # print \\\"err-=\\\",err2\\n if(max(err1, err2) < mpmath.power(10, -prec)):\\n Yin = Y\\n break\\n # t=max(1.0,abs(mpmath.log10(prec)-mpmath.log10(self.prec)))\\n # Yin=t/mpmath.mpf(Ns[0]+Ns[1])*mpmath.mpf(2.0) ## This should be good on average\\n # Yin=Yin*mpmath.mpf(0.2)\\n print(\\\"err={0}\\\".format(max(err1, err2)))\\n print(\\\"Yin={0}\\\".format(Yin))\\n sys.stdout.flush()\\n # Qadd=40\\n try:\\n if(ef):\\n CC = vv_harmonic_wmwf_phase2_2_ef(self, Ns, Is, prec, Yin, Qadd_in=Qadd)\\n else:\\n CC = vv_harmonic_wmwf_phase2_2(M, P, C, Ns, Is, prec, Yin)\\n for x in CC.keys():\\n C[x] = CC[x]\\n except KeyboardInterrupt:\\n print(\\\"Manually stopping...\\\")\",\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 c(self):\\n if self.__c is not None:\\n return self.__c\\n else:\\n raise ValueError(\\\"Run .fit() first!\\\")\",\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 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 get_vars_and_coefficients(elements, start=3):\\n return [var for var in elements[start + 1::2]], [float(coeff) for coeff in elements[start::2]]\",\n \"def coefficients(self, force_characters = False) :\\n if len(self.__coefficients) == 0 :\\n return dict()\\n elif not force_characters and len(self.__coefficients) == 1 :\\n return self.__coefficients.values()[0] \\n else :\\n return self.__coefficients\",\n \"def get_model(self, last=True, a=None, c=None):\\n \\n if (a is not None) and (c is not None) and (not last):\\n return np.outer(a, c)\\n elif last and (a is None) and (c is None):\\n if len(self.rijs) == 0:\\n raise(PE.PyAValError(\\\"Last model requested, but no model exists yet.\\\", \\\\\\n where=\\\"SysRem\\\", \\\\\\n solution=\\\"Call 'iterate' first.\\\"))\\n a, c = self.ac[-1][0], self.ac[-1][1]\\n return np.outer(a, c)\\n else:\\n raise(PE.PyAValError(\\\"Unknown combination of parameters given.\\\", \\\\\\n where=\\\"SysRem.get_model\\\", \\\\\\n solution=\\\"Specify either a and c or use 'last=True'.\\\"))\",\n \"def __getslice__(self, i, j):\\n return self.__getitem__(slice(i,j))\",\n \"def splineval(x,C,X):\\n m = len(X)\\n i = findsubintervals(x,X) \\n G = zeros(m)\\n for j in range(m):\\n k = i[j]\\n t = X[j] - x[k]\\n G[j]=C[k,:]* t**array([[0],[1],[2],[3]])\\n return G\",\n \"def __delslice__(self, *args):\\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___delslice__(self, *args)\",\n \"def __delslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___delslice__(self, *args)\",\n \"def __call__(self, start):\\r\\n return self._iterate(start)\",\n \"def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\\n coherence_values =[]\\n model_list =[]\\n for i in range(start,limit,step):\\n model=gensim.models.ldamodel.LdaModel(doc_term_matrix, num_topics=i, random_state=2, id2word=dictionary, iterations=10)\\n model_list.append(model)\\n coherence_model =CoherenceModel(model=model, texts=texts, dictionary=dictionary,coherence='c_v')\\n coherence_values.append(coherence_model.get_coherence())\\n return model_list,coherence_values\",\n \"def coeff(self):\\n return self._coeff\",\n \"def getC(self):\\n\\t\\treturn self.c\",\n \"def test10b(self):\\n a = np.arange(10)\\n b = bcolz.carray(a, rootdir=self.rootdir)\\n bi = b.iter(2, 10, 2)\\n ai = iter(a[2:10:2])\\n # print \\\"result:\\\", [i for i in bi]\\n self.assertEqual([i for i in ai], [i for i in bi])\\n self.assertEqual([i for i in ai], [i for i in bi])\",\n \"def get_proj_coeffs(self, src):\\n self.proj_coeffs = parallel.call_and_bcast(self.get_array, src)\",\n \"def get_controlpoints(self, index):\\n if index < 2: # is index in very beginning\\n current_controlpoints = self.controlpoints[0:4] # use first points\\n elif index > len(self.controlpoints) - 2: # is index in very end\\n current_controlpoints = self.controlpoints[-4:] # use last points\\n else:\\n current_controlpoints = self.controlpoints[index - 2:index + 2]\\n return current_controlpoints\",\n \"def getContents(self):\\r\\n cont=[]\\r\\n for i in range (len(self._indices)):\\r\\n cont.append(self._dataset.getPoint(self._indices[i]))\\r\\n return cont\",\n \"def test03c(self):\\n a = np.arange(1e3)\\n b = bcolz.carray(a, chunklen=10, rootdir=self.rootdir)\\n sl = slice(990, 998, 2)\\n # print \\\"b[sl]->\\\", `b[sl]`\\n assert_array_equal(a[sl], b[sl], \\\"Arrays are not equal\\\")\",\n \"def __getslice__(self, *args):\\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___getslice__(self, *args)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74884826","0.6898075","0.6749403","0.6479534","0.6430882","0.62302166","0.6188517","0.59158885","0.5810388","0.5780244","0.56624705","0.56085896","0.5574313","0.5566425","0.55087215","0.5465687","0.5429033","0.5421072","0.5413725","0.53567517","0.5345228","0.5335757","0.5319425","0.5318317","0.53081983","0.5288195","0.5286638","0.5285024","0.528301","0.5271648","0.52680147","0.5227575","0.5215635","0.5215635","0.5202695","0.520189","0.5185502","0.5183351","0.51752806","0.5151667","0.5151667","0.5130609","0.5126467","0.51124936","0.51032245","0.5102937","0.5090461","0.508762","0.50805825","0.5063531","0.5060976","0.50575274","0.5033769","0.50270665","0.5024213","0.5015244","0.501453","0.5012567","0.5010971","0.50106853","0.5009292","0.500509","0.4999035","0.49916154","0.49755496","0.49645686","0.49441102","0.49411598","0.49389908","0.49221545","0.4916073","0.49074146","0.4898777","0.48856205","0.4865238","0.4861277","0.48562953","0.48546052","0.48538917","0.48458338","0.4844677","0.4843183","0.4836494","0.4807461","0.4802021","0.47987628","0.47917405","0.47814694","0.47803515","0.47777444","0.4776459","0.4775573","0.4774102","0.47573954","0.47572652","0.47550908","0.4751179","0.4750263","0.47396916","0.47315025"],"string":"[\n \"0.74884826\",\n \"0.6898075\",\n \"0.6749403\",\n \"0.6479534\",\n \"0.6430882\",\n \"0.62302166\",\n \"0.6188517\",\n \"0.59158885\",\n \"0.5810388\",\n \"0.5780244\",\n \"0.56624705\",\n \"0.56085896\",\n \"0.5574313\",\n \"0.5566425\",\n \"0.55087215\",\n \"0.5465687\",\n \"0.5429033\",\n \"0.5421072\",\n \"0.5413725\",\n \"0.53567517\",\n \"0.5345228\",\n \"0.5335757\",\n \"0.5319425\",\n \"0.5318317\",\n \"0.53081983\",\n \"0.5288195\",\n \"0.5286638\",\n \"0.5285024\",\n \"0.528301\",\n \"0.5271648\",\n \"0.52680147\",\n \"0.5227575\",\n \"0.5215635\",\n \"0.5215635\",\n \"0.5202695\",\n \"0.520189\",\n \"0.5185502\",\n \"0.5183351\",\n \"0.51752806\",\n \"0.5151667\",\n \"0.5151667\",\n \"0.5130609\",\n \"0.5126467\",\n \"0.51124936\",\n \"0.51032245\",\n \"0.5102937\",\n \"0.5090461\",\n \"0.508762\",\n \"0.50805825\",\n \"0.5063531\",\n \"0.5060976\",\n \"0.50575274\",\n \"0.5033769\",\n \"0.50270665\",\n \"0.5024213\",\n \"0.5015244\",\n \"0.501453\",\n \"0.5012567\",\n \"0.5010971\",\n \"0.50106853\",\n \"0.5009292\",\n \"0.500509\",\n \"0.4999035\",\n \"0.49916154\",\n \"0.49755496\",\n \"0.49645686\",\n \"0.49441102\",\n \"0.49411598\",\n \"0.49389908\",\n \"0.49221545\",\n \"0.4916073\",\n \"0.49074146\",\n \"0.4898777\",\n \"0.48856205\",\n \"0.4865238\",\n \"0.4861277\",\n \"0.48562953\",\n \"0.48546052\",\n \"0.48538917\",\n \"0.48458338\",\n \"0.4844677\",\n \"0.4843183\",\n \"0.4836494\",\n \"0.4807461\",\n \"0.4802021\",\n \"0.47987628\",\n \"0.47917405\",\n \"0.47814694\",\n \"0.47803515\",\n \"0.47777444\",\n \"0.4776459\",\n \"0.4775573\",\n \"0.4774102\",\n \"0.47573954\",\n \"0.47572652\",\n \"0.47550908\",\n \"0.4751179\",\n \"0.4750263\",\n \"0.47396916\",\n \"0.47315025\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7475892"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11796,"cells":{"query":{"kind":"string","value":"Obtains a double information item. getdouinf(self,whichdinf_)"},"document":{"kind":"string","value":"def getdouinf(self,whichdinf_):\n dvalue_ = ctypes.c_double()\n res = __library__.MSK_XX_getdouinf(self.__nativep,whichdinf_,ctypes.byref(dvalue_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n dvalue_ = dvalue_.value\n _dvalue_return_value = dvalue_\n return (_dvalue_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 getdouinf(self,whichdinf_): # 3\n if not isinstance(whichdinf_,dinfitem): raise TypeError(\"Argument whichdinf has wrong type\")\n res,resargs = self.__obj.getdouinf(whichdinf_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dvalue_return_value = resargs\n return _dvalue_return_value","def getintinf(self,whichiinf_): # 3\n if not isinstance(whichiinf_,iinfitem): raise TypeError(\"Argument whichiinf has wrong type\")\n res,resargs = self.__obj.getintinf(whichiinf_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ivalue_return_value = resargs\n return _ivalue_return_value","def getDip(self):\n return self._dip","def getD(self):\r\n return self.D","def getDi(self, n=None, label=None):\n return self.di[label][n]","def get_details(disease):\n\treturn d_desc_map[disease]","def XPLMGetDatad_f(inRefcon):\n pass","def idd_info(self):\n if self._idd_info is None:\n bunchdt, block, data, commdct, idd_index, versiontuple = idfreader1(\n self.idfname, self.iddname, self, commdct=None, block=None\n )\n self._block = block\n self._idd_info = commdct\n self._idd_index = idd_index\n self._idfobjects = bunchdt\n self._model = data\n self._idd_version = versiontuple\n return self._idd_info","def get_wotd():\n\treturn wotd","def GetDatum(self, *args):\n return _XCAFDoc.XCAFDoc_DimTolTool_GetDatum(self, *args)","def __getitem__(self, item):\n return self.hdus[item]","def getintinf(self,whichiinf_):\n ivalue_ = ctypes.c_int32()\n res = __library__.MSK_XX_getintinf(self.__nativep,whichiinf_,ctypes.byref(ivalue_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n ivalue_ = ivalue_.value\n _ivalue_return_value = ivalue_\n return (_ivalue_return_value)","def driehoekig(self):\n return self._driehoekig.get_waarde()","def getDouble(self, int: int, int2: int) -> float:\n ...","def FindDatum(self, *args):\n return _XCAFDoc.XCAFDoc_DimTolTool_FindDatum(self, *args)","def getiddname(self):\n return self.iddname","def x(self):\r\n return self.unif[0]","def getlintinf(self,whichliinf_): # 3\n if not isinstance(whichliinf_,liinfitem): raise TypeError(\"Argument whichliinf has wrong type\")\n res,resargs = self.__obj.getlintinf(whichliinf_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ivalue_return_value = resargs\n return _ivalue_return_value","def get_dcmgnd(self):\n return self.dcmgnd","def doi(self):\n return LiteratureReader(self.record).doi","def _get(weather_data, item):\r\n return weather_data.get(item, \"\")","def dn(self):\n return self.dndlogdp.mul(self.dlogdp)","def getNeighbor(self, neighborID):","def getDensityEstimate(self):\n return self.density","def getFood(self):\n return self.data.food","def den(self):\n return self.den","def info(self):\n return self.nfo","def hdu_info(self):\n return self._hdusinfo","def get_dih(year):\r\n return common.get_dict(get_dih_filename(year), 'DaysInHospital', int)","def getValue(self):\n return DPxGetDinValue()","def dish_get_info() -> Dish:\r\n return Dish(input(\"Please enter the dish's name: \"),\r\n float(input(\"Please enter the price of the dish: \")),\r\n int(input(\"Please enter the number of calories of the dish: \")))","def get_usage_id_from_aside(self, aside_id):\n raise NotImplementedError()","def z(self):\r\n return self.unif[2]","def dentist_id(self) -> float:\n return self._dentist_id","def getSubintinfo(self,value):\n if value in self.subintinfo.keys():\n return self.subintinfo[value][-1]\n return None","def __getitem__(self, value):\n return self.d.get(value, 0)","def y(self):\r\n return self.unif[1]","def get_info(self, pos):\n if pos in self._mine:\n return mine_data(self._mine[pos])\n return {}","def getDip(self):\n return DEFAULT_DIP","def getelem(self,num):\n #return self.M.conf()['elements'][num]\n return self.lat[num]","def get_inf_data(self, driver):\r\n if 'Found 0 Title(s)' != driver.find_element_by_xpath('/html/body/h3').text:\r\n # select custom information\r\n driver.find_element_by_xpath('//*[@id=\"display_format_custom\"]').click()\r\n # select all useful information\r\n for i in range(1, 9):\r\n for j in range(1, 5):\r\n if (i == 1 and j < 4) or (i == 2 and j == 1) or (i == 2 and j == 4) or (i == 3 and j == 4) or (\r\n i == 5 and j > 1) or i == 6 or (i == 7 and j < 3) or (i == 8 and j == 3):\r\n driver.find_element_by_xpath(\r\n '/html/body/form/ul[2]/table/tbody/tr[' + str(i) + ']/td[' + str(j) + ']/input[2]').click()\r\n # display\r\n driver.find_element_by_xpath('//*[@id=\"btn_find_display\"]').click()\r\n\r\n # the number of records\r\n number = len(driver.find_elements_by_xpath('//*[@id=\"list_form\"]/ul'))\r\n # all useful information\r\n for i in range(1, number + 1):\r\n self.doi_l.append('')\r\n information_l = driver.find_element_by_xpath('//*[@id=\"list_form\"]/ul[' + str(i) + ']').text.split('\\n')\r\n self.record_number_l.append(re.findall(r'(?<== ).*$', information_l[0])[0])\r\n reaction_method = re.findall(r'(?<== ).*$', information_l[1])[0]\r\n self.reaction_method_l.append(reaction_method)\r\n self.data_type_l.append(re.findall(r'(?<== ).*$', information_l[2])[0])\r\n self.qdb_code(reaction_method)\r\n self.qdb_process_l.append(self.qdb_process)\r\n prod = re.findall(r'(?<== ).*$', information_l[5])[0]\r\n self.x_unit_l.append(re.findall(r'(?<== ).*$', information_l[6])[0])\r\n self.y_unit_l.append(re.findall(r'(?<== ).*$', information_l[7])[0])\r\n self.ref_number_l.append(re.findall(r'(?<== ).*$', information_l[8])[0])\r\n author = re.findall(r'(?<== ).*$', information_l[9])[0]\r\n rewrite_author = re.sub('[$]', ' ', author)\r\n self.author_l.append(rewrite_author)\r\n self.title_l.append(re.findall(r'(?<== ).*$', information_l[10])[0])\r\n self.journal_l.append(re.findall(r'(?<== ).*$', information_l[11])[0])\r\n self.volume_l.append(re.findall(r'(?<== ).*$', information_l[12])[0])\r\n self.page_l.append(re.findall(r'(?<== ).*$', information_l[13])[0])\r\n self.publish_year_l.append(re.findall(r'(?<== ).*$', information_l[14])[0])\r\n reaction_formula = re.findall(r'(?<== ).*$', information_l[15])[0]\r\n self.reaction_formula_l.append(reaction_formula)\r\n self.decompose_nifs(reaction_formula, prod)\r\n self.reactants_l.append(self.reactants)\r\n self.products_l.append(self.products)\r\n df_information = pd.DataFrame([self.record_number_l, self.reaction_formula_l, self.reaction_method_l,\r\n self.qdb_process_l, self.reactants_l, self.products_l, self.data_type_l,\r\n self.x_unit_l, self.y_unit_l, self.ref_number_l, self.doi_l,\r\n self.title_l, self.author_l, self.journal_l, self.volume_l, self.page_l,\r\n self.publish_year_l])\r\n df_information = df_information.T\r\n df_information.rename(columns={0: 'record_number', 1: 'reaction_formula', 2: 'reaction_method',\r\n 3: 'QDB_process', 4: 'reactants', 5: 'products',\r\n 6: 'Data_type', 7: 'temperature_unit', 8: 'rate_coefficient_unit',\r\n 9: 'reference_number', 10: 'DOI',\r\n 11: 'title', 12: 'author', 13: 'journal', 14: 'volume', 15: 'page',\r\n 16: 'publish_year'},\r\n inplace=True)\r\n else:\r\n df_information = pd.DataFrame()\r\n return df_information","def get_usage_id_from_aside(self, aside_id):\n return aside_id.usage_id","def getd(self, node):\n\n return self.daq.getDouble(f'/{self.device_id}/{node}')","def get_nh_tun_dip(self):\n return int(self.get('nhr_tun_dip'))","def get_doi(self):\n\n return self._dois","def XPLMGetDatad(inDataRef):\n return float","def read_double(self):\n return self._packers[\"d\"].unpack(self.read(8))[0]","def __getitem__(self,item):\n if self._dosort is not None:\n i = self._sorted_indices[item-1]\n else:\n i = item\n return super(AHFCatalogue,self).__getitem__(i)","def GetDMXInformation(self):\n if self.cur_uid is None:\n return\n self._get_dmx_personality()","def getDataDict(self):\n #code begins here \n return self.__dflst,self.__dfwells","def device_get_information_about(pnd, buf):\n return _nfc.device_get_information_about(pnd, buf)","def double(self):\n return self._double","def getMJD(self,full=False,numwrap=float):\n if full:\n return numwrap(self.header['STT_IMJD'])+(numwrap(self.header['STT_SMJD'])+numwrap(self.header['STT_OFFS']))/numwrap(86400)\n return numwrap(self.header['STT_IMJD'])+numwrap(self.header['STT_OFFS'])","def get_droid(did):\n conn = create_connection(db_location)\n c = conn.cursor()\n c.execute(\"SELECT * FROM droids WHERE droid_uid = \" + did)\n print(\"DEBUG: *****\") \n droid = dict((c.description[i][0], value) for i, value in enumerate(c.fetchone()))\n if __debug__:\n print(droid)\n conn.commit()\n conn.close()\n return (droid)","def other_entry(self):\r\n l = self.other_entries()\r\n assert len(l) == 1\r\n return l[0][1]","def __getitem__(self, where):\n return self._data[where]","def _select_single(self, disc):\n sqlstmt = \"SELECT h FROM %s WHERE d=?\" % self.VIEW\n pickup = self.cursor.execute(sqlstmt, (disc,))\n picked = pickup.fetchone()\n if picked is not None:\n # picked = (h,)\n return picked[0]\n else:\n raise KeyError(str(disc))","def getInfo(self,item):\r\n return ''","def __getitem__(self, item) -> Union[MoleculeDatapoint, List[MoleculeDatapoint]]:\n return self.data[item]","def _get_tagged_doc(self, doi):\n\n return self.tagged_docs[list(map(lambda x: x.tags[0], self.tagged_docs)).index(doi)]","def _getOneModelInfo(nupicModelID):\n return _iterModels([nupicModelID]).next()","def get_info_inst(self):\n return self.get_info(\"INST\")","def rond(self):\n return self._rond.get_waarde()","def getdualobj(self,whichsol_):\n dualobj_ = ctypes.c_double()\n res = __library__.MSK_XX_getdualobj(self.__nativep,whichsol_,ctypes.byref(dualobj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n dualobj_ = dualobj_.value\n _dualobj_return_value = dualobj_\n return (_dualobj_return_value)","def get_bond_info(self):\n return","def _get_drug_entry(self,cid):\n result = None\n search_str = \"drugbank:drug/drugbank:drugbank-id/.[@primary='true']/..[drugbank:drugbank-id='%s']\" % (cid) \n result = DrugBank._top_root.find(search_str,DrugBank.NAMESPACES)\n return result","def get_info(self, info):\r\n pass","def getObjectInfo(fluiddb, objectId):\n return fluiddb.objects[objectId].get(showAbout=True)","def get_weather(self):\n return self.__weather","def _get_information(self):\n pass","def get_deb_info(deb_path, *args):\n return (get_deb_item(deb_path, arg) for arg in args)","def get_item_detail(item_id):\n pass","def get_dx(self):\n return self.__dx","def get_data_info(f_obj):\n\n return {dat_info : getattr(f_obj, dat_info) for dat_info in get_obj_desc()['freq_info']}","def mjd(self):\n # unWISE FITS files are coadds so no MJD info\n return None","def get_dmi(data):\n if data is None:\n raise EmptyDataError('[!] Invalid data value')\n\n result = TA.DMI(data)\n if result is None:\n raise IndicatorException\n return result","def data(self):\n return self.d","def get_disc_number(self) -> Optional[int]:\n return self.disc_number","def getFileInfo(self, fid):\n if fid in self.files:\n return self.files[fid].toInfoData()\n\n return self.db.getFileInfo(fid)","def getDmd(self):\n for obj in aq_chain(self):\n if getattr(obj, 'id', None) == 'dmd': return obj","def get_ddi(self, doi, format='ddi'):\n response = self.api.get_dataset_export(doi, format)\n return DDI(response.content)","def get_new_data(self):\n if self.is_dethist:\n return self._generate_dethist_data()\n else:\n return self._generate_data()","def get(self, item_id, class_id):\n return get_item_info_with_spell(item_id, class_id)","def __getitem__(self, item):\n if not hasattr(self, 'hdu_list'):\n self.update_hdu_list()\n\n ext, ver, ver_sent = self._extract_item(item)\n\n try:\n # if it is an int\n hdu = self.hdu_list[ext]\n except Exception:\n # might be a string\n ext = mks(ext)\n if not self.case_sensitive:\n mess = '(case insensitive)'\n ext = ext.lower()\n else:\n mess = '(case sensitive)'\n\n if ver > 0:\n key = '%s-%s' % (ext, ver)\n if key not in self.hdu_map:\n raise IOError(\"extension not found: %s, \"\n \"version %s %s\" % (ext, ver, mess))\n hdu = self.hdu_map[key]\n else:\n if ext not in self.hdu_map:\n raise IOError(\"extension not found: %s %s\" % (ext, mess))\n hdu = self.hdu_map[ext]\n\n return hdu","def idd_index(self):\n if self._idd_index is None:\n bunchdt, block, data, commdct, idd_index, versiontuple = idfreader1(\n self.idfname, self.iddname, self, commdct=None, block=None\n )\n self._block = block\n self._idd_info = commdct\n self._idd_index = idd_index\n self._idfobjects = bunchdt\n self._model = data\n self._idd_version = versiontuple\n return self._idd_index","def get_list_density(self,typ,file_number):\n if typ == 'emis':\n return self.beam_emis[file_number].densities\n elif typ == 'atte':\n return self.beam_atte[file_number].densities\n else:\n raise NameError('No list with this name: {0}'.format(typ))","def getlintinf(self,whichliinf_):\n ivalue_ = ctypes.c_int64()\n res = __library__.MSK_XX_getlintinf(self.__nativep,whichliinf_,ctypes.byref(ivalue_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n ivalue_ = ivalue_.value\n _ivalue_return_value = ivalue_\n return (_ivalue_return_value)","def get_debiet_inlaatduiker(verval, m_constant=1, schuifhoogte=1.78, breedte_duiker=1.75):\n return m_constant * schuifhoogte * breedte_duiker * np.sqrt(2 * 9.81 * verval)","def grabDaily(self):\n raise NotImplemented(\"method should be redefined in a subclass\")","def __getitem__(self, item):\n if 0 >= item < len(self.stations):\n return self.stations[item]","def getDip(self):\n return constants.DEFAULT_DIP","def _nodata_value(self):\n try:\n nodata = float(self._info[\"bands\"][0][\"noDataValue\"])\n except KeyError:\n nodata = None\n return nodata","def get_vdw_info(self):\n return","def GetDetailsItem(self):\r\n if self.details: return self.details.GetDetailsItem()\r\n return None","def getDiameter(self):\n\n hdr = self.header\n if \"cd1_1\" in hdr:\n self.D = abs(hdr[\"cd1_1\"]) * hdr[\"naxis1\"]\n elif \"cdelt1\" in hdr:\n self.D = abs(hdr[\"cdelt1\"]) * hdr[\"naxis1\"]\n else:\n print(\"Warning: no coordinate information found in input header;\")\n print(\" pupil width assumed to be 6.5 meters\")\n self.D = 6.5","def return_zeropoint():\n return 22.5","def get_stat_obj(self, nn_id):\n try:\n obj = models.NN_DEF_LIST_INFO.objects.get(nn_id=nn_id)\n return getattr(obj, \"automl_stat\")\n except Exception as e:\n raise Exception(e)","def get_dominance(self):\n trait = self.traitDao.get_dominance(self.name)\n dominance = trait[1]\n return dominance","def get_furniture():","def dmap_info(self, out=None):\n binfile = self.config.dataset.binfile\n dmap = BinnedPhotonData(binfile)\n print ('File: %s ' %binfile, file=out)\n print ('\\n index emin emax type nside photons', file=out)\n total = 0\n def bignum(n):\n t = '%9d' % n\n return ' '+' '.join([t[0:3],t[3:6],t[6:]])\n for i,band in enumerate(dmap):\n fmt = '%5d'+2*'%10d'+2*'%6d'+'%12s'\n print (fmt % (i, round(band.emin()), round(band.emax()), \n band.event_class()&15, band.nside(), bignum(band.photons())))\n total += band.photons()\n print ('total%45s'% bignum(total), file=out)\n return dmap"],"string":"[\n \"def getdouinf(self,whichdinf_): # 3\\n if not isinstance(whichdinf_,dinfitem): raise TypeError(\\\"Argument whichdinf has wrong type\\\")\\n res,resargs = self.__obj.getdouinf(whichdinf_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _dvalue_return_value = resargs\\n return _dvalue_return_value\",\n \"def getintinf(self,whichiinf_): # 3\\n if not isinstance(whichiinf_,iinfitem): raise TypeError(\\\"Argument whichiinf has wrong type\\\")\\n res,resargs = self.__obj.getintinf(whichiinf_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _ivalue_return_value = resargs\\n return _ivalue_return_value\",\n \"def getDip(self):\\n return self._dip\",\n \"def getD(self):\\r\\n return self.D\",\n \"def getDi(self, n=None, label=None):\\n return self.di[label][n]\",\n \"def get_details(disease):\\n\\treturn d_desc_map[disease]\",\n \"def XPLMGetDatad_f(inRefcon):\\n pass\",\n \"def idd_info(self):\\n if self._idd_info is None:\\n bunchdt, block, data, commdct, idd_index, versiontuple = idfreader1(\\n self.idfname, self.iddname, self, commdct=None, block=None\\n )\\n self._block = block\\n self._idd_info = commdct\\n self._idd_index = idd_index\\n self._idfobjects = bunchdt\\n self._model = data\\n self._idd_version = versiontuple\\n return self._idd_info\",\n \"def get_wotd():\\n\\treturn wotd\",\n \"def GetDatum(self, *args):\\n return _XCAFDoc.XCAFDoc_DimTolTool_GetDatum(self, *args)\",\n \"def __getitem__(self, item):\\n return self.hdus[item]\",\n \"def getintinf(self,whichiinf_):\\n ivalue_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getintinf(self.__nativep,whichiinf_,ctypes.byref(ivalue_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n ivalue_ = ivalue_.value\\n _ivalue_return_value = ivalue_\\n return (_ivalue_return_value)\",\n \"def driehoekig(self):\\n return self._driehoekig.get_waarde()\",\n \"def getDouble(self, int: int, int2: int) -> float:\\n ...\",\n \"def FindDatum(self, *args):\\n return _XCAFDoc.XCAFDoc_DimTolTool_FindDatum(self, *args)\",\n \"def getiddname(self):\\n return self.iddname\",\n \"def x(self):\\r\\n return self.unif[0]\",\n \"def getlintinf(self,whichliinf_): # 3\\n if not isinstance(whichliinf_,liinfitem): raise TypeError(\\\"Argument whichliinf has wrong type\\\")\\n res,resargs = self.__obj.getlintinf(whichliinf_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _ivalue_return_value = resargs\\n return _ivalue_return_value\",\n \"def get_dcmgnd(self):\\n return self.dcmgnd\",\n \"def doi(self):\\n return LiteratureReader(self.record).doi\",\n \"def _get(weather_data, item):\\r\\n return weather_data.get(item, \\\"\\\")\",\n \"def dn(self):\\n return self.dndlogdp.mul(self.dlogdp)\",\n \"def getNeighbor(self, neighborID):\",\n \"def getDensityEstimate(self):\\n return self.density\",\n \"def getFood(self):\\n return self.data.food\",\n \"def den(self):\\n return self.den\",\n \"def info(self):\\n return self.nfo\",\n \"def hdu_info(self):\\n return self._hdusinfo\",\n \"def get_dih(year):\\r\\n return common.get_dict(get_dih_filename(year), 'DaysInHospital', int)\",\n \"def getValue(self):\\n return DPxGetDinValue()\",\n \"def dish_get_info() -> Dish:\\r\\n return Dish(input(\\\"Please enter the dish's name: \\\"),\\r\\n float(input(\\\"Please enter the price of the dish: \\\")),\\r\\n int(input(\\\"Please enter the number of calories of the dish: \\\")))\",\n \"def get_usage_id_from_aside(self, aside_id):\\n raise NotImplementedError()\",\n \"def z(self):\\r\\n return self.unif[2]\",\n \"def dentist_id(self) -> float:\\n return self._dentist_id\",\n \"def getSubintinfo(self,value):\\n if value in self.subintinfo.keys():\\n return self.subintinfo[value][-1]\\n return None\",\n \"def __getitem__(self, value):\\n return self.d.get(value, 0)\",\n \"def y(self):\\r\\n return self.unif[1]\",\n \"def get_info(self, pos):\\n if pos in self._mine:\\n return mine_data(self._mine[pos])\\n return {}\",\n \"def getDip(self):\\n return DEFAULT_DIP\",\n \"def getelem(self,num):\\n #return self.M.conf()['elements'][num]\\n return self.lat[num]\",\n \"def get_inf_data(self, driver):\\r\\n if 'Found 0 Title(s)' != driver.find_element_by_xpath('/html/body/h3').text:\\r\\n # select custom information\\r\\n driver.find_element_by_xpath('//*[@id=\\\"display_format_custom\\\"]').click()\\r\\n # select all useful information\\r\\n for i in range(1, 9):\\r\\n for j in range(1, 5):\\r\\n if (i == 1 and j < 4) or (i == 2 and j == 1) or (i == 2 and j == 4) or (i == 3 and j == 4) or (\\r\\n i == 5 and j > 1) or i == 6 or (i == 7 and j < 3) or (i == 8 and j == 3):\\r\\n driver.find_element_by_xpath(\\r\\n '/html/body/form/ul[2]/table/tbody/tr[' + str(i) + ']/td[' + str(j) + ']/input[2]').click()\\r\\n # display\\r\\n driver.find_element_by_xpath('//*[@id=\\\"btn_find_display\\\"]').click()\\r\\n\\r\\n # the number of records\\r\\n number = len(driver.find_elements_by_xpath('//*[@id=\\\"list_form\\\"]/ul'))\\r\\n # all useful information\\r\\n for i in range(1, number + 1):\\r\\n self.doi_l.append('')\\r\\n information_l = driver.find_element_by_xpath('//*[@id=\\\"list_form\\\"]/ul[' + str(i) + ']').text.split('\\\\n')\\r\\n self.record_number_l.append(re.findall(r'(?<== ).*$', information_l[0])[0])\\r\\n reaction_method = re.findall(r'(?<== ).*$', information_l[1])[0]\\r\\n self.reaction_method_l.append(reaction_method)\\r\\n self.data_type_l.append(re.findall(r'(?<== ).*$', information_l[2])[0])\\r\\n self.qdb_code(reaction_method)\\r\\n self.qdb_process_l.append(self.qdb_process)\\r\\n prod = re.findall(r'(?<== ).*$', information_l[5])[0]\\r\\n self.x_unit_l.append(re.findall(r'(?<== ).*$', information_l[6])[0])\\r\\n self.y_unit_l.append(re.findall(r'(?<== ).*$', information_l[7])[0])\\r\\n self.ref_number_l.append(re.findall(r'(?<== ).*$', information_l[8])[0])\\r\\n author = re.findall(r'(?<== ).*$', information_l[9])[0]\\r\\n rewrite_author = re.sub('[$]', ' ', author)\\r\\n self.author_l.append(rewrite_author)\\r\\n self.title_l.append(re.findall(r'(?<== ).*$', information_l[10])[0])\\r\\n self.journal_l.append(re.findall(r'(?<== ).*$', information_l[11])[0])\\r\\n self.volume_l.append(re.findall(r'(?<== ).*$', information_l[12])[0])\\r\\n self.page_l.append(re.findall(r'(?<== ).*$', information_l[13])[0])\\r\\n self.publish_year_l.append(re.findall(r'(?<== ).*$', information_l[14])[0])\\r\\n reaction_formula = re.findall(r'(?<== ).*$', information_l[15])[0]\\r\\n self.reaction_formula_l.append(reaction_formula)\\r\\n self.decompose_nifs(reaction_formula, prod)\\r\\n self.reactants_l.append(self.reactants)\\r\\n self.products_l.append(self.products)\\r\\n df_information = pd.DataFrame([self.record_number_l, self.reaction_formula_l, self.reaction_method_l,\\r\\n self.qdb_process_l, self.reactants_l, self.products_l, self.data_type_l,\\r\\n self.x_unit_l, self.y_unit_l, self.ref_number_l, self.doi_l,\\r\\n self.title_l, self.author_l, self.journal_l, self.volume_l, self.page_l,\\r\\n self.publish_year_l])\\r\\n df_information = df_information.T\\r\\n df_information.rename(columns={0: 'record_number', 1: 'reaction_formula', 2: 'reaction_method',\\r\\n 3: 'QDB_process', 4: 'reactants', 5: 'products',\\r\\n 6: 'Data_type', 7: 'temperature_unit', 8: 'rate_coefficient_unit',\\r\\n 9: 'reference_number', 10: 'DOI',\\r\\n 11: 'title', 12: 'author', 13: 'journal', 14: 'volume', 15: 'page',\\r\\n 16: 'publish_year'},\\r\\n inplace=True)\\r\\n else:\\r\\n df_information = pd.DataFrame()\\r\\n return df_information\",\n \"def get_usage_id_from_aside(self, aside_id):\\n return aside_id.usage_id\",\n \"def getd(self, node):\\n\\n return self.daq.getDouble(f'/{self.device_id}/{node}')\",\n \"def get_nh_tun_dip(self):\\n return int(self.get('nhr_tun_dip'))\",\n \"def get_doi(self):\\n\\n return self._dois\",\n \"def XPLMGetDatad(inDataRef):\\n return float\",\n \"def read_double(self):\\n return self._packers[\\\"d\\\"].unpack(self.read(8))[0]\",\n \"def __getitem__(self,item):\\n if self._dosort is not None:\\n i = self._sorted_indices[item-1]\\n else:\\n i = item\\n return super(AHFCatalogue,self).__getitem__(i)\",\n \"def GetDMXInformation(self):\\n if self.cur_uid is None:\\n return\\n self._get_dmx_personality()\",\n \"def getDataDict(self):\\n #code begins here \\n return self.__dflst,self.__dfwells\",\n \"def device_get_information_about(pnd, buf):\\n return _nfc.device_get_information_about(pnd, buf)\",\n \"def double(self):\\n return self._double\",\n \"def getMJD(self,full=False,numwrap=float):\\n if full:\\n return numwrap(self.header['STT_IMJD'])+(numwrap(self.header['STT_SMJD'])+numwrap(self.header['STT_OFFS']))/numwrap(86400)\\n return numwrap(self.header['STT_IMJD'])+numwrap(self.header['STT_OFFS'])\",\n \"def get_droid(did):\\n conn = create_connection(db_location)\\n c = conn.cursor()\\n c.execute(\\\"SELECT * FROM droids WHERE droid_uid = \\\" + did)\\n print(\\\"DEBUG: *****\\\") \\n droid = dict((c.description[i][0], value) for i, value in enumerate(c.fetchone()))\\n if __debug__:\\n print(droid)\\n conn.commit()\\n conn.close()\\n return (droid)\",\n \"def other_entry(self):\\r\\n l = self.other_entries()\\r\\n assert len(l) == 1\\r\\n return l[0][1]\",\n \"def __getitem__(self, where):\\n return self._data[where]\",\n \"def _select_single(self, disc):\\n sqlstmt = \\\"SELECT h FROM %s WHERE d=?\\\" % self.VIEW\\n pickup = self.cursor.execute(sqlstmt, (disc,))\\n picked = pickup.fetchone()\\n if picked is not None:\\n # picked = (h,)\\n return picked[0]\\n else:\\n raise KeyError(str(disc))\",\n \"def getInfo(self,item):\\r\\n return ''\",\n \"def __getitem__(self, item) -> Union[MoleculeDatapoint, List[MoleculeDatapoint]]:\\n return self.data[item]\",\n \"def _get_tagged_doc(self, doi):\\n\\n return self.tagged_docs[list(map(lambda x: x.tags[0], self.tagged_docs)).index(doi)]\",\n \"def _getOneModelInfo(nupicModelID):\\n return _iterModels([nupicModelID]).next()\",\n \"def get_info_inst(self):\\n return self.get_info(\\\"INST\\\")\",\n \"def rond(self):\\n return self._rond.get_waarde()\",\n \"def getdualobj(self,whichsol_):\\n dualobj_ = ctypes.c_double()\\n res = __library__.MSK_XX_getdualobj(self.__nativep,whichsol_,ctypes.byref(dualobj_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n dualobj_ = dualobj_.value\\n _dualobj_return_value = dualobj_\\n return (_dualobj_return_value)\",\n \"def get_bond_info(self):\\n return\",\n \"def _get_drug_entry(self,cid):\\n result = None\\n search_str = \\\"drugbank:drug/drugbank:drugbank-id/.[@primary='true']/..[drugbank:drugbank-id='%s']\\\" % (cid) \\n result = DrugBank._top_root.find(search_str,DrugBank.NAMESPACES)\\n return result\",\n \"def get_info(self, info):\\r\\n pass\",\n \"def getObjectInfo(fluiddb, objectId):\\n return fluiddb.objects[objectId].get(showAbout=True)\",\n \"def get_weather(self):\\n return self.__weather\",\n \"def _get_information(self):\\n pass\",\n \"def get_deb_info(deb_path, *args):\\n return (get_deb_item(deb_path, arg) for arg in args)\",\n \"def get_item_detail(item_id):\\n pass\",\n \"def get_dx(self):\\n return self.__dx\",\n \"def get_data_info(f_obj):\\n\\n return {dat_info : getattr(f_obj, dat_info) for dat_info in get_obj_desc()['freq_info']}\",\n \"def mjd(self):\\n # unWISE FITS files are coadds so no MJD info\\n return None\",\n \"def get_dmi(data):\\n if data is None:\\n raise EmptyDataError('[!] Invalid data value')\\n\\n result = TA.DMI(data)\\n if result is None:\\n raise IndicatorException\\n return result\",\n \"def data(self):\\n return self.d\",\n \"def get_disc_number(self) -> Optional[int]:\\n return self.disc_number\",\n \"def getFileInfo(self, fid):\\n if fid in self.files:\\n return self.files[fid].toInfoData()\\n\\n return self.db.getFileInfo(fid)\",\n \"def getDmd(self):\\n for obj in aq_chain(self):\\n if getattr(obj, 'id', None) == 'dmd': return obj\",\n \"def get_ddi(self, doi, format='ddi'):\\n response = self.api.get_dataset_export(doi, format)\\n return DDI(response.content)\",\n \"def get_new_data(self):\\n if self.is_dethist:\\n return self._generate_dethist_data()\\n else:\\n return self._generate_data()\",\n \"def get(self, item_id, class_id):\\n return get_item_info_with_spell(item_id, class_id)\",\n \"def __getitem__(self, item):\\n if not hasattr(self, 'hdu_list'):\\n self.update_hdu_list()\\n\\n ext, ver, ver_sent = self._extract_item(item)\\n\\n try:\\n # if it is an int\\n hdu = self.hdu_list[ext]\\n except Exception:\\n # might be a string\\n ext = mks(ext)\\n if not self.case_sensitive:\\n mess = '(case insensitive)'\\n ext = ext.lower()\\n else:\\n mess = '(case sensitive)'\\n\\n if ver > 0:\\n key = '%s-%s' % (ext, ver)\\n if key not in self.hdu_map:\\n raise IOError(\\\"extension not found: %s, \\\"\\n \\\"version %s %s\\\" % (ext, ver, mess))\\n hdu = self.hdu_map[key]\\n else:\\n if ext not in self.hdu_map:\\n raise IOError(\\\"extension not found: %s %s\\\" % (ext, mess))\\n hdu = self.hdu_map[ext]\\n\\n return hdu\",\n \"def idd_index(self):\\n if self._idd_index is None:\\n bunchdt, block, data, commdct, idd_index, versiontuple = idfreader1(\\n self.idfname, self.iddname, self, commdct=None, block=None\\n )\\n self._block = block\\n self._idd_info = commdct\\n self._idd_index = idd_index\\n self._idfobjects = bunchdt\\n self._model = data\\n self._idd_version = versiontuple\\n return self._idd_index\",\n \"def get_list_density(self,typ,file_number):\\n if typ == 'emis':\\n return self.beam_emis[file_number].densities\\n elif typ == 'atte':\\n return self.beam_atte[file_number].densities\\n else:\\n raise NameError('No list with this name: {0}'.format(typ))\",\n \"def getlintinf(self,whichliinf_):\\n ivalue_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getlintinf(self.__nativep,whichliinf_,ctypes.byref(ivalue_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n ivalue_ = ivalue_.value\\n _ivalue_return_value = ivalue_\\n return (_ivalue_return_value)\",\n \"def get_debiet_inlaatduiker(verval, m_constant=1, schuifhoogte=1.78, breedte_duiker=1.75):\\n return m_constant * schuifhoogte * breedte_duiker * np.sqrt(2 * 9.81 * verval)\",\n \"def grabDaily(self):\\n raise NotImplemented(\\\"method should be redefined in a subclass\\\")\",\n \"def __getitem__(self, item):\\n if 0 >= item < len(self.stations):\\n return self.stations[item]\",\n \"def getDip(self):\\n return constants.DEFAULT_DIP\",\n \"def _nodata_value(self):\\n try:\\n nodata = float(self._info[\\\"bands\\\"][0][\\\"noDataValue\\\"])\\n except KeyError:\\n nodata = None\\n return nodata\",\n \"def get_vdw_info(self):\\n return\",\n \"def GetDetailsItem(self):\\r\\n if self.details: return self.details.GetDetailsItem()\\r\\n return None\",\n \"def getDiameter(self):\\n\\n hdr = self.header\\n if \\\"cd1_1\\\" in hdr:\\n self.D = abs(hdr[\\\"cd1_1\\\"]) * hdr[\\\"naxis1\\\"]\\n elif \\\"cdelt1\\\" in hdr:\\n self.D = abs(hdr[\\\"cdelt1\\\"]) * hdr[\\\"naxis1\\\"]\\n else:\\n print(\\\"Warning: no coordinate information found in input header;\\\")\\n print(\\\" pupil width assumed to be 6.5 meters\\\")\\n self.D = 6.5\",\n \"def return_zeropoint():\\n return 22.5\",\n \"def get_stat_obj(self, nn_id):\\n try:\\n obj = models.NN_DEF_LIST_INFO.objects.get(nn_id=nn_id)\\n return getattr(obj, \\\"automl_stat\\\")\\n except Exception as e:\\n raise Exception(e)\",\n \"def get_dominance(self):\\n trait = self.traitDao.get_dominance(self.name)\\n dominance = trait[1]\\n return dominance\",\n \"def get_furniture():\",\n \"def dmap_info(self, out=None):\\n binfile = self.config.dataset.binfile\\n dmap = BinnedPhotonData(binfile)\\n print ('File: %s ' %binfile, file=out)\\n print ('\\\\n index emin emax type nside photons', file=out)\\n total = 0\\n def bignum(n):\\n t = '%9d' % n\\n return ' '+' '.join([t[0:3],t[3:6],t[6:]])\\n for i,band in enumerate(dmap):\\n fmt = '%5d'+2*'%10d'+2*'%6d'+'%12s'\\n print (fmt % (i, round(band.emin()), round(band.emax()), \\n band.event_class()&15, band.nside(), bignum(band.photons())))\\n total += band.photons()\\n print ('total%45s'% bignum(total), file=out)\\n return dmap\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8139567","0.61747223","0.57404494","0.56027913","0.54675555","0.5288993","0.5271653","0.52566004","0.5161955","0.5105282","0.50878364","0.50546545","0.5054538","0.5047877","0.5047438","0.5029467","0.50197035","0.5018882","0.5017841","0.49776104","0.49767607","0.49569342","0.4952468","0.49438515","0.49278337","0.4920823","0.49157676","0.49137434","0.49049678","0.4890677","0.48809803","0.48717287","0.48458096","0.4823374","0.4817194","0.48158354","0.4807081","0.47933325","0.47769243","0.4773711","0.47616038","0.47550577","0.47550118","0.47466376","0.47379878","0.47071412","0.46974784","0.46937597","0.46860552","0.46756518","0.46718282","0.46685395","0.46566525","0.46539316","0.464045","0.4639294","0.46281934","0.4618016","0.46142462","0.46040678","0.46018177","0.45981267","0.4597718","0.45976257","0.45936114","0.45902607","0.45794323","0.45710352","0.4569134","0.45658213","0.45625454","0.4560121","0.45586056","0.45578438","0.45492017","0.45457578","0.45441717","0.45427465","0.45341665","0.45338544","0.45305184","0.4517248","0.4517016","0.45143485","0.4511906","0.45082206","0.45058808","0.45054844","0.45050555","0.45020273","0.44983566","0.4494376","0.44943124","0.44931036","0.4491824","0.44862363","0.4484888","0.44790292","0.44751695","0.4475067"],"string":"[\n \"0.8139567\",\n \"0.61747223\",\n \"0.57404494\",\n \"0.56027913\",\n \"0.54675555\",\n \"0.5288993\",\n \"0.5271653\",\n \"0.52566004\",\n \"0.5161955\",\n \"0.5105282\",\n \"0.50878364\",\n \"0.50546545\",\n \"0.5054538\",\n \"0.5047877\",\n \"0.5047438\",\n \"0.5029467\",\n \"0.50197035\",\n \"0.5018882\",\n \"0.5017841\",\n \"0.49776104\",\n \"0.49767607\",\n \"0.49569342\",\n \"0.4952468\",\n \"0.49438515\",\n \"0.49278337\",\n \"0.4920823\",\n \"0.49157676\",\n \"0.49137434\",\n \"0.49049678\",\n \"0.4890677\",\n \"0.48809803\",\n \"0.48717287\",\n \"0.48458096\",\n \"0.4823374\",\n \"0.4817194\",\n \"0.48158354\",\n \"0.4807081\",\n \"0.47933325\",\n \"0.47769243\",\n \"0.4773711\",\n \"0.47616038\",\n \"0.47550577\",\n \"0.47550118\",\n \"0.47466376\",\n \"0.47379878\",\n \"0.47071412\",\n \"0.46974784\",\n \"0.46937597\",\n \"0.46860552\",\n \"0.46756518\",\n \"0.46718282\",\n \"0.46685395\",\n \"0.46566525\",\n \"0.46539316\",\n \"0.464045\",\n \"0.4639294\",\n \"0.46281934\",\n \"0.4618016\",\n \"0.46142462\",\n \"0.46040678\",\n \"0.46018177\",\n \"0.45981267\",\n \"0.4597718\",\n \"0.45976257\",\n \"0.45936114\",\n \"0.45902607\",\n \"0.45794323\",\n \"0.45710352\",\n \"0.4569134\",\n \"0.45658213\",\n \"0.45625454\",\n \"0.4560121\",\n \"0.45586056\",\n \"0.45578438\",\n \"0.45492017\",\n \"0.45457578\",\n \"0.45441717\",\n \"0.45427465\",\n \"0.45341665\",\n \"0.45338544\",\n \"0.45305184\",\n \"0.4517248\",\n \"0.4517016\",\n \"0.45143485\",\n \"0.4511906\",\n \"0.45082206\",\n \"0.45058808\",\n \"0.45054844\",\n \"0.45050555\",\n \"0.45020273\",\n \"0.44983566\",\n \"0.4494376\",\n \"0.44943124\",\n \"0.44931036\",\n \"0.4491824\",\n \"0.44862363\",\n \"0.4484888\",\n \"0.44790292\",\n \"0.44751695\",\n \"0.4475067\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7271567"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11797,"cells":{"query":{"kind":"string","value":"Obtains a double parameter. getdouparam(self,param_)"},"document":{"kind":"string","value":"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)"},"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 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 getParam(self,param):\n if param in self.params.keys():\n return self.params[param]\n else:\n return None","def get_param(self, param):\n return self.params.get(param, None)","def param(self, *args, **kwargs):\n return self.options.param(*args,**kwargs)","def _get_one_param(self, param_name):\n return getattr(self, '__' + param_name)","def get_param(self, parameter):\n if self.c.get(1).get('data'):\n return self.c.get(1).data.get(parameter, None)\n return None","def param(self):\n return self._param","def get_param_values(self,obs_name,param):\n\n return self.datasets[obs_name][param]","def get_trial_param(self, trial_id: int, param_name: str) -> float:\n raise NotImplementedError","def _param(self) ->nn.Parameter:\n return next(self.parameters())","def get_parameter(self, param):\n try:\n result = self._data[\"queryResult\"][\"parameters\"][param]\n except KeyError:\n result = None\n\n return result","def getDouble(self, int: int, int2: int) -> float:\n ...","def getParameter(self, name):","def point_to_param(self, pt):\n r = self.p2 - self.p1\n return (pt - self.p1).dot(r) / r.square()","def get(self, param, phase=\"last\", name=\"Main\"):\n df = self.summary(name=name)\n if param not in df.columns:\n raise KeyError(f\"@param must be in {', '.join(df.columns)}.\")\n if phase == \"last\":\n phase = df.index[-1]\n return df.loc[phase, param]","def gui_get_param(self,param_name):\n return self._tkvars[param_name].get()","def param_to_point(self, param):\n return self.p1 + param * (self.p2 - self.p1)","def get_parameter(cur, par):\n cur.execute(\"SELECT value FROM parameters WHERE par='%s';\" % par)\n return cur.fetchone()[0]","def get_param(self):\n\t\treturn handle_to_object(call_sdk_function('PrlResult_GetParam', self.handle))","def get_param(self, param_name):\n if hasattr(self, param_name):\n return getattr(self, param_name)\n else:\n return None","def d_Drepp_d_par(self,par):\n sinE = np.sin(self.E())\n cosE = np.cos(self.E())\n # first term\n term1 = -cosE*self.prtl_der('alpha',par)\n # second term\n term2 = (self.alpha()*sinE - \\\n (self.beta()+self.GAMMA)*cosE)*self.prtl_der('E',par)\n # Third term\n term3 = -sinE*(self.prtl_der('beta',par)+self.prtl_der('GAMMA',par))\n\n return term1+term2+term3","def get_param_duration(param):\n\n # dummy value\n value = rospy.Duration(1)\n\n try:\n # only a default value in case the param gets fuzzed.\n value = rospy.Duration(get_param_num(param))\n except ValueError:\n err_msg = (\n \"Param %s has the invalid value '%s'.\"\n % (param, rospy.get_param(param)))\n rospy.logerr(err_msg)\n rospy.signal_shutdown(err_msg)\n value = rospy.Duration(1)\n return value","def d_alpha_d_par(self,par):\n\n if par not in self.binary_params:\n errorMesg = par + \"is not in binary parameter list.\"\n raise ValueError(errorMesg)\n\n if par in ['A1','A1DOT']:\n dername = 'd_alpha_d_'+par\n return getattr(self,dername)()\n\n else:\n dername = 'd_omega_d_'+par # For parameters only in Ae\n if hasattr(self,dername):\n cosOmg=np.cos(self.omega())\n return self.a1()/c.c*cosOmg*getattr(self,dername)()\n else:\n return np.longdouble(np.zeros(len(self.tt0)))","def getParameter(self, *args):\n return _libsbml.Model_getParameter(self, *args)","def getParameter(self, *args):\n return _libsbml.KineticLaw_getParameter(self, *args)","def get_value(self, param, freq):\n p = self.get_parameter(param)\n value = p.get_value(freq)\n return value","def Getdxdparam(Mda,Mdb,Xa):\n\n Xb = Xa.copy()\n #Xb[iulag] = Xa[iulag] + (1-Xa[iq]*Xa[iM])*(Mdb.ubar-Mda.ubar)\n Xb[Mdb.nX:Mdb.nXY] = Mdb.F(Xb[Mdb.interpstates])\n Xb[Mdb.nXY:] = Mdb.Static(Xb)\n\n if CLArgs.param == \"b\":\n D = Mdb.b() - Mda.b()\n else:\n D = Mdb.tau - Mda.tau\n\n return (Xb[iM] - Xa[iM])/D","def patience_param(x):\n # -- TODO: make this do something!\n return x","def from_param(self):\n return self._dither","def get_param(param):\n try:\n return get_param_server().getParam(param)\n except socket.error:\n raise RosParamIOException(\"Unable to communicate with master!\")","def d_Drep_d_par(self,par):\n sinE = np.sin(self.E())\n cosE = np.cos(self.E())\n # first term\n term1 = -sinE*self.prtl_der('alpha',par)\n # second term\n term2 = -(self.alpha()*cosE+ \\\n (self.beta()+self.GAMMA)*sinE)*self.prtl_der('E',par)\n # Third term\n term3 = cosE*(self.prtl_der('beta',par)+self.prtl_der('GAMMA',par))\n\n return term1+term2+term3","def def_paramt():\n Zeff = 1.0\n amu = 2.0\n mf = mp*amu\n return Zeff, amu,mf","def get_measurements(self, param):\n return tuple(self.__buffer[param])","def d_beta_d_par(self,par):\n if par not in self.binary_params:\n errorMesg = par + \"is not in binary parameter list.\"\n raise ValueError(errorMesg)\n\n if par in ['A1','ECC','EDOT','DTH','A1DOT']:\n dername = 'd_beta_d_'+par\n return getattr(self,dername)()\n\n else:\n dername = 'd_omega_d_'+par # For parameters only in omega\n if hasattr(self,dername):\n eTheta = self.eTheta()\n a1 = self.a1()\n sinOmg = np.sin(self.omega())\n return -a1/c.c*(1-eTheta**2)**0.5*sinOmg*getattr(self,dername)()\n else:\n return np.longdouble(np.zeros(len(self.tt0)))","def get_custom_param(plot):\n return Plot.get_custom_param(plot)","def _get_disparity_parameter(self, norm_weight, degree):\n return 1 - (degree - 1) * integrate.quad(lambda x: (1 - x)**(degree - 2), 0, norm_weight)[0]","def getDouble(self, address: ghidra.program.model.address.Address) -> float:\n ...","def double(self):\n return self._double","def A(param):\n return (param.delta + param.nu + param.mu0) * (param.beta - param.nu)","def posterior_sample_parameter(self, parameter):\n pass","def p_hid(request) -> float:\n return request.param","def getParam(self, params, name):\n return params.get(name)","def getD(self):\r\n return self.D","def getd(self, node):\n\n return self.daq.getDouble(f'/{self.device_id}/{node}')","def get_param(self, param_name, memo=None):\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 explicit_attr = getattr(self, param_name)\n if explicit_attr is not None:\n return explicit_attr\n else:\n return self.build_param(param_name, memo=memo)","def _get_par(vid, par, *args):\n arguments = cc.pack(par, *args)\n ret = traci.vehicle.getParameter(vid, \"carFollowModel.\" + arguments)\n return cc.unpack(ret)","def getReal( self, par, path, unit = None ):\n\n return self.db.getRealPar( par, path, unit = unit )","def get_params(self):","def getParam(self):\n return self.__alpha0, self.__alpha1, self.__beta, self.__eta","def get_parameter(self, key):\n return self._params[key]","def get_last_measurement(self, param):\n return self.__buffer[param][-1]","def objective(self, param):\n self.__init__(param, self.data)\n # return self.rmse() + self.penalty()\n return self.rmse() + self.penalty()","def get_param_with_name(self, param_name):\n return self.params[param_name]","def _load_parameter(self):","def my_method(self, param1):\n return param1","def peturb(param):\n ann = param.annotation\n if ann == inspect._empty:\n ann = 'normal'\n if type(ann)==str:\n if ann == 'normal':\n return param.default + np.random.normal()\n elif ann == 'positive':\n return abs(param.default + np.random.normal())\n elif type(ann) == tuple:\n # Get a number from uniform random distribution\n # bounded by values in the annotation tuple.\n if type(ann[0]) == float:\n return np.random.uniform(*ann)\n elif type(ann[0]) == int:\n return np.random.randint(*ann)\n else:\n print('Unrecognised function annotation.')","def as_param(self):\n self._clean()\n return self.param","def gen_parameter(self, g, ng, p):\n pass","def d_Dre_d_par(self,par):\n sinE = np.sin(self.E())\n cosE = np.cos(self.E())\n # First term\n term1 = self.alpha()*(-self.prtl_der('er',par)-self.prtl_der('E',par)*sinE)\n # Second term\n term2 = (cosE-self.er())*self.prtl_der('alpha',par)\n # Third term\n term3 = (self.prtl_der('beta',par)+self.prtl_der('GAMMA',par))*sinE\n # Fourth term\n term4 = (self.beta()+self.GAMMA)*cosE*self.prtl_der('E',par)\n\n return term1 + term2 + term3 +term4","def double_value(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"double_value\")","def getVzoneOneParam(self, param):\n\n # return only the param of the first p_zone of the v_zone\n \t# if we want to know th CH parameter, we use the channels dict of\n \t# the v_amp object\n\tif param == \"CH\":\n\t return(param, self.v_amp_obj.channels[self.v_amp_obj._pzones[self.v_params[\"childs\"][0]][param]])\n\telse:\n value = self.v_amp_obj._pzones[self.v_params[\"childs\"][0]][param]\n return(param, value)","def _get_one_param_theta(self, param_name, deep = True):\n if(deep and self._FLAG_TYPE == 'collection' and param_name == 'list_func'):\n theta = []\n for sub in self.list_func_free:\n theta.append(sub.theta)\n else:\n vals = self._get_one_param(param_name)\n mask = self._get_one_free_mask(param_name)\n theta = [v for n, v in enumerate(vals) if mask[n]]\n if (len(theta) == 0):\n res = np.array(theta)\n else:\n res = np.hstack(theta)\n return res","def LastParameter(*args):\n return _Geom2dLProp.Geom2dLProp_Curve2dTool_LastParameter(*args)","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 get_admittance(self, param_name: list = ['Y11', 'Y21']):\n # TODO: move the plot in this analysis module. Renderer should recover the entire data\n return self.renderer.plot_params(param_name)","def calc_param(self,tri_coord,node_coord):\n x_basis, y_basis = node_coord\n A = []\n for j,coord in enumerate(tri_coord):\n xi, yi = coord \n A.append([1.0,xi,yi])\n if xi == x_basis and yi == y_basis:\n special = j\n continue \n\n b = np.zeros(3,dtype=float)\n b[special] = 1.0\n\n param = np.linalg.solve(A,b)\n\n return param[1:] #ignore /alpha because its falls out of gradient\n\n # print(calc_param((0,1,11),0))","def get_p_arg(self):\n\ttry:\n\t arg = sys.argv[5]\n\t p = float(arg) \n\texcept IndexError:\n\t print \"The probability of packet loss, p, must be provided.\"\n\t sys.exit(\"Example usage:\\n\\nclient.py myfile.txt receivedfile.txt 127.0.0.1 6060 0.0\") \n\tif (p < 0.0 or p > 1.0):\n\t print \"p value must be between 0.0 and 1.0 inclusive.\"\n\t sys.exit(\"Example usage:\\n\\nclient.py myfile.txt receivedfile.txt 127.0.0.1 6060 0.0\")\t\n\telse:\n\t return p","def point_to_parameter(self, pt):\n uv = ShapeAnalysis_Surface(self.surface()).ValueOfUV(\n gp_Pnt(pt[0], pt[1], pt[2]), 1e-9\n )\n return np.array(uv.Coord())","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 get_prob(cls, word, **given):\n fields = 'pos phr lmk rel deg'\n params = dict((f, None) for f in fields.split())\n params.update(given)\n return cls.query.filter_by(word=word, **params).one()","def get_param( self, param, default=None, header='content-type', unquote=1, decode=None ):\n param = _Message.get_param( self, param, default, header, unquote )\n\n if type(param) is TupleType:\n return '' # TODO\n\n #if param is default or not decode:\n # return param\n\n # XXX why is this commented out???\n #return recode_header( param, self._hint_charset )\n\n # XXX _get_params_preserve does not support Header objects\n return param","def _get_param_doc(self, param_name: str) -> Optional[DocstringParam]:\n for param in self._doc.params:\n if param.arg_name == param_name:\n return param\n\n return None","def get_parametervalue(self, 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 node_event_value = self.get_nodevalue(node_event)\n\n return node_event_value","def get(self) -> float:\n ...","def parameters(self):\n #print \"in instrument.parameter()\"\n return self._params","def grid_param(self, param):\n return griddata(self.zvals, param, self.ygrid, method='linear')","def get_speed(self):\n return self.get_par(\"slew_speed\")","def d_model(request) -> int:\n return request.param","def getParameter(self, parameter):\n if not self.proxy:\n self.proxy = self.session.service(\"ALExpressiveListening\")\n return self.proxy.getParameter(parameter)","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 get_dependent_param_points(self, param):\n if param == SHAPE_STRING:\n return self.shape_at, self.shape_value\n elif param == LOCATION_STRING:\n return self.loc_at, self.loc_value\n elif param == SCALE_STRING:\n return self.scale_at, self.scale_value\n else:\n err_msg = \"Parameter '{}' is unknown.\".format(param)\n raise ValueError(err_msg)","def get_param(self, step_id, name):\n step_params = self._params.get(step_id)\n return step_params.get(name) if step_params else None","def calculate_parameters(self, item):","def _getParameterValue(self, name, target = ''):\n warnings.warn(\"This function is deprecated; parameters have been replaced with uniform inputs in 1.38.\", DeprecationWarning, stacklevel = 2)\n return None","def __getattr__(self, attribute):\n return self.parameters.get(attribute, None)","def _handleInput(self, paramInput):\n super()._handleInput(paramInput)\n pFind = paramInput.findFirst('p')\n if pFind != None:\n self.p = pFind.value\n else: self.raiseAnError(IOError,'p value needed for Geometric distribution')\n self.initializeDistribution()","def dgdy(self, X):\n \n return 3*X[1]**2","def get_params(self, deep=...):\n ...","def _get_ssm_param(self, parameter_name):\n response = self.ssm_client.get_parameter(Name=parameter_name)\n res = response.get(\"Parameter\", {})\n cwa_parameter = res.get(\"Value\", {})\n return cwa_parameter","def get_diameters():\n return Global_Module.global_diameters","def parameters(self):","def get_a_param(self):\n value = self.memory[self.exec_ptr + 1]\n Vm.validate_value(value)\n return value","def _get_single_par(vid, par, *args):\n ret = PlexeImp._get_par(vid, par, *args)\n return ret[0]","def get_param(self, name):\n for param in self._parameters:\n if name == param._name:\n return param\n pass\n new_param = self._add_param(name)\n return new_param","def ex_4pdeer(param): \r\n param = _parsargs(param,npar=1) \r\n \r\n # Dipolar pathways\r\n lam = param[0]\r\n pathways = [\r\n [1-lam],\r\n [lam, 0]\r\n ]\r\n return pathways","def gb2_density(self, arg):\n return (self.param_a * arg**(self.param_a*self.param_p-1) /\n (self.param_b**(self.param_a*self.param_p) *\n scp.beta(self.param_p, self.param_q) *\n (1 + (arg / self.param_b) ** self.param_a) **\n (self.param_p+self.param_q)))","def get_double_power_law(self, alpha, beta, M_star):\n\t\tdenom = 10.0**(0.4*(alpha + 1.0)*(self.M_grid - M_star))\n\t\tdenom += 10.0**(0.4*(beta + 1.0)*(self.M_grid - M_star))\n\t\tdn = 1.0/denom\n\t\tdn /= np.sum(dn)\n\t\treturn dn","def get(self):\n return self._params","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 getMenuItemParameter(self):\r\n return self.parameter"],"string":"[\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 getParam(self,param):\\n if param in self.params.keys():\\n return self.params[param]\\n else:\\n return None\",\n \"def get_param(self, param):\\n return self.params.get(param, None)\",\n \"def param(self, *args, **kwargs):\\n return self.options.param(*args,**kwargs)\",\n \"def _get_one_param(self, param_name):\\n return getattr(self, '__' + param_name)\",\n \"def get_param(self, parameter):\\n if self.c.get(1).get('data'):\\n return self.c.get(1).data.get(parameter, None)\\n return None\",\n \"def param(self):\\n return self._param\",\n \"def get_param_values(self,obs_name,param):\\n\\n return self.datasets[obs_name][param]\",\n \"def get_trial_param(self, trial_id: int, param_name: str) -> float:\\n raise NotImplementedError\",\n \"def _param(self) ->nn.Parameter:\\n return next(self.parameters())\",\n \"def get_parameter(self, param):\\n try:\\n result = self._data[\\\"queryResult\\\"][\\\"parameters\\\"][param]\\n except KeyError:\\n result = None\\n\\n return result\",\n \"def getDouble(self, int: int, int2: int) -> float:\\n ...\",\n \"def getParameter(self, name):\",\n \"def point_to_param(self, pt):\\n r = self.p2 - self.p1\\n return (pt - self.p1).dot(r) / r.square()\",\n \"def get(self, param, phase=\\\"last\\\", name=\\\"Main\\\"):\\n df = self.summary(name=name)\\n if param not in df.columns:\\n raise KeyError(f\\\"@param must be in {', '.join(df.columns)}.\\\")\\n if phase == \\\"last\\\":\\n phase = df.index[-1]\\n return df.loc[phase, param]\",\n \"def gui_get_param(self,param_name):\\n return self._tkvars[param_name].get()\",\n \"def param_to_point(self, param):\\n return self.p1 + param * (self.p2 - self.p1)\",\n \"def get_parameter(cur, par):\\n cur.execute(\\\"SELECT value FROM parameters WHERE par='%s';\\\" % par)\\n return cur.fetchone()[0]\",\n \"def get_param(self):\\n\\t\\treturn handle_to_object(call_sdk_function('PrlResult_GetParam', self.handle))\",\n \"def get_param(self, param_name):\\n if hasattr(self, param_name):\\n return getattr(self, param_name)\\n else:\\n return None\",\n \"def d_Drepp_d_par(self,par):\\n sinE = np.sin(self.E())\\n cosE = np.cos(self.E())\\n # first term\\n term1 = -cosE*self.prtl_der('alpha',par)\\n # second term\\n term2 = (self.alpha()*sinE - \\\\\\n (self.beta()+self.GAMMA)*cosE)*self.prtl_der('E',par)\\n # Third term\\n term3 = -sinE*(self.prtl_der('beta',par)+self.prtl_der('GAMMA',par))\\n\\n return term1+term2+term3\",\n \"def get_param_duration(param):\\n\\n # dummy value\\n value = rospy.Duration(1)\\n\\n try:\\n # only a default value in case the param gets fuzzed.\\n value = rospy.Duration(get_param_num(param))\\n except ValueError:\\n err_msg = (\\n \\\"Param %s has the invalid value '%s'.\\\"\\n % (param, rospy.get_param(param)))\\n rospy.logerr(err_msg)\\n rospy.signal_shutdown(err_msg)\\n value = rospy.Duration(1)\\n return value\",\n \"def d_alpha_d_par(self,par):\\n\\n if par not in self.binary_params:\\n errorMesg = par + \\\"is not in binary parameter list.\\\"\\n raise ValueError(errorMesg)\\n\\n if par in ['A1','A1DOT']:\\n dername = 'd_alpha_d_'+par\\n return getattr(self,dername)()\\n\\n else:\\n dername = 'd_omega_d_'+par # For parameters only in Ae\\n if hasattr(self,dername):\\n cosOmg=np.cos(self.omega())\\n return self.a1()/c.c*cosOmg*getattr(self,dername)()\\n else:\\n return np.longdouble(np.zeros(len(self.tt0)))\",\n \"def getParameter(self, *args):\\n return _libsbml.Model_getParameter(self, *args)\",\n \"def getParameter(self, *args):\\n return _libsbml.KineticLaw_getParameter(self, *args)\",\n \"def get_value(self, param, freq):\\n p = self.get_parameter(param)\\n value = p.get_value(freq)\\n return value\",\n \"def Getdxdparam(Mda,Mdb,Xa):\\n\\n Xb = Xa.copy()\\n #Xb[iulag] = Xa[iulag] + (1-Xa[iq]*Xa[iM])*(Mdb.ubar-Mda.ubar)\\n Xb[Mdb.nX:Mdb.nXY] = Mdb.F(Xb[Mdb.interpstates])\\n Xb[Mdb.nXY:] = Mdb.Static(Xb)\\n\\n if CLArgs.param == \\\"b\\\":\\n D = Mdb.b() - Mda.b()\\n else:\\n D = Mdb.tau - Mda.tau\\n\\n return (Xb[iM] - Xa[iM])/D\",\n \"def patience_param(x):\\n # -- TODO: make this do something!\\n return x\",\n \"def from_param(self):\\n return self._dither\",\n \"def get_param(param):\\n try:\\n return get_param_server().getParam(param)\\n except socket.error:\\n raise RosParamIOException(\\\"Unable to communicate with master!\\\")\",\n \"def d_Drep_d_par(self,par):\\n sinE = np.sin(self.E())\\n cosE = np.cos(self.E())\\n # first term\\n term1 = -sinE*self.prtl_der('alpha',par)\\n # second term\\n term2 = -(self.alpha()*cosE+ \\\\\\n (self.beta()+self.GAMMA)*sinE)*self.prtl_der('E',par)\\n # Third term\\n term3 = cosE*(self.prtl_der('beta',par)+self.prtl_der('GAMMA',par))\\n\\n return term1+term2+term3\",\n \"def def_paramt():\\n Zeff = 1.0\\n amu = 2.0\\n mf = mp*amu\\n return Zeff, amu,mf\",\n \"def get_measurements(self, param):\\n return tuple(self.__buffer[param])\",\n \"def d_beta_d_par(self,par):\\n if par not in self.binary_params:\\n errorMesg = par + \\\"is not in binary parameter list.\\\"\\n raise ValueError(errorMesg)\\n\\n if par in ['A1','ECC','EDOT','DTH','A1DOT']:\\n dername = 'd_beta_d_'+par\\n return getattr(self,dername)()\\n\\n else:\\n dername = 'd_omega_d_'+par # For parameters only in omega\\n if hasattr(self,dername):\\n eTheta = self.eTheta()\\n a1 = self.a1()\\n sinOmg = np.sin(self.omega())\\n return -a1/c.c*(1-eTheta**2)**0.5*sinOmg*getattr(self,dername)()\\n else:\\n return np.longdouble(np.zeros(len(self.tt0)))\",\n \"def get_custom_param(plot):\\n return Plot.get_custom_param(plot)\",\n \"def _get_disparity_parameter(self, norm_weight, degree):\\n return 1 - (degree - 1) * integrate.quad(lambda x: (1 - x)**(degree - 2), 0, norm_weight)[0]\",\n \"def getDouble(self, address: ghidra.program.model.address.Address) -> float:\\n ...\",\n \"def double(self):\\n return self._double\",\n \"def A(param):\\n return (param.delta + param.nu + param.mu0) * (param.beta - param.nu)\",\n \"def posterior_sample_parameter(self, parameter):\\n pass\",\n \"def p_hid(request) -> float:\\n return request.param\",\n \"def getParam(self, params, name):\\n return params.get(name)\",\n \"def getD(self):\\r\\n return self.D\",\n \"def getd(self, node):\\n\\n return self.daq.getDouble(f'/{self.device_id}/{node}')\",\n \"def get_param(self, param_name, memo=None):\\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 explicit_attr = getattr(self, param_name)\\n if explicit_attr is not None:\\n return explicit_attr\\n else:\\n return self.build_param(param_name, memo=memo)\",\n \"def _get_par(vid, par, *args):\\n arguments = cc.pack(par, *args)\\n ret = traci.vehicle.getParameter(vid, \\\"carFollowModel.\\\" + arguments)\\n return cc.unpack(ret)\",\n \"def getReal( self, par, path, unit = None ):\\n\\n return self.db.getRealPar( par, path, unit = unit )\",\n \"def get_params(self):\",\n \"def getParam(self):\\n return self.__alpha0, self.__alpha1, self.__beta, self.__eta\",\n \"def get_parameter(self, key):\\n return self._params[key]\",\n \"def get_last_measurement(self, param):\\n return self.__buffer[param][-1]\",\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 get_param_with_name(self, param_name):\\n return self.params[param_name]\",\n \"def _load_parameter(self):\",\n \"def my_method(self, param1):\\n return param1\",\n \"def peturb(param):\\n ann = param.annotation\\n if ann == inspect._empty:\\n ann = 'normal'\\n if type(ann)==str:\\n if ann == 'normal':\\n return param.default + np.random.normal()\\n elif ann == 'positive':\\n return abs(param.default + np.random.normal())\\n elif type(ann) == tuple:\\n # Get a number from uniform random distribution\\n # bounded by values in the annotation tuple.\\n if type(ann[0]) == float:\\n return np.random.uniform(*ann)\\n elif type(ann[0]) == int:\\n return np.random.randint(*ann)\\n else:\\n print('Unrecognised function annotation.')\",\n \"def as_param(self):\\n self._clean()\\n return self.param\",\n \"def gen_parameter(self, g, ng, p):\\n pass\",\n \"def d_Dre_d_par(self,par):\\n sinE = np.sin(self.E())\\n cosE = np.cos(self.E())\\n # First term\\n term1 = self.alpha()*(-self.prtl_der('er',par)-self.prtl_der('E',par)*sinE)\\n # Second term\\n term2 = (cosE-self.er())*self.prtl_der('alpha',par)\\n # Third term\\n term3 = (self.prtl_der('beta',par)+self.prtl_der('GAMMA',par))*sinE\\n # Fourth term\\n term4 = (self.beta()+self.GAMMA)*cosE*self.prtl_der('E',par)\\n\\n return term1 + term2 + term3 +term4\",\n \"def double_value(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"double_value\\\")\",\n \"def getVzoneOneParam(self, param):\\n\\n # return only the param of the first p_zone of the v_zone\\n \\t# if we want to know th CH parameter, we use the channels dict of\\n \\t# the v_amp object\\n\\tif param == \\\"CH\\\":\\n\\t return(param, self.v_amp_obj.channels[self.v_amp_obj._pzones[self.v_params[\\\"childs\\\"][0]][param]])\\n\\telse:\\n value = self.v_amp_obj._pzones[self.v_params[\\\"childs\\\"][0]][param]\\n return(param, value)\",\n \"def _get_one_param_theta(self, param_name, deep = True):\\n if(deep and self._FLAG_TYPE == 'collection' and param_name == 'list_func'):\\n theta = []\\n for sub in self.list_func_free:\\n theta.append(sub.theta)\\n else:\\n vals = self._get_one_param(param_name)\\n mask = self._get_one_free_mask(param_name)\\n theta = [v for n, v in enumerate(vals) if mask[n]]\\n if (len(theta) == 0):\\n res = np.array(theta)\\n else:\\n res = np.hstack(theta)\\n return res\",\n \"def LastParameter(*args):\\n return _Geom2dLProp.Geom2dLProp_Curve2dTool_LastParameter(*args)\",\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 get_admittance(self, param_name: list = ['Y11', 'Y21']):\\n # TODO: move the plot in this analysis module. Renderer should recover the entire data\\n return self.renderer.plot_params(param_name)\",\n \"def calc_param(self,tri_coord,node_coord):\\n x_basis, y_basis = node_coord\\n A = []\\n for j,coord in enumerate(tri_coord):\\n xi, yi = coord \\n A.append([1.0,xi,yi])\\n if xi == x_basis and yi == y_basis:\\n special = j\\n continue \\n\\n b = np.zeros(3,dtype=float)\\n b[special] = 1.0\\n\\n param = np.linalg.solve(A,b)\\n\\n return param[1:] #ignore /alpha because its falls out of gradient\\n\\n # print(calc_param((0,1,11),0))\",\n \"def get_p_arg(self):\\n\\ttry:\\n\\t arg = sys.argv[5]\\n\\t p = float(arg) \\n\\texcept IndexError:\\n\\t print \\\"The probability of packet loss, p, must be provided.\\\"\\n\\t sys.exit(\\\"Example usage:\\\\n\\\\nclient.py myfile.txt receivedfile.txt 127.0.0.1 6060 0.0\\\") \\n\\tif (p < 0.0 or p > 1.0):\\n\\t print \\\"p value must be between 0.0 and 1.0 inclusive.\\\"\\n\\t sys.exit(\\\"Example usage:\\\\n\\\\nclient.py myfile.txt receivedfile.txt 127.0.0.1 6060 0.0\\\")\\t\\n\\telse:\\n\\t return p\",\n \"def point_to_parameter(self, pt):\\n uv = ShapeAnalysis_Surface(self.surface()).ValueOfUV(\\n gp_Pnt(pt[0], pt[1], pt[2]), 1e-9\\n )\\n return np.array(uv.Coord())\",\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 get_prob(cls, word, **given):\\n fields = 'pos phr lmk rel deg'\\n params = dict((f, None) for f in fields.split())\\n params.update(given)\\n return cls.query.filter_by(word=word, **params).one()\",\n \"def get_param( self, param, default=None, header='content-type', unquote=1, decode=None ):\\n param = _Message.get_param( self, param, default, header, unquote )\\n\\n if type(param) is TupleType:\\n return '' # TODO\\n\\n #if param is default or not decode:\\n # return param\\n\\n # XXX why is this commented out???\\n #return recode_header( param, self._hint_charset )\\n\\n # XXX _get_params_preserve does not support Header objects\\n return param\",\n \"def _get_param_doc(self, param_name: str) -> Optional[DocstringParam]:\\n for param in self._doc.params:\\n if param.arg_name == param_name:\\n return param\\n\\n return None\",\n \"def get_parametervalue(self, 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 node_event_value = self.get_nodevalue(node_event)\\n\\n return node_event_value\",\n \"def get(self) -> float:\\n ...\",\n \"def parameters(self):\\n #print \\\"in instrument.parameter()\\\"\\n return self._params\",\n \"def grid_param(self, param):\\n return griddata(self.zvals, param, self.ygrid, method='linear')\",\n \"def get_speed(self):\\n return self.get_par(\\\"slew_speed\\\")\",\n \"def d_model(request) -> int:\\n return request.param\",\n \"def getParameter(self, parameter):\\n if not self.proxy:\\n self.proxy = self.session.service(\\\"ALExpressiveListening\\\")\\n return self.proxy.getParameter(parameter)\",\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 get_dependent_param_points(self, param):\\n if param == SHAPE_STRING:\\n return self.shape_at, self.shape_value\\n elif param == LOCATION_STRING:\\n return self.loc_at, self.loc_value\\n elif param == SCALE_STRING:\\n return self.scale_at, self.scale_value\\n else:\\n err_msg = \\\"Parameter '{}' is unknown.\\\".format(param)\\n raise ValueError(err_msg)\",\n \"def get_param(self, step_id, name):\\n step_params = self._params.get(step_id)\\n return step_params.get(name) if step_params else None\",\n \"def calculate_parameters(self, item):\",\n \"def _getParameterValue(self, name, target = ''):\\n warnings.warn(\\\"This function is deprecated; parameters have been replaced with uniform inputs in 1.38.\\\", DeprecationWarning, stacklevel = 2)\\n return None\",\n \"def __getattr__(self, attribute):\\n return self.parameters.get(attribute, None)\",\n \"def _handleInput(self, paramInput):\\n super()._handleInput(paramInput)\\n pFind = paramInput.findFirst('p')\\n if pFind != None:\\n self.p = pFind.value\\n else: self.raiseAnError(IOError,'p value needed for Geometric distribution')\\n self.initializeDistribution()\",\n \"def dgdy(self, X):\\n \\n return 3*X[1]**2\",\n \"def get_params(self, deep=...):\\n ...\",\n \"def _get_ssm_param(self, parameter_name):\\n response = self.ssm_client.get_parameter(Name=parameter_name)\\n res = response.get(\\\"Parameter\\\", {})\\n cwa_parameter = res.get(\\\"Value\\\", {})\\n return cwa_parameter\",\n \"def get_diameters():\\n return Global_Module.global_diameters\",\n \"def parameters(self):\",\n \"def get_a_param(self):\\n value = self.memory[self.exec_ptr + 1]\\n Vm.validate_value(value)\\n return value\",\n \"def _get_single_par(vid, par, *args):\\n ret = PlexeImp._get_par(vid, par, *args)\\n return ret[0]\",\n \"def get_param(self, name):\\n for param in self._parameters:\\n if name == param._name:\\n return param\\n pass\\n new_param = self._add_param(name)\\n return new_param\",\n \"def ex_4pdeer(param): \\r\\n param = _parsargs(param,npar=1) \\r\\n \\r\\n # Dipolar pathways\\r\\n lam = param[0]\\r\\n pathways = [\\r\\n [1-lam],\\r\\n [lam, 0]\\r\\n ]\\r\\n return pathways\",\n \"def gb2_density(self, arg):\\n return (self.param_a * arg**(self.param_a*self.param_p-1) /\\n (self.param_b**(self.param_a*self.param_p) *\\n scp.beta(self.param_p, self.param_q) *\\n (1 + (arg / self.param_b) ** self.param_a) **\\n (self.param_p+self.param_q)))\",\n \"def get_double_power_law(self, alpha, beta, M_star):\\n\\t\\tdenom = 10.0**(0.4*(alpha + 1.0)*(self.M_grid - M_star))\\n\\t\\tdenom += 10.0**(0.4*(beta + 1.0)*(self.M_grid - M_star))\\n\\t\\tdn = 1.0/denom\\n\\t\\tdn /= np.sum(dn)\\n\\t\\treturn dn\",\n \"def get(self):\\n return self._params\",\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 getMenuItemParameter(self):\\r\\n return self.parameter\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7216291","0.6473787","0.63799834","0.6056583","0.6039823","0.60313606","0.5998796","0.59530497","0.5941797","0.5872715","0.5843549","0.5813088","0.5800583","0.576759","0.57210326","0.56473833","0.5598247","0.5590956","0.55658185","0.5548109","0.5548043","0.5540383","0.55133027","0.5474782","0.54703546","0.54520005","0.5437544","0.54323894","0.5427863","0.54197294","0.5416418","0.5409379","0.54072446","0.53958994","0.53615284","0.5356121","0.53545886","0.53166175","0.5308789","0.52848285","0.5281689","0.52772504","0.5275805","0.5272419","0.5239845","0.52307194","0.52243143","0.51983464","0.51962525","0.5194923","0.5186655","0.517258","0.51721746","0.51505923","0.5107932","0.51071554","0.5092622","0.5088609","0.50712925","0.5070144","0.5062235","0.5055798","0.50540954","0.5053547","0.5049263","0.50385207","0.50384104","0.5027609","0.50274426","0.50213826","0.50160813","0.50129807","0.50098175","0.5001551","0.49908704","0.49903646","0.49742767","0.49675903","0.49672735","0.49672732","0.49672487","0.4955111","0.4953137","0.49521378","0.495169","0.49502078","0.4948015","0.4944961","0.49389708","0.49357024","0.4931563","0.4927061","0.49147323","0.4906998","0.49042028","0.48964185","0.4880301","0.48724565","0.48674098","0.4863594"],"string":"[\n \"0.7216291\",\n \"0.6473787\",\n \"0.63799834\",\n \"0.6056583\",\n \"0.6039823\",\n \"0.60313606\",\n \"0.5998796\",\n \"0.59530497\",\n \"0.5941797\",\n \"0.5872715\",\n \"0.5843549\",\n \"0.5813088\",\n \"0.5800583\",\n \"0.576759\",\n \"0.57210326\",\n \"0.56473833\",\n \"0.5598247\",\n \"0.5590956\",\n \"0.55658185\",\n \"0.5548109\",\n \"0.5548043\",\n \"0.5540383\",\n \"0.55133027\",\n \"0.5474782\",\n \"0.54703546\",\n \"0.54520005\",\n \"0.5437544\",\n \"0.54323894\",\n \"0.5427863\",\n \"0.54197294\",\n \"0.5416418\",\n \"0.5409379\",\n \"0.54072446\",\n \"0.53958994\",\n \"0.53615284\",\n \"0.5356121\",\n \"0.53545886\",\n \"0.53166175\",\n \"0.5308789\",\n \"0.52848285\",\n \"0.5281689\",\n \"0.52772504\",\n \"0.5275805\",\n \"0.5272419\",\n \"0.5239845\",\n \"0.52307194\",\n \"0.52243143\",\n \"0.51983464\",\n \"0.51962525\",\n \"0.5194923\",\n \"0.5186655\",\n \"0.517258\",\n \"0.51721746\",\n \"0.51505923\",\n \"0.5107932\",\n \"0.51071554\",\n \"0.5092622\",\n \"0.5088609\",\n \"0.50712925\",\n \"0.5070144\",\n \"0.5062235\",\n \"0.5055798\",\n \"0.50540954\",\n \"0.5053547\",\n \"0.5049263\",\n \"0.50385207\",\n \"0.50384104\",\n \"0.5027609\",\n \"0.50274426\",\n \"0.50213826\",\n \"0.50160813\",\n \"0.50129807\",\n \"0.50098175\",\n \"0.5001551\",\n \"0.49908704\",\n \"0.49903646\",\n \"0.49742767\",\n \"0.49675903\",\n \"0.49672735\",\n \"0.49672732\",\n \"0.49672487\",\n \"0.4955111\",\n \"0.4953137\",\n \"0.49521378\",\n \"0.495169\",\n \"0.49502078\",\n \"0.4948015\",\n \"0.4944961\",\n \"0.49389708\",\n \"0.49357024\",\n \"0.4931563\",\n \"0.4927061\",\n \"0.49147323\",\n \"0.4906998\",\n \"0.49042028\",\n \"0.48964185\",\n \"0.4880301\",\n \"0.48724565\",\n \"0.48674098\",\n \"0.4863594\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7105613"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11798,"cells":{"query":{"kind":"string","value":"Computes the dual objective value associated with the solution. getdualobj(self,whichsol_)"},"document":{"kind":"string","value":"def getdualobj(self,whichsol_):\n dualobj_ = ctypes.c_double()\n res = __library__.MSK_XX_getdualobj(self.__nativep,whichsol_,ctypes.byref(dualobj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n dualobj_ = dualobj_.value\n _dualobj_return_value = dualobj_\n return (_dualobj_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 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 getprimalobj(self,whichsol_):\n primalobj_ = ctypes.c_double()\n res = __library__.MSK_XX_getprimalobj(self.__nativep,whichsol_,ctypes.byref(primalobj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n primalobj_ = primalobj_.value\n _primalobj_return_value = primalobj_\n return (_primalobj_return_value)","def getprimalobj(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getprimalobj(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _primalobj_return_value = resargs\n return _primalobj_return_value","def dual_objective(self, dual_coeffs):\n primal = self.model._sdca_primal_dual_relation(self.l_l2sq,\n dual_coeffs)\n prox_l2_value = 0.5 * self.l_l2sq * np.linalg.norm(primal) ** 2\n return self.model.dual_loss(dual_coeffs) - prox_l2_value","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 objective_val(self):\n return self.m.ObjVal","def get_sol(self):","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 getdualsolutionnorms(self,whichsol_):\n nrmy_ = ctypes.c_double()\n nrmslc_ = ctypes.c_double()\n nrmsuc_ = ctypes.c_double()\n nrmslx_ = ctypes.c_double()\n nrmsux_ = ctypes.c_double()\n nrmsnx_ = ctypes.c_double()\n nrmbars_ = ctypes.c_double()\n res = __library__.MSK_XX_getdualsolutionnorms(self.__nativep,whichsol_,ctypes.byref(nrmy_),ctypes.byref(nrmslc_),ctypes.byref(nrmsuc_),ctypes.byref(nrmslx_),ctypes.byref(nrmsux_),ctypes.byref(nrmsnx_),ctypes.byref(nrmbars_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nrmy_ = nrmy_.value\n _nrmy_return_value = nrmy_\n nrmslc_ = nrmslc_.value\n _nrmslc_return_value = nrmslc_\n nrmsuc_ = nrmsuc_.value\n _nrmsuc_return_value = nrmsuc_\n nrmslx_ = nrmslx_.value\n _nrmslx_return_value = nrmslx_\n nrmsux_ = nrmsux_.value\n _nrmsux_return_value = nrmsux_\n nrmsnx_ = nrmsnx_.value\n _nrmsnx_return_value = nrmsnx_\n nrmbars_ = nrmbars_.value\n _nrmbars_return_value = nrmbars_\n return (_nrmy_return_value,_nrmslc_return_value,_nrmsuc_return_value,_nrmslx_return_value,_nrmsux_return_value,_nrmsnx_return_value,_nrmbars_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 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 objective(self):\n return self._objective","def _get_rhs(self, thermo, chem_pot_array, beta):\n num_singlets = len(self._ground_states) - 1\n matrix = np.zeros((num_singlets, num_singlets))\n energy_vector = np.zeros(num_singlets)\n for i in range(num_singlets):\n for j in range(num_singlets):\n ref_singlet = thermo[0][get_singlet_name(\n self._singlet_names[j])]\n singlet = thermo[i +\n 1][get_singlet_name(self._singlet_names[j])]\n matrix[i, j] = ref_singlet - singlet\n ref_energy = thermo[0][\"energy\"] / \\\n len(self._ground_states[0][\"atoms\"])\n energy = thermo[i + 1][\"energy\"] / \\\n len(self._ground_states[i + 1][\"atoms\"])\n energy_vector[i] = ref_energy - energy\n inv_matrix = np.linalg.inv(matrix)\n rhs = inv_matrix.dot(energy_vector) / beta - chem_pot_array / beta\n return rhs","def get_solution(self):\r\n return self.solution","def get_objective(self, X_v, U_v, X_last_p, U_last_p):\n objective = None\n return objective","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 dual(self):\n return dual_array(self)","def recover_dual_variables(task, sol, inverse_data):\n dual_vars = dict()\n\n # Dual variables for the inequality constraints\n suc_len = sum(ell for _, ell in inverse_data['suc_slacks'])\n if suc_len > 0:\n suc = [0.] * suc_len\n task.getsucslice(sol, 0, suc_len, suc)\n dual_vars.update(MOSEK._parse_dual_var_block(suc, inverse_data['suc_slacks']))\n\n # Dual variables for the original equality constraints\n y_len = sum(ell for _, ell in inverse_data['y_slacks'])\n if y_len > 0:\n y = [0.] * y_len\n task.getyslice(sol, suc_len, suc_len + y_len, y)\n y = [-val for val in y]\n dual_vars.update(MOSEK._parse_dual_var_block(y, inverse_data['y_slacks']))\n\n # Dual variables for SOC and EXP constraints\n snx_len = sum(ell for _, ell in inverse_data['snx_slacks'])\n if snx_len > 0:\n snx = np.zeros(snx_len)\n task.getsnxslice(sol, inverse_data['n0'], inverse_data['n0'] + snx_len, snx)\n dual_vars.update(MOSEK._parse_dual_var_block(snx, inverse_data['snx_slacks']))\n\n # Dual variables for PSD constraints\n for j, (id, dim) in enumerate(inverse_data['psd_dims']):\n sj = [0.] * (dim * (dim + 1) // 2)\n task.getbarsj(sol, j, sj)\n dual_vars[id] = vectorized_lower_tri_to_mat(sj, dim)\n\n # Now that all dual variables have been recovered, find those corresponding\n # to the exponential cone, and permute the entries to reflect the CVXPY\n # standard for the exponential cone.\n for con in inverse_data['constraints']:\n if isinstance(con, ExpCone):\n cid = con.id\n perm = expcone_permutor(con.num_cones(), MOSEK.EXP_CONE_ORDER)\n dual_vars[cid] = dual_vars[cid][perm]\n return dual_vars","def dual_objective_expression_rule(_m):\r\n\r\n # Build limits\r\n t_1 = sum(- (m.mu_2[z, y] * m.SOLAR_BUILD_LIMITS[z]) - (m.mu_3[z, y] * m.WIND_BUILD_LIMITS[z]) - (\r\n m.mu_4[z, y] * m.STORAGE_BUILD_LIMITS[z]) for z in m.Z for y in m.Y)\r\n\r\n # Min power output\r\n t_2 = sum(\r\n m.sigma_1[g, y, s, t] * m.P_MIN[g] for g in m.G.difference(m.G_STORAGE) for y in m.Y for s in m.S for t\r\n in m.T)\r\n\r\n # Max power - existing generators\r\n t_3 = sum(\r\n - m.sigma_2[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_THERM for y in m.Y for s in m.S\r\n for t in m.T)\r\n\r\n # Max power - existing wind\r\n t_4 = sum(\r\n - m.sigma_4[g, y, s, t] * m.Q_W[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_WIND for y in\r\n m.Y for s in m.S for t in m.T)\r\n\r\n # Max power - existing solar\r\n t_5 = sum(\r\n - m.sigma_6[g, y, s, t] * m.Q_S[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_SOLAR for y in\r\n m.Y for s in m.S for t in m.T)\r\n\r\n # Max power - existing hydro\r\n t_6 = sum(\r\n - m.sigma_8[g, y, s, t] * m.P_H[g, y, s, t] * (1 - m.F[g, y]) for g in m.G_E_HYDRO for y in m.Y for s in\r\n m.S for t in m.T)\r\n\r\n # Max charging power - existing storage\r\n t_7 = sum(\r\n - m.sigma_11[g, y, s, t] * m.P_IN_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_STORAGE for y in m.Y for s in\r\n m.S for t in m.T)\r\n\r\n # Max discharging power - existing storage\r\n t_8 = sum(\r\n - m.sigma_13[g, y, s, t] * m.P_OUT_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_STORAGE for y in m.Y for s in\r\n m.S for t in m.T)\r\n\r\n # Max energy - existing storage units\r\n t_9 = sum(\r\n - m.sigma_16[g, y, s, t] * m.Q_MAX[g] for g in m.G_E_STORAGE for y in m.Y for s in m.S for t in m.T)\r\n\r\n # Min energy - interval end\r\n t_10 = sum(m.sigma_18[g, y, s] * m.Q_END_MIN[g] for g in m.G_STORAGE for y in m.Y for s in m.S)\r\n\r\n # Max energy - interval end\r\n t_11 = sum(- m.sigma_19[g, y, s] * m.Q_END_MAX[g] for g in m.G_STORAGE for y in m.Y for s in m.S)\r\n\r\n # Ramp-up constraint - generators\r\n t_12 = sum(\r\n - m.sigma_20[g, y, s, t] * m.RR_UP[g] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for s in m.S\r\n for t in m.T)\r\n\r\n # Ramp-up constraint - initial power output - generators\r\n t_13 = sum(\r\n - m.sigma_20[g, y, s, m.T.first()] * m.P0[g, y, s] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y\r\n for s in m.S)\r\n\r\n # Ramp-down constraint - generators\r\n t_18 = sum(\r\n - m.sigma_23[g, y, s, t] * m.RR_DOWN[g] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for s in m.S\r\n for t in m.T)\r\n\r\n # Ramp-down constraint - initial power output - generators\r\n t_19 = sum(\r\n m.sigma_23[g, y, s, m.T.first()] * m.P0[g, y, s] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for\r\n s in m.S)\r\n\r\n # Min powerflow\r\n t_24 = sum(m.sigma_27[l, y, s, t] * m.POWERFLOW_MIN[l] for l in m.L for y in m.Y for s in m.S for t in m.T)\r\n\r\n # Max powerflow\r\n t_25 = sum(\r\n - m.sigma_28[l, y, s, t] * m.POWERFLOW_MAX[l] for l in m.L for y in m.Y for s in m.S for t in m.T)\r\n\r\n # Demand\r\n t_26 = sum(m.lamb[z, y, s, t] * m.DEMAND[z, y, s, t] for z in m.Z for y in m.Y for s in m.S for t in m.T)\r\n\r\n # Initial storage unit energy\r\n t_27 = sum(m.zeta_1[g, y, s, m.T.first()] * m.Q0[g, y, s] for g in m.G_STORAGE for y in m.Y for s in m.S)\r\n\r\n return (t_1 + t_2 + t_3 + t_4 + t_5 + t_6 + t_7 + t_8 + t_9 + t_10 + t_11 + t_12 + t_13 + t_18 + t_19 + t_24\r\n + t_25 + t_26 + t_27)","def define_objective(m):\r\n\r\n # Dual objective function\r\n m.OBJECTIVE = Objective(expr=m.DUAL_OBJECTIVE_EXPRESSION, sense=maximize)\r\n\r\n return m","def _objfunc(self, dv_dict):\n\n fail = 0\n metadata = self.metadata\n system = self.root\n\n try:\n for name in self.indep_list:\n self.set_desvar(name, dv_dict[name])\n\n # Execute the model\n #print(\"Setting DV\")\n #print(dv_dict)\n\n self.iter_count += 1\n update_local_meta(metadata, (self.iter_count,))\n\n try:\n with self.root._dircontext:\n system.solve_nonlinear(metadata=metadata)\n\n # Let the optimizer try to handle the error\n except AnalysisError:\n fail = 1\n\n func_dict = self.get_objectives() # this returns a new OrderedDict\n func_dict.update(self.get_constraints())\n\n # Record after getting obj and constraint to assure they have\n # been gathered in MPI.\n self.recorders.record_iteration(system, metadata)\n\n # Get the double-sided constraint evaluations\n #for key, con in iteritems(self.get_2sided_constraints()):\n # func_dict[name] = np.array(con.evaluate(self.parent))\n\n except Exception as msg:\n tb = traceback.format_exc()\n\n # Exceptions seem to be swallowed by the C code, so this\n # should give the user more info than the dreaded \"segfault\"\n print(\"Exception: %s\" % str(msg))\n print(70*\"=\",tb,70*\"=\")\n fail = 1\n func_dict = {}\n\n #print(\"Functions calculated\")\n #print(func_dict)\n return func_dict, fail","def solve(self):\n self.m.optimize()\n if self.m.status == GRB.OPTIMAL:\n self.solution = self.sol_as_mat()\n return self.solution","def objective(self):\n pass","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 get_objective(self, sampler=None):\n def objective(params):\n circuit = self.get_circuit(params)\n circuit.make_cache()\n return self.get_energy(circuit, sampler)\n\n def obj_expect(params):\n circuit = self.get_circuit(params)\n circuit.make_cache()\n return self.get_energy_sparse(circuit)\n\n if sampler is not None:\n return objective\n if self.sparse is None:\n self.make_sparse()\n return obj_expect","def return_solver(self):\r\n sol = ht3_solver(self.mesh)\r\n sol.max_T = self.max_T\r\n sol.d_T = self.d_T\r\n sol.saved_data = self.saved_data\r\n sol.node_map = self.node_map\r\n return sol","def getprosta(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getprosta(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value = resargs\n _prosta_return_value = prosta(_prosta_return_value)\n return _prosta_return_value","def get_dual_val(self, var_name, pos):\n val = self.get_other_value(self.dual_var, var_name, pos)\n if not self.pdv_to_csv: # if not saved to csv file\n return val\n else: # otherwise, we should get the file path and read from the file to array or mat\n f_path = os.path.join(self.root_dir, 'dual_vars', var_name, str(val) + '.csv')\n df = pd.read_csv(f_path, header = None) # first read csv file into a pandas data frame and then transform\n return np.asmatrix(df.values)","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 getObjective(self, *args):\n return _libsbml.FbcModelPlugin_getObjective(self, *args)","def get_sol_value(self):\n return float(self.data[2])","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 solve(self, model, solver=\"cbc\", with_duals=False, **solver_kwargs):\n logging.info(\"Optimising using %s.\", solver)\n\n solver_kwargs[\"tee\"] = solver_kwargs.get(\"tee\", True)\n\n self.meta[\"solph_version\"] = solph.__version__\n self.meta[\"solver\"] = solver\n self.meta[\"solver_start\"] = datetime.datetime.now()\n\n if with_duals:\n model.receive_duals()\n\n model.solve(solver=solver, solve_kwargs=solver_kwargs)\n\n self.meta[\"solver_end\"] = datetime.datetime.now()\n\n self.es.results[\"main\"] = solph.processing.results(model)\n self.meta.update(solph.processing.meta_results(model))\n self.es.results[\"param\"] = solph.processing.parameter_as_dict(self.es)\n self.es.results[\"meta\"] = self.meta\n\n self.results = self.es.results","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 get_approx_solution(self, solver):\n tour = solver.solve(self)\n print('The cost is {}.'.format(get_cost(tour,self)))\n self.tours[solver.__class__.__name__] = tour\n return tour","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 get_objective(\n self,\n fun: bool = True,\n res: bool = True,\n max_sensi_order: int = 2,\n fim_for_hess: bool = False,\n ):\n if fim_for_hess:\n fhess = self.get_ffim()\n else:\n fhess = self.get_fs2nllh()\n\n return pypesto.Objective(\n fun=self.get_fnllh() if fun else None,\n grad=self.get_fsnllh() if fun and max_sensi_order >= 1 else None,\n hess=fhess if fun and max_sensi_order >= 2 else None,\n res=self.get_fres() if res else None,\n sres=self.get_fsres() if res and max_sensi_order >= 1 else None,\n )","def rhs(self, inv_method=None):\n if inv_method is None:\n self._rhs = self.mass_matrix_full.LUsolve(self.forcing_full)\n else:\n self._rhs = (self.mass_matrix_full.inv(inv_method,\n try_block_diag=True) * self.forcing_full)\n return self._rhs","def fetch(self):\n return self.sol","def get_best_individual(self):\n return self._best_indv","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 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 get_solution(self):\n return self._generate_solution()","def get_dual_change_value(self, var_change_name, pos):\n return self.get_other_value(self.dual_var_change, var_change_name, pos)","def compute_dual_effective_cost_scipy(w: types.WorkloadSpace, workload_mat: types.WorkloadMatrix,\n cost_per_buffer: types.StateSpace,\n method: str = 'revised simplex') \\\n -> Tuple[Optional[types.WorkloadSpace], types.StateSpace, float]:\n # We remove the columns of the workload matrix which are all zeros because they correspond to\n # inequalities which are always satisfied as we assume that the cost per buffer is always\n # positive. This is needed for scipy linprog because if not done, the method will return\n # status 4: Numerical difficulties encountered. The other methods may not need this step\n non_zeros_columns = np.logical_not(np.all(np.isclose(workload_mat, 0.), axis=0))\n workload_mat = workload_mat[:, non_zeros_columns]\n cost_per_buffer = cost_per_buffer[non_zeros_columns, :]\n res = linprog(c=-w, A_ub=workload_mat.T, b_ub=cost_per_buffer, bounds=(None, None),\n method=method)\n if res.success:\n eff_cost = - res.fun # Dual problem is: max c_bar @ w; while here we do: min - c_bar @ w\n c_bar = res.x[:, None]\n else:\n c_bar = None\n if res.status == 2: # Problem appears to be infeasible\n eff_cost = None\n elif res.status == 3: # Problem appears to be unbounded\n eff_cost = np.inf\n elif res.status == 4: # We should not return anything\n raise exceptions.ScipyLinprogStatusError(\"Scipy encountered numerical difficulties\")\n return c_bar, None, eff_cost","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 potentialSolver(self, max_it, tol):\n\n dx2 = 1.0/(self.dh[0]*self.dh[0]); # dx^2\n dy2 = 1.0/(self.dh[1]*self.dh[1]); # dy^2\n dz2 = 1.0/(self.dh[2]*self.dh[2]); # dz^2\n \n L2 = 0.0 # norm\n \n converged = False\n \n # solve potential\n for it in np.arange(1,max_it+1):\n for i in np.arange(1,self.ni-1):\n for j in np.arange(1,self.nj-1):\n for k in np.arange(1,self.nk-1):\n #standard internal open node\n phi_new = (self.rho[i][j][k]/self.EPS_0 +\\\n dx2*(self.phi[i-1][j][k] + self.phi[i+1][j][k]) +\\\n dy2*(self.phi[i][j-1][k] + self.phi[i][j+1][k]) +\\\n dz2*(self.phi[i][j][k-1] + self.phi[i][j][k+1]))/(2*dx2+2*dy2+2*dz2)\n \n # sucessive over relaxation \n self.phi[i,j,k] = self.phi[i,j,k] + 1.4*(phi_new - self.phi[i][j][k])\n \n\n #check for convergence*/\n if it%25==0:\n sum = 0;\n for i in np.arange(1,self.ni-1):\n for j in np.arange(1,self.nj-1):\n for k in np.arange(1,self.nk-1):\n\n R = -self.phi[i][j][k]*(2*dx2+2*dy2+2*dz2) +\\\n self.rho[i][j][k]/self.EPS_0 +\\\n dx2*(self.phi[i-1][j][k] + self.phi[i+1][j][k]) +\\\n dy2*(self.phi[i][j-1][k] + self.phi[i][j+1][k]) +\\\n dz2*(self.phi[i][j][k-1] + self.phi[i][j][k+1])\n\n sum += R*R;\n\n\n L2 = np.sqrt(sum/(self.ni*self.nj*self.nk));\n #print(\"iter: \"+str(it)+\", L2 = \"+str(L2))\n if (L2<tol):\n converged = True\n break\n \n if (converged==False):\n print(\"Gauss-Seidel failed to converge, L2 = \"+str(L2))\n \n return converged","def slo(self, objective=99.99):\n self.objective = objective\n return objective","def solve(self):\n\n if self.degree > 2:\n return \"The polynomial degree is strictly greater than 2, I can't solve.\"\n \n elif self.degree == 0:\n \"\"\"a * X^0 = 0\"\"\" \n a = self.all_terms[0].coefficient\n if a != 0:\n return \"The eqution has no solution\"\n else:\n return \"Every real number is a solution\"\n\n elif self.degree == 1:\n \"\"\"a * X^1 + b * X^0 = 0\"\"\"\n a = self.all_terms[1].coefficient\n b = self.all_terms[0].coefficient\n return formula.linear(a, b)\n\n elif self.degree == 2:\n \"\"\"a * X^2 + b * X^1 + c * X^0 = 0\"\"\"\n a = self.all_terms[2].coefficient\n b = self.all_terms[1].coefficient\n c = self.all_terms[0].coefficient\n discriminant = (b ** 2) - (4 * a * c)\n two_a = 2 * a\n if discriminant == 0:\n return formula.linear(two_a, b)\n else:\n if discriminant > 0:\n return formula.quadratic(two_a, b, discriminant)\n else:\n return formula.quadratic(two_a, b, discriminant, simple=False)","def compute(self, solver=\"cbc\", **kwargs):\n self.table2es()\n logging.info(\"Creating the linear model...\")\n model = solph.Model(self.es)\n logging.info(\"Done. Optimise the model.\")\n self.solve(model, solver=solver, **kwargs)","def solver_objects(\n kappa, f, u_D, Nx, Ny, degree=1,\n linear_solver='Krylov', # Alt: 'direct'\n abs_tol=1E-5, # Absolute tolerance in Krylov solver\n rel_tol=1E-3, # Relative tolerance in Krylov solver\n max_iter=1000, # Max no of iterations in Krylov solver\n log_level=PROGRESS, # Amount of solver output\n dump_parameters=False, # Write out parameter database?\n ):\n # Create mesh and define function space\n mesh = UnitSquareMesh(Nx, Ny)\n V = FunctionSpace(mesh, 'P', degree)\n\n def boundary(x, on_boundary):\n return on_boundary\n\n bc = DirichletBC(V, u_D, boundary)\n\n # Define variational problem\n u = TrialFunction(V)\n v = TestFunction(V)\n a = kappa*dot(grad(u), grad(v))*dx\n L = f*v*dx\n\n # Compute solution\n u = Function(V)\n problem = LinearVariationalProblem(a, L, u, bc)\n solver = LinearVariationalSolver(problem)\n\n if linear_solver == 'Krylov':\n solver.parameters['linear_solver'] = 'gmres'\n solver.parameters['preconditioner'] = 'ilu'\n prm = solver.parameters['krylov_solver'] # short form\n prm['absolute_tolerance'] = abs_tol\n prm['relative_tolerance'] = rel_tol\n prm['maximum_iterations'] = max_iter\n print(parameters['linear_algebra_backend'])\n set_log_level(log_level)\n if dump_parameters:\n info(parameters, True)\n solver_parameters = {'linear_solver': 'gmres',\n 'preconditioner': 'ilu'}\n else:\n solver_parameters = {'linear_solver': 'lu'}\n\n solver.solve()\n return u","def get_objective(self, objtype=None, get_value=True):\n if objtype is None:\n # Find active objective\n if self.act_objective is not None:\n obj = self.act_objective\n else:\n raise ValueError('No active objective found.')\n\n else:\n assert objtype in self.objectives.keys(), 'Requested objective does not exist. Please choose from {}'.format(\n self.objectives.keys())\n obj = self.objectives[objtype]\n\n if get_value:\n return value(obj)\n else:\n return obj","def get_rhs(self, mol_orbs, task, ops):\n if 'x' in ops or 'y' in ops or 'z' in ops:\n prop = {k: v for k, v in zip('xyz', self.get_dipole(task))}\n den = mol_orbs.get_density(task.molecule)\n da = den.alpha_to_numpy(0)\n db = den.beta_to_numpy(0)\n D = da + db\n S = self.get_overlap(task)\n mo = mol_orbs.alpha_to_numpy()\n\n matrices = tuple([mo.T@(S@D@prop[p].T - prop[p].T@D@S)@mo for p in ops])\n gradients = tuple([self.mat2vec(m, mol_orbs, task) for m in matrices])\n return gradients","def objective(self, params, x, data):\n # make residual per data set\n ndata, nx = data.shape\n resid = 0.0*data[:]\n resid[0, :] = data[0, :] - self.thermo(params, x)\n resid[1, :] = data[1, :] - self.density(params, x)\n # now flatten this to a 1D array, as minimize() needs\n return resid.flatten()","def objective(self,w):\n diffs = self.get_y_times_diffs(self.get_split_weights(w))\n #print diffs, sigmoid(diffs)\n obj = -np.sum(np.log(sigmoid(diffs))) #negative, since minimising\n # regularisation\n obj += 0.5 * self.alpha * np.dot(w[:self.interp_index[0]], w[:self.interp_index[0]])\n return obj","def _dualize(self, block, unfixed=[]):\n #\n # Collect linear terms from the block\n #\n A, b_coef, c_rhs, c_sense, d_sense, vnames, cnames, v_domain = collect_linear_terms(block, unfixed)\n #\n # Construct the block\n #\n if isinstance(block, Model):\n dual = ConcreteModel()\n else:\n dual = Block()\n for v, is_indexed in vnames:\n if is_indexed:\n setattr(dual, v+'_Index', Set(dimen=None))\n setattr(dual, v, Var(getattr(dual, v+'_Index')))\n else:\n setattr(dual, v, Var())\n for cname, is_indexed in cnames:\n if is_indexed:\n setattr(dual, cname+'_Index', Set(dimen=None))\n setattr(dual, cname, Constraint(getattr(dual, cname+'_Index')))\n setattr(dual, cname+'_lower_', Var(getattr(dual, cname+'_Index')))\n setattr(dual, cname+'_upper_', Var(getattr(dual, cname+'_Index')))\n else:\n setattr(dual, cname, Constraint())\n setattr(dual, cname+'_lower_', Var())\n setattr(dual, cname+'_upper_', Var())\n dual.construct()\n #\n # Add variables\n #\n # TODO: revisit this hack. We shouldn't be calling\n # _getitem_when_not_present()\n #\n for name, ndx in b_coef:\n v = getattr(dual, name)\n if not ndx in v:\n v._getitem_when_not_present(ndx)\n #\n # Construct the objective\n #\n if d_sense == minimize:\n dual.o = Objective(expr=sum(- b_coef[name,ndx]*getattr(dual,name)[ndx] for name,ndx in b_coef), sense=d_sense)\n else:\n dual.o = Objective(expr=sum(b_coef[name,ndx]*getattr(dual,name)[ndx] for name,ndx in b_coef), sense=d_sense)\n #\n # Construct the constraints\n #\n for cname in A:\n c = getattr(dual, cname)\n c_index = getattr(dual, cname+\"_Index\") if c.is_indexed() else None\n for ndx,terms in iteritems(A[cname]):\n if not c_index is None and not ndx in c_index:\n c_index.add(ndx)\n expr = 0\n for term in terms:\n v = getattr(dual,term.var)\n if not term.ndx in v:\n v.add(term.ndx)\n expr += term.coef * v[term.ndx]\n if not (cname, ndx) in c_rhs:\n c_rhs[cname, ndx] = 0.0\n if c_sense[cname,ndx] == 'e':\n c.add(ndx, expr - c_rhs[cname,ndx] == 0)\n elif c_sense[cname,ndx] == 'l':\n c.add(ndx, expr - c_rhs[cname,ndx] <= 0)\n else:\n c.add(ndx, expr - c_rhs[cname,ndx] >= 0)\n for (name, ndx), domain in iteritems(v_domain):\n v = getattr(dual, name)\n flag = type(ndx) is tuple and (ndx[-1] == 'lb' or ndx[-1] == 'ub')\n if domain == 1:\n if flag:\n v[ndx].domain = NonNegativeReals\n else:\n v.domain = NonNegativeReals\n elif domain == -1:\n if flag:\n v[ndx].domain = NonPositiveReals\n else:\n v.domain = NonPositiveReals\n else:\n if flag:\n # TODO: verify that this case is possible\n v[ndx].domain = Reals\n else:\n v.domain = Reals\n return dual","def check_primal_and_dual_solutions(self):\r\n\r\n # Solve primal and dual models\r\n self.primal.solve_model(self.m_p)\r\n self.dual.solve_model(self.m_d)\r\n\r\n # Elements to check\r\n elements = {\r\n 'power - existing thermal':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_THERMAL', 'is_dual': True}\r\n },\r\n 'power - existing wind':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_WIND', 'is_dual': True}\r\n },\r\n 'power - existing solar':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_SOLAR', 'is_dual': True}\r\n },\r\n 'power - existing hydro':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_HYDRO', 'is_dual': True}\r\n },\r\n # 'charging power - existing storage':\r\n # {\r\n # 'primal': {'id': 'p_in', 'is_dual': False},\r\n # 'dual': {'id': 'CHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\r\n # },\r\n # 'discharging power - existing storage':\r\n # {\r\n # 'primal': {'id': 'p_out', 'is_dual': False},\r\n # 'dual': {'id': 'DISCHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\r\n # },\r\n 'power - candidate thermal':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_THERMAL', 'is_dual': True}\r\n },\r\n 'power - candidate wind':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_WIND', 'is_dual': True}\r\n },\r\n 'power - candidate solar':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_SOLAR', 'is_dual': True}\r\n },\r\n 'charging power - candidate storage':\r\n {\r\n 'primal': {'id': 'p_in', 'is_dual': False},\r\n 'dual': {'id': 'CHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\r\n },\r\n 'discharging power - candidate storage':\r\n {\r\n 'primal': {'id': 'p_out', 'is_dual': False},\r\n 'dual': {'id': 'DISCHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\r\n },\r\n 'locational marginal prices':\r\n {\r\n 'primal': {'id': 'POWER_BALANCE', 'is_dual': True},\r\n 'dual': {'id': 'lamb', 'is_dual': False}\r\n },\r\n 'load shedding':\r\n {\r\n 'primal': {'id': 'p_V', 'is_dual': False},\r\n 'dual': {'id': 'LOAD_SHEDDING_POWER', 'is_dual': True}\r\n },\r\n }\r\n\r\n print(f\"Checking primal and dual solutions\")\r\n for el_id, e in elements.items():\r\n # Absolute, non-zero and max absolute difference\r\n absolute_diff, non_zero_diff, max_abs_diff = self.compare_primal_and_dual_components(e)\r\n\r\n print('---------------------------------------------------------------------------')\r\n print(f\"Primal component: {e['primal']['id']}, Dual component: {e['dual']['id']}')\")\r\n print(f'Total keys: {len(absolute_diff)}')\r\n print(f\"Max absolute difference: {max_abs_diff}\")\r\n print(f\"Non-zero diff: {non_zero_diff}\")","def solve(self):\n is_valid = self.verify_sub_matrixes()\n \n if not is_valid:\n raise ValueError((\n \"El determinante es igual a cero \"\n \"el método no puede continuar\"\n ))\n \n (lower, upper) = self.doolittle_factorization()\n\n lower_solution_vector = lower.solve_matrix(matrix=None, vector=self.vector.vector)\n lower_solution_vector.print_vector()\n upper_solution_vector = upper.solve_matrix(\n matrix=None, vector=lower_solution_vector.vector)\n upper_solution_vector.print_vector()\n\n comprobation = self.matrix.comprobation(upper_solution_vector.vector)\n return comprobation","def get_solution(self):\n return self.P_plot[-1]","def get_objective(self):\n self.objective = 0\n for r in self.routes:\n r.update_route(self.vrpdata)\n self.objective += r.distance\n # all() returns True if all elements of the iterable are true\n self.solutionValid = (all([r.tourValid for r in self.routes]) and len(self.routes) <= self.vrpdata.MaxNumVeh)\n if self.solutionValid:\n return self.objective\n return -1","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 potentialSolver2(self, max_it, tol):\n\n dx2 = 1.0/(self.dh[0]*self.dh[0]); # dx^2\n dy2 = 1.0/(self.dh[1]*self.dh[1]); # dy^2\n dz2 = 1.0/(self.dh[2]*self.dh[2]); # dz^2\n \n L2 = 0.0 # norm\n \n converged = False\n \n \n # solve potential\n for it in np.arange(1,max_it+1):\n \"\"\"\n for i in np.arange(1,self.ni-1):\n for j in np.arange(1,self.nj-1):\n for k in np.arange(1,self.nk-1):\n \"\"\" \n \"\"\"\n #standard internal open node\n phi_new = (self.rho[i][j][k]/self.EPS_0 +\\\n dx2*(self.phi[i-1][j][k] + self.phi[i+1][j][k]) +\\\n dy2*(self.phi[i][j-1][k] + self.phi[i][j+1][k]) +\\\n dz2*(self.phi[i][j][k-1] + self.phi[i][j][k+1]))/(2*dx2+2*dy2+2*dz2)\n \n # sucessive over relaxation \n self.phi[i,j,k] = self.phi[i,j,k] + 1.4*(phi_new - self.phi[i][j][k])\n \n \"\"\" \n #standard internal open node\n self.phi[1:self.ni-1,1:self.nj-1,1:self.nk-1] = \\\n (self.rho[1:self.ni-1,1:self.nj-1,1:self.nk-1]/self.EPS_0 +\\\n dx2*(self.phi[0:self.ni-2,1:self.nj-1,1:self.nk-1] + self.phi[2:self.ni,1:self.nj-1,1:self.nk-1])+\\\n dy2*(self.phi[1:self.ni-1,0:self.nj-2,1:self.nk-1] + self.phi[1:self.ni-1,2:self.nj,1:self.nk-1])+\\\n dz2*(self.phi[1:self.ni-1,1:self.nj-1,0:self.nk-2] + self.phi[1:self.ni-1,1:self.nj-1,2:self.nk]))/(2*dx2+2*dy2+2*dz2)\n \n \"\"\"\n # sucessive over relaxation \n self.phi[1:self.ni-1,1:self.nj-1,1:self.nk-1] = self.phi[1:self.ni-1,1:self.nj-1,1:self.nk-1] +\\\n 1.8*(self.phi_new[1:self.ni-1,1:self.nj-1,1:self.nk-1] - \\\n self.phi[1:self.ni-1,1:self.nj-1,1:self.nk-1])\n \"\"\"\n \n #check for convergence*/\n if it%25==0:\n sum = 0;\n \"\"\"\n for i in np.arange(1,self.ni-1):\n for j in np.arange(1,self.nj-1):\n for k in np.arange(1,self.nk-1):\n \"\"\"\n\n self.R[1:self.ni-1,1:self.nj-1,1:self.nk-1] = \\\n -self.phi[1:self.ni-1,1:self.nj-1,1:self.nk-1]*(2*dx2+2*dy2+2*dz2) +\\\n self.rho[1:self.ni-1,1:self.nj-1,1:self.nk-1]/self.EPS_0 +\\\n dx2*(self.phi[0:self.ni-2,1:self.nj-1,1:self.nk-1] + self.phi[2:self.ni,1:self.nj-1,1:self.nk-1]) +\\\n dy2*(self.phi[1:self.ni-1,0:self.nj-2,1:self.nk-1] + self.phi[1:self.ni-1,2:self.nj,1:self.nk-1]) +\\\n dz2*(self.phi[1:self.ni-1,1:self.nj-1,0:self.nk-2] + self.phi[1:self.ni-1,1:self.nj-1,2:self.nk])\n\n sum = np.sum(self.R**2)\n\n L2 = np.sqrt(sum/(self.ni*self.nj*self.nk));\n #print(\"iter: \"+str(it)+\", L2 = \"+str(L2))\n if (L2<tol):\n converged = True\n break\n\n if (converged==False):\n print(\"Gauss-Seidel failed to converge, L2 = \"+str(L2))\n \n return converged","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 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 _objective_function(self, thetas, X, Y):\n \n # Convert thetas vector to form total_cost can understand\n thetas = self.reshape_thetas(thetas, 'list')\n self.thetas = thetas\n \n # Get cost function value\n fval = self.total_cost(X, Y, thetas)\n \n # Get derivatives using back propagation\n Deltas = self.get_gradients(X, Y)\n dfval = self.reshape_thetas(Deltas, 'vector')\n \n return fval, dfval","def define_expressions(m):\r\n\r\n def dual_objective_expression_rule(_m):\r\n \"\"\"Expression for dual objective function\"\"\"\r\n\r\n # Build limits\r\n t_1 = sum(- (m.mu_2[z, y] * m.SOLAR_BUILD_LIMITS[z]) - (m.mu_3[z, y] * m.WIND_BUILD_LIMITS[z]) - (\r\n m.mu_4[z, y] * m.STORAGE_BUILD_LIMITS[z]) for z in m.Z for y in m.Y)\r\n\r\n # Min power output\r\n t_2 = sum(\r\n m.sigma_1[g, y, s, t] * m.P_MIN[g] for g in m.G.difference(m.G_STORAGE) for y in m.Y for s in m.S for t\r\n in m.T)\r\n\r\n # Max power - existing generators\r\n t_3 = sum(\r\n - m.sigma_2[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_THERM for y in m.Y for s in m.S\r\n for t in m.T)\r\n\r\n # Max power - existing wind\r\n t_4 = sum(\r\n - m.sigma_4[g, y, s, t] * m.Q_W[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_WIND for y in\r\n m.Y for s in m.S for t in m.T)\r\n\r\n # Max power - existing solar\r\n t_5 = sum(\r\n - m.sigma_6[g, y, s, t] * m.Q_S[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_SOLAR for y in\r\n m.Y for s in m.S for t in m.T)\r\n\r\n # Max power - existing hydro\r\n t_6 = sum(\r\n - m.sigma_8[g, y, s, t] * m.P_H[g, y, s, t] * (1 - m.F[g, y]) for g in m.G_E_HYDRO for y in m.Y for s in\r\n m.S for t in m.T)\r\n\r\n # Max charging power - existing storage\r\n t_7 = sum(\r\n - m.sigma_11[g, y, s, t] * m.P_IN_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_STORAGE for y in m.Y for s in\r\n m.S for t in m.T)\r\n\r\n # Max discharging power - existing storage\r\n t_8 = sum(\r\n - m.sigma_13[g, y, s, t] * m.P_OUT_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_STORAGE for y in m.Y for s in\r\n m.S for t in m.T)\r\n\r\n # Max energy - existing storage units\r\n t_9 = sum(\r\n - m.sigma_16[g, y, s, t] * m.Q_MAX[g] for g in m.G_E_STORAGE for y in m.Y for s in m.S for t in m.T)\r\n\r\n # Min energy - interval end\r\n t_10 = sum(m.sigma_18[g, y, s] * m.Q_END_MIN[g] for g in m.G_STORAGE for y in m.Y for s in m.S)\r\n\r\n # Max energy - interval end\r\n t_11 = sum(- m.sigma_19[g, y, s] * m.Q_END_MAX[g] for g in m.G_STORAGE for y in m.Y for s in m.S)\r\n\r\n # Ramp-up constraint - generators\r\n t_12 = sum(\r\n - m.sigma_20[g, y, s, t] * m.RR_UP[g] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for s in m.S\r\n for t in m.T)\r\n\r\n # Ramp-up constraint - initial power output - generators\r\n t_13 = sum(\r\n - m.sigma_20[g, y, s, m.T.first()] * m.P0[g, y, s] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y\r\n for s in m.S)\r\n\r\n # Ramp-down constraint - generators\r\n t_18 = sum(\r\n - m.sigma_23[g, y, s, t] * m.RR_DOWN[g] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for s in m.S\r\n for t in m.T)\r\n\r\n # Ramp-down constraint - initial power output - generators\r\n t_19 = sum(\r\n m.sigma_23[g, y, s, m.T.first()] * m.P0[g, y, s] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for\r\n s in m.S)\r\n\r\n # Min powerflow\r\n t_24 = sum(m.sigma_27[l, y, s, t] * m.POWERFLOW_MIN[l] for l in m.L for y in m.Y for s in m.S for t in m.T)\r\n\r\n # Max powerflow\r\n t_25 = sum(\r\n - m.sigma_28[l, y, s, t] * m.POWERFLOW_MAX[l] for l in m.L for y in m.Y for s in m.S for t in m.T)\r\n\r\n # Demand\r\n t_26 = sum(m.lamb[z, y, s, t] * m.DEMAND[z, y, s, t] for z in m.Z for y in m.Y for s in m.S for t in m.T)\r\n\r\n # Initial storage unit energy\r\n t_27 = sum(m.zeta_1[g, y, s, m.T.first()] * m.Q0[g, y, s] for g in m.G_STORAGE for y in m.Y for s in m.S)\r\n\r\n return (t_1 + t_2 + t_3 + t_4 + t_5 + t_6 + t_7 + t_8 + t_9 + t_10 + t_11 + t_12 + t_13 + t_18 + t_19 + t_24\r\n + t_25 + t_26 + t_27)\r\n\r\n # Dual objective expression\r\n m.DUAL_OBJECTIVE_EXPRESSION = Expression(rule=dual_objective_expression_rule)\r\n\r\n def scenario_revenue_rule(_m, y, s):\r\n \"\"\"Total revenue collected from wholesale electricity sales\"\"\"\r\n\r\n if y != m.Y.last():\r\n # Scaling factor\r\n scaling_factor = m.DELTA[y] * m.RHO[y, s]\r\n\r\n # Revenue from electricity sales (wholesale) = $/MWh x MWh\r\n return sum((m.lamb[z, y, s, t] / scaling_factor) * m.DEMAND[z, y, s, t] * m.RHO[y, s] for z in m.Z\r\n for t in m.T)\r\n\r\n else:\r\n # Scaling factor\r\n scaling_factor = m.DELTA[y] * m.RHO[y, s] * (1 + (1 / m.INTEREST_RATE))\r\n\r\n # Revenue from electricity sales (wholesale)\r\n return sum((m.lamb[z, y, s, t] / scaling_factor) * m.DEMAND[z, y, s, t] * m.RHO[y, s] for z in m.Z\r\n for t in m.T)\r\n\r\n # Revenue from wholesale electricity sales for each scenario\r\n m.SCENARIO_REVENUE = Expression(m.Y, m.S, rule=scenario_revenue_rule)\r\n\r\n def scenario_average_price_rule(_m, y, s):\r\n \"\"\"Average price for a given scenario\"\"\"\r\n\r\n return m.SCENARIO_REVENUE[y, s] / m.SCENARIO_DEMAND[y, s]\r\n\r\n # Scenario demand weighted average wholesale price\r\n m.SCENARIO_AVERAGE_PRICE = Expression(m.Y, m.S, rule=scenario_average_price_rule)\r\n\r\n def year_average_price_rule(_m, y):\r\n \"\"\"Average price for a given year\"\"\"\r\n\r\n # Total revenue\r\n return sum(m.SCENARIO_REVENUE[y, s] for s in m.S) / sum(m.SCENARIO_DEMAND[y, s] for s in m.S)\r\n\r\n # Year demand weighted average wholesale price\r\n m.YEAR_AVERAGE_PRICE = Expression(m.Y, rule=year_average_price_rule)\r\n\r\n return m","def rhs(self):\n return self._args[1]","def objective(self, x):\n pass","def objective(self, x):\n pass","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 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 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 get_sol_value(self, mesh_num, el_num, Y, x, count_lift=True):\n m = self._meshes[mesh_num]\n e = m.elements[el_num]\n val = 0.\n for i, g in enumerate(e.dofs):\n if g == -1:\n if count_lift:\n val += e.shape_function(i, x)*e.get_dirichlet_value(i)\n else:\n val += e.shape_function(i, x)*Y[g]\n #print val, e.dofs\n return val","def solve_return_conv(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 convergence_criteria = exit_info['val']\n\n print (exit_info['msg'])\n print (\"Exiting with ||grad[L]|| = {0:e}\".format(convergence_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, convergence_criteria]","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 _solve(self) -> CasADiArrayType:\n solver_input = {\"x0\": self.x0, \"p\": self.p}\n if self.opt_type in CONSTRAINED_OPT:\n solver_input[\"lbg\"] = self._lbg\n solver_input[\"ubg\"] = self._ubg\n self._solution = self._solver(**solver_input)\n self._stats = self._solver.stats()\n self._stats[\"solution\"] = self._solution\n return self._solution[\"x\"]","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_instance(instance, solver='glpk', mipgap=0.01):\n solver = ReferenceModel.SolverFactory(solver)\n solver.options['mipgap'] = mipgap\n instance.preprocess()\n _results = solver.solve(instance, suffixes=['dual'])\n instance.solutions.store_to(_results)\n return(_results)","def set_obj_fun(self):\n\n # disable button \"Edit Objective Function\"\n # self.ui.pb_edit_obj_func.setEnabled(False)\n a_str = str(self.le_a.text())\n state_a = self.is_le_addr_ok(self.le_a)\n b_str = str(self.le_b.text())\n state_b = self.is_le_addr_ok(self.le_b)\n c_str = str(self.le_c.text())\n state_c = self.is_le_addr_ok(self.le_c)\n func = str(self.le_of.text())\n def get_value_exp():\n A = 0.\n B = 0.\n C = 0.\n if state_a:\n A = self.mi.get_value(a_str)\n if state_b:\n B = self.mi.get_value(b_str)\n if state_c:\n C = self.mi.get_value(c_str)\n if func == \"\":\n return 0\n return eval(func)\n\n self.objective_func = get_value_exp\n\n return self.objective_func","def tot_grad_obj(self,x0,gradient):\n tmp = self.tot_objective(self.rebuild_param(x0,**self.fixed))\n obj = tmp[0]\n if gradient:\n grad =self.fix_par(tmp[1], **self.fixed)[0]\n return obj,grad\n else:\n return obj","def lu_solve(self, rhs):\n if not self.domain == rhs.domain:\n raise DMDomainError(\"Domains must match: %s != %s\" % (self.domain, rhs.domain))\n\n # XXX: As for inv we should consider whether to return a matrix over\n # over an associated field or attempt to find a solution in the ring.\n # For now we follow the existing DomainMatrix convention...\n if not self.domain.is_Field:\n raise DMDomainError(\"Field expected, got %s\" % self.domain)\n\n m, n = self.shape\n j, k = rhs.shape\n if m != j:\n raise DMShapeError(\"Matrix size mismatch: %s * %s vs %s * %s\" % (m, n, j, k))\n sol_shape = (n, k)\n\n # XXX: The Flint solve method only handles square matrices. Probably\n # Flint has functions that could be used to solve non-square systems\n # but they are not exposed in python-flint yet. Alternatively we could\n # put something here using the features that are available like rref.\n if m != n:\n return self.to_ddm().lu_solve(rhs.to_ddm()).to_dfm()\n\n try:\n sol = self.rep.solve(rhs.rep)\n except ZeroDivisionError:\n raise DMNonInvertibleMatrixError(\"Matrix det == 0; not invertible.\")\n\n return self._new(sol, sol_shape, self.domain)","def calc_obj_value(ts11,s11):\n pe = [] # point errors\n te = 0 # total error\n\n for i in range(0, len(TARGET_S11)):\n pe.append((TARGET_S11[i]-s11[i])**2/(TARGET_S11[i])**2)\n te+=pe[i]\n\n ae = te/len(pe) # average error\n obj = 1/abs(ae) # objective value\n\n return (obj, pe)","def eval(self, Vobj):\n try:\n return Vobj.evaluated_on(self)\n except AttributeError:\n return self.A() * Vobj + self.b()","def solve(self, solver):\n solver.solve()","def dual_problem(\n states: list[np.ndarray], probs: list[float] = None, dist_method=\"min-error\"\n) -> float:\n constraints = []\n meas = []\n\n dim_x, _ = states[0].shape\n\n y_var = cvxpy.Variable((dim_x, dim_x), hermitian=True)\n objective = cvxpy.Minimize(cvxpy.trace(cvxpy.real(y_var)))\n\n dim = int(np.log2(dim_x))\n dim_list = [2] * int(np.log2(dim_x))\n sys_list = list(range(1, dim, 2))\n # dim_list = [3, 3]\n\n if dist_method == \"min-error\":\n for i, _ in enumerate(states):\n meas.append(cvxpy.Variable((dim_x, dim_x), PSD=True))\n constraints.append(\n cvxpy.real(y_var - probs[i] * states[i])\n >> partial_transpose(meas[i], sys=sys_list, dim=dim_list)\n )\n\n if dist_method == \"unambiguous\":\n for j, _ in enumerate(states):\n sum_val = 0\n for i, _ in enumerate(states):\n if i != j:\n sum_val += cvxpy.real(cvxpy.Variable()) * probs[i] * states[i]\n meas.append(cvxpy.Variable((dim_x, dim_x), PSD=True))\n constraints.append(\n cvxpy.real(y_var - probs[j] * states[j] + sum_val)\n >> partial_transpose(meas[j], sys=sys_list, dim=dim_list)\n )\n\n meas.append(cvxpy.Variable((dim_x, dim_x), PSD=True))\n constraints.append(\n cvxpy.real(y_var) >> partial_transpose(meas[-1], sys=sys_list, dim=dim_list)\n )\n\n problem = cvxpy.Problem(objective, constraints)\n sol_default = problem.solve()\n\n # print(np.around(y_var.value, decimals=3))\n\n return sol_default","def evaluate_rhs(self, t, y, ydot=np.empty(0)):\n raise NotImplementedError","def objective(self, adjacency=None, R=None):\n raise NotImplementedError()","def calculateCost(self,sol,weights):\n\t\treturn sum([x.value*y if x != None else 0 \\\n\t\t\t\t\tfor x,y in zip(sol,weights)])","def __call__(self, r=None, t=None, o=None):\n return self._sol(as_o(r,t,o))[0]","def _get_obj(self, weights, alpha_vector):\r\n assert (len(alpha_vector.columns) == 1)\r\n return cvx.Minimize(-alpha_vector.T.values[0]*weights)","def find_solution(self):\r\n for solution in self.solutions:\r\n if self.fitting_function.is_legal_solution(solution):\r\n return solution\r\n return None","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 get_solution(self):\n objective_value = self.solver.objective.value\n status = self.solver.status\n variables = pd.Series(data=self.solver.primal_values)\n\n fluxes = empty(len(self.reactions))\n rxn_index = list()\n var_primals = self.solver.primal_values\n\n for (i, rxn) in enumerate(self.reactions):\n rxn_index.append(rxn.id)\n fluxes[i] = var_primals[rxn.id] - var_primals[rxn.reverse_id]\n\n fluxes = pd.Series(index=rxn_index, data=fluxes, name=\"fluxes\")\n\n solution = Solution(objective_value=objective_value, status=status,\n fluxes=fluxes)\n\n self.solution = solution\n\n self.solution.raw = variables\n\n self.\\\n solution.values = pd.DataFrame.from_dict({k:v.unscaled\n for k,v in self._var_dict.items()},\n orient = 'index')\n\n return solution","def _get_obj(self, out_pattern):\n x_tilde = self._get_x_tilde(out_pattern)\n sig_u_tilde = amath.exp(self._get_u_tilde(log_sigmoid=True))\n V_times_sig_u_tilde = sig_u_tilde.dot(self.variables[\"V\"])\n\n obj = - 0.5 * self.out_dim * amath.log(2.0 * amath.pi) \\\n - 0.5 * amath.inner(x_tilde, x_tilde)\n obj = obj + amath.inner(x_tilde, V_times_sig_u_tilde)\n obj = obj - 0.5 * amath.inner(V_times_sig_u_tilde, V_times_sig_u_tilde)\n obj = obj - 0.5 * amath.inner(sig_u_tilde,\n (1.0 - sig_u_tilde)\n * amath.diag(self.variables[\"V\"].dot(\n self.variables[\"V\"].transpose())))\n return obj","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 _create_result_object(self, rclass, jsol=None):\n res = rclass(self.model)\n\n # Process JSON solution\n #self.context.log(3, \"JSON data:\\n\", jsol)\n self.last_json_result = jsol\n\n # Parse JSON solution\n if jsol:\n # Parse JSON\n stime = time.time()\n jsol = parse_json_string(jsol)\n self.process_infos.incr(CpoProcessInfos.TOTAL_JSON_PARSE_TIME, time.time() - stime)\n # Build result structure\n res._add_json_solution(jsol, self.solver.expr_map)\n\n # Process Log\n if self.log_data is not None:\n res._set_solver_log(''.join(self.log_data))\n self.log_data = []\n res.process_infos.update(self.process_infos)\n return res","def evaluate_rhs(self, x, V):\n rhs = self._numeric_system(x, V, *self.model.params.values()).ravel()\n return rhs","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 getprimalsolutionnorms(self,whichsol_):\n nrmxc_ = ctypes.c_double()\n nrmxx_ = ctypes.c_double()\n nrmbarx_ = ctypes.c_double()\n res = __library__.MSK_XX_getprimalsolutionnorms(self.__nativep,whichsol_,ctypes.byref(nrmxc_),ctypes.byref(nrmxx_),ctypes.byref(nrmbarx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nrmxc_ = nrmxc_.value\n _nrmxc_return_value = nrmxc_\n nrmxx_ = nrmxx_.value\n _nrmxx_return_value = nrmxx_\n nrmbarx_ = nrmbarx_.value\n _nrmbarx_return_value = nrmbarx_\n return (_nrmxc_return_value,_nrmxx_return_value,_nrmbarx_return_value)","def get_best_solution(self):\n if not self.tours:\n raise Exception('No solution has been computed yet')\n scores = {s:get_cost(self.tours[s],self) for s in self.tours}\n best = min(scores,key=scores.get)\n print('The best solution is given by {} with score {}'.format(best,scores[best]))\n return self.tours[best]"],"string":"[\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 getprimalobj(self,whichsol_):\\n primalobj_ = ctypes.c_double()\\n res = __library__.MSK_XX_getprimalobj(self.__nativep,whichsol_,ctypes.byref(primalobj_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n primalobj_ = primalobj_.value\\n _primalobj_return_value = primalobj_\\n return (_primalobj_return_value)\",\n \"def getprimalobj(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getprimalobj(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _primalobj_return_value = resargs\\n return _primalobj_return_value\",\n \"def dual_objective(self, dual_coeffs):\\n primal = self.model._sdca_primal_dual_relation(self.l_l2sq,\\n dual_coeffs)\\n prox_l2_value = 0.5 * self.l_l2sq * np.linalg.norm(primal) ** 2\\n return self.model.dual_loss(dual_coeffs) - prox_l2_value\",\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 objective_val(self):\\n return self.m.ObjVal\",\n \"def get_sol(self):\",\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 getdualsolutionnorms(self,whichsol_):\\n nrmy_ = ctypes.c_double()\\n nrmslc_ = ctypes.c_double()\\n nrmsuc_ = ctypes.c_double()\\n nrmslx_ = ctypes.c_double()\\n nrmsux_ = ctypes.c_double()\\n nrmsnx_ = ctypes.c_double()\\n nrmbars_ = ctypes.c_double()\\n res = __library__.MSK_XX_getdualsolutionnorms(self.__nativep,whichsol_,ctypes.byref(nrmy_),ctypes.byref(nrmslc_),ctypes.byref(nrmsuc_),ctypes.byref(nrmslx_),ctypes.byref(nrmsux_),ctypes.byref(nrmsnx_),ctypes.byref(nrmbars_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nrmy_ = nrmy_.value\\n _nrmy_return_value = nrmy_\\n nrmslc_ = nrmslc_.value\\n _nrmslc_return_value = nrmslc_\\n nrmsuc_ = nrmsuc_.value\\n _nrmsuc_return_value = nrmsuc_\\n nrmslx_ = nrmslx_.value\\n _nrmslx_return_value = nrmslx_\\n nrmsux_ = nrmsux_.value\\n _nrmsux_return_value = nrmsux_\\n nrmsnx_ = nrmsnx_.value\\n _nrmsnx_return_value = nrmsnx_\\n nrmbars_ = nrmbars_.value\\n _nrmbars_return_value = nrmbars_\\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 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 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 objective(self):\\n return self._objective\",\n \"def _get_rhs(self, thermo, chem_pot_array, beta):\\n num_singlets = len(self._ground_states) - 1\\n matrix = np.zeros((num_singlets, num_singlets))\\n energy_vector = np.zeros(num_singlets)\\n for i in range(num_singlets):\\n for j in range(num_singlets):\\n ref_singlet = thermo[0][get_singlet_name(\\n self._singlet_names[j])]\\n singlet = thermo[i +\\n 1][get_singlet_name(self._singlet_names[j])]\\n matrix[i, j] = ref_singlet - singlet\\n ref_energy = thermo[0][\\\"energy\\\"] / \\\\\\n len(self._ground_states[0][\\\"atoms\\\"])\\n energy = thermo[i + 1][\\\"energy\\\"] / \\\\\\n len(self._ground_states[i + 1][\\\"atoms\\\"])\\n energy_vector[i] = ref_energy - energy\\n inv_matrix = np.linalg.inv(matrix)\\n rhs = inv_matrix.dot(energy_vector) / beta - chem_pot_array / beta\\n return rhs\",\n \"def get_solution(self):\\r\\n return self.solution\",\n \"def get_objective(self, X_v, U_v, X_last_p, U_last_p):\\n objective = None\\n return objective\",\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 dual(self):\\n return dual_array(self)\",\n \"def recover_dual_variables(task, sol, inverse_data):\\n dual_vars = dict()\\n\\n # Dual variables for the inequality constraints\\n suc_len = sum(ell for _, ell in inverse_data['suc_slacks'])\\n if suc_len > 0:\\n suc = [0.] * suc_len\\n task.getsucslice(sol, 0, suc_len, suc)\\n dual_vars.update(MOSEK._parse_dual_var_block(suc, inverse_data['suc_slacks']))\\n\\n # Dual variables for the original equality constraints\\n y_len = sum(ell for _, ell in inverse_data['y_slacks'])\\n if y_len > 0:\\n y = [0.] * y_len\\n task.getyslice(sol, suc_len, suc_len + y_len, y)\\n y = [-val for val in y]\\n dual_vars.update(MOSEK._parse_dual_var_block(y, inverse_data['y_slacks']))\\n\\n # Dual variables for SOC and EXP constraints\\n snx_len = sum(ell for _, ell in inverse_data['snx_slacks'])\\n if snx_len > 0:\\n snx = np.zeros(snx_len)\\n task.getsnxslice(sol, inverse_data['n0'], inverse_data['n0'] + snx_len, snx)\\n dual_vars.update(MOSEK._parse_dual_var_block(snx, inverse_data['snx_slacks']))\\n\\n # Dual variables for PSD constraints\\n for j, (id, dim) in enumerate(inverse_data['psd_dims']):\\n sj = [0.] * (dim * (dim + 1) // 2)\\n task.getbarsj(sol, j, sj)\\n dual_vars[id] = vectorized_lower_tri_to_mat(sj, dim)\\n\\n # Now that all dual variables have been recovered, find those corresponding\\n # to the exponential cone, and permute the entries to reflect the CVXPY\\n # standard for the exponential cone.\\n for con in inverse_data['constraints']:\\n if isinstance(con, ExpCone):\\n cid = con.id\\n perm = expcone_permutor(con.num_cones(), MOSEK.EXP_CONE_ORDER)\\n dual_vars[cid] = dual_vars[cid][perm]\\n return dual_vars\",\n \"def dual_objective_expression_rule(_m):\\r\\n\\r\\n # Build limits\\r\\n t_1 = sum(- (m.mu_2[z, y] * m.SOLAR_BUILD_LIMITS[z]) - (m.mu_3[z, y] * m.WIND_BUILD_LIMITS[z]) - (\\r\\n m.mu_4[z, y] * m.STORAGE_BUILD_LIMITS[z]) for z in m.Z for y in m.Y)\\r\\n\\r\\n # Min power output\\r\\n t_2 = sum(\\r\\n m.sigma_1[g, y, s, t] * m.P_MIN[g] for g in m.G.difference(m.G_STORAGE) for y in m.Y for s in m.S for t\\r\\n in m.T)\\r\\n\\r\\n # Max power - existing generators\\r\\n t_3 = sum(\\r\\n - m.sigma_2[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_THERM for y in m.Y for s in m.S\\r\\n for t in m.T)\\r\\n\\r\\n # Max power - existing wind\\r\\n t_4 = sum(\\r\\n - m.sigma_4[g, y, s, t] * m.Q_W[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_WIND for y in\\r\\n m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Max power - existing solar\\r\\n t_5 = sum(\\r\\n - m.sigma_6[g, y, s, t] * m.Q_S[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_SOLAR for y in\\r\\n m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Max power - existing hydro\\r\\n t_6 = sum(\\r\\n - m.sigma_8[g, y, s, t] * m.P_H[g, y, s, t] * (1 - m.F[g, y]) for g in m.G_E_HYDRO for y in m.Y for s in\\r\\n m.S for t in m.T)\\r\\n\\r\\n # Max charging power - existing storage\\r\\n t_7 = sum(\\r\\n - m.sigma_11[g, y, s, t] * m.P_IN_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_STORAGE for y in m.Y for s in\\r\\n m.S for t in m.T)\\r\\n\\r\\n # Max discharging power - existing storage\\r\\n t_8 = sum(\\r\\n - m.sigma_13[g, y, s, t] * m.P_OUT_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_STORAGE for y in m.Y for s in\\r\\n m.S for t in m.T)\\r\\n\\r\\n # Max energy - existing storage units\\r\\n t_9 = sum(\\r\\n - m.sigma_16[g, y, s, t] * m.Q_MAX[g] for g in m.G_E_STORAGE for y in m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Min energy - interval end\\r\\n t_10 = sum(m.sigma_18[g, y, s] * m.Q_END_MIN[g] for g in m.G_STORAGE for y in m.Y for s in m.S)\\r\\n\\r\\n # Max energy - interval end\\r\\n t_11 = sum(- m.sigma_19[g, y, s] * m.Q_END_MAX[g] for g in m.G_STORAGE for y in m.Y for s in m.S)\\r\\n\\r\\n # Ramp-up constraint - generators\\r\\n t_12 = sum(\\r\\n - m.sigma_20[g, y, s, t] * m.RR_UP[g] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for s in m.S\\r\\n for t in m.T)\\r\\n\\r\\n # Ramp-up constraint - initial power output - generators\\r\\n t_13 = sum(\\r\\n - m.sigma_20[g, y, s, m.T.first()] * m.P0[g, y, s] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y\\r\\n for s in m.S)\\r\\n\\r\\n # Ramp-down constraint - generators\\r\\n t_18 = sum(\\r\\n - m.sigma_23[g, y, s, t] * m.RR_DOWN[g] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for s in m.S\\r\\n for t in m.T)\\r\\n\\r\\n # Ramp-down constraint - initial power output - generators\\r\\n t_19 = sum(\\r\\n m.sigma_23[g, y, s, m.T.first()] * m.P0[g, y, s] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for\\r\\n s in m.S)\\r\\n\\r\\n # Min powerflow\\r\\n t_24 = sum(m.sigma_27[l, y, s, t] * m.POWERFLOW_MIN[l] for l in m.L for y in m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Max powerflow\\r\\n t_25 = sum(\\r\\n - m.sigma_28[l, y, s, t] * m.POWERFLOW_MAX[l] for l in m.L for y in m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Demand\\r\\n t_26 = sum(m.lamb[z, y, s, t] * m.DEMAND[z, y, s, t] for z in m.Z for y in m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Initial storage unit energy\\r\\n t_27 = sum(m.zeta_1[g, y, s, m.T.first()] * m.Q0[g, y, s] for g in m.G_STORAGE for y in m.Y for s in m.S)\\r\\n\\r\\n return (t_1 + t_2 + t_3 + t_4 + t_5 + t_6 + t_7 + t_8 + t_9 + t_10 + t_11 + t_12 + t_13 + t_18 + t_19 + t_24\\r\\n + t_25 + t_26 + t_27)\",\n \"def define_objective(m):\\r\\n\\r\\n # Dual objective function\\r\\n m.OBJECTIVE = Objective(expr=m.DUAL_OBJECTIVE_EXPRESSION, sense=maximize)\\r\\n\\r\\n return m\",\n \"def _objfunc(self, dv_dict):\\n\\n fail = 0\\n metadata = self.metadata\\n system = self.root\\n\\n try:\\n for name in self.indep_list:\\n self.set_desvar(name, dv_dict[name])\\n\\n # Execute the model\\n #print(\\\"Setting DV\\\")\\n #print(dv_dict)\\n\\n self.iter_count += 1\\n update_local_meta(metadata, (self.iter_count,))\\n\\n try:\\n with self.root._dircontext:\\n system.solve_nonlinear(metadata=metadata)\\n\\n # Let the optimizer try to handle the error\\n except AnalysisError:\\n fail = 1\\n\\n func_dict = self.get_objectives() # this returns a new OrderedDict\\n func_dict.update(self.get_constraints())\\n\\n # Record after getting obj and constraint to assure they have\\n # been gathered in MPI.\\n self.recorders.record_iteration(system, metadata)\\n\\n # Get the double-sided constraint evaluations\\n #for key, con in iteritems(self.get_2sided_constraints()):\\n # func_dict[name] = np.array(con.evaluate(self.parent))\\n\\n except Exception as msg:\\n tb = traceback.format_exc()\\n\\n # Exceptions seem to be swallowed by the C code, so this\\n # should give the user more info than the dreaded \\\"segfault\\\"\\n print(\\\"Exception: %s\\\" % str(msg))\\n print(70*\\\"=\\\",tb,70*\\\"=\\\")\\n fail = 1\\n func_dict = {}\\n\\n #print(\\\"Functions calculated\\\")\\n #print(func_dict)\\n return func_dict, fail\",\n \"def solve(self):\\n self.m.optimize()\\n if self.m.status == GRB.OPTIMAL:\\n self.solution = self.sol_as_mat()\\n return self.solution\",\n \"def objective(self):\\n pass\",\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 get_objective(self, sampler=None):\\n def objective(params):\\n circuit = self.get_circuit(params)\\n circuit.make_cache()\\n return self.get_energy(circuit, sampler)\\n\\n def obj_expect(params):\\n circuit = self.get_circuit(params)\\n circuit.make_cache()\\n return self.get_energy_sparse(circuit)\\n\\n if sampler is not None:\\n return objective\\n if self.sparse is None:\\n self.make_sparse()\\n return obj_expect\",\n \"def return_solver(self):\\r\\n sol = ht3_solver(self.mesh)\\r\\n sol.max_T = self.max_T\\r\\n sol.d_T = self.d_T\\r\\n sol.saved_data = self.saved_data\\r\\n sol.node_map = self.node_map\\r\\n return sol\",\n \"def getprosta(self,whichsol_): # 3\\n if not isinstance(whichsol_,soltype): raise TypeError(\\\"Argument whichsol has wrong type\\\")\\n res,resargs = self.__obj.getprosta(whichsol_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _prosta_return_value = resargs\\n _prosta_return_value = prosta(_prosta_return_value)\\n return _prosta_return_value\",\n \"def get_dual_val(self, var_name, pos):\\n val = self.get_other_value(self.dual_var, var_name, pos)\\n if not self.pdv_to_csv: # if not saved to csv file\\n return val\\n else: # otherwise, we should get the file path and read from the file to array or mat\\n f_path = os.path.join(self.root_dir, 'dual_vars', var_name, str(val) + '.csv')\\n df = pd.read_csv(f_path, header = None) # first read csv file into a pandas data frame and then transform\\n return np.asmatrix(df.values)\",\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 getObjective(self, *args):\\n return _libsbml.FbcModelPlugin_getObjective(self, *args)\",\n \"def get_sol_value(self):\\n return float(self.data[2])\",\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 solve(self, model, solver=\\\"cbc\\\", with_duals=False, **solver_kwargs):\\n logging.info(\\\"Optimising using %s.\\\", solver)\\n\\n solver_kwargs[\\\"tee\\\"] = solver_kwargs.get(\\\"tee\\\", True)\\n\\n self.meta[\\\"solph_version\\\"] = solph.__version__\\n self.meta[\\\"solver\\\"] = solver\\n self.meta[\\\"solver_start\\\"] = datetime.datetime.now()\\n\\n if with_duals:\\n model.receive_duals()\\n\\n model.solve(solver=solver, solve_kwargs=solver_kwargs)\\n\\n self.meta[\\\"solver_end\\\"] = datetime.datetime.now()\\n\\n self.es.results[\\\"main\\\"] = solph.processing.results(model)\\n self.meta.update(solph.processing.meta_results(model))\\n self.es.results[\\\"param\\\"] = solph.processing.parameter_as_dict(self.es)\\n self.es.results[\\\"meta\\\"] = self.meta\\n\\n self.results = self.es.results\",\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 get_approx_solution(self, solver):\\n tour = solver.solve(self)\\n print('The cost is {}.'.format(get_cost(tour,self)))\\n self.tours[solver.__class__.__name__] = tour\\n return tour\",\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 get_objective(\\n self,\\n fun: bool = True,\\n res: bool = True,\\n max_sensi_order: int = 2,\\n fim_for_hess: bool = False,\\n ):\\n if fim_for_hess:\\n fhess = self.get_ffim()\\n else:\\n fhess = self.get_fs2nllh()\\n\\n return pypesto.Objective(\\n fun=self.get_fnllh() if fun else None,\\n grad=self.get_fsnllh() if fun and max_sensi_order >= 1 else None,\\n hess=fhess if fun and max_sensi_order >= 2 else None,\\n res=self.get_fres() if res else None,\\n sres=self.get_fsres() if res and max_sensi_order >= 1 else None,\\n )\",\n \"def rhs(self, inv_method=None):\\n if inv_method is None:\\n self._rhs = self.mass_matrix_full.LUsolve(self.forcing_full)\\n else:\\n self._rhs = (self.mass_matrix_full.inv(inv_method,\\n try_block_diag=True) * self.forcing_full)\\n return self._rhs\",\n \"def fetch(self):\\n return self.sol\",\n \"def get_best_individual(self):\\n return self._best_indv\",\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 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 get_solution(self):\\n return self._generate_solution()\",\n \"def get_dual_change_value(self, var_change_name, pos):\\n return self.get_other_value(self.dual_var_change, var_change_name, pos)\",\n \"def compute_dual_effective_cost_scipy(w: types.WorkloadSpace, workload_mat: types.WorkloadMatrix,\\n cost_per_buffer: types.StateSpace,\\n method: str = 'revised simplex') \\\\\\n -> Tuple[Optional[types.WorkloadSpace], types.StateSpace, float]:\\n # We remove the columns of the workload matrix which are all zeros because they correspond to\\n # inequalities which are always satisfied as we assume that the cost per buffer is always\\n # positive. This is needed for scipy linprog because if not done, the method will return\\n # status 4: Numerical difficulties encountered. The other methods may not need this step\\n non_zeros_columns = np.logical_not(np.all(np.isclose(workload_mat, 0.), axis=0))\\n workload_mat = workload_mat[:, non_zeros_columns]\\n cost_per_buffer = cost_per_buffer[non_zeros_columns, :]\\n res = linprog(c=-w, A_ub=workload_mat.T, b_ub=cost_per_buffer, bounds=(None, None),\\n method=method)\\n if res.success:\\n eff_cost = - res.fun # Dual problem is: max c_bar @ w; while here we do: min - c_bar @ w\\n c_bar = res.x[:, None]\\n else:\\n c_bar = None\\n if res.status == 2: # Problem appears to be infeasible\\n eff_cost = None\\n elif res.status == 3: # Problem appears to be unbounded\\n eff_cost = np.inf\\n elif res.status == 4: # We should not return anything\\n raise exceptions.ScipyLinprogStatusError(\\\"Scipy encountered numerical difficulties\\\")\\n return c_bar, None, eff_cost\",\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 potentialSolver(self, max_it, tol):\\n\\n dx2 = 1.0/(self.dh[0]*self.dh[0]); # dx^2\\n dy2 = 1.0/(self.dh[1]*self.dh[1]); # dy^2\\n dz2 = 1.0/(self.dh[2]*self.dh[2]); # dz^2\\n \\n L2 = 0.0 # norm\\n \\n converged = False\\n \\n # solve potential\\n for it in np.arange(1,max_it+1):\\n for i in np.arange(1,self.ni-1):\\n for j in np.arange(1,self.nj-1):\\n for k in np.arange(1,self.nk-1):\\n #standard internal open node\\n phi_new = (self.rho[i][j][k]/self.EPS_0 +\\\\\\n dx2*(self.phi[i-1][j][k] + self.phi[i+1][j][k]) +\\\\\\n dy2*(self.phi[i][j-1][k] + self.phi[i][j+1][k]) +\\\\\\n dz2*(self.phi[i][j][k-1] + self.phi[i][j][k+1]))/(2*dx2+2*dy2+2*dz2)\\n \\n # sucessive over relaxation \\n self.phi[i,j,k] = self.phi[i,j,k] + 1.4*(phi_new - self.phi[i][j][k])\\n \\n\\n #check for convergence*/\\n if it%25==0:\\n sum = 0;\\n for i in np.arange(1,self.ni-1):\\n for j in np.arange(1,self.nj-1):\\n for k in np.arange(1,self.nk-1):\\n\\n R = -self.phi[i][j][k]*(2*dx2+2*dy2+2*dz2) +\\\\\\n self.rho[i][j][k]/self.EPS_0 +\\\\\\n dx2*(self.phi[i-1][j][k] + self.phi[i+1][j][k]) +\\\\\\n dy2*(self.phi[i][j-1][k] + self.phi[i][j+1][k]) +\\\\\\n dz2*(self.phi[i][j][k-1] + self.phi[i][j][k+1])\\n\\n sum += R*R;\\n\\n\\n L2 = np.sqrt(sum/(self.ni*self.nj*self.nk));\\n #print(\\\"iter: \\\"+str(it)+\\\", L2 = \\\"+str(L2))\\n if (L2<tol):\\n converged = True\\n break\\n \\n if (converged==False):\\n print(\\\"Gauss-Seidel failed to converge, L2 = \\\"+str(L2))\\n \\n return converged\",\n \"def slo(self, objective=99.99):\\n self.objective = objective\\n return objective\",\n \"def solve(self):\\n\\n if self.degree > 2:\\n return \\\"The polynomial degree is strictly greater than 2, I can't solve.\\\"\\n \\n elif self.degree == 0:\\n \\\"\\\"\\\"a * X^0 = 0\\\"\\\"\\\" \\n a = self.all_terms[0].coefficient\\n if a != 0:\\n return \\\"The eqution has no solution\\\"\\n else:\\n return \\\"Every real number is a solution\\\"\\n\\n elif self.degree == 1:\\n \\\"\\\"\\\"a * X^1 + b * X^0 = 0\\\"\\\"\\\"\\n a = self.all_terms[1].coefficient\\n b = self.all_terms[0].coefficient\\n return formula.linear(a, b)\\n\\n elif self.degree == 2:\\n \\\"\\\"\\\"a * X^2 + b * X^1 + c * X^0 = 0\\\"\\\"\\\"\\n a = self.all_terms[2].coefficient\\n b = self.all_terms[1].coefficient\\n c = self.all_terms[0].coefficient\\n discriminant = (b ** 2) - (4 * a * c)\\n two_a = 2 * a\\n if discriminant == 0:\\n return formula.linear(two_a, b)\\n else:\\n if discriminant > 0:\\n return formula.quadratic(two_a, b, discriminant)\\n else:\\n return formula.quadratic(two_a, b, discriminant, simple=False)\",\n \"def compute(self, solver=\\\"cbc\\\", **kwargs):\\n self.table2es()\\n logging.info(\\\"Creating the linear model...\\\")\\n model = solph.Model(self.es)\\n logging.info(\\\"Done. Optimise the model.\\\")\\n self.solve(model, solver=solver, **kwargs)\",\n \"def solver_objects(\\n kappa, f, u_D, Nx, Ny, degree=1,\\n linear_solver='Krylov', # Alt: 'direct'\\n abs_tol=1E-5, # Absolute tolerance in Krylov solver\\n rel_tol=1E-3, # Relative tolerance in Krylov solver\\n max_iter=1000, # Max no of iterations in Krylov solver\\n log_level=PROGRESS, # Amount of solver output\\n dump_parameters=False, # Write out parameter database?\\n ):\\n # Create mesh and define function space\\n mesh = UnitSquareMesh(Nx, Ny)\\n V = FunctionSpace(mesh, 'P', degree)\\n\\n def boundary(x, on_boundary):\\n return on_boundary\\n\\n bc = DirichletBC(V, u_D, boundary)\\n\\n # Define variational problem\\n u = TrialFunction(V)\\n v = TestFunction(V)\\n a = kappa*dot(grad(u), grad(v))*dx\\n L = f*v*dx\\n\\n # Compute solution\\n u = Function(V)\\n problem = LinearVariationalProblem(a, L, u, bc)\\n solver = LinearVariationalSolver(problem)\\n\\n if linear_solver == 'Krylov':\\n solver.parameters['linear_solver'] = 'gmres'\\n solver.parameters['preconditioner'] = 'ilu'\\n prm = solver.parameters['krylov_solver'] # short form\\n prm['absolute_tolerance'] = abs_tol\\n prm['relative_tolerance'] = rel_tol\\n prm['maximum_iterations'] = max_iter\\n print(parameters['linear_algebra_backend'])\\n set_log_level(log_level)\\n if dump_parameters:\\n info(parameters, True)\\n solver_parameters = {'linear_solver': 'gmres',\\n 'preconditioner': 'ilu'}\\n else:\\n solver_parameters = {'linear_solver': 'lu'}\\n\\n solver.solve()\\n return u\",\n \"def get_objective(self, objtype=None, get_value=True):\\n if objtype is None:\\n # Find active objective\\n if self.act_objective is not None:\\n obj = self.act_objective\\n else:\\n raise ValueError('No active objective found.')\\n\\n else:\\n assert objtype in self.objectives.keys(), 'Requested objective does not exist. Please choose from {}'.format(\\n self.objectives.keys())\\n obj = self.objectives[objtype]\\n\\n if get_value:\\n return value(obj)\\n else:\\n return obj\",\n \"def get_rhs(self, mol_orbs, task, ops):\\n if 'x' in ops or 'y' in ops or 'z' in ops:\\n prop = {k: v for k, v in zip('xyz', self.get_dipole(task))}\\n den = mol_orbs.get_density(task.molecule)\\n da = den.alpha_to_numpy(0)\\n db = den.beta_to_numpy(0)\\n D = da + db\\n S = self.get_overlap(task)\\n mo = mol_orbs.alpha_to_numpy()\\n\\n matrices = tuple([mo.T@(S@D@prop[p].T - prop[p].T@D@S)@mo for p in ops])\\n gradients = tuple([self.mat2vec(m, mol_orbs, task) for m in matrices])\\n return gradients\",\n \"def objective(self, params, x, data):\\n # make residual per data set\\n ndata, nx = data.shape\\n resid = 0.0*data[:]\\n resid[0, :] = data[0, :] - self.thermo(params, x)\\n resid[1, :] = data[1, :] - self.density(params, x)\\n # now flatten this to a 1D array, as minimize() needs\\n return resid.flatten()\",\n \"def objective(self,w):\\n diffs = self.get_y_times_diffs(self.get_split_weights(w))\\n #print diffs, sigmoid(diffs)\\n obj = -np.sum(np.log(sigmoid(diffs))) #negative, since minimising\\n # regularisation\\n obj += 0.5 * self.alpha * np.dot(w[:self.interp_index[0]], w[:self.interp_index[0]])\\n return obj\",\n \"def _dualize(self, block, unfixed=[]):\\n #\\n # Collect linear terms from the block\\n #\\n A, b_coef, c_rhs, c_sense, d_sense, vnames, cnames, v_domain = collect_linear_terms(block, unfixed)\\n #\\n # Construct the block\\n #\\n if isinstance(block, Model):\\n dual = ConcreteModel()\\n else:\\n dual = Block()\\n for v, is_indexed in vnames:\\n if is_indexed:\\n setattr(dual, v+'_Index', Set(dimen=None))\\n setattr(dual, v, Var(getattr(dual, v+'_Index')))\\n else:\\n setattr(dual, v, Var())\\n for cname, is_indexed in cnames:\\n if is_indexed:\\n setattr(dual, cname+'_Index', Set(dimen=None))\\n setattr(dual, cname, Constraint(getattr(dual, cname+'_Index')))\\n setattr(dual, cname+'_lower_', Var(getattr(dual, cname+'_Index')))\\n setattr(dual, cname+'_upper_', Var(getattr(dual, cname+'_Index')))\\n else:\\n setattr(dual, cname, Constraint())\\n setattr(dual, cname+'_lower_', Var())\\n setattr(dual, cname+'_upper_', Var())\\n dual.construct()\\n #\\n # Add variables\\n #\\n # TODO: revisit this hack. We shouldn't be calling\\n # _getitem_when_not_present()\\n #\\n for name, ndx in b_coef:\\n v = getattr(dual, name)\\n if not ndx in v:\\n v._getitem_when_not_present(ndx)\\n #\\n # Construct the objective\\n #\\n if d_sense == minimize:\\n dual.o = Objective(expr=sum(- b_coef[name,ndx]*getattr(dual,name)[ndx] for name,ndx in b_coef), sense=d_sense)\\n else:\\n dual.o = Objective(expr=sum(b_coef[name,ndx]*getattr(dual,name)[ndx] for name,ndx in b_coef), sense=d_sense)\\n #\\n # Construct the constraints\\n #\\n for cname in A:\\n c = getattr(dual, cname)\\n c_index = getattr(dual, cname+\\\"_Index\\\") if c.is_indexed() else None\\n for ndx,terms in iteritems(A[cname]):\\n if not c_index is None and not ndx in c_index:\\n c_index.add(ndx)\\n expr = 0\\n for term in terms:\\n v = getattr(dual,term.var)\\n if not term.ndx in v:\\n v.add(term.ndx)\\n expr += term.coef * v[term.ndx]\\n if not (cname, ndx) in c_rhs:\\n c_rhs[cname, ndx] = 0.0\\n if c_sense[cname,ndx] == 'e':\\n c.add(ndx, expr - c_rhs[cname,ndx] == 0)\\n elif c_sense[cname,ndx] == 'l':\\n c.add(ndx, expr - c_rhs[cname,ndx] <= 0)\\n else:\\n c.add(ndx, expr - c_rhs[cname,ndx] >= 0)\\n for (name, ndx), domain in iteritems(v_domain):\\n v = getattr(dual, name)\\n flag = type(ndx) is tuple and (ndx[-1] == 'lb' or ndx[-1] == 'ub')\\n if domain == 1:\\n if flag:\\n v[ndx].domain = NonNegativeReals\\n else:\\n v.domain = NonNegativeReals\\n elif domain == -1:\\n if flag:\\n v[ndx].domain = NonPositiveReals\\n else:\\n v.domain = NonPositiveReals\\n else:\\n if flag:\\n # TODO: verify that this case is possible\\n v[ndx].domain = Reals\\n else:\\n v.domain = Reals\\n return dual\",\n \"def check_primal_and_dual_solutions(self):\\r\\n\\r\\n # Solve primal and dual models\\r\\n self.primal.solve_model(self.m_p)\\r\\n self.dual.solve_model(self.m_d)\\r\\n\\r\\n # Elements to check\\r\\n elements = {\\r\\n 'power - existing thermal':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_THERMAL', 'is_dual': True}\\r\\n },\\r\\n 'power - existing wind':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_WIND', 'is_dual': True}\\r\\n },\\r\\n 'power - existing solar':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_SOLAR', 'is_dual': True}\\r\\n },\\r\\n 'power - existing hydro':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_HYDRO', 'is_dual': True}\\r\\n },\\r\\n # 'charging power - existing storage':\\r\\n # {\\r\\n # 'primal': {'id': 'p_in', 'is_dual': False},\\r\\n # 'dual': {'id': 'CHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\\r\\n # },\\r\\n # 'discharging power - existing storage':\\r\\n # {\\r\\n # 'primal': {'id': 'p_out', 'is_dual': False},\\r\\n # 'dual': {'id': 'DISCHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\\r\\n # },\\r\\n 'power - candidate thermal':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_THERMAL', 'is_dual': True}\\r\\n },\\r\\n 'power - candidate wind':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_WIND', 'is_dual': True}\\r\\n },\\r\\n 'power - candidate solar':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_SOLAR', 'is_dual': True}\\r\\n },\\r\\n 'charging power - candidate storage':\\r\\n {\\r\\n 'primal': {'id': 'p_in', 'is_dual': False},\\r\\n 'dual': {'id': 'CHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\\r\\n },\\r\\n 'discharging power - candidate storage':\\r\\n {\\r\\n 'primal': {'id': 'p_out', 'is_dual': False},\\r\\n 'dual': {'id': 'DISCHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\\r\\n },\\r\\n 'locational marginal prices':\\r\\n {\\r\\n 'primal': {'id': 'POWER_BALANCE', 'is_dual': True},\\r\\n 'dual': {'id': 'lamb', 'is_dual': False}\\r\\n },\\r\\n 'load shedding':\\r\\n {\\r\\n 'primal': {'id': 'p_V', 'is_dual': False},\\r\\n 'dual': {'id': 'LOAD_SHEDDING_POWER', 'is_dual': True}\\r\\n },\\r\\n }\\r\\n\\r\\n print(f\\\"Checking primal and dual solutions\\\")\\r\\n for el_id, e in elements.items():\\r\\n # Absolute, non-zero and max absolute difference\\r\\n absolute_diff, non_zero_diff, max_abs_diff = self.compare_primal_and_dual_components(e)\\r\\n\\r\\n print('---------------------------------------------------------------------------')\\r\\n print(f\\\"Primal component: {e['primal']['id']}, Dual component: {e['dual']['id']}')\\\")\\r\\n print(f'Total keys: {len(absolute_diff)}')\\r\\n print(f\\\"Max absolute difference: {max_abs_diff}\\\")\\r\\n print(f\\\"Non-zero diff: {non_zero_diff}\\\")\",\n \"def solve(self):\\n is_valid = self.verify_sub_matrixes()\\n \\n if not is_valid:\\n raise ValueError((\\n \\\"El determinante es igual a cero \\\"\\n \\\"el método no puede continuar\\\"\\n ))\\n \\n (lower, upper) = self.doolittle_factorization()\\n\\n lower_solution_vector = lower.solve_matrix(matrix=None, vector=self.vector.vector)\\n lower_solution_vector.print_vector()\\n upper_solution_vector = upper.solve_matrix(\\n matrix=None, vector=lower_solution_vector.vector)\\n upper_solution_vector.print_vector()\\n\\n comprobation = self.matrix.comprobation(upper_solution_vector.vector)\\n return comprobation\",\n \"def get_solution(self):\\n return self.P_plot[-1]\",\n \"def get_objective(self):\\n self.objective = 0\\n for r in self.routes:\\n r.update_route(self.vrpdata)\\n self.objective += r.distance\\n # all() returns True if all elements of the iterable are true\\n self.solutionValid = (all([r.tourValid for r in self.routes]) and len(self.routes) <= self.vrpdata.MaxNumVeh)\\n if self.solutionValid:\\n return self.objective\\n return -1\",\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 potentialSolver2(self, max_it, tol):\\n\\n dx2 = 1.0/(self.dh[0]*self.dh[0]); # dx^2\\n dy2 = 1.0/(self.dh[1]*self.dh[1]); # dy^2\\n dz2 = 1.0/(self.dh[2]*self.dh[2]); # dz^2\\n \\n L2 = 0.0 # norm\\n \\n converged = False\\n \\n \\n # solve potential\\n for it in np.arange(1,max_it+1):\\n \\\"\\\"\\\"\\n for i in np.arange(1,self.ni-1):\\n for j in np.arange(1,self.nj-1):\\n for k in np.arange(1,self.nk-1):\\n \\\"\\\"\\\" \\n \\\"\\\"\\\"\\n #standard internal open node\\n phi_new = (self.rho[i][j][k]/self.EPS_0 +\\\\\\n dx2*(self.phi[i-1][j][k] + self.phi[i+1][j][k]) +\\\\\\n dy2*(self.phi[i][j-1][k] + self.phi[i][j+1][k]) +\\\\\\n dz2*(self.phi[i][j][k-1] + self.phi[i][j][k+1]))/(2*dx2+2*dy2+2*dz2)\\n \\n # sucessive over relaxation \\n self.phi[i,j,k] = self.phi[i,j,k] + 1.4*(phi_new - self.phi[i][j][k])\\n \\n \\\"\\\"\\\" \\n #standard internal open node\\n self.phi[1:self.ni-1,1:self.nj-1,1:self.nk-1] = \\\\\\n (self.rho[1:self.ni-1,1:self.nj-1,1:self.nk-1]/self.EPS_0 +\\\\\\n dx2*(self.phi[0:self.ni-2,1:self.nj-1,1:self.nk-1] + self.phi[2:self.ni,1:self.nj-1,1:self.nk-1])+\\\\\\n dy2*(self.phi[1:self.ni-1,0:self.nj-2,1:self.nk-1] + self.phi[1:self.ni-1,2:self.nj,1:self.nk-1])+\\\\\\n dz2*(self.phi[1:self.ni-1,1:self.nj-1,0:self.nk-2] + self.phi[1:self.ni-1,1:self.nj-1,2:self.nk]))/(2*dx2+2*dy2+2*dz2)\\n \\n \\\"\\\"\\\"\\n # sucessive over relaxation \\n self.phi[1:self.ni-1,1:self.nj-1,1:self.nk-1] = self.phi[1:self.ni-1,1:self.nj-1,1:self.nk-1] +\\\\\\n 1.8*(self.phi_new[1:self.ni-1,1:self.nj-1,1:self.nk-1] - \\\\\\n self.phi[1:self.ni-1,1:self.nj-1,1:self.nk-1])\\n \\\"\\\"\\\"\\n \\n #check for convergence*/\\n if it%25==0:\\n sum = 0;\\n \\\"\\\"\\\"\\n for i in np.arange(1,self.ni-1):\\n for j in np.arange(1,self.nj-1):\\n for k in np.arange(1,self.nk-1):\\n \\\"\\\"\\\"\\n\\n self.R[1:self.ni-1,1:self.nj-1,1:self.nk-1] = \\\\\\n -self.phi[1:self.ni-1,1:self.nj-1,1:self.nk-1]*(2*dx2+2*dy2+2*dz2) +\\\\\\n self.rho[1:self.ni-1,1:self.nj-1,1:self.nk-1]/self.EPS_0 +\\\\\\n dx2*(self.phi[0:self.ni-2,1:self.nj-1,1:self.nk-1] + self.phi[2:self.ni,1:self.nj-1,1:self.nk-1]) +\\\\\\n dy2*(self.phi[1:self.ni-1,0:self.nj-2,1:self.nk-1] + self.phi[1:self.ni-1,2:self.nj,1:self.nk-1]) +\\\\\\n dz2*(self.phi[1:self.ni-1,1:self.nj-1,0:self.nk-2] + self.phi[1:self.ni-1,1:self.nj-1,2:self.nk])\\n\\n sum = np.sum(self.R**2)\\n\\n L2 = np.sqrt(sum/(self.ni*self.nj*self.nk));\\n #print(\\\"iter: \\\"+str(it)+\\\", L2 = \\\"+str(L2))\\n if (L2<tol):\\n converged = True\\n break\\n\\n if (converged==False):\\n print(\\\"Gauss-Seidel failed to converge, L2 = \\\"+str(L2))\\n \\n return converged\",\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 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 _objective_function(self, thetas, X, Y):\\n \\n # Convert thetas vector to form total_cost can understand\\n thetas = self.reshape_thetas(thetas, 'list')\\n self.thetas = thetas\\n \\n # Get cost function value\\n fval = self.total_cost(X, Y, thetas)\\n \\n # Get derivatives using back propagation\\n Deltas = self.get_gradients(X, Y)\\n dfval = self.reshape_thetas(Deltas, 'vector')\\n \\n return fval, dfval\",\n \"def define_expressions(m):\\r\\n\\r\\n def dual_objective_expression_rule(_m):\\r\\n \\\"\\\"\\\"Expression for dual objective function\\\"\\\"\\\"\\r\\n\\r\\n # Build limits\\r\\n t_1 = sum(- (m.mu_2[z, y] * m.SOLAR_BUILD_LIMITS[z]) - (m.mu_3[z, y] * m.WIND_BUILD_LIMITS[z]) - (\\r\\n m.mu_4[z, y] * m.STORAGE_BUILD_LIMITS[z]) for z in m.Z for y in m.Y)\\r\\n\\r\\n # Min power output\\r\\n t_2 = sum(\\r\\n m.sigma_1[g, y, s, t] * m.P_MIN[g] for g in m.G.difference(m.G_STORAGE) for y in m.Y for s in m.S for t\\r\\n in m.T)\\r\\n\\r\\n # Max power - existing generators\\r\\n t_3 = sum(\\r\\n - m.sigma_2[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_THERM for y in m.Y for s in m.S\\r\\n for t in m.T)\\r\\n\\r\\n # Max power - existing wind\\r\\n t_4 = sum(\\r\\n - m.sigma_4[g, y, s, t] * m.Q_W[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_WIND for y in\\r\\n m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Max power - existing solar\\r\\n t_5 = sum(\\r\\n - m.sigma_6[g, y, s, t] * m.Q_S[g, y, s, t] * m.P_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_SOLAR for y in\\r\\n m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Max power - existing hydro\\r\\n t_6 = sum(\\r\\n - m.sigma_8[g, y, s, t] * m.P_H[g, y, s, t] * (1 - m.F[g, y]) for g in m.G_E_HYDRO for y in m.Y for s in\\r\\n m.S for t in m.T)\\r\\n\\r\\n # Max charging power - existing storage\\r\\n t_7 = sum(\\r\\n - m.sigma_11[g, y, s, t] * m.P_IN_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_STORAGE for y in m.Y for s in\\r\\n m.S for t in m.T)\\r\\n\\r\\n # Max discharging power - existing storage\\r\\n t_8 = sum(\\r\\n - m.sigma_13[g, y, s, t] * m.P_OUT_MAX[g] * (1 - m.F[g, y]) for g in m.G_E_STORAGE for y in m.Y for s in\\r\\n m.S for t in m.T)\\r\\n\\r\\n # Max energy - existing storage units\\r\\n t_9 = sum(\\r\\n - m.sigma_16[g, y, s, t] * m.Q_MAX[g] for g in m.G_E_STORAGE for y in m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Min energy - interval end\\r\\n t_10 = sum(m.sigma_18[g, y, s] * m.Q_END_MIN[g] for g in m.G_STORAGE for y in m.Y for s in m.S)\\r\\n\\r\\n # Max energy - interval end\\r\\n t_11 = sum(- m.sigma_19[g, y, s] * m.Q_END_MAX[g] for g in m.G_STORAGE for y in m.Y for s in m.S)\\r\\n\\r\\n # Ramp-up constraint - generators\\r\\n t_12 = sum(\\r\\n - m.sigma_20[g, y, s, t] * m.RR_UP[g] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for s in m.S\\r\\n for t in m.T)\\r\\n\\r\\n # Ramp-up constraint - initial power output - generators\\r\\n t_13 = sum(\\r\\n - m.sigma_20[g, y, s, m.T.first()] * m.P0[g, y, s] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y\\r\\n for s in m.S)\\r\\n\\r\\n # Ramp-down constraint - generators\\r\\n t_18 = sum(\\r\\n - m.sigma_23[g, y, s, t] * m.RR_DOWN[g] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for s in m.S\\r\\n for t in m.T)\\r\\n\\r\\n # Ramp-down constraint - initial power output - generators\\r\\n t_19 = sum(\\r\\n m.sigma_23[g, y, s, m.T.first()] * m.P0[g, y, s] for g in m.G_THERM.union(m.G_E_HYDRO) for y in m.Y for\\r\\n s in m.S)\\r\\n\\r\\n # Min powerflow\\r\\n t_24 = sum(m.sigma_27[l, y, s, t] * m.POWERFLOW_MIN[l] for l in m.L for y in m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Max powerflow\\r\\n t_25 = sum(\\r\\n - m.sigma_28[l, y, s, t] * m.POWERFLOW_MAX[l] for l in m.L for y in m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Demand\\r\\n t_26 = sum(m.lamb[z, y, s, t] * m.DEMAND[z, y, s, t] for z in m.Z for y in m.Y for s in m.S for t in m.T)\\r\\n\\r\\n # Initial storage unit energy\\r\\n t_27 = sum(m.zeta_1[g, y, s, m.T.first()] * m.Q0[g, y, s] for g in m.G_STORAGE for y in m.Y for s in m.S)\\r\\n\\r\\n return (t_1 + t_2 + t_3 + t_4 + t_5 + t_6 + t_7 + t_8 + t_9 + t_10 + t_11 + t_12 + t_13 + t_18 + t_19 + t_24\\r\\n + t_25 + t_26 + t_27)\\r\\n\\r\\n # Dual objective expression\\r\\n m.DUAL_OBJECTIVE_EXPRESSION = Expression(rule=dual_objective_expression_rule)\\r\\n\\r\\n def scenario_revenue_rule(_m, y, s):\\r\\n \\\"\\\"\\\"Total revenue collected from wholesale electricity sales\\\"\\\"\\\"\\r\\n\\r\\n if y != m.Y.last():\\r\\n # Scaling factor\\r\\n scaling_factor = m.DELTA[y] * m.RHO[y, s]\\r\\n\\r\\n # Revenue from electricity sales (wholesale) = $/MWh x MWh\\r\\n return sum((m.lamb[z, y, s, t] / scaling_factor) * m.DEMAND[z, y, s, t] * m.RHO[y, s] for z in m.Z\\r\\n for t in m.T)\\r\\n\\r\\n else:\\r\\n # Scaling factor\\r\\n scaling_factor = m.DELTA[y] * m.RHO[y, s] * (1 + (1 / m.INTEREST_RATE))\\r\\n\\r\\n # Revenue from electricity sales (wholesale)\\r\\n return sum((m.lamb[z, y, s, t] / scaling_factor) * m.DEMAND[z, y, s, t] * m.RHO[y, s] for z in m.Z\\r\\n for t in m.T)\\r\\n\\r\\n # Revenue from wholesale electricity sales for each scenario\\r\\n m.SCENARIO_REVENUE = Expression(m.Y, m.S, rule=scenario_revenue_rule)\\r\\n\\r\\n def scenario_average_price_rule(_m, y, s):\\r\\n \\\"\\\"\\\"Average price for a given scenario\\\"\\\"\\\"\\r\\n\\r\\n return m.SCENARIO_REVENUE[y, s] / m.SCENARIO_DEMAND[y, s]\\r\\n\\r\\n # Scenario demand weighted average wholesale price\\r\\n m.SCENARIO_AVERAGE_PRICE = Expression(m.Y, m.S, rule=scenario_average_price_rule)\\r\\n\\r\\n def year_average_price_rule(_m, y):\\r\\n \\\"\\\"\\\"Average price for a given year\\\"\\\"\\\"\\r\\n\\r\\n # Total revenue\\r\\n return sum(m.SCENARIO_REVENUE[y, s] for s in m.S) / sum(m.SCENARIO_DEMAND[y, s] for s in m.S)\\r\\n\\r\\n # Year demand weighted average wholesale price\\r\\n m.YEAR_AVERAGE_PRICE = Expression(m.Y, rule=year_average_price_rule)\\r\\n\\r\\n return m\",\n \"def rhs(self):\\n return self._args[1]\",\n \"def objective(self, x):\\n pass\",\n \"def objective(self, x):\\n pass\",\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 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 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 get_sol_value(self, mesh_num, el_num, Y, x, count_lift=True):\\n m = self._meshes[mesh_num]\\n e = m.elements[el_num]\\n val = 0.\\n for i, g in enumerate(e.dofs):\\n if g == -1:\\n if count_lift:\\n val += e.shape_function(i, x)*e.get_dirichlet_value(i)\\n else:\\n val += e.shape_function(i, x)*Y[g]\\n #print val, e.dofs\\n return val\",\n \"def solve_return_conv(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 convergence_criteria = exit_info['val']\\n\\n print (exit_info['msg'])\\n print (\\\"Exiting with ||grad[L]|| = {0:e}\\\".format(convergence_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, convergence_criteria]\",\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 _solve(self) -> CasADiArrayType:\\n solver_input = {\\\"x0\\\": self.x0, \\\"p\\\": self.p}\\n if self.opt_type in CONSTRAINED_OPT:\\n solver_input[\\\"lbg\\\"] = self._lbg\\n solver_input[\\\"ubg\\\"] = self._ubg\\n self._solution = self._solver(**solver_input)\\n self._stats = self._solver.stats()\\n self._stats[\\\"solution\\\"] = self._solution\\n return self._solution[\\\"x\\\"]\",\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_instance(instance, solver='glpk', mipgap=0.01):\\n solver = ReferenceModel.SolverFactory(solver)\\n solver.options['mipgap'] = mipgap\\n instance.preprocess()\\n _results = solver.solve(instance, suffixes=['dual'])\\n instance.solutions.store_to(_results)\\n return(_results)\",\n \"def set_obj_fun(self):\\n\\n # disable button \\\"Edit Objective Function\\\"\\n # self.ui.pb_edit_obj_func.setEnabled(False)\\n a_str = str(self.le_a.text())\\n state_a = self.is_le_addr_ok(self.le_a)\\n b_str = str(self.le_b.text())\\n state_b = self.is_le_addr_ok(self.le_b)\\n c_str = str(self.le_c.text())\\n state_c = self.is_le_addr_ok(self.le_c)\\n func = str(self.le_of.text())\\n def get_value_exp():\\n A = 0.\\n B = 0.\\n C = 0.\\n if state_a:\\n A = self.mi.get_value(a_str)\\n if state_b:\\n B = self.mi.get_value(b_str)\\n if state_c:\\n C = self.mi.get_value(c_str)\\n if func == \\\"\\\":\\n return 0\\n return eval(func)\\n\\n self.objective_func = get_value_exp\\n\\n return self.objective_func\",\n \"def tot_grad_obj(self,x0,gradient):\\n tmp = self.tot_objective(self.rebuild_param(x0,**self.fixed))\\n obj = tmp[0]\\n if gradient:\\n grad =self.fix_par(tmp[1], **self.fixed)[0]\\n return obj,grad\\n else:\\n return obj\",\n \"def lu_solve(self, rhs):\\n if not self.domain == rhs.domain:\\n raise DMDomainError(\\\"Domains must match: %s != %s\\\" % (self.domain, rhs.domain))\\n\\n # XXX: As for inv we should consider whether to return a matrix over\\n # over an associated field or attempt to find a solution in the ring.\\n # For now we follow the existing DomainMatrix convention...\\n if not self.domain.is_Field:\\n raise DMDomainError(\\\"Field expected, got %s\\\" % self.domain)\\n\\n m, n = self.shape\\n j, k = rhs.shape\\n if m != j:\\n raise DMShapeError(\\\"Matrix size mismatch: %s * %s vs %s * %s\\\" % (m, n, j, k))\\n sol_shape = (n, k)\\n\\n # XXX: The Flint solve method only handles square matrices. Probably\\n # Flint has functions that could be used to solve non-square systems\\n # but they are not exposed in python-flint yet. Alternatively we could\\n # put something here using the features that are available like rref.\\n if m != n:\\n return self.to_ddm().lu_solve(rhs.to_ddm()).to_dfm()\\n\\n try:\\n sol = self.rep.solve(rhs.rep)\\n except ZeroDivisionError:\\n raise DMNonInvertibleMatrixError(\\\"Matrix det == 0; not invertible.\\\")\\n\\n return self._new(sol, sol_shape, self.domain)\",\n \"def calc_obj_value(ts11,s11):\\n pe = [] # point errors\\n te = 0 # total error\\n\\n for i in range(0, len(TARGET_S11)):\\n pe.append((TARGET_S11[i]-s11[i])**2/(TARGET_S11[i])**2)\\n te+=pe[i]\\n\\n ae = te/len(pe) # average error\\n obj = 1/abs(ae) # objective value\\n\\n return (obj, pe)\",\n \"def eval(self, Vobj):\\n try:\\n return Vobj.evaluated_on(self)\\n except AttributeError:\\n return self.A() * Vobj + self.b()\",\n \"def solve(self, solver):\\n solver.solve()\",\n \"def dual_problem(\\n states: list[np.ndarray], probs: list[float] = None, dist_method=\\\"min-error\\\"\\n) -> float:\\n constraints = []\\n meas = []\\n\\n dim_x, _ = states[0].shape\\n\\n y_var = cvxpy.Variable((dim_x, dim_x), hermitian=True)\\n objective = cvxpy.Minimize(cvxpy.trace(cvxpy.real(y_var)))\\n\\n dim = int(np.log2(dim_x))\\n dim_list = [2] * int(np.log2(dim_x))\\n sys_list = list(range(1, dim, 2))\\n # dim_list = [3, 3]\\n\\n if dist_method == \\\"min-error\\\":\\n for i, _ in enumerate(states):\\n meas.append(cvxpy.Variable((dim_x, dim_x), PSD=True))\\n constraints.append(\\n cvxpy.real(y_var - probs[i] * states[i])\\n >> partial_transpose(meas[i], sys=sys_list, dim=dim_list)\\n )\\n\\n if dist_method == \\\"unambiguous\\\":\\n for j, _ in enumerate(states):\\n sum_val = 0\\n for i, _ in enumerate(states):\\n if i != j:\\n sum_val += cvxpy.real(cvxpy.Variable()) * probs[i] * states[i]\\n meas.append(cvxpy.Variable((dim_x, dim_x), PSD=True))\\n constraints.append(\\n cvxpy.real(y_var - probs[j] * states[j] + sum_val)\\n >> partial_transpose(meas[j], sys=sys_list, dim=dim_list)\\n )\\n\\n meas.append(cvxpy.Variable((dim_x, dim_x), PSD=True))\\n constraints.append(\\n cvxpy.real(y_var) >> partial_transpose(meas[-1], sys=sys_list, dim=dim_list)\\n )\\n\\n problem = cvxpy.Problem(objective, constraints)\\n sol_default = problem.solve()\\n\\n # print(np.around(y_var.value, decimals=3))\\n\\n return sol_default\",\n \"def evaluate_rhs(self, t, y, ydot=np.empty(0)):\\n raise NotImplementedError\",\n \"def objective(self, adjacency=None, R=None):\\n raise NotImplementedError()\",\n \"def calculateCost(self,sol,weights):\\n\\t\\treturn sum([x.value*y if x != None else 0 \\\\\\n\\t\\t\\t\\t\\tfor x,y in zip(sol,weights)])\",\n \"def __call__(self, r=None, t=None, o=None):\\n return self._sol(as_o(r,t,o))[0]\",\n \"def _get_obj(self, weights, alpha_vector):\\r\\n assert (len(alpha_vector.columns) == 1)\\r\\n return cvx.Minimize(-alpha_vector.T.values[0]*weights)\",\n \"def find_solution(self):\\r\\n for solution in self.solutions:\\r\\n if self.fitting_function.is_legal_solution(solution):\\r\\n return solution\\r\\n return None\",\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 get_solution(self):\\n objective_value = self.solver.objective.value\\n status = self.solver.status\\n variables = pd.Series(data=self.solver.primal_values)\\n\\n fluxes = empty(len(self.reactions))\\n rxn_index = list()\\n var_primals = self.solver.primal_values\\n\\n for (i, rxn) in enumerate(self.reactions):\\n rxn_index.append(rxn.id)\\n fluxes[i] = var_primals[rxn.id] - var_primals[rxn.reverse_id]\\n\\n fluxes = pd.Series(index=rxn_index, data=fluxes, name=\\\"fluxes\\\")\\n\\n solution = Solution(objective_value=objective_value, status=status,\\n fluxes=fluxes)\\n\\n self.solution = solution\\n\\n self.solution.raw = variables\\n\\n self.\\\\\\n solution.values = pd.DataFrame.from_dict({k:v.unscaled\\n for k,v in self._var_dict.items()},\\n orient = 'index')\\n\\n return solution\",\n \"def _get_obj(self, out_pattern):\\n x_tilde = self._get_x_tilde(out_pattern)\\n sig_u_tilde = amath.exp(self._get_u_tilde(log_sigmoid=True))\\n V_times_sig_u_tilde = sig_u_tilde.dot(self.variables[\\\"V\\\"])\\n\\n obj = - 0.5 * self.out_dim * amath.log(2.0 * amath.pi) \\\\\\n - 0.5 * amath.inner(x_tilde, x_tilde)\\n obj = obj + amath.inner(x_tilde, V_times_sig_u_tilde)\\n obj = obj - 0.5 * amath.inner(V_times_sig_u_tilde, V_times_sig_u_tilde)\\n obj = obj - 0.5 * amath.inner(sig_u_tilde,\\n (1.0 - sig_u_tilde)\\n * amath.diag(self.variables[\\\"V\\\"].dot(\\n self.variables[\\\"V\\\"].transpose())))\\n return obj\",\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 _create_result_object(self, rclass, jsol=None):\\n res = rclass(self.model)\\n\\n # Process JSON solution\\n #self.context.log(3, \\\"JSON data:\\\\n\\\", jsol)\\n self.last_json_result = jsol\\n\\n # Parse JSON solution\\n if jsol:\\n # Parse JSON\\n stime = time.time()\\n jsol = parse_json_string(jsol)\\n self.process_infos.incr(CpoProcessInfos.TOTAL_JSON_PARSE_TIME, time.time() - stime)\\n # Build result structure\\n res._add_json_solution(jsol, self.solver.expr_map)\\n\\n # Process Log\\n if self.log_data is not None:\\n res._set_solver_log(''.join(self.log_data))\\n self.log_data = []\\n res.process_infos.update(self.process_infos)\\n return res\",\n \"def evaluate_rhs(self, x, V):\\n rhs = self._numeric_system(x, V, *self.model.params.values()).ravel()\\n return rhs\",\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 getprimalsolutionnorms(self,whichsol_):\\n nrmxc_ = ctypes.c_double()\\n nrmxx_ = ctypes.c_double()\\n nrmbarx_ = ctypes.c_double()\\n res = __library__.MSK_XX_getprimalsolutionnorms(self.__nativep,whichsol_,ctypes.byref(nrmxc_),ctypes.byref(nrmxx_),ctypes.byref(nrmbarx_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n nrmxc_ = nrmxc_.value\\n _nrmxc_return_value = nrmxc_\\n nrmxx_ = nrmxx_.value\\n _nrmxx_return_value = nrmxx_\\n nrmbarx_ = nrmbarx_.value\\n _nrmbarx_return_value = nrmbarx_\\n return (_nrmxc_return_value,_nrmxx_return_value,_nrmbarx_return_value)\",\n \"def get_best_solution(self):\\n if not self.tours:\\n raise Exception('No solution has been computed yet')\\n scores = {s:get_cost(self.tours[s],self) for s in self.tours}\\n best = min(scores,key=scores.get)\\n print('The best solution is given by {} with score {}'.format(best,scores[best]))\\n return self.tours[best]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8319954","0.6920154","0.6874271","0.63215345","0.6047877","0.5961174","0.59548587","0.59537184","0.58911043","0.5846982","0.5843001","0.5692419","0.5645508","0.5627053","0.5615924","0.5534404","0.5509056","0.5460137","0.5445977","0.5432232","0.5387754","0.5381813","0.53387713","0.5336079","0.53168064","0.5313718","0.5278235","0.52592915","0.52289516","0.5227192","0.52105886","0.520849","0.5198985","0.51971835","0.51903194","0.5182043","0.5171627","0.51657504","0.5160079","0.5155458","0.5148735","0.5135916","0.5093906","0.508405","0.5080983","0.50460786","0.5037124","0.50311095","0.5024426","0.5022463","0.5021971","0.5010482","0.5007559","0.5001164","0.4993654","0.4992824","0.49668315","0.49665105","0.49662426","0.49661565","0.4966002","0.49656045","0.49621534","0.4961148","0.495954","0.49590197","0.49587992","0.49516472","0.49516472","0.49491152","0.49347177","0.49332222","0.49322322","0.4912373","0.4911664","0.49098396","0.49017817","0.4899344","0.48954576","0.48939627","0.48915514","0.48879233","0.48870894","0.48855016","0.48772562","0.48765817","0.4871432","0.48691398","0.48605064","0.48538348","0.48534355","0.48519132","0.48517334","0.4850593","0.48498514","0.484563","0.48412582","0.4840825","0.48358437","0.48356795"],"string":"[\n \"0.8319954\",\n \"0.6920154\",\n \"0.6874271\",\n \"0.63215345\",\n \"0.6047877\",\n \"0.5961174\",\n \"0.59548587\",\n \"0.59537184\",\n \"0.58911043\",\n \"0.5846982\",\n \"0.5843001\",\n \"0.5692419\",\n \"0.5645508\",\n \"0.5627053\",\n \"0.5615924\",\n \"0.5534404\",\n \"0.5509056\",\n \"0.5460137\",\n \"0.5445977\",\n \"0.5432232\",\n \"0.5387754\",\n \"0.5381813\",\n \"0.53387713\",\n \"0.5336079\",\n \"0.53168064\",\n \"0.5313718\",\n \"0.5278235\",\n \"0.52592915\",\n \"0.52289516\",\n \"0.5227192\",\n \"0.52105886\",\n \"0.520849\",\n \"0.5198985\",\n \"0.51971835\",\n \"0.51903194\",\n \"0.5182043\",\n \"0.5171627\",\n \"0.51657504\",\n \"0.5160079\",\n \"0.5155458\",\n \"0.5148735\",\n \"0.5135916\",\n \"0.5093906\",\n \"0.508405\",\n \"0.5080983\",\n \"0.50460786\",\n \"0.5037124\",\n \"0.50311095\",\n \"0.5024426\",\n \"0.5022463\",\n \"0.5021971\",\n \"0.5010482\",\n \"0.5007559\",\n \"0.5001164\",\n \"0.4993654\",\n \"0.4992824\",\n \"0.49668315\",\n \"0.49665105\",\n \"0.49662426\",\n \"0.49661565\",\n \"0.4966002\",\n \"0.49656045\",\n \"0.49621534\",\n \"0.4961148\",\n \"0.495954\",\n \"0.49590197\",\n \"0.49587992\",\n \"0.49516472\",\n \"0.49516472\",\n \"0.49491152\",\n \"0.49347177\",\n \"0.49332222\",\n \"0.49322322\",\n \"0.4912373\",\n \"0.4911664\",\n \"0.49098396\",\n \"0.49017817\",\n \"0.4899344\",\n \"0.48954576\",\n \"0.48939627\",\n \"0.48915514\",\n \"0.48879233\",\n \"0.48870894\",\n \"0.48855016\",\n \"0.48772562\",\n \"0.48765817\",\n \"0.4871432\",\n \"0.48691398\",\n \"0.48605064\",\n \"0.48538348\",\n \"0.48534355\",\n \"0.48519132\",\n \"0.48517334\",\n \"0.4850593\",\n \"0.48498514\",\n \"0.484563\",\n \"0.48412582\",\n \"0.4840825\",\n \"0.48358437\",\n \"0.48356795\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79599565"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11799,"cells":{"query":{"kind":"string","value":"Obtains an integer information item. getintinf(self,whichiinf_)"},"document":{"kind":"string","value":"def getintinf(self,whichiinf_):\n ivalue_ = ctypes.c_int32()\n res = __library__.MSK_XX_getintinf(self.__nativep,whichiinf_,ctypes.byref(ivalue_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n ivalue_ = ivalue_.value\n _ivalue_return_value = ivalue_\n return (_ivalue_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 getintinf(self,whichiinf_): # 3\n if not isinstance(whichiinf_,iinfitem): raise TypeError(\"Argument whichiinf has wrong type\")\n res,resargs = self.__obj.getintinf(whichiinf_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ivalue_return_value = resargs\n return _ivalue_return_value","def getlintinf(self,whichliinf_): # 3\n if not isinstance(whichliinf_,liinfitem): raise TypeError(\"Argument whichliinf has wrong type\")\n res,resargs = self.__obj.getlintinf(whichliinf_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ivalue_return_value = resargs\n return _ivalue_return_value","def getlintinf(self,whichliinf_):\n ivalue_ = ctypes.c_int64()\n res = __library__.MSK_XX_getlintinf(self.__nativep,whichliinf_,ctypes.byref(ivalue_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n ivalue_ = ivalue_.value\n _ivalue_return_value = ivalue_\n return (_ivalue_return_value)","def getdouinf(self,whichdinf_): # 3\n if not isinstance(whichdinf_,dinfitem): raise TypeError(\"Argument whichdinf has wrong type\")\n res,resargs = self.__obj.getdouinf(whichdinf_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dvalue_return_value = resargs\n return _dvalue_return_value","def getInteger(self):","def getInteger(self):","def getInteger(self):\n pass","def getI(self):\n\n return self.i","def ins_ii(self):\n return self._ins_ii","def ipi(self):\n return self._ipi","def XPLMGetDatai_f(inRefcon):\n return int","def get_int2(self):\n pass","def GetInteger(self,prompt=''):\n\t\treturn self.acad.ActiveDocument.Utility.GetInteger(prompt)","def _getIntFeature(self):\n\n # create args\n valueToGet = c_int64()\n\n errorCode = VimbaDLL.featureIntGet(self._handle,\n self._name,\n byref(valueToGet))\n if errorCode != 0:\n raise VimbaException(errorCode)\n\n return valueToGet.value","def getdouinf(self,whichdinf_):\n dvalue_ = ctypes.c_double()\n res = __library__.MSK_XX_getdouinf(self.__nativep,whichdinf_,ctypes.byref(dvalue_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n dvalue_ = dvalue_.value\n _dvalue_return_value = dvalue_\n return (_dvalue_return_value)","def _get_iops(self, report):\n match = re.search(\"iops\\=(\\d+)\", report)\n if match:\n return int(match.group(1))","def getSubintinfo(self,value):\n if value in self.subintinfo.keys():\n return self.subintinfo[value][-1]\n return None","def i(self):\n return self._i","def getIR2() -> int:\n pass","def get_inttemp(self):\n return self.read_register(4104, 1, 3)","def XPLMGetDatai(inDataRef):\n return int","def getInteger(self):\n assert self._is_int is True\n return self._value","def ied_num(self) -> str:\n return pulumi.get(self, \"ied_num\")","def getIR1() -> int:\n pass","def instrID(self):\n return self.query('*IDN?')","def getInteger(self):\n return self.value if self.isInteger() else None","def getInt(self):\n try:\n data, n = self._buf.pop(_I_LEN)\n \n if n != _I_LEN:\n raise SerializationError('There is not enough data left.')\n \n return _I_STRUCT.unpack(data)[0]\n except StructError as e:\n raise SerializationError('Data is not a valid integer: '\n '{0}'.format(e))","def __int__(self):\n return self.get_raw_int()","def get_ibat(self):\n return self.read_register(4097, 1, 3)","def getint(self, option):\n return getint(self.name, option)","def GetI(self, *args):\n return _table.Table_GetI(self, *args)","def getInteger(self):\n return _libsbml.ASTNode_getInteger(self)","def readInt(self) -> int:\n return self._unpack('!i', 4)","def info(self) -> int:","def get_int(self, item: str) -> int:\n return int(self[item])","def get_list_index(self):\r\n s = self.query('LIST:IND?')\r\n return int(s)","def get_list_index(self):\r\n s = self.query('LIST:IND?')\r\n return int(s)","def ion(self):\n return self._ion","def id(self):\n if (len(self.value) > 1) and isinstance(self.value[1], int):\n return self.value[1]\n return -1","def getint(self, option, argument=None):\n value = self.get(option, argument)\n if value: return int(value)\n else: return 0","def Get(self, vtkInformation, p_int):\n ...","def geti(self, node):\n\n return self.daq.getInt(f'/{self.device_id}/{node}')","def getId(self):\n if self.id: return self.id\n reader = self.getReader()\n subData = reader.findSubRecord('INTV','LAND')\n (self.gridX,self.gridY) = struct.unpack('ii',subData)\n self.id = '[%d,%d]' % (self.gridX,self.gridY)\n return self.id","def ion_id(self):\n return self._ion_id","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 getIR5() -> int:\n pass","def I(self) :\n\t\ttry :\n\t\t\treturn self._I\n\t\texcept Exception as e:\n\t\t\traise e","def __get_isin(instrument):\n return instrument['isin']","def get_info_inst(self):\n return self.get_info(\"INST\")","def getNumber():","def info(self) -> list[int]:","def getint(self, option, default=None):\n\t\treturn self._get_raw(option, 'int', default)","def innulo(self):\n for i in range(self.n):\n if not comozero(self[i]):\n return i\n return None","def __index__(self):\n return int(self)","def get_list_index(self):\r\n _debug('simq03b_api.get_list_index')\r\n \r\n s = self.query('LIST:IND?')\r\n return int(s)","def get_int(self, name):\n return self.field(name).toInt()[0]","def get_inchikey(self):\n if not self.inchikey:\n _pubchem_id_type_checker(self.id_type)\n r = requests.get(f\"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/{self.id_type}/{self.drug_id}/property/InChIKey/json\")\n response = r.json()\n data = response['PropertyTable']['Properties'][0]\n self.inchikey = data['InChIKey']\n return self.inchikey","def itag(self):\n return self.__itag","def ref_ii(self):\n return self._ref_ii","def getIR3() -> int:\n pass","def inf(self):\n return self._inf","def _get(self) -> int:\n while self.stack: \n data, i = self.stack.pop()\n if i+1 < len(data): self.stack.append((data, i+1)) #backtracking point \n if data[i].isInteger(): return data[i].getInteger()\n if not data[i].getList(): continue #empty list \n self.stack.append((data[i].getList(), 0)) #push nested list on stack\n return None","def get_imei():\n # Read the IMEI number high and low from the XBee device.\n sh = xbee.atcmd(AT_CMD_SH)\n sl = xbee.atcmd(AT_CMD_SL)\n if sh is None or sl is None:\n return None\n\n # Transform the values to hexadecimal strings.\n sh_string = binascii.hexlify(sh).decode().upper()\n sl_string = binascii.hexlify(sl).decode().upper()\n\n imei = sh_string + sl_string\n if len(imei) > 15:\n imei = imei[-15:]\n return imei","def get_inst(self, inst):\n if (type(inst) is str):\n return index_of(inst, self._param_val[0])\n elif (type(inst) is int):\n return self._param_val[0][inst]\n else:\n raise TypeError(\"Trying to get_inst for inst of type %s (not int or str).\" % type(inst))","def _get_igp_isis(self):\n return self.__igp_isis","def get_izz(self, comm = None):\n with open(self.filepath, \"r\") as file:\n return file.readline().split()[1]","def __getitem__(self, i):\n idx, Z0 = None, None\n\n ion = None\n if type(i) == str:\n ion = self.getIonByName(i)\n else:\n if len(i) == 2:\n idx = i[0]\n Z0 = i[1]\n else:\n idx = i\n\n ion = self.ions[idx]\n\n if Z0 is not None:\n return ion[Z0]\n else:\n return ion","def getint(self, section, option):\n return int(self.get(section, option))","def image_id_at(self, i):\n return i","def unpack_int(self, offset):\n return struct.unpack_from(str(\"<i\"), self._buf, self._offset + offset)[0]","def get_info(cls, icao_id: str) -> Tuple[str, str]:\n if len(cls.mapping) == 0:\n cls._generate_mapping()\n return cls.mapping.get(icao_id.lower(), ('Unknown', 'Unknown'))","def getInt(self, int: int, int2: int) -> int:\n ...","def __int__(self):\n return int(self.obj)","def __getitem__(self, item: str) -> int:\n return self.stoi[item]","def info(self):\n return self.nfo","def get_imei(self):\n raise DeviceException(DeviceException.FEATURE_NOT_IMPLEMENTED)","def getInt(self, addr: ghidra.program.model.address.Address) -> int:\n ...","def value(self, p_int): # real signature unknown; restored from __doc__\n pass","def getInt(self, item, default=0):\n value = self.getSection(CFG_GENERAL, item)\n return default if not value else int(value)","def itos(self, i):\n token = self._itos.get(i)\n return token if token else 'UNK'","def get_nh_idx(self):\n return int(self.get('nhr_id'))","def InterfaceIndex(self) -> int:","def InterfaceIndex(self) -> int:","def get_Ui(iid):\n try: \n return len(trainSet.ir[trainSet.to_inner_iid(iid)])\n except ValueError:\n return 0","def info(self, id):","def getDi(self, n=None, label=None):\n return self.di[label][n]","def __int__(self):\n return int(self.q[0])","def get_list_index(self):\r\n return self.n","def info(self, list: list[int], /) -> list[int]:","def I_int(expt, z):\n u = kperp * r(z) / (2. * np.pi) # UV plane: |u| = d / lambda\n nu = expt['nu_line'] / (1. + z)\n fov = (1.02 / (nu * expt['Ddish']) * (3e8 / 1e6))**2.\n \n l = 3e8 / (nu * 1e6) # Wavelength (m)\n u_min = expt['Dmin'] / l\n u_max = expt['Dmax'] / l\n \n # New calc.\n n_u = expt['Ndish']*(expt['Ndish'] - 1.) * l**2. * np.ones(u.shape) \\\n / (2. * np.pi * (expt['Dmax']**2. - expt['Dmin']**2.) )\n n_u[np.where(u < u_min)] = 1. / INF_NOISE\n n_u[np.where(u > u_max)] = 1. / INF_NOISE\n \n # Interferometer multiplicity factor, /I/\n I = 4./9. * fov / n_u\n return I","def output_integer(state, key, data):\n return int(state[key])","def get_nh_tun_dip(self):\n return int(self.get('nhr_tun_dip'))","def _getOneModelInfo(nupicModelID):\n return _iterModels([nupicModelID]).next()","def getValue(self) -> int:\n ...","def get_ident():\n return -1","def I (self, n):","def get_ispin(outcar):\n os.system(\"grep 'ISPIN' %s | awk '{print $3}' > .ispin\" % outcar)\n with open('.ispin', 'r') as ispin_file:\n f_ispin = int(ispin_file.readline().split()[0])\n return f_ispin","def getIntValue(self, *args):\n return _libsbml.ConversionProperties_getIntValue(self, *args)","def getint(self, section, option, default=None):\r\n return self.get(section, option, type=int, default=default)","def get_info(self, info):\r\n pass"],"string":"[\n \"def getintinf(self,whichiinf_): # 3\\n if not isinstance(whichiinf_,iinfitem): raise TypeError(\\\"Argument whichiinf has wrong type\\\")\\n res,resargs = self.__obj.getintinf(whichiinf_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _ivalue_return_value = resargs\\n return _ivalue_return_value\",\n \"def getlintinf(self,whichliinf_): # 3\\n if not isinstance(whichliinf_,liinfitem): raise TypeError(\\\"Argument whichliinf has wrong type\\\")\\n res,resargs = self.__obj.getlintinf(whichliinf_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _ivalue_return_value = resargs\\n return _ivalue_return_value\",\n \"def getlintinf(self,whichliinf_):\\n ivalue_ = ctypes.c_int64()\\n res = __library__.MSK_XX_getlintinf(self.__nativep,whichliinf_,ctypes.byref(ivalue_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n ivalue_ = ivalue_.value\\n _ivalue_return_value = ivalue_\\n return (_ivalue_return_value)\",\n \"def getdouinf(self,whichdinf_): # 3\\n if not isinstance(whichdinf_,dinfitem): raise TypeError(\\\"Argument whichdinf has wrong type\\\")\\n res,resargs = self.__obj.getdouinf(whichdinf_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _dvalue_return_value = resargs\\n return _dvalue_return_value\",\n \"def getInteger(self):\",\n \"def getInteger(self):\",\n \"def getInteger(self):\\n pass\",\n \"def getI(self):\\n\\n return self.i\",\n \"def ins_ii(self):\\n return self._ins_ii\",\n \"def ipi(self):\\n return self._ipi\",\n \"def XPLMGetDatai_f(inRefcon):\\n return int\",\n \"def get_int2(self):\\n pass\",\n \"def GetInteger(self,prompt=''):\\n\\t\\treturn self.acad.ActiveDocument.Utility.GetInteger(prompt)\",\n \"def _getIntFeature(self):\\n\\n # create args\\n valueToGet = c_int64()\\n\\n errorCode = VimbaDLL.featureIntGet(self._handle,\\n self._name,\\n byref(valueToGet))\\n if errorCode != 0:\\n raise VimbaException(errorCode)\\n\\n return valueToGet.value\",\n \"def getdouinf(self,whichdinf_):\\n dvalue_ = ctypes.c_double()\\n res = __library__.MSK_XX_getdouinf(self.__nativep,whichdinf_,ctypes.byref(dvalue_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n dvalue_ = dvalue_.value\\n _dvalue_return_value = dvalue_\\n return (_dvalue_return_value)\",\n \"def _get_iops(self, report):\\n match = re.search(\\\"iops\\\\=(\\\\d+)\\\", report)\\n if match:\\n return int(match.group(1))\",\n \"def getSubintinfo(self,value):\\n if value in self.subintinfo.keys():\\n return self.subintinfo[value][-1]\\n return None\",\n \"def i(self):\\n return self._i\",\n \"def getIR2() -> int:\\n pass\",\n \"def get_inttemp(self):\\n return self.read_register(4104, 1, 3)\",\n \"def XPLMGetDatai(inDataRef):\\n return int\",\n \"def getInteger(self):\\n assert self._is_int is True\\n return self._value\",\n \"def ied_num(self) -> str:\\n return pulumi.get(self, \\\"ied_num\\\")\",\n \"def getIR1() -> int:\\n pass\",\n \"def instrID(self):\\n return self.query('*IDN?')\",\n \"def getInteger(self):\\n return self.value if self.isInteger() else None\",\n \"def getInt(self):\\n try:\\n data, n = self._buf.pop(_I_LEN)\\n \\n if n != _I_LEN:\\n raise SerializationError('There is not enough data left.')\\n \\n return _I_STRUCT.unpack(data)[0]\\n except StructError as e:\\n raise SerializationError('Data is not a valid integer: '\\n '{0}'.format(e))\",\n \"def __int__(self):\\n return self.get_raw_int()\",\n \"def get_ibat(self):\\n return self.read_register(4097, 1, 3)\",\n \"def getint(self, option):\\n return getint(self.name, option)\",\n \"def GetI(self, *args):\\n return _table.Table_GetI(self, *args)\",\n \"def getInteger(self):\\n return _libsbml.ASTNode_getInteger(self)\",\n \"def readInt(self) -> int:\\n return self._unpack('!i', 4)\",\n \"def info(self) -> int:\",\n \"def get_int(self, item: str) -> int:\\n return int(self[item])\",\n \"def get_list_index(self):\\r\\n s = self.query('LIST:IND?')\\r\\n return int(s)\",\n \"def get_list_index(self):\\r\\n s = self.query('LIST:IND?')\\r\\n return int(s)\",\n \"def ion(self):\\n return self._ion\",\n \"def id(self):\\n if (len(self.value) > 1) and isinstance(self.value[1], int):\\n return self.value[1]\\n return -1\",\n \"def getint(self, option, argument=None):\\n value = self.get(option, argument)\\n if value: return int(value)\\n else: return 0\",\n \"def Get(self, vtkInformation, p_int):\\n ...\",\n \"def geti(self, node):\\n\\n return self.daq.getInt(f'/{self.device_id}/{node}')\",\n \"def getId(self):\\n if self.id: return self.id\\n reader = self.getReader()\\n subData = reader.findSubRecord('INTV','LAND')\\n (self.gridX,self.gridY) = struct.unpack('ii',subData)\\n self.id = '[%d,%d]' % (self.gridX,self.gridY)\\n return self.id\",\n \"def ion_id(self):\\n return self._ion_id\",\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 getIR5() -> int:\\n pass\",\n \"def I(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._I\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def __get_isin(instrument):\\n return instrument['isin']\",\n \"def get_info_inst(self):\\n return self.get_info(\\\"INST\\\")\",\n \"def getNumber():\",\n \"def info(self) -> list[int]:\",\n \"def getint(self, option, default=None):\\n\\t\\treturn self._get_raw(option, 'int', default)\",\n \"def innulo(self):\\n for i in range(self.n):\\n if not comozero(self[i]):\\n return i\\n return None\",\n \"def __index__(self):\\n return int(self)\",\n \"def get_list_index(self):\\r\\n _debug('simq03b_api.get_list_index')\\r\\n \\r\\n s = self.query('LIST:IND?')\\r\\n return int(s)\",\n \"def get_int(self, name):\\n return self.field(name).toInt()[0]\",\n \"def get_inchikey(self):\\n if not self.inchikey:\\n _pubchem_id_type_checker(self.id_type)\\n r = requests.get(f\\\"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/{self.id_type}/{self.drug_id}/property/InChIKey/json\\\")\\n response = r.json()\\n data = response['PropertyTable']['Properties'][0]\\n self.inchikey = data['InChIKey']\\n return self.inchikey\",\n \"def itag(self):\\n return self.__itag\",\n \"def ref_ii(self):\\n return self._ref_ii\",\n \"def getIR3() -> int:\\n pass\",\n \"def inf(self):\\n return self._inf\",\n \"def _get(self) -> int:\\n while self.stack: \\n data, i = self.stack.pop()\\n if i+1 < len(data): self.stack.append((data, i+1)) #backtracking point \\n if data[i].isInteger(): return data[i].getInteger()\\n if not data[i].getList(): continue #empty list \\n self.stack.append((data[i].getList(), 0)) #push nested list on stack\\n return None\",\n \"def get_imei():\\n # Read the IMEI number high and low from the XBee device.\\n sh = xbee.atcmd(AT_CMD_SH)\\n sl = xbee.atcmd(AT_CMD_SL)\\n if sh is None or sl is None:\\n return None\\n\\n # Transform the values to hexadecimal strings.\\n sh_string = binascii.hexlify(sh).decode().upper()\\n sl_string = binascii.hexlify(sl).decode().upper()\\n\\n imei = sh_string + sl_string\\n if len(imei) > 15:\\n imei = imei[-15:]\\n return imei\",\n \"def get_inst(self, inst):\\n if (type(inst) is str):\\n return index_of(inst, self._param_val[0])\\n elif (type(inst) is int):\\n return self._param_val[0][inst]\\n else:\\n raise TypeError(\\\"Trying to get_inst for inst of type %s (not int or str).\\\" % type(inst))\",\n \"def _get_igp_isis(self):\\n return self.__igp_isis\",\n \"def get_izz(self, comm = None):\\n with open(self.filepath, \\\"r\\\") as file:\\n return file.readline().split()[1]\",\n \"def __getitem__(self, i):\\n idx, Z0 = None, None\\n\\n ion = None\\n if type(i) == str:\\n ion = self.getIonByName(i)\\n else:\\n if len(i) == 2:\\n idx = i[0]\\n Z0 = i[1]\\n else:\\n idx = i\\n\\n ion = self.ions[idx]\\n\\n if Z0 is not None:\\n return ion[Z0]\\n else:\\n return ion\",\n \"def getint(self, section, option):\\n return int(self.get(section, option))\",\n \"def image_id_at(self, i):\\n return i\",\n \"def unpack_int(self, offset):\\n return struct.unpack_from(str(\\\"<i\\\"), self._buf, self._offset + offset)[0]\",\n \"def get_info(cls, icao_id: str) -> Tuple[str, str]:\\n if len(cls.mapping) == 0:\\n cls._generate_mapping()\\n return cls.mapping.get(icao_id.lower(), ('Unknown', 'Unknown'))\",\n \"def getInt(self, int: int, int2: int) -> int:\\n ...\",\n \"def __int__(self):\\n return int(self.obj)\",\n \"def __getitem__(self, item: str) -> int:\\n return self.stoi[item]\",\n \"def info(self):\\n return self.nfo\",\n \"def get_imei(self):\\n raise DeviceException(DeviceException.FEATURE_NOT_IMPLEMENTED)\",\n \"def getInt(self, addr: ghidra.program.model.address.Address) -> int:\\n ...\",\n \"def value(self, p_int): # real signature unknown; restored from __doc__\\n pass\",\n \"def getInt(self, item, default=0):\\n value = self.getSection(CFG_GENERAL, item)\\n return default if not value else int(value)\",\n \"def itos(self, i):\\n token = self._itos.get(i)\\n return token if token else 'UNK'\",\n \"def get_nh_idx(self):\\n return int(self.get('nhr_id'))\",\n \"def InterfaceIndex(self) -> int:\",\n \"def InterfaceIndex(self) -> int:\",\n \"def get_Ui(iid):\\n try: \\n return len(trainSet.ir[trainSet.to_inner_iid(iid)])\\n except ValueError:\\n return 0\",\n \"def info(self, id):\",\n \"def getDi(self, n=None, label=None):\\n return self.di[label][n]\",\n \"def __int__(self):\\n return int(self.q[0])\",\n \"def get_list_index(self):\\r\\n return self.n\",\n \"def info(self, list: list[int], /) -> list[int]:\",\n \"def I_int(expt, z):\\n u = kperp * r(z) / (2. * np.pi) # UV plane: |u| = d / lambda\\n nu = expt['nu_line'] / (1. + z)\\n fov = (1.02 / (nu * expt['Ddish']) * (3e8 / 1e6))**2.\\n \\n l = 3e8 / (nu * 1e6) # Wavelength (m)\\n u_min = expt['Dmin'] / l\\n u_max = expt['Dmax'] / l\\n \\n # New calc.\\n n_u = expt['Ndish']*(expt['Ndish'] - 1.) * l**2. * np.ones(u.shape) \\\\\\n / (2. * np.pi * (expt['Dmax']**2. - expt['Dmin']**2.) )\\n n_u[np.where(u < u_min)] = 1. / INF_NOISE\\n n_u[np.where(u > u_max)] = 1. / INF_NOISE\\n \\n # Interferometer multiplicity factor, /I/\\n I = 4./9. * fov / n_u\\n return I\",\n \"def output_integer(state, key, data):\\n return int(state[key])\",\n \"def get_nh_tun_dip(self):\\n return int(self.get('nhr_tun_dip'))\",\n \"def _getOneModelInfo(nupicModelID):\\n return _iterModels([nupicModelID]).next()\",\n \"def getValue(self) -> int:\\n ...\",\n \"def get_ident():\\n return -1\",\n \"def I (self, n):\",\n \"def get_ispin(outcar):\\n os.system(\\\"grep 'ISPIN' %s | awk '{print $3}' > .ispin\\\" % outcar)\\n with open('.ispin', 'r') as ispin_file:\\n f_ispin = int(ispin_file.readline().split()[0])\\n return f_ispin\",\n \"def getIntValue(self, *args):\\n return _libsbml.ConversionProperties_getIntValue(self, *args)\",\n \"def getint(self, section, option, default=None):\\r\\n return self.get(section, option, type=int, default=default)\",\n \"def get_info(self, info):\\r\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8475795","0.7179274","0.71271324","0.6888658","0.61792326","0.61792326","0.61284137","0.6106969","0.6090202","0.60640436","0.5953424","0.5949886","0.5806069","0.5791027","0.57118326","0.57102764","0.5709367","0.5658817","0.56567997","0.5631905","0.5562323","0.55616623","0.55563307","0.55512255","0.555092","0.551458","0.5497488","0.5486335","0.5473889","0.5445329","0.54386944","0.538869","0.5365809","0.535481","0.53363186","0.53297937","0.53297937","0.5321655","0.529433","0.5294321","0.5289532","0.5287744","0.5267451","0.5257109","0.5248606","0.5241545","0.5219158","0.5214118","0.51992893","0.5195465","0.5192678","0.5187006","0.5175759","0.5170035","0.51689076","0.51650494","0.5145682","0.51410604","0.513934","0.51181835","0.51172787","0.51086324","0.51075125","0.51072186","0.5102389","0.5093389","0.5084813","0.50847924","0.5084157","0.50591755","0.5030943","0.50216544","0.5007081","0.49981785","0.49787098","0.4976291","0.49735463","0.49645662","0.4960034","0.49555752","0.49520767","0.4951684","0.4951684","0.49512744","0.49496353","0.49328014","0.4926225","0.49236232","0.49143684","0.49119928","0.49078324","0.49041843","0.48946518","0.48881385","0.488391","0.48793966","0.48754936","0.48734358","0.48722705","0.48719907"],"string":"[\n \"0.8475795\",\n \"0.7179274\",\n \"0.71271324\",\n \"0.6888658\",\n \"0.61792326\",\n \"0.61792326\",\n \"0.61284137\",\n \"0.6106969\",\n \"0.6090202\",\n \"0.60640436\",\n \"0.5953424\",\n \"0.5949886\",\n \"0.5806069\",\n \"0.5791027\",\n \"0.57118326\",\n \"0.57102764\",\n \"0.5709367\",\n \"0.5658817\",\n \"0.56567997\",\n \"0.5631905\",\n \"0.5562323\",\n \"0.55616623\",\n \"0.55563307\",\n \"0.55512255\",\n \"0.555092\",\n \"0.551458\",\n \"0.5497488\",\n \"0.5486335\",\n \"0.5473889\",\n \"0.5445329\",\n \"0.54386944\",\n \"0.538869\",\n \"0.5365809\",\n \"0.535481\",\n \"0.53363186\",\n \"0.53297937\",\n \"0.53297937\",\n \"0.5321655\",\n \"0.529433\",\n \"0.5294321\",\n \"0.5289532\",\n \"0.5287744\",\n \"0.5267451\",\n \"0.5257109\",\n \"0.5248606\",\n \"0.5241545\",\n \"0.5219158\",\n \"0.5214118\",\n \"0.51992893\",\n \"0.5195465\",\n \"0.5192678\",\n \"0.5187006\",\n \"0.5175759\",\n \"0.5170035\",\n \"0.51689076\",\n \"0.51650494\",\n \"0.5145682\",\n \"0.51410604\",\n \"0.513934\",\n \"0.51181835\",\n \"0.51172787\",\n \"0.51086324\",\n \"0.51075125\",\n \"0.51072186\",\n \"0.5102389\",\n \"0.5093389\",\n \"0.5084813\",\n \"0.50847924\",\n \"0.5084157\",\n \"0.50591755\",\n \"0.5030943\",\n \"0.50216544\",\n \"0.5007081\",\n \"0.49981785\",\n \"0.49787098\",\n \"0.4976291\",\n \"0.49735463\",\n \"0.49645662\",\n \"0.4960034\",\n \"0.49555752\",\n \"0.49520767\",\n \"0.4951684\",\n \"0.4951684\",\n \"0.49512744\",\n \"0.49496353\",\n \"0.49328014\",\n \"0.4926225\",\n \"0.49236232\",\n \"0.49143684\",\n \"0.49119928\",\n \"0.49078324\",\n \"0.49041843\",\n \"0.48946518\",\n \"0.48881385\",\n \"0.488391\",\n \"0.48793966\",\n \"0.48754936\",\n \"0.48734358\",\n \"0.48722705\",\n \"0.48719907\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80981195"},"document_rank":{"kind":"string","value":"1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":117,"numItemsPerPage":100,"numTotalItems":95772,"offset":11700,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTkyNjI1MSwianRpIjoiYTNiNzY1YjgtMzljMC00ODM0LWE3NWUtZmM2OWI4N2NiZjQ4Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU5Mjk4NTEsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.CjeWx-pS6VwJJyT1O8_6ARE1Od-JyqC_fSeTKaUNZyKeqieYu7rc1HopsZhTdAaSj3klyC2GjBonLB2XjuUPDQ","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 |
|---|---|---|---|---|---|---|
Runs garbage collection on allocated space, to prevent memoryleaks. This function should be called once at the end of your scriptfile. | def cleanup(self):
self.loader.cleanup()
self.Loaded = False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collect_garbage(self) -> None:\n pass",
"def _run_garbage_collection():\n sleep_time = 0.5\n done = False\n while not done:\n collected = gc.collect(2)\n logger.info(\"{} objects collected\".format(collected))\n if collected:\n logger.info(\"Sleeping for {} sec... | [
"0.75592524",
"0.73604804",
"0.73248816",
"0.72240883",
"0.7211107",
"0.7113082",
"0.68933505",
"0.67799926",
"0.66687465",
"0.66379446",
"0.661931",
"0.661931",
"0.661931",
"0.6553741",
"0.6529691",
"0.65203846",
"0.65177214",
"0.64993566",
"0.6487079",
"0.6476979",
"0.64546... | 0.0 | -1 |
Return a node partition based on the Modularity Matrix eigenvectors. This method calculates the eigenvector v associated with the second largest eigenvalue of the Modularity Matrix. Where the Modularity Matrix is defined as | def spectral_modularity_partition(G):
try:
import numpy as np
except:
raise ImportError("spectral_partition() \
requires NumPy: http://scipy.org/")
k = np.matrix(G.degree().values())
m = G.number_of_edges()
B = nx.adj_matrix(G) - (k.transpose() * k) / (2.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def partition_by_eigenvector(graph):\n ###TODO\n pass",
"def get_second_eigenvector(graph):\n ###TODO\n pass",
"def eigen_decomp(matrix):\n w = None\n v = None\n ### YOUR CODE HERE\n w,v=np.linalg.eig(matrix)\n ### END YOUR CODE\n return w, v",
"def calculate_biggest_eigenvalue(... | [
"0.64587164",
"0.6063995",
"0.5738871",
"0.5670563",
"0.5506475",
"0.5493674",
"0.5468006",
"0.54519945",
"0.5425612",
"0.538626",
"0.53837824",
"0.53741413",
"0.53188396",
"0.53172624",
"0.5283776",
"0.52623904",
"0.52556574",
"0.5252896",
"0.522213",
"0.5203772",
"0.5151679... | 0.5227392 | 18 |
Partition a graph G into two communities using greedy modularity maximization. The algorithm works by selecting a node to change communities by which will maximize the modularity. The swap is made and the community structure with the highest modularity is kept. | def greedy_max_modularity_partition(G, C_init=None, max_iter=10):
if G.is_multigraph():
raise nx.NetworkXError("greed_max_modularity() not defined for multigraph")
if C_init is None:
m1 = G.order() / 2
m2 = G.order() - m1
C = nxt.random_partition(G.nodes(), partition_sizes=[m1, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def greedy_modularity_communities(G, weight=None):\n\n # Count nodes and edges\n N = len(G.nodes())\n m = sum([d.get('weight', 1) for u, v, d in G.edges(data=True)])\n q0 = 1.0 / (2.0*m)\n\n # Map node labels to contiguous integers\n label_for_node = dict((i, v) for i, v in enumerate(G.nodes()))\... | [
"0.7985895",
"0.7863971",
"0.7855224",
"0.7789124",
"0.7097632",
"0.69124085",
"0.6684569",
"0.65042293",
"0.6500123",
"0.64222026",
"0.6420805",
"0.6327584",
"0.61868286",
"0.60904104",
"0.60830534",
"0.6052372",
"0.5930056",
"0.5916552",
"0.5912683",
"0.59000045",
"0.588164... | 0.7307236 | 4 |
Recursively partition the graph G using the the algorithm defined by partition function depth times. | def recursive_partition(G,
partition_function,
depth,
dendogram=False,
**kwargs):
C = [set(G)]
if dendogram:
D = nx.Graph()
for _ in range(depth):
C_next = []
for c in C:
C_nex... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, g, n_partitions):\n\n def _iterative_cutting(g, p):\n \"\"\"helper function (iterative version)\"\"\"\n\n to_be_processed = [g]\n K = math.ceil(len(g.nodes()) / p)\n\n res = []\n while len(to_be_processed) > 0:\n\n g = ... | [
"0.6947835",
"0.6692274",
"0.6523319",
"0.64699167",
"0.61639374",
"0.61390674",
"0.6134492",
"0.5891478",
"0.5791794",
"0.57705605",
"0.5603623",
"0.5438325",
"0.54188836",
"0.53874147",
"0.5370432",
"0.5352181",
"0.534992",
"0.5345076",
"0.53345937",
"0.53251743",
"0.529117... | 0.8169132 | 0 |
Function to handle data files. | def openFile(dir):
# If path is a directory
if os.path.isdir(dir):
# Check file sizes from first file
Nt = len(glob.glob(dir + "/1_*.txt")) # Nt
tmp = np.loadtxt(dir + "/1_0.txt")
Ny, Nx = tmp.shape
# Output array
V = np.zeros((Nt, 2, Ny, Nx))
for n in ra... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_file_data(filename):",
"def process_data(*args, **kwargs):\n\n filepath = kwargs[\"filepath\"]\n func = kwargs[\"func\"]\n \n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.... | [
"0.7081344",
"0.6695296",
"0.6569301",
"0.652842",
"0.6515002",
"0.6513492",
"0.6484186",
"0.64830065",
"0.64793694",
"0.64597356",
"0.644613",
"0.644613",
"0.644039",
"0.64369124",
"0.64328086",
"0.64319426",
"0.63926923",
"0.6369196",
"0.63599026",
"0.6351072",
"0.6330499",... | 0.0 | -1 |
Open vector field with independent directories. | def openVectorWT(w_dir, t_dir):
tx_dir, ty_dir = t_dir
W = openFile(w_dir)
Tx = openFile(tx_dir)
Ty = openFile(ty_dir)
assert Tx.shape == Ty.shape, "Tx and Ty must have same dimensions."
Nt, Nc = W.shape
Ny, Nx = Tx.shape
V = np.zeros((Nt, 2, Ny, Nx))
for n in range(Nt):
V[n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vol_open_path(volpath, open_flags=VMDK_OPEN_DEFAULT):\n dhandle = get_uint(0)\n ihandle = get_uint(0)\n key = c_uint32(0)\n\n res = lib.DiskLib_OpenWithInfo(volpath.encode(), open_flags,\n byref(key), byref(dhandle),\n byref(ihandl... | [
"0.61692727",
"0.55802864",
"0.55706215",
"0.55380136",
"0.5490979",
"0.5486886",
"0.5469945",
"0.54595834",
"0.542374",
"0.5422143",
"0.54089767",
"0.53765655",
"0.5329017",
"0.532043",
"0.5300465",
"0.52988756",
"0.5256425",
"0.524063",
"0.524063",
"0.524063",
"0.52376485",... | 0.51535165 | 25 |
Copy the example source files after the package is installed to an install test subdirectory for use during `spack test run`. | def cache_test_sources(self):
self.cache_extra_test_sources([join_path("examples", "apps")]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_build_tests(self):\n # Now copy the relative files\n self.cache_extra_test_sources(self.build_relpath)\n\n # Ensure the path exists since relying on a relative path at the\n # same level as the normal stage source path.\n mkdirp(self.install_test_root)",
"def setup(se... | [
"0.6967905",
"0.6740082",
"0.64299864",
"0.6414573",
"0.62844384",
"0.6230936",
"0.62204343",
"0.6206577",
"0.61558056",
"0.61538935",
"0.6142146",
"0.613289",
"0.61247826",
"0.61047703",
"0.6058998",
"0.60412216",
"0.6023817",
"0.6023817",
"0.6018945",
"0.6014771",
"0.601258... | 0.6493857 | 2 |
Tests the creation of LASCOMap using FITS. | def test_fitstoLASCO(lasco):
assert isinstance(lasco, LASCOMap) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_initialized() -> None:\n MapieClassifier()",
"def test_initialized() -> None:\n MapieRegressor()",
"def test_make_ec_map():\n pass",
"def test_on_map_of_constants(synthetic_checkerboard):\n img = synthetic_checkerboard['img']\n di = synthetic_checkerboard['cdi']\n\n cpp_vorimg = te... | [
"0.642239",
"0.6291131",
"0.61287856",
"0.58283335",
"0.5784628",
"0.5760995",
"0.5741576",
"0.5735342",
"0.56763834",
"0.56645465",
"0.5649165",
"0.56062466",
"0.5596278",
"0.5567189",
"0.55620426",
"0.55452317",
"0.55275035",
"0.5512195",
"0.5509079",
"0.5508",
"0.55052185"... | 0.6583202 | 0 |
Test the is_datasource_for method of LASCOMap. Note that header data to be provided as an argument can be a MetaDict object. | def test_is_datasource_for(lasco):
assert lasco.is_datasource_for(lasco.data, lasco.meta) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_datasource_for(eit_map):\n assert eit_map.is_datasource_for(eit_map.data, eit_map.meta)",
"def is_datasource_for(cls, **kwargs):\n if 'source' in kwargs.keys():\n if kwargs.get('source', ''):\n return kwargs.get('source', '').lower().startswith(cls._source)\n ... | [
"0.74529046",
"0.6727229",
"0.57659274",
"0.57537",
"0.5483662",
"0.5415242",
"0.5341589",
"0.52654755",
"0.5249164",
"0.522096",
"0.5166681",
"0.5155394",
"0.5150383",
"0.5117535",
"0.5107733",
"0.50836915",
"0.50509775",
"0.50261915",
"0.5001209",
"0.49781814",
"0.495897",
... | 0.762238 | 0 |
Tests the measurement property of the LASCOMap object. | def test_measurement(lasco):
assert lasco.measurement == "white-light" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_measurement(eit_map):\n assert eit_map.measurement.value in [195, 171]",
"def test_unit_of_measurement(self):\n for name in self.sensor_dict:\n sensor = self.sensor_dict[name][\"sensor\"]\n assert self.sensor_dict[name][\"units\"] == sensor.unit_of_measurement",
"def te... | [
"0.7147487",
"0.70588326",
"0.6354547",
"0.6340808",
"0.62565184",
"0.622547",
"0.62038094",
"0.614342",
"0.6091599",
"0.5935994",
"0.5892707",
"0.58639777",
"0.5852712",
"0.58456856",
"0.57708514",
"0.57647926",
"0.5735114",
"0.5731323",
"0.57311",
"0.5704018",
"0.5696798",
... | 0.70623285 | 1 |
Tests the observatory property of the LASCOMap object. | def test_observatory(lasco):
assert lasco.observatory == "SOHO" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_observatory(eit_map):\n assert eit_map.observatory == \"SOHO\"",
"def check_observatory(self):\n assert self.observatory in ALL_OBSERVATORIES, \\\n \"Invalid observatory \" + repr(self.observatory) + \" in \" + repr(self.filename)",
"def observatories():\n\n obs_db = {}\n\n ... | [
"0.70502925",
"0.5905423",
"0.5631481",
"0.55709904",
"0.53638643",
"0.53604794",
"0.535205",
"0.53501725",
"0.53381866",
"0.53307277",
"0.5307905",
"0.5280215",
"0.52539486",
"0.5241711",
"0.51865333",
"0.5175831",
"0.51357836",
"0.5132824",
"0.51225543",
"0.50932837",
"0.50... | 0.62336797 | 1 |
Tests that rotation metadata is correctly removed for JPEG2000 images provided by Helioviewer.org. | def test_helioviewer_rotation(lasco, lasco_helioviewer):
np.testing.assert_allclose(lasco.rotation_matrix,
[[0.999966, -0.008296], [0.008296, 0.999966]], rtol=1e-6)
np.testing.assert_array_equal(lasco_helioviewer.rotation_matrix, [[1., 0.], [0., 1.]]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_exif_data_removed_from_added_thumbnail(self):\n\n # The image that has GPS data:\n path = \"tests/core/fixtures/images/tester_exif_gps.jpg\"\n\n # Double-check the original image does have some GPS data:\n exif_dict = piexif.load(path)\n self.assertEqual(len(exif_dict[\"... | [
"0.7091541",
"0.6875018",
"0.6684414",
"0.6390415",
"0.62990373",
"0.6178711",
"0.60326755",
"0.5979566",
"0.5919938",
"0.59135807",
"0.58806163",
"0.5868729",
"0.5854647",
"0.57815886",
"0.5773003",
"0.5765147",
"0.57515615",
"0.5746518",
"0.573411",
"0.57289803",
"0.5725085... | 0.5038498 | 95 |
Show running config send command | def show_run(ssh, tell):
command = "sh run int " + tell
ssh.send('terminal length 0\n')
time.sleep(1)
ssh.recv(1000)
ssh.send(command + '\n')
time.sleep(2)
result = ssh.recv(20000).decode('ascii')
k = result.split()
first_steep = Table.chown_run_in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def config(self, ctx):\n await ctx.send_help(ctx.command)",
"def config(ctx):\n if not ctx.invoked_subcommand:\n cfg = ctx.obj['cfg']\n for section in cfg.sections():\n print(\"[\", section, \"]\")\n for option in cfg[section]:\n print(option, \"... | [
"0.66976076",
"0.6623098",
"0.65153956",
"0.64066315",
"0.64043736",
"0.637134",
"0.6354019",
"0.6347334",
"0.63209146",
"0.627261",
"0.6210578",
"0.61829835",
"0.6150153",
"0.6137609",
"0.6073267",
"0.60719395",
"0.6047735",
"0.6041152",
"0.6037163",
"0.6018082",
"0.590123",... | 0.0 | -1 |
get value from query dict by key | def get_value(self, query_dict, k):
if k in query_dict:
return query_dict[k]
return '' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getitem__(self, key):\n return self.query(key)",
"def find(cls, key):\r\n return cls.query().get(key)",
"def getSpecific(self, keyword, key):",
"def __getitem__(self, key):\n return self.keyvaluepair_set.get(key=key).value",
"def __getitem__(self, key):\n return self.param... | [
"0.7332471",
"0.7147431",
"0.7024943",
"0.68989784",
"0.6870327",
"0.6814481",
"0.6814481",
"0.68065345",
"0.67460203",
"0.67460203",
"0.67460203",
"0.67460203",
"0.673437",
"0.67057145",
"0.6673087",
"0.6667873",
"0.66438895",
"0.66222626",
"0.6579917",
"0.6568693",
"0.65685... | 0.8017572 | 0 |
Sort the contents of a directory by last modified date. | def _sorted_ls(path):
def _get_modified_time(f):
return os.stat(os.path.join(path, f)).st_mtime
return list(sorted(os.listdir(path), key=_get_modified_time)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_files_list(dirname, date_order, rdate_order):\n file_list = os.listdir(dirname)\n file_mtimes = dict.fromkeys(file_list)\n for f in file_list:\n if f[0] == '.':\n print \"Skipping file: \", f\n del file_mtimes[f]\n continue\n if date_order or rdate_or... | [
"0.6985285",
"0.64413553",
"0.62352586",
"0.62184626",
"0.62138826",
"0.62063205",
"0.6144919",
"0.6113929",
"0.6106303",
"0.6105642",
"0.60882264",
"0.5959481",
"0.5916365",
"0.58824",
"0.5865776",
"0.58552366",
"0.582905",
"0.57945603",
"0.57103086",
"0.5698084",
"0.567522"... | 0.7565834 | 0 |
Return a filepath that contains data about the next change to test. | def get_next_change_file():
path = '/tmp/perf/'
changes_to_test = _sorted_ls(path)
if changes_to_test:
return os.path.join(path, changes_to_test[0]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def track_path(self, filename):\n return os.path.join(os.path.dirname(__file__), 'testdata', filename)",
"def track_path(self, filename):\n return os.path.join(os.path.dirname(__file__), 'testdata', filename)",
"def track_path(self, filename):\n return os.path.join(os.path.dirname(__file__... | [
"0.6054829",
"0.6054829",
"0.6054829",
"0.58682424",
"0.58409095",
"0.57942855",
"0.57845366",
"0.56979823",
"0.5621857",
"0.56051517",
"0.5566274",
"0.5565939",
"0.5536013",
"0.5490917",
"0.54188967",
"0.5408854",
"0.53556013",
"0.5328166",
"0.53233224",
"0.5313902",
"0.5301... | 0.77210146 | 0 |
convert area in rad^2 to km^2 | def area_rad_to_km(area_rad):
r_earth = 6.37122e3 # SHR_CONST_REARTH, in km
circ = 2*np.pi*r_earth
foo = xr.ufuncs.sqrt(area_rad.copy())
foo *= r_earth
area_km = foo**2
return area_km | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_ha_to_km2(nb):\n return nb / 100",
"def rad(area) :\n return sqrt(area/pi)",
"def km2m(km):\n return km * 1000",
"def km2_area(polygons):\n\n reprojected_polygons = [reproject(p) for p in polygons]\n return ops.cascaded_union(reprojected_polygons).area * 1e-6",
"def mi_to_m... | [
"0.71732515",
"0.6853367",
"0.6772503",
"0.6647387",
"0.65435016",
"0.65118784",
"0.6350859",
"0.6348541",
"0.63110465",
"0.622082",
"0.61698097",
"0.61480993",
"0.61387134",
"0.6091799",
"0.60879374",
"0.60483783",
"0.60402596",
"0.60270166",
"0.60109943",
"0.6004886",
"0.59... | 0.80855316 | 0 |
given a coupler history file, opened as Xarray dataset, print both the LAND and GLC icemask extent in km^2 By default, take the cell area from the mapping file | def print_CPL_icemask_areas(ds,
area_xg='domg_aream', area_xl='doml_aream',
fldname_xg='g2x_Sg_icemask', fldname_xl='x2l_Sg_icemask'):
glc_area = ds[area_xg].squeeze()
lnd_area = ds[area_xl].squeeze()
# convert area to km^2
glc_area_km = area_rad... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getCl(filename):\n powSpec = pf.getdata(filename,1)\n temps = powSpec.field('TEMPERATURE')\n ell = np.arange(temps.size)\n return ell,temps",
"def openFullProfFile(self, filename):\n handle = open(filename)\n lines = handle.readlines()\n handle.close()\n atoms = []\n bo... | [
"0.555418",
"0.54992247",
"0.52755046",
"0.5268108",
"0.5262233",
"0.52601403",
"0.51926464",
"0.5186282",
"0.51615363",
"0.5161252",
"0.5121497",
"0.5097654",
"0.5093182",
"0.5093085",
"0.5066861",
"0.50287604",
"0.5013134",
"0.4981871",
"0.4980489",
"0.4980489",
"0.49727952... | 0.0 | -1 |
given a coupler history file, opened as Xarray dataset, print both the LAND and GLC glacier cover extent in km^2 By default, take the cell area from the mapping file | def print_CPL_glaciercover_areas(ds,
area_xg='domg_aream', area_xl='doml_aream',
fldname_xg='g2x_Sg_ice_covered', fldname_xl='x2l_Sg_ice_covered'):
glc_area = ds[area_xg].squeeze()
lnd_area = ds[area_xl].squeeze()
# convert area to km^2
glc_area_k... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GeneralProfile(binsz, filename):\n data_image = fits.open(filename)\n data_image= data_image[1]\n glons = np.arange(LonLow, LonHigh+binsz, binsz) \n glon_bounds = Table()\n glon_bounds['CHANNEL'] = np.arange(len(glons) - 1)\n glon_bounds['GLON_MIN'] = np.float64(glons[:-1])\n glon_bound... | [
"0.5609589",
"0.5601541",
"0.55048555",
"0.54529756",
"0.54399264",
"0.54399264",
"0.54047024",
"0.5373725",
"0.5327029",
"0.5310322",
"0.5244416",
"0.52235144",
"0.51699317",
"0.5160779",
"0.5159728",
"0.5151086",
"0.5126497",
"0.5102221",
"0.5094817",
"0.50947016",
"0.50794... | 0.49858493 | 32 |
Process the VariableDeclarator(s) declared in this VariableDeclaration object. A VariableDeclarator is added to the children of this VariableDeclaration object | def _processDecl(self, decl):
if not decl:
pass
elif isinstance(decl, list):
self.setNumChildren(len(decl))
count = 0
for k in decl:
if isinstance(k, Declarator):
self.setChild(count, k)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _declaration_variable(self, node: ET.Element):\n # variable names\n variables_and_values = self.transform_all_subnodes(\n self.get_one(node, './variables'), skip_empty=True,\n ignored={'entity-decl-list__begin', 'entity-decl-list','attr-spec' })\n if not variables_and... | [
"0.6810748",
"0.6578837",
"0.6053343",
"0.59713",
"0.5935473",
"0.59103817",
"0.5832406",
"0.5818021",
"0.5752003",
"0.5744763",
"0.5647307",
"0.5632013",
"0.56183445",
"0.55835354",
"0.5564148",
"0.55624855",
"0.5562174",
"0.55372584",
"0.5528148",
"0.55208623",
"0.5486831",... | 0.5426866 | 25 |
The specifier(s) associated with this VariableDeclaration is added to the _spec __slots__ list. For example, specifiers like int or char | def _processSpec(self, spec):
if isinstance(spec, list):
for k in spec:
if isinstance(k, Specifier):
self._spec.append(k)
else:
raise NotAValidSpecifierError(str(type(k)))
elif isinstance(spec, Specifier):
se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_slots(self, var_list):\n # No slots needed by default\n for v in var_list:\n self._zeros_slot(v, \"m\", self._name)\n self._zeros_slot(v, \"v\", self._name)",
"def _create_slots(self, var_list):\n # No slots needed by default\n for v in var_list:\n ... | [
"0.52604717",
"0.52604717",
"0.5201867",
"0.51641715",
"0.51444745",
"0.4977457",
"0.49642998",
"0.49199918",
"0.49157214",
"0.48912543",
"0.48886278",
"0.48856738",
"0.48667434",
"0.48653454",
"0.4856379",
"0.48158437",
"0.48061067",
"0.4801965",
"0.47877678",
"0.4766531",
"... | 0.49605078 | 7 |
Initializes VariableDeclaration class with a list or singleton of leading specifiers, and a list or singleton of VariableDeclarators. Specifiers are stored in __slots__._spec while the Declarators are set as the Traversable's children. | def __init__(self, spec, decl=None):
self._spec = []
self.initialize()
self._processDecl(decl)
self._processSpec(spec) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _declaration_variable(self, node: ET.Element):\n # variable names\n variables_and_values = self.transform_all_subnodes(\n self.get_one(node, './variables'), skip_empty=True,\n ignored={'entity-decl-list__begin', 'entity-decl-list','attr-spec' })\n if not variables_and... | [
"0.6275647",
"0.6155617",
"0.578357",
"0.57416016",
"0.5729255",
"0.5677622",
"0.5557472",
"0.55549604",
"0.5540802",
"0.55403185",
"0.5524431",
"0.55118006",
"0.55104923",
"0.547801",
"0.54737693",
"0.5459073",
"0.5390455",
"0.53899175",
"0.5386324",
"0.5367685",
"0.53676826... | 0.56387484 | 6 |
Returns the Ansi C representation of this object, for example int a,b | def __repr__(self):
retval = ''
for k in self._spec:
retval += '%s ' % k
retval += ','.join([repr(k) for k in self.getChildren()])
return retval | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def coerce(self, c):\n\t# Default: use printable representation\n\treturn `c`",
"def __int__(self):\n return int(str(self),2)",
"def c_str(self):\n return _libsbml.string_c_str(self)",
"def SBO_intToString(*args):\n return _libsbml.SBO_intToString(*args)",
"def intToString(*args):\n r... | [
"0.57383925",
"0.5585024",
"0.55128294",
"0.54797655",
"0.53910303",
"0.53130233",
"0.53029615",
"0.52937627",
"0.5279769",
"0.5232459",
"0.52185833",
"0.5182179",
"0.51701224",
"0.5169652",
"0.5156103",
"0.5145193",
"0.51357955",
"0.5125165",
"0.51012224",
"0.5080231",
"0.50... | 0.0 | -1 |
Used by pickle or copy to get the state associated with the class. The returned items dict contains _spec object from hir.his class and the contents of the __slots__ of the bases of this class | def items(self):
items = {}
items['_spec'] = self._spec
for k in VariableDeclaration.__bases__:
if hasattr(k, 'items'):
supitems = k.items(self)
for k, v in list(supitems.items()):
items[k] = v
return dict(items) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getstate__(self):\n result_dict = {}\n for pickle_attr in self.pickle_attrs:\n result_dict[pickle_attr] = self.__dict__[pickle_attr]\n return result_dict",
"def __getstate__(self):\n\n state = {}\n for key in self.__slots__:\n state[key] = getattr(se... | [
"0.7187282",
"0.71103054",
"0.7069516",
"0.7069516",
"0.6906408",
"0.6862938",
"0.68552864",
"0.6698801",
"0.6671827",
"0.6624724",
"0.65399987",
"0.6491391",
"0.64735425",
"0.6471728",
"0.6461241",
"0.64488274",
"0.64174575",
"0.6403766",
"0.6386938",
"0.63760126",
"0.635302... | 0.59476286 | 49 |
Returns the results of self.items() call when called by pickle or copy | def __getstate__(self):
return dict(self.items()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self):\n return self.get_items()",
"def __call__(self):\n return self._main._items()",
"def __call__(self):\n return self._main._values()",
"def iteritems(self):",
"def get_items(self):\r\n return self.items()",
"def call(self) -> List[Dict]:",
"def get(self):\n... | [
"0.7648818",
"0.7183077",
"0.7022631",
"0.68654466",
"0.6727224",
"0.66725326",
"0.6459323",
"0.6459323",
"0.63707757",
"0.6357433",
"0.63273364",
"0.6308503",
"0.6262151",
"0.6262151",
"0.62585175",
"0.6251573",
"0.6216152",
"0.6208589",
"0.6191592",
"0.6191592",
"0.6166413"... | 0.67192966 | 6 |
Blindly sets state based on the items like statedict | def __setstate__(self, statedict):
for k, v in list(statedict.items()):
setattr(self, k, v) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_state( self ):",
"def set_state(canvas, state):\n for key, value in state.items():\n set_attribute(canvas, key, value)",
"def __setstate__(self, state):\n exclude_keys = ['_is_zero', '_is_positive', '_is_nonneg']\n for key,value in state.items():\n if key in exclude_k... | [
"0.6640367",
"0.6613323",
"0.6503467",
"0.64254403",
"0.6403489",
"0.6403489",
"0.61876637",
"0.61295795",
"0.6122762",
"0.61179495",
"0.60977346",
"0.6040049",
"0.60339725",
"0.6022381",
"0.5904621",
"0.5903245",
"0.58718616",
"0.5868567",
"0.5868567",
"0.5859888",
"0.584155... | 0.6726932 | 1 |
Return twotuple consisting of last tick and timestamp according to the provider. | def lastTick(): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def last_tick_time(self):\n return self.last_tick_",
"def _get_inner_time(self):\n timestamp = self.last_two_timestamps[0]\n if not timestamp:\n return '0000-00-00 00:00:00'\n return str(timestamp)",
"def CopyToStatTimeTuple(self):\n if self._number_of_seconds is None:\n ... | [
"0.6058038",
"0.5908135",
"0.5744919",
"0.57218426",
"0.56084853",
"0.55859977",
"0.55482435",
"0.55307746",
"0.5460453",
"0.54443234",
"0.5392926",
"0.53773177",
"0.5367498",
"0.53394985",
"0.53265816",
"0.53179586",
"0.52925235",
"0.5291002",
"0.5247732",
"0.52462095",
"0.5... | 0.54950005 | 8 |
Decorator for checking function arguments. | def accepts(*argtlst):
def acceptsfun(fun):
def accept(*args):
if len(args) != len(argtlst):
raise TypeError('Expected %d argument(s) (%d given)' % (len(argtlst), len(args)))
try:
return fun(*[ t(a) for (t,a) in zip(argtlst,args) ])
except ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_arg(function: Callable, arg_name: str, value: Any) -> None:\n Annotation = function.__annotations__.get(arg_name)\n if not is_valid(value, Annotation):\n raise ArgumentError(function, arg_name, value)",
"def check_inputs(function):\n def decorated(self, data, *args, **kwargs):\n ... | [
"0.7241338",
"0.71735525",
"0.70431894",
"0.70084655",
"0.69977546",
"0.6993821",
"0.69763994",
"0.69633996",
"0.69536203",
"0.69426346",
"0.6865531",
"0.6863752",
"0.6848815",
"0.6838962",
"0.683391",
"0.6827481",
"0.68118894",
"0.67408526",
"0.6676843",
"0.6661639",
"0.6583... | 0.0 | -1 |
Create a new enum class with the given names and values. | def Enum(name,names,values=None):
e = new.classobj(name,(EnumBase,),{})
e._initialize(names,values)
return e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_enum(name, fields, values=None):\n Enum = namedtuple(name, fields)\n if values is None:\n return Enum(*fields)\n return Enum(*values)",
"def Enum(name,names,values=None):\n e = type(name,(EnumBase,),{})\n e._initialize(names,values)\n return e",
"def enum(cls, options, value... | [
"0.8069038",
"0.77621424",
"0.7693179",
"0.67224294",
"0.64560413",
"0.64129335",
"0.6399653",
"0.6381578",
"0.6362524",
"0.6356486",
"0.6356486",
"0.6356486",
"0.6356486",
"0.6295191",
"0.6264949",
"0.622943",
"0.61205715",
"0.6103254",
"0.6100767",
"0.6011548",
"0.597129",
... | 0.79247373 | 1 |
Check out a license feature from the license server ahead of time. checkoutlicense(self,feature_) | def checkoutlicense(self,feature_):
res = __library__.MSK_XX_checkoutlicense(self.__nativep,feature_)
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkoutlicense(self,feature_): # 3\n if not isinstance(feature_,feature): raise TypeError(\"Argument feature has wrong type\")\n res = self.__obj.checkoutlicense(feature_)\n if res != 0:\n raise Error(rescode(res),\"\")",
"def checkinlicense(self,feature_): # 3\n if not isinstance... | [
"0.8759661",
"0.76959074",
"0.7425138",
"0.5600213",
"0.5537508",
"0.5521356",
"0.548995",
"0.54747057",
"0.5455874",
"0.5384995",
"0.53284734",
"0.5324986",
"0.5264816",
"0.511373",
"0.5108549",
"0.5074378",
"0.50742406",
"0.506557",
"0.49946",
"0.4989902",
"0.49843487",
"... | 0.8638246 | 1 |
Check in a license feature back to the license server ahead of time. checkinlicense(self,feature_) | def checkinlicense(self,feature_):
res = __library__.MSK_XX_checkinlicense(self.__nativep,feature_)
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkinlicense(self,feature_): # 3\n if not isinstance(feature_,feature): raise TypeError(\"Argument feature has wrong type\")\n res = self.__obj.checkinlicense(feature_)\n if res != 0:\n raise Error(rescode(res),\"\")",
"def checkoutlicense(self,feature_):\n res = __library__.MSK_XX... | [
"0.8578027",
"0.7463757",
"0.7390654",
"0.5651141",
"0.5581003",
"0.5463787",
"0.5270471",
"0.52479464",
"0.5114471",
"0.5090737",
"0.5089456",
"0.503208",
"0.49925858",
"0.4985686",
"0.4984008",
"0.49691615",
"0.49654573",
"0.49629948",
"0.49579397",
"0.4957077",
"0.49551493... | 0.8341613 | 1 |
Check in all unused license features to the license token server. checkinall(self) | def checkinall(self):
res = __library__.MSK_XX_checkinall(self.__nativep)
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkinlicense(self,feature_):\n res = __library__.MSK_XX_checkinlicense(self.__nativep,feature_)\n if res != 0:\n raise Error(rescode(res),Env.getcodedesc(rescode(res))[1])",
"def checkinlicense(self,feature_): # 3\n if not isinstance(feature_,feature): raise TypeError(\"Argument feature has... | [
"0.63698614",
"0.6221822",
"0.5931282",
"0.5740085",
"0.5677093",
"0.55289596",
"0.542468",
"0.5373415",
"0.52878785",
"0.5231693",
"0.5146559",
"0.5137509",
"0.5130173",
"0.51154923",
"0.5080095",
"0.5038724",
"0.5035732",
"0.5019301",
"0.50055707",
"0.49882403",
"0.4987122"... | 0.5811207 | 3 |
Preallocates a thread pool. setupthreads(self,numthreads_) | def setupthreads(self,numthreads_):
res = __library__.MSK_XX_setupthreads(self.__nativep,numthreads_)
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _setup_workers(self, num_workers):\n self.pool = []\n\n for _ in range(num_workers):\n self.pool.append(Thread(target=self.threadloop))\n\n for a_thread in self.pool:\n a_thread.setDaemon(True)\n a_thread.start()",
"def setNumThreads(self, num):\r\n ... | [
"0.67134285",
"0.664693",
"0.66045773",
"0.65749764",
"0.65331626",
"0.6397342",
"0.63319147",
"0.6289505",
"0.6270324",
"0.6233809",
"0.6203612",
"0.6121451",
"0.6111992",
"0.61068666",
"0.6090031",
"0.60645485",
"0.603164",
"0.60059214",
"0.5992909",
"0.59382534",
"0.59107"... | 0.71301144 | 0 |
Prints an intro to message stream. echointro(self,longver_) | def echointro(self,longver_):
res = __library__.MSK_XX_echointro(self.__nativep,longver_)
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def echointro(self,longver_): # 3\n res = self.__obj.echointro(longver_)\n if res != 0:\n raise Error(rescode(res),\"\")",
"def print_intro(self):\n \n print(\"Did you know that most insect migrations are intergenerational, meaning that offspring continue their parent's journey?\\n... | [
"0.67646956",
"0.6153699",
"0.6104376",
"0.59173465",
"0.58073634",
"0.56515396",
"0.550261",
"0.5483312",
"0.54654086",
"0.54641277",
"0.54259765",
"0.54012567",
"0.5313122",
"0.5312723",
"0.5293063",
"0.5257486",
"0.52515185",
"0.5221724",
"0.521526",
"0.5208508",
"0.520083... | 0.58583564 | 4 |
Obtains a short description of a response code. getcodedesc(code_) | def getcodedesc(code_):
symname_ = (ctypes.c_char * value.max_str_len)()
str_ = (ctypes.c_char * value.max_str_len)()
res = __library__.MSK_XX_getcodedesc(code_,symname_,str_)
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1])
_symname_retval = symname_.value.decode("utf-8... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_description(self, code):\n try:\n return self.message[str(code)]\n except KeyError:\n return \"Unknown (\" + str(code) + \")\"",
"def get_error_description(self, code):\n self.c.execute(\"SELECT * FROM errorcode WHERE code=%d\" % code)\n return self.c.fet... | [
"0.7868941",
"0.7265099",
"0.71852154",
"0.7024114",
"0.68930936",
"0.65707564",
"0.6471099",
"0.61716706",
"0.6068444",
"0.60453534",
"0.5955093",
"0.59224325",
"0.59201384",
"0.5877441",
"0.5869272",
"0.5843591",
"0.584061",
"0.58223593",
"0.58223593",
"0.58223593",
"0.5772... | 0.7308555 | 1 |
Obtains MOSEK version information. getversion() | def getversion():
major_ = ctypes.c_int32()
minor_ = ctypes.c_int32()
revision_ = ctypes.c_int32()
res = __library__.MSK_XX_getversion(ctypes.byref(major_),ctypes.byref(minor_),ctypes.byref(revision_))
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1])
major_ = major_.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_version(self):",
"def get_version(self):\n pass",
"def version():\n\n pass",
"def get_version():\r\n return __version__",
"def get_version():\n return 1",
"def version(self):\n return self._get(\"version\")",
"def version():\n return __VERSION__",
"def getversion(se... | [
"0.75196946",
"0.72990847",
"0.7296857",
"0.7293538",
"0.72827107",
"0.72488153",
"0.72436064",
"0.72343117",
"0.72194815",
"0.7173161",
"0.71342564",
"0.710485",
"0.710485",
"0.710485",
"0.7074583",
"0.7065602",
"0.7065602",
"0.7030908",
"0.7029974",
"0.7016896",
"0.7016896"... | 0.657121 | 88 |
Directs all output from a 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_linkfiletoenvstream(self.__nativep,whichstream_,filename_,append_)
if res != 0:
raise Error(rescode(res),Env.getcodedesc(re... | {
"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)\... | [
"0.8230284",
"0.8075898",
"0.78177077",
"0.5887671",
"0.5797239",
"0.5779203",
"0.5750145",
"0.5621454",
"0.5610482",
"0.5507235",
"0.54676473",
"0.53902507",
"0.5375923",
"0.52373123",
"0.5158702",
"0.5143754",
"0.5114576",
"0.5060143",
"0.5022461",
"0.49955034",
"0.4979789"... | 0.7388001 | 3 |
Enables debug information for the license system. putlicensedebug(self,licdebug_) | def putlicensedebug(self,licdebug_):
res = __library__.MSK_XX_putlicensedebug(self.__nativep,licdebug_)
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putlicensedebug(self,licdebug_): # 3\n res = self.__obj.putlicensedebug(licdebug_)\n if res != 0:\n raise Error(rescode(res),\"\")",
"def set_debug(self, debug):\n self.debug = debug",
"def debug(self, debug):\n\n self._debug = debug",
"def setDebug():\n\tglobal debug\n\tde... | [
"0.89798224",
"0.6667688",
"0.6555875",
"0.6333619",
"0.6249309",
"0.62011623",
"0.610661",
"0.60838914",
"0.6049035",
"0.59686905",
"0.59017515",
"0.58994114",
"0.58361554",
"0.57802033",
"0.5760369",
"0.5759169",
"0.5745018",
"0.5731944",
"0.5715388",
"0.57055396",
"0.56213... | 0.871731 | 1 |
Input a runtime license code. putlicensecode(self,code_) | def putlicensecode(self,code_):
_code_minlength = value.license_buffer_length
if value.license_buffer_length > 0 and code_ is not None and len(code_) != value.license_buffer_length:
raise ValueError("Array argument code is not long enough: Is %d, expected %d" % (len(code_),value.license_buffer_length))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putlicensecode(self,code): # 3\n if code is None:\n code_ = None\n else:\n try:\n code_ = memoryview(code)\n except TypeError:\n try:\n _tmparr_code = array.array(\"i\",code)\n except TypeError:\n raise TypeError(\"Argument code ha... | [
"0.8494057",
"0.69254506",
"0.6896676",
"0.6758022",
"0.66151047",
"0.6599839",
"0.6433473",
"0.6400917",
"0.6400902",
"0.6374781",
"0.6374781",
"0.6374159",
"0.6374159",
"0.6353943",
"0.634261",
"0.6289735",
"0.6273301",
"0.62477654",
"0.6194605",
"0.6178612",
"0.61690074",
... | 0.8169754 | 1 |
Control whether mosek should wait for an available license if no license is available. putlicensewait(self,licwait_) | def putlicensewait(self,licwait_):
res = __library__.MSK_XX_putlicensewait(self.__nativep,licwait_)
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putlicensewait(self,licwait_): # 3\n res = self.__obj.putlicensewait(licwait_)\n if res != 0:\n raise Error(rescode(res),\"\")",
"def license(self, license):\n\n self._license = license",
"def accept_license():\r\n msg, status = \"\", True\r\n\r\n try:\r\n sleep(5)\r\n ... | [
"0.8608505",
"0.61256635",
"0.6123673",
"0.5970834",
"0.5939061",
"0.58548045",
"0.5833694",
"0.5822022",
"0.57427907",
"0.5724147",
"0.56733525",
"0.5650425",
"0.5636824",
"0.5599726",
"0.5547277",
"0.5536273",
"0.5516888",
"0.5485797",
"0.5453408",
"0.5441463",
"0.5418717",... | 0.84320354 | 1 |
Set the path to the license file. putlicensepath(self,licensepath_) | def putlicensepath(self,licensepath_):
if isinstance(licensepath_,unicode):
licensepath_ = licensepath_.encode("utf-8",errors="replace")
res = __library__.MSK_XX_putlicensepath(self.__nativep,licensepath_)
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def putlicensepath(self,licensepath_): # 3\n res = self.__obj.putlicensepath(licensepath_)\n if res != 0:\n raise Error(rescode(res),\"\")",
"def license(self, license):\n\n self._license = license",
"def upload_license(self):\n param = self.module.params[\"param\"]\n lice... | [
"0.88523585",
"0.71379733",
"0.6935849",
"0.67722934",
"0.67470366",
"0.6633295",
"0.63802004",
"0.6364983",
"0.6364983",
"0.6364983",
"0.6364983",
"0.6364983",
"0.6356385",
"0.6281657",
"0.62535214",
"0.62287545",
"0.6200384",
"0.61479205",
"0.6000918",
"0.59174573",
"0.5909... | 0.8595367 | 1 |
Computes vector addition and multiplication by a scalar. axpy(self,n_,alpha_,x_,y_) | def axpy(self,n_,alpha_,x_,y_):
_x_minlength = (n_)
if (n_) > 0 and x_ is not None and len(x_) != (n_):
raise ValueError("Array argument x is not long enough: Is %d, expected %d" % (len(x_),(n_)))
if x_ is None:
raise ValueError("Argument x cannot be None")
if x_ is None:
raise ValueEr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def axpy(self,n_,alpha_,x,y): # 3\n if x is None: raise TypeError(\"Invalid type for argument x\")\n if x is None:\n x_ = None\n else:\n try:\n x_ = memoryview(x)\n except TypeError:\n try:\n _tmparr_x = array.array(\"d\",x)\n except TypeErr... | [
"0.74733484",
"0.60606563",
"0.60547745",
"0.5907904",
"0.5887075",
"0.58122003",
"0.57062954",
"0.55805856",
"0.5475544",
"0.5468158",
"0.54651934",
"0.54209197",
"0.541921",
"0.5392053",
"0.5377396",
"0.5374282",
"0.5341102",
"0.5317172",
"0.5305736",
"0.52814037",
"0.52740... | 0.7002267 | 1 |
Computes the inner product of two vectors. dot(self,n_,x_,y_) | def dot(self,n_,x_,y_):
_x_minlength = (n_)
if (n_) > 0 and x_ is not None and len(x_) != (n_):
raise ValueError("Array argument x is not long enough: Is %d, expected %d" % (len(x_),(n_)))
if x_ is None:
raise ValueError("Argument x cannot be None")
if x_ is None:
raise ValueError("Arg... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vec_dot(x, y):\r\n return sum(a * b for a, b in zip(x, y))",
"def vector_dot(x, y):\n\n if(len(x) != len(y)):\n raise ValueError(\"vector lengths differ\")\n else:\n # return x1*y1+x2*y2+...xn*yn\n return sum([x[i] * y[i] for i in range(len(x))])",
"def dot(x,y):\n\treturn sum([... | [
"0.7724258",
"0.7549486",
"0.7496103",
"0.7352206",
"0.732253",
"0.73218113",
"0.73188835",
"0.72784954",
"0.7264976",
"0.7238966",
"0.72267455",
"0.72038215",
"0.7178318",
"0.7174941",
"0.7155059",
"0.71423924",
"0.7082818",
"0.70600927",
"0.70422834",
"0.7040143",
"0.703703... | 0.67537916 | 42 |
Computes dense matrix times a dense vector product. gemv(self,transa_,m_,n_,alpha_,a_,x_,beta_,y_) | def gemv(self,transa_,m_,n_,alpha_,a_,x_,beta_,y_):
_a_minlength = ((n_) * (m_))
if ((n_) * (m_)) > 0 and a_ is not None and len(a_) != ((n_) * (m_)):
raise ValueError("Array argument a is not long enough: Is %d, expected %d" % (len(a_),((n_) * (m_))))
if a_ is None:
raise ValueError("Argument a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gemv(self,transa_,m_,n_,alpha_,a,x,beta_,y): # 3\n if not isinstance(transa_,transpose): raise TypeError(\"Argument transa has wrong type\")\n if a is None: raise TypeError(\"Invalid type for argument a\")\n if a is None:\n a_ = None\n else:\n try:\n a_ = memoryview(a... | [
"0.79829377",
"0.7744591",
"0.6858341",
"0.6395874",
"0.6372895",
"0.6168905",
"0.5858304",
"0.5748902",
"0.5633987",
"0.5538611",
"0.5522281",
"0.5460428",
"0.54414135",
"0.53847235",
"0.5364882",
"0.5344385",
"0.53304935",
"0.5325173",
"0.5298962",
"0.52883595",
"0.5280534"... | 0.792816 | 1 |
Performs a dense matrix multiplication. gemm(self,transa_,transb_,m_,n_,k_,alpha_,a_,b_,beta_,c_) | def gemm(self,transa_,transb_,m_,n_,k_,alpha_,a_,b_,beta_,c_):
_a_minlength = ((m_) * (k_))
if ((m_) * (k_)) > 0 and a_ is not None and len(a_) != ((m_) * (k_)):
raise ValueError("Array argument a is not long enough: Is %d, expected %d" % (len(a_),((m_) * (k_))))
if a_ is None:
raise ValueError(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gemm(self,transa_,transb_,m_,n_,k_,alpha_,a,b,beta_,c): # 3\n if not isinstance(transa_,transpose): raise TypeError(\"Argument transa has wrong type\")\n if not isinstance(transb_,transpose): raise TypeError(\"Argument transb has wrong type\")\n if a is None: raise TypeError(\"Invalid type for a... | [
"0.797159",
"0.6984396",
"0.68837875",
"0.6616794",
"0.6429315",
"0.6350331",
"0.6247334",
"0.60708445",
"0.57550097",
"0.5749969",
"0.5730603",
"0.5723331",
"0.56517935",
"0.56435114",
"0.5592844",
"0.5431652",
"0.5431652",
"0.5407086",
"0.53182805",
"0.5267758",
"0.51957977... | 0.7851844 | 1 |
Performs a rankk update of a symmetric matrix. syrk(self,uplo_,trans_,n_,k_,alpha_,a_,beta_,c_) | def syrk(self,uplo_,trans_,n_,k_,alpha_,a_,beta_,c_):
_a_minlength = ((n_) * (k_))
if ((n_) * (k_)) > 0 and a_ is not None and len(a_) != ((n_) * (k_)):
raise ValueError("Array argument a is not long enough: Is %d, expected %d" % (len(a_),((n_) * (k_))))
if a_ is None:
raise ValueError("Argument... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def syrk(self,uplo_,trans_,n_,k_,alpha_,a,beta_,c): # 3\n if not isinstance(uplo_,uplo): raise TypeError(\"Argument uplo has wrong type\")\n if not isinstance(trans_,transpose): raise TypeError(\"Argument trans has wrong type\")\n if a is None: raise TypeError(\"Invalid type for argument a\")\n ... | [
"0.7740319",
"0.5345925",
"0.5307275",
"0.5296073",
"0.51972955",
"0.51492083",
"0.51247174",
"0.5056189",
"0.50554264",
"0.5031491",
"0.5031491",
"0.50260574",
"0.50148124",
"0.49368647",
"0.4909689",
"0.48912278",
"0.4885631",
"0.48802838",
"0.47982484",
"0.4793419",
"0.478... | 0.6716742 | 1 |
Computes a Cholesky factorization of sparse matrix. computesparsecholesky(self,multithread_,ordermethod_,tolsingular_,anzc_,aptrc_,asubc_,avalc_) | def computesparsecholesky(self,multithread_,ordermethod_,tolsingular_,anzc_,aptrc_,asubc_,avalc_):
n_ = None
if n_ is None:
n_ = len(anzc_)
elif n_ != len(anzc_):
raise IndexError("Inconsistent length of array anzc")
if n_ is None:
n_ = len(aptrc_)
elif n_ != len(aptrc_):
rai... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def computesparsecholesky(self,multithread_,ordermethod_,tolsingular_,anzc,aptrc,asubc,avalc): # 3\n n_ = None\n if n_ is None:\n n_ = len(anzc)\n elif n_ != len(anzc):\n raise IndexError(\"Inconsistent length of array anzc\")\n if n_ is None:\n n_ = len(aptrc)\n elif ... | [
"0.7571543",
"0.5584805",
"0.5499678",
"0.5440372",
"0.53172606",
"0.52564216",
"0.52468324",
"0.52155113",
"0.51478595",
"0.514581",
"0.5113303",
"0.51123667",
"0.50142163",
"0.5004762",
"0.49805087",
"0.4961134",
"0.49372533",
"0.49125624",
"0.49123773",
"0.48156536",
"0.47... | 0.7723914 | 0 |
Solves a sparse triangular system of linear equations. sparsetriangularsolvedense(self,transposed_,lnzc_,lptrc_,lsubc_,lvalc_,b_) | def sparsetriangularsolvedense(self,transposed_,lnzc_,lptrc_,lsubc_,lvalc_,b_):
n_ = None
if n_ is None:
n_ = len(b_)
elif n_ != len(b_):
raise IndexError("Inconsistent length of array b")
if n_ is None:
n_ = len(lnzc_)
elif n_ != len(lnzc_):
raise IndexError("Inconsistent le... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sparsetriangularsolvedense(self,transposed_,lnzc,lptrc,lsubc,lvalc,b): # 3\n if not isinstance(transposed_,transpose): raise TypeError(\"Argument transposed has wrong type\")\n n_ = None\n if n_ is None:\n n_ = len(b)\n elif n_ != len(b):\n raise IndexError(\"Inconsistent leng... | [
"0.8438944",
"0.54154617",
"0.5376753",
"0.53284895",
"0.5311217",
"0.52064824",
"0.51942706",
"0.51143026",
"0.49586394",
"0.4949625",
"0.49304515",
"0.49237528",
"0.48860884",
"0.4882467",
"0.48812094",
"0.48605898",
"0.48446497",
"0.4842036",
"0.48256987",
"0.48181957",
"0... | 0.8483068 | 0 |
Computes a Cholesky factorization of a dense matrix. potrf(self,uplo_,n_,a_) | def potrf(self,uplo_,n_,a_):
_a_minlength = ((n_) * (n_))
if ((n_) * (n_)) > 0 and a_ is not None and len(a_) != ((n_) * (n_)):
raise ValueError("Array argument a is not long enough: Is %d, expected %d" % (len(a_),((n_) * (n_))))
if isinstance(a_,numpy.ndarray) and not a_.flags.writeable:
raise ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cholesky(A):\n n = len(A)\n\n # Create zero matrix for L\n L=np.zeros((n,n))\n\n # Perform the Cholesky decomposition\n for i in range(n):\n for k in range(i+1):\n tmp_sum = sum(L[i][j] * L[k][j] for j in xrange(k))\n \n if (i == k): # Diagonal elements\n\... | [
"0.6387226",
"0.61609155",
"0.59897465",
"0.5824701",
"0.58079535",
"0.57489014",
"0.5693151",
"0.5668508",
"0.55993855",
"0.55892885",
"0.5545973",
"0.54544604",
"0.54271024",
"0.5358241",
"0.52774936",
"0.52692854",
"0.52478313",
"0.5180107",
"0.5173138",
"0.5172021",
"0.51... | 0.59195656 | 3 |
Computes all eigenvalues of a symmetric dense matrix. syeig(self,uplo_,n_,a_,w_) | def syeig(self,uplo_,n_,a_,w_):
_a_minlength = ((n_) * (n_))
if ((n_) * (n_)) > 0 and a_ is not None and len(a_) != ((n_) * (n_)):
raise ValueError("Array argument a is not long enough: Is %d, expected %d" % (len(a_),((n_) * (n_))))
if a_ is None:
raise ValueError("Argument a cannot be None")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def syeig(self,uplo_,n_,a,w): # 3\n if not isinstance(uplo_,uplo): raise TypeError(\"Argument uplo has wrong type\")\n if a is None: raise TypeError(\"Invalid type for argument a\")\n if a is None:\n a_ = None\n else:\n try:\n a_ = memoryview(a)\n except TypeError:... | [
"0.81746787",
"0.65293",
"0.6337791",
"0.6227501",
"0.61773306",
"0.61583877",
"0.6139027",
"0.6113723",
"0.6071404",
"0.5926805",
"0.5913415",
"0.5867152",
"0.5759248",
"0.574515",
"0.5692723",
"0.5674269",
"0.5673998",
"0.56041884",
"0.5596572",
"0.55924916",
"0.5584967",
... | 0.7566581 | 1 |
Computes all the eigenvalues and eigenvectors of a symmetric dense matrix, and thus its eigenvalue decomposition. syevd(self,uplo_,n_,a_,w_) | def syevd(self,uplo_,n_,a_,w_):
_a_minlength = ((n_) * (n_))
if ((n_) * (n_)) > 0 and a_ is not None and len(a_) != ((n_) * (n_)):
raise ValueError("Array argument a is not long enough: Is %d, expected %d" % (len(a_),((n_) * (n_))))
if isinstance(a_,numpy.ndarray) and not a_.flags.writeable:
rai... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def syeig(self,uplo_,n_,a,w): # 3\n if not isinstance(uplo_,uplo): raise TypeError(\"Argument uplo has wrong type\")\n if a is None: raise TypeError(\"Invalid type for argument a\")\n if a is None:\n a_ = None\n else:\n try:\n a_ = memoryview(a)\n except TypeError:... | [
"0.762131",
"0.7074867",
"0.69879603",
"0.62617326",
"0.61253196",
"0.6078475",
"0.60627306",
"0.59903747",
"0.5944606",
"0.59257424",
"0.58683413",
"0.579515",
"0.5727938",
"0.56910884",
"0.56848043",
"0.56688",
"0.56349427",
"0.5588005",
"0.55771166",
"0.55645275",
"0.55502... | 0.6196969 | 4 |
Stops all threads and delete all handles used by the license system. licensecleanup() | def licensecleanup():
res = __library__.MSK_XX_licensecleanup()
if res != 0:
raise Error(rescode(res),Env.getcodedesc(rescode(res))[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def licensecleanup(): # 3\n res = _msk.Env.licensecleanup()\n if res != 0:\n raise Error(rescode(res),\"\")",
"def cleanup(self):\n logger.debug('Beginning cleanup ...')\n self.stop()\n\n #Clear subscriptions\n for sub, dev in self.subs.items():\n dev.clear... | [
"0.69909203",
"0.6920652",
"0.68741477",
"0.67954016",
"0.67419183",
"0.66345876",
"0.6611523",
"0.65672064",
"0.65559095",
"0.65391237",
"0.64915305",
"0.64907163",
"0.6485269",
"0.64084905",
"0.6398197",
"0.6396374",
"0.6393423",
"0.6385401",
"0.6373598",
"0.63693166",
"0.6... | 0.69317514 | 1 |
Construct a new Task object. Task(env=None,maxnumcon=0,maxnumvar=0,nativep=None,other=None) | def __init__(self,env=None,maxnumcon=0,maxnumvar=0,nativep=None,other=None):
self.__library = __library__
self.__nativep = None
self.__schandle = None
self._lock = threading.RLock()
if isinstance(env,Task):
other = env
env = None
try:
if native... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, env: MTEnv, n_tasks: int):\n super().__init__(env=env)\n self.n_tasks = n_tasks\n self.tasks: List[TaskStateType]\n self._are_tasks_set = False",
"def __init__(self,target, name = \"\", prio = 10, period = 0, time2run = 0):\n Task.taskid += 1\n self.ti... | [
"0.6326176",
"0.603982",
"0.60264874",
"0.6022742",
"0.59993345",
"0.59907025",
"0.5958645",
"0.5920431",
"0.58973324",
"0.58927375",
"0.5887594",
"0.5862502",
"0.58127207",
"0.5787428",
"0.5779538",
"0.57600564",
"0.5736297",
"0.57311445",
"0.57209444",
"0.5698059",
"0.56828... | 0.7289506 | 0 |
Set the progress callback function. If func is None, progress callbacks are detached and disabled. | def set_Progress(self,func):
if func is None:
self.__progress_func = None
#res = self.__library.MSK_XX_putcallbackfunc(self.__nativep,None,None)
else:
self.__progress_func = func
res = self.__library.MSK_XX_putcallbackfunc(self.__nativep,self.__progress_cb,N... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_Progress(self,func):\n self.__obj.set_Progress(func)",
"def progress_callback(self, func):\n self.curl.setopt(pycurl.PROGRESSFUNCTION, func)",
"def svn_client_ctx_t_progress_func_set(svn_client_ctx_t_self, svn_ra_progress_notify_func_t_progress_func): # real signature unknown; restored fr... | [
"0.76210636",
"0.73132217",
"0.67322624",
"0.62459373",
"0.62381303",
"0.62043667",
"0.6173967",
"0.5974019",
"0.59440875",
"0.59214973",
"0.5781422",
"0.5741869",
"0.56839746",
"0.5624882",
"0.5602486",
"0.55898565",
"0.55845433",
"0.5564059",
"0.552527",
"0.548637",
"0.5454... | 0.7993551 | 0 |
Set the progress callback function. If func is None, progress callbacks are detached and disabled. | def set_InfoCallback(self,func):
if func is None:
self.__infocallback_func = None
#res = self.__library.MSK_XX_putcallbackfunc(self.__nativep,None,None)
else:
self.__infocallback_func = func
res = self.__library.MSK_XX_putcallbackfunc(self.__nativep,self.__p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_Progress(self,func):\n if func is None:\n self.__progress_func = None\n #res = self.__library.MSK_XX_putcallbackfunc(self.__nativep,None,None)\n else:\n self.__progress_func = func \n res = self.__library.MSK_XX_putcallbackfunc(self.__nativep,self.__pr... | [
"0.7993143",
"0.76209813",
"0.731291",
"0.6731336",
"0.62466186",
"0.62391365",
"0.6202586",
"0.6175435",
"0.5974019",
"0.59456486",
"0.5923007",
"0.5781629",
"0.5743591",
"0.56242794",
"0.56027734",
"0.559077",
"0.55824286",
"0.5566021",
"0.55251116",
"0.5486115",
"0.5453305... | 0.568447 | 13 |
Analyze the data of a task. analyzeproblem(self,whichstream_) | def analyzeproblem(self,whichstream_):
res = __library__.MSK_XX_analyzeproblem(self.__nativep,whichstream_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def analyzeproblem(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.analyzeproblem(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"... | [
"0.7656688",
"0.66234916",
"0.61678094",
"0.61584896",
"0.61026996",
"0.5949743",
"0.58778185",
"0.58460075",
"0.57688403",
"0.5664479",
"0.5660007",
"0.5589258",
"0.55565846",
"0.5520233",
"0.54727155",
"0.5431195",
"0.53835535",
"0.5372877",
"0.5285206",
"0.52802324",
"0.52... | 0.7232371 | 1 |
Analyze the names and issue an error for the first invalid name. analyzenames(self,whichstream_,nametype_) | def analyzenames(self,whichstream_,nametype_):
res = __library__.MSK_XX_analyzenames(self.__nativep,whichstream_,nametype_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def analyzenames(self,whichstream_,nametype_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n if not isinstance(nametype_,nametype): raise TypeError(\"Argument nametype has wrong type\")\n res = self.__obj.analyzenames(whichstream_,namety... | [
"0.856169",
"0.6125964",
"0.5851493",
"0.5766484",
"0.5649188",
"0.5622006",
"0.552477",
"0.55118906",
"0.55098695",
"0.5505015",
"0.55004114",
"0.54994583",
"0.5494788",
"0.5492459",
"0.5460644",
"0.5454948",
"0.54511714",
"0.544296",
"0.5436722",
"0.5407702",
"0.53509",
"... | 0.7843094 | 1 |
Print information related to the quality of the solution. analyzesolution(self,whichstream_,whichsol_) | def analyzesolution(self,whichstream_,whichsol_):
res = __library__.MSK_XX_analyzesolution(self.__nativep,whichstream_,whichsol_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def analyzesolution(self,whichstream_,whichsol_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.analyzesolution(whichstream_,w... | [
"0.75133455",
"0.73406655",
"0.73010904",
"0.71412873",
"0.6950169",
"0.6524369",
"0.6344713",
"0.6188728",
"0.6182085",
"0.61371076",
"0.6001918",
"0.59650385",
"0.58990616",
"0.5842757",
"0.5792607",
"0.57907444",
"0.5786922",
"0.5766608",
"0.57455885",
"0.56941146",
"0.569... | 0.7367534 | 1 |
Prepare a task for basis solver. initbasissolve(self,basis_) | def initbasissolve(self,basis_):
_basis_minlength = self.getnumcon()
if self.getnumcon() > 0 and basis_ is not None and len(basis_) != self.getnumcon():
raise ValueError("Array argument basis is not long enough: Is %d, expected %d" % (len(basis_),self.getnumcon()))
if isinstance(basis_,numpy.ndarray) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initbasissolve(self,basis): # 3\n _copyback_basis = False\n if basis is None:\n basis_ = None\n else:\n try:\n basis_ = memoryview(basis)\n except TypeError:\n try:\n _tmparr_basis = array.array(\"i\",basis)\n except TypeError:\n ... | [
"0.75565845",
"0.5916598",
"0.5722016",
"0.56191593",
"0.55989605",
"0.5507775",
"0.5439479",
"0.54063594",
"0.53515726",
"0.5298564",
"0.5275407",
"0.5211078",
"0.5209353",
"0.5185748",
"0.5182779",
"0.51719403",
"0.5151672",
"0.51347935",
"0.5133514",
"0.5122238",
"0.510710... | 0.72589576 | 1 |
Solve a linear equation system involving a basis matrix. solvewithbasis(self,transp_,numnz_,sub_,val_) | def solvewithbasis(self,transp_,numnz_,sub_,val_):
_numnz_tmp = ctypes.c_int32(numnz_)
_sub_minlength = self.getnumcon()
if self.getnumcon() > 0 and sub_ is not None and len(sub_) != self.getnumcon():
raise ValueError("Array argument sub is not long enough: Is %d, expected %d" % (len(sub_),self.getnum... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solvewithbasis(self,transp_,numnz_,sub,val): # 3\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(\"i\",sub)\n except TypeError:\n ... | [
"0.8131871",
"0.59714824",
"0.5729135",
"0.5628742",
"0.56091446",
"0.55008405",
"0.5451734",
"0.5344914",
"0.53239274",
"0.5203088",
"0.52024055",
"0.5150057",
"0.50754064",
"0.50556207",
"0.5001627",
"0.49995735",
"0.49937603",
"0.49653995",
"0.49574485",
"0.4943281",
"0.49... | 0.81305534 | 1 |
Computes conditioning information for the basis matrix. basiscond(self) | def basiscond(self):
nrmbasis_ = ctypes.c_double()
nrminvbasis_ = ctypes.c_double()
res = __library__.MSK_XX_basiscond(self.__nativep,ctypes.byref(nrmbasis_),ctypes.byref(nrminvbasis_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
nrmbasis_ = nrmbasis_.valu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def basiscond(self): # 3\n res,resargs = self.__obj.basiscond()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nrmbasis_return_value,_nrminvbasis_return_value = resargs\n return _nrmbasis_return_value,_nrminvbasis_return_value",
"def _t... | [
"0.7401494",
"0.64921606",
"0.57574993",
"0.5757078",
"0.57382345",
"0.5737791",
"0.57173383",
"0.5618264",
"0.56107277",
"0.56098473",
"0.5554291",
"0.5542242",
"0.55095696",
"0.54061496",
"0.53822345",
"0.53605485",
"0.53488797",
"0.53358144",
"0.5334709",
"0.5314764",
"0.5... | 0.7459991 | 0 |
Appends a number of constraints to the optimization task. appendcons(self,num_) | def appendcons(self,num_):
res = __library__.MSK_XX_appendcons(self.__nativep,num_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def appendcons(self,num_): # 3\n res = self.__obj.appendcons(num_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def add_constraint(self, constraint):\n constraint_type = constraint[0]\n if constraint_type == 'time':\n d... | [
"0.71618354",
"0.5837507",
"0.57036966",
"0.5674323",
"0.5663676",
"0.564805",
"0.5605173",
"0.5550933",
"0.55144083",
"0.544833",
"0.5390708",
"0.5379284",
"0.5327178",
"0.5318046",
"0.5295975",
"0.529086",
"0.52815723",
"0.5280168",
"0.527523",
"0.52032757",
"0.51928747",
... | 0.7185383 | 0 |
Appends a number of variables to the optimization task. appendvars(self,num_) | def appendvars(self,num_):
res = __library__.MSK_XX_appendvars(self.__nativep,num_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 addVars(self, *indexes, **kwargs):\n ...",
"def add_variables(self, variables):\n for variable in variables:\n ... | [
"0.7904469",
"0.59922993",
"0.5989691",
"0.5909687",
"0.5740387",
"0.56843954",
"0.5554662",
"0.55087805",
"0.5492639",
"0.54744667",
"0.5469596",
"0.5469596",
"0.5439085",
"0.54265416",
"0.5411829",
"0.54096377",
"0.5366153",
"0.5350174",
"0.53359",
"0.53321993",
"0.5321142"... | 0.77544904 | 1 |
Removes a number of constraints. removecons(self,subset_) | def removecons(self,subset_):
num_ = None
if num_ is None:
num_ = len(subset_)
elif num_ != len(subset_):
raise IndexError("Inconsistent length of array subset")
if subset_ is None:
raise ValueError("Argument subset cannot be None")
if subset_ is None:
raise ValueError("Argum... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def removecons(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\"... | [
"0.82700276",
"0.7332951",
"0.72566605",
"0.67324287",
"0.6559985",
"0.64388156",
"0.63277817",
"0.6175792",
"0.614257",
"0.5968138",
"0.5823671",
"0.580427",
"0.5789894",
"0.57380253",
"0.5684563",
"0.5578613",
"0.55290496",
"0.5512659",
"0.53915256",
"0.5372119",
"0.5369376... | 0.81802654 | 1 |
Removes a number of variables. removevars(self,subset_) | def removevars(self,subset_):
num_ = None
if num_ is None:
num_ = len(subset_)
elif num_ != len(subset_):
raise IndexError("Inconsistent length of array subset")
if subset_ is None:
raise ValueError("Argument subset cannot be None")
if subset_ is None:
raise ValueError("Argum... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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\"... | [
"0.8907224",
"0.78043014",
"0.7453004",
"0.6692764",
"0.6318491",
"0.6138598",
"0.59088767",
"0.58950776",
"0.5870665",
"0.58485174",
"0.5781053",
"0.5777436",
"0.5639282",
"0.5567257",
"0.55330926",
"0.54802483",
"0.5447446",
"0.53930205",
"0.53908974",
"0.5379356",
"0.53722... | 0.854354 | 1 |
Removes a number of symmetric matrices. removebarvars(self,subset_) | def removebarvars(self,subset_):
num_ = None
if num_ is None:
num_ = len(subset_)
elif num_ != len(subset_):
raise IndexError("Inconsistent length of array subset")
if subset_ is None:
raise ValueError("Argument subset cannot be None")
if subset_ is None:
raise ValueError("Ar... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 subse... | [
"0.8297461",
"0.72585315",
"0.72363526",
"0.5895367",
"0.5825849",
"0.5779745",
"0.5747303",
"0.5530704",
"0.55015457",
"0.5498184",
"0.5485593",
"0.531922",
"0.5292177",
"0.5290783",
"0.5279861",
"0.52415746",
"0.52012604",
"0.5192966",
"0.51445",
"0.51120937",
"0.509378",
... | 0.8265025 | 1 |
Removes a number of conic constraints from the problem. removecones(self,subset_) | def removecones(self,subset_):
num_ = None
if num_ is None:
num_ = len(subset_)
elif num_ != len(subset_):
raise IndexError("Inconsistent length of array subset")
if subset_ is None:
raise ValueError("Argument subset cannot be None")
if subset_ is None:
raise ValueError("Argu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def removecones(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\... | [
"0.83215594",
"0.7152217",
"0.7077424",
"0.6343125",
"0.609424",
"0.5977655",
"0.5930062",
"0.5876391",
"0.5876157",
"0.57800704",
"0.5628625",
"0.5434072",
"0.5406661",
"0.5321781",
"0.5312178",
"0.52586085",
"0.5258399",
"0.52080315",
"0.51944923",
"0.5176773",
"0.513047",
... | 0.8212127 | 1 |
Appends semidefinite variables to the problem. appendbarvars(self,dim_) | def appendbarvars(self,dim_):
num_ = None
if num_ is None:
num_ = len(dim_)
elif num_ != len(dim_):
raise IndexError("Inconsistent length of array dim")
if dim_ is None:
raise ValueError("Argument dim cannot be None")
if dim_ is None:
raise ValueError("Argument dim may not be... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def appendbarvars(self,dim): # 3\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 num_ is None: num_ = 0\n if dim is None: raise TypeError(\"Invalid type for argument dim\")\n if dim... | [
"0.8159918",
"0.55441445",
"0.53684646",
"0.533822",
"0.5304417",
"0.5274865",
"0.51826805",
"0.51651216",
"0.5160413",
"0.5143895",
"0.5126579",
"0.49842018",
"0.49332213",
"0.4922936",
"0.48779106",
"0.47955328",
"0.47735283",
"0.47632688",
"0.47520563",
"0.47460842",
"0.47... | 0.80840147 | 1 |
Appends a new conic constraint to the problem. appendcone(self,ct_,conepar_,submem_) | def appendcone(self,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 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\"... | [
"0.8244941",
"0.77045983",
"0.74703234",
"0.74599016",
"0.7063304",
"0.6341248",
"0.6041502",
"0.5657737",
"0.53261125",
"0.5275094",
"0.5257149",
"0.5240593",
"0.52378",
"0.5133575",
"0.51317424",
"0.51154685",
"0.51087177",
"0.50969726",
"0.5067879",
"0.50591534",
"0.501998... | 0.8216878 | 1 |
Appends a new conic constraint to the problem. appendconeseq(self,ct_,conepar_,nummem_,j_) | def appendconeseq(self,ct_,conepar_,nummem_,j_):
res = __library__.MSK_XX_appendconeseq(self.__nativep,ct_,conepar_,nummem_,j_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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)",
"... | [
"0.86378384",
"0.75416327",
"0.7458773",
"0.7380015",
"0.73679143",
"0.646462",
"0.6225315",
"0.51709515",
"0.5064763",
"0.48974988",
"0.48840624",
"0.48815274",
"0.48780966",
"0.4864251",
"0.48559266",
"0.47979966",
"0.4756608",
"0.4728465",
"0.47282267",
"0.4699578",
"0.467... | 0.86945903 | 0 |
Appends multiple conic constraints to the problem. appendconesseq(self,ct_,conepar_,nummem_,j_) | def appendconesseq(self,ct_,conepar_,nummem_,j_):
num_ = None
if num_ is None:
num_ = len(ct_)
elif num_ != len(ct_):
raise IndexError("Inconsistent length of array ct")
if num_ is None:
num_ = len(conepar_)
elif num_ != len(conepar_):
raise IndexError("Inconsistent length of... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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)",
"... | [
"0.804992",
"0.7942396",
"0.7373104",
"0.67235893",
"0.66512686",
"0.5923257",
"0.5694749",
"0.5581973",
"0.5278961",
"0.5160132",
"0.5134265",
"0.49960873",
"0.49393323",
"0.49215627",
"0.49015927",
"0.48836264",
"0.4847048",
"0.47998023",
"0.479868",
"0.479868",
"0.47911417... | 0.74851453 | 2 |
Changes the bounds for one constraint. chgconbound(self,i_,lower_,finite_,value_) | def chgconbound(self,i_,lower_,finite_,value_):
res = __library__.MSK_XX_chgconbound(self.__nativep,i_,lower_,finite_,value_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def chgconbound(self,i_,lower_,finite_,value_): # 3\n res = self.__obj.chgconbound(i_,lower_,finite_,value_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)",
"def chgbound(self,accmode_,i_,lower_,finite_,value_): # 3\n if not isinstance(accmode... | [
"0.9179256",
"0.7916872",
"0.7376792",
"0.7195413",
"0.6016417",
"0.6004763",
"0.5817389",
"0.5724509",
"0.5707932",
"0.56736284",
"0.5642968",
"0.5613745",
"0.5549648",
"0.5543372",
"0.55391896",
"0.5501184",
"0.5480884",
"0.5466385",
"0.5455747",
"0.54376006",
"0.543686",
... | 0.8795833 | 1 |
Changes the bounds for one variable. chgvarbound(self,j_,lower_,finite_,value_) | def chgvarbound(self,j_,lower_,finite_,value_):
res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 chgconbound(self,i_,lower_,finite_,value_): # 3\n res = self.__obj.chgconbound(i_... | [
"0.92657965",
"0.6645805",
"0.66179705",
"0.63044393",
"0.6285839",
"0.6257946",
"0.62345994",
"0.61521727",
"0.57639956",
"0.56784153",
"0.5577765",
"0.5573649",
"0.54211104",
"0.5403837",
"0.53660214",
"0.52223015",
"0.52110463",
"0.5164623",
"0.51575583",
"0.51482254",
"0.... | 0.8982534 | 1 |
Obtains a single coefficient in linear constraint matrix. getaij(self,i_,j_) | def getaij(self,i_,j_):
aij_ = ctypes.c_double()
res = __library__.MSK_XX_getaij(self.__nativep,i_,j_,ctypes.byref(aij_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
aij_ = aij_.value
_aij_return_value = aij_
return (_aij_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 getqobjij(self,i_,j_): # 3\n res,resargs = self.__obj.... | [
"0.7493986",
"0.64376247",
"0.6392298",
"0.6329551",
"0.6308506",
"0.6269593",
"0.6261952",
"0.6139124",
"0.61024755",
"0.61000454",
"0.60522926",
"0.5945203",
"0.5922942",
"0.59190106",
"0.5905818",
"0.5891535",
"0.5806236",
"0.578032",
"0.57758945",
"0.5755988",
"0.572439",... | 0.7658511 | 0 |
Obtains the number nonzeros in a rectangular piece of the linear constraint matrix. getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_) | def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_):
numnz_ = ctypes.c_int32()
res = __library__.MSK_XX_getapiecenumnz(self.__nativep,firsti_,lasti_,firstj_,lastj_,ctypes.byref(numnz_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
numnz_ = numnz_.value
_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getapiecenumnz(self,firsti_,lasti_,firstj_,lastj_): # 3\n res,resargs = self.__obj.getapiecenumnz(firsti_,lasti_,firstj_,lastj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numnz_return_value = resargs\n return _numnz_return_value"... | [
"0.81828666",
"0.7616695",
"0.75478375",
"0.7345593",
"0.7094305",
"0.69862217",
"0.6438003",
"0.6430326",
"0.61437994",
"0.5996406",
"0.57730126",
"0.5638592",
"0.55720025",
"0.5547465",
"0.5504438",
"0.54538846",
"0.54515296",
"0.5447022",
"0.5447022",
"0.5447022",
"0.54470... | 0.79388636 | 1 |
Obtains the number of nonzero elements in one column of the linear constraint matrix getacolnumnz(self,i_) | def getacolnumnz(self,i_):
nzj_ = ctypes.c_int32()
res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
nzj_ = nzj_.value
_nzj_return_value = nzj_
return (_nzj_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getacolnumnz(self,i_): # 3\n res,resargs = self.__obj.getacolnumnz(i_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nzj_return_value = resargs\n return _nzj_return_value",
"def getarownumnz(self,i_): # 3\n res,resargs = self.... | [
"0.78955895",
"0.6964569",
"0.66872597",
"0.6391909",
"0.63877714",
"0.632295",
"0.63077927",
"0.6225606",
"0.6197037",
"0.61326903",
"0.61255556",
"0.60932916",
"0.60414433",
"0.60414433",
"0.5994617",
"0.5992448",
"0.59544677",
"0.5947154",
"0.5945124",
"0.5940501",
"0.5913... | 0.7779385 | 1 |
Obtains one column of the linear constraint matrix. getacol(self,j_,subj_,valj_) | def getacol(self,j_,subj_,valj_):
nzj_ = ctypes.c_int32()
_subj_minlength = self.getacolnumnz((j_))
if self.getacolnumnz((j_)) > 0 and subj_ is not None and len(subj_) != self.getacolnumnz((j_)):
raise ValueError("Array argument subj is not long enough: Is %d, expected %d" % (len(subj_),self.getacolnu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getacol(self,j_,subj,valj): # 3\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_... | [
"0.7543346",
"0.6976331",
"0.6404439",
"0.6330695",
"0.6330695",
"0.6330695",
"0.6279653",
"0.6225708",
"0.6189424",
"0.61752653",
"0.61163116",
"0.5897423",
"0.5880596",
"0.5877866",
"0.5869485",
"0.58650994",
"0.58379793",
"0.57342744",
"0.56699353",
"0.564279",
"0.5636988"... | 0.74751633 | 1 |
Obtains a sequence of columns from the coefficient matrix. getacolslice(self,first_,last_,ptrb_,ptre_,sub_,val_) | def getacolslice(self,first_,last_,ptrb_,ptre_,sub_,val_):
maxnumnz_ = self.getacolslicenumnz((first_),(last_))
_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 ... | {
"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... | [
"0.68298435",
"0.68189096",
"0.6633351",
"0.6410343",
"0.635861",
"0.62143683",
"0.59577066",
"0.58845204",
"0.5850808",
"0.5840428",
"0.5830795",
"0.5818751",
"0.5787353",
"0.5729822",
"0.57094395",
"0.5652637",
"0.5626832",
"0.5602785",
"0.557522",
"0.55677515",
"0.5536629"... | 0.8195238 | 0 |
Obtains the number of nonzero elements in one row of the linear constraint matrix getarownumnz(self,i_) | def getarownumnz(self,i_):
nzi_ = ctypes.c_int32()
res = __library__.MSK_XX_getarownumnz(self.__nativep,i_,ctypes.byref(nzi_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
nzi_ = nzi_.value
_nzi_return_value = nzi_
return (_nzi_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getacolnumnz(self,i_):\n nzj_ = ctypes.c_int32()\n res = __library__.MSK_XX_getacolnumnz(self.__nativep,i_,ctypes.byref(nzj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nzj_ = nzj_.value\n _nzj_return_value = nzj_\n return (_nzj_return_value... | [
"0.7470606",
"0.7418583",
"0.72705734",
"0.6671628",
"0.65264225",
"0.64747626",
"0.6341131",
"0.62474984",
"0.62369037",
"0.6191386",
"0.61299306",
"0.60664266",
"0.60327625",
"0.60295826",
"0.59809643",
"0.58457065",
"0.5787058",
"0.57626253",
"0.567574",
"0.56754756",
"0.5... | 0.70268023 | 3 |
Obtains one row of the linear constraint matrix. getarow(self,i_,subi_,vali_) | def getarow(self,i_,subi_,vali_):
nzi_ = ctypes.c_int32()
_subi_minlength = self.getarownumnz((i_))
if self.getarownumnz((i_)) > 0 and subi_ is not None and len(subi_) != self.getarownumnz((i_)):
raise ValueError("Array argument subi is not long enough: Is %d, expected %d" % (len(subi_),self.getarownu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getarow(self,i_,subi,vali): # 3\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_... | [
"0.6920191",
"0.6274343",
"0.62173253",
"0.62173253",
"0.62173253",
"0.6196485",
"0.5856732",
"0.5815419",
"0.5781744",
"0.5771555",
"0.5736372",
"0.56483686",
"0.5642731",
"0.5605737",
"0.55912125",
"0.5473553",
"0.5381739",
"0.53433347",
"0.5286959",
"0.52848995",
"0.525561... | 0.6652325 | 1 |
Obtains the number of nonzeros in a slice of columns of the coefficient matrix. getacolslicenumnz(self,first_,last_) | def getacolslicenumnz(self,first_,last_):
numnz_ = ctypes.c_int64()
res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
numnz_ = numnz_.value
_numnz_return_value = numnz... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getarowslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numnz_ = numnz_.value\n _numnz_return_v... | [
"0.7030425",
"0.6908309",
"0.69077706",
"0.68798804",
"0.67562634",
"0.6697987",
"0.60632735",
"0.60440516",
"0.59911394",
"0.5967844",
"0.59044164",
"0.5890069",
"0.58208543",
"0.57897",
"0.5725127",
"0.5724517",
"0.57188284",
"0.5676727",
"0.5675189",
"0.5605064",
"0.559714... | 0.7720494 | 0 |
Obtains the number of nonzeros in a slice of rows of the coefficient matrix. getarowslicenumnz(self,first_,last_) | def getarowslicenumnz(self,first_,last_):
numnz_ = ctypes.c_int64()
res = __library__.MSK_XX_getarowslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
numnz_ = numnz_.value
_numnz_return_value = numnz... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getacolslicenumnz(self,first_,last_):\n numnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getacolslicenumnz64(self.__nativep,first_,last_,ctypes.byref(numnz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numnz_ = numnz_.value\n _numnz_return_v... | [
"0.6821424",
"0.6395617",
"0.63196206",
"0.6160555",
"0.6055939",
"0.5976274",
"0.5861332",
"0.57293874",
"0.57270485",
"0.5675843",
"0.5640828",
"0.5561198",
"0.5522415",
"0.5509788",
"0.5488033",
"0.5468512",
"0.5459757",
"0.54302907",
"0.5399739",
"0.5388458",
"0.5382514",... | 0.7653431 | 0 |
Obtains a sequence of rows from the coefficient matrix. getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_) | def getarowslice(self,first_,last_,ptrb_,ptre_,sub_,val_):
maxnumnz_ = self.getarowslicenumnz((first_),(last_))
_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 ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 e... | [
"0.72424006",
"0.7017984",
"0.6397862",
"0.6392683",
"0.6295936",
"0.62946856",
"0.62616587",
"0.6202416",
"0.6158898",
"0.60779953",
"0.5999932",
"0.59529024",
"0.5874609",
"0.5798113",
"0.57943004",
"0.5755523",
"0.56610376",
"0.5640397",
"0.5582677",
"0.55730736",
"0.55696... | 0.7663158 | 0 |
Obtains a sequence of rows from the coefficient matrix in sparse triplet format. getarowslicetrip(self,first_,last_,subi_,subj_,val_) | def getarowslicetrip(self,first_,last_,subi_,subj_,val_):
maxnumnz_ = self.getarowslicenumnz((first_),(last_))
_subi_minlength = (maxnumnz_)
if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):
raise ValueError("Array argument subi is not long enough: Is %d, expected %d" % (len(subi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getarowslicetrip(self,first_,last_,subi,subj,val): # 3\n maxnumnz_ = self.getaslicenumnz(accmode.con,(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 ... | [
"0.7290529",
"0.70560914",
"0.6958755",
"0.61386895",
"0.5720467",
"0.5619456",
"0.56170875",
"0.5535816",
"0.5501307",
"0.5492698",
"0.5491768",
"0.54850245",
"0.5449865",
"0.5407904",
"0.5361041",
"0.5322481",
"0.53132904",
"0.5291811",
"0.5257537",
"0.5243826",
"0.5230079"... | 0.7172589 | 1 |
Obtains a sequence of columns from the coefficient matrix in triplet format. getacolslicetrip(self,first_,last_,subi_,subj_,val_) | def getacolslicetrip(self,first_,last_,subi_,subj_,val_):
maxnumnz_ = self.getacolslicenumnz((first_),(last_))
_subi_minlength = (maxnumnz_)
if (maxnumnz_) > 0 and subi_ is not None and len(subi_) != (maxnumnz_):
raise ValueError("Array argument subi is not long enough: Is %d, expected %d" % (len(subi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 ... | [
"0.77380747",
"0.69819945",
"0.67847955",
"0.6653259",
"0.6169519",
"0.5882231",
"0.58708996",
"0.57938963",
"0.570199",
"0.56744456",
"0.56735504",
"0.5606898",
"0.5576777",
"0.5566366",
"0.5544074",
"0.55073357",
"0.5492098",
"0.54868627",
"0.5441892",
"0.5436611",
"0.54346... | 0.7907325 | 0 |
Obtains bound information for one constraint. getconbound(self,i_) | def getconbound(self,i_):
bk_ = ctypes.c_int32()
bl_ = ctypes.c_double()
bu_ = ctypes.c_double()
res = __library__.MSK_XX_getconbound(self.__nativep,i_,ctypes.byref(bk_),ctypes.byref(bl_),ctypes.byref(bu_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getconbound(self,i_): # 3\n res,resargs = self.__obj.getconbound(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 ret... | [
"0.8117996",
"0.67920166",
"0.6751894",
"0.67239743",
"0.6467672",
"0.62063897",
"0.6088784",
"0.6081014",
"0.6081014",
"0.6081014",
"0.6081014",
"0.6081014",
"0.6081014",
"0.6081014",
"0.6081014",
"0.6065061",
"0.5971675",
"0.59263587",
"0.59000784",
"0.59000784",
"0.5864605... | 0.7855516 | 1 |
Obtains bound information for one variable. getvarbound(self,i_) | def getvarbound(self,i_):
bk_ = ctypes.c_int32()
bl_ = ctypes.c_double()
bu_ = ctypes.c_double()
res = __library__.MSK_XX_getvarbound(self.__nativep,i_,ctypes.byref(bk_),ctypes.byref(bl_),ctypes.byref(bu_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 ret... | [
"0.88780814",
"0.6618329",
"0.6337327",
"0.6297966",
"0.6271888",
"0.6069767",
"0.606333",
"0.603762",
"0.59093964",
"0.5864278",
"0.5855671",
"0.57791764",
"0.5742324",
"0.5674004",
"0.56727177",
"0.56428105",
"0.5599474",
"0.55750924",
"0.5573686",
"0.5553733",
"0.554883",
... | 0.8567469 | 1 |
Obtains bounds information for a slice of the constraints. getconboundslice(self,first_,last_,bk_,bl_,bu_) | def getconboundslice(self,first_,last_,bk_,bl_,bu_):
_bk_minlength = ((last_) - (first_))
if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):
raise ValueError("Array argument bk is not long enough: Is %d, expected %d" % (len(bk_),((last_) - (first_))))
if isinstance(b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 ... | [
"0.83687973",
"0.7650801",
"0.7564731",
"0.7493438",
"0.74723506",
"0.7434169",
"0.7281272",
"0.72708595",
"0.69236696",
"0.6754937",
"0.67351925",
"0.6625706",
"0.6625706",
"0.6625706",
"0.6625706",
"0.6625706",
"0.6625706",
"0.6625706",
"0.6625706",
"0.64139324",
"0.6381346... | 0.80538183 | 1 |
Obtains bounds information for a slice of the variables. getvarboundslice(self,first_,last_,bk_,bl_,bu_) | def getvarboundslice(self,first_,last_,bk_,bl_,bu_):
_bk_minlength = ((last_) - (first_))
if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):
raise ValueError("Array argument bk is not long enough: Is %d, expected %d" % (len(bk_),((last_) - (first_))))
if isinstance(b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 ... | [
"0.8427269",
"0.76399803",
"0.7581138",
"0.72763824",
"0.7077902",
"0.6877357",
"0.67139685",
"0.65998864",
"0.64918834",
"0.63755333",
"0.63566226",
"0.6347929",
"0.63300616",
"0.62415",
"0.6238382",
"0.62190634",
"0.62190634",
"0.62190634",
"0.62190634",
"0.62190634",
"0.62... | 0.8042054 | 1 |
Obtains one objective coefficient. getcj(self,j_) | def getcj(self,j_):
cj_ = ctypes.c_double()
res = __library__.MSK_XX_getcj(self.__nativep,j_,ctypes.byref(cj_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
cj_ = cj_.value
_cj_return_value = cj_
return (_cj_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 jac(self):\n return self.unit_jac if self._jac is None else se... | [
"0.7897348",
"0.694601",
"0.649423",
"0.64774793",
"0.6358544",
"0.6241598",
"0.62322664",
"0.62282366",
"0.61798847",
"0.6176942",
"0.60854423",
"0.6068824",
"0.60608375",
"0.6059429",
"0.60535663",
"0.6037667",
"0.60261023",
"0.6021581",
"0.6002902",
"0.5994126",
"0.5985048... | 0.7652591 | 1 |
Obtains all objective coefficients. getc(self,c_) | def getc(self,c_):
_c_minlength = self.getnumvar()
if self.getnumvar() > 0 and c_ is not None and len(c_) != self.getnumvar():
raise ValueError("Array argument c is not long enough: Is %d, expected %d" % (len(c_),self.getnumvar()))
if isinstance(c_,numpy.ndarray) and not c_.flags.writeable:
rais... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def coefficients(self) :\n return self.__coefficients",
"def coefficients(self) :\n raise NotImplementedError",
"def coefficients(self):\r\n return self.coef_['x']",
"def coefficients(self):\n return self._coefficients",
"def coefficients(self):\n return self._coefficient... | [
"0.65427005",
"0.63974696",
"0.63769525",
"0.635677",
"0.635677",
"0.625591",
"0.62489706",
"0.6245763",
"0.6245763",
"0.61773825",
"0.61445075",
"0.612955",
"0.6082035",
"0.60051066",
"0.5921834",
"0.5798229",
"0.57925135",
"0.5790028",
"0.57677597",
"0.5748355",
"0.56996524... | 0.61323375 | 11 |
Obtains the fixed term in the objective. getcfix(self) | def getcfix(self):
cfix_ = ctypes.c_double()
res = __library__.MSK_XX_getcfix(self.__nativep,ctypes.byref(cfix_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
cfix_ = cfix_.value
_cfix_return_value = cfix_
return (_cfix_return_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 getCF(self):\r\n return self.lEq;",
"def CFL(self):\n ... | [
"0.7102011",
"0.69387436",
"0.61814743",
"0.61706793",
"0.6118419",
"0.6102831",
"0.60178024",
"0.59100634",
"0.58950984",
"0.5895022",
"0.5848309",
"0.58352464",
"0.5788822",
"0.5725575",
"0.57214016",
"0.56842697",
"0.56441844",
"0.56353074",
"0.56272084",
"0.56272084",
"0.... | 0.723974 | 0 |
Obtains a cone. getcone(self,k_,submem_) | def getcone(self,k_,submem_):
ct_ = ctypes.c_int32()
conepar_ = ctypes.c_double()
nummem_ = ctypes.c_int32()
_submem_minlength = self.getconeinfo((k_))[2]
if self.getconeinfo((k_))[2] > 0 and submem_ is not None and len(submem_) != self.getconeinfo((k_))[2]:
raise ValueError("Array argument su... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 ... | [
"0.85851794",
"0.74542636",
"0.6922491",
"0.6714365",
"0.6663073",
"0.6131412",
"0.61234295",
"0.60180366",
"0.59645844",
"0.5906362",
"0.5844101",
"0.55410284",
"0.5464269",
"0.5432204",
"0.54281247",
"0.54012233",
"0.5308824",
"0.5305274",
"0.5274296",
"0.52731514",
"0.5224... | 0.783578 | 1 |
Obtains information about a cone. getconeinfo(self,k_) | def getconeinfo(self,k_):
ct_ = ctypes.c_int32()
conepar_ = ctypes.c_double()
nummem_ = ctypes.c_int32()
res = __library__.MSK_XX_getconeinfo(self.__nativep,k_,ctypes.byref(ct_),ctypes.byref(conepar_),ctypes.byref(nummem_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescod... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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... | [
"0.88562346",
"0.65607196",
"0.6062003",
"0.6057762",
"0.6046421",
"0.6028648",
"0.57615036",
"0.57012206",
"0.5669752",
"0.56304246",
"0.5603641",
"0.5487404",
"0.5487404",
"0.53959966",
"0.53923273",
"0.53695613",
"0.5355254",
"0.5324863",
"0.531304",
"0.5291991",
"0.528119... | 0.85250115 | 1 |
Obtains a sequence of coefficients from the objective. getclist(self,subj_,c_) | def getclist(self,subj_,c_):
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("Argument subj may ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def coefficients(self) :\n return self.__coefficients",
"def coefficients(self):\n return self._coefficients",
"def coefficients(self):\n return self._coefficients",
"def coefficients(self) :\n raise NotImplementedError",
"def coefficients(self):\r\n return self.coef_['x'... | [
"0.61023015",
"0.60017306",
"0.60017306",
"0.6000582",
"0.58433247",
"0.581803",
"0.581803",
"0.57937485",
"0.5704129",
"0.55387217",
"0.5518362",
"0.5512744",
"0.5497052",
"0.54852647",
"0.5466663",
"0.54118663",
"0.54016364",
"0.537931",
"0.5366499",
"0.5345296",
"0.5335419... | 0.70591795 | 0 |
Obtains a sequence of coefficients from the objective. getcslice(self,first_,last_,c_) | def getcslice(self,first_,last_,c_):
_c_minlength = ((last_) - (first_))
if ((last_) - (first_)) > 0 and c_ is not None and len(c_) != ((last_) - (first_)):
raise ValueError("Array argument c is not long enough: Is %d, expected %d" % (len(c_),((last_) - (first_))))
if isinstance(c_,numpy.ndarray) and ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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(\"Ar... | [
"0.74884826",
"0.6898075",
"0.6749403",
"0.6479534",
"0.6430882",
"0.62302166",
"0.6188517",
"0.59158885",
"0.5810388",
"0.5780244",
"0.56624705",
"0.56085896",
"0.5574313",
"0.5566425",
"0.55087215",
"0.5465687",
"0.5429033",
"0.5421072",
"0.5413725",
"0.53567517",
"0.534522... | 0.7475892 | 1 |
Obtains a double information item. getdouinf(self,whichdinf_) | def getdouinf(self,whichdinf_):
dvalue_ = ctypes.c_double()
res = __library__.MSK_XX_getdouinf(self.__nativep,whichdinf_,ctypes.byref(dvalue_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
dvalue_ = dvalue_.value
_dvalue_return_value = dvalue_
return (_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getdouinf(self,whichdinf_): # 3\n if not isinstance(whichdinf_,dinfitem): raise TypeError(\"Argument whichdinf has wrong type\")\n res,resargs = self.__obj.getdouinf(whichdinf_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dvalue_re... | [
"0.8139567",
"0.61747223",
"0.57404494",
"0.56027913",
"0.54675555",
"0.5288993",
"0.5271653",
"0.52566004",
"0.5161955",
"0.5105282",
"0.50878364",
"0.50546545",
"0.5054538",
"0.5047877",
"0.5047438",
"0.5029467",
"0.50197035",
"0.5018882",
"0.5017841",
"0.49776104",
"0.4976... | 0.7271567 | 1 |
Obtains a double parameter. getdouparam(self,param_) | def getdouparam(self,param_):
parvalue_ = ctypes.c_double()
res = __library__.MSK_XX_getdouparam(self.__nativep,param_,ctypes.byref(parvalue_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
parvalue_ = parvalue_.value
_parvalue_return_value = parvalue_
r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 =... | [
"0.7216291",
"0.6473787",
"0.63799834",
"0.6056583",
"0.6039823",
"0.60313606",
"0.5998796",
"0.59530497",
"0.5941797",
"0.5872715",
"0.5843549",
"0.5813088",
"0.5800583",
"0.576759",
"0.57210326",
"0.56473833",
"0.5598247",
"0.5590956",
"0.55658185",
"0.5548109",
"0.5548043"... | 0.7105613 | 1 |
Computes the dual objective value associated with the solution. getdualobj(self,whichsol_) | def getdualobj(self,whichsol_):
dualobj_ = ctypes.c_double()
res = __library__.MSK_XX_getdualobj(self.__nativep,whichsol_,ctypes.byref(dualobj_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
dualobj_ = dualobj_.value
_dualobj_return_value = dualobj_
ret... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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_retu... | [
"0.8319954",
"0.6920154",
"0.6874271",
"0.63215345",
"0.6047877",
"0.5961174",
"0.59548587",
"0.59537184",
"0.58911043",
"0.5846982",
"0.5843001",
"0.5692419",
"0.5645508",
"0.5627053",
"0.5615924",
"0.5534404",
"0.5509056",
"0.5460137",
"0.5445977",
"0.5432232",
"0.5387754",... | 0.79599565 | 1 |
Obtains an integer information item. getintinf(self,whichiinf_) | def getintinf(self,whichiinf_):
ivalue_ = ctypes.c_int32()
res = __library__.MSK_XX_getintinf(self.__nativep,whichiinf_,ctypes.byref(ivalue_))
if res != 0:
_,msg = self.__getlasterror(res)
raise Error(rescode(res),msg)
ivalue_ = ivalue_.value
_ivalue_return_value = ivalue_
return (_i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getintinf(self,whichiinf_): # 3\n if not isinstance(whichiinf_,iinfitem): raise TypeError(\"Argument whichiinf has wrong type\")\n res,resargs = self.__obj.getintinf(whichiinf_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ivalue_re... | [
"0.8475795",
"0.7179274",
"0.71271324",
"0.6888658",
"0.61792326",
"0.61792326",
"0.61284137",
"0.6106969",
"0.6090202",
"0.60640436",
"0.5953424",
"0.5949886",
"0.5806069",
"0.5791027",
"0.57118326",
"0.57102764",
"0.5709367",
"0.5658817",
"0.56567997",
"0.5631905",
"0.55623... | 0.80981195 | 1 |
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